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Saying Goodbye: 10 Years, a 19% Annual Return, and 17 Investing Lessons

9 years 7 months and 6 days. This is how much time has passed since I started managing my family’s investment portfolio of US stocks on 26 October 2010. 19.5% versus 12.7%. These are the respective annual returns of my family’s portfolio (without dividends) and the S&P 500 (with dividends) in that period.

I will soon have to say goodbye to the portfolio. Jeremy Chia (my blogging partner) and myself have co-founded a global equities investment fund. As a result, the lion’s share of my family’s investment portfolio will soon be liquidated so that the cash can be invested in the fund.

The global equities investment fund will be investing with the same investment philosophy that underpins my family’s portfolio, so the journey continues. But my heart’s still heavy at having to let the family portfolio go. It has been a huge part of my life for the past 9 years 7 months and 6 days, and I’m proud of what I’ve achieved (I hope my parents are too!).

In the nearly-10 years managing the portfolio, I’ve learnt plenty of investing lessons. I want to share them here, to benefit those of you who are reading, and to mark the end of my personal journey and the beginning of a new adventure. I did not specifically pick any number of lessons to share. I’m documenting everything that’s in my head after a long period of reflection.

Do note that my lessons may not be timeless, because things change in the markets. But for now, they are the key lessons I’ve picked up.

Lesson 1: Focus on business fundamentals, not macroeconomic or geopolitical developments – there are always things to worry about

My family’s portfolio has many stocks that have gone up multiple times in value. A sample is given below:

Some of them are among the very first few stocks I bought; some were bought in more recent years. But what’s interesting is that these stocks produced their gains while the world experienced one crisis after another.

You see, there were always things to worry about in the geopolitical and macroeconomic landscape since I started investing. Here’s a short and incomplete list (you may realise how inconsequential most of these events are today, even though they seemed to be huge when they occurred):

2010 – European debt crisis; BP oil spill; May 2010 Flash Crash
2011 – Japan earthquake; Middle East uprising
2012 – Potential Greek exit from Eurozone; Hurricane Sandy
2013 – Cyprus bank bailouts; US government shutdown; Thailand uprising
2014 – Oil price collapse
2015 – Crash in Euro dollar against the Swiss Franc; Greece debt crisis
2016 – Brexit; Italy banking crisis
2017 – Bank of England hikes interest rates for first time in 10 years
2018 – US-China trade war
2019 – Australia bushfires; US President impeachment; appearance of COVID-19 in China
2020 (thus far) – COVID-19 becomes global pandemic

The stocks mentioned in the table above produced strong business growth over the years I’ve owned them. This business growth has been a big factor in the returns they have delivered for my family’s portfolio. When I was studying them, my focus was on their business fundamentals – and this focus has served me well.

In a 1998 lecture for MBA students, Warren Buffett was asked about his views on the then “tenuous economic situation and interest rates.“ He responded:

“I don’t think about the macro stuff. What you really want to do in investments is figure out what is important and knowable. If it is unimportant and unknowable, you forget about it. What you talk about is important but, in my view, it is not knowable.

Understanding Coca-Cola is knowable or Wrigley’s or Eastman Kodak. You can understand those businesses that are knowable. Whether it turns out to be important depends where your valuation leads you and the firm’s price and all that. But we have never not bought or bought a business because of any macro feeling of any kind because it doesn’t make any difference.

Let’s say in 1972 when we bought See’s Candy, I think Nixon [referring to former US President, Richard Nixon] put on the price controls a little bit later, but so what! We would have missed a chance to buy something for [US]$25 million that is producing [US]$60 million pre-tax now. We don’t want to pass up the chance to do something intelligent because of some prediction about something we are no good on anyway.”

Lesson 2: Adding to winners work

I’ve never shied away from adding to the winners in my portfolio, and this has worked out well. Here’s a sample, using some of the same stocks shown in the table in Lesson 1.

Adding to winners is hard to achieve, psychologically. As humans, we tend to anchor to the price we first paid for a stock. After a stock has risen significantly, it’s hard to still see it as a bargain. But I’ll argue that it is stocks that have risen significantly over a long period of time that are the good bargains. It’s counterintuitive, but hear me out.

The logic here rests on the idea that stocks do well over time if their underlying businesses do well. So, the stocks in my portfolio that have risen significantly over a number of years are likely – though not always – the ones with businesses that are firing on all cylinders. And stocks with businesses that are firing on all cylinders are exactly the ones I want to invest in.

Lesson 3: The next Amazon, is Amazon

When I first bought shares of Amazon in April 2014 at US$313, its share price was already more than 200 times higher than its IPO share price of US$1.50 in May 1997. That was an amazing annual return of around 37%.

But from the time I first invested in Amazon in April 2014 to today, its share price has increased by an even more impressive annual rate of 40%. Of course, it is unrealistic to expect Amazon to grow by a further 200 times in value from its April 2014 level over a reasonable multi-year time frame. But a stock that has done very well for a long period of time can continue delivering a great return. Winners often keep on winning.

Lesson 4: Focus on business quality and don’t obsess over valuation

It is possible to overpay for a company’s shares. This is why we need to think about the valuation of a business. But I think it is far more important to focus on the quality of a business – such as its growth prospects and the capability of the management team – than on its valuation.

If I use Amazon as an example, its shares carried a high price-to-free cash flow (P/FCF) ratio of 72 when I first invested in the company in April 2014. But Amazon’s free cash flow per share has increased by 1,000% in total (or 48% annually) from US$4.37 back then to US$48.10 now, resulting in the overall gain of 681% in its share price.

Great companies could grow into their high valuations. Amazon’s P/FCF ratio, using my April 2014 purchase price and the company’s current free cash flow per share, is just 6.5 (now that’s a value stock!). But there’s no fixed formula that can tell you what valuation is too high for a stock. It boils down to subjective judgement that is sometimes even as squishy as an intuitive feeling. This is one of the unfortunate realities of investing. Not everything can be quantified.

Lesson 5: The big can become bigger – don’t obsess over a company’s market capitalisation

I’ve yet to mention Mastercard, but I first invested in shares of the credit card company on 3 December 2014 at US$89 apiece. Back then, it already had a huge market capitalisation of around US$100 billion, according to data from Ycharts. Today, Mastercard’s share price is US$301, up more than 200% from my initial investment.

A company’s market capitalisation alone does not tell us much. It is the company’s (1) valuation, (2) size of the business, and (3) addressable market, that can give us clues on whether it could be a good investment opportunity. In December 2014, Mastercard’s price-to-earnings (P/E) ratio and revenue were both reasonable at around 35 and US$9.2 billion, respectively. Meanwhile, the company’s market opportunity still looked significant, since cashless transactions represented just 15% of total transactions in the world back then.

Lesson 6: Don’t ignore “obvious” companies just because they’re well known

Sticking with Mastercard, it was an obvious company that was already well-known when I first invested in its shares. In the first nine months of 2014, Mastercard had more than 2 billion credit cards in circulation and had processed more than 31.4 billion transactions. Everyone could see Mastercard and know that it was a great business. It was growing rapidly and consistently, and its profit and free cash flow margins were off the charts (nearly 40% for both).

The company’s high quality was recognised by the market – its P/E ratio was high in late 2014 as I mentioned earlier. But Mastercard still delivered a fantastic annual return of around 25% from my December 2014 investment.

I recently discovered a poetic quote by philosopher Arthur Schopenhauer: “The task is… not so much to see what no one has yet seen, but to think what nobody has yet thought, about that which everyone sees.” This is so applicable to investing.

Profitable investment opportunities can still be found by thinking differently about the data that everyone else has. It was obvious to the market back in December 2014 that Mastercard was a great business and its shares were valued highly because of this. But by thinking differently – with a longer-term point of view – I saw that Mastercard could grow at high rates for a very long period of time, making its shares a worthy long-term investment. From December 2014 to today, Mastercard’s free cash flow per share has increased by 158% in total, or 19% per year. Not too shabby.

Lesson 7: Be willing to lose sometimes

We need to take risks when investing. When I first invested in Shopify in September 2016, it had a price-to-sales (P/S) ratio of around 12, which is really high for a company with a long history of making losses and producing meagre cash flow. But Shopify also had a visionary leader who dared to think and act long-term. Tobi Lütke, Shopify’s CEO and co-founder, penned the following in his letter to investors in the company’s 2015 IPO prospectus (emphases are mine):

“Over the years we’ve also helped foster a large ecosystem that has grown up around Shopify. App developers, design agencies, and theme designers have built businesses of their own by creating value for merchants on the Shopify platform. Instead of stifling this enthusiastic pool of talent and carving out the profits for ourselves, we’ve made a point of supporting our partners and aligning their interests with our own. In order to build long-term value, we decided to forgo short-term revenue opportunities and nurture the people who were putting their trust in Shopify. As a result, today there are thousands of partners that have built businesses around Shopify by creating custom apps, custom themes, or any number of other services for Shopify merchants.

This is a prime example of how we approach value and something that potential investors must understand: we do not chase revenue as the primary driver of our business. Shopify has been about empowering merchants since it was founded, and we have always prioritized long term value over short-term revenue opportunities. We don’t see this changing…

… I want Shopify to be a company that sees the next century. To get us there we not only have to correctly predict future commerce trends and technology, but be the ones that push the entire industry forward. Shopify was initially built in a world where merchants were simply looking for a homepage for their business. By accurately predicting how the commerce world would be changing, and building what our merchants would need next, we taught them to expect so much more from their software.

These underlying aspirations and values drive our mission: make commerce better for everyone. I hope you’ll join us.”

Shopify was a risky proposition. But it paid off handsomely. In investing, I think we have to be willing to take risks and accept that we can lose at times. But failing at risk-taking from time to time does not mean our portfolios have to be ruined. We can take intelligent risks by sizing our positions appropriately. Tom Engle is part of The Motley Fool’s investing team in the US. He’s one of the best investors the world has never heard of. When it comes to investing in risky stocks that have the potential for huge returns, Tom has a phrase I love: “If it works out, a little is all you need; if it doesn’t, a little is all you want.”

I also want to share a story I once heard from The Motley Fool’s co-founder Tom Gardner. Once, a top-tier venture capital firm in the US wanted to improve the hit-rate of the investments it was making. So the VC firm’s leaders came up with a process for the analysts that could reduce investing errors. The firm succeeded in improving its hit-rate (the percentage of investments that make money). But interestingly, its overall rate of return became lower. That’s because the VC firm, in its quest to lower mistakes, also passed on investing in highly risky potential moonshots that could generate tremendous returns.

The success of one Shopify can make up for the mistakes of many other risky bets that flame out. To hit a home run, we must be willing to miss at times.

Lesson 8: The money is made on the holding, not the buying and selling

My family’s investment portfolio has over 50 stocks. It’s a collection that was built steadily over time, starting with the purchase of just six stocks on 26 October 2010. In the 9 years, 7 months and 6 days since, I’ve only ever sold two stocks voluntarily: (1) Atwood Oceanics, an owner of oil rigs; and (2) National Oilwell Varco, a supplier of parts and equipment that keep oil rigs running. Both stocks were bought on 26 October 2010.

David Gardner is also one of the co-founders of The Motley Fool (Tom Gardner is his brother). There’s something profound David once said about portfolio management that resonates with me:

“Make your portfolio reflect your best vision for our future.”

The sales of Atwood Oceanics and National Oilwell Varco happened because of David’s words. Part of the vision I have for the future is a world where our energy-needs are met entirely by renewable sources that do not harm the precious environment we live in. For this reason, I made the rare decision to voluntarily part ways with Atwood Oceanics and National Oilwell Varco in September 2016 and June 2017, respectively.

My aversion to selling is by design – because I believe it strengthens my discipline in holding onto the winners in my family’s portfolio. Many investors tend to cut their winners and hold onto their losers. Even in my earliest days as an investor, I recognised the importance of holding onto the winners in driving my family portfolio’s return. Being very slow to sell stocks has helped me hone the discipline of holding onto the winners. And this discipline has been a very important contributor to the long run performance of my family’s portfolio.

The great Charlie Munger has a saying that one of the keys to investing success is “sitting on your ass.” I agree. Patience is a virtue. And talking about patience…

Lesson 9: Be patient – some great things take time

Some of my big winners needed only a short while before they took off. But there are some that needed significantly more time. Activision Blizzard is one such example. As I mentioned earlier, I invested in its shares in October 2010. Then, Activision Blizzard’s share price went nowhere for more than two years before it started rocketing higher.

Peter Lynch once said: “In my investing career, the best gains usually have come in the third or fourth year, not in the third or fourth week or the third or fourth month.” The stock market does not move according to our own clock. So patience is often needed.

Lesson 10: Management is the ultimate source of a company’s economic moat

In my early days as an investor, I looked for quantifiable economic moats. These are traits in a company such as (1) having a network effect, (2) being a low-cost producer, (3) delivering a product or service that carries a high switching cost for customers, (4) possessing intangible assets such as intellectual property, and (5) having efficient scale in production.

But the more I thought about it, the more I realised that a company’s management team is the true source of its economic moat, or lack thereof.

Today, Netflix has the largest global streaming audience with a pool of 183 million subscribers around the world. Having this huge base of subscribers means that Netflix has an efficient scale in producing content, because the costs can be spread over many subscribers. Its streaming competitors do not have this luxury. But this scale did not appear from thin air. It arose because of Netflix’s CEO and co-founder, Reed Hastings, and his leadership team.

The company was an early pioneer in the streaming business when it launched its streaming service in 2007. In fact, Netflix probably wanted to introduce streaming even from its earliest days. Hastings said the following in a 2007 interview with Fortune magazine:

“We named the company Netflix for a reason; we didn’t name it DVDs-by-mail. The opportunity for Netflix online arrives when we can deliver content to the TV without any intermediary device.”

When Netflix first started streaming, the content came from third-party producers. In 2013, the company launched its first slate of original programming. Since then, Netflix has ramped up its original content budget significantly. The spending has been done smartly, as Netflix has found plenty of success with its original programming. For instance, in 2013, the company became the first streaming provider to be nominated for a primetime Emmy. And in 2018 and 2019, the company snagged 23 and 27 Emmy wins, respectively.

A company’s current moat is the result of management’s past actions; a company’s future moat is the result of management’s current actions. Management is what creates the economic moat.

Lesson 11: Volatility in stocks is a feature, not a bug

Looking at the table in Lesson 1, you may think that my investment in Netflix was smooth-sailing. It’s actually the opposite.

I first invested in Netflix shares on 15 September 2011 at US$26 after the stock price had fallen by nearly 40% from US$41 in July 2011. But the stock price kept declining afterward, and I bought more shares at US$16 on 20 March 2012. More pain was to come. In August 2012, Netflix’s share price bottomed at less than US$8, resulting in declines of more than 70% from my first purchase, and 50% from my second.

My Netflix investment was a trial by fire for a then-young investor – I had started investing barely a year ago before I bought my first Netflix shares. But I did not panic and I was not emotionally affected. I already knew that stocks – even the best performing ones – are volatile over the short run. But my experience with Netflix drove the point even deeper into my brain.

Lesson 12: Be humble – there’s so much we don’t know

My investment philosophy is built on the premise that a stock will do well over time if its business does well too. But how does this happen?

In the 1950s, lawmakers in the US commissioned an investigation to determine if the stock market back then was too richly priced. The Dow (a major US stock market benchmark) had exceeded its peak seen in 1929 before the Great Depression tore up the US market and economy. Ben Graham, the legendary father of value investing, was asked to participate as an expert on the stock market. Here’s an exchange during the investigation that’s relevant to my discussion:

“Question to Graham: When you find a special situation and you decide, just for illustration, that you can buy for 10 and it is worth 30, and you take a position, and then you cannot realize it until a lot of other people decide it is worth 30, how is that process brought about – by advertising, or what happens?

Graham’s response: That is one of the mysteries of our business, and it is a mystery to me as well as to everybody else. We know from experience that eventually the market catches up with value. It realizes it in one way or another.”

More than 60 years ago, one of the most esteemed figures in the investment business had no idea how stock prices seemed to eventually reflect their underlying economic values. Today, I’m still unable to find any answer. If you’ve seen any clues, please let me know! This goes to show that there’s so much I don’t know about the stock market. It’s also a fantastic reminder for me to always remain humble and be constantly learning. Ego is the enemy.

Lesson 13: Knowledge compounds, and read outside of finance

Warren Buffett once told a bunch of students to “read 500 pages… every day.” He added, “That’s how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it.”

I definitely have not done it. I read every day, but I’m nowhere close to the 500 pages that Buffett mentioned. Nonetheless, I have experienced first hand how knowledge compounds. Over time, I’ve been able to connect the dots faster when I analyse a company. And for companies that I’ve owned shares of for years, I don’t need to spend much time to keep up with their developments because of the knowledge I’ve acquired over the years.

Reading outside of finance has also been really useful for me. I have a firm belief that investing is only 5% finance and 95% everything else. Reading about psychology, society, history, science etc. can make us even better investors than someone who’s buried neck-deep in only finance books. Having a broad knowledge base helps us think about issues from multiple angles. This brings me to Arthur Schopenhauer’s quote I mentioned earlier in Lesson 6: “The task is… not so much to see what no one has yet seen, but to think what nobody has yet thought, about that which everyone sees.”

Lesson 14: The squishy things matter

Investing is part art and part science. But is it more art than science? I think so. The squishy, unquantifiable things matter. That’s because investing is about businesses, and building businesses involves squishy things.

Jeff Bezos said it best in his 2005 Amazon shareholders’ letter (emphases are mine):

“As our shareholders know, we have made a decision to continuously and significantly lower prices for customers year after year as our efficiency and scale make it possible. This is an example of a very important decision that cannot be made in a math-based way.

In fact, when we lower prices, we go against the math that we can do, which always says that the smart move is to raise prices. We have significant data related to price elasticity. With fair accuracy, we can predict that a price reduction of a certain percentage will result in an increase in units sold of a certain percentage. With rare exceptions, the volume increase in the short term is never enough to pay for the price decrease.

However, our quantitative understanding of elasticity is short-term. We can estimate what a price reduction will do this week and this quarter. But we cannot numerically estimate the effect that consistently lowering prices will have on our business over five years or ten years or more.

Our judgment is that relentlessly returning efficiency improvements and scale economies to customers in the form of lower prices creates a virtuous cycle that leads over the long term to a much larger dollar amount of free cash flow, and thereby to a much more valuable Amazon.com. We’ve made similar judgments around Free Super Saver Shipping and Amazon Prime, both of which are expensive in the short term and—we believe—important and valuable in the long term.”

On a related note, I was also attracted to Shopify when I came across Tobi Lütke’s letter to investors that I referenced in Lesson 7. I saw in Lütke the same ability to stomach short-term pain, and the drive toward producing long-term value, that I noticed in Bezos. This is also a great example of how knowledge compounds.

Lesson 15: I can never do it alone

Aaron Bush is one of the best investors I know of at The Motley Fool, and he recently created one of the best investing-related tweet-storms I have seen. In one of his tweets, he said: “Collaboration can go too far. Surrounding yourself with a great team or community is critical, but the moment decision-making authority veers democratic your returns will begin to mean-revert.”

I agree with everything Aaron said. Investment decision-making should never involve large teams. But at the same time, having a community or team around us is incredibly important for our development; their presence enables us to view a problem from many angles, and it helps with information gathering and curation.

I joined one of The Motley Fool’s investment newsletter services in 2010 as a customer. The service had wonderful online forums and this dramatically accelerated my learning curve. In 2013, I had the fortune to join an informal investment club in Singapore named Kairos Research. It was founded by Stanley Lim, Cheong Mun Hong, and Willie Keng. They are also the founders of the excellent Asia-focused investment education website, Value Invest Asia. I’ve been a part of Kairos since and have benefited greatly. I’ve made life-long friends and met countless thoughtful, kind, humble, and whip-smart people who have a deep passion for investing and knowledge. The Motley Fool’s online forums and the people in Kairos have helped me become a better human being and investor over the years.

I’ve also noticed – in these group interactions – that the more I’m willing to give, the more I receive. Giving unconditionally and sincerely without expecting anything in return, paradoxically, results in us having more. Giving is a superpower.

Lesson 16: Be honest with myself about what I don’t know

When we taste success in the markets, it’s easy for ego to enter the picture. We may look into the mirror and proclaim: “I’m a special investor! I’ve been great at picking growth stocks – this knowledge must definitely translate to trading options, shorting commodities, and underwriting exotic derivatives. They, just like growth stocks, are all a part of finance, isn’t it?”

This is where trouble comes. The entrance of ego is the seed of future failure. In the biography of Warren Buffett, The Snowball: Warren Buffett and the Business of Life, author Alice Schroeder shared this passage about Charlie Munger:

“[Munger] dread falling prey to what a Harvard Law School classmate of his had called “the Shoe Button Complex.”

“His father commuted daily with the same group of men,” Munger said. “One of them had managed to corner the market in shoe buttons – a really small market, but he had it all. He pontificated on every subject, all subjects imaginable. Cornering the market on shoe buttons made him an expert on everything. Warren and I have always sensed it would be a big mistake to behave that way.”

The Shoe Button Complex can be applied in a narrower sense to investing too. Just because I know something about the market does not mean I know everything. For example, a few years after I invested in Atwood Oceanics and National Oilwell Varco, I realised I was in over my head. I have no ability to predict commodity prices, but the business-health of the two companies depends on the price of oil. Since I came to the realisation, I have stayed away from additional commodity-related companies. In another instance, I know I can’t predict the movement of interest rates, so I’ve never made any investment decision that depended on interest rates as the main driver.

Lesson 17: Be rationally optimistic

In Lesson 1, I showed that the world had lurched from one crisis to another over the past decade. And of course, we’re currently battling COVID-19 now. But I’m still optimistic about tomorrow. This is because one key thing I’ve learnt about humanity is that our progress has never happened smoothly. It took us only 66 years to go from the first demonstration of manned flight by the Wright brothers at Kitty Hawk to putting a man on the moon. But in between was World War II, a brutal battle across the globe from 1939 to 1945 that killed an estimated 66 million, according to National Geographic.

This is how progress is made, through the broken pieces of the mess that Mother Nature and our own mistakes create. Morgan Housel has the best description of this form of rational optimism that I’ve come across:

“A real optimist wakes up every morning knowing lots of stuff is broken, and more stuff is about to break.

Big stuff. Important stuff. Stuff that will make his life miserable. He’s 100% sure of it.

He starts his day knowing a chain of disappointments awaits him at work. Doomed projects. Products that will lose money. Coworkers quitting. He knows that he lives in an economy due for a recession, unemployment surely to rise. He invests his money in a stock market that will crash. Maybe soon. Maybe by a lot. This is his base case.

He reads the news with angst. It’s a fragile world. Every generation has been hit with a defining shock. Wars, recessions, political crises. He knows his generation is no different.

This is a real optimist. He’s an optimist because he knows all this stuff does not preclude eventual growth and improvement. The bad stuff is a necessary and normal path that things getting better over time rides on. Progress happens when people learn something new. And they learn the most, as a group, when stuff breaks. It’s essential.

So he expects the world around him to break all the time. But he knows – as a matter of faith – that if he can survive the day-to-day fractures, he’ll capture the up-and-to-the-right arc that learning and hard work produces over time.”

To me, investing in stocks is, at its core, the same as having faith in the long-term potential of humanity. There are 7.8 billion individuals in the world today, and the vast majority of us will wake up every morning wanting to improve the world and our own lot in life – this is ultimately what fuels the global economy and financial markets. Miscreants and Mother Nature will wreak havoc from time to time. But I have faith in the collective positivity of humanity. When there’s a mess, we can clean it up. This has been the story of our long history – and the key driver of the return my family’s portfolio has enjoyed immensely over the past 9 years, 7 months, and 6 days.

My dear portfolio, goodbye.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I, the author, will be making sell-trades on the stocks mentioned in this article over the coming weeks.

The Latest Thoughts From American Technology Companies On AI (2026 Q2)

A collection of quotes on artificial intelligence, or AI, from the management teams of US-listed technology companies in the 2026 Q2 earnings season.

The way I see it, artificial intelligence (or AI), really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Since then, developments in AI have progressed at a breathtaking pace.

We’re thick in the action of the latest earnings season for the US stock market – for the second quarter of 2026 – and I thought it would be useful to collate some of the interesting commentary I’ve come across in earnings conference calls, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. This is an ongoing series. For the older commentary:

2023 Q1 – here and here
2023 Q2 – here and here
2023 Q3 – here and here
2023 Q4 – here and here
2024 Q1 – here and here
2024 Q2 – here and here
2024 Q3 – here and here
2024 Q4 – here, here, and here
2025 Q1 – here and here
2025 Q2 – here and here
2025 Q3 – here, here, and here
2025 Q4 – here, here, here, and here
2026 Q1 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

Alphabet (NASDAQ: GOOG)

Alphabet’s management recently announced new AI models, Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber; the Gemini Flash models are Alphabet’s workhorse models and management is seeing lots of demand for them because of their performance and cost; Gemini 3.5 Flash Cyber has the same performance as other larger, frontier cyber models; Alphabet is testing Gemini 3.5 Pro; Alphabet has started pre-training for Gemini 4 and management is excited by its progress; developers and enterprises have strong token usage of Alphabet’s models; 9 million developers are building with Alphabet’s models each month; Alphabet’s model APIs (application programming interfaces) processed 22 billion tokens per minute in 2026 Q2 (was 16 billion tokens per minute in 2026 Q1); management launched the Omni video model in May and it has led to a 40% increase in daily users creating videos on the Gemini app; Alphabet’s Gemma family of small open models that can run locally on devices, have been downloaded over 900 million times, with the latest Gemma 4 been downloaded 300 million times since launching in April; the Gemini app now has 950 million monthly active users (was 750 million in 2025 Q4), with daily users up 3x from a year ago; Gemini Spark, the personal AI agent within the Gemini app, is now available globally; the Gemini Flash models are great for enterprise needs;

Yesterday, we announced new models, Gemini 3.6 Flash and 3.5 Flash-Lite, which are cost-effective and highly efficient. We are seeing tons of demand for our workhorse Gemini Flash series because it hits the sweet spot of performance and cost. We also launched Gemini 3.5 Flash Cyber, which I’m really excited about. Paired with our CodeMender agent, it finds and fixes vulnerabilities and delivers performance at the frontier comparable to far bigger cyber models. Gemini 3.5 Pro is currently in testing, and our team is already building the next generation of models. We have started our most ambitious pre-training run yet for Gemini 4 and are excited by the progress we are seeing at the frontier.

Demand for our models is translating to strong token usage across developers and enterprise customers, and we continue to be supply constrained, a sign of momentum and rapid adoption. More than 9 million developers are building each month with our models across our APIs and key developer products. Our model APIs are now processing approximately 22 billion tokens per minute. That’s up from 16 billion just a quarter ago.

This quarter, we launched Omni. It allows users to create anything from any input, starting with video. Since launching at I/O in May, there’s been a 40% increase in daily active users creating videos on the Gemini app. Our Gemma family of open models, small enough to run on local devices, are hugely popular. These models have been downloaded over 900 million times, and our latest Gemma 4 models have been downloaded over 300 million times since launching in April…

…The Gemini app, which now has 950 million monthly active users with daily active users tripling in the last year. Users love new agentic features like Daily Brief and our personalized agent, Gemini Spark, which is now available in the U.S. and internationally…

…If you think about an area like customer service, you need really good voice quality, you need live streaming, you need the ability to reason on that. If you’re a professional services firm, you need high-quality summarization, content generation, et cetera. Flash does very well on all of that.

Antigravity, Alphabet’s 1st-party agentic coding solution, has more than 2.4 million weekly active users; Antigravity has helped a team in Google Chrome compress a 2-year delivery timeline into 3 months; 83% of Alphabet’s sales team are using Gemini-powered tools every weeks, and achieving a 20% higher win rate; Gemini-powered agentic solutions are addressing 75% of support queries

Our agentic development platform, Antigravity, allows anyone to build in the agent-first era. It has more than 2.4 million weekly active users. Antigravity is a powerful tool for users and enterprises, and it’s completely accelerated how we build internally. As just one example, a team in Chrome is now on track to accelerate delivery by 8 times, compressing a 2-year timeline into 3 months through model-driven refactoring…

…83% of our sales team uses Gemini-assisted tools weekly, driving up to a 20% higher win rate when using customized pitch narratives. Our ads customer support teams use Gemini-powered agentic solutions that now autonomously address 75% of support queries, freeing them to solve ur customers’ most complex challenges.

AI continues to be expansionary for Google Search; Google Search usage hit an all-time high during the recent World Cup; management recently combined AI Overviews and AI Mode into a seamless Search experience; management is infusing Google Search with frontier capabilities such as AI agents; Alphabet’s AI features are driving higher usage of Google Search; AI Mode in Search has surpassed 1 billion monthly active users since launching in October 2025 and is driving incremental growth in overall Search queries; Alphabet’s AI features are now sending billions of clicks per week to websites; Alphabet has lowered the cost of AI Mode responses to its lowest level since launch despite introducing more AI capabilities; management is encouraged with the monetisation on even commercial queries that show AI Overviews; users are asking more detailed questions in AI Overviews and AI Mode, leading to more opportunities for more relevant ads; management is deploying and testing new advertising formats in AI Mode, including Text Ads, Direct Offers, and Highlighted Answers; Highlighted Answers is showing early user traction

With Search, AI continues to drive an expansionary moment with new experiences resonating with users and driving growth in queries. As a big football fan, I was particularly excited to see Search usage hit an all-time high during the World Cup this year…

…We recently brought together AI Overviews and AI Mode into one seamless Search experience that combines our frontier capabilities with the best of the web. We are continuing to incorporate more frontier capabilities into Search with agents, personal intelligence, and notebooks. Our AI-powered features are driving increased Search usage. Since expanding AI Mode globally last October, we have surpassed 1 billion monthly active users. Just like AI Overviews, AI Mode is driving an incremental increase in Search queries overall, and we are now sending billions of clicks to websites every week through AI features in Search…

…Thanks to our engineering and hardware optimizations, this quarter, we reduced the cost of AI Mode responses to its lowest level since launch, even as we have brought more advanced AI capabilities…

…We continue to be encouraged with monetization performance on queries that show AI Overviews, even as we’ve expanded AI Overviews to more commercial queries. Across AI Overviews and AI Mode, people are asking more specific and detailed questions, providing opportunities for more relevant ads. Within AI Mode, we continue to test and deploy a range of new ad formats. For Text Ads, we improve performance by adding contextual site links based on the conversation. Direct Offers is gaining momentum, with partners like IHG Hotels & Resorts soon surfacing special offers during trip planning. In Highlighted Answers, our latest experience, placing clearly marked sponsored links inside list responses is showing early user traction.

Alphabet’s management recently launched Ask Youtube, which is powered by Gemini and allows users to ask complex questions about Youtube videos; early engagement with Ask Youtube is encouraging, with 140 million users in June 2026; management is widening the Ask experience to Youtube’s search experience; Youtube introduced creator shows and custom sponsorships, where AI is used to dynamically surface videos tailored to a brand’s desired moment; Kate Spade worked with Youtube creators and saw a 3.25% brand lift in purchase intent; Youtube’s direct response growth is fueled by brands’ usage of Demand Gen; Arc’teryx used Demand Gen to achieve a 70% better return on advertising spend

We’re also bringing the power of conversational AI directly into the YouTube experience. Ask YouTube uses our Gemini models to let people ask complex questions about individual videos, get quick takeaways, and jump straight to moments in those videos. The early engagement is encouraging. More than 140 million users engaged with Ask YouTube on the Watch page in June 2026. We are bringing that Ask experience to the wider search experience on YouTube…

…Over 550 million watched them on their televisions. This made the FIFA World Cup 2026 the most viewed World Cup in YouTube history. Advertisers connected with fans through FIFA channel takeovers, Gemini-powered soccer-themed sponsorships, and game day mastheads…

… More broadly, on YouTube, we introduced an exclusive slate of creator shows making it easier for brands to tap into the fandom of creators. We launched custom sponsorships to put brands at the heart of the world’s biggest moments as they unfold on YouTube, using AI to dynamically surface videos tailored to a brand’s desired moment. Brands continue to partner with YouTube creators to engage new audiences. Kate Spade reached Gen Z through a first-of-its-kind YouTube creator campaign and partnered with creators Ellie Thumann and Hannah Meloche. By leveraging multiple format videos on YouTube, the brand drove a 3.25% brand lift in purchase intent…

SMBs continue to fuel our direct response growth through campaigns like Demand Gen. They leverage our AI to scale visual storytelling across YouTube, Shorts, and now Google Maps. To attract high-value shoppers across North America and Europe, outdoor brand Arc’teryx used Demand Gen to achieve a 70% better return on their ad spend compared to other paid channels.

Google Cloud’s momentum is driven by Alphabet’s vertical integration of the entire AI technology stack; the Gemini models are a key driver of Google Cloud’s growth and they are integrated into all of Google Cloud’s products; Google Cloud is seeing strong, diversified demand across products, customers, geographies, and industries; Google Cloud is enjoying growth from winning new customers (customer acquisition doubled year-on-year in 2026 Q2), expanding with existing customers (existing customers are exceeding commitments by 50% in 2026 Q2, an acceleration from 2026 Q1), and partners (Google Cloud Marketplace transactions were up 7x year-on-year in 2026 Q2); the Gemini Enterprise platform is seeing rapid adoption, with its Agent Development Kit reaching nearly 70 million total downloads in 2026 Q2; nearly 90% of the Fortune 100 companies are using Gemini Enterprise; businesses are using Gemini to build custom agents, automate processes, and more; 500 Google Cloud customers processed over 1 trillion tokens each over the last 12 months (was 330 in 2026 Q1), with 2,000 customers consuming over 100 billion tokens each; Booking Holdings has expanded a multi-year commitment with Google Cloud; it’s still early days for AI-native and AI-enabled workloads on the cloud, and it’s also early days even for overall workloads that have shifted to the cloud

Next, Cloud. Our continued momentum is driven by our integrated AI portfolio consisting of chips, models, data, security, and agent platforms, all designed to work together. Gemini continues to be a key driver of growth and is deeply integrated across all of our cloud products, including Gemini Enterprise, data analytics, cybersecurity, and Google Workspace. We are seeing strong, diversified demand across products, customers, geographies, and industries. Our product differentiation is driving expansion in three ways. We are winning new customers, more than doubling our acquisition velocity year-over-year. We are deepening our relationships with existing customers who are expanding their usage and exceeding their commitments by more than 50%, also, an acceleration over last quarter. We are driving growth with partners, with transactions on Google Cloud Marketplace growing over seven times year-over-year.

One of the strongest parts of our growth comes from the rapid adoption of our Gemini Enterprise platform. It’s differentiated with easy-to-use tools to build agents and automate processes, connectivity to enterprise systems, cost management, and governance tools. In Q2, Agent Development Kit, our framework for building and deploying enterprise AI agents, reached nearly 70 million total downloads. As I said, nearly 90% of Fortune 100 are using Gemini Enterprise. We have customers like PepsiCo for AI and analytics solutions, Intel to streamline core processes, HSBC for wealth management, Bell Canada for customer engagement, Macy’s for commerce experiences, and SIGNAL IDUNA for knowledge management. More broadly, Gemini is transforming how millions of businesses use AI to build custom agents, automate processes, improve cybersecurity, manage customer relationships, streamline data analytics, collaborate effectively, and more.

All of this momentum is driving growth in our paid token usage. Nearly 500 Cloud customers have each processed more than 1 trillion tokens in the last year, and usage is so much deeper than that. Over the last 12 months, more than 2,000 enterprises consumed over 100 billion tokens…

…Booking Holdings expanded a multi-year cloud commitment and are partnering closely

to advance our AI-powered ad formats. It’s also deploying Google’s AI technology to enable new

customer experiences like agentic dining reservations on OpenTable, helping restaurants get

discovered and booked right when it matters most…

…We used to talk about cloud itself, very small percentage of overall workloads and enterprises have shifted to cloud. Think about what percentage of workloads are really AI native and AI enabled. It again feels very early.

