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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.

More Of The Latest Thoughts From American Technology Companies On AI (2026 Q3)

Last month, I published The Latest Thoughts From American Technology Companies On AI (2026 Q3). In it, I shared commentary in earnings conference calls for the third quarter of 2026, from the leaders of US-listed 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.

A few more technology companies I’m watching hosted earnings conference calls for 2026’s third quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. 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
2026 Q2 – here, here, here, and here

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

Alphabet (NASDAQ: GOOG)

Alphabet’s AI investments are driving growth in Search, including commercial queries; the growth rate in queries in Search is the highest seen in the last 5 years; management is building a seamless experience across AI Overviews, AI Mode, and Search; queries in AI mode are 3x longer than traditional searches; 20% of AI Mode searches globally are multimodal, and Alphabet has crossed 50 billion visual searches per month; management is looking to bring agentic experiences to Search; many queries on AI Mode are upper funnel, which allows Alphabet to capture new, high-value commercial intent; management is also seeing that the majority of AI Mode queries are incremental, which creates more monetisation opportunities; management has found that users are comfortable with advertising on AI Mode and AI Overviews, with similar click-through rates between sponsored and organic product list items within AI Mode; management is now scaling high-quality ads within AI Overviews and AI Mode; management is taking the monetisation of AI Overviews and AI Mode global for the 1st time; AI Mode is currently monetising at a lower rate than traditional search, but management feels very good about the trajectory

Search has strong momentum. Our AI investments are driving increased usage and overall query growth, including commercial queries. In fact, our year-over-year query growth rate is the highest it’s been in the last five years…

…We are building one seamless experience across AI Overviews, AI Mode, and traditional Search. People are using our AI features more, searching in entirely new ways. Their queries are longer, personalized, and multimodal. Queries in AI Mode are on average three times longer than traditional searches, and one in five AI Mode searches globally use voice, images, or documents. We have now crossed over 50 billion visual searches per month…

…We think of Search holistically, a unified product spanning traditional results and AI-powered Search. On AI Mode, many of these queries are upper funnel queries, allowing us to capture new, high-value commercial intent. In addition, the majority of AI Mode queries are incremental, creating more monetization opportunities over time. Additionally, our user research shows that people are comfortable with ads on these surfaces and find them highly relevant. Click-through rates are already similar between sponsored and organic product list items within AI Mode. Based on these collective results, we are now scaling high-quality ads within AI Overviews and AI Mode to more users and queries. This includes taking our monetization footprint global for the first time. Today, we are announcing the expansion of ads in AI Mode into international markets alongside multi-turn ads in AI Mode in the U.S…

…It’s worth noting that the strong growth in AI Mode queries had an impact because of the lower monetization we were seeing in AI Mode. To be clear, we feel very good about our trajectory here. Our ad experiments have shown promising results, and we are now scaling ads within both AI Overviews and AI Mode to more users and more queries, including, as I mentioned, international markets and multi-turn ads in the U.S.

Alphabet’s 1st-party model APIs processed 32 billion tokens per minute in 2026 Q3, up 40% sequentially (was 22 billion tokens per minute in 2026 Q2), and up 4x from a year ago; Alphabet’s 2 latest models, Gemini 4 Argon and Gemini 4 Carbon, are leading the frontier; Argon’s usage in its 1st week ramped up 3.5x faster than Gemini 3, despite capacity constraints; management sees Argon performing well in finance, legal, cybersecurity, and science; customers are using Argon to untangle legacy code bases, accelerate scientific research, improve products, and more; Alphabet has used Argon internally over the last few weeks to ship 35% more code with 30% fewer errors; Gemini 4 Carbon is currently Alphabet’s strongest frontier model and it ranks No.1 on the recently updated Artificial Analysis Intelligence Index at 59; Carbon outperforms Argon across nearly all domains; management is rolling out Carbon to a trusted cyber defenders and the US government first, before expanding access; management recently introduced Genie 4, Alphabet’s most advanced world model; management sees Alphabet being at the point where AI is accelerating the company’s AI training and launch speed, and management expects continued acceleration because of RSI (recursive self-improvement); Alphabet will soon be releasing more efficient models, including the Carbon family of models; Alphabet’s Gemini Flash cyber models are frontier standard for cybersecurity and can patch vulnerabilities 2.6x more reliably than other models; the Gemini Flash cyber models will become even better when Argon and Carbon are rolled out

Our first-party model APIs are now processing nearly 32 billion tokens per minute, 40% more than last quarter and more than four times what we processed a year ago. With Gemini 4 Argon and now Carbon, which we announced today, we are excited to be leading at the frontier…

…We are seeing strong demand and usage for Argon, which we launched two weeks ago. In fact, on our developer platforms, usage in the first week ramped up 3 and a half times faster than Gemini 3, even with capacity constraints. Argon is proving to do really well in areas like finance, legal, cybersecurity, and science. Developers and Cloud customers are already using it for things like untangling legacy code bases, accelerating scientific research, robotics, discovering product improvements, and domain-specific tasks requiring in-depth reasoning. At Google, just in the last few weeks, teams across Chrome, Ads, and Search have used Argon to write and optimize code much faster, shipping 35% more code with 30% fewer errors.

Argon is just the start of the Gemini 4 series. Today, we announced Carbon. It is our strongest frontier model with state-of-the-art cybersecurity and long-horizon capabilities. It ranks number one on the recently updated Artificial Analysis Intelligence Index at 59. Carbon outperforms Argon across almost every domain, like full-stack web development, multimodal understanding, complex knowledge work, and scientific reasoning. We are rolling Carbon out first to a subset of trusted cyber defenders before we expand access, and it is being tested by the U.S. government.

Beyond our Gemini 4 series, yesterday we announced Genie 4, our most advanced world model yet that simulates safe, realistic, interactive worlds with native audio in real-time. This is an important building block for training robots and agents…

…We are at the point where AI is helping us increase our training and launch velocity. I would expect the next 12 months to see continued acceleration because of RSI. We have more models coming soon, including more efficient versions to help developers optimize for speed and cost and to deploy across our billions of users…

…We are working on distilled versions of our Carbon models so that we can deploy it across a billion-user scale efficiently, both from a compute, from a latency standpoint, and from a quality standpoint…

…Our Gemini Flash cyber models are frontier models that automate vulnerability discovery across 20 programming languages and patch 2.6 x more reliably than other models. There is even better performance to come as we roll out Argon and Carbon.

The Gemini app is now at 1 billion users, and is Google’s fastest-growing product ever; management is attempting to bring agentic experiences to the Gemini app; the integration of the Gemini 4 models into the Gemini app has been a big step forward for Alphabet in introducing agentic experiences into the app

The Gemini app is now at 1 billion users and is the fastest-growing product in Google’s history…

…People already put their trust in Google to get things done in their daily lives, and our leadership here remains an important differentiator. We focus first on getting these agentic experiences right with the Gemini agent for enterprise, because the bar for security, safety, and governance is extremely high. As a next step, we are bringing these features to Search and also, of course, to our billion users on the Gemini app. People are already using the Gemini app to do more than ask questions. They are using it to do things, including complex multistep agentic queries. Our Gemini 4 series has been a really big step forward here and has already been handling these complex tasks really well.

Alphabet’s management is focused on providing the right agentic experience with the Gemini agent for enterprise; management thinks agents will be a unifying layer for Alphabet’s products; the Universal Commerce Protocol (UCP) for agentic commerce currently enables direct checkout for hundreds of thousands of brands; management is combining UCP with the Agent Payments Protocol for agentic commerce; management has launched Universal Cart for retailers to maintain their relationships with customers while helping to capture sales and avoid abandoned carts in the agentic commerce experience; management wants to enhance the product discovery experience for users through the agentic experience; management is confident that Alphabet can provide compelling advertising models for agentic use cases; management sees subscriptions and payments as other ways of monetising agentic use cases; management sees agentic use cases as an expansion opportunity for Alphabet; management thinks safety and trust are very important considerations for consumers when engaging with agentic experiences

We focus first on getting these agentic experiences right with the Gemini agent for enterprise, because the bar for security, safety, and governance is extremely high…

…Agents will operate as a unifying layer across our products. You will see us extend these agentic experiences beyond Search in the Gemini app to Chrome, Android, and our Workspace products as well…

…The Universal Commerce Protocol, UCP, is being rapidly adopted by retailers. Today, UCP enables direct checkout for hundreds of thousands of brands across Google, including Chewy, Nike, Tapestry, and many more. We are combining this with the Agent Payments Protocol as we build for the future of commerce. Our focus spans two fronts, unlocking agentic capabilities that act on behalf of the consumer, and designing practical tools that gives shoppers confidence and control. We launched Universal Cart this quarter to support that second track, mirroring how people actually shop today across Google. This allows retailers to maintain their relationships with customers while helping to capture sales and avoid abandoned carts…