Alphabet’s management is seeing strong interest for Google Cloud’s AI-powered security offerings; 90% of Fortune 100 companies are Google Cloud Security users; 90% of Wiz customers are using AI-powered security features; in 2026 Q2, the number of AI workloads protected by Google Cloud’s security platform saw a 45% sequential increase; Alphabet has a new security offering called Google AI Threat Defense

We’re also seeing strong interest in our AI-powered security platform, which is differentiated because it integrates threat intelligence, cyber response prioritization with Wiz, AI-automated scanning, code remediation, and monitoring. Today, 90% of Fortune 100 are Google Cloud Security users, nearly 90% of Wiz customers are using AI-powered security features, and we have seen a more than 45% quarter-over-quarter increase in the number of AI workloads scanned and protected by our security platform. Our security tools are being used to protect critical infrastructure, including financial services organizations such as Morgan Stanley, telecommunication providers such as Telus, healthcare organizations such as Texas Children’s Hospital, software companies such as Atlassian, and several government agencies. With our new Google AI Threat Defense, we are really excited to bring our new cyber model and CodeMender to help our customers defend against AI threats.

Alphabet’s management believes Google Cloud offers the industry’s widest range of AI accelerators, including Alphabet’s latest TPU 8t and 8i, and NVIDIA’s latest Vera Rubin GPU platform; Google Cloud’s Virgo Network connects a million AI accelerators across multiple data center sites into a unified supercomputer; Google Cloud has native support for AI accelerator programming languages, which enables workload portability across GPUs and TPUs; Google Cloud’s agent-optimised Axion CPU has 30% better performance per dollar than other CPUs; Google Cloud’s AI infrastructure is seeing strong demand from a wide variety of companies, such as AI labs, technology companies, financial services providers, pharmaceuticals, and robotics companies

We offer the industry’s broadest range of accelerators from Google and NVIDIA, including the new NVIDIA Vera Rubin platform and TPU 8t and 8i, which delivers strong price performance…

…First, our Virgo Network, which is designed to meet the needs of modern large-scale AI workloads. It allows customers to connect a million AI accelerators across multiple data center sites into a unified supercomputer. Second, our software stack has native support for JAX, PyTorch, vLLM, and SGLang, enabling workload portability across GPUs and TPUs. Third, our new agent-optimized Google Axion CPU provides 30% better performance per dollar compared to peer offerings.

We are seeing strong growth and demand for our AI infrastructure offerings from leading labs such as Ineffable Intelligence, next-generation AI builders, including Kakao, financial services like Deutsche Börse Group, pharmaceutical companies such as Pfizer and Roche, and robotics and spatial intelligence companies such as World Labs.

Waymo has introduced a new vehicle, Oasis, to riders; Oasis is the 1st vehicle powered by the 6th generation Waymo Driver

Waymo introduced its newest vehicle, Oasis, to public riders. This is the first vehicle powered by the sixth-generation Waymo Driver and will welcome more riders in the coming months.

Alphabet’s management continues to accelerate the deployment of Gemini across the company’s entire advertising infrastructure; Gemini improves Search Ads’ ability to find relevant ads for longer searches that were previously difficult to monetize; in 2026 Q2, Gemini drove a 20% improvement in showing highly relevant ads in Shopping ads; 500,000 advertisers have adopted AI Max; users of AI Max enjoy 15% more conversions or value on Search at similar ROAs; AAA Auto Club Enterprises used AI Max to drive a 17% improvement in conversion volume and an 11% decrease in cost per lead; half of Alphabet’s SMB (small, medium business) advertising customers use its generative AI tools for creative development

We continue to accelerate the deployment of Gemini across our entire ads infrastructure to boost performance in three areas mentioned before: ads quality, advertiser tools, and AI user experiences.

First, ads quality. At Google Marketing Live, we showcased how Gemini improves query understanding, allowing us to find relevant ads for longer searches previously difficult to monetize. The core engine of our Search Ads relies on a dual prediction, delivering immediate utility for the user while maximizing measurable value for the advertiser. Gemini completely supercharges this capability. We use Gemini’s advanced reasoning to decode the nuances of longer, more detailed queries. With Shopping ads, for instance, we drove a 20% improvement in showing highly relevant ads, helping shoppers immediately find the best match.

Second, advertiser tools. Take AI Max. It’s out of beta, and 500,000 advertisers have already adopted it. Those who adopt our AI-powered campaigns like AI Max or Performance Max, see an average of 15% more conversions or value on Search at a similar ROAs. AAA Auto Club Enterprises used AI Max to personalize creative assets and capture growth from increasingly detailed insurance searches. This led to a 17% improvement in conversion volume and an 11% decrease in cost per lead. We see strong adoption of our generative AI creative tools, which makes creative development easier, especially for SMBs. In fact, over half of our SMB customers globally use AI to create or optimize their creatives…

Merchants are rapidly adopting Alphabet’s UCP (Universal Commerce Protocol) for agentic commerce

In collaboration with the retail industry, we established the open-source Universal Commerce Protocol, UCP, as the new standard for agentic commerce. Merchants are rapidly adopting UCP, with Target and Steve Madden now live, while new members have joined the UCP Shopping and Food Tech Councils to help steer its vision.

Google Cloud had 82% revenue growth in 2026 Q2 (was 63% in 2026 Q1) driven by growth in GCP; GCP grew at a higher rate than Google Cloud’s overall growth; Google Cloud’s growth was driven by core GCP, AI solutions, and AI infrastructure; Google Cloud started recognising revenue for the first time in 2026 Q2 from selling TPUs; Google Cloud’s revenue growth still accelerated materially even after excluding TPU sales; Google Cloud operating margin was 35.6% (was 32.9% in 2026 Q1 and 20.7% in 2025 Q2); Google Cloud backlog grew 11% sequentially to $514 billion in 2026 Q2 (was $462 billion in 2026 Q1); most of Google Cloud’s backlog are GCP contracts and just over 50% of the backlog is expected to be recognised as revenue in the next 2 years; when external TPU sales agreements are signed, they are reflected in Google Cloud’s backlog; Google Cloud’s $514 billion in 2026 Q2 primarily consists of GCP contracts; external TPU sales impact Alphabet’s operating cash flow because the company needs to build ahead of selling the systems; the use of TPUs in Google Cloud brings better margins

Cloud revenues were up 82% to $24.8 billion, driven primarily by GCP, which grew faster than cloud overall. Core GCP, AI solutions, and AI infrastructure were all important drivers of growth. We also began to recognize revenues from TPU system sales, which we delivered to customer data centers for the first time in Q2. Cloud revenue growth accelerated meaningfully even after excluding the impact of TPU system sales. Cloud operating income was $8.8 billion, more than tripling year-over-year, and operating margin increased from 20.7% in the second quarter last year to 35.6%. Google Cloud’s backlog increased by more than $50 billion sequentially, reaching $514 billion in the second quarter. The increase was driven by strong demand for our enterprise AI offerings. The majority of the backlog is related to typical GCP contracts from a broad mix of customers, and we expect to recognize just over 50% of the total backlog as revenue over the next 24 months…

…In terms of revenue recognition and how we look at the TPU system sales. The way to think about it is the following. When we sign the agreements that I’ve mentioned in the prepared remarks, they would be then reflected in the cloud backlog. The vast majority of the $514 billion of cloud backlog is the GCP agreements, but the TPU system sales are reflected in that backlog. We start building inventory to be able to sell those systems. You see that impact on the cash from operations because we built ahead, obviously, as we’re building that business and ramping up. Once we start delivering the sales, generally that’s when we start recognizing revenue…

…On the TPU margins, we don’t break out margins for any specific products or infrastructure component. Certainly, there are benefits from designing and manufacturing our own chips.

Alphabet’s management is seeing significant demand within Google Cloud; management continues to expect to recognise most of the revenue of external TPU shipments in 2027; management expects to use 3rd party centers in 2026 Q3 as a bridge while building internal compute capacity, as Google Cloud remains constrained by supply, and the use of 3rd-party data centers will result in near-term margin pressure; management has raised capex guidance for 2026 to $195 billion to $205 billion (was previously $180 billion to $190 billion; 2025’s capex was $91.4 billion, which was itself up 65% from $55.4 billion in 2024, and 2024’s capex was up 69% from 2023); the higher capex guidance is because of an acceleration in the delivery of capacity to meet growing demand (i.e. Google Cloud is pulling-forward capex); management continues to expect 2027’s capex to be much higher than 2026’s; management expects Alphabet’s free cash flow to remain under pressure because of AI-related capex; management continues to see attractive returns on Alphabet’s capex; the supply constraint of Google Cloud comes from strong demand from both external customers and internal use cases; the use of 3rd-party data centers is to support large customers at a high near-term cost, but there’s still high ROI (return on investment) over the entire life-time of the deals with these customers; when Alphabet’s input costs for capex goes up, management is able to raise prices for its services; Alphabet’s level of capex in 2027 is driven by strong demand indicators that management is seeing, and the dynamics in the market look even better today compared to a year ago

In Google Cloud, we are seeing significant demand for products and services, which we expect to drive strong growth. As I mentioned earlier, we started delivering TPU system to customer data centers in the second quarter. We continue to expect to recognize a relatively small portion of the revenues from our existing TPU system sales agreements this year, ramping as we exit 2026. We anticipate the vast majority of the revenues from these agreements will be realized in 2027. Given the supply-constrained environment, we plan to expand the use of third-party capacity in Q3 as a bridging strategy while we build out more internal capacity. This strategy allows us to keep growing our customer base and capture greater overall value. However, it will create modest margin pressure in the near term as we utilize this capacity…

…We are updating our full-year 2026 CapEx guidance range to $195 billion-$205 billion, up from our previous estimate of $180 billion-$190 billion. The increase in the range is primarily due to an acceleration in the delivery of capacity to meet growing demand. As we previously shared, we continue to expect our CapEx to increase significantly in 2027 and will provide more details at a later date…

…We expect the free cash flow will remain under pressure, driven by our investments in technical infrastructure, which enables us to capitalize on the AI opportunity and continue to drive attractive returns. Q2 represented another strong quarter…

…It feels like we are in very early innings of what feels like secular shift across multiple areas. In our core information businesses, just the possibilities when I see what all you can do with the absolute frontier capabilities, there’s still a lot of work ahead to translate all that into experiences for our consumer users. You can think about end-to-end agentic experiences to really meaningfully do a lot more for them. All of that looks like extraordinary opportunities with extraordinary returns for executing well on those opportunities…

…We are seeing very strong demand, both from external cloud customers as well as across the business…

…I think on the bridge deal, the main thing I would say is, look, on the margin, there are very, very large customers of ours on Cloud who we are trying to support them through this extraordinary moment. The incremental opportunities they are bringing to us, while a short-term cost over a few months may be very high, in the lifetime of the deal, as we bring more capacity on, is highly ROI positive…

…To the extent that our input cost is going up to us, we reflect that in our ability to price our solutions and see returns there. All of that is factored into how we are planning…

…Our compute capacity investments in 2027, to the first question I answered, I think we are seeing strong demand indicators, including long-term deals, the existing deals which we have, which are renewing with exceptional demand on a moving forward basis. We are using all that to plan and invest accordingly. I think if anything, the dynamics look healthier than where we were about a year ago, that’s what gives us the confidence to undertake those investments.

Alphabet’s management is aware that the company does not have frontier capabilities in coding and agentic coding and the teams are working on it; 3.6 Flash is already showing improvement in coding compared to 3.5 Flash; management is very confident that Alphabet will be at the frontier again and they are being very ambitious with Gemini 4; Gemini 4 will be a much larger base model compared to the previous generation; management expects Alphabet to pick up the pace to almost a monthly-cadence when it comes to model releases; management wants Gemini 4 to compete at the frontier level of where the frontier will be when Gemini 4 is released

There are areas where we’ve acknowledged we need to improve. Coding and agentic coding is an example of that, and the teams are very focused on it…

…In terms of agentic coding, we are iterating, and you will see us make continued iterations. 3.6 Flash, for example, compared to 3.5 Flash, jumped over 10 points in DeepSuite as a benchmark, and it is more token efficient doing so. We are using it internally. We are testing it with many customers in coding. We see the progress just in six weeks from the prior version to the new version, and you will see more continued iterations on that as well.

In terms of the frontier, we are both very committed and very confident of being at the frontier. For the next generation of frontier, you’re going to need much larger base models. We are now training Gemini 4, and we’re being very ambitious with it…

…We will need Gemini 4 as a larger base model to compete at that frontier level, so we are focused on executing on that well…

…On the speed of model releases, I do think you will see us continue to pick up pace…

…Picking up pace and releasing models almost at a monthly cadence is part of our roadmap as we are building Gemini 4 as well…

…We want to compete at the frontier level of where the frontier will be when Gemini 4 comes out, we are applying a lot of our compute and effort in that direction.

Alphabet’s management’s priority with TPUs is to allocate to internal teams for frontier model development; to balance demand for TPUs, Alphabet is using both TPUs and GPUs to serve its models

In terms of allocating our TPUs, look, our first priority is making sure we are allocating what we need to compete at the frontier in terms of AGI development…

…Given the extraordinary demand to balance the external demand, even for most cloud customers, we are using both TPUs and GPUs mainly for serving our models. Think Vertex AI, Gemini Enterprise, the momentum we see. We’re using it or agentic workloads, et cetera. We are using it for those purposes…

Amazon (NASDAQ: AMZN)

AWS grew 36.7% year-on-year in 2026 Q2 (was 28% in 2026 Q1) and is now growing at its fastest pace in 18 quarters; AWS added $4.6 billion in revenue sequentially, 80% more than the largest increase; AWS’s backlog is $496 billion in 2026 Q2 (was $364 billion in 2026 Q1), up triple-digits year-on-year; AWS’s run rate has reached $169 billion (was $150 billion in 2026 Q1); AWS’s AI business revenue run rate is $25 billion in 2026 Q2 (was $15 billion in 2026 Q1), up triple digits year-on-year; management thinks customers are choosing AWS for AI for 4 reasons, namely, (1) AWS’s broad capabilities, (2) customers want their AI inference to be at where their other applications and data reside, and this happens to be in AWS, and (3) AWS has the strongest security and operational performance; AWS is seeing growth in both AI and non-AI workloads, and growth in one is driving growth in the other; AI workloads drive non-AI workloads because (1) post-training reinforcement learning and agent tool use are mostly run on CPUs, and AWS’s Graviton CPU has 30%-40% better price performance than competitors, and (2) AI workloads need databases, and this is an AWS strength too; management sees enterprises as being very early in using inference at scale; management now believes AWS can be a trillion-dollar annual revenue business in time, up from the previous view of a “few hundred billion dollar revenue business”; management is seeing customers who want to benefit from AI accelerate their migration to the cloud; management is seeing a strong correlation in customers’ AI spend and core growth in AWS; AWS is on track to doubling its 2025 power capacity by 2027

Revenue growth of 36.7% year-over-year, accelerating for the fifth straight quarter, our fastest growth in 18 quarters back when AWS was less than half its current revenue size. We added over $4.6 billion in revenue quarter-over-quarter, about 80% more than our largest increase ever. Our backlog stands at $496 billion, growing triple digits year-over-year. AWS is now a $169 billion annualized revenue run rate business, which, for perspective, would place it 24th on the Fortune 500 list if it was a standalone company…

…Our AI revenue run rate climbed significantly quarter-over-quarter, and is now also over $25 billion, growing triple-digit percentages year-over-year.

Customers choose AWS because we offer the broadest capabilities. They want their AI inference to reside near their other applications and data, and more of it resides in AWS than anywhere else. Because AWS has the strongest security and operational performance, we’re seeing strong growth across both AI and non-AI, what we call core, and growth in one is driving growth in the other. Growth in AI drives core because post-training reinforcement learning and agent tool use is mostly done on CPUs versus AI accelerators. This is an advantage for AWS, as our Graviton chip is the strongest CPU chip, offering up to 30%-40% better price performance than other options. You need a place to store this AI data and to run vector databases, which are also emblematic of a meaningful edge for AWS because we have the broadest and most capable functionality by a fair bit in these core infrastructure areas. We feel similarly about the AI stack, top to bottom. We have a unique offering that customers are excited about…

…Remember, enterprises are still very early in using inference at scale in their current production applications. We long believed AWS could become a few hundred billion-dollar revenue business and now believe it’ll be at least double that, and very possibly be a trillion-dollar annual revenue business for us in time, with very appealing accompanying free cash flow and return on invested capital…

…Increasingly, customers seeking the full benefits of AI are accelerating their transition to the cloud. We see a strong linkage between AI spend and core growth. As customers invest in AI, we see a corresponding increase in core consumption…

…We’re on pace with the capacity build that we talked about a few quarters ago, where we said we expect to have double the power capacity by the end of 2027 that we had in 2025, and we continue to be on that track.

AWS’s chips business is now at a $25 billion annual revenue run rate in 2026 Q2 (was $20 billion in 2026 Q1), up triple-digits year-on-year; management thinks AWS’s Tranium AI chip and Graviton CPU both have leading price-performance; OpenAI and Anthropic, the 2 highest-profile AI labs, have multi-year, multi-gigawatt commitments to Trainium; a growing number of AI startups, and larger technology companies, are also adopting Trainium; Graviton is used by 98% of AWS’s top 1,000 EC2 customers; Graviton revenue commitments are up 3x sequentially in 2026 Q2; Graviton 5 is growing nearly 2x faster than Graviton 4 did; AWS continues to have a deep partnership with NVIDIA, as management knows that customers want choice; management sees incredible demand for Trainium and some customers who are interested in obtaining Trainium chips outside of AWS; management thinks there’s a real chance AWS will start selling Trainium chips to 3rd party data centers

Our chips business now has an annual revenue run rate of over $25 billion, growing triple-digit percentages year-over-year…

…We are unusually well-positioned for this AI inflection, given our leading price-performance chips in both AI with Trainium and CPU with Graviton. In addition to the two leading AI labs in the world, Anthropic and OpenAI, making multi-year, multi-gigawatt commitments to Trainium, an increasing number of AI startups are also adopting Trainium, including unicorns like Neurorobotics and Odyssey, joining startups like Twelve Labs, Descartes Labs, Poolside AI, Karakuri, Metagenomi, NetoAI, and Splash Music, and larger companies like Uber and Pinterest all adopting Trainium.Graviton is used by 98% of our top 1,000 EC2 customers. The revenue commitments have increased nearly three times quarter-over-quarter, and Graviton5 is growing nearly 2x faster as Graviton4 did.

We also continue to have a deep partnership with Nvidia, and we’ll continue making AWS the best place to run Nvidia chips, as we have customers who will run on Nvidia for as long as we can foresee, and we believe strongly that customers want choice…

…We just have an incredible amount of demand for Trainium. There are a lot of customers who are very excited about using it in the form that we’re providing right now. We do have an increasing number of customers who are interested in us providing the Trainium chips to them, separate from our cloud, and we’re actively having those conversations and exploring, and I expect there’s a real chance we’ll do that in the future.

Amazon’s management continues to think that technical companies will build their own foundation models, and AWS’s SageMaker AI service helps them do that; Bedrock, AWS’s fully-managed service for companies to build upon frontier models, provides high-performance and cost-effective inference; Bedrock provides the best selection of leading models at superior performance and with governance and security; Bedrock continues to grow rapidly; building agents at production scale is hard, so Amazon Bedrock Agents provides the building blocks for organisations to build and manage agents; management thinks most companies will use turnkey agentic services; AWS’s coding agent Kiro is 50% more cost-effective than competitors, and usage has tripled sequentially in 2026 Q2; Amazon Q is an AI work companion that lets users manage leading SaaS tools and take action for users; Amazon Q was recently made more capable with autonomous agents and more integrations with SaaS tools; Amazon Q’s customers include large multinational companies; AWS has agentic services such as Amazon Connect for call centers and AWS Transform for software migration; management recently released AWS Continuum, an agentic service leveraging frontier models for cybersecurity; management expects AWS Continuum to grow quickly; all 5 major airlines, and many leading banks and healthcare companies, are users of Amazon Connect; Amazon Connect is growing very quickly;

As we’ve been saying for 18 months now, technically competent companies are going to build their own foundation models. Not the really big frontier models, but smaller models that leverage their proprietary data. There is no easier service for this than our SageMaker AI service. Customers also need a high-performance, cost-effective inference service, and that’s what Amazon Bedrock provides. Bedrock not only provides the best selection of leading models at superior performance and with the governance and security controls that companies need, it’s also continuing to grow incredibly quickly…

…After you’ve built an agent, you have a lot of muck to worry about. A production agent needs somewhere secure to run, memory so it holds context, an identity so it can act on a user’s behalf, tools and data to connect to, and a way to watch what it’s doing once real traffic hits. Stitching all that together reliably is hard, and it’s stalled many production deployments. It’s why we’ve built Amazon Bedrock Agents. It provides building blocks as managed infrastructure, and our teams keep iterating, recently adding features like policies which give companies deterministic controls over what agents can do, payments so agents can execute transactions autonomously, web search to ground agents’ knowledge without having to leave AWS, and a new harness that further speeds up how fast customers can put this all together, including creating the agent with Strands Agents. While companies will construct their own purpose-built agents from the ground up, most will also use turnkey agentic services…

…Our own spec-driven Kiro, which is up to 50% more cost-effective than others and tripled in usage quarter-over-quarter…

…Amazon Q, an intelligent AI work companion that helps you manage, search, and automate your digital workload across email, calendar, local or cloud files, and custom workflows. Unlike other offerings in this space, Q also lets you manage across leading SaaS tools like Slack, Salesforce, Jira, Teams, and ServiceNow. Q enforces a company’s existing access controls so each person sees only what they’re cleared to see. Then it takes action: scheduling meetings, drafting and sending email, updating a CRM record, building a dashboard, and more. In Q2, we made Q even more capable, adding autonomous agents that customers set up in plain language to run continuously in the background and carry out multi-step tasks, a personalized activity feed that pulls email, messages, calendars, and tasks into one prioritized view, and 16 new integrations, including Adobe, Moody’s, and Snowflake. Q has momentum, with 3M, Allianz, AstraZeneca, Autodesk, BMW, Exxon, FINRA, Hyundai, Intuit, Mondelēz International, Moody’s, the NBA, the NFL, Sun Life, and Southwest Airlines all using it.

We also have services like Amazon Connect, our call center service, and AWS Transform, which automates software migration growing quickly…

…We recently released AWS Continuum, which discovers, prioritizes, validates, and remediates code vulnerabilities. It starts by ingesting the backlog of vulnerabilities a team already has and then leverages the new frontier models to run comprehensive scans. Continuum uses agents in each company’s own business context to prioritize what matters, reasoning through questions like, “Is the affected component deployed? Is it reachable? Is it in a production path? What’s the impact if it’s exploited?” Then it validates vulnerabilities in a sandbox so teams aren’t chasing false positives. Finally, it recommends the fix. It is hard to talk with enterprises about AI right now without their mentioning security. We expect Continuum to grow quickly…

…Amazon Connect, which is our call center service, which is used by all five major leading airline providers, as well as many of the leading banks and healthcare companies, continues to grow very quickly.

Amazon’s management has clear line of sight to strong financial returns on the company’s originally-planned capex of $200 billion, or higher, for 2026; Amazon’s capex has 2 components, the data centers, and servers; spending on data centers is done 2 years before servers are slotted in to start monetisation; once a data center has servers in place, AWS immediately starts generating significant revenue; a data center can be monetised for 30-plus years without needing startup capital again; servers are typically purchased months before they are put into service; management will only buy servers when they see strong demand signals; servers take slightly less than 3 years to breakeven and have 5-6 years of useful life; most of AWS’s AI capacity is contracted for at least 5 years, so AWS earns significant free cash flow on servers in the 2-3 years after breakeven; AWS has a strong track record of pulling forward breakeven periods for servers; AWS typically gets 5-6 generations of server economics from data centers, with subsequent generations after the 1st having better overall economics because the upfront data center spending is not needed; when there’s demand for many data centers, Amazon has to spend ahead of time before the data centers can come online and be monetised, and management sees very compelling revenue, free cash flow, and ROIC (return on invested capital) a few years after the data centers are being monetised; management has gone through the same monetisation cycle during the 1st era of cloud computing; cloud computing’s demand-build was more gradual than AI; management is seeing margins and returns for Amazon’s AI build tracking slightly ahead of the cloud computing build at the same point of evolution; management has raised Amazon’s capex for 2026 to $220 billion (capex was $128 billion in 2025, and $83 billion in 2024 because of higher cost of memory chips; even with the higher capex guidance, management sees AWS as being supply-constrained in 2026 and 2027, and possibly 2028, with 2028 demand being striking; the lion’s share of AWS’s capacity in 2027 is already reserved, and quite a bit of capacity for 2028 is also already reserved

Earlier this year, we said we plan to invest approximately $200 billion in cash CapEx in 2026, the majority of which to support AI and AWS. At this level of spend and higher, we have clear line of sight to strong financial returns…

…There are two major parts of the investment, the data centers and the servers and networking equipment that go into them. These have different capital cycles. Data center capital is spent starting two years before we can put servers into them to start monetizing. Once a data center opens with servers plugged in, we start generating significant revenue right away and then get to monetize these data centers for 30-plus years without having to spend that startup capital again. Servers and networking equipment operate on a shorter cycle. We typically purchase these a few months before putting them into service, so we have strong visibility into customer demand before we trigger the spend. If the demand isn’t there, we won’t spend the capital.

For servers and networking equipment, on average, it takes a little less than three years to break even on that investment. The servers currently have a useful life of at least five to six years, and most of our AI capacity these days is being contracted for at least five-year terms. That means that we’re driving significant free cash flow on the servers and networking equipment in the two to three years after we break even. It’s also worth noting that AWS has a strong track record of pulling forward break evens on server equipment where we’ve already made meaningful progress and finding ways to extend the useful life of this equipment without sacrificing customer experience. For our data centers, which have 30-plus-year useful lives, we should get at least five to six generations of server economics, like I explained earlier, with subsequent generations after the first having even better overall economics because we don’t have to repeat that upfront data center investment I mentioned earlier.

This means in the short term, when demand is necessitating so many data centers being built simultaneously in advance of when we can start monetizing them, we’ll spend a lot of CapEx and encounter free cash flow headwinds until these data centers come online, can be monetized, and we get a few years into these servers being utilized. As we get a few years out and the revenue growth outpaces the incremental CapEx growth, which will happen at some point, the resulting revenue, free cash flow, and return on invested capital is very compelling. We’ve done this before in the first era of cloud computing, just over a longer time horizon, where demand built more gradually than it has in AI. We see the margins and returns in AI tracking what we saw with Core at the same point of evolution, actually a little ahead.

We now believe we will spend approximately $220 billion in cash CapEx in 2026. The higher cost of memory pushing this number up from our prior estimate of about $200 billion. Even at that amount, we will still not have enough capacity to meet all the demand we have in 2026, and I believe this dynamic will also be true in 2027, too. In fact, the demand we already have for 2028 is striking…

…We have so much demand right now. Apart from what we’ve talked about in 2026, the lion’s share of capacity in 2027, we’re adding a lot of capacity, as I mentioned just a few minutes ago, is largely reserved, and we have quite a bit of capacity that’s already been reserved for 2028.

Over 350 million Amazon customers have used Alexa for Shopping, Amazon’s agentic shopping assistant, in the last 12 months; in 2026 Q2, active users of Alexa for Shopping was up nearly 100% year-on-year, and interactions was up 5x; management expanded Amazon Lens to 10 additional countries and it’s now available in 21 countries; in the US, customers who use Alexa for Shopping spend 40% more per order than those who don’t; customers who’ve tried Alexa+ are signing up for Prime at 25% higher rates

Customers love Alexa for Shopping, our agentic AI shopping assistant. It offers personalized recommendations, product comparisons, price history, and the ability to automate shopping through features like price alerts and auto-buy. Over 350 million customers have used it in the last 12 months, and engagement accelerated in Q2, with active users nearly doubling and interactions up over 5x year-over-year. We also expanded Amazon Lens, which lets customers take a photo of anything they see and instantly find the same or similar items on Amazon, to 10 additional countries, and it’s now available in 21 countries around the world…

…We find that everywhere Alexa goes, it drives momentum for the business. For example, in the U.S., customers who use Alexa for Shopping spend an average of over 40% more per order than those who don’t. Customers who’ve tried Alexa+ are signing up for Prime at nearly 25% higher rates.

Ads Agent is one of Amazon’s AI-powered advertising tools and it lowers campaign setup time from hours to minutes; advertisers using Ads Agent see 8% lower cost per impression and 6% lower cost per acquisition; management has expanded Ads Agent to 11 new countries in 2026 so far

We make it easy to create, launch, and optimize full-funnel campaigns using AI-powered tools, including Ads Agent, which turns hours of setup and targeting into minutes. Advertisers using Ads Agent targeting see 8% lower cost per impression and 6% lower cost per acquisition, and we’ve expanded it to 11 new countries this year.

Amazon’s management thinks that AWS can be wildly successful even if Amazon does not have its own frontier model, because there is not going be just one model to rule the world; management still wants Amazon to pursue building frontier models because it gives Amazon more control over costs and model-features; management thinks there will be at least 6 frontier models that are equally good over the next few years, and Amazon’s model will be among the mix

AWS and Amazon can have a wildly successful business without its own frontier model. A lot of that is because there is not going to be one model to rule the world. You already see that right now. You see it. It’s not just Anthropic, or it’s not just OpenAI. You see increasingly more and more companies being interested in the open models as well…

…All that said, we are pursuing our own frontier model, and we’re doing it for a few reasons. First of which is it just gives us additional control over cost. Cost for our own consumer applications, also we’re trying to drive costs down for customers. Having a player like ourselves that’s always focused on trying to take the price performance and the cost down for customers all the time, we think will help keep the models more cost effective for customers. I think also it allows us to have more control over prioritization on what models focus on. We have, both from our own external customers as well as our internal customers inside the company, certain priorities that matter that we want the models trained especially well for, then it gives us some control on speed. My view of it is that within the next few years, you’re going to have at least a half dozen models that are comparably good to each other.

In the 2025 Q4 earnings call, Amazon’s management said market demand for AI compute looked like a barbell with AI labs on one end spending a lot on compute for just a handful of applications, and with enterprises on the other end using AI for productivity purposes; now, the adoption curve for AI still looks like a barbell, with the middle being enterprise production workloads that are mostly not using inference pervasively; management thinks the middle of the barbell will become the largest AI workloads, and that AI will change every customer experience and lead to the invention of brand-new experiences; management does not know if the trajectory of the middle of the barbell will be as steep as seen currently with the ends of the barbell

We see this adoption curve in AI right now is very barbellled. There is, on one end of the barbell, the AI labs are consuming gobs and gobs of compute, and there are a few runaway successful generative AI applications like Claude Code and ChatGPT. On the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. These are things like automating customer service or business process automation or fraud or things like that. In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren’t. That is going to change very significantly over time. In my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too. I think we’re still in the relative early stages of how much demand there’s going to be for AI. I think it’s going to change every customer experience that we know. I think that it will invent all sorts of new ones that we never imagined. I don’t know if the trajectory of that middle part of the barbell will be the same wildly steep trajectory that we’ve seen with the current barbell AI labs piece.

It seems that most of AWS’s contracts do not have built-in protections for cost inflation

[Question] Could you talk about how your pricing strategy at AWS incorporates future cost inflation? Do your longer-term contracts allow for stable return profiles despite cost inflation?

[Answer] What I would say is that most of the deals that you sign, there’s a certain amount of your demand that is on demand, where there aren’t contracts. A large amount of it tends to be deals and agreements that you’ve signed. The deals that you sign, those will be the prices and those will be the agreements that we have over the duration of that contract. New agreements that you sign, you always take into account what your costs are and how you ultimately build a price that you agree to with your customers. I think it’s no secret right now to any company in the world that there are inflated prices right now on some of the components like memory and hard drives and SSDs.

Apple (NASDAQ: AAPL)

Apple’s management recently unveiled the new Siri AI, and they are thrilled with the response from early users; Apple’s work on Apple Intelligence are done in a way that’s personal and private, with AI models that are running on-device and on servers using private cloud compute; management thinks Apple’s differentiating factor with AI is its massive unified memory bandwidth, industry-leading power-efficient performance, and deep on-device intelligence; management thinks Apple’s products are the best hardware for users to experience AI; Apple recently unveiled new AI-powered accessibility features; one AI-powered accessibility feature is for power wheelchair users to control drive systems with just their eyes, using the Apple Vision Pro; Siri AI had a public beta a few weeks ago, and the feedback has been great; Siri AI is private, based on a user’s personal context, and integrated into iOS; management is unsure what the compute costs will be for the new Siri AI, but they do see upgrade possibilities on iCloud+ for heavy users; management is still unclear if the introduction of Siri AI will result in a step-change in Apple’s cost structure; Siri AI’s initial roll out will not include China and Europe for regulatory reasons, but management is working with the relevant authorities to solve the problems

This year’s WWDC was a wonderful showcase of our latest innovations. We were tremendously excited to unveil the all-new Siri AI, a completely reimagined version of Siri that is profoundly capable, deeply personal, and integrated seamlessly across our platforms. We’ve been absolutely thrilled by the response from people who’ve been using Siri AI in the developer and public betas. The reviews from early users have been phenomenal, it’s been so wonderful to hear from people who are excited about the capabilities we’ve built. It underscores our philosophy that building AI that is private and based on personal context can change how users find information and get things done with our products in a way that truly enriches their lives…

…We’re excited about the work we’re doing on the next generation of Apple Intelligence, including Siri AI and the AI features we’re developing across our platforms. These experiences are intuitive and useful, while also deeply integrated in a way that’s personal and private with the latest models running on-device and on servers using private cloud compute…

…What sets Apple apart is the unique combination of massive unified memory bandwidth, industry-leading power-efficient performance, and deep on-device intelligence, all built around the customer experience from the ground up. The result is that Apple has created the world’s best hardware to experience AI, whether using Apple Intelligence, including Siri AI, or third-party offerings…

… In honor of Global Accessibility Awareness Day, we unveiled new features to help users get more out of the products they use every day. New intelligent capabilities are coming to VoiceOver, Magnifier, Voice Control, and Accessibility Reader to make them more useful and intuitive. We’re also using on-device speech recognition to generate subtitles for video content without captions. Apple Vision Pro is adding a feature for power wheelchair users to control drive systems using just their eyes…

…[Question] Just on iOS 27 and Apple Intelligence, went into public beta earlier this month. Could you talk about learnings from the public beta? Will the new Siri AI be a demand driver for iPhones this holiday?

[Answer] We released it to the public for a public beta a few weeks ago, the continued feedback is really, really great. I think it’s a very big idea to have AI that’s private, that’s based on your personal context, and that’s integrated across the operating system… In terms of what it means for compute cost, it’s obviously early going for us. I don’t want to say that we have a complete plan for that. We do believe there will be people that want to use it a lot. We will have some kind of upgrade possibilities on iCloud+ where people can buy up the stack on iCloud+. We’ll see how the pickup for that is…

…[Question] Is it right to think that the capital intensity of Apple will change in the future because of Siri AI?

[Answer] We use some third-party cloud, and we do our own data centers. There will be a mix. Generally speaking, as you know, we have been growing our OpEx and spending more in AI in general and quite a bit more. There are other locations on the P&L other than OpEx, like COGS etc., that also have AI expenditures. We’ll see what Siri AI does from the cost side of it. There’s also the ability when people use it a lot for them to move up on an iCloud Plan as well. What the balance of that is a bit uncertain at the moment…

…[Question] When you announced Siri AI, you also did mention, along with the rollout, that probably we won’t have the initial rollout in China and Europe. Just wanted to get your updated thoughts on that front.

[Answer] You look at the EU, we’re working closely with the commission. Obviously, our complete desire is to launch everything everywhere at the same time. That’s always the philosophy that we have. We have not been able to do that in the European Union, but we’re working closely with them to try to get to something that would allow us to offer Siri AI there. It is offered or will be offered for the Mac there, because the Mac is not covered by the same regulations as the iPhone and the iPad. Net-net, we’re working with them and hope to reach some sort of solution. You look at China, last week we received approval to ship sort of the original features of Apple Intelligence, things like cleanup and so forth. We’re working now through the rollout of those, and there will be more work required down the road for Siri AI. We’re at the front end of that.

Apple’s management thinks the Mac is an AI powerhouse, with excellent on-device inference and creation capabilities; management is seeing customers use the Mac Mini for agentic AI, and deploy clusters of Mac Studios to run frontier models locally; more companies are choosing Macs for their on-device AI advantages, including Disney and Credit Agricole; Credit Agricole is using on-device AI on MacBook Pros to reduce manual processing time of regulatory workflows by 80%

Mac delivered its best June quarter yet with $10.4 billion in revenue, growing an impressive 29% from a year ago despite significant supply constraints. This revenue growth was driven by the incredible strength of our latest lineup with MacBook Pro and the all-new MacBook Neo. According to IDC, we gained share globally. We also set a June quarter revenue record in developed markets and an all-time record in emerging markets with particular strength in Greater China, where we had an all-time revenue record. In addition, we achieved all-time records for upgraders and customers new to Mac.

With the power of Apple silicon, the Mac lineup delivers outstanding power-efficient performance, massive memory bandwidth, and next-level AI capabilities. Mac continues to be the ultimate AI powerhouse, excelling at high throughput, on-device inference, and creation across a broad range of AI workloads. We’re seeing customers increasingly put those capabilities to work, from using Mac Mini as a powerful platform for agentic AI to deploying clusters of Mac Studio systems to run frontier class models locally…

…More companies are choosing Mac for on-device AI advantages, including lower costs, better performance, and enhanced privacy and security. At Disney, creative teams are increasingly turning to Mac for on-device AI workflows that reduce overall cloud token costs and keep their IP secure. Crédit Agricole, France’s leading retail bank, is using on-device AI on MacBook Pro to streamline regulatory workflows, reducing manual processing time by over 80%.

Users of AirPods are using live translation, powered by Apple Intelligence

Meanwhile, we continue raising the bar across our AirPods lineup, whether it’s the immersive listening experience of AirPods Pro 3 or the premium listening experience and exceptional active noise cancellation of AirPods Max 2. With live translation powered by Apple Intelligence, people are crossing language barriers and connecting like never before.

Apple’s management will reinvest tariff refunds into the US; Apple recently announced a new agreement with Broadcom to design custom silicon and wireless connectivity technologies; management expects the new agreement with Broadcom to exceed $30 billion, and it is part of Apple’s $600 billion manufacturing commitment to the US, and the largest commitment to-date; Apple will soon open the Apple Advanced Manufacturing Center in Houston; the Apple Advanced Manufacturing Center is in a facility where Apple is currently assembling AI servers and will soon manufacture Mac Minis; the Apple Advanced Manufacturing Center will be imparting the processes Apple uses to make its products to strengthen the USA’s entire advanced manufacturing ecosystem; Apple will be sourcing 100 million components from TSMC’s Arizona fab in 2026 and management is really pleased about the fab; Apple’s use of the TSMC Arizona fab is part of Apple’s $600 billion commitment

Last year, we made a $600 billion commitment to the U.S. over four years, and now, as we said before, we plan to reinvest the tariff refunds we’ve received into the U.S. We’re pleased with the progress we’ve already made advancing the American supply chain. Earlier this month, Apple announced a new agreement with Broadcom to design and produce custom silicon components and cutting-edge wireless connectivity technologies. The new multi-year agreement with Broadcom, which is part of Apple’s American Manufacturing Program, is expected to exceed $30 billion. This marks our largest-ever American manufacturing program commitment. It’s also an important step forward in our work to build an end-to-end silicon supply chain here in the U.S.

We’re excited for the upcoming opening of the Apple Advanced Manufacturing Center in Houston. The center is located in a facility where we currently assemble advanced AI servers. Later this year, we’ll make Mac Mini there, too. The center will teach students, supplier employees, and business of all sizes the same innovative processes we use to make our products. The goal is to empower American manufacturers to take their work to the next level and strengthen the entire advanced manufacturing ecosystem…

…In Arizona, we do source over 100 million components this year out of Arizona, it is part of our $600 billion commitment to the U.S., and we could not be more pleased with how that fab has ramped and is producing for us.

Apple’s management thinks it will be a great idea if there were more memory chip suppliers; management had to reluctantly raise the prices of Apple products to deal with a 100-year flood on memory pricing; it’s still unclear to management if Apple’s price hikes will impact demand

In terms of the sources of supply, primarily the DRAM market has three suppliers. Obviously if there were more suppliers, that would be good, and it would help us on the supply side and perhaps the pricing side. It’s unclear on the pricing side, it could help on the supply side…

…We reluctantly raised prices, I would say. We did it because we’re in what I would characterize as a 100-year flood on the memory pricing, with exponential increases in memory prices…

…Obviously, we’ve now had to increase prices on iPad and Mac — and the price elasticity there, it’s just too early to come to a definitive conclusion of what happens there, because it takes a little while for the channels to adjust since there’s channel inventory, and it takes a while for the consumer to respond. And so we’ll understand that more in the weeks ahead.

ASML (NASDAQ: ASML)

ASML’s management is seeing strong end-market demand that is motivating its customers to raise their capex; management is keen to support its customers’ demand; management is seeing both Logic and DRAM customers entering long-term agreements with their customers and having unprecedented visibility on future demand; management is seeing Logic customers add a lot of capacity on existing advanced nodes because of AI-related demand; management is seeing Logic customers aggressively ramp the 2nm node and even start ramping the 1.4nm node; the dynamics in the Logic segment are driving both an increase in litho intensity and more demand for litho; management sees a clear need for more supply of memory chips; management is seeing Memory customers accelerate capacity plans; management is seeing advanced Memory nodes calling for higher lithographic intensity, for both EUV (including low-NA EUV) and DUV (immersion belongs to DUV); management is seeing a perfect storm for ASML on DRAM (memory chip) in 2026 and beyond

The end market demand, this has motivated our customers to increase their CapEx but also accelerate all their plans. This really creates a need for more systems basically starting this year. We are doing the same on our side, extending output, extending basically our teams so that we can support them moving forward…

…What we also see for both Logic and DRAM is that our customers are getting long-term agreements with their own customers, which really invites them to commit for the long term. Because they have what I would call a quite unprecedented visibility on what will happen to the market…

…Quite a bit happening with Logic. So first, if we look at the existing advanced nodes 5nm, 4nm, 3nm, we see that our customers are trying to add a lot of capacity there now. This is because there is a huge demand on those technologies coming from AI. At the same time, the 2nm ramp is done as aggressively as possible. We see customers adding capacity, accelerating their plans and even start basically to look at the 1.4nm ramp…

…These dynamics in the logic segments are driving both an increase in litho intensity and greater demand for advanced lithography…

…If we look at the price of Memory today, either for DDR or for HBM, there is a clear need for more supply. This is translating into, again, acceleration of capacity plans from our customers. So this is happening with all customers. On top of that, as we discussed previously, the latest nodes are calling for more litho, for higher litho intensity, both on EUV but also advanced immersion…

…DRAM lithography intensity is rising as customers migrate to advanced nodes. This includes both EUV and deep UV immersion with EUV Low-NA growth driven by the increased replacement of multi-patterning with more cost-effective single-exposed EUV…

…HBM will require more wafers. So there’s a volume effect again. So that’s one element. The second element is, of course, the number of EUV and immersion layers, which has increased basically on the nodes that are ramping very, very strongly right now. So the 1c node, for example, which is going to be an enormous node, or even 1b are using more EUV layers. So this is really this combination, which creates a bit the perfect storm for ASML on DRAM this year and most probably the next few years to come.

ASML’s management is having very constructive discussions with customers for long-term business; ASML has already received nearly all the EUV orders needed for 2027, despite the company adding 30% more EUV capacity for 2027 compared to 2026; ASML has already received large orders for EUV for 2028, and this has driven ASML to increase EUV capacity in 2028 by another 30%; the capacity additions for EUV for 2027 and 2028 are for low-NA EUV systems; ASML can achieve the capacity additions by optimising existing cleanroom space, and has space to increase capacity by even more if needed; the 30% capacity increase in EUV for 2027 refers to tools, while the actual wafer capacity will be increased by 45%; the cleanroom optimisation does not require ASML to sacrifice any of its high-NA EUV supply; management thinks it’s possible that ASML’s anticipated capacity additions will increase further; the current planned capacity addition for 2028 is an act of preemption by ASML’s management, but they are based off strong customer signals; management sees plenty of operating leverage to come down the road for ASML

We are having very constructive discussions with our customers on the long term. Their own visibility to their business allows them to share with us also, longer than, I would say, usual visibility on their business. We are talking of course about next year, but also beyond that. Now this has also translated practically into very strong order bookings through the first half of 2026.

If we look into more detail starting with 2027, there we are pretty much already close to receive all the EUV orders we need for 2027. This is with us adding about 30% capacity for EUV in 2027 versus 2026. When we look at 2028, we have received already a large number of orders from our customers for EUV. This has also invited us very strongly to investigate another 30% increase in our EUV capacity for 2028. Now, of course, when EUV grows DUV grows as well. Immersion is going to be important. Also for 2027 and for 2028 we are going to look into a 30% increase of our capacity for both years…

… For 2027, we are now close to being fully covered with orders for Low-NA EUV, and we are planning to increase our Low-NA EUV capacity by around 30%. Looking ahead to 2028, we have already received a significant number of Low-NA EUV orders. Strong demand forecasts from our customers have led us to investigate a further 30% capacity increase for that year…

…[Question] The 30% increase in ’28, which implies 110 tools. And I mean, Christophe, you mentioned investigating. The word is precise. Do you need a new cleanroom for that?

[Answer] The number we are mentioning, we can achieve basically by optimizing the existing cleanroom space in the right way. So this is also why we can create basically that improvement in the short term…

…The balance between demand and supply as we see it today gets us to the 30%, right? So that’s the way we do it. If customers are going to come to ASML and say, “Hey, ASML, we need considerably more.” Then just as we’ve been doing it in the past couple of months, we need to look ourselves in the eye, we need to look at all the supply chain and just see what can further be done…

…When it comes to EUV in particular, right, the tool mix that we’re going to ship next year will be Es and Fs, while this year, it’s a combination of Ds and Es. And if you recognize the difference in output, then in essence what you’re looking at is not 30% improvement of wafer capacity that we’re adding, but approximately 45%…

…The optimization I was referring to before is really basically across all products. Now I think everyone understands that, of course, a lot more is being done today on Low-NA and immersion, for example, than High-NA. But we are not sacrificing, I would say, any of our High-NA supply by doing the rest of the optimization…

…[Question] The capacity increase to 85 and 110 units, you are meeting the demand, not undershipping. Is that correct?

[Answer] I think that as you have noticed in the last few months, I don’t think we have reached yet a stable state on what the demand will be for ’27, certainly not for ’28. So we keep on revising basically with our customers what that demand is. And again, the whole goal of our supply is to follow that demand. So I would not say that we are done with this discussion…

…We’re investigating the 110 scenario for EUV in Low-NA by 2028. Of course, we don’t have orders for 110 EUV Low-NA at this stage. So we’re not waiting. We’re preempting…

…The demand signals that we’re getting from customers also when it comes to ’28 are sufficiently strong for us to seriously investigate this 110 number and the related number on immersion that we signaled to you…

…In the past, we increased the headcount of R&D quite substantially. I would say that today, we believe that with the team that we have today, we can really entertain a very aggressive roadmap going forward. So all in all, I think you will continue to see us manage both R&D and SG&A quite nicely. And as a result of that, the operating leverage that you imply, I think the operating leverage will indeed become better in the quarters and the years to come.

Intel is now using ASML’s high-NA EUV systems in production for its most advanced nodes; management expects to soon enter discussions with all of ASML’s customers on how/when high-NA EUV systems will be inserted into their high-volume manufacturing flows; the maturity of the high-NA EUV systems is maturing to the level required for high-volume manufacturing; management believes that the high-NA EUV system, at single exposure, will bring cost benefits to customers; management thinks the high-NA EUV system’s cost will have more advantages over low-NA EUV once the high-NA EUV system matures; management thinks the partnership with Intel is the clearest example of the progress high-NA EUV is making in terms of its maturity; Intel was the first to get the high-NA EUV systems, so it became the first to implement in production; management thinks both logic and DRAM are good candidates for high-NA EUV systems; there’s very little fungibility in the optical tools used in low-NA EUV and high-NA EUV systems

As you may have read it, Intel is basically now using High NA in production on their most advanced products. So it means that some of the products you buy today from Intel have been created with an High NA machine. So this is, of course, a very important milestone. This is the proof of the maturity of the tool. We talked a lot about that in the last quarter. We are seeing that happening with all customers and therefore expect to enter that discussion with all our customers on how exactly and when exactly the tool will be inserted in high volume manufacturing…

…Intel Foundry is using ASML High-NA EUV technology on the Intel 18A process node to produce a subset of its Intel Core Ultra Series 3 processors…

… We are continuing to work very closely with our customers to prove the value of High-NA technology for their process technology road maps. In parallel, the maturity of the platform is improving towards the level required for insertion into high-volume manufacturing…

…Every new generation of lithography system ASML ever brought to market was with a strong intention to reduce the cost of patterning. So when you look at High-NA single expose, the design of the tool, the performance of the tool will be such that it provides a cost benefit to our customer…

…We are still basically working on bringing the High-NA platform to the level of maturity of Low-NA. And when you achieve that, this is practically the time where the cost of High-NA is going basically to provide an advantage versus the existing technology…

…I think the key again for High-NA to be cost-effective, to beat the cost of Low-NA plus immersion multi-patterning is to bring High-NA to the right maturity…

…We are very happy with the press release this morning about Intel, because this is, I would say, maybe the strongest sign so far that we’re getting there…

…We talked about Intel today. I think you know that Intel was first to get the technology. So they are first to implement it in production…

…The opportunity for DRAM is significant also because the volume is also significant. But there’s no real change there. I think we still see both logic and DRAM being a good candidate for High-NA. The reason for that is both DRAM and advanced logic will be shifting more and more towards multi-patterning Low-NA over time. So that applies to both…

…In terms of the fungibility of equipment for High-NA and Low-NA, particularly when it comes to ZEISS, because that’s the way, C.J., I interpret your question, that really isn’t there. It’s totally different tools that you need that ZEISS needs to produce a High-NA optic versus a Low-NA optic. So it’s not that there is fungibility that you can use High-NA tools to get more Low-NA output.

Intel (NASDAQ: INTC)

Intel’s management sees demand outpacing supply for the company (despite growing its supply) as well as the semiconductor industry; the supply shortage is expected to last for some time; management sees a rapid and sustained build-out of compute infrastructure for the semiconductor industry; Intel’s customers are signalling a strong and sustainable spending environemnt

Strong demand for our products continue to outpace our growing supply…

…Industry is facing one of the most severe supply constraints in its history across leading-edge logic silicon wafers, memory and substrates. These shortages will persist for the foreseeable future…

…Intel is uniquely positioned to benefit from the overwhelming demand for compute as the entire industry continue a rapid and sustained build-out of compute infrastructure…

…Customers continue to signal a strong and sustainable spending environment driven by the unprecedented demand for AI compute. Industry-wide supply constraints across wafers, memory and substrates remain the dominant challenge our customers are facing to support the AI infrastructure build-out.

Intel’s management sees the transition to agentic AI systems driving CPU density higher in AI data centers; management is seeing customers place growing importance to Intel’s x86 CPUs in AI data centers; Intel’s Xeon 6 server CPU product continues to be one of the company’s fastest ramping products ever; Intel’s design services business (where Intel helps customers build custom AI chips) saw revenue grow 3x year-on-year in 2026 Q2, driven with the help of the x86 CPU franchise; management’s outlook for server CPU demand has increased yet again and management expects strong growth for the industry for 2026-2028; management thinks the CPU-to-GPU ratio in AI systems is now at parity, and could even skew towards CPUs in the future; management sees significant growth in the CPU market

As AI expands from training to inference and increasingly to agentic and multi-agent systems, general purpose server CPU density continue to increase, and our core server CPU franchise is growing faster than ever…

…Demand accelerate across cloud and enterprise as customers increasingly recognize the critical role that CPUs in general and x86 CPUs, in particular, play in the AI infrastructure…

…Xeon 6 continue to be one of the fastest ramping products in Intel history, reflecting improving execution and strong customer demand…

…We continue to make steady progress in our newly announced design services business with revenue growing nearly 3x year-over-year. We see tremendous opportunities to leverage our strong x86-based general purpose computing franchise to build more purpose-built computing products for the AI era…

…Our outlook for server CPU demand has improved again since our last earnings report and we’re forecasting strong double-digit unit growth for the industry this year and next, with momentum extending into 2028…

…Lip-Bu has talked in the past about the ratio of CPU to GPU going up. And we now believe we’re almost in parity at this point and could eventually even skew more to CPUs on a unit basis…

…From all the inputs we’re getting from our customers in terms of the level of spend and also the long-term agreements we put in place and the visibility we’ve gotten, we feel like the growth is going to be significant.

Intel’s management is more confident than ever of the company’s foundry roadmap for leading-edge chips; in 2026 Q2, Intel’s leading-edge 18A node exceeded internal volume and yield expectations, and saw output increase meaningfully; Intel is now ramping multiple new products on the 18A node while supporting demand for existing products; the ramp up of 18A for Intel’s internal products provides validation as the company engages external customers; management has started risk-production for the 18A-P node, which has performance and power improvements over 18A; Intel’s 14A node is outpacing the 18A node’s development; customer engagements for 14A has increasing momentum and management is increasingly confident that the node will be a highly competitive node in performance, power, density, cost, and schedule; Intel remains on track for 14A risk production for internal products in 2027 H2; management has decided to fully commit to a high volume ramp for 14A in 2028; the 18A node is now in volume production across commercial and consumer products in the PC client segment; the 18A node’s output was 25% over target in 2026 Q1, and up 50% sequentially; nearly all of Intel Foundry’s revenue in 2026 Q2 was from internal demand; Intel Foundry has lowered the cost of Intel’s Panther Lake SKU by 50% year-to-date in 2026 Q2, with further reductions expected for the rest of 2026 and 2027; the 18A node’s quarter-to-date yield for 2026 Q3 are ahead of targets set earlier this year

My confidence in our foundry process road map has grown significantly since joining over a year ago. I am more confident than ever of the strategic in significant and unique value proposition of Intel Foundry. During Q2, our factories across Intel 7, Intel 3 and Intel 18A exceeded internal volume targets, driven by improving yields, better cycle times and increasing wafer starts. 18A output increased meaningfully in the quarter. Yields continue to track ahead of expectations. We are now ramping multiple new products on 18A, while supporting growing demand for our lead products, including Panther Lake and Wildcat Lake. I keep raising the bar on the internal targets, and the team continues to meet the challenge. The successful volume ramp of 18A for our internal products provide important validations as our Intel Foundry engaged with external customers. We also began risk production of 18A-P, providing additional performance and power advantages, while maintaining IP and design compatibility with Intel 18A, positioning 18A-P as a competitive node for external customers…

…I’m encouraged by our progress on Intel 14A. Defect density and transistor performance are all outpacing 18A development. PDK 0.5 is now complete, and PDK 0.9 is on track for October. We continue to build out and validate the IP portfolio for 14A as we position the 14A family for broad-based adoptions across a wide range of customers. I’m pleased to see the increasing momentum on customer engagements for Intel 14A, and I’m increasingly confident that the 14A will be a highly competitive process offering across key vectors of performance, power, density, cost and schedule. With encouraging external customer progress and increased demand for our internal products, we remain on track for 14A risk production for our internal products in second half of 2027, and we make the decision in Q2 to fully committed to high-volume ramp in 2028…

…In our core PC client segment, Intel 18A is now in volume production across multiple commercial and consumer products. Our factory output continues to increase sequentially every month. The successful high-volume ramp of 18A for our internal products provides important validation as Intel Foundry engaged with external customers…

…Our Client Group has now brought 18A to full scale with 400-plus designs for Series 3 across consumer and commercial…

…Turning to Intel Foundry, revenue of $5.8 billion was up 6% sequentially on higher fab volumes driven by strong growth in Intel 18A with output approximately 25% above target and up more than 50% quarter-over-quarter. External foundry revenue was $293 million in the quarter…

…Intel Foundry has driven down the cost of our primary Panther Lake SKU by roughly 50% year-to-date and is on track for an additional 20% this year with further meaningful reductions planned in 2027…

…Our wafer output across our major nodes exceeded expectations from 90 days ago, and Q3 quarter-to-date 18A yields are trending ahead of targets set in March.

Customer interest in Intel’s advanced packaging technology, EMIB-T (Embedded Multi-die Interconnect Bridge with Through-Silicon Vias) remains very high; EIMB-T has a growing backlog, and yields are hitting targets; management wants to ramp EMIB-T into high volume production in 2027

On advanced packaging, customer interest for EMIB-T continue to be very high. Technology is compelling, providing capabilities for advanced AI silicon solutions, which are not possible with today’s mainstream offerings. We continue to have a growing EMIB-T backlog, yield and reliability are hitting targets, and we focused on ramping the technology into high volume and high quality to support customer ramps in 2027.

Intel’s management sees a growing opportunity for AI with edge devices; management thinks the market for edge and physical AI will match that of enterprise AI; Intel had 130 Series 3 design wins for edge AI in 2026 Q2

We recently renamed our PC business to our Client Computing and Physical AI Group or CCPG. We did this to recognize the growing opportunity for AI at the edge…

…We expect enterprise adoption of AI to be a long-term tailwind for CCPG, but our AI-driven market prospects don’t stop there as the edge and physical AI opportunity is likely to at least match the client TAM over time. CCPG showcased this growing opportunity with 130 Series 3 design wins for edge AI applications, including brain and control deployments for robotics.

Intel has a multi-year collaboration with SambaNova, an AI inference chip startup

We also extended our heterogeneous AI strategy through multiyear collaboration with SambaNova. We are pleased with their growing momentum as we work with them to drive performance and power improvements with disaggregated inference.

Intel’s management thinks the company is the only one that can build the whole of compute solutions for AI, from traditional CPUs and GPUs, to custom AI chips

We are the only company that can design, manufacturing, build the entire range of computing solutions from general purpose, traditional CPUs and GPUs to more purpose-built ASICs and CPUs optimized for agentic AI, as we increasingly move from compute dominant by system on chip towards system in package.

Intel’s vPro mangeability software had 1,500% growth in the last 4 quarters, demonstrating that manageability and enhanced security are critical for agentic deployments

On the commercial side, activations for our market-leading vPro manageability software have surged 1,500% over the last 4 quarters, underpinning that manageability and enhanced security are critical must-haves in the agentic workplace.

Intel’s management now expects capital expenditure to be more than $20 billion in 2026, which is up from the previous expectation of around $18 billion; management is aggressively locking in tool purchase orders from vendors; management expects Intel’s capital expenditure in 2027 to be much higher than 2026’s level; Intel’s total capital expenditure on tools and space in the U.S. is approaching $100 billion for 2021-2026; management is committed to have capital expenditure match customer demand; Intel’s capital expenditure is fairly broad-based and includes advanced packaging, although more is allocated for front-end manufacturing than advanced packaging; the higher capital expenditure for 2026 is a signal of management’s confidence in Intel’s customers, and the higher visibility management now has on the long-term demand outlook; management sees significant returns from Intel’s capital expenditure; Intel’s external customers became excited when they started to see the 14A node’s PDK (process design kit yield, and this gave management comfort to invest more in capital expenditure; most of Intel’s capital expenditure in 2026 will be for tools for Intel’s leading edge nodes; management thinks Intel has sufficient liquidity on the balance sheet for now to fund its capital expenditure, while prepayments from customers are also helpful, although the company may need to tap the capital markets if the business becomes really successful and more capital expenditure is needed to fulfil demand; management is willing to commit capital expenditure only if there are customer commitments

Due to strong customer demand signals, we’re raising our outlook for 2026 and now expect our CapEx to be more than $20 billion, which is up significantly versus our expectations entering the year. We’re also aggressively locking in tool purchase orders from our vendors, accelerating our clean room build-outs and actively securing supply of substrates and memory. As a result, we’re forecasting 2027 capital expenditures to be significantly above the 2026 levels with the vast majority spent across our U.S. network. In fact, as we look back from 2021 through 2026, our total capital spending in tools and space in the U.S. is approaching $100 billion, significantly higher than any other semiconductor company over that time frame. We remain committed to tightly matching our expenditures with customer demand and remain financially disciplined as we capture the growth ahead…

…The CapEx is fairly broad-based. It’s going to include advanced packaging. . As Lip-Bu talked about, we’re pretty excited about our prospects on EMIB-T. And so we will be investing in that. That said, the cost of a fab for the front end is much more expensive than a packaging facility. So it will be skewed towards the front end…

…This increased investment is a signal of our confidence in customers across all of our business units. We feel very confident, particularly in places where we’ve gotten long-term agreements that we now have the signal to be able to kind of forecast out what the outlook looks like for the next few years in terms of demand, and we’re putting forth the capacity in anticipation of that across all of our businesses…

…That’s why you see the CapEx going up next year. But over time, they generate significant return. And particularly now as we migrate towards a model where we keep these processes on longer, the returns are quite significant…

…The engagement with the customer, the feedback have been very positive, tremendous demand for our own products and also external foundry customer engagement that give me the confidence the moment they’re starting to see the 0.9 PDK the yield, they’re starting to get excited about what kind of product they want to run that and how much capacity we can provide them. So those are very positive signs that they are really serious about going forward. And that’s why, as I mentioned earlier, I don’t put CapEx unless I see the yield performance, the IP is ready to serve the customer and also customer engagement, the level of engagement I see…

…Most of the CapEx dollars are going to at this point is tooling. We’ll increase tooling in ’26 by 40% relative to ’25. So we’re investing a significant amount in tooling. And it’s where you might expect, it’s Intel 3, it’s 18A, it will be 18A-P…

…[Question] As you are planning these investments for the back half of the year and into next year, how are you thinking about the balance sheet?

[Answer] We feel like we’re in a really good place from a balance sheet perspective. We have over $30 billion of cash. We have a $10 billion revolver. So we’ve got $40 billion of liquidity… We have seen, by the way, our customers willing to invest with us. And we’ve had prepays from customers that we’ve been — that has enabled us to unlock capacity that’s helped us. That said, if we’re super successful, which we’re driving to, we may need to tap the capital markets to drive some more investment…

…We’re just going to be very careful around making bets ahead of customer commitments. I think that’s the most significant change with Lip-Bu is until we really know that we’ve got the customers, we don’t want to put a significant amount of capital.

Intel’s management thinks the ASIC (application specific integrated circuit) for AI business has a market opportunity of more than $100 billion; management expects the annualised revenue run rate for Intel’s ASIC for AI business to increase rapidly

[Question] On the ASIC business. I guess based on what was disclosed last quarter, it’s about a $1.2 billion run rate business now growing well for the company. How do we think about the diversity of that business? And just you’ve announced Fortinet. I’m just curious of how you’re thinking about the growth profile of that business?

[Answer] This is a massive opportunity. I think potentially it’s over $100 billion TAM market…

…Growth rates of ASICs. I mean I would say that today, we’re probably running at about a $2 billion run rate or at least approaching a $2 billion run rate for that business. We think in the not-too-distant future, we’ll be at a $4 billion run rate for that business.

Intel’s management sees memory chips having a severe supply shortage; Intel is collaborating with all 3 major memory chip companies; management sees memory chips as a bottleneck in AI currently, and so Intel is working on memory, with the recent hire of SK Hynix’s CEO (SK Hynix is one of the 3 major memory chip companies)

Memory becomes a big supply constraint challenge. And we’re collaborating with the 3 big memory vendors. That’s very important to serve our customers as our #1 priority. The next thing, as you recall, Intel has a rich history in the memory. And recently, we hired Seok-Hee Lee to join us. He used to be the CEO of SK Hynix. And clearly, memory, it becomes the bottleneck, a lot of AI infrastructure and pain point for customers. And we’re also looking at how other areas that we can integrate compute and memory and also how the stacking and then how can we use the memory more — utilization more efficiently. So I think there’s a lot of areas we are working on. Stay tuned, and we will work on that.

Mastercard (NYSE: MA)

Mastercard’s management is seeing higher demand for the company’s security solutions; Mastercard is able to identify threats before they materialise through Recorded Future’s capabilities (Recorded Future was acquired by Mastercard in 2024 Q4 and it provides AI-powered solutions for real-time visibility into potential threats related to fraud); Mastercard Threat Intelligence was launched in 2026 and in its first 3 quarters, it has identified more than 7 million card-testing transactions in 192 countries and prevented $172 million in fraud; Mastercard Merchant Trust Services is a new suite of AI-powered capabilities to help identify fraudulent merchants

We’re seeing increased demand for Mastercard’s robust and unique security solutions as clients navigate the ever-expanding threat landscape. Today, we have differentiated capabilities that span cybersecurity, identity, and fraud…

…We’re identifying threats before they materialize. How? We’re bringing Recorded Future’s market-leading intelligence capabilities to our clients globally and seeing strong engagement across sectors. This quarter, we partnered with Wipro, a global information technology and consulting company, to further scale our capabilities. Building up on our acquisition of Recorded Future, we also launched Mastercard Threat Intelligence specifically for payment fraud. In its first three quarters, Threat Intelligence has identified more than 7 million card testing transactions across 192 countries. Stopping that activity prevented an estimated $172 million in fraud linked to malicious domains. That’s real value to us, our customers, and of course, our cardholders…

…Mastercard Merchant Trust Services is a new suite of AI-powered capabilities to help identify fraudulent merchants. Keeping the scammers from setting up shop will reduce fraud, cut out disputes, and provide greater security. The goal, real transactions for real purchases from real merchants.

Mastercard’s management believes agentic commerce is the next evolution in payments and is a significant opportunity for the company as it leads to incremental transactions and services; Mastercard Agent Pay helps power secure and trusted agentic transactions across Mastercard’s global acceptance network; management expects cards to prevail in an agentic world; early engagement with Mastercard Agent Pay is encouraging; management sees agentic commerce giving rise to a new class of payments in machine-to-machine (M2M) payments; management believes M2M payments expands Mastercard’s addressable market; management recently announced Mastercard Agent Pay for Machines to allow AI agents to purchase low-value digital services at machine speed; Mastercard is currently the only network enabling M2M payments; there were already 30 industry leaders participating in Mastercard Agent Pay for Machines at its launch; management sees agentic transactions in consumer-oriented use cases happening through existing card networks, and these transactions also bring the opportunity for services; Mastercard Agent Pay has a capability called Verifiable Intent, developed together with Google, that allows consumers to challenge a transaction; management sees agentic transactions in B2B use cases happening through existing card networks; management sees agentic transactions in M2M as possibly requiring different types of payment infrastructure, such as stablecoins

Agentic commerce is the next evolution in payments, where the importance of security, transparency, and control only increase. Agentic commerce creates a significant opportunity for Mastercard. It leads to incremental transactions and even more opportunity for our services. Through Mastercard Agent Pay, we’re helping power secure and trusted agentic transactions across our global acceptance network using tokenization, zero liability protections, and unique dispute resolution capabilities. These capabilities are just a few of the reasons why we expect cards will prevail in an agentic world, both in consumer and commercial use cases. It’s early days, but engagement across the globe is energizing.

The rise of agentic commerce also brings about an entirely new class of payment use cases, machine-to-machine payments. This is an expansion of our addressable market and one that we are at the forefront. We recently announced Mastercard Agent Pay for Machines, which enables AI agents to purchase low-value digital services such as APIs, compute, data, content at machine speed. With on-chain permissioning and off-chain settlement, Mastercard is the only network enabling machine-to-machine payments. An ecosystem is rallying behind us. At launch, we had more than 30 industry leaders participating, including Adyen, Ant International, BVNK, Checkout.com, Cloudflare, Coinbase, and OKX. We are a first mover in this space and one with credibility and trust to deliver…

…There’s the consumer-oriented use cases in agentic commerce where our keyword search turns changes, and we may use agents for that. That could be an LLM, that could be a first-party agent by a large retailer. There are transactions that are now then delegated to agents, and that can happen very well through the existing card networks. What you need for that is, that’s what merchants always need. They need reach, they need predictable user experiences. That’s the same, that’s true for consumers. We really believe that cards will prevail in that world. This is a tremendous opportunity for us also on the services side through tokenization, inside tokens and so forth…

…We have additional capabilities that we put into the Agent Pay protocol from us, and one of them is Verifiable Intent, which allows you to basically challenge a transaction, say, “I never wanted to buy this,” and then the chargeback process can kick back in. This was innovated together with Google…

…If you look on the B2B side, you can see there’s a range of agentic commerce transactions that can happen where you have an agent that does purchasing for a company. That can very well happen on the card ecosystem, very similar to what I just said on the consumer side. Amounts, speeds, purposes, they will need the protections, they need the global reach, all of that can apply, and we believe that’s a continued opportunity for us, particularly on the services side again…

…Machine-to-machine payments. That is low-ticket, micro-ticket transaction that happens at very high velocity. For that, we can see a world emerging where different kind of underlying infrastructure is required. For that, we’ve put out our protocol, which is an evolution of Agent Pay, which is Agent Pay for Machines. This is the only network protocol that’s out there today to facilitate that. Now, the underlying infrastructure for that, we’ve mapped it out. You can start to see that there is a transaction that is recognized from one agent to another, these machines talking to each other, but the settlement happens through different kinds of rails. That could involve stablecoins, but it also could involve different types of settlements. We’re actually quite open to that. What it needs is the immediacy of these agents to recognize that transaction, and that is what Agent Pay for Machines actually does.

Meta Platforms (NASDAQ: META)

Meta’s management sees the company’s AI investments accelerating all parts of the core business; the AI investments are improving users’ experiences, performance for advertisers, and the speed of shipping new experiences; management is optimistic about the integration of LLMs (large language models) into Meta’s recommendation systems for Instagram and Facebook because the models the company to show more relevant and engaging content; management thinks the new Muse Image and Muse Video models will lead to a huge expansion of new and personalised content to show people; Meta is using LLMs in its advertising systems to (1) improve the prediction and ranking of ads that are shown, and (2) drive significant increases in advertising relevance and conversions on both Instagram and Facebook; management sees clear signs of Meta’s AI investments paying off because Meta’s advertising business is reporting faster year-over-year revenue growth than any other competitor; 9 million small businesses are now using at least one of Meta’s Gen AI advertising creative tools (was 8 million in 2026 Q1); management is rolling out end-to-end creative solutions for advertisers and Muse Image will be important for this as it can produce better ad variations; Muse Image is getting great feedback so far; management has launched Meta One, a subscription that provides more tools and AI features; management will explore different tiers and pricing options for Meta One as demand grows; recent new experiences Meta has launched include Instagram Instants, Forum, and Seller; management will use recommendation systems to scale new experiences; adoption of Image Generation more than doubled among advertisers in 2026 Q2

We are now at a point where our investments in AI are accelerating every major part of our core business. They’re improving the experience for people using our apps, driving better performance for advertisers, and helping our teams build new experiences and ship faster…

…In Instagram and Facebook, I am very optimistic about our work to integrate large language models into our recommendation systems. LLMs add a first principles understanding of what the content is about and why it is compelling, as well as a deeper understanding of what people are interested in and what their goals are when they are using our apps. This means that we can show more relevant and engaging content that better reflects people’s goals and interests. Our new Muse Image and Muse Video models will also dramatically expand the universe of content that people can discover across our platforms. There are already two large sets of content to draw from. First, from your friends and the people you follow, and second, from creators that you don’t follow. Now there’s going to be a whole new and nearly infinite universe of personalized content. This is going to make our services a lot more useful and engaging for people.