…We also want to enhance, not just automate away the discovery experience that people frankly love in so many cases. Users like choice, and they want to consider things way beyond price, like brand loyalty, customer service and so on. We’re really architecting these agentic workflows in a way that we can intentionally design the time and the space for users to explore those choices. So simple example, an agent could respond, “Here’s the insurance product we found, and here’s another strong option, one of which is a higher premium, maybe a lower deductible, and they are currently offering a very special discount.” Given that there’s almost never only one perfect option for a decision, we’re actually confident that we can actually provide really interesting, compelling advertising models for agentic use cases. And obviously you can imagine going beyond us with subscriptions and other payment methods…

…I would view it as an expansion opportunity, right? Any time, just like a few years ago, being able to incorporate AI chat use cases in Search allowed people to ask more and do more in Search, which is what is playing out in our usage. I think agents would again be an opportunity to expand the types of problems we solve for our users… I’m excited about agents and I would view it as a new frontier, which brings in a new set of use cases to Search, and so we are excited to work through that…

…When people are approaching these agentic experiences, we clearly see people are concerned about safety and trust. It is a very high factor in how they want to use these things. Over decades, the trust we have built by delivering these experiences in a safe way across many consumer products with scale in billions of users, as well as this putting in end-to-end as we are building our models and agents, deploying them both with extensive testing, the kind of real-time monitoring we do as we deploy these at scale, I think all of that will end up being a huge differentiator.

Waymo now has more than 300 million fully autonomous miles; Waymo is now operating in 15 major cities in the USA and will be rolling out internationally soon

Waymo is on a great trajectory. We have now driven more than 300 million fully autonomous miles. Waymo now operates in 15 major U.S. cities and just recently opened up service to the public in San Antonio, San Diego, and Tampa, with planned expansions into Canada, Germany, Singapore, and Spain.

Among Alphabet’s other long-term initiatives, Isomorph is using Gemini 4 Agron for drug research

Isomorphic is using Argon to support drug design research.

Alphabet’s management continues to deploy Gemini models across Alphabet’s entire advertising infrastructure; the Gemini models are improving the performance of Alphabet’s advertising business in advertising quality, advertiser tools, and user experiences; in advertising quality, the integration of Gemini models into ads infrastructure is improving how Alphabet decodes user intent and drive ads relevance; in advertising quality, management upgraded product listing ads in 2026 Q3, which drove a 19% increase in satisfaction and a 50% drop in dissatisfaction; in advertising quality, management has delivered 18% incremental advertiser value and an 8% increase in user sentiment on Discover Ads in 2026; in advertiser tools, management is introducing a way to test different budgets and ROI targets across multiple Search campaigns in a single AI Max A/B test; in advertiser tools, management has integrated Gemini-powered AI creative tools into AI Max, and advertisers have generated over 1.5 billion campaign assets in 2026 Q3, up 7x year-on-year; in advertiser tools, Telstra used AI Max to achieve a 52% increase in conversions and 36% reduction in cost per order

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

First, ads quality. The integration of Gemini models into our ads infrastructure is improving how we decode user intent and drive ads relevance. For example, in Q3, we made the biggest update to product listing ads in a decade, adding new AI capabilities to help users make more confident purchase decisions. Already, we have seen satisfaction increase by 19% and dissatisfaction drop more than half. We are also seeing AI models enable better prediction of clicks and conversions, stronger overall performance, and deeper personalization of ads. For instance, through several innovations in personalization modeling, we delivered 18% incremental advertiser value and an 8% increase in user sentiment on Discover Ads in 2026. Overall, AI is accelerating our ads development and drives the majority of our product launches.

Second, advertiser tools. We are ensuring every advertiser, from global brands to small businesses, can seamlessly participate in our AI services. AI Max and Performance Max continue to see strong adoption. To give marketers even more control, we are now introducing a way to test different budgets and ROI targets across multiple Search campaigns in a single AI Max A/B test. To drive better performance, we integrated our AI creative tools directly into AI Max. In Q3 alone, Gemini models helped advertisers generate over 1.5 billion campaign assets, up over 7x year-over-year. For example, by using AI Max to capture long-form and multilingual queries, Telstra saw a 52% increase in conversions and a 36% reduction in cost per order, showing up in key moments however their customers search.