For ads, we are using LLMs to improve how our systems predict and rank the ads that we show. We’ve expanded the context that we can take into account around a person’s organic and ads activity to determine an ad’s relevance, driving significant increases in relevance and conversions on both Facebook and Instagram. On a dollar basis, our ads business is reporting faster year-over-year revenue growth than any other company’s reported ad business. These AI investments are paying off.

We are also seeing a lot of demand for our new AI-powered creative tools. 9 million small businesses on our platforms are now using at least one of our AI ad creative tools, and we’re rolling out new end-to-end creative solutions that help advertisers translate performance data into their creative decisions. Muse Image is going to supercharge this. The model can analyze images, improve its own work, and produce better ad variations based on advertiser input. We’re getting great feedback on this so far.

We also just launched Meta One, a new subscription offering that provides more tools and AI features across our apps. As demand grows, we’re going to offer a variety of different tiers and pricing options there as well…

…Earlier this year, we shipped Instagram Instants. We also just launched Forum, a standalone groups app, and Seller, a standalone marketplace app. I expect it to become a lot easier to ship new apps, we are planning to build out more ideas and use our recommendation systems to scale them to the people who will find them interesting, as we’ve done with Threads…

…Image Generation, which now lets advertisers produce more creatives at scale from existing content, including a new ability to create images from video assets, saw adoption more than double this quarter.

Meta’s management is building new personal agents that will be the foundation for new products and revenue streams in the future; Muse Spark will be a base for Meta’s personal agents; management thinks personal agents will need to work right out of the box, compared to enterprise agents where software engineers are more willing to make them work; management thinks WhatsApp will become an important surface for individuals to interact with multiple agents; WhatsApp is already the leading surface where people engage with Meta AI; management wants to deliver a private and secure AI experience for individuals; Meta recently launched incognito mode on WhatsApp and the Meta AI app for users to have private conversations with their assistant; management believes personal agents will be a massive market a few years down the road

We are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead…

…One reason that we are so focused on making Muse Spark great at agentic capabilities is that we think that there’s a very big opportunity to ship a few types of agents that are aligned with our mission and business. The first is personal agents. Soon, we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want…

…The first domain that agents have really taken off in is coding. Engineers are more technical and willing to spend time making those agents work. To build great personal agents, this needs to be a great consumer product that just works out of the box and is easy enough for billions of people to adopt and use…

…As we move towards a future where we’re all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important. WhatsApp is already the leading surface where people engage with Meta AI. As we build out a platform for more agents across our messaging apps, we’re going to innovate on how to deliver a private and secure AI experience. We launched incognito mode this quarter on WhatsApp and the Meta AI app, allowing people to have private conversations with their assistant that even Meta can’t see. We’re planning to make strong privacy and security a fundamental part of the agents that we’re building as well…

…We think that consumer personal agents is going to end up being an extremely important and massive market. I think that it’s extremely unlikely if you look out five years from now, for example, that whatever period of time you want, that you don’t have billions of people with a personal agent that understands your goals and that is just working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about, whether it’s helping you with your health or your hobbies or your personal finances or your productivity in running your home better or improving and enhancing your relationships, helping with your career.

Meta’s management sees a large opportunity to sell to businesses, including business agents and compute; Muse Spark 1.1 is an agentic coding model that is available through Meta’s new public API (application programming interface); management is ramping up distribution of Muse Spark and is making it easier for enterprises to adopt the model; management recently made Meta Business Agents available globally on WhatsApp and Messenger, and will soon roll it out on Instagram; 1 million businesses are already using Meta Business Agents; Meta Business Agents can learn from daily conversations with customers and bring insights to businesses; management’s goal is to build a business-in-a-box service for an individual to start and run a business on Meta’s platforms; management intends to monetise Business Agents through subscriptions and volume-based pricing, and eventually results-based pricing; management thinks results-based pricing will allow Meta to run efficient auctions over the company’s compute capacity; management recently introduced Meta Business Agent Platform for enterprises to build and manage agents at scale on WhatsApp; Brazilian car rental company Movida used a business agent on WhatsApp and saw a 44% increase in daily bookings compared to a year ago, with 85% of conversations in WhatsApp resolved entirely by the AI agent without human assistance

We see a large enterprise opportunity to sell to businesses, including APIs, Business Agents, potentially selling Compute directly and other services that we’re building for large customers…

…Muse Spark 1.1 is a strong agentic encoding model that is very efficient and excels at computer use, tool use, and multimodal understanding. It’s available through our new public API. We are ramping up distribution through partner channels and more coding agents over the coming weeks. We are also building out features to make it easier for enterprises to adopt Muse Spark…

…We made Meta business agents available globally this quarter on WhatsApp and Messenger. There are already more than 1 million businesses using them to talk to their customers or complete sales every week. We’re rolling business agents out on Instagram now, too. One interesting thing about having an agent talk to your customers every day is that it learns over time and can bring all of those insights back to you. We’re building more agentic capabilities to summarize all these conversations, digest what happened overnight, and surface what customers are asking for. Soon it’ll go further, including suggesting ways to grow your business, giving you competitive intelligence and real-time insights into what’s working and what’s not. Over time, we’d like to build this into a business-in-a-box service that can help you start and run a whole business using Meta’s platforms. In terms of how we will monetize these, we have a mix of subscriptions, volume-based pricing. I expect that we’re going to continue to evolve more of these products to be like our ad systems, where businesses only pay us when we achieve results for them. Over time, that will let us run an efficient auction over our compute, similar to how we do that for advertisers today…

…Earlier this month, we also introduced the Meta Business Agent Platform, which gives enterprises the infrastructure to build, customize, and deploy their business agent at scale on WhatsApp. The platform provides larger businesses with enterprise-grade controls, guardrails, and measurement built in so they can define rules and offer personalized experiences, starting within the messaging apps that their customers already use. Movida, one of Brazil’s largest rental car companies with nearly 400 locations, deployed a business agent on WhatsApp to handle the entire booking flow, from vehicle selection and pricing to payment, in a single conversation. Returning customers could complete a reservation in as few as three messages. In a one-month period, Movida reported a 44% increase in daily bookings through WhatsApp when compared to the same period in the prior year, that 85% of conversations in the channel were resolved entirely by the AI agent without human assistance.

Meta’s management thinks the trajectory of Meta Superintelligence Labs (MSL) is strong; MSL recently shipped the Muse Spark 1.1 and Muse Image models; Meta AI has seen a 60% increase in daily users since Muse Spark was integrated with it; Muse Spark 1.1 is an agentic coding model that is available through Meta’s new public API (application programming interface); management still believes in open sourced models, and expects MSL to release open source models soon; management thinks that closed models can be more jagged in intelligence than open sourced models; some experiences on Meta’s apps are not possible to be built by others because Meta has always taken a full-stack approach to its technology stack and this is why management wants control over the models Meta is using, and hence why the company is building its own models; management thinks current open source models are not as good as frontier models, so they do not want to use them; management thinks companies who invest in building models will be rewarded well over time

It’s been a little more than a year since we launched Meta Superintelligence Labs, our trajectory is strong. In the last month, we shipped Muse Spark 1.1 and Muse Image. Since we rebuilt Meta AI and integrated Muse Spark, we have seen a 60% increase in the number of people interacting with the assistant each day, that continues to grow quickly week-over-week…

…On open source, I think we have always felt like open source was an important part of the ecosystem, it’s good for the world…

…In ramping up Meta Superintelligence Labs, in some ways, actually counterintuitively, it takes some more work to do open source models because if you’re doing something as a closed system that you’re only building for your own use cases, it can be a little more jagged. Whereas if you release it as open and it’s going to be used for a lot of things, you want to make it more well-rounded…

…We expect that we will get back to releasing some open source models at some point soon…

…The question is, do we think that because there are some open-weight models that we can just rely on those? Right now, the open-source models are not as strong as the frontier models, so no is the basic answer…

…Some qualitative experiences are just not even possible for others to build because we go all the way down the stack. It just seems to me pretty clear that having kind of sovereignty over building your own models is going to be an important part of that stack going forward, which is why it is important for Meta, but is also why other people care about open source and why open source matters overall…

…I get that this is a big investment and it’s a big bet. We see the technology working. We’re happy with the trajectory of the lab. I’m excited about the products that are coming we believe that this is going to be a big thing. I get that this is sort of a big bet across the industry. My personal bet is that the people who invest in this are going to be rewarded and feel very good over time.

Meta’s management continues to see glasses as the ideal form factor for hardware that people use to interact with AI because they can be worn throughout the day and can provide insights while people remain present in the moment; Meta recently released its own line of Meta glasses in collaboration with EssilorLuxottica, and these glasses come with Muse Spark out of the box; early sales for the Meta glasses have exceeded management’s expectations

As we get closer to personal superintelligence, we are also going to need hardware that allows you to seamlessly interact with it. Glasses are the ideal form factor since they can be with you throughout the day and they can assist you without pulling you away from the moment. Our glasses remain one of the fastest-growing consumer electronics of all time. We continue adding to the lineup. We just released our own line of Meta glasses in collaboration with EssilorLuxottica, including a style that we designed with Kylie Jenner. They’re the first glasses to ship with Muse Spark out of the box so that they can understand what you’re seeing and give even more helpful answers. Early sales have been strong, exceeding our expectations.

Meta’s management sees the company as the only one in the world building AI with the main goal of putting it in people’s hands and distributing it widely

We’re the only major company building AI with the primary goal of putting superintelligence directly into people’s hands. Rather than centralizing superintelligence, we are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them. That’s the way that society has always made progress. I think that these are the right values for building a positive AI future, and if we help build this, then I think that we will continue to build a very strong business as well.

Improvements to Meta’s Feed and Reels recommendations drove a double-digit year-on-year increase in global time spent on Instagram; improvements to rankings drove a 9% year-on-year increase in video time spent on Facebook, and over 10% in US and Canada

On Instagram, global time spent this quarter grew double digits year-over-year this quarter, largely driven by improvements to our Feed and Reels recommendations. On Facebook, video time spent increased 9% globally year-over-year and over 10% within the U.S. and Canada, where it was driven by ranking improvements.

LLMs improve Meta’s ranking and recommendation systems by (1) making existing systems smarter by understanding content and generating better training data, (2) helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes, and (3) increasing the level of personalisation in recommendations; on engineering development, Meta recently had every public Reels and Feed post on Instagram being automatically analysed by an LLM, and management is working to include more surfaces on Facebook; on engineering development, management recently began using the Muse family of models to understand content and early results are positive; on increasing the level of personalisation, management recently shipped the largest single-release ranking improvement to date on Reels, which drove a 15 basis point increase in sessions on Instagram, and this ranking improvement is now being brought to Feed; on increasing the level of personalisation, Meta’s largest ranking models can now identify high-quality new Reels at creation, and over half of all recommended content on Instagram Feed is now less than one day old, more than double from 2025 Q2; on increasing the level of personalisation, users can now use natural language prompts to tune their recommendations on Instagram and Facebook, and early users have an 80% retention rate; Meta is building its next-generation recommendation systems, and the effort includes building foundation models for simultaneously recommending organic content and advertising, and developing LLM-native recommender systems; on building next-generation recommendation systems, in 2026 H1, Meta had continuously pre-trained a large-scale model with recommendations data, and observed healthy scaling laws; managment sees room to continue improving Meta’s recommendation systems into 2027

We are finding that LLMs are increasingly capable of delivering ranking and recommendations gains.

First, they make our existing systems smarter by understanding what the content is actually about and generating better training data.

Second, LLM-powered agents are also helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes. Earlier this year, we reached a milestone of every public Reels and Feed post on Instagram being automatically processed through an LLM and analyzed across dimensions from topics to tone, and we’re working towards including more surfaces on Facebook as well. These signals can then be passed to downstream applications across ranking, recommendations, and content policy enforcement, which is a key building block toward greater personalization. This quarter, we also began using our Muse family of models to conduct content understanding across signals like video topic classification and summarization, and we’ve seen positive early results.

Finally, our recommendations are also becoming more personalized, surfacing more fresh content while giving people more direct control over what they see. On Reels, we shipped our largest single-release ranking improvement to date, combining faster inference with a new architecture that draws on deeper user history to improve predictions. This drove a 15 basis point increase in sessions on Instagram, with particular strength in reshares and time spent, which are both strong indicators of better content to user matching. We are now bringing this to Feed, where early results look comparable. We are also getting new content to people more quickly. Investments we’ve made in more real-time infrastructure and modeling improvements on new videos are allowing our largest ranking models to now identify high-quality new Reels at creation. On Instagram Feed, over half of all recommended content is now less than one day old, more than double from a year ago. We’re also giving people more direct control of the content they see. Today, Instagram users can visit the Your Algo page, which lets users write natural language prompts to tune their recommendations. Similarly, on Facebook, we launched Shape Your Feed. Early results show over 80% retention among users who engage with it.

Looking forward, we’re executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that are designed to power organic content and ads recommendations simultaneously, as well as developing LLM native recommender systems. We hit our first research milestone this half by continuously pre-training a large-scale model with recommendations data, and observing healthy scaling laws in the process. We’re encouraged by this milestone and expect continued progress in the second half of the year…

…We certainly see further headroom to continue improving recommendations over the rest of the year and into 2027. We expect that will help us drive additional gains on both engagement on Facebook and Instagram.

Meta continues to enhance its systems to show advertising to users at the optimal time and location; management recently introduced Meta Generative Recommender, which uses LLMs to reason about ad content and user preferences simultaneously; early pilots of the Meta Generative Recommender architecture drove a 1% increase in app event conversions on Instagram; in 2026 Q2, management improved Meta’s user understanding models, which when combined with GEM (Generative Ads Model), drove an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook; Advantage+, Meta’s suite of AI-powered advertising automation tools, is now at a $75 billion revenue run rate (was $60 billion in 2025 Q3); advertisers who use multiple tools within Advantage+ see compounding gains; Indian apparel company Underneat adopted Advantage+ and saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions

The first part of this work is optimizing the level of ads within organic engagement. Here, we continue to enhance our systems to show ads at the optimal time and location…

…Within our ad systems, we’re delivering performance gains as we deploy more complex and predictive models. This quarter, we introduced Meta Generative Recommender, a paradigm shift in how our ad system works. Rather than scoring every possible ad individually, we are now using LLMs to reason about ad content and user preferences together and predict the best ad for each person. This makes our ad matching more intelligent and more precise, which compounds performance gains for advertisers. We deployed the first generative model into our ads retrieval system and saw notable improvements in ads performance. Early pilots using LLMs to better understand user preferences drove a 1% increase in app event conversions on Instagram. In Q2, we also advanced our user understanding models to analyze ads and organic activity, simultaneously improve both user experience and advertiser performance. Combined with our GEM model for ads ranking and sequence learning, these advancements generated an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook…

…Our AI-powered Advantage+ end-to-end solutions continue to grow, reaching over $75 billion in annual revenue run rate this quarter. We’re working to deepen adoption as advertisers who leverage multiple tools see compounding performance gains…

…Underneat, an online apparel brand in India, had been setting up each campaign manually across Facebook and Instagram. After adopting Advantage+ sales campaigns layered with Advantage+ audience placements and budget optimization, they saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions.

Meta’s management continues to invest aggressively in AI infrastructure to meet growing AI usage; management recently announced a new strategic venture with BlackRock for a 1 GW data center; management expects a significant part of Meta’s compute to go towards training models, growing the core business, and building AI agents and new products; Meta is getting a lot of offers for its compute at a significant premium over what it paid; management’s approach to building compute capacity has 3 key elements, (1) the industry has historically under-built for AI adoption, (2) management has high confidence in Meta’s ability to utilize the capacity effectively, and (3) industry capacity will remain tight for some time; management’s current plans are to maximise capacity for 2026 and 2027, and to have flexibility to grow compute in 2028 and beyond if needed; the flexibility comes from the long-lived nature of the compute assets and Meta’s efforts in developing custom AI chips; management thinks that Meta’s models, consumer experiences, and enterprise offerings will be the best and highest ROI use of its compute infrastructure; a substantial amount of Meta’s compute goes towards training models; management believes there’s a much higher margin on selling intelligence rather than on selling compute directly; management believes that near-term compute capacity is more valuable than long-term capacity; Meta is compute-constrained today, so much so that there are many ROI-positive areas that management would put compute toward if there was any capacity

As AI usage in our products and businesses continues to ramp, we continue to invest aggressively in infrastructure to meet the demand. Yesterday, as part of our Meta Compute effort, we announced a new strategic venture with BlackRock to develop a new one gigawatt data center in El Paso, Texas. Overall, we expect that a significant portion of our compute is going to go towards training our models, growing our core business, and delivering personal agents and new products. We also expect to grow a large business serving large customers as well…

…We’re getting a lot of offers for compute at a significant premium over what we paid for it, and we have more coding and productivity tools on our roadmap as well…

…Our approach to building capacity is strongly influenced by several key elements. First, the broad environment for building infrastructure is dynamic and uncertain in both near-term and longer-term time horizons. The industry has under-built historically for the wave of AI adoption, making existing capacity, including our own, extremely valuable. Longer-term, the supply chains need to be built out to support the capacity that we anticipate we and others will need for AI-powered experiences. Second, we have high confidence in our ability to utilize capacity to scale and build on top of our existing experiences, as well as continue to invest in foundational models that will create substantial new opportunities. Consequently, our current plans are geared towards maximizing 2026 and 2027 capacity. When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels, and we are confident that this will be true in this timeframe as well. Longer-term, it’s harder to predict the exact usage scaling curves…

…We believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute. Therefore, our longer-term capacity strategy aims to give us the flexibility to continue growing compute in 2028 and beyond by laying down data center and network foundations to accommodate future server decisions. The long-lived nature of these assets inherently provides the flexibility that will make it possible to adjust our investment to the pace of AI adoption. In addition, we have been making strategic investments in areas like our internal custom silicon effort, which will provide long-term strategic flexibility and supply chain leverage…

…Finally, we believe that overall industry capacity is going to remain tight for the foreseeable future. As we’ve said earlier, we strongly believe that the models, consumer experiences, and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure. Those enterprise offerings have the potential to take multiple forms, as Mark mentioned. Agentic tools, our API, or monetizing compute directly, given outsized market demand…

…A substantial amount of the compute goes towards training models to be a leading lab, and I think that’s an important investment…

…We believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly…

…Generally, we believe near-term capacity is more valuable than long-term capacity…

…We are today and expect to be in the sort of foreseeable future demand-constrained. That really includes our core business too, where we still have numerous ROI-positive places that we would put compute toward if we had it.

Meta’s management believes that the company’s distribution advantage will give the company the opportunity to serve AI products to many users, even if the company’s models are not on the frontier

We believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses. This should be true regardless of whether our models are on the frontier.

Microsoft (NASDAQ: MSFT)

Microsoft’s management added 31 new data centers across 5 continents in 2026 Q2 (FY2026 Q4), or 1 gigawatt of compute capacity; Microsoft is bringing capacity online faster than ever; Microsoft has reduced dock-to-live times for new GPUs by nearly 50% over the last 12 months; Microsoft is on track to double its overall compute footprint in 2 years;

We added 31 new data centers across five continents this quarter, bringing the total to 88 this year as we expand our footprint in response to accelerating demand. We’re also bringing capacity online faster than ever. Over the last fiscal year, we’ve reduced dock-to-live times for new GPUs in our largest regions by nearly 50%. All up, we added another gigawatt of capacity this quarter and remain on track to roughly double our overall capacity in just two years.

Microsoft’s management is optimising silicon across systems and software to get more from the company’s compute infrastructure; Microsoft has increased the throughput for Copilot workloads by 4x since the start of FY2026; Microsoft’s AI infrastructure utilises chips from NVIDIA, AMD, and itself (Maia); Microsoft’s Maia 200 chip continues to scale, has 30% better tokens per dollar compared to other leading AI chips, and now supports both OpenAI and MAI (Microsoft AI) models; Microsoft will be among the first cloud providers to provide the latest generation GPUs from AMD (Helios) and NVIDIA (Vera Rubin); management sees CPUs as being just as important as GPUs when running agentic workloads; Microsoft’s Cobalt CPUs are powering 1st-party and 3rd-party workloads; management expects to have Microsoft’s own Cobalt 200 racks in over 25 data centers around the world by the end of July

We’re also getting more from the infrastructure we already have by optimizing across silicon systems and software. For example, we increased the throughput for Copilot workloads 4x since the start of the year…

…We also continue to modernize our fleet with our own silicon innovation alongside the latest from NVIDIA and AMD. Maia 200 continues to scale. It delivers 30% better performance per dollar than the latest generation hardware in our fleet and is now supporting both OpenAI and MAI models. And we will be among the first cloud providers to deploy next generation rack-scale AI infrastructure based on AMD Helios and NVIDIA Vera Rubin.

When it comes to running agents, CPUs are just as important as GPUs. Our Cobalt VMs are powering both our own first-party workloads as well as workloads for customers, including Adobe, Arm, Elastic, OpenAI, Sprinklr, and TomTom. By the end of this month, we expect to have our Cobalt 200 racks in over 25 data centers around the world as we rapidly expand capacity.

Microsoft’s management thinks customers want the right model for the right task; management thinks Microsoft offers the broadest selection of models, more than 11,000, among the cloud hyperscalers; the number of customers building with models from different providers has increased by 5x since the start of FY2026; Levi Strauss is using Foundry to bring together models from OpenAI and Anthropic, and 1,000 domain-specific agents; management is building Foundry to be the complete app and agent stack, which gives agents access to the IQ layer, tools, durable state and memory, sandboxes, rubrics, and evals; there are more than 100,00 Foundry customers and revenue doubled year-on-year in 2026 Q2 (FY2026 Q4); Telefonica is using foundry for its first wave of agents tackling mission-critical network operations; the number of Foundry customers at an annualised run rate of 1 trillion tokens was up 4x year-on-year in 2026 Q2 (FY2026 Q4)

Every customer wants the right model for each task based on quality, latency, cost, and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the latest from OpenAI, Anthropic, Mistral, xAI, as well as our own MAI family. Since the start of the year, we have seen 5x increase in the number of customers building with models from multiple providers. Levi Strauss & Co., for example, is using models from OpenAI and Anthropic on Foundry as it brings more than 1,000 domain-specific agents into a unified enterprise AI platform…

…Beyond model choice, data, and context, we are building Foundry as the complete app and agent stack. It gives agents access to the IQ layer, the tools they use, along with durable state and memory, secure sandboxes, rubrics and evals, and even their own self-improvement loops. We now have 100,000 Foundry customers and revenue more than doubled year- over- year. Telefónica, for example, adopted Foundry as the foundation of its corporate agentic platform with its first wave of agents tackling mission-critical network operations. All up, the number of Foundry customers at 1 trillion tokens annualized run rate increased 4x year- over- year.

Microsoft recently announced more than a dozen new models across modalities, and all offer cost-efficient inference; Microsoft’s models are co-designed with its own AI chips; MAI (Microsoft AI) models have 40% better performance per watt when they are run on Maia 200 chips; management is building a new model system where the harness, context, memory, and action space are separate from the models, meaning every model is substitutable; Microsoft is using the new model system in its products; developers using MAI-Code-1-Flash on GitHub Copilot are getting higher code acceptance rates and 10% lower median token usage while still getting frontier capabilities; MAI-Code-1-Flash is delivering comparable quality to GPT-5.6 in Excel for common tasks with much lower costs; MAI-Cyber-1-Flash achieves better security performance than much larger models but at half the cost; MAI-Voice-2-Flash has led to an 89% reduction of GPU costs in Dynamics 365; MAI-Image-2.5 has led to an 84% reduction in GPU costs in PowerPoint; any company can use Microsoft’s new model system in Foundry; management thinks that it will be increasingly clear that organisations want AI system providers who will help them with outcomes and knowledge creation, and not simply extract knowledge, and this direction of travel has led to the architectural design of separating the harness from the model, so that the model layer is swappable; management thinks that the recent Hugging Face breach by an unreleased OpenAI model is a good example of why enterprises cannot depend on just one model; MAI-Cyber-1-Flash can achieve better security performance than much larger models but at half the cost because 90% of the tasks are done by MAI-Cyber-1-Flash whereas the remaining 10% is done by the frontier model; the work done by Microsoft on model diversification also helps with margin improvement

We are also accelerating our own model development. We announced more than a dozen new models across image, voice, transcription, coding, security, including our first reasoning model, MAI Thinking-1, all with cost-efficient inference at the core for the enterprise use cases. We are co-designing these models with our silicon. We are seeing 40% better performance per watt when running MAI models on Maia 200. More importantly, we are building a new model system where the harness, context, memory, and action space are separate from any one model family, thereby moving the frontier on the cost-to-outcome curve. It’s not just about cost. It also has the added benefit of business continuity and resilience because every model is substitutable.

This is the system we are using in our products with great results. For example, millions of developers have used MAI-Code-1-Flash on GitHub Copilot, achieving higher code acceptance rates and 10% lower median token usage while still having access to frontier capabilities from OpenAI and Anthropic. In Excel, MAI-Code-1-Flash is delivering comparable quality to GPT-5.6 for the most common tasks while operating at significantly lower costs. In security, MAI-Cyber-1-Flash achieves better performance than much larger Mythos model, but at half the cost when combined with our multi-agent security harness. More broadly, across our model implementations, we are seeing significant efficiency gains, including 89% reduction of GPU costs in Dynamics 365 with MAI-Voice-2-Flash and up to 84% reduced GPU costs in PowerPoint with MAI-Image-2.5. This system is available to any company to use as part of Foundry…

…Every firm is going to evaluate who are the providers who are helping them with their outcomes and their knowledge creation. I think that that is now fairly clear, and it’s going to become clearer by the day. This is not going to be about, come in and take all my knowledge and benefit yourself, whereas I am not getting anything out of it. Given that direction of travel, we are very clear about the architectural design of the platform, which is you’ve got to keep your harness separate from the model. The harness will ensure that your memory, your context, all of that is external. That means any given model at any given time is swappable…

…If you look even at the Hugging Face incident, the biggest thing that we should take away from that is you can’t depend on any one model. You will maybe need multiple models to even remediate some challenges that get caused by one model. That’s the way to think about it, which is you can’t be subject to the refusal of the one model…

…Essentially you can have Mythos level performance, with 50% less cost because of this MAI-Cyber-1-Flash. The reason is because 90% of the tasks are done by the MAI-Cyber-1-Flash model, and 10% of the tasks, you still go to the frontier. This is sort of that mixing of the right model for the right task in what is essentially a pipeline job, is a super important characteristic…

…The work, frankly, on model diversification also is a margin improvement opportunity. Being able to serve the best possible outcome with a more efficient, or both efficient in terms of token usage and efficient in terms of cost structure, are also margin levers.

Microsoft’s management sees the data estate shifting from primarily supporting human users to supporting agents; Microsoft’s customers are adopting the company’s AI-optimised databases; PostgreSQL revenue was up 55% in 2026 Q2 (FY2026 Q4); the number of PostgreSQL customers who also use Foundry was up 80% in 2026 Q2 (FY2026 Q4); customers are increasingly choosing PostgreSQL as the database for their AI workloads; Microsoft has Horizon DB, a fully-managed PostgreSQL service that has 3x the throughput of self-managed deployments; Fabric customers grew 60% year-on-year in 2026 Q2 (FY2026 Q4) to 40,000 (was 35,000 in 2026 Q1); 17,000 customers now use both Fabric and Foundry, up 60% year-on-year (was 15,000 in 2026 Q1); management recently introduced the agent-fore Rayfin SDK (software development kit) for building apps in Fabric; tens of thousands of customers, including 90% of the Fortune 500, are grounding their agents; management recently introduced Web IQ, which allows agents access to the web; Web IQ is already being used by popular AI assistants, including ChatGPT; Agent 365 is a control plane for managing agents’ governance, identity, and security; tens of thousands of companies are already using Agent 365 to manage 40 million agents in 2026 Q2 (FY2026 Q4) (was “tens of millions of agents” in 2026 Q1)

The data estate is evolving from primarily supporting apps used by people to supporting agents. Customers are rapidly adopting our AI-optimized databases like Cosmos DB and PostgreSQL to give agents fast, secure access to real-time data and context they need for memory and retrieval.PostgreSQL revenue was up 55%, accelerating for the third consecutive quarter. Also, the number of PostgreSQL customers also using Foundry increased 80% as customers increasingly choose it as the database for AI workloads. We are going further with Horizon DB, our new fully managed PostgreSQL service on Azure, which delivers three times the throughput of self-managed deployments…

…We now have over 40,000 paid Fabric customers, up more than 60% year-over-year, and over 17,000 customers now use Foundry and Fabric, up 60% year-over-year as enterprises connect agents to real-time operational, analytical, and unstructured data in Fabric…

…Tens of thousands of customers, including nearly 90% of the Fortune 500, are already grounding their agents in enterprise context with Foundry, Fabric, and Work IQ. This quarter, we introduced Web IQ, which gives agents access to real-world intelligence from across the web. It is already being used by the most popular AI assistants, including ChatGPT…

…With Agent 365, we offer a control plane that extends companies’ existing governance, identity, security, and management frameworks to agents they build. Just two months in, Agent 365 now has nearly 40 million agents registered across tens of thousands of companies.

There are over 30 million Microsoft 365 Copilot seats in 2026 Q2 (FY2026 Q4) (was “over 20 million” in 2026 Q1), with net seat adds up more than 100% sequentially; management made Copilot generally available in June 2026 to help customers complete multi-step tasks; management recently introduced Autopilots, which are autonomous long-running agents powered by OpenClaw; in 2026 Q3 (FY2027 Q1), management will bring all of Microsoft’s Copilot experiences together in one super app that spans both consumer and commercial experiences; management has been improving Copilot and recent customer feedback has been good; Copilot’s user satisfaction scores have doubled over the last 3 quarters; Copilot’s latency was reduced by 25% in 2026 Q2 (FY2026 Q4); usage intensity of Copilot is at a record, with conversations per user up nearly 100% year-on-year in 2026 Q2 (FY2026 Q4), and average weekly engagement on par with Outlook and Teams; the time taken for Copilot to move from deployment to high usage has fallen from months to just days over the last 12 months; the number of Copilot customers with more than 50,000 seats was up 7x year-on-year in 2026 Q2 (FY2026 Q4); the number of Copilot customers deploying Copilot to the majority of their knowledge workers was up 75% sequentially in 2026 Q2 (FY2026 Q4); Copilot has saved NHS England employees 43 minutes per year, and NHS England is rolling out Copilot to 505,000 employees; KPMG is rolling out Copilot to 276,000 employees; HSBC has committed to 200,000 Copilot seats; many customers have bought 60,000 or more Copilot seats; management recently added usage-based billing to Copilot Cowork; the super app will have Chat, CoWork, Autopilot, and Code in it; management thinks the addition of usage-based billing to Copilot Cowork has increased Microsoft’s addressable market

We now have over 30 million paid Microsoft 365 Copilot seats with net seat adds more than doubling quarter-over-quarter…

…Last month, we made Copilot generally available, helping customers complete multi-step tasks grounded in their work data while meeting enterprise security and compliance requirements. This quarter, we also introduced Autopilots, autonomous long-running agents with full enterprise compliance, including always-on personal agent powered by OpenClaw.

This quarter, we are bringing these Copilot experiences together, including code in one super app spanning both consumer and commercial experiences. This is a major step forward and I look forward to sharing more soon. More broadly, we have steadily been improving the quality and performance of Copilot and have been delighted by the recent customer feedback. Over the last three quarters, user satisfaction scores have doubled and are now at an all-time high. This quarter alone, we cut latency by 25%. These quality improvements, together with continued product innovation, are driving record usage intensity. The number of conversations per user nearly doubled year-over-year. Average weekly engagement is on par with Outlook and Teams. The time from deployment to what we think of as high usage, meaning monthly active usage, about 80% across a customer’s user base, has fallen from months to just days over the past year. The number of customers with more than 50,000 seats increased over 7x year-over-year, and the number of enterprise customers deploying Copilot to the majority of their information workers grew nearly 75% quarter-over-quarter, a signal of how central Copilot has become to their operations.

NHS England, for example, is rolling out Copilot to 505,000 clinicians and staff, the largest healthcare deployment of its kind after a trial showed it saved employees an average of 43 minutes per day. KPMG is expanding its deployment across its global workforce of more than 276,000 professional, HSBC committed to 200,000 seats to accelerate its workforce transformation. AstraZeneca, Boeing, Infosys, Coke Inc., Procter & Gamble, Stellantis, Tata Consultancy Services, University of Pittsburgh Medical Center, Wells Fargo, and Wipro each purchased 60,000 or more…

…Earlier this month, we added usage-based billing to Copilot Cowork with thousands of customers already paying for and actively using it…

…We now have Chat, CoWork, Autopilot, Code all coming to essentially what is going to become this flagship super app that various roles can use it…

…If I think about historically Office compared to what Microsoft Copilot is much more narrower. This is the first time where you really have an enterprise-wide tool, which has a both per seat and usage-based pricing. The TAM is much more expansive.

Microsoft’s management has added consumption plans to its business model; customer service within Microsoft Dynamics 365’s MCPs (model context protocols) have seen the highest usage-based credit consumption, up 4x sequentially in 2026 Q2 (FY2026 Q4); management recently introduced usage-based billing to GitHub Copilot; after the introduction of usage-based billing, GitHub Copilot had significant consumption revenue and continued to have business and enterprise seat growth

In addition to this, we are also evolving our business model beyond per seat to per seat plus consumption, further expanding our TAM and delivering more customer value. Earlier this month, we added usage-based billing to Copilot Cowork with thousands of customers already paying for and actively using it…

…In Biz Apps, we have been reinventing Microsoft Dynamics 365 for an agent-first world. We are exposing over 650,000 MCP actions across sales, finance, supply chain, HR, and customer service so that agents can now access business context and take action using the same data models, rules, permissions, security guardrails, and audit trails as any application user. We are also moving from seats to seats plus consumption model. Customer service is at the forefront of this transformation with usage-based credit consumption in this category up 4x quarter-over-quarter with customers like Northern Trust using our tools to drive proactive intelligence…

…GitHub Copilot now has 50 million users. This quarter, we introduced usage-based billing and have continued to see business and enterprise seat growth and also significant consumption revenue after the new model went into effect. Copilot revenue accelerated over 60% quarter-over-quarter.

Microsoft’s management recently launched Microsoft Frontier Company for customers to build AI systems that learn and improve with usage, through customers’ workflows, domain knowledge, and judgment; Microsoft will embed 6,000 industry and engineering experts with customers as part of Microsoft Frontier Company; Microsoft has completed over 330 of such projects with 164 customers; Microsoft’s teams worked with Novo Nordisk to build an agent that analyses clinical data under strict compliance requirements; Microsoft worked with LSEG to embed AI into LSEG Workspace

There is a tremendous opportunity to turn customers’ workflows, domain knowledge, and accumulated judgment into AI systems that learn and improve with every usage. To help customers capture that opportunity this month, we launched Microsoft Frontier Company, the largest outcome-driven engineering organization in the industry. We will embed 6,000 industry and engineering experts with customers to co-design, co-innovate, and continuously improve AI systems at scale. We’ve been testing this model over the past year, completing over 330 projects across 164 customers, including many of the world’s leading companies across industries. For example, our FD teams worked with Novo Nordisk to build an agent that helps analyze clinical data while meeting its strict compliance requirements. We partnered with LSEG to embed AI into LSEG Workspace, helping finance professionals ask complex questions and quickly find answers across structured and unstructured financial content.

Microsoft’s management sees a big opportunity for Windows to become a place for unmetered intelligence by combining on-device compute with enterprise-grade security

We see significant opportunity for Windows to become the offload for unmetered intelligence, combining powerful on-device compute with enterprise-grade security.

1/3 of Microsoft’s cloud and AI-related capex in 2026 Q2 (FY2026 Q4) are for long-lived assets that will support long-term monetisation, while the other 2/3 are for CPUs and GPUs; management is able to easily slowdown Microsoft’s capex, both for the short-lived and long-lived assets, depending on the demand environment; in thinking about Microsoft’s capex, management is studying history, such as the railroad boom-and-bust described in the book 1873

Capital expenditures were $41 billion, including the impact from higher component pricing as noted in our guide. Roughly two-thirds of our CapEx was for short-lived assets, primarily CPUs and GPUs, as customers increasingly build solutions that leverage both AI and non-AI infrastructure. The remaining spend was for long-lived assets. This quarter, total finance leases were $5.6 billion and were primarily for large data center sites, and cash paid for PP&E was $35.8 billion…

…You’ve seen our CapEx really pivot toward what I would call and do call short-lived assets, which really, right, that’s CPUs and GPUs that have relatively shorter lead times. If the demand environment changes, you just slow down what is, in fact, the largest component, right, and the driver of COGS. The investment into land and data center builds is actually quite flexible, right? It’s a smaller percentage of the overall cost structure, and timing can be changed on much of that, especially on the builds. You can stagger the timing of the build-out of, as I was saying, some of the GPUs and CPUs that you plan to put in. When you think about being able to manage through that, hyperscalers have been doing that for quite a long time in terms of having the flexibility and the understanding of manage those changes in demand…

…All of us are reading this, “1873” is the book to be read. So in my mind, I think you’ve got to get the product shape right. That’s sort of a lot of what we are focused on. You have to get the portfolio right. Amy talked about what we are doing, whether it’s in Copilot or the super app, bringing all the form factors or all the way to Azure and the agent first sort of primitives in Azure. You kind of have to really get that portfolio to all come together. The mix of customers is super important. You have to recognize the breadth, the geo mix, the segment mix, the workload mix, and you got to really think about all of those when you’re even building capacity. You’ve got to run an efficient railroad.

Azure grew revenue by 43% in 2026 Q2 (FY2025 Q4) (was 40% in 2026 Q1); Azure’s revenue growth was better than expected because of efficiency gains in the compute fleet and the earlier delivery of new capacity; Azure continues to be constrained by capacity; the new capacity was quickly monetised; Azure’s revenue in 2026 Q2 also benefitted from stronger than expected GitHub Copilot consumption following the June business model change to usage-based pricing; Azure’s compute infrastructure is fungible when it comes to running any model family; Azure has a very diverse book of business; Azure has been able to adjust pricing to counter a rise in hardware costs

In Azure and other cloud services, revenue grew 43% against a prior year that included accelerating growth. Customer demand continues to exceed available capacity. Revenue growth was ahead of expectations, driven by efficiency gains across our CPU and GPU fleet, as well as process improvements to enable earlier delivery of new capacity. That additional in-quarter capacity for Azure was quickly monetized. Results also benefited from stronger than expected GitHub Copilot consumption following the June business model change to align pricing with usage and value…

…Given that we continue to see growing demand, no matter what model is chosen or what model family or whether it’s run a model of your own, the Azure platform’s quite efficient at delivering that. Think about that infrastructure as being pretty fungible…

…The other thing is, that’s important, Mark, is you just have an incredibly diverse book of business, by geo, by segment, by industry…

…[Question] How do you manage through the hardware price increases that we’re seeing, the component prices?

[Answer] We’re adding this capacity, to your point, but a lot of this obviously is also being sold in newer contracts, and we’re able to have the pricing reflect it, but keep value.

From FY2027 onwards, management will extend the useful life of Microsoft’s data centers and offices from 15 years to 25 years; the extension of the useful life will only have minimal benefit to FY2027’s operating income; the extension of the useful life will have greater impact to capital expenditures, as the extension will shift Microsoft’s future data center leases from finance leases to operating leases (finance leases are included in capital expenditures, while operating leases are not); management now expects 2026’s total capital expenditure to be $175 billion (was previously $190 billion) because of the shift from finance leases to operating leases

Effective at the start of FY 2027, we are extending the estimated useful life of our data centers and office buildings from 15- 25 years, reflecting our operating history and expected use of these assets. The impact of this update is reflected in today’s guidance. This change affects only the timing of future depreciation and is expected to have a minimal benefit to FY 2027 operating income. The greater impact is on capital expenditures, as more of our future data center leases will shift from finance leases to operating leases as a result of this update. Finance leases are included in capital expenditures, while operating leases are not.

Outside of this useful life impact, our calendar year 2026 CapEx investment expectations remain unchanged. However, the shift from finance to operating leases adjusts our expectation to approximately $175 billion…

…At the company level, with strong commercial momentum, we continue to expect another fiscal year of double-digit revenue and operating income growth. Operating expenses should grow in the mid to high single digits, reflecting continued investment in R&D compute capacity, talent, and data. We expect FY 2027 capital expenditures will grow year-over-year, given demand signals across our portfolio. Even as we invest to meet growing demand, full fiscal year operating margins should be down less than a point. In addition, we expect to remain free cash flow positive in FY 2027.

Netflix (NASDAQ: NFLX)

Netflix’s management is using LLMs (large language models) to improve title discovery and understanding of member preferences; management is using AI to improve Netflix’s search function; in 2026 H1, GenAI (generative AI) workflows were used in 300 of Netflix’s titles, with the workflows mostly in post-production; the use of GenAI helps Netflix to deliver higher quality output faster and at lower cost, and also produce sequences that otherwise would have to be skipped; it’s still early days for Netflix’s acquisition of InterPositive (a startup focused on providing AI tools for filmmakers) but Netflix is already seeing the impact of genAI on its productions; Netflix has other genAI tools outside of InterPositive; management believes that the presence of AI tools does not change the fact that it takes great artists to create great content; Netflix is using AI tools for set references, pre-vis, VFX, sequence prep, and shot planning; management is seeing AI use cases in content production scaling faster and faster; a recent Netflix documentary, American Experiment, features 17 minutes of AI-enhanced footage that were produced twice as fast and at half the cost compared to non-AI options

We are leveraging LLMs to improve title discovery and to better understand member preferences. We’re also enhancing search for our members with new voice search functionality and AI-powered natural language search…

…In 2026, GenAI workflows have been used in roughly 300 of our titles, with the largest concentration of work in post-production. We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods. In some cases, productions would have had to leave out key shots and sequences in the absence of GenAI technology. For example, Glory (India), Brasil 70: A Saga do Tri (Brazil), and The American Experiment (US) utilized GenAI tools to create highly complex sequences (e.g., enhanced crowds, historical battle sequences, and worldbuilding establishing shots)…

…It’s early days for InterPositive, but we’re broadly seeing that gen AI is starting to have an impact across hundreds of our productions. So important to note that we have other gen AI tools in addition to InterPositive…

…On the content side, we believe it takes great artists to make something great, and AI is not changing that. AI will give creators better tools to bring their visions to life. Movies are being made by people who make movies. AI provides them with better tools to make them even better…

…So today, our talent leverages tools for things like set references and pre-vis and VFX and sequence prep and shot planning, which all makes the production itself so much more smooth and efficient and fast. And that’s just the beginning. We’re seeing it across the entire production life cycle and AI — those use cases are scaling faster and faster. So our documentary series we just released called American Experiment. That series features 17 minutes of AI-enhanced footage. It enabled us to expand the scope of the series in ways that just wouldn’t have been feasible before. Those 17 minutes, they were produced twice as fast and at half the cost of previous options.

Netflix’s management has expanded Netflix’s AI-powered advertising tools across its full advertising lifecycle

In Q2, we expanded our AI-powered tools across the full advertising lifecycle, from planning and creative production to campaign management, optimization, and reporting.

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s management is seeing very strong demand for TSMC’s leading edge nodes; TSMC’s capital expenditure is always in anticipation of growth in future years; management has raised capex guidance for 2026 to US$60 billion to US$64 billion partly because of agentic AI (previous guidance is for US$52 billion to US$56 billion; capex growth at the high end of the new guidance would be 56% from 2025’s capex of US$41 billion); management does not foresee any bottlenecks to TSMC’s capacity expansion plans; most of TSMC’s capex for 2026 will be for advanced process technologies; management now expects TSMC to grow revenue by above 40% in USD terms in 2026 (previous guidance was for growth to above 30%); TSMC’s capex in the last 3 years was ~US$100 billion, and the next 3 years is now expected to be much, much higher (previous guidance was for it to be “much higher”), although management does not have a specific 3-year capex outlook to share for 2026-2028; management now thinks the AI accelerators business will have an even stronger CAGR for 2024-2029 than the high-end of the mid-to-high-50% CAGR communicated in the 2026 Q1 earnings call; it’s very likely for TSMC’s capex guidance for 2026 to continue increasing; management thinks the next few years will look really good for TSMC; management now thinks the CAGRs for 2nm, 3nm, and 5nm nodes will be even higher than the very strong CAGRs mentioned during a recent symposium; TSMC’s capex guidance for 2026 was raised partly because customers are willing to cooperate with TSMC (hinting at pre-payments?), and because of price inflation of tools

Demand for our leading-edge technologies is very strong…

…At TSMC, a higher level of capital expenditures is always correlated to higher growth opportunities in the following years…

…Given the continued strong structural demand from our customers, including the newly emerging Agentic AI market, we have decided to raise our full year 2026 capital budget to be between USD 60 billion and USD 64 billion as we continue to invest heavily to support our customers’ growth. We always collaborate closely with the tool suppliers well in advance to prepare the capacity, whether it is a strong up cycle or down cycle, just like our customers collaborate with us well in advance to plan our capacity. Thus, we do not foresee any bottlenecks to our capacity expansion plans.

About 70% to 80% of the 2026 capital budget will be allocated for advanced process technologies. About 10% will be spent for specialty technologies and about 10% to 20% will be spent for advanced packaging, testing, mask-making and others…

…Supported by our robust technology differentiation and the broad customer base, we now expect our full year 2026 revenue growth to be slightly above 40% year-over-year in U.S. dollar terms…

…We do not have a number to share with you. But as you know, we invest CapEx this year for the future business opportunity. And as long as there are business opportunities, we will not hesitate to invest. As you can hear from our prepared remarks that we — our conviction in the megatrend, AI megatrend multiyear is very strong, and we are stepping up the CapEx, including increasing this year’s CapEx. Last time, we said our CapEx in the next 3 years will be significantly higher than the CapEx in the past 3 years. Now is the — the CapEx in the next 3 years will be even more significantly higher than the past 3 years…

…If you read our message that we continue to invest more. We increased the CapEx with a good reason. So if you’re asking about the AI’s CAGR, let me give you not a number, but it’s stronger and stronger and stronger. So we don’t give you the number today because it continue to increase. So we don’t know how to answer this question, but stronger than what we said before…

…This year, we say we increased the CapEx from $52 billion to $56 billion, now $60 billion to $64 billion. And you bet, that will continue to increase…

…Because of the revenue corresponding to our investment, right, because we know we forecast our demand, and then we make an assessment, and then we do the CapEx. Next few years is going to be a very good business for TSMC. That’s all I can say…

…[Question] I noticed that during your symposium that you actually mentioned about 2-nanometer family capacity growth will be growing at around 70% CAGR from ’26 to ’28 and N3 plus and N5 to grow by 25% CAGR from ’22 to ’27. So I was just wondering, are those numbers still right assumptions today?

[Answer] We showed the chart. Okay. Now it’s bigger. That’s what I say…

…[Question] From year-to-date, so TSMC raised the CapEx guidance by almost USD 10 billion. So can you give me some color where is the upside from? How you guys see the difference from 6 months ago?

[Answer] The most important reason is because of the demand continued to increase, and we feel the pressure from the customer to drive TSMC, not drive actually, to cooperate with TSMC for the capacity increase. That’s one of the major reasons. The second reason is inflation. Now we buy the tools with inflation price.

TSMC’s management sees very robust AI-related demand; management continues to see very strong signals and positive outlooks from TSMC’s customers’ customers, who are the cloud service providers; management’s conviction in the AI megatrend remains very high; management thinks demand for chips, driven by AI, will be incredibly strong to at least 2030, but is unsure if there will be a dip in-between; management thinks the collective trend of AI is so robust it is creating a new industry altogether

AI-related demand continues to be extremely robust. The AI megatrend continues to drive the need for more and more computation, which supports the robust demand for leading-edge silicon. Our customers and customers’ customers, who are mainly the cloud service providers, continue to provide us with a very strong signal and positive outlook. Thus, our conviction in the multi-year AI megatrend remains very high…

…I believe from this day on all the way to probably 2029, 2030, the demand is very strong. Whether in between there’s a dip or not, I’m not very sure. But the trend is so robust that I believe we are witnessing a kind of a new industry. I would like to say the new industry called AI industry, which is so common in our daily life because it’s going to affect our automotive, affect the humanoids, robot, and also impact to all the industry. So by the amount of money we put in, I mean, including all the CSPs, this alone is a very important new industry to the world. And so the demand will be there. And the fundamental thing is semiconductor chips, and most of them in TSMC.

TSMC’s management sees the emergence of agentic AI leading to a resurgence of the importance of CPUs (central processing units) in AI data centers; management thinks the resurgence of CPUs is positive for TSMC because nearly all the companies behind the major CPU architectures are customers of TSMC

The emergence of Agentic AI is leading to a resurgence in the role of CPUs in AI data centers, which drives more silicon demand in addition to AI accelerators. We believe this is positive for TSMC as no matter what CPU approach is taken, whether it’s x86, ARM-based, or RISC-V architecture, they are almost all TSMC’s customers. We are already collaborating closely with our CPU customers and working to support them with the most advanced technologies and necessary capacity, so they can capture the Agentic AI market opportunities.

TSMC’s management collaborates closely with its customers, and customers’ customers, when planning the company’s capacity; management is aware that while the CEOs of TSMC’s customers’ customers are telling their own truths, the combined picture is not the truth, and management is adjusting for that in thinking about true end-demand so that TSMC does not end up with over-capacity

To address the structural increase in overall long-term semiconductor market demand profile, TSMC collaborates closely with our customers and our customers’ customers to plan our capacity. Given the fundamental complexity of leading-edge technologies and the design-in and lead time involved, we also have a very good idea of their multiyear product road map and production plans. This is important because it takes more than 5 years to develop the technology and product, prepare the capacity, and ramp it up to high-volume production. Internally, TSMC employs a disciplined capacity planning system to assess the market demand from both a top-down and bottom-up approach. This is a continuous and ongoing process. Based on our assessment, we are stepping up our CapEx investment to increase our capacity, to support our customers’ future growth…

…Now remember that I believe every customer tell me the truth, everyone. You put all the truths together, it’s not the truth. So we have to make some of the judgment. You know what I mean, since you are laughing. Because all the customers are very aggressive, right? That’s the CEO’s job. CEO got to be aggressive. So they give me the number of their demand, and I believe they try their best to tell me the truth. So I put all together, all the truths together is not a truth. Mark down that word. So yes, we do a very careful judgment. May not be correct, may not be correct, but we did carefully and because this is a big money, right?

TSMC’s management has announced an additional US$100 billion investment in Arizona for fabs for nodes of 2nm-and-below, in partnership with TSMC’s US customers and the US government; management believes the latest Arizona investment will strengthen the US’s semiconductor supply chain; the additional US$100 billion investment in Arizona will see TSMC build at least 4 additional fabs, with both front-end and back-end fabs; TSMC is also receiving government support in the USA, similar to Intel, but TSMC does not announce the support; there’s no firm timeline for the additional US$100 billion in investment, as it depends on the market situation and customers’ demand, but management wants to move fast

With a strong collaboration and support from our leading U.S. customers and the U.S. federal state and city governments, we would like to announce an additional USD 100 billion investment in Arizona. This is to build several more semiconductor logical wafer fab for 2-nanometer and below technologies as well as advanced packaging fabs to support the strong multiyear demand from our leading U.S. customers. We believe this investment will help to further foster the development of the U.S. semiconductor ecosystem, strengthen the supply chain, and support an increasing number of high-tech, high-paying jobs in the United States…

…We announced additional $100 billion investment in Arizona. How many fabs? Many. So actually, let me say that, say probably, additional 4 more fabs will be built.

[Question] And that’s combining front- and back-end?

[Answer] Yes…

…The other one in the U.S., they got a very strong U.S. government support. We also got the government support, by the way, although we don’t announce it…

…[Question] Do you have any schedule or time frame to share about this additional $100 billion?

[Answer] Most of the time, it depends on the market situation and our customers’ demand. So if you ask me to give you a firm schedule, no, we don’t have it today. But we do have a plan. And we speed it up. We try to speed it up as fast as possible.

TSMC’s management is building new fabs in Taiwan; management is continuing to convert 5nm tools for 3nm capacity in Taiwan; management is focusing on flexible capacity support among the N7, N5, and N3 nodes; the upcoming A14 node has 10-15 speed improvement at the same power compared to N2, or 25-30 power improvement at the same speed, and a nearly 20% chip density gain; the A14 node is on track and progressing well; management has introduced the A13 and A12 extensions, which are both superior to A14; A13 and A12 are scheduled for volume production 2029; management believes the A14 family will be an even larger and long-lasting node than N2; management wants to move as fast as possible in Taiwan and other countries

We are building 13 leading-edge and advanced packaging fab in Taiwan over the next several years, and we will continue to further invest in Taiwan…

…We continue to convert 5-nanometer tools to support 3-nanometer capacity in Taiwan…

…We are also focusing on capacity optimization across node, which including flexible capacity support among N7, N5 and N3 nodes…

…Our A14 technology representing the second generation of nanosheet transistors and deliver another full node stride from N2 with performance and power benefit to address the incessant need for high-performance and energy-efficient computing. Compared with N2, A14 will provide 10 to 15 speed improvement at the same power or 25 to 30 power improvement at the same speed and close to 20% chip density gain. A14 technology development is on track and progressing well…

…We also introduced A13 and A12 as extension of the A14 family. A13 represents a further advancement of A14, achieving an over 6% die area saving through an innovative 97% optical shrink. Through continuous design technology co-optimization, A13 also drive further performance and power efficiency improvement. A13 design rule are backward compatible with A14 to ensure smooth IP migration. We also introduced A12, which will bring our innovative superpower rail technology to the A14 platform for superior performance, power, and area benefit. Both A13 and A12 are scheduled for volume production in 2029. We believe A14 and its derivative technologies will propel our A14 family to be an even larger and long-lasting node for TSMC than N2. Just like 2-nanometer technology is a larger and longer-lasting node than 3-nanometer, and here further extend our technology leadership position well into the future…

…We’re also moving the new fabs and the facilities in Taiwan as fast as possible. And the same thing, we try to bring up a new fab in the Japan as fast as possible. Because of the situation today is the demand and the supply, the gap is so big. So we are working very hard to narrow the gap.

TSMC’s management has demonstrated 90% device performance and 90% SRAM yield; management is seeing strong customer interest and engagement from smartphone and HPC AI applications

Internal product-like vehicle demonstrated close to 90% device performance and close to 90% 256 megabits SRAM yield. We are observing a strong level of customer interest and engagement from both smartphone and HPC AI applications and customer now tape-out activity is ongoing and ahead of schedule. Pre-production will start in 2027 and volume production is scheduled for 2028.

When dealing with foundry competition, TSMC’s management thinks 3 things are the most important, namely, technology, manufacturing, and customer trust; management is not that concerned about foundry competition because they think choosing a foundry partner requires deep work to understand the foundry’s technological road map and it’s not as simple as changing a brand of milk from a convenience store; management is welcoming competing advanced packaging technologies because it lessens TSMC’s load and provides more flexibility for customers; advanced packaging is a back-end business, and it’s where TSMC is less worried about competition

The most important thing as we continue to say is the technology, manufacturing, and customer trust. These 3 fundamental never change. For my 30-some years, 40 years career, it’s always the most important thing. And that’s always the TSMC’s secret recipe to win the business. So from my – from the competition point of view, choosing a technology, ramping it up is not buying a milk from 7-Eleven. Well, I’m using that — I’m quoting the sentence for my customer, anyway. It says that you’re choosing a kind of a technology partner, it is no shortcut. You need to understand the technology. You need to really utilize it using the test chip, and then something, and work together, and then prepare the capacity and ramp it up. That’s why I would say, it takes about 5 years. It’s not that today, you think this milk is better, you go to the next store, it’s a 7-Eleven. You don’t like it, you go to another store. No…

…[Question] My question is regarding the new advanced packaging technology. We noticed that especially the EMIB-T is gaining traction. So how will TSMC react this request?

[Answer] Our packaging capacity is so tight that now it’s limited by customers’ growth. So we welcome that additional flexibility in the market. And so that will help TSMC’s front-end wafer business growth, which is a majority part of TSMC’s business. The technology looks good, according to the newspaper. And we hope they will be successful and so that share some of the loading from TSMC. Today, we’re working very hard to shorten the gap between the demand and the capacity. And so as I said, we welcome to have this additional alternatives, and so the flexibility for my customer…

…[Question] If these technologies have some small problem, and then ask our company to support. So how our company accommodate it?

[Answer] Our #1 is to support our customers’ success. So whatever that we can hear about our customers’ business, we want to win…

…The front-end’s wafer business and the back-end’s business are 2 different things, right? If they are the same, then you can expect ASE become the front-end competitor also. It’s 2 different things. And I also say that since our capacity in the back-end is so in shortage mode, the gap is bigger. And so, I welcome that the competitor offers some of the flexibility to my customer so that their front-end wafer can be put into the package, and that help TSMC’s front-end wafer business.

TSMC’s management is diligently checking to make sure its AI chips do not end up in its customers’ inventory and are actually being put into production

Are we sure that we deliver the chips to our customer, and they were not put into inventory? So we — actually, we are checking the AI data centers progress, the building, the location, the demand, the racks, we’re checking all that to make sure that TSMC chips will not be put in inventory.

TSMC’s management is not worried about customer concentration for AI-related demand; management sees a lot of new players in the AI industry

[Question] Could I ask about the risk that you see around customer concentration, as AI demand continues to significantly outgrow other end markets? I think your exposure to your top 5 customers is becoming meaningfully larger than at any point in your history.

[Answer] No, that’s not our concern. Besides what you say the customers are growing bigger and bigger, we are very happy about it. And some of the customers also growing very fast. So it’s not — Jim, it’s not what you said that the bigger customer is growing bigger and bigger. No. I mean that’s — there’s a lot of new player in the AI industry.

TSMC has started production for its COUPE (Compact Universal Photonic Engine) platform; COUPE is TSMC’s silicon photonics platform designed to send data using light instead of copper wire; management believes AI data centers will need to lower power consumption while increasing communication bandwidth, and these are important functions of the COUPE platform; management believes COUPE’s demand will grow in the next few years and it will become a fairly important technology for TSMC

[Question] When should we expect the COUPE platform to have a material contribution to your top line?

[Answer] We start the production right now, and it will be ramped up. As time goes by, I think the AI data center need to lower down the power consumption and increase the bandwidth of the communication channel. So I believe the COUPE will continue to increase the demand, and then will become a fairly important technology in the next few years.

All the silicon-roads for AI lead to TSMC

[Question] You mentioned about the Agentic AI and the CPU growth potential. But can you give us more update among that AI, different kind of chips between GPU, accelerators or CPU? What you see the growth potential and your visibility?

[Answer] I don’t think I can give you a very specific number. But let me share with you. All of them are in TSMC. And they’re also using the same kind of leading-edge technologies. We’re working with our customers to allocate the wafer, the supply to balance the CPU, GPU, XPUs ratio.

In mature nodes, there is a shortage for nodes that are related to AI, such as for power management ICs (integrated circuits); the demand for mature nodes from consumer products is not high

The mature node cover a lot of different segments. Only the one which related to AI is in shortage, which is the most important one, is the #1 is power management IC, because all the AI data centers need a lot of power management. And those are the mature node technology like 0.18 micron, 90-nanometer or something like that. Those are in shortage definitely. And also the sensor portion because of — you need a lot of sensor to detect the environmental information and put into the AI data center to analyze it. Other than that, other area, just like you pointed out, the consumer product is not in a high demand. And so other segment is not so strong demand. And as I pointed out in my statement, other area, no, it’s not so much of, say, in a lot of shortage, not at all.

Tesla (NASDAQ: TSLA)

Tesla’s management is seeing very high take rate of FSD in locations where it is approved; management thinks consumers are purchasing FSD primarily, instead of buying Tesla vehicles; management thinks Tesla will enjoy similar uptick in demand as it gets approval for FSD in different countries; 55% of Tesla’s vehicle deliveries in North America in 2026 Q2 had FSD subscriptions enabled; FSD now has 1.5 million paid customers globally (1.3 million in 2026 Q1), 55% of which are paid upfront and 45% are subscriptions; management has turned off upfront payments for FSD, so they expect FSD monetisation to come from subscriptions in the future; the Robotaxi fleet is currently running v15 (version 15) of FSD; v15 has 7 major parallel tracks of improvement over v14; Robotaxi’s v15 already has 40% of the tracks merged together; management thinks v15 will further improve the safety profile of the Robotaxi fleet; management thinks it makes sense to upgrade all Tesla vehicles to at least Hardware 4

We’re seeing in locations that have FSD approved, we’re seeing a very high take rate of FSD. In fact, I think for a lot of people, they’re actually buying Tesla Full Self-Driving with a car attached, as opposed to a car with FSD. They’re coming into our stores in the U.S. and telling me they want the Full Self-Driving and with whatever car it comes with, essentially. Clearly this is a significant demand driver and as we get approval for FSD in different countries, I think we’ll see a similar uptick in demand…

…In Q2, we had, in North America, about 55% of our deliveries had FSD subscription at the time of delivery enabled. Overall, FSD attach rates continue to improve, reaching nearly 1.5 million paid customers globally, of which 55% is upfront purchases and the remaining 45% is subscriptions. We expect that the bulk of the growth in FSD monetization will come from subscriptions as we’ve removed the purchase option in most markets…

…The currently operating Robotaxi fleet is already running early versions of the V15 FSD software that we had referred to in the past. For V15, we had planned roughly about seven major improvement tracks, and they’re all happening in parallel. The early V15 builds that are running on Robotaxi have already 40% of those tracks merged together, and that’s what’s running in the fleet right now. As we continue to complete our work on V15, we will see that the car is going to be ridiculously safe and capable…

…I think it’s going to make sense to upgrade all cars that have less than Hardware four. Any cars that have cameras, basically, because otherwise it would be probably too many modifications. Anything that’s set up for cameras, it’ll be financially sensible at some point to upgrade them. I think we’d want to upgrade them to the next generation of AI board.

Tesla’s management thinks the company’s energy business will be crucial for scaling up AI data centers; power constraints are a major issue for the deployment of AI data centers; power can cycle dramatically in AI data centers, especially for training runs, so the data centers require fast-acting power electronics to smooth out the huge changes in power

The energy business is also growing incredibly fast and, I think will be crucial for the scale-up of artificial intelligence data centers…

…We think power constraints are going to be, they already are a major issue for AI. Just turning on the AI computers, the AI compute demand is so high that even the hyperscalers are having trouble turning on their AI compute and finding the power, and then smoothing the power, especially for the training runs where the power cycles dramatically in a very short period of time. You can have, during a training run, the power consumption can drop by 70% for 100 milliseconds. You really need fast-acting, advanced power electronics to be able to smooth out the massive changes in power, especially during their training runs. That’s why SpaceX has bought so many Megapacks for the data centers. It’s actually mostly for smoothing out the power for the training runs.

Tesla’s management said during the 2026 Q1 call that the company will increase capital expenditure significantly, partly for AI-related investments; management is confident that the capex will yield massive returns; the higher capex in 2026 Q2 caused free cash flow to become negative; management continues to expect capex for 2026 to be more than $25 billion; management is tapping on the debt markets to help fund Tesla’s capex

This is a massive CapEx year. I’m confident that all the things that we’re investing in will yield incredible returns. Really, maybe the best CapEx returns that we’ve ever seen…

…Our free cash flow ended up being negative for the quarter. Most of the reason for it going negative is because CapEx more than doubled sequentially. We expect it to increase further in the second half of 2026. We continue to expect that CapEx for this year will be more than $25 billion. CapEx will grow for the next two or three years as we expand our Robotaxi fleet, expand our production capacity for Optimus, make investments for semiconductor fab, install solar manufacturing capacity, and AI compute infrastructure, in addition to all the other expansions we’ll do for other manufacturing for automotive. In addition to using our cash for such investments, we are being opportunistic in securing certain debt facilities that will give us the capacity to borrow up to $30 billion to help accelerate such investments.

Tesla’s management will soon start production for Optimus, the company’s autonomous humanoid robot; management continues to think Optimus will be the biggest product ever; no one has ever built an autonomous humanoid robot that can do tasks without any programming; there are many challenges in the electromechanical design of Optimus; management foresees very substantial challenges in scaling the production of Optimus, as the degree of difficulty is proportionate to the newness of a robotic part, and there is no existing supply chain for Optimus; Tesla has in-house a significant amount of Optimus’ production; Tesla has an Optimus production line in Fremont, California; management expects the initial part of the Optimus production S-curve to be long and flat; management thinks a lot of videos of robots on the internet are pre-programmed or remote controlled; management believes Optimus will be the first humanoid robot capable of doing generalised tasks; Optimus will come with hands that have the same dexterity as the human hand, and then eventually have superhuman dexterity; management is confident that the same AI technology for self-driving cars can be used for a digital version of Optimus; the development of Digital Optimus is in partnership with SpaceX’s (an Elon Musk company) Grok models; management thinks having Optimus take on the human form-factor gives the robot access to huge amount of learning-data as the form factor allows the robot to learn directly from humans; workers at Tesla factories are providing the data for Optimus to learn from; Optimus benefits from a reinforcement learning loop when a large number of robots practice their tasks; management has the same end-to-end AI approach for Optimus’s AI as they do for FSD; management sees Optimus 4 as having much higher vertical integration than Optimus 3; management sees 10 million units a year for Optimus 4, compared to 1 million units annually for Optimus 3

We will soon start production with Optimus…

…I think Optimus will be the biggest product ever…

…It’s one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that if you simply ask it to do something or show it a video, it can do the task without any programming. No one’s ever achieved this. There are many challenges in the electromechanical design of the robot to achieve sufficient dexterity, also to be very reliable and have long wear and tear. Meaning, it needs to be out in the field and not break down…

…The production scaling challenge is very substantial. This is going to be the hardest product to scale manufacturing that we’ve ever made at Tesla, because everything on the robot is new. The difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot…

…With Optimus, there is no supply chain. We’ve had to build up a supply chain in its entirety, or in-house the production. We actually have in-housed a tremendous amount. The Optimus production line that we’re building out in Fremont, in place of what used to be the Model S, X production, it looks incredible…

…Optimus will follow the normal S-curve of a manufacturing ramp, but the initial portion of the S-curve will be quite flat and long because of the newness of the parts in the robot.

You’ve probably seen lots of impressive demonstrations of robots on the internet, but those demonstrations you’re seeing are pre-programmed or remote controlled. There is no humanoid robot that is actually able to do generalized tasks. Optimus will be the first one that is capable of doing that, where it’s not just a demo, it’s genuinely useful in day-to-day life. And Optimus is designed to have full human dexterity. A hand that has the same level of dexterity, if not higher, than a human hand. The human hand is an incredible thing. The more you study the human hand, the more you realize how amazing hands are. It’s more than just opposable thumbs. The nuances of how human hands work are amazing. The closer you look, the more amazed you are. Optimus will have that capability. It will have human and then superhuman dexterity…

…We have Digital Optimus, which is basically driving a computer screen, I guess you could call it computer use or something like that, but driving a computer screen in the same way you drive a car. For the car, it’s pixels in or photons in and controls out. The same thing is true for Optimus and Digital Optimus. It’s photons in, controls out. We feel confident we can adapt the same Tesla AI technology that we developed for self-driving cars to have a self-driving computer screen or self-driving computer, essentially. The self-driving computer, where the very low cost, Tesla AI computer can handle all of the sort of real-time tasks. They’re doing real-time video control of the screen of the computer. It’s not like screenshots type of thing, it’s real-time video, at high frame rate. Digital Optimus will be important, obviously, for physical Optimus because physical Optimus needs to be able to operate computers. It can’t come up to a touch screen and not know what to do. It’s got to have a generalized touch screen and computer use capability.

That’s looking promising and this is in partnership with SpaceX. SpaceX’s Grok, sort of the big model that is the manager of Digital Optimus and tells Digital Optimus what to do, provides it with a series of tasks, then Digital Optimus goes and does those tasks…

…Optimus has been designed to not just match the appearance of human beings, but also the functionality and dexterity of humans. One of the main reasons for this approach is that having the human form factor and function allows us to learn from humans on how to perform a wide variety of tasks. This opens up the entire world to provide data for training Optimus. Just like FSD, we have access to a broad fleet of humans giving us data from all of the workers at our factory…

…When we have a large number of Optimus robots practicing their tasks in what we call the Optimus Academy. The data from the bots experiencing the task themselves will be invaluable and help us close any minor form factor gaps that may exist between the bot and the humans. This is also when the reinforcement learning loop kicks in, where the bot initially attempts some tasks, fails sometimes, learns from both the successes and failures of those tasks, and eventually learns to master those tasks at perhaps a superhuman level. Our AI strategy for Optimus is aligned the same. It’s the same end-to-end strategy that drives FSD, pixels in, controls out. Just like FSD, we expect it to work broadly. In FSD, you can get in the car, type in an address, and then hit start, and it just handles all of driving from park to park. The same thing is going to be true for Optimus as well. You’re going to just ask it to do anything, and then it should just perform the entire task on its own without you having to do anything along the way…

…For Optimus 4, which will be built in Austin, that will be a much more vertically integrated supply system for Optimus 4. That would aim to have an order of magnitude more production of Optimus 4 than Optimus 3. Sort of aspirationally 10 million units a year versus 1 million units a year of Optimus 3. With all the caveats there, which is insanely difficult to scale production.