Google Cloud had 103% revenue growth in 2026 Q3 (was 82% in 2026 Q2) driven by growth in AI infrastructure and solutions; Google Cloud is seeing huge demand and management is working hard to bring more capacity online; Google Cloud is seeing strong demand for TPUs across a wide range of customers; Google Cloud now has 18 products with more than $1 billion in annual revenue; more than 2,600 customers have used at least 100 billion tokens in the last 12 months (was 2,000 in 2026 Q2); 75 customers have consumed at least 10 trillion tokens in the last 12 months; nearly 90% of the Fortune 100 are using Gemini Enterprise; customers who used Gemini Enterprise in 2025 Q3 have 4x-ed their spend in 2026 Q3; Google Cloud has sold more than 100 million seats for knowledge work across all its AI services; Gemini Enterprise now includes the Gemini agent that can research and answer questions, write and run code, and automate process flows; BNP Paribas is deploying Gemini Enterprise to 65,000 employees to streamline workflows; Tata Steel deployed 300 specialised AI Agents with Google Cloud in 9 months and cut service times for customers by 50%; Ulta Beauty used Google Cloud to build the Ulta AI shopping assistant, which has a 3x higher conversion rate than traditional browsing; Gemini is driving acceleration for Google Cloud’s database, analytics, and cybersecurity products; Etsy is using Google Cloud’s BigQuery and Icceberg products to perform critical data joins up to 60% faster;

Google Cloud revenues grew 103%. Our AI infrastructure and solutions are the major drivers for Cloud growth, and our customers see us as the leading provider here, so there’s huge demand. We are working hard to bring more capacity online to meet this long-term demand. At the same time, we are seeing strong demand for our TPU systems across a wide range of customers, and we expect that to continue as we scale…

…Google Cloud now has 18 product lines with more than $1 billion each in annual revenue…

…Over the past 12 months, more than 2,600 customers have used at least 100 billion tokens, and nearly 75 at least 10 trillion tokens on Gemini Enterprise. Nearly 90% of the Fortune 100 use Gemini Enterprise, and customers who use Gemini Enterprise in Q3 2025 more than quadruple their spend with us year over year…

…Across all our AI services, we have sold more than 100 million seats for knowledge work. Gemini Enterprise now includes the Gemini agent that is deeply integrated across our entire Cloud portfolio. It can research and answer questions, write and run code, and automate process flows with one agent, one API, and a single prompt window. It gives companies strong security and cost controls and the widest choice of frontier models. Customers include BNP Paribas, who is deploying Gemini Enterprise to more than 65,000 employees to streamline workflows while maintaining strict enterprise-grade security and data governance. Tata Steel, who deployed more than 300 specialized AI agents in just nine months to enhance manufacturing safety, optimize supply chains, and cut service times for customers by 50%. Ulta Beauty, who built the Ulta AI shopping assistant, resulting in a 3x higher conversion rate than traditional browsing on their site.

Gemini is also driving acceleration for our database, analytics, and cybersecurity products. Gemini can now help users find, analyze, and visualize information from structured and unstructured data. Gemini uses our Knowledge Catalog to find the right sources and definitions of information, improving accuracy, lowering token cost, and making responses more reliable. Gemini uses our unified lakehouse to access information from multiple systems with zero copy. To respond to agentic analysis, BigQuery improved query speed over 40% year-over-year, while reducing query processing cost by over 50% year-over-year. Customers include Deutsche Telekom, which uses BigQuery Cloud Storage and Knowledge Catalog to build a unified data lakehouse across 40-plus legacy on-premise systems. Etsy, which migrated three petabytes of data to unified lakehouse. By using BigQuery and Iceberg, they now perform critical data joins up to 60% faster.

The growing risk of cybersecurity threats is also driving strong demand for our AI-powered security platform. Gemini and Google Threat Intelligence autonomously analyzes millions of external events every day with 98% accuracy. More than 95% of Wiz customers are adopting AI-powered features, and growth continues. These security tools protect critical infrastructure, including financial institutions like VanOorte and Jack Henry, and telecommunication providers such as AT&T. All of this is powered by our leading AI infrastructure, spanning our TPU systems, Axion CPUs, the latest Nvidia GPUs, and our Agent Compute Engine, which is specifically designed for AI agents.