Tesla’s management is trying to scale Robotaxi as fast as possible, but they are doing so while being very careful not to harm anyone; Robotaxi has been introduced in a number of cities in Florida, Texas, and the Bay Area; management expects Robotaxi’s miles driven to increase by more than 10% a week; Robotaxi is in 7 markets in the US currently; management expects to accelerate the ramp of Robotaxi throughout the year and expand into new US markets; Robotaxi has driven more than 380,000 miles unsupervised with zero incidents; Robotaxi did not require LIDAR, radars, HD maps and more to drive safely; management thinks Robotaxi’s current safety record is validation of the company’s entire AI approach to autonomy; Robotaxi’s number of unsupervised miles has been growing double-digits every week since the start of 2026; it has been taking management relatively less effort to launch Robotaxi in new cities and management expects the time to launch in a new city to trend towards zero; management expects the Robotaxi business to be fully vertically integrated; management thinks Robotaxi will not face any demand challenges; the Robotaxi fleet is still small (only in the dozens) but management wants to expand into new cities to test the generalisability of Robotaxi’s autonomy technology; vehicles in the Robotaxi fleet are driving 24/7, so Tesla can get a lot of miles from them

There are, I think, 30,000 – 40,000 automotive deaths per year in the U.S. alone, most of those do not generate any press or maybe, you never really read about almost any of those. If we injure even one person, it’ll be worldwide headline news, and regulators will immediately clamp down on our activities. We don’t want to injure anyone. We’re going as fast as humanly possible in scaling Robotaxi, but while trying to ensure that we do not harm anyone at all, and ideally do not even run over a pet. That’s really the constraint is we want to grow as fast as possible with Robotaxi without harm to anyone…

…We’ve opened up in a number of cities in Florida and in Texas, obviously in the Bay Area. We’ll continue to scale, I think, very rapidly with more than 10% a week in terms of miles driven…

…We continue to grow the Tesla Robotaxi fleet and have expanded to a total of seven markets in the U.S. We expect the ramp of the fleet to accelerate throughout the year, along with expansion into new U.S. markets…

…In terms of safety, the program has had an impeccable safety record. We have driven more than 380,000 miles of unsupervised Robotaxi, now across six cities in two different states. We have had zero notable incidents. Any reports have been of other actors impacting us when we were stationary…

…Historically, the so-called experts have always claimed that you need LiDARs, radars, HD maps, and the entire kitchen sink to drive safely. Here we show that such is not true. You can have safe, comfortable, and affordable autonomy with just cameras. This record should be a huge validation of Tesla’s entire AI approach…

…Since the beginning of this year, we have grown at double-digit growth rates to the number of unsupervised miles that the fleet drives every week…

…For expanding to new cities, it has been relatively less effort on our front. We expect that the time to launch to a new city will continue to trend towards zero, towards an end where we operate in entire states as a whole instead of going city by city…

…[Question] Would you ever consider third-party distribution partnerships, such as with rideshare providers, to increase utilization? Is the plan for now to keep Robotaxi fully vertically integrated?

[Answer] We expect to be vertically integrated with Robotaxi as we are in the rest of our business. I don’t think we’re going to have any demand challenges with Robotaxi. The economics will be so compelling that I think we will really have a lot more desire to use the service than I think demand will outstrip our ability to service the demand…

…[Question] On Robotaxi. If I look at the launches, you’ve been adding cities, the number of units is still, it looks like based on media reports, sort of in the dozens as opposed to hundreds. Why not just sort of scale up Austin or one or two cities before adding cities, what do you need to sort of get that higher volume numbers in a major city? What sort is the roadblock to start adding more vehicles on the ground?

[Answer] The reason we have been expanding across different cities instead of just doubling down on a single city, is that we want to make sure that our stack is a very general one. It is a general one. We just want to both prove to ourselves and to other folks that it is working across a lot of different cities without too much effort per city…

…In terms of miles versus vehicles, since these vehicles operating the Robotaxi fleet drive basically continuously as opposed to human drivers who use vehicles for maybe a couple of hours a day or something like that. These vehicles are in mostly continuous operation, which means that even for a few vehicles, you can get a lot of miles out of them, that’s why we are tracking the amount of unsupervised miles.

Tesla’s management expects to announce the location for the TeraFab soon; management believes TeraFab is a necessary project for Tesla because the company will be constrained by the supply of AI chips, otherwise, for the scaling of Optimus; management has placed semiconductor manufacturing orders for the development fab in Austin; the development fab will have lithography mask production, logic, memory, packaging, and chip testing all under one roof, which is unique among fabs, and gives it a rapid iterative cycle to test high-risk ideas

The Terafab, we expect to announce a location soon, and provide more details about our plans in that regard…

…I do think Terafab is going to be an amazing initiative and a necessary one, and one without which we will be constrained in our ability to scale Optimus production, because we simply won’t have enough AI chips. It’s crucial to solve that, and we’ll have to solve memory, logic, and packaging in order to scale Optimus…

…We’ve placed equipment orders for our development fab in Austin. That development fab, I think, is pretty cool because it’s intended to have lithography mask production, and then logic, memory and packaging and chip testing all under one roof. You can have a very fast iterative cycle, and try out new chip designs very quickly and see if they work. I don’t think such a building exists anywhere on Earth. This is really going to be super helpful as we try some exciting, adventurous, high-risk, high-payoff bets on AI chips.

Tesla’s management is building a Megapod design that combines an AI computer with an x86 CPU; the Megapod comes in a box that can be placed anywhere, and thus can allow Tesla to scale AI compute with disaggregated electricity production, such as its Supercharger network; Tesla’s Supercharger network has 7GW of power, and growing

We’re also building out a Megapod design that has Tesla AI computer with x86. It’s a pairing, an x86 computer with a Tesla AI computer in a box, and it’s got this Digital Optimus in a box, and in a Megapod, kind of like the Megapack packaging. Where we put just a large number of AI full plus x86 combos in a giant box, essentially. These boxes can be placed anywhere in the country or outside the country. This allows us to scale AI compute using disaggregated electricity production. Because there’s lots of places all around the world, including at our Superchargers. I think we’ve got something like seven gigawatts of power at our Superchargers and growing. We can place Megapods at many of these Superchargers and have distributed power for AI.

Tesla’s management thinks the Cybercab, which is the autonomous vehicle for the company’s Robotaxi fleet, is a phenomenal product; the manufacturing targets for Cybercab are now roughly aligned with the growth of Robotaxi’s unsupervised miles; v15 of FSD will also work on Cybercab; the Cybercab is a new form factor, so Tesla needs to accumulate driving data that is specific to the Cybercab before the company can put a lot of them on the road

Regarding the Cybercab, first of all, it’s a phenomenal product. Anyone who rides in it instantly falls in love with the experience. We have aligned our manufacturing targets to roughly match the projected growth rate of the unsupervised miles. The same V15 models that power the Model Y and other platforms will also work on Cybercabs…

…Because it is a new vehicle chassis, we need to accumulate driving data that is specific to the Cybercab before we can put a lot of them on the road.

Tesla’s management is seeing Tesla’s suppliers making investments to support Optimus; Samsung and Micron are providing good support to Tesla

Our suppliers have been great, they have made and are making tremendous investments in support of Optimus and Robotaxi and whatnot. Samsung and TSMC, in particular, are building fabs, TSMC in Arizona and Samsung in Texas, and putting in tens of billions to build AI compute for Optimus and Robotaxi. Panasonic has also invested many billions in increasing battery cell production…

…The Samsung fab, that’s going to be pretty significantly dedicated to future projects. That’s a massive investment, multi-billion dollar. We’re seeing the same level of investment going to memory and also new specific items like metal injection molded parts, flexible printed circuits, and all sorts of nonlinear technologies that are more based for the robot as opposed to the traditional vehicle supply chain we’ve had…

…I’d actually also like to thank Micron for giving us memory allocation. They’ve got to make some very tough decisions on memory allocation. We really appreciate Micron making room for Tesla in the years to come and giving us actually a very significant allocation on reasonable terms given the pretty insane pricing of memory these days.

There are increasing levels of collaboration between Tesla and SpaceX, and management appears to be hinting at some form of combination; Starlink, in particular, is important for Tesla because Starlink ensures that Robotaxis have constant internet connectivity

As you can tell from all the many collaborations on so many fronts with SpaceX, there’s more and more overlap, especially with Terafab, that’s really going to be a gigantic project. Obviously, we can’t talk about combining companies and that kind of thing on an earnings call. It’s got to be done with the appropriate process…

…We continue to benefit from our relationship with SpaceX, they’ve been a great partner, we have numerous beneficial transactions with them. Earlier this year, we deepened our relationship through an investment and a framework agreement. This will allow us to continue to work with them on projects that Elon mentioned, like Terafab and Digital Optimus…

…You’ve got Grok in the car. Grok helping drive Digital Optimus. You also got Starlink being integrated into the Cybercab, Starlink will be integrated into all our car vehicles, at least for markets that Starlink is active. Because for a Robotaxi situation, you need to have coverage everywhere. There are many places, even in Silicon Valley, where the cellular coverage is terrible or sometimes non-existent, which is surprising for Silicon Valley. I know when I drive to work, those first 10, 15 minutes, I can’t actually do any calls because the cellular connectivity is so bad. We can’t have Robotaxis getting stuck in these Bermuda Triangles of lack of cellular connectivity. Starlink with its ability to do connectivity anywhere is actually quite important, so we don’t have Robotaxis missing in action. Obviously if people are sitting in the car, they go, “Blip,” and want to do high productivity stuff or want entertainment. With Starlink, you can watch 4K live sports in the car and with very low cost per gigabyte of data, as that’s really not feasible via the cellular system.

There are a shortage of truck drivers in the USA and a fully autonomous Tesla Semi will be important to address the shortage; management’s focus at the moment is on solving autonomy for Tesla’s high-volume vehicles before doing to for the Semi, which is a low-volume vehicle; management thinks autonomy for the Semi will come in 2027, and in time for the scaling up towards volume-production of the Semi

There is a really serious shortage of truckers. There just aren’t enough people around who want to drive trucks, which are crucial for transport throughout America. An autonomous Semi is actually going to be very important to address the shortage of truck drivers. Obviously will be great for improving safety, and making it easier on truck drivers to have a self-driving Semi is going to dramatically improve safety and comfort for truck drivers that use the Tesla Semi. Since the total number of units of the Tesla Semi is still low, and will continue to be a very small percentage, even by the end of this year, of our total vehicle fleet. It makes sense for us to focus our self-driving efforts on our high-volume vehicles, Model 3 and Y, and solving self-driving for, and really getting to the point where it’s generalized, unsupervised self-driving for those vehicles, and Cybercab. We expect to get self-driving working on the Tesla Semi probably around the end of this year or early next year. I just don’t want it to be a distraction on the march of nines of safety for self-driving on three, Y, and Cybercab. It’s taking a bit of a backseat for the next six months or so for autonomous Semi. It will definitely be working next year and in time for the scale-up to high production of the Tesla Semi.

The Tesla chip team is making fast progress on the AI5 chip; management is very excited about the design of the AI6 chip as they think it’s going to be the best edge computing chip in the world

The Tesla chip team is really doing great work and making incredibly fast progress on AI5. I’m very excited about the design of the Tesla AI6 chip. I think it’s going to be the best edge computing chip in the world.

Visa (NASDAQ: V)

When generative AI first emerged, Visa’s management quickly deployed the technology to engineering, client service, and model orchestration; with the emergence of agentic AI, management has used the technology to build end-to-end pipelines with human oversight and autonomous capability, leading to a reduction in the size of product development teams from 10 or more people to 2 or 4; teams in Visa that use agentic AI have showed 80% more code commits and a 65% increase in the pace of feature development; Visa now has more than 150 AI-powered applications; in the last 12 months, Visa has shipped more than 300 major product releases with the help of AI; management has eliminated certain roles in Visa to fund AI-related investments; Visa has an AI financial assistant, which allows banks to provide white-lablled AI insights to cardholders; Visa has the Vulnerability Agentic Harness to allow users to use frontier models to fix problems; Visa has increased the velocity of its consulting projects with the help of AI; management thinks Visa still has enormous opportunity ahead to deploy AI within the company

AI is changing how work gets done at Visa. With the dawn of the generative AI era, we moved quickly to deploy AI across our enterprise to assist us in areas like engineering, client service, and model orchestration. As we enter the era of agentic AI, we are going beyond AI assistance and harnessing the power of AI to execute work and tasks with our supervision. We have progressed materially in product development and engineering, deploying new tools, plugins, agent skills, and persistent sessions to create an end-to-end pipeline with human oversight and autonomous capability. As a result of the unlocks we can realize with this new tooling, we are reforming our product development teams that used to be 10 or more into smaller and more nimble agentic squads of two to four. The results are meaningful for those teams that are using the agentic tool chain, with 80% more code commits and 80% plus improvement in requirement definition from 30 days to five days, which has translated to 65% plus faster feature development…

…We now have more than 150 AI-powered applications, and over the last 12 months, we have shipped more than 300 major product releases…

…Today, we announced that we are eliminating roles, with the majority being in our technology and product teams, to ensure that we are continuing to position Visa for future growth…

…The investments that we have in front of us are enormous. I think what we’ve shown over the last couple of years is that we can drive efficiencies, we can take the savings that we generate from those efficiencies, and we can invest those savings against the strategic levers that we laid out at our strategy at our Investor Day and deliver accelerated performance as a result….

…We continue to develop new products, including our AI financial assistant, enabling banks to white label our AI-powered financial insights from their data and Visa’s network data for their cardholders right in the bank’s own app and website…

…We built the Visa Vulnerability Agentic Harness, an orchestration layer that allows us to use models like Mythos to find and fix issues at AI speed. It is available now on GitHub to our clients, along with a technical blueprint, remediation, and validation agents…

…For our advisory and other portfolio, in addition to the strong marketing services revenue growth, we’ve also increased the velocity of our consulting projects through the help of AI. Just this past quarter alone, for over 700 clients across 100-plus countries and territories, we delivered 1,200 consulting projects, which is more than we delivered for all of 2019…

…I think we still have enormous opportunity ahead of us there, and we’re just going to continue to lean in to using these tools to drive efficiency and effectiveness, shipping products better, faster, and ultimately, better serving our clients in the ecosystem.

Visa’s new agentic-AI-driven way of working has made big impacts on its initiatives for stablecoins and agentic commerce; management believes that agentic commerce will expand Visa’s addressable market; Visa recently enabled new seller capabilities and infrastructure for agentic commerce; Visa is partnering with OpenAI to enable secure payments in agentic commerce; Visa is partnering with Meta Platforms to enable consumers to transact in Facebook and Instagram seamlessly and securely with Visa tokens; management thinks the world of commerce is still only in the very early stages of a major adoption curve for agentic payments; management thinks the adoption curve of agentic payments will follow the same patterns as previous technological cycles; management thinks that trust will be the ultimate accelerant for adoption of agentic payments; Visa’s products within agentic commerce have all been about building trust for consumers

As a result of this new way of working, we are able to design, build, and ship products at an increased velocity with continuous innovation and improvement. I want to touch on two areas where we are deploying our new way of working with great impact, stablecoin and agentic commerce…

…If stablecoins are reshaping the back end of commerce, we see AI as transforming the front end. We believe agentic commerce will expand our addressable market and drive future growth for Visa. This quarter, we continued to work across the ecosystem by enabling new seller capabilities such as our Agent Score and Agent Directory and building infrastructure such as our Token Assurance Framework to ensure agent-initiated transactions are transparent and trusted…

…We are excited to be partnering with OpenAI to enable secure Visa payments within agentic commerce. Through the partnership, Visa will provide its global network, credentialing capabilities, and security infrastructure to support agentic commerce experiences, helping consumers and businesses interact and transact with confidence. As part of our partnership with Meta, Visa is enabling new ways to pay across Facebook and Instagram, powered by Visa Intelligent Commerce, allowing consumers to transact seamlessly and securely with Visa tokens…

…We believe that AI and agentic commerce will expand our addressable market. We believe we’re in the very early stages of what’s going to be a major adoption curve in payments. I think to get a sense of how this progresses from here, it’s instructive to look at other kind of major cycles that we’ve been through, whether it was e-commerce or mobile commerce, tokenization, tap-to-pay. These innovations and these kind of major forces, they followed a similar pattern, right? You have an early period where Visa and other players are establishing standards, we’re announcing, launching, and shipping new products, as you mentioned. You migrate into the early adoption period of the curve, which ultimately then leads to growing consumer momentum and ultimately broad scale. All of those kind of ones that I mentioned previously have gone through that, and they’ve achieved that broad scale. We don’t think agentic commerce will be any different…

…I think the ultimate thing that’s going to accelerate that adoption is going to be trust. Trust that the payment is secure, trust that the agent is authorized, trust that the transaction reflects the consumer’s actual intent, and then the protections exist if something goes wrong. If you look at the products that we’ve announced over the last several quarters, they’re all directly intended to address that trust and ensure that our users have trust in using Visa credentials to make agentic commerce transactions. That’s where we’re investing.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet, Amazon, Apple, ASML, Mastercard, Meta Platforms, Microsoft, Netflix, TSMC, and Visa. Holdings are subject to change at any time.

What We’re Reading (Week Ending 02 August 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 02 August 2026:

1. Cyprus – “weird” opportunities galore – Swen Lorenz

In 1999, the main index of the Cyprus Stock Exchange rose nearly tenfold, making it the world’s best-performing stock market.

It was one of history’s greatest investment manias:

Brokers had such a backlog of orders that it took them up to three weeks to clear a trade.
Some Cypriot farmers reportedly sold their sheep to invest the proceeds in the stock market.
Hastily arranged IPOs were oversubscribed by a factor of 50-100x.
One IPO by a small cruise company attracted subscriptions equivalent to 10% of Cyprus’ GDP.
A small shopping mall suddenly reached a valuation of EUR 2bn, roughly equal to the entire market cap of the Cyprus stock market before the boom began.

The business section of Britain’s The Guardian reported at the time:

“Everyone, housewives, cleaning ladies, ministers and businessmen are in on the act … The mania has reached such heights that from Cyprus’ taverna-terraced beaches to its remote mountain villages few now talk of anything else.”

It ended much like almost every investment mania ends: in tears.

After breaking records on the way up, the Cypriot stock market broke records on the way down, too. The market index ultimately lost 99%, which, according to some, remains the largest decline ever suffered by a national stock market index. By comparison, Greece “only” lost 92.5% during its sovereign debt crisis, while Germany’s hyperinflation between 1918-1922 saw the market fall by 97%.

2. You just hired a million bad employees – George Sivulka

For the first time in history, humans are cheaper than software.

And AI is creating more jobs than it eliminates…

…Managing AI is harder than managing people, because AI scales dysfunction instantly. Fortunately, we can learn from the past:

Agent workforces and human workforces fail in the same way.

Understanding the 7 major parallels between the two will unlock the next trillion dollars of AI value creation…

…1. Tokenmaxxing is throwing bodies at the problem…

…People are spending so much on tokens because they don’t know how to use them.

Maybe 1 in 100 employees knows how to give AI context. It’s a rare breed of person that can articulate a process clearly, that has the patience to empathize with a polluted context window, or even understands what that means.

Give an agent harness to the other 99 people and they will produce “loops”.

2. Loops are meetings about meetings…

…3. Wasted tokens are the new headcount bloat…

…Just like 80% of employees do nothing, 80% of tokens today do nothing.

People create more people. Tokens create more tokens. Looping is the new empire building.

4. 100X tokens are the new 10X engineers…

…In the same way a handful of employees make others 10X as productive, for any given job some amount of token context can cut AI effort down by orders of magnitude. There exist tokens that will give you 100X as much leverage.

Humans are cheaper than tokens on average, but good tokens are cheaper at scale…

…5. Context hoarding is the latest job security tactic.

There’s a massive political problem with AI inside the firm, and it will only get worse.

Employees don’t want to teach AI systems their secret sauce….

…6. Evals are the new OKRs.

The best way to manage a token workforce is the same as the best way to manage humans: by defining what good looks like.

The one breakout AI use case that escaped politics is coding. It expanded the pie and made every engineer better.

The mechanism is evals. 99% of AI revenue today is coding because coding has built-in evals. Code runs or it doesn’t…

…7. The next trillion-dollar opportunity is the transformation company.

Enterprises have been buying foundation model commits, the application layer, and internal builds for years now. All of it conceals a brutal truth about the economics:

Nobody has AI working reliably yet…

…In fact, the next biggest businesses won’t be eating existing services spend. They will sell a net-new type of service to existing players:

“AI transformation companies” will be 10X larger than any neofirm.

Transformation sounds like a one-off project. But there’s a Jevons paradox at work: every use case an organization adopts surfaces ten more. The more AI-enabled a firm becomes, the more transformation it consumes, while the frontier of what’s possible advances daily. Ongoing AI transformation efforts will become the only way to compete.

3. Data-Center Builders Are Racing to Offload Stakes Worth Billions – Anissa Gardizy

Data-center builders and operators across the U.S. are working with bankers to sell majority equity stakes worth tens of billions of dollars in their companies this summer, according to people familiar with the efforts…

…Sales of data-center operators are on the rise as owners of these firms seek exits and investor interest in owning the physical infrastructure behind advanced artificial intelligence grows. A massive backlog of demand for server capacity pushed companies to pursue novel strategies to secure more of it, from renting chips from direct competitors to launching data centers into orbit…

…Whether there are enough buyers with the means to absorb so many large deals at once this summer remains an open question. Ravi Purohit, the co-head of infrastructure at Paul Weiss, said that while plenty of investors want exposure to data centers, only a handful of firms can afford multibillion-dollar deals…

…But as these megadeals scale up, developers are increasingly running into fierce local opposition, driven by residents’ anxieties over rising utility bills, noise and advanced AI services in general. In some cases, it has pushed developers to pause or walk away from projects. This all makes investing in data centers riskier than it was in the past, investors said.

“I think people are downplaying the significance of Nimbyism in this country right now,” Purohit said, adding that companies looking to sell this summer will be scrutinized for how they plan to handle it.

“The more they can demonstrate to buyers that they have a constructive relationship with these communities…that actually goes a really long way,” he said.

4. How Terrorist Groups Are Using A.I. to Gain an Edge in Battle – Dustin Volz and Eric Schmitt

When a gang of motorcycle-riding members of Boko Haram attacked a military base in eastern Nigeria a couple of years ago, they were stymied by a defensive trench surrounding the complex.The extremists regrouped. Before launching another assault, they asked A.I. for help.

“We saw in a movie how motorcycles can jump over bridges,” a former Boko Haram commander told Antonia Juelich, a terrorism and technology researcher at Cambridge University. “We used A.I. to learn how to do this. We gave it information, like what motorcycles we use and the distance we need to jump and so on, and it gave us steps on what we have to do.”

Using tips from chatbots, mechanics modified the motorcycles to allow for faster acceleration and top speed. The riders dug their own holes, filled them with broken glass and fire, and practiced jumps — sometimes with fatal outcomes — until they achieved enough aerial liftoff to mount a successful attack, defectors said…

…Until recently, the Islamic State, Al Qaeda and other extremists primarily used A.I. in the information-operations realm — propaganda production, translation, recruitment and security tradecraft. But that has evolved as jihadists have turned to A.I. for tactical on-the-ground advantages, according to current and former U.S. military and counterterrorism officials and independent researchers…

…Daniel Byman, a terrorism expert at Georgetown University and co-author of a report about A.I. and the future of terrorism released on Friday by the Center for Strategic and International Studies, said terrorist groups were “mixing and matching” from different A.I. systems, seeking to avoid technical guardrails established by the A.I. companies. Dr. Juelich’s research also found that Boko Haram was platform agnostic, interchangeably working with OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and xAI’s Grok, as well as the Chinese firm DeepSeek…

…Defectors recounted attending organized training sessions focused on how to best leverage the powers of generative A.I. models to inform or enhance their uses of the technology.

The trainings, in which laptops were equipped with virtual private networks and encryption software, were delivered via transnational jihadist networks often led by members of the Islamic State, interviewees said. Common topics included managing an account on an A.I. platform, suggestions on generating useful answers and tips on evading safety restrictions.

5. Are We Repeating the Biggest Mistake of 1873? (Transcript here) – Merryn Somerset Webb and Liaquat Ahamed

Liaquat Ahamed: What started it was the gold rush. Europe, which was the centre of the world, had gone through a terrible depression in the 1840s, fuelled by bad harvests and by revolution. They actually thought every government in Europe was going to fall and that we’d have the equivalent of the Russian Revolution across Europe. That didn’t happen — but at the same time, they discovered gold in the United States, and that provided the fuel to get the global economy going.

You had bankers like the Rothschilds, who had made a ton of money in the early part of the 19th century lending to governments, jumping on the bandwagon and starting to lend for infrastructure — particularly the railroads — but to the private sector. You got a massive boom in lending, and it was a boom based on the bond market. Everyone thinks the bond market is a sleepy place where people who don’t want to take risk put their money, but it was essentially the bond market that provided the impetus to growth. It grew by five times in the two decades from 1850 to 1870…

…Merryn Somerset Webb: That’s interesting, and we’ll come on to it — whether economies are incredibly resilient and whether the best thing to do is just leave them alone. Because when we get to 1873, we find out that everyone decided not to leave it alone. They succumbed to what we’d call “something must be done”-ism, and that’s when things started to get nasty.

But the pivotal moment is this war between Prussia and France that starts in 1870. It’s a short war, but it has enormous repercussions, because France is obliged to pay enormous reparations to Germany — to the tune of how much?

Liaquat Ahamed: Around a billion dollars.

Merryn Somerset Webb: A billion dollars, which would be the equivalent of $1.2 to $1.3 trillion today. And Prussia thought this was fine, because so much money would take France forever to pay off. This would keep them down and out of the way — the enemy dealt with indefinitely. Instead, France somehow managed to go out and get the bond markets to give them a billion bucks over two years. And it was the Rothschilds who were at the centre of that, because they had incredible reach.

Liaquat Ahamed: They raised two bond issues which together raised a billion dollars. But the most important thing was that in one case the issue was three times oversubscribed, and in the other, twelve times…

…Merryn Somerset Webb: And the London Stock Exchange went crazy.

Liaquat Ahamed: Because everyone who didn’t get into that bond issue still had their money.

Merryn Somerset Webb: Exactly.

Liaquat Ahamed: And in the US, the railroads had been a perfectly rational boom until then — and then suddenly railroad bond issues doubled. We went up to $500 million a year in an economy where that was roughly 5% of GDP.

Merryn Somerset Webb: Which today would be $1.5 trillion in the US. So that $1.5 trillion — the sort of money that goes into a SpaceX today — is what went into the railroads then. Fascinating. And then of course that billion dollars went to Germany over a two-year period.

Liaquat Ahamed: Yes, and they then had 25% of GDP to play with in cash.

Merryn Somerset Webb: And that was totally mismanaged.

Liaquat Ahamed: They could have fed it into the economy at a slower pace. But injecting 25% of GDP in liquid cash into an economy that was relatively unsophisticated — where everyone who owned government bonds suddenly found their bonds paid off —

Merryn Somerset Webb: Paid off meaning redeemed.

Liaquat Ahamed: Yes. They looked around and said, “What do I do with this money?” and started hunting for opportunities. And lo and behold, a whole lot of charlatans appeared on the scene to relieve them of it. It was not only a stock market boom but a giant IPO boom. The stock market, which had maybe 30 or 40 companies listed, suddenly ballooned to 500 or 600. A lot of them were banks, a lot were real estate, a lot were railroad companies — but there were also all sorts of things, like companies set up to explore the northern regions of Europe, or to look for banana plantations in West Africa. It was a crazy time…

…Merryn Somerset Webb: So the crash starts in Vienna. But because there had been a global boom and a global bubble, it spreads. There’s contagion across the world.

Liaquat Ahamed: Yes. You actually get a false period of calm. It crashes in Vienna, everyone says there’s going to be a global disaster — and then nothing happens for three or four months. So people said, maybe Vienna was just overpriced. It was a local incident. Everywhere else, earnings will rise to match prices and it’ll be fine.

But meanwhile, Jay Cooke — the premier investment banker, who had raised $2 billion for the Union government during the Civil War — suddenly found that because of the disruption in Europe he couldn’t raise capital. He ran out of money in the middle of constructing the second transcontinental railroad. It was a little like the Lehman Brothers story: he started injecting his own bank’s money into the project, and still couldn’t complete it. When he announced that he couldn’t raise the capital, there was total panic — the same sort of psychological panic that happened after Lehman. People said, “If Jay Cooke, a friend of the president and the most well-connected banker in the United States, can’t raise $100 million, what hope do we have?”

Every railroad stopped construction. There were 500 railroad companies in the United States; by the end of the year a third of them had stopped paying dividends, and within five years half of them had defaulted.

Merryn Somerset Webb: Everything comes crashing down.

Liaquat Ahamed: Everything comes crashing down.

Merryn Somerset Webb: And at the same time everything’s still going horribly in Europe — 70% of the banks in Vienna have gone bust. It’s global carnage…

…Liaquat Ahamed: What caused the boom to end? I think two things. One is that everyone tried to build railroads at the same time, so they started competing against each other and railroad profitability began to decline. The equivalent today is all of the hyperscalers trying to build AI infrastructure at once, and the price of tokens starting to collapse — which, by the way, has already started.

Merryn Somerset Webb: It’s already happening.

Liaquat Ahamed: It changes the economics of their investment. And at the same time, because of the disruption in Europe and the war, the price of capital started rising — and there are some signs of that happening now. Until recently we reassured ourselves that we could finance this boom out of the profits of the giant technology companies. But even that has proved inadequate, and they’re now going out and borrowing.

So the combination of declining profitability and a rising cost of capital at some point causes a crunch. In the US case, when Jay Cooke announced he couldn’t complete his railroad — the equivalent today would be OpenAI declaring, “Actually, we think we’ve miscalculated, we’re not going to be able to complete a whole model, and we’re going to have to sell to Microsoft.” Can you imagine the panic that would occur in the market for AI infrastructure?

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google). Holdings are subject to change at any time.

The View On Consumer Spending From The Largest Payments Companies (2026 Q2)

Mastercard and Visa can feel the pulse of consumer spending – what are they seeing now?

Mastercard (NYSE: MA) and Visa (NYSE: V) are two of the largest payments companies in the world. As a result, they have a great view on consumer spending that’s taking place. With both companies reporting their earnings results for the second quarter of 2026 earlier this week, the bottom line is that consumer spending remains strong in the USA and other parts of the world. Here’s what they are seeing.

*What’s shown in italics between the two horizontal lines below are quotes from Mastercard and Visa’s management teams that I picked up from their earnings conference calls.

From Mastercard

1. Mastercard’s management sees consumers and businesses being healthy and continuing to spend and they are supported by positive job growth, low unemployment, and purchasing power; management is monitoring geopolitical risks; management is seeing economies around the world adapting to changing conditions; the fundamentals of consumer and business spending and travel remain healthy

Consumers and businesses are healthy and continue to spend, supported by positive job growth, low unemployment, and real purchasing power in many major economies. At the same time, we continue to monitor geopolitical uncertainty and its potential economic impacts…

…Around the world, economies are adapting to changing conditions, with consumers and businesses continuing to demonstrate resilience…

…Overall, the underlying fundamentals of consumer and business spending and travel remain healthy.

2. Worldwide GDV (gross dollar volume) was up 8% year-on-year in 2026 Q2 in constant-currency basis; cross-border volume was up 12% globally in constant-currency, driven by both travel and non-travel cross-border spending (cross-border volume growth was 13% in 2026 Q1); switched transactions was up 9% year-on-year in 2026 Q2; card growth was 5% in 2026 Q2, with Mastercard ending the quarter with 3.7 billion cards in circulation (there were 3.7 billion cards in 2026 Q1, and year-on-year growth was 5% then); domestic assessments were up 10%, cross-border assessments were up 20% and transaction processing assessments were up 12%

I’ll speak to the growth rates of our key volume drivers for the second quarter on a local currency basis. Worldwide gross dollar volume, or GDV, increased by 8% year-over-year. In the U.S., GDV increased by 6%, with credit growth of 10% and debit growth of 1%. As a reminder, the Capital One debit portfolio migration was basically complete in Q1. Excluding the impacts from that migration, our U.S. debit GDV growth would have been 8%. Outside of the U.S., GDV increased 9%, with credit growth of 9% and debit growth of 10%. Cross-border volume increased 12% globally for the quarter, reflecting continued growth in both travel and non-travel related cross-border spending…

…Switched transactions grew 9% year-over-year in Q2…

…Card growth was 5%. Globally, there are 3.7 billion Mastercard and Maestro-branded cards issued…

…All growth rates are described on a currency-neutral basis unless otherwise noted. Looking quickly at each key metric. Domestic assessments were up 10%, while worldwide GDV grew 8%. The two PPT difference is primarily driven by pricing. Cross-border assessments increased 20%, while cross-border volumes increased 12%. The eight PPT difference is driven primarily by pricing in international markets and mix. Transaction processing assessments were up 12%, while switched transactions grew 9%. The three PPT difference is primarily due to favorable mix and pricing, partially offset by lower revenue from FX volatility, and other network assessments were $326 million this quarter.

3. In 2026 Q2, Mastercard’s operating metrics had good year-on-year growth and were stable sequentially; in July 2026 so far, Mastercard’s operating metrics continue to be strong with worldwide switched volume growth of 9% (6% in the USA, and 11% outside of the USA), switched transactions growth of 9%, and cross-border volume growth of 11%; card-not-present ex-travel’s sequential decline in growth rate was driven by timing; Mastercard’s US business had some benefit from the World Cup in 2026 Q2, but it was hard to quantify; Mastercard’s US business continues to have healthy consumer and business spending trends; management is seeing strong spending trends in both mass and affluent consumers in the US and around the world, although affluent consumers have higher growth in spending

Let me comment on the operating metric trends for Q2 and the first four weeks of July. Switched metrics were generally in line with Q1, and underlying spend remained stable. Of note, excluding Capital One debit, on a like-for-like basis, U.S. switched volume growth was 10%, or two PPT higher sequentially. This increase was driven by higher spend on fuel and overall strong consumer and business spending.

Moving to our cross-border metrics, our overall cross-border volume growth remained healthy at 12% in the second quarter. Cross-border card-not-present ex-travel remained strong at 20%, benefiting from increased card-not-present spend from Venezuela and the timing of large retail promotional events. While cross-border travel was down sequentially, relative to the April metrics we discussed on our last earnings call, we saw improved growth in the quarter due to lower impacts from the developments in the Middle East and timing of holidays.

As we look at the first four weeks of July, our metrics remain relatively stable and strong. Looking specifically at card-not-present ex travel, let’s focus on July compared to June. The sequential decline is primarily driven by timing, including the large retail promotional events that happened in June this year as compared to July last year, and by mix of days…

…The strong underlying consumer and business spending, which we’re seeing in the U.S. There has been a tailwind which has come on account of higher fuel prices, so let’s recognize that. You probably have some impact coming through from the World Cup as well, as it relates to the second quarter in particular. Hard to really quantify what that is just because we can’t really identify exactly what that is. I would tell you the underlying consumer and business spending trends continue to hold up well in the U.S. To your point, if I look at it’s broad-based. We see it across credit and debit. We see it across consumer and commercial…

…I would say they’re holding up well, both across mass and affluent. Certainly in the U.S., but across the world as well. We try and track the best we can as it relates to what we’re seeing in spending patterns based on the product codes that are out in the market, which serve the different categories of customers. When we look at that, we’re seeing generally strong trends, across both mass and affluent. What you do tend to see is higher growth in the affluent side of spending. That’s kind of not a new phenomenon. That’s been with us for some time now.

From Visa

1. US payments volume growth was good at 10% in 2026 Q2 (FY2026 Q3), a growth rate not seen since FY2019; there was good growth in both US credit and debit volumes; growth across consumer spend bands improved sequentially, with the highest spend band continuing to grow the fastest; both discretionary and non-discretionary spend remained strong; management did not see a deterioration in spend in the lower bands

U.S. payment volume grew 10% year-over-year, up about two points from Q2, a growth rate not seen since fiscal 2019, excluding the post-COVID recovery, with both card present and card not present growth accelerating strongly. U.S. payments volume growth was the result of several factors, including higher tax refunds, the cost of fuel, retail, including the timing of promotional shopping events, strong Visa Direct growth, and FIFA-related spend. U.S. credit rose 11% year-over-year, up more than a point from Q2. Debit accelerated by more than two points from Q2 to grow 9% year-over-year. Growth across consumer spend band saw incremental improvement from Q2, with the highest spend band continuing to grow the fastest. Across our volume, both discretionary and non-discretionary spend remained strong. We do not see signs of the lower spend consumer weakening in our volumes.

2. Visa’s cross-border volume growth remained strong in 2026 Q2 (FY2026 Q3) at 12%, up from 11% in 2026 Q1

Q3 total cross-border volume grew 12% year-over-year, up more than a point from Q2. Cross-border e-commerce volume was up 16%, three points above Q2, primarily driven by retail, including the timing of promotional shopping events. Travel-related cross-border volume was up 10%, consistent with Q2. While the conflict continued to be an offsetting factor, commercial and U.S. inbound continued to improve, and in June, the FIFA World Cup boosted inbound North America and Latin America volume.

3. Payments volume on Visa’s network continues to grow in July 2026, with US payments volume up 9%, cross-border volume up 14%, e-commerce volume up 18%, and processed transactions up 9%

Now, let’s look at drivers through July 21st, with volume growth in constant dollars. U.S. payments volume was up 9%, with both credit and debit up 9% year-over-year. A step down from June, primarily due to retail, including the timing of promotional shopping events, a lack of a day’s mix benefit that helped June, and the change in the cost of fuel. For cross-border volume excluding transactions within Europe, total volume grew 14% year-over-year, with e-commerce up 18% and travel up 12%. Processed transactions grew 9% year-over-year.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have a vested interest in Mastercard and Visa. Holdings are subject to change at any time.

What We’re Reading (Week Ending 26 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

Here are the articles for the week ending 26 July 2026:

1. Keynote speech by Chinese President Xi Jinping at opening ceremony of 2026 World AI Conference

We human beings must answer the questions posed by our times: How to get along with thinking machines? How to ensure security when algorithm is part of decision making? How to tackle ethical challenges by technologies through adaptive governance? How to realize AI for all when the divide keeps widening? These questions demand serious consideration and real answers from the whole international community.

In China’s view, all countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security. We should join hands to build a just and equitable system for global AI governance. To this end, I wish to share four observations.

First, we should adhere to the principle of openness and win-win and boost innovation-driven development. As a new engine of world economic growth and an accelerator for the shift of growth drivers, AI is moving from the digital world into the physical world. We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing. We should facilitate technological innovation, industrial development and scenario-based application of AI. We should make coordinated advances in the transformation and upgrade of traditional industries, the cultivation and growth of emerging industries and forward-looking planning for future industries, so that all sectors and businesses can benefit from AI.

Second, we should strengthen risk-awareness and ensure that AI is secure and controllable. AI should be a trusted tool for humanity. We should take seriously the various types of inherent and secondary risks that AI may trigger. We should put in place laws and regulations, technological monitoring, early warning and emergency response systems in order to strengthen the line of security, prevent abuses and malicious use, and ensure that AI is always under human control. In the meantime, we should jointly oppose overstretching the national security concept in the field of AI and placing one country’s security over that of others.

2. The AI Bubble? – Nothing Linear?

Five very different numbers get quoted in this debate, and they blur together constantly:

$725B — 2026 AI capex from the big four hyperscalers alone (Microsoft, Google, Amazon, Meta).
~$1.5T — estimated total AI infrastructure spend across the whole industry in 2026.
~$3T — Sequoia’s David Cahn’s estimate of the annual revenue AI must eventually earn to justify that build-out.
~$50–100B — roughly what the largest AI firms actually earn today: OpenAI ~$25B run-rate, Anthropic ~$30–47B ARR, plus Google/Microsoft AI revenue.
The gap — the distance between the last two is the entire debate. It is the widest it has ever been, and widening…

…The three genuine choke points:

Advanced AI chips. Nvidia holds roughly 80% of the AI-accelerator market, and TSMC is effectively the sole advanced-node foundry able to fabricate the leading chips — virtually every leading chip, Nvidia’s, AMD’s, and hyperscalers’ custom silicon alike, passes through it. TSMC’s advanced-node capacity and CoWoS packaging are reportedly booked out for years, with Nvidia taking a large share. A single geopolitical or manufacturing disruption in Taiwan would immediately choke the entire industry’s ability to train or serve frontier models. Lesson: sovereign AI ultimately needs sovereign or allied access to advanced chip fabrication — software alone cannot route around this bottleneck.

Electricity & data-centre capacity. The IEA projects data-centre electricity demand roughly doubling from ~415 TWh (2024) to ~945 TWh by 2030, with AI the dominant driver. In some US states and countries (Ireland, Virginia) data centres already draw a fifth or more of total electricity. Power availability, not chip supply, is increasingly the binding constraint on how fast new capacity can be built. AI’s physical footprint — power, water, land — is becoming as strategically important as its digital one, and draws the same local political resistance as any heavy industry.

Frontier model training. Roughly six labs — OpenAI, Google, Anthropic, xAI, DeepSeek, Zhipu — can credibly compete at the very top, because training a genuinely frontier model costs tens to hundreds of millions in compute alone. But this choke point compresses faster than the physical ones: open-weight releases (Llama, DeepSeek, Qwen, GLM) have shown near-frontier capability at a fraction of the presumed cost. Algorithmic efficiency keeps lowering the compute needed to reach yesterday’s frontier.

A fourth layer, high-quality training data, is a contested rather than settled choke point: the freely-scrapeable internet text that trained the first LLMs is finite and non-renewing, and a fast-growing share of new web content is itself AI-generated — raising the risk of models training on their own degraded output (”model collapse”). Licensing disputes and lawsuits are actively reshaping what data can legally be used, pushing labs toward paid licensing, synthetic data and proprietary enterprise data. Data is shifting from an abundant free input to a scarce, litigated, paid-for one — a structural change in the industry’s cost base most forecasts do not yet price in…

…Part 5 · The bull case — why this may not be a bubble

The technology genuinely works. Unlike some past manias, the product is real and used daily by hundreds of millions. Generative AI reached an estimated 53% adoption among the general population within about three years — faster than the PC or the internet.

Revenue is growing very fast. The leading labs are scaling revenue at rates rarely seen: Anthropic went from roughly $1B to a ~$30–47B run-rate inside 18 months, and OpenAI reached a ~$25B run-rate. Fast growth can, in principle, close even a very wide gap.

The spenders are the strongest companies in history. Unlike the debt-laden telecom builders of 2000, the biggest AI spenders are among the most profitable firms ever. Nvidia earned roughly $120B in net income last year; Microsoft, Google and Amazon are cash machines. They will not simply collapse if returns are slow.

Valuations are rich, but not insane. The NASDAQ-100’s forward P/E is around 26x today, versus roughly 60x at the 2000 dot-com peak. Expensive, and concentrated in a few names — but not the pure fantasy of the last great tech bubble.

Individuals are extracting real value now. Even where enterprise ROI is unclear, the estimated value of generative-AI tools to US consumers reached about $172B annually by early 2026, with median value per user roughly tripling year over year.

Part 6 · The bear case — why it may be a bubble…

…Big-four AI capex has gone from ~$90B (2020) to $147B (2023) to $410B (2025) to $725B (2026). The gap itself has widened from a $200B question (2023) to $600B (2024) to an estimated ~$800B+ annual gap in 2026 — Allianz pegs the capex-vs-revenue divergence at ~46%, exceeding the ~32% seen in the 2001 telecom bubble.The cash-flow squeeze. PIMCO estimates Big Tech capex will consume ~94% of operating cash flow over the next two years, up from ~40% in 2023 — meaning for every $100 earned, only ~$6 is left for dividends, buybacks, salaries and everything else. For the first time, the hyperscalers are also leaning on debt: the big five raised roughly $108B in new debt in 2025.

Enterprise ROI — the crack in the revenue side. The core bull rebuttal is “enterprises will pay, because AI makes them radically more efficient.” The data from the industry’s own consultants is, so far, harsher. MIT Project NANDA found 95% of enterprise GenAI pilots showed zero measurable P&L impact (2025, 300 deployments). BCG’s “AI at Scale” survey of 1,800 executives found only ~26% of companies are generating meaningful financial value from AI. S&P Global found 42% of companies abandoned most AI projects in 2025 — more than double the prior year. McKinsey’s 2026 State of AI found fewer than 20% of pilots reach enterprise-scale production.

Most failures are not the technology’s fault — MIT found ~70% of the work to make AI pay off is process and workflow redesign, not the model. But from a market standpoint the cause barely matters: if the ROI is not showing up in the P&L, the revenue needed to justify the capex is not showing up either…

…The technology can be completely real and the bubble can still burst. That is not a contradiction — it is the normal pattern of every industrial build-out in history. The pattern runs four steps: high returns attract capital; capital keeps flowing until overcapacity is built; overcapacity triggers collapse; survivors inherit the wreckage cheaply and, when demand finally catches up, make fortunes on assets others paid to build.

3.Maybe Intelligence Ain’t All That – Clifford Sosin

Superintelligence arrived. You probably didn’t notice, because it turned out to be kind of incremental…

…We hold a thin scatter of facts about the world, and intelligence or reasoning is whatever fills the space between them. It extrapolates between things we already know, and LLMs are incredible at that. In studying our words, they learned the structure of our thoughts. We will soon have it in unlimited supply, at 200 IQ, for close to nothing.

Where the space between the facts behaves well, this is close to godlike. Real estate law isn’t hard. We made it up, it’s internally consistent, and a model holding every statute and ruling should beat any lawyer for free. Coding, math and most administrative work are similarly benign. What makes them easy is that they have relatively smooth solution spaces and are tractably verifiable.

Most of what matters doesn’t behave like that. The universe is mostly the emergent behavior of complex systems. Stir cream into coffee. Watch a storm build out of nothing but atmospheric temperature differences and water vapor. The local rules are simple and the tornado is not. In systems like these, no amount of reasoning delivers the answer, because there’s no shortcut hiding in the gaps. You have to run the thing. And running it has a ceiling of its own, since small errors compound, which is why no supercomputer will ever give you a clean two-week forecast. The smallest object that can perfectly simulate the universe is the universe. Human systems are similarly complex…

…An AI will hand you a genuinely clever design for a jet turbine blade. It might be far more likely to work than anything your engineers came up with. It’ll still probably fail, because that’s the base rate at the edge of what anyone knows. The only way to find out is to build the blade and try to break it.

That’s the real limit on learning, and it doesn’t care how smart you are. Coming up with ideas was never the hard part. The hard part is how fast reality answers them.

4. A Stock Certificate From 1941 Taught Me More About AI Than Anyone from OpenAI – Francis Huang

But I think the railroad story, if you take it seriously, tells you six things about what happens next.

First, the technology will work. That part isn’t in doubt. Railroads worked. AI works. The question was never “will trains move faster than horses?” or “will language models generate useful output?” The answer to both was always yes. The technology question is settled. Everything that follows is a finance question and an ownership question, and those are much harder.

Second, the buildout will be larger than anyone currently projects. In 1850, America had 8,879 miles of track. By 1860, it had 30,626. Nobody in 1850 would have believed that number. AI infrastructure spending has quadrupled since 2022 and shows no sign of slowing. McKinsey’s $6.7 trillion projection for 2030 might end up being low. When a technology changes the cost structure of everything, the capital required to build it out has a way of exceeding every estimate, including the ones that already seemed crazy.

Third, the financial system will change in ways we can’t anticipate. Railroads didn’t just use the existing capital markets; they created new ones. Bond markets, equity markets, underwriting syndicates, credit analysis, bankruptcy law, corporate governance, even the concept of the limited liability corporation, all got reshaped or invented to handle railroad finance. AI will do the same. We don’t know what the new financial instruments will look like yet. Maybe compute futures. Maybe revenue-sharing tokens tied to model performance. Maybe something nobody has named yet. The railroad precedent says the instruments themselves will be part of the story.

Fourth, a crisis will come, and its trigger will be something nobody is watching right now. In 1873, it was a Viennese real estate bubble. In 1893, it was the cumulative effect of rate wars nobody thought would last. Whatever hits AI won’t be “AI doesn’t work.” It’ll come from some adjacent system that’s quietly entangled with the AI buildout in ways nobody has mapped. A chip supply disruption. A sovereign debt crisis in a country that’s heavily invested in AI infrastructure. An energy bottleneck. Something nobody is talking about at Davos or on the All-In Podcast. The black swan, by definition, is the one you’re not looking for.

Fifth, the people who build it and the people who own it long-term will be different. The merchants of Boston who funded the Boston & Worcester Railroad in 1831 were not the Vanderbilt family that controlled it in 1900, and neither of them were CSX, which runs it today. OpenAI’s current investors, Anthropic’s current investors, the hyperscalers currently spending $700 billion a year: these may or may not be the entities that own and profit from AI infrastructure in 2050. Morgan got rich not by building railroads but by picking up the pieces after other people’s railroads collapsed. Someone will play that role in AI. We don’t know their name yet.

Sixth, and this is the one I keep coming back to: the infrastructure will outlast everyone’s financial projections. The tracks the Boston & Albany laid in the 1840s are still carrying freight in 2026. Data centers being built today will run computation, in some form, for decades after the companies that built them have been restructured, merged, acquired, or dissolved. The physical layer endures. The capital layer above it churns.

5. Why Are Berries Everywhere, in Every Season? Driscoll’s – Julia Moskin

In just the last decade, berries have completed the journey from fragile, local, seasonal treat to worldwide refrigerator staple and marketing juggernaut. Global production has tripled since 2000, according to research from the U.N.’s Food and Agriculture Organization, and still cannot keep up with demand. In sales and volume, berries are the fastest-growing category in American produce, according to data from the U.S. Department of Agriculture.

Most of that growth has been driven by Driscoll’s, a $7 billion California company that began as a multifamily farm in 1904, patented its first strain of strawberries in 1958 and is still controlled by family members. In 1989, its board made what the company calls the Meadowood Declaration, a resolution that seemed preposterous at the time: to make all four berries available, in every season, in every part of the world.

Today the company is the undisputed global market leader, shipping four billion containers of highly perishable fruit across 60 countries each year…

…According to Circana, a market research firm, Driscoll’s is now the second-highest-earning brand in American supermarkets, behind only Coca-Cola…

…Instead of owning land, the company owns the genetic material of its berries and the knowledge of how best to plant, pick and transport them. It subcontracts with farmers around the world to grow those breeds according to its specifications, then handles sales and distribution after harvest.

But global access to berries has a cost, measured in metrics like water consumption, pollution, pesticides and labor practices. Driscoll’s has come under fire on all four fronts…

…Inside a nearby laboratory, where two full-time sensory scientists make their assessments, 210 raspberry varieties were laid out in a grid of plastic pints. Some had been bred for visual appeal, with more shapely shoulders, uniform drupelets and less “hair” (the thin red styles that sprout where the berry is pollinated). Others were developed to maximize yield, with fewer thorns and better “plant architecture” — tall, fluffy stalks that make the berries easy to pick. Each cultivar is tested for qualities like P.S.I., the interior pressure that determines whether a berry will yield to the teeth with an explosive, juicy pop.

Out of those 210 strains, said Kyle Rak, the company’s chief raspberry scientist, perhaps two will make it to market…

…Many Driscoll’s berries are no longer planted in soil, but grown in pots filled with carefully balanced mixtures of organic materials like coconut fiber and moss. This system of substrate farming was developed over centuries in the Netherlands to produce maximum yields from minimal land.

It requires a substantial start-up investment by Driscoll’s growers, who also absorb the costs of ever-shifting factors like labor, weather, equipment and rent. The company provides seed plants and “inputs” like soil treatments, along with technical support and marketing dollars. After harvest, the company retrieves the filled clamshells, then compensates the grower according to the price those berries command. According to Driscoll’s, growers receive 75 to 80 percent of the revenue…

…Demand for berries has exploded in the United States because of overlapping recent trends: more snacking and the rise of “functional” foods that promise specific health benefits…

…Berries are shifting entire economies.

In 2023, they became Mexico’s most lucrative agricultural export, surpassing avocados, beer and tequila. On Moldovan plantations and in Andean highlands, growers of low-margin crops like sugar cane and corn have switched to berries, which command premium prices.

In 2025, China overtook the United States as the world’s largest blueberry producer. Driscoll’s, the first foreign berry company allowed to operate there, now has about 8,000 acres under cultivation.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Amazon, Meta Platforms, Microsoft, and TSMC. Holdings are subject to change at any time.

Warren Buffett’s Latest Wisdom

Takeaways from CNBC’s latest interview of the Oracle of Omaha.

Warren Buffett was interviewed for an hour by CNBC’s Becky Quick last week. He spent a good chunk of time during the interview sharing his thoughts on philanthropy, but he also discussed investing matters. In this article, I want to share my investing-related takeaways from Buffett’s latest interview. Before I get to it, I would like to thank my friend Thomas Chua for performing a great act of public service – Thomas posted a transcript of the interview at his excellent investing website Steady Compounding a few days after it happened.

The italicised passages between the two horizontal lines below are direct quotes from the interview.

1. Buffett initiated Berkshire Hathaway’s large position in Alphabet shares, even though Alphabet is laying out enormous capital expenditure for AI infrastructure, because he thinks Alphabet has a great chance of winning with their AI-related capital expenditure; Buffett thinks Alphabet is only ranked 5th or 6th in terms of the businesses he likes that are in Berkshire’s portfolio

[Warren Buffett] I initiated it, but I normally wouldn’t give you that answer on something like that, but I will, because I am not doing anything that he doesn’t approve of, and he’s not doing anything I don’t approve of…

…[Warren Buffett] The real question with Google, and all of its competitors now, is they’re all laying out hundreds of billions.

[Becky Quick] They’re big cap-ex spenders, the biggest.

[Warren Buffett] Yeah, and that’s real money. If our railroad were to lay out 300 million, or a billion, or 200 billion, that kind of money wasn’t even put into the railroad business, in terms of developing it. That’s the game they’re playing now. They won’t play that game with computer software.

[Becky Quick] So when they were asset light you didn’t like them, and the markets loved them. Now that they are spending heavily on cap-ex, a lot of shareholders don’t like them as much because they don’t…

[Warren Buffett] They’re more likely to be a winner, based on their record, than probably 90% or 95% of what will get merchandised through Wall Street, because Wall Street is only selling something…

…[Becky Quick] Why do you like Alphabet above all others, and what made you initiate this position? What was the eureka moment?

[Warren Buffett] I would say that I don’t like it as well as at least four or five other businesses that we own.

2. Buffett looks at buying shares and buying an entire company the same way – he’s analysing the quality of the underlying businesses

[Becky Quick] Okay, so you’re counting fully owned companies as well.

[Warren Buffett] We are always making the choice between whether we’ll buy marketable securities or the company. We look at it the same way. There are some minor exceptions, we can’t set dividend policy, for example, if we don’t own it, but the chances of those being material, the important thing is to buy a good business, and to buy it on the right terms, and then get the right person to run it.

3. A good business is one that can earn a high return on capital for a long period of time; American Express looks like a much better business than banks because it earns materially higher returns on capital while taking lower risk

[Warren Buffett] When I say a very good business, I mean something you can expect to earn high returns on capital over a long period of time…

…[Warren Buffett] So a good business is one that earns a lot more than the returns on essentially riskless investments, which you could define as Treasuries. But if you take something like American Express, most of the banks earn 13% or 14% on capital. If I asked everybody to guess what American Express would get, they would come up with some figure similar, but it’s so different, it earns 30% plus on capital, and does not incur more risk in doing so than the banks that earn 13% or 14%.

4. The key to investing is to find companies that can earn high returns on capital for a long period of time

[Warren Buffett] The trick in investing is to find businesses that are going to earn high returns on capital for an extended period of time, and that’s what happened with Berkshire for a long period of time.

5. Wall Street, to its detriment, often overlooks the internal rate of return a business earns

[Warren Buffett] I can’t recall a report on Wall Street that really gets into the internal rates of return that the business is actually earning. What’s more important is what a business is earning, but they ask all these questions about what will happen next quarter, and it’s ridiculous.

6. Buffett thinks the hyperscalers are all making AI-related capital expenditures not necessarily because they want to, but because they have to

[Becky Quick] But I’m talking about why Alphabet versus the other Magnificent Seven, or the other hyperscalers who are doing the same thing, spending a lot of money, Amazon, Microsoft, whoever it may be, to try and win in this position of AI.

[Warren Buffett] Well, I don’t want to sit around knocking the others. They don’t have any choice. They’re now playing a game, in many cases, that they don’t want to play. IBM would have loved it if they just kept playing the game IBM was playing in the ’30s, the ’40s, the ’50s, and the ’60s, and then somebody came along and said, we’ll get a better result for you, achieving the objective of all the customers you have, because that’s all you’re going to have, either happy customers or you don’t have customers, over time. The customer’s not dumb.

7. Wonderful businesses attract competition, and the key to investing is determining how long a wonderful business can stay wonderful

[Warren Buffett] But if you have a wonderful business, you are going to be subject to attack. So it’s not a question of whether it was wonderful yesterday, it’s the question of how long it is going to be wonderful.

8. Coca-Cola is currently entangled in a lawsuit with the US government over taxes which could have massive implications for American businesses

[Becky Quick] We talked about Coca-Cola briefly, the long time position you’ve held for more than 45 years. There is a major lawsuit with the government that could look at action, I believe, going all the way back to 1996 with Coca-Cola. The IRS has said that they owe them roughly $20 billion, of which they’ve paid…

[Warren Buffett] About $10 billion or so.

[Becky Quick] But we’re going to hear about whether the activities, and this has to do with their overseas business, some of the accounting that goes back and forth, Coca-Cola says they thought they had an agreement in 1996 that stood for how they should behave. The government’s now looking for more money and saying that’s not the case. It’s not just Coca-Cola that’s riding on this, so there’s a lot of other American businesses doing the same thing.

[Warren Buffett] A huge number, which is why the derivative effects of the suit could be the biggest in American history.

9. Buffett thinks the latest chair of the Federal Reserve, Kevin Warsh, is a good choice

[Becky Quick] But you think Kevin [Warsh] knows a lot and is a…

[Warren Buffett] A very, yeah, I think he was a good choice.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet. Holdings are subject to change at any time.

What We’re Reading (Week Ending 19 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

Here are the articles for the week ending 19 July 2026:

1. The Great Wave Has Arrived – Tang Jie

If there is one thing we have learned over the past twenty years, it is this:

The greatest commercial opportunities never lie in minor adjustments to products or business models. They arise when the ceiling of intelligence itself makes a leap…

…Whoever can push that ceiling even one inch higher will be able to redefine the boundaries of what thousands of industries are capable of achieving. That single inch is precisely what the new generation of AI companies grounded in first principles are competing to secure…

…We have a simple but demanding definition of AGI:

AGI is not the intelligence of a single genius. It is the aggregate of all human intelligence.

It should be capable of creating original knowledge on the level of the theory of relativity. That is the only standard by which we measure whether the true summit has been reached.

On the road toward that destination, several mountains must be crossed. They are also where today’s technological wave is surging most powerfully.

The First Mountain: Long-Horizon Task Capability

The most exciting breakthrough today is teaching models to complete extremely long tasks—not merely answering questions immediately, but planning and executing over weeks, months, or even years…

…The Second Mountain: Fully Autonomous Agent Systems

Building on long-horizon capabilities, groups of agents that can operate independently, collaborate with one another, and work around the clock will become a new form of productivity…

…The Third Mountain: Self-Evolution

This is the most difficult—and also the most compelling—mountain of all.

AI training AI is already taking shape. Models are beginning to write their own code, clean and synthesize their own data, and train themselves…

…What will happen after these three mountains have been crossed?

AI will begin to learn what the “self” is and what self-awareness means. Beyond that, it may begin to touch human emotion. Farther still lies consciousness itself.

From perception to cognition, from cognition to general intelligence, and from general intelligence toward artificial superintelligence, or ASI—the road has already been laid…

…When AGI arrives, today’s applications may all need to be rebuilt as AI-native systems—or may no longer be needed at all.

Operating systems themselves may be rewritten. In the future, when you turn on a computer, what you see may be an “LLM OS,” with every function generated on demand.

Going deeper still, this represents a challenge to the von Neumann architecture that has underpinned computing for the past eighty years…

…As the supply of high-quality human-generated data approaches exhaustion, we will turn computing power into fuel for evolution.

This means building factories for high-quality synthetic data, using AI-versus-AI competition through self-play to generate knowledge from scratch, and giving systems the ability to reconstruct their own code within secure sandboxes.

The goal is to free the pace of evolution from the physical limitations of human engineers…

…The more powerful AI becomes, the more robust its safety constraints must be.

From the very beginning, Zhipu established a guiding principle:

AI must serve human well-being and national strategic priorities.

The company rejects bolt-on safety patches. Instead, it seeks to encode human ethics, social norms, and national laws and regulations into the model’s value function as foundational axioms.

We plan to commit resources on the scale of tens of billions to advancing mechanistic interpretability—clarifying the neural logic behind model decisions and transforming black-box systems into transparent, explainable ones.

2. The Reverse Information Paradox – Satya Nadella

In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!

Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.

That is what I think of as the Reverse Information Paradox…

… That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm’s right to align models to their enterprise accountability obligations.

3. Away From the Casino Tables: The Wildest Value Gap Since 1999 – Sam Ziff

The chart below splits global equities into four valuation buckets, running from deep value to extreme growth, with each cohort’s current valuation shown as a premium or discount to its own 40-year median…

…The cheapest bucket globally (“deep value”) trades at a 45% discount to its historical average, while extreme growth sits at a 40% premium. Shallow value outside the US remains meaningfully cheap. The further you move from US large-cap growth, the more likely you are to find something trading below its long-run valuation. This is the backdrop against which we invest…

…The last time markets were this concentrated and this expensive, the next decade belonged to the parts of the market the crowd had forgotten. From the end of 1999 to the end of 2009, the MSCI World and S&P 500 delivered close to a zero total return, while emerging markets doubled and value outperformed growth.

4. The 221-year-old company that reinvented itself — 4 times – Eric Markowitz

Meet Jean-Joseph D’Ieteren.

In 1805, the 14-year-old orphan took over a small workshop in Brussels and started making wheels. Over the next 220 years, the company D’Ieteren founded became a carriage maker, then a builder of custom car bodies, then an importer of American automobiles, then the exclusive Belgian distributor of Volkswagen, then the world leader in automotive glass repair, and then — in a move that raised eyebrows across Europe — the owner of Moleskine, the Italian notebook brand.

Today, the D’Ieteren Group operates in more than 40 countries, employs over 32,000 people, and generates annual revenues exceeding $8.2 billion…

…The D’Ieteren family maintains a private museum tucked inside an ordinary working building on the Rue du Mail — roughly 5,000 square feet that most people in Brussels don’t know exists. There’s no sign outside. You call ahead, state your reason for visiting, and wait to hear if you’ve been accepted. If you are, you walk up a ramp, down a long corridor, and through a large sliding door. Then the city disappears…

…The company had walked away from its core business four times, yet it had preserved, with extraordinary care, a museum’s worth of carriages, photographs, and tools. It wasn’t the behavior of a company that had escaped its past. It was the behavior of a company that understood its past so precisely that it knew exactly what to keep and what to release…

…For most of the 19th century, D’Ieteren was a carriage maker of growing renown. It won medals at international exhibitions, earned the title of Supplier to the Royal Court, and built some of the most celebrated horse-drawn vehicles in Europe…

…In 1898, while still producing horse-drawn carriages, they built bodywork for 12 electric vehicles commissioned by Camille Jenatzy, the Belgian race car driver who would soon become the first person to break the 62 mph (100 km/h) land speed record…

…After a fire destroyed the old workshops around 1903, the family rebuilt with modernized facilities capable of handling the new work on a greater scale. For the next two decades, horse-drawn and automotive production coexisted — in many cases with the same craftsmen serving the same clients, who often owned both kinds of vehicles…

…On the eve of the 1929 stock market crash, D’Ieteren employed nearly 500 craftsmen and was exporting 65% of its production to Argentina, Egypt, Spain, and the United States. A single car would cost approximately $468,700 in today’s dollars.

But Lucien had also spent 15 years sitting with an uncomfortable truth.

During World War I, while assigned to a military vehicle depot in Le Havre, he had watched American cars roll through by the thousands: Studebakers, Packards, Fords. Standardized, efficiently built, priced for ordinary people. He came home understanding that the future was volume, not bespoke cars…

…What the crash did for Lucien — what crisis so often does — was to remove the social and psychological costs of changing. The reputation, the pride, the loyalty to craftsmen: all of it was real, and all of it had kept him from acting on what he already knew. The crash stripped that away…

…Crisis has a way of burning off the unnecessary, leaving behind only what has always been true.

One of the most commonly asked questions is, “What do you do?”…

…The better question — the harder question — is not what we do, but who we are.

Consider D’Ieteren.

In a narrow technical sense, they were a carriage maker, then a carmaker, then a glass repair company. If you’d asked them in 1850 what they did, they’d have said carriages. But that wasn’t really who they were. Who they were was a tight-knit family obsessed with quality, committed to the long view, and devoted to finding better ways for people to move through the world. The carriage was just the expression of that deeper question at the time.

5. A Framework for Frontier AI and the Dawning of a New Age – Demis Hassabis

AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile – it is much more akin to the discovery of electricity or fire…

…The magnitude of this technology’s impact will be unprecedented, perhaps 10x of the Industrial Revolution at 10x the speed. It will help us solve some of the biggest problems society faces from accelerating drug discovery to developing new clean energy sources to creating novel advanced materials. We could even reach a point where resources are no longer the limiting factor for human progress, leading to an amazing new era of abundance…

…Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance. On the horizon, we will need robust safeguards to maintain control of increasingly agentic, recursively self-improving systems – and tackle unknown issues that will only become clearer over time…

…The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous. The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organisation, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives…

…A model would qualify as ‘Frontier-class’ if it meets certain thresholds on a set of benchmarks determined by the Standards Body and regularly updated to keep pace with evolving AI capabilities. Organisations with ‘Frontier Models’ as defined by those benchmarks would be deemed ‘Frontier Labs’, and be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research, and more.

Initially, Frontier Labs would voluntarily share models with the Standards Body for review up to 30 days before release. Once the assessment protocol is shown to be effective and robust, formalisation could quickly follow, meaning that Frontier Models would be required to pass it to be deployed in the US market…

…Model assessments should include rigorous scientific evaluations of capabilities in cybersecurity, biological threats and other high-risk domains…

…Even if we solve these hard technical challenges, there will be further complex economic and philosophical questions to tackle: what sorts of new economic models will be needed to help everyone thrive in a post-scarcity world? What values do we want to live by, what will meaning and purpose be, and how might even the human condition itself change? Resolving these questions obviously cannot and should not be left to technologists alone. It requires every part of society to come together to help define this new chapter.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (Demis Hassabis is the CEO of Google Deepmind, an Alphabet company) and Microsoft (Satya Nadella is the CEO of the company). Holdings are subject to change at any time.

What The USA’s Largest Bank Thinks About The State Of The Country’s Economy In Q2 2026

Insights from JPMorgan Chase’s management on the health of American consumers and businesses in the second quarter of 2026.

JPMorgan Chase (NYSE: JPM) is currently the largest bank in the USA by total assets. Because of this status, it is naturally able to feel the pulse of the country’s economy. The bank’s latest earnings conference call – for the second quarter of 2026 – was held earlier this week and contained useful insights on the state of American consumers and businesses. The bottom-line is this: the US economy remains resilient, but the risks to the global and US economy are shifting, with the consequences unknown.

What’s shown between the two horizontal lines below are quotes from JPMorgan’s management team that I picked up from the call.

1. The US economy remained resilient in 2026 Q2, as businesses continued to invest and hire; the economy’s resilience is driven partly by AI-related capital investments; the risks to the US and global economy are shifting, and it’s anybody’s guess as to how the risks will eventually play out; consumers and small businesses in the USA continue to show resilience, with strong employment driving spending; management thinks it’s really hard to untangle AI-related and non-AI-related capital investments; consumer spending remains robust across income segments; consumer delinquencies are lower than expected; management does not see a K-shaped economy in the USA; management thinks the US economy is in a slightly higher than normal inflationary environment

The U.S. economy has demonstrated notable resiliency this year, with stronger business investment and hiring. This strength is being supported by several tailwinds, including AI-driven capital investment, fiscal stimulus and the benefits of more efficient regulation. However, several risks are shifting below the surface like tectonic plates, including geopolitical tensions and wars, sticky inflation, large global fiscal deficits and elevated asset prices. We cannot predict how these forces will ultimately play out. They may remain manageable, but they could also cause meaningful disruptions when they shift or collide…

…Consumers and small businesses continue to show resilience despite elevated gas prices and inflation with higher tax refunds and a solid labor market contributing to strong spend growth…

…We do see some decent kind of CapEx and associated loan growth across the franchise. And at least on the surface, some of that does not appear to be AI related. However, I was a little reluctant to draw that conclusion too strongly just because the AI theme has started to proliferate in so many different parts of the economy, right? It’s like the comments about data centers wind up creating a lot of demand for like plumbers and electricians, right? So you wind up seeing it in sort of slightly nonobvious places. And so any given bit of loan growth or CapEx that you see that doesn’t superficially look like it’s AI-related might still be…

…Spend is kind of fine robust and across income segments. It seems like a bit of a tailwind there from tax refunds. Delinquencies are a little lower than we expected. And again, that’s a better performance. You see pretty much across the board by kind of FICO score. There’s some of that economic heterogeneity data came out from the Fed recently, which also I think doesn’t give a lot of support to the K-shape narrative essentially…

…From our perspective, through all the various dimensions, there’s not like that much there in terms to support the K-shape narrative…

…We are in a slightly higher than normal inflationary environment.

2. Net charge-offs for the whole bank (effectively bad loans that JPMorgan can’t recover) was flat at US$2.4 billion compared to a year ago (charge-offs was $2.3 billion in 2026 Q1)

Credit costs were $2.5 billion, with net charge-offs of $2.4 billion and a net reserve build of $149 million.

3. JPMorgan’s investment banking fees were up 27% in 2026 Q2 from a year ago because of strong performance in equity underwriting and mergers & acquisitions (M&A); management still sees a robust pipeline for capital markets activities; management thinks there’s some pull-forward in investment banking fees; management thinks the capital markets environment is close to as good as it gets, but they do not know how long it will last

IB fees were up 30% year-on-year, reflecting double-digit growth across all products with particularly strong performance in equity underwriting. While this quarter’s performance was supported by both some large ECM deals and the acceleration of the closure of some M&A transactions, the pipeline remains quite robust. And the current activity levels seem to be encouraging more activity. As a result, while conversion will obviously be dependent on market conditions, we expect activity levels to remain healthy…

…To what extent would this quarter’s results like particularly elevated as a result of some of the large high-profile IPOs and other capital raisings in particular. And I think clearly, there was some pull forward. And clearly, the large deals contributed meaningfully to this quarter results…

…It’s getting close to as good as it gets. We just don’t know how long it’s going to last…

…I just think we’re in a very healthy active exuberant market with very high prices and very high volumes, and we benefit from that. We just don’t know how long it will continue. Could it get a lot better than this? It can get better. But how much better? I don’t know.

4. Management now expects credit card net charge-offs for 2026 to be 3.2% (previous expectation was 3.4%; was around 3.3% in 2025)

We now expect Card net charge-off rate to be approximately 3.2% and reflecting better-than-expected consumer credit performance.

5. Management sees the market as being extremely risk-on

The market is clearly extremely risk-on and we’re kind of takers of that. And we’re trying to strike the right balance between supporting all our clients and being appropriately cautious in an environment that has some complicated dynamics in it.

6. Management is seeing some credit deals for data center development that they think are questionable

For whatever reason, I think the data center underwriting space is one that resonates with me as a kind of bellwether for what people are doing. And we passed on some deals that — obviously, because when you look at the data center stuff, the key question is like what happens with power supply, what happens with tenants, what happens with — it’s a well-discussed thing. And we have a pretty precise framework to govern what we’re willing to do and what we’re not willing to do in that space across those types of risks. And we saw some deals come through where we were just like, “Yes, we’re not doing that.”