Alphabet’s TPU 8t and 8i have up to 2.7x better training performance and 80% better inference price performance than previous TPUs; Alphabet recently partnered Meta Platforms to deliver TorchTPU, which allows users to train, serve, and deploy PyTorch models on TPUs; nearly 100 customers use TPUs today; Anthropic and Apple are both users of TPUs, with Apple performing pre-training of its foundation models with the latest generation TPUs; new frontier model companies Mirendil and Physical Intelligence are users of TPUs; Alphabet will be delivering TPUs to Oracle

TPU 8t and 8i offer up to 2.7 x better training performance and 80% better inference price performance, respectively, versus our previous generation of TPUs. To make it easy for developers to port workloads to TPUs, we recently worked closely with Meta to deliver TorchTPU, which allows users to train, serve, and deploy PyTorch models on TPUs using the tools they are already familiar with and rely on. Nearly 100 customers use TPUs today, and we are seeing strong and growing demand from many others. In addition to our work with Anthropic, both directly and via Broadcom, and public reference customers like Apple, who are scaling pre-training of their foundation models on the latest generation of TPUs, we are also working with new frontier model companies, including Mirendil and Physical Intelligence, capital markets firms including Citadel Securities and Jump Trading, universities like Caltech, and public sector service providers, including Cassava and Cirrascale. Given continued strong and accelerating demand, we have recently entered into partnerships to deliver TPU systems with both Crux AI and Oracle, with more in the works.

Alphabet had $924 billion in pre-commencement leases, and other obligations of $152 billion, at the end of 2026 Q3; these reflect maximum obligations, and the actual amounts can vary

At the end of the third quarter, we had future purchase commitments of $924 billion in pre-commencement leases and other obligations of $152 billion. These dollar amounts reflect the maximum contractual obligations. However, a number of these agreements have variable pricing and volume flexibility, which could result in lower spend.

Google Cloud had 103% revenue growth in 2026 Q3 (was 82% in 2026 Q2), driven by growth in GCP (including AI infrastructure, AI solutions, and core GCP) and TPU system sales; Google Cloud’s revenue growth was still very strong even if TPU system sales were excluded; Google Cloud operation margin was 36.2% (was 35.6% in 2026 Q2 and 23.7% in 2025 Q3); Google Cloud backlog grew 9% sequentially to $561 billion in 2026 Q3 (was $514 billion in 2026 Q2); most of Google Cloud’s backlog are GCP contracts and just over 55% of the backlog is expected to be recognised as revenue in the next 2 years; a gigawatt of TPU systems appear to cost $35 billion; when Alphabet sells TPUs to 3rd parties, the inventory is built only after a contract is signed; Alphabet receives cash for the TPU system sales after the systems are delivered

The Google Cloud segment delivered record growth in the third quarter. Cloud revenues were up 103% to $30.7 billion, driven by strong growth in GCP, with strength in AI, including both AI infrastructure and AI solutions, and core GCP. TPU system sales were also a meaningful contributor. GCP accounted for the vast majority of the revenue generated in Q3. The Cloud revenue growth rate, excluding the impact of TPU systems, was again incredibly strong. Cloud operating income was $11.1 billion, more than tripling year over year. Operating margin increased from 23.7% in the third quarter last year to 36.2%. The third-party bridging capacity, which we discussed on our prior call, came online only at the very end of Q3 and therefore had limited negative impact to our margin in the quarter. Google Cloud’s backlog was $561 billion in the third quarter, with the majority of the backlog related to typical GCP contracts. We expect to recognize just over 55% of the total backlog as revenues over the next 24 months…

…[Question] If a gigawatt of TPU full system costs around $35 billion for the compute and all the components around it, what percent of that are we actually recognizing as revenue?…

…When we sign an agreement with a customer to sell TPU systems, once we sign that agreement you’ll first see that showing up in our backlog. Then we start building inventory. Think about this from a CapEx versus cash perspective. We build the inventory ahead of getting the revenue stream in. I think I’ve alluded to this on the previous call. You see inventory balances go up, where previously inventory balances primarily were some of our hardware products. Now those TPU systems that are not yet sold will be showing up, so you see cash going out, and we’re building up the inventory base. As we start delivering and billing customers for these TPU systems, then you see cash coming in…

…We procure components to build inventory, we only do that after we have signed contracts