So it’s normal, I guess, it’s competitive, and people are eager to be involved. And in some cases, there’s ironically some element of like relationship lending that’s happening through the data center space, when it’s kind of a start-up entity that’s building the data center. So that’s part of the story a little bit, too. But I don’t think we’re screaming from the rooftops that underwriting is — underwriting standards have collapsed, but I think you see normal pressures, and we’re navigating those in the way that we do, which is we do flex in some moments for particularly important clients in situations where we feel like it’s the right thing to do. But in general, we try to be the one that holds the line and make sure that we’re guided by our own risk appetite and a kind of appropriately skeptical view of the environment.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 12 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

Here are the articles for the week ending 12 July 2026:

1. AI’s Value Capture problem – Jaya Gupta

Imagine State Farm, Progressive, Allstate, Travelers, Chubb, AIG, Liberty Mutual, and 100-plus smaller carriers all running claims through the same model. Every carrier feeds it the same stream of context: the accident description, photos, repair estimate, adjuster’s note, borderline approval, fraud flag, override, payout, appeal, recovery outcome.

At first this is obviously useful. The model moves claims faster, flags suspicious cases, learns which repair estimates run inflated, which medical patterns look strange, and which overrides later become losses.

But if the same model learns from every carrier, is your claims judgment still your advantage? The underwriting exception that protected your loss ratio becomes a benchmark. The fraud pattern your team caught early becomes a feature sold back to the market…

…If everyone gets the same edge, customers keep it. Imagine an auto manufacturer using a model to negotiate semiconductors, resin, freight, contract manufacturing capacity, and substitute parts. The edge is buying better than the next manufacturer: knowing which supplier shortage is real, which quote embeds excess margin, and when preserving supply matters more than squeezing price. If every manufacturer runs procurement through the same model, the model does not just lower costs. It makes buying more “similar”. The best buyer loses the spread between its process and everyone else’s…

…The model also captures what compounds. Imagine 1,000 resource-constrained biotechs using Claude for Life Sciences because they do not have the internal platform of massive pharma company. Each company owns its compound, lab cost, failed program, and regulatory trail. But the workbench can see the pattern across all of them: which tox signal killed the program, which assay gave false confidence, which endpoint was weak, and which patient subgroup wasn’t the right one. If it sits across enough biotechs and pharmas, it can see failure patterns no single company can see. While data advantage is in exclusivity, a shared workbench breaks exclusivity by aggregation. And because Anthropic intends to develop drugs of its own, the tool you adopt for efficiency is built by the entity whose endgame may be to do what you do, using what it learned by watching the field do it…

…Data rights are not learning rights. Companies know how to negotiate retention, confidentiality, security, access controls, and training opt-outs. But the more important question is who owns the derived judgment: tasks, feedback loops, evals, workflow traces, corrections, failure modes, decision patterns, agent skills, and product insights. Once the model company knows the hard problem, it can acquire the job logic another way…

…The gain is front-loaded; the dependency compounds. The first adoption creates a real productivity jump. But once competitors run the same model, that jump becomes the baseline, and what remains is not your edge, it is your dependency on the next upgrade. Everyone will capture the first uplift but the vendor captures the recurring learning curve. Year one, the factory model reduces downtime, but then every rival has the same predictive maintenance workflow and the vendor owns the process intuition you now depend on.

2. High Bandwidth Flash: The Full Report – Austin Lyons

What’s interesting is that it has the same read bandwidth as an HBM4 stack, but with roughly 10x the capacity. And it’s made of NAND, not DRAM like HBM. (NAND is the cheap stuff.)

The first samples of the memory itself are expected very soon from Sandisk, sometime in the second half of 2026. Samples of the first AI inference devices built with HBF follow in early 2027…

…NAND flash has traditionally been used for information storage, i.e. where data lives when it’s not being used. Cheap, dense, and non-volatile. But slooooooowwwww….

But memory is where the accelerator keeps information handy during computation, and it has to be fast enough that compute never waits…

…So how could NAND flash possibly be used as memory?

Well, could there be a way to get the right data to the accelerator in time for computation, even with NAND’s slow reads? If you start the read it WAY before the accelerator needs the data, it could work?

In addition to intelligently scheduling when data is requested, one can also try to achieve high bandwidth from NAND. Bandwidth is the amount of data that arrives per unit of time. So if you know the read is going to take a long time, well, might as well have a bunch of reads running in parallel, if possible, right?

HBF unlocks high bandwidth by stacking NAND dies to increase the “width” or the parallelism. Each die is divided into many sub-arrays, which are small blocks of NAND that can each be read at the same time, independently of one another. An HBF stack places 16 of these dies behind a single interface, thousands of bits wide, so a huge number of sub-arrays are available to read at once. Any single read still takes thousands of nanoseconds, but thousands of reads can run in parallel, so a lot of data can be moved simultaneously…

…Conventional flash ships about 14 GB/s behind a PCIe 5.0 NVMe controller. Packaged as HBF, the same material delivers 1.6 TB/s. Roughly 100x the bandwidth, from packaging alone…

…HBF’s latency is still 10-100x slower than HBM. But if the accelerator knows what data it needs in advance, it can prefetch it and avoid waiting on any single slow read. The argument for HBF is that inference decode is the perfect workload…

…Inference cost scales with GPU count, and for today’s massive frontier models, GPU count is often driven by memory capacity per GPU.

Why?

Well, you need enough HBM to hold all the weights, but HBM is co-packaged with the accelerator; a fixed amount of HBM is bonded into each GPU package. So you can’t add memory without adding GPUs. Hence, bigger models mean more GPUs.

Of course, weights aren’t the only thing the accelerator needs to store in memory and acccess quickly and often. The KV cache and activations sit in memory too, and both stay in HBM or DRAM.

But the KV cache takes new writes every token; NAND’s endurance can’t handle that. NAND has much lower write endurace.

And activations need low-latency random access that NAND is too slow to give.

So HBF is for storing model weights.

And frontier weights are huge and always wanting to be even bigger. Wouldn’t it be nice to hold the model in significantly cheaper memory than HBM?

Recall that a 70B parameter model at fp16 (2 bytes per parameter) requires 70 × 10⁹ × 2 = 140 GB just for weights. A 1T parameter model thus needs 1,000 × 10⁹ × 2 = 2 TB. But that’s a lot of HBM; today’s shipping HBM4 stacks hold 36 GB (12-Hi); 16-Hi parts push 48 GB, and the JEDEC spec tops out at 64 GB. So a large model needs many stacks, which means many GPUs. Expensive!

That also means more interconnect (to move data around all those GPUs), which requires more power and, ultimately, a higher cost per output token (watts and $).

But HBF can provide 512 GB of capacity per stack!

So 512 GB per stack versus 48 GB for HBM4 is roughly 10x the capacity at the same bandwidth…

…For the same bandwidth, HBF has up to 8-16x the capacity at roughly 2x the power.Yes, HBF has worse bandwidth per watt, but for model weight storage one can argue the metrics that matter most are capacity and capacity per watt, where HBF wins.

SanDisk also claims a similar cost to an HBM stack despite 8-16x the capacity, which works out to roughly 10x lower cost per GB.

I thought NAND was way cheaper?!The raw NAND is cheap per bit, but an HBF stack isn’t cheap.

3. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 1 – Tim Isgro

Starting in October 1992, each Russian citizen would be given a paper voucher that could be used, in government-organized auctions, to purchase a portion of a Russian business. 147 million vouchers would be printed, one for each Russian citizen, and distributed until January 1993 via local branches of the State Savings Bank. To obtain the vouchers, a citizen needed only to pay a nominal fee of 25 rubles (equivalent to 10 cents US)…

…By January, 144 million of the vouchers had been picked up and were circulating in public.

Importantly, the vouchers were freely tradable, which could help poorer Russian citizens monetize their vouchers if they chose to (and many did), and free tradability helped attract larger pools of capital that were needed in privatizing the economy.

Once Russian citizens had their vouchers, they could either hold them and use them at the auctions or they could trade them immediately for cash, typically on the streets of Russia. As soon as the vouchers began trading, it was apparent that they were trading for very low prices…

…As the chart shows, from 1992 to 1994, these vouchers were trading on the streets of Russia for anywhere from 5 to 25 USD each…

…144 million privatization vouchers were distributed to Russian citizens. These vouchers were mandated by the government to be converted into the ownership of 29% of all businesses in the country…

…Using a generous value of $20 per voucher, that implies the entirety of Russian businesses was worth $14.4bn at the time (equal to $20 x 144,000,000 vouchers / 20%). As if such an extreme undervaluation needs a comparison, the market cap of Exxon alone, a single US company, at its stock price low in 1993 was $71.7bn…

…Why were the vouchers trading at such incredibly low values?…

…First, inflation was rampant in Russia at that time. Prior to the fall of communism, the prices of many goods and services were set by the state. Starting in 1989 and in a few stages in the years following, Russian leaders slowly freed prices from government control. The effect was a massive inflation that gripped the country in the 1990’s…

…Second, and perhaps most obviously, the primary owners of the vouchers were ordinary Russian citizens. Now, the voucher auctions were designed to be as simple as possible, to the point where a citizen did not even need to know anything about a company to participate…

…Finally, and for interesting reasons, the vouchers were given a “face value” of 10,000 rubles (approximately 25 USD as of the end of 1992, albeit highly volatile)…

…The apparent effect of that 10,000 ruble face value (again, about 25 USD as of late 1992) on the trading price cannot be overstated.

It inadvertently created an anchor around which the vouchers traded…

…From December 1992 to June 1994, 15,052 Russian businesses were taken private, in whole or in part, almost all of them at prices that were incredibly low compared to their counterparts in other countries.

Boycko, Shleifer, and Vishny describe two of the bargains:

VAZ, the auto maker of the popular Lada cars, came out of its auction with a total market value of $45 million. As a point of comparison, in 1991, Fiat reportedly offered the Russian government $2 billion for the company.
Gazprom, the gigantic Russian natural gas monopoly, emerged from its auction with a market value of $228 million. This was roughly 1/1000th the value of put on the company by foreign investment banks, presumably by comparing it to other natural gas companies around the world…

…1994, for example, was an incredibly volatile year. The ROS index of 30 Russian stocks (created by CSFB) surged from 116 at the start of the year to a peak of 1,669 in September of that year. Yes, the index was up 1,338% in nine months. That is not a typo. It later dropped to a low of 443 in April 1995…

…So, 1994 was an important year for Russian privatization as an investment. It marks the end of the “voucher period” when Russian vouchers were trading at absurdly low prices and marks the deeper involvement of foreign investors (and the re-pricing and volatility that came with them). It is the time when the price of Russian businesses went from absurdly low to merely very low.

4. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 2 – Tim Isgro

In Part One of our case study of Li Lu’s investment in Russia, we discussed the the fall of communism, the government plan to privatize Russian businesses, and the voucher system that was used to convey interests in those businesses to private citizens.

Now, I would like to focus on one of the two companies mentioned by Li to try to get a more specific sense of what he saw at the time. That company is Lukoil…

…Lukoil was formed in 1991 with the merger of three companies, the Langepas Oil Company, Urai, and Kogalym (the “Luk” in Lukoil), and in November 1992, Boris Yeltsin officially designated Lukoil one of three integrated holding companies. In those early days of the company’s formation and Russian market privatization, the trading of stocks was in its infancy and systems were rudimentary…

…Lukoil was cheap in those days, but how cheap was it? Let’s take a look at the valuation of Lukoil versus that of another leading oil company at the time, Exxon.

While Lukoil’s trading prices implied a market cap of 20 cents to 50 cents per barrel of proven oil and gas reserves around the time of Li’s purchase, Exxon’s valuation implied a market cap around $6 to $8 per barrel. In other words, Lukoil was trading about 5% the value of Exxon on a reserve basis ($0.35 divided by $7). At the time, the price per barrel of crude oil on world markets was around $20.

5. A Penny on the Dollar: Li Lu’s Bet on Russian Privatization, Part 3 – Tim Isgro

Li speaks briefly and vaguely about the market prices of Lukoil and Gazprom around this time and is not very specific about the exact time when he bought and sold the stock, but he does provide some clues:

Forget about the earnings. Just… the assets on the balance sheet. At the time oil prices [in the world market], I think the four or five year average was around twenty dollars [per barrel], and the [value per barrel of proven oil reserves for Lukoil was] at really low prices… about 10 cents to 20 cents per proven barrel of oil on the balance sheet and that’s not even counting the earnings… This is how low it went. It was ridiculous.

From this quote and others throughout the talk, it seems that Li’s main focus was just how cheap Lukoil was trading relative to how much proven oil reserves were on its balance sheet. He seems to have virtually ignored any loss (or any income) that the company was making at the time, reasoning that such an extreme undervaluation relative to the company’s assets dwarfed the numbers on the income statement…

…All in all, with the limited information we have, I think the most reasonable conclusion is that Li made his initial investment in Lukoil somewhere in early 1995 to mid-1996 when the stock was trading around $3 billion to $5 billion in market cap and $0.30 to $0.50 per proven barrel of oil and gas reserves, and he sold it somewhere around mid-1997 to mid-1998, in the region of $12 billion to $20 billion of market cap and $1.25 to $2.00 per proven barrel of reserves. (Recall his comments from earlier, that he sold “two years after” he first bought it and that the $2.00 price per proven barrel of oil no longer looked protected.)

With those crude, round numbers, his investment would have made him anywhere from 2.5 to 6.6 times his money in just over two years of time…

…Li talks about the dramatic cheapness of Lukoil and other Russian stocks at the time, and he was right to some extent. But that cheapness had its limits. Below, I show the price of Lukoil’s stock from 1993 to 2021. I also show a variation of the chart with Lukoil’s market cap versus the value of its proven oil reserves (a measure of Lukoil’s “cheapness”).

The charts paint a picture that is difficult to rectify: For virtually the entire period from 1993 to 2021, Lukoil appeared to be cheap, trading at a market cap that almost never valued the entire company greater than 8% of the value of its proven oil and gas in the ground. Exxon (and later Exxon-Mobil), by comparison, averaged a market cap of 34% of the value of its reserves from 1993 to 2021.

So, at all points, an investor might have thought that Lukoil was cheap. And yes, buying the company’s stock in 1995 and holding it for two years, like Li, would have produced a great return. Even holding it for 10 years, from, say, June 1995 to June 2005 would have produced an annualized return (excluding dividends) of 21%, as the stock went from $5.21 to $34.75.

However, the next 10 years, through June 2015, would have only produced an annualized return of only 3%, despite the company appearing cheap in June 2005, when its market value was only 4.7% of the value of its reserves…

…This case study was particularly enjoyable for me because the lessons are so difficult to tease out. Simply buying Lukoil stock at any point in its history because it was cheap relative to other companies around the world would have been a mixed bag. Buying in the 1990’s or early 2000’s would likely have worked out great. Buying in the late 2000’s or the 2010’s would likely have been poor. At all times, Lukoil looked cheap versus Exxon and other western oil companies.

It is very difficult to know how to think about this issue, but one thing to keep in mind is the timing of Li’s investment. In the early to mid 90’s, Russia was emerging from communism and still getting accustomed to the cultural shift toward capitalism and democracy. One could argue that, although corruption was still rampant, the prevailing winds were blowing in the direction of a country getting more used to democracy and slowly reaping the benefits of capitalist markets. These trends could serve as a gradual but important kind of catalyst to close the gap between price and value. In Russia, for example, these changes would slowly lead to more Western investors participating in Russian markets through the 1990s and 2000s.

But it’s important to realize too that the lack of such change (or timing) could make for a difficult investing situation, whereby an investor thinks a stock is cheap by some measure but that situation sticks around for many years.

So one takeaway from Li’s investment is that extreme cheapness is a great thing to hunt for, but seek to have it come along with a changing situation or an outright catalyst.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

Company Notes Series (#15): Northern Ocean

Editor’s note: This is the latest edition in the “Company Notes Series”, where we periodically share our notes on companies we’ve studied in the recent past but currently have no vested interest in (we may invest in or sell shares in the companies mentioned at any time). The notes are raw and not updated, and the “as of” date for the data is given at the start of the notes. The previous edition in the series can be found here. Please share your thoughts on the series through the “Contact Us” page; your feedback will determine if we continue with it. Thanks in advance!

Start of notes for Northern Ocean

Data as of 2025-11-19

Northern Ocean is listed in Norway’s Oslo Stock Exchange, with the ticker symbol NOL
Northern Ocean is the owner of 2 oil rigs as of 16 November 2025, namely, Deepsea Bollsta and Deepsea Mira:

*Figure 1; Source: NOL 2025-09-10 investor presentation*

Both Deepsea Bollsta and Deepsea Mira were acquired by Northern Ocean in 2017. Deepsea Bollsta was acquired for US$400 million while Deepsea Mira was acquired for US$365 million; it’s interesting to note that Deepsea Mira’s construction cost was around US$720 million. Both Deepsea Bollsta and Deepsea Mira are managed by Odfjell Drilling and have similar specifications and ages, as shown in Figure 1
On 17 November 2025, Northern Ocean announced the sale of Deepsea Bollsta to Odfjell Drilling for US$480 million; management intends to return capital to shareholders when the sale is complete.
On 18 November 2025, Northern Ocean’s stock price was NOK 8.13. The company has 303.2154 million shares outstanding as of 30 June 2025, and 9.5 million outstanding and unvested options, given a fully-diluted share count of 312.7154 million. Northern Ocean’s market capitalisation is thus NOK 2.542 billion, or US$251.7 million.
As of 30 June 2025, Northern Ocean has US$28.1 million in cash, US$299.3 million in debt, and US$248.7 million in related-party debt. This gives Northern Ocean an enterprise value of US$771.6 million. After selling Deepsea Bollsta, Northern Ocean’s enterprise value would become US$291.6 million. And now that Northern Ocean has only one asset left, if management has no ambition to buy more assets, it makes sense for the company to sell Deepsea Mira. It’s likely Deepsea Mira fetches a price that is around US$365 million or higher. Assuming a sale price of US$365 million, Northern Ocean would thus have a negative enterprise value of US$73.4 million, and this compares with a market capitalization of US$251.7 million, which equates to an upside of around 30%.
Northern Ocean has positive operating cash flow of US$4.3 million in 2025 H1, with capex of US$35.8 million, and interest expense of US$30.3 million. With the cash inflow from the sale of Deepsea Bollsta, Northern Ocean can pay down significant amounts of its debt, reducing interest expense by, say, two-thirds; the sale of Deepsea Bollsta will also reduce capex. All in, Northern Ocean’s pro-forma financials for 2025 H1 could look something like this: positive operating cash flow of US$22 million, with capex of US$18 million, and interest expense of US$10 million. This means Northern Ocean’s enterprise value, post sale of Deepsea Bollsta, will be reducing over time (a good thing for equity holders in terms of the bargain they are getting), while waiting for the sale of Deepsea Mira.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any company mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 05 July 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

Here are the articles for the week ending 05 July 2026:

1. As AI Companies Race for Power, Amazon and Google Have the Lead – Lee Jinjoo

Amazon has an incumbent advantage. It is the world’s largest cloud provider and has been building a lot of data centers over the past two decades. The company’s operating, self-built data centers in the U.S. consume up to roughly 9 gigawatts of power, according to Aterio, a data provider. That is comparable with the generation capacity of North Dakota.

By comparison, Microsoft and Alphabet’s Google each have self-built data centers that use up to about 5 gigawatts of power, while Meta Platforms’ data centers have a roughly 4 gigawatt capacity. So far, most of the hyperscalers’ data center capacity is self-built, rather than rented from data-center operators…

…Based on estimates from Aterio, which tracks company announcements, utility filings, building permits and satellite data, Amazon is expected to add the most data center and power capacity in the U.S. through 2030. But Google is expected to add capacity at the fastest rate. In fact, including leased capacity from third-party data center owners, Google will have significantly closed its gap with Amazon by 2030, according to Aterio…

…Amazon plans to build out most of its own capacity, while Google is expected to rely more heavily on leases. Based on Aterio’s data, about a quarter of Google’s expected data center capacity in 2030 is expected to come from leases. Self-built can take longer but is the cheaper option over the long term…

…Google has proved that it can get speed with clean energy. At least three of its planned Texas data centers will be able to skip the long queue to connect to the grid because they are being built next to solar and wind projects, according to a report from Cleanview. In two of these data centers, Google will build the solar and wind capacity through its Intersect Power subsidiary. Texas’ power market rules allow faster grid connection if the data center is co-located with a new source of power. In all three cases, the solar and wind capacity well exceeds that of the data center…

…Microsoft, for example, struck a 20-year agreement with Chevron to power its AI data center in Texas with an off-grid, natural-gas-fired power plant. Both Meta and Amazon have plans for such projects, according to data compiled by Cleanview. Amazon hasn’t publicly confirmed its involvement, but its data center in Fayette County, Ohio, is the only planned source of large power demand near a permitted off-grid, natural-gas-power project, according to Cleanview’s Thomas. In most cases, hyperscalers eventually want these data centers to connect to the grid.

2. What’s the Real Depreciation Curve of a GPU? It Depends on What It Actually Did – Pietro Sette

Identical GPU hardware can age very differently depending on how it’s used:

A GPU running steady inference at, 60–70% utilization, under moderate thermals, day in and day out
vs. a GPU running irregular training workloads that repeatedly spike to 95–100% utilization and push thermal limits every afternoon

On paper these two might be the exact same model of GPU. In practice, their aging is radically different. One might still be going strong and profitable after 5+ years (indeed, some 2016-era GPUs are still in active cloud service), while the other might effectively be worn out – or at least no longer economically viable in 3 years or less…

…Consider a mid-market lender financing several GPU deployments in the 0–50MW range (hundreds of high-end GPUs across multiple customers).

Their original underwriting assumed:

~80% steady utilization on each GPU (a consistent workload level)
~5.5 year useful economic life for the GPUs (before resale or obsolescence)
No meaningful variance across different customers or workload types (every GPU in the fleet treated uniformly)

But when real telemetry data was collected at the GPU level, here’s what was actually observed:…

…Result: The fleet’s effective depreciation curve varied by 30–45% across different end-customers, even though the GPUs were identical models. In other words, certain customer workloads drove their hardware to lose value almost half again faster than others…

…Different operational events and stressors affect how fast a GPU “ages” or loses reliable performance. Thermal stress, power stress, and workload intensity are chief among them.

3. Rory Johnston on Why His $200 Oil Prediction Didn’t Turn Out Right (Transcript here) – Joe Weisenthal, Tracy Alloway, and Rory Johnston

[Joe Weisenthal]: Let’s start back in early March. Remind listeners what your take and the general wisdom was in the first couple of weeks of March about how long this could persist. And remind people why the Strait of Hormuz was seen as the choke point among choke points when it comes to oil.

[Rory Johnston]: Yeah, let’s transport ourselves back to our last conversation.

[Joe Weisenthal]: We need to get the time-travel-machine music, right?

[Rory Johnston]: They can add that. So, the reason it was such a massive deal — and still remains, I’d say. While we’ve avoided the doomsday prophecies, it is still by far the largest supply disruption in the market’s history. And for the numbers, for the barrel counting, for Tracy: the total flow through Hormuz prior to the war was roughly 20 million barrels a day. We knew we weren’t going to lose all of that, because we had some offsets — the Saudi East-West pipeline, the Emirati pipeline to Fujairah on the Gulf of Oman. But netting out all of those known rerouting options — which, again, at the time we didn’t know if they would fully work, because they’d never been fully tested, though they did work, thankfully — even after netting those off, we were still down roughly 13 million barrels a day of Gulf oil production, excluding Iran, that had been forcibly shut in for the duration of this crisis and is only now beginning to pick back up. That’s a lot of oil. That’s more than 13% of global supply. The reason we thought prices were going to hit $150 or even $200 a barrel is that when you have a supply shock that large without any more offsets, you end up at demand-destructive pricing really, really fast. And to destroy that level, the depth of that demand — we had never seen that before, but $200 a barrel seemed like the reasonable price at which it would happen. Now, thankfully, we did not have to destroy that demand. And what we’ll talk about shortly, I’m sure, is all the ways the system adapted and flexed. I think we saw this most notably, above all, in China.

[Tracy Alloway]: Okay, why don’t we just dive into it? Give us your overview on what happened and why we didn’t actually hit $200 a barrel.

[Rory Johnston]: The two biggest things — one on the fundamentals, the barrel-counting side — was China. We always knew China had huge stockpiles of oil, but we didn’t know how it was going to react to this crisis. What we’ve seen is that Chinese crude oil imports — into the world’s largest crude oil importer — fell by upwards of 5 million barrels a day between the three-month average prior to the war and June. We’re not quite done this month, but that’s roughly where we’re trending for June so far. That 5 million barrels a day was upwards of half of the total spot-market supply hit to Asia, and it allowed a lot of those other Asian importers to not have the competition they would otherwise have had for the barrels they were importing. So the countries that were hit hardest, and the governments that were most panicked — South Korea, Australia, Japan, Taiwan, and so on — there was a period where the Prime Minister of Australia was coming out daily and announcing the government’s successful acquisition of a cargo of diesel. It felt very COVID-y. Those importers saw imports collapse through March and April, but through May and into June they actually recovered basically to pre-war levels. And the largest facilitator of that was the fact that China was not competing for any of the other barrels — it absorbed so much of the shock itself.

[Tracy Alloway]: Just on China specifically — I have so many questions already — do you have any sense of how much of this was genuine demand destruction or substitution in China versus just releasing from stockpiles?

[Rory Johnston]: It’s a good question, and the firm answer is we don’t have 100% certainty as to the exact composition of that swing. We know the oil going in fell by 5 to 6 million barrels a day, products included. But in terms of actual demand destruction — and you guys were actually in China very recently — all the mobility indicators showed no notable decline. The level of implied demand destruction we see is striking, and importantly, China does not publish official demand data, and, very importantly, it doesn’t publish official inventory data either. So we’re left feeling around in the shadows. The implied demand destruction through this crisis was on par with the steepest in history, and on par in volume with the COVID-zero demand shock in 2022. But you guys were in China; I have not seen any reporting that indicates that level of lockdown. So we start asking, what’s going on in the middle? Typically, you’d assume that level of demand destruction without COVID-zero lockdowns would have to be driven by massive price increases. But part of what happened here is that China basically throttled the ability of domestic retail prices to rise through their normal regulatory procedures. Petrol prices in Beijing only rose maybe 30%, versus the doubling we saw globally. So again, it just doesn’t track for me that all of that, or even most of it, was demand destruction.

So then we go to substitution or outright releases of strategic petroleum reserves. The one thing we can say is that the inventories we can see — the floating-roof crude oil storage tanks — are still very, very high, roughly where they stood at the beginning of this crisis. As far as we can tell, they’re not drawing down aggressively on those stocks, at least not yet. The caveat is that with satellite analysis we can’t see underground storage caverns and their proper SPR. They have at least six storage caverns that we know of, about 131 million barrels. The likelihood is that they’ve been drawing those down, because crude oil imports fell far faster than refining run rates. So again, it had to be made up somewhere. The Occam’s Razor here is that they’ve been silently releasing additional crude inventories. But above and beyond that, crude refining run rates also fell dramatically, by 3 to 3.5 million barrels a day. So where’s the implied demand destruction? This is where we get one of two things. Either a very large release of refined product stocks — we know China has large stocks of refined products like gasoline, diesel, and jet fuel. We have virtually no firm information on those levels, and we can’t track them closely day-to-day or week-to-week, because unlike crude they don’t have floating roofs, so we have to infer. Those stockpiles are upwards of a billion barrels, but the implication is that they’re drawing them down very rapidly. The other thing we could have seen — and your colleague Javier Blas was on this very early — is the potential to switch some petrochemical feedstocks from oil-derived products like naphtha and LPG toward more gas-based products, natural gas, or even, in the extreme, coal-based chemical products.

4. How to Buy Cheap Claude Tokens in China – Qian Zilan

Underneath the handful of labs sits a much larger market, one that has been operating in public on GitHub, Taobao, Twitter, and Telegram. It is a grey economy of API proxies (commonly called “transfer stations,” 中转站) that lets Chinese developers access Anthropic’s models at as low as 10% of the official price. The participants extend far beyond selective experienced AI researchers, and the motivations are much broader than building a frontier model to catch up. Everyone who wants to use more advanced AI models or tools, be they university professors and students, tech workers, individual developers, or hobbyists, uses API proxies.1 The logs they generate may have become a commodity, traded for purposes ranging from model training to targeted fraud.

Meanwhile, every layer of control frontier US AI companies have added (geoblocking, phone verification, credit card requirements, and now live biometric KYC checks) has produced a corresponding layer of evasion infrastructure. These new SMS farms and biometric harvesting operations have implications that extend beyond geopolitics into how frontier AI safety frameworks are designed…

…A transfer station (中转站) is what the Chinese developer ecosystem calls an API proxy–an overseas server that sits between a developer and Anthropic’s infrastructure. It accepts API requests, forwards them as if they originated from the transfer station’s location, and passes the response back.2 The user redirects their software to the proxy’s server instead of Anthropic’s, and pays the API proxy RMB via WeChat or Alipay.3 This sidesteps both the VPN and the overseas credit card needed for direct access. Prominent transfer stations are catalogued in community repositories and ranked by real-time price and uptime. Below them, a longer tail of small and individual projects comes and goes.

While this setup sounds functionally identical to legitimate Western API aggregators like OpenRouter, transfer stations operate in an entirely different universe of legality and trust. Legitimate aggregators exist to simplify developer workflows, charging standard rates based on transparent enterprise agreements. Transfer stations, conversely, are built explicitly for evasion, routing data through unaccountable middlemen…

…A transfer station is not a sole entity. It sits in the middle of a layered supply chain, with most participants never interacting with each other directly.

Upstream are the resource providers: account merchants who bulk-register or acquire Anthropic accounts at scale; SMS verification platforms that supply the foreign phone numbers needed to pass sign-up checks; and, at the more technical end, reverse engineers who analyze Anthropic’s client code to find authentication shortcuts or detect when detection logic has changed. The payment infrastructure with card merchants and proxy networks also enables overseas billing from inside China…

…Almost no one operates the full chain. Most participants own one or two links and monetise those well, resulting in a resilient, modular system. AI model providers can suspend individual operators, but the upstream account pools and downstream customer base remain intact. So long as there are developers who want access to Claude and identity black markets willing to supply the credentials, which are both durable features, a replacement can be stood up quickly…

…The most curious thing, however, is not how to get access to Claude or Claude Code in China, but how to get it at a ridiculously low price–usually priced at 1 RMB per $1 of tokens — 70–90% below official prices. According to public discussions, there are at least three ways a transfer station makes this possible–often described as “one fish, three meals (一鱼三吃):

Meal 1: The markup on access. This is possible because of the upstream resource providers who can stack proxies using at least five relatively “innocent” tactics:

bulk-registering API accounts to farm Anthropic’s $5 free credit
reselling unused quota from others’ accounts
corporate/educational discount arbitrage
“APImaxxing” — one $200 Max plan carved up among multiple users via tokens-per-hour quotas, exploiting the gap between Anthropic’s flat subscription price and the far higher cost of equivalent pay-per-token API access…

…Meal 2: Swapping models and inflating tokens. Because users’ inputs and model outputs are mediated through a proxy, users cannot verify which model their request was actually routed to. A user selects Opus 4.7, but the proxy can silently route to Sonnet, Haiku, or, in the worst case, GLM or Qwen, and fraudulently relabel the output…

…Meal 3: The logs are the product. This is perhaps the most important part as it intersects with data privacy and distillation. Every request that passes through a proxy — full prompt, full response, tool calls, iterations — is sitting on the proxy operator’s server. For AI coding agents, those logs contain long reasoning chains, real engineering decisions, repository context, and human-verified correct outputs. This makes them an ideal dataset for post-training: for supervised fine-tuning on real engineering tasks, and, where full reasoning traces are captured, for distilling Claude’s reasoning patterns into smaller models.

5. How funerals keep Africa poor – David Oks

A modest, mid-level funeral in Ghana costs about $5,000 U.S. dollars; a “befitting” one can easily cost $15,000 or $20,000. And all this in a country with a median income of about $1,500 per year. Ghana is known for its particularly ornate funeral culture; but it’s not the only place in sub-Saharan Africa with a culture of exorbitantly expensive funerals. The average household in KwaZulu-Natal in eastern South Africa, for example, spends the equivalent of an adult’s annual income on a single funeral. We see the same tendency for ultra-expensive funerals in a striking number of places: the Democratic Republic of the Congo, Kenya, Nigeria, Benin, Cameroon, Mozambique, the Ivory Coast. It’s often observed, in fact, that families will spend more money on burying the dead than on keeping the sick alive: indeed, in the Kagera region of northern Tanzania, families spend 50 percent more money on funerals than on medical care…

…The answer, I think, is that the funeral isn’t really about the deceased. Funerals function as a costly signal of kinship group loyalty: and in that context, the expense of the funeral is the point. And, in turn, funerals tell us quite a lot about why so many societies across Africa have had so much trouble achieving economic “takeoff.” Kinship societies are actively hostile to economic growth, because economic growth undermines the basis of kinship: that is why kinship societies demand constant, visible sacrifices of wealth—funerals being the most spectacular—that make it extraordinarily difficult for any individual to accumulate capital, reinvest their assets, and pull ahead. The funeral is a window into a system of wealth destruction that serves, above all else, to keep people poor…

…African societies, by and large, are kinship societies.

So what are kinship societies?

You can think of modern societies as large collections of individuals, their lives structured by impersonal institutions like states and corporations. Kinship societies are much older: they are, in fact, the oldest and most durable type of human society. In a kinship society, life is centered on the extended family: the “clan,” the lineage, the tribe—a group that often includes many people who aren’t actually related. These kinship networks don’t act anything like nuclear families in modern societies. They are highly functional organisms: most of the functions provided by states in the modern world—protection from harm, credit, dispute resolution, eldercare, social insurance—are instead provided by the kinship network. If you fall sick, the kinship group will care for you; if you need cash, the kinship group will lend you money; if a stranger wrongs you, the kinship group will avenge you.

Of course, a kinship network isn’t a charity. It’s more like a mutual aid society that you’re born into and can’t leave: what the kinship group gives, the kinship group must also take. A huge amount of life in kinship societies is structured by the obligations that people owe to their kin…

…In a kinship society, nothing that you earn is truly yours. If you make money beyond the point of subsistence, you’ll be expected to share it with your less-fortunate relatives; if you start a business, you’ll be expected to hire your cousins or nephews or in-laws, even if they’re not the best possible employees; if you buy a car, you’ll be expected to lend it out to relatives who need it.

The result is a constant process of redistribution from the most productive members of a kinship group to the least productive. This informal redistribution is a constant feature of life in African societies: 93 percent of Kenyan entrepreneurs agree that success in business leads to financial demands from family and friends. South Africans even have a name for the sharing obligations that define African kinship groups: “the black tax.”…

…If the productive members of the group can defect—removing their resources from the common pool—then the whole system of mutual obligation begins to unravel. If a productive individual can simply withdraw from sharing obligations, then the network must demand more from those who remain, increasing the incentive to defect: so the entire delicate machinery of mutual obligation collapses in a slow cascade. This is the death spiral for kinship networks.

So from the perspective of the kinship network, wealth is a threat…

…You can think of funerals as another wealth destruction ritual. The genius of it is that it can’t be evaded: it is a public ceremony virtually dedicated to the immolation of wealth. In private, you might be able to evade your sharing obligations by hiding your earnings or your savings; but in public, at the funeral, the claims that your kin make on your wealth are at their most visible and least avoidable. You can’t simply not show up to your uncle’s funeral; and, if you show up, you will obviously be expected to contribute a handsome sum.

And this logic is even more powerful for those who are suspected of shirking their kinship obligations. It’s at the funeral where you must signal your willingness to honor sharing obligations most loudly. The lavishness of the funeral is a costly signal of continued commitment to the system of mutual obligation that holds the kinship group together. The point is that it’s expensive and incommensurate with your means.

This is why Ghanaian funerals, for example, have tended to grow only more lavish with time…

…And so the lavish funeral, in the end, is not a strange cultural quirk of African life, but the most visible manifestation of a social order oriented toward the destruction of accumulated surplus. And until the grip of that social order loosens, much of the wealth that Africa produces will continue to go, quite literally, into the ground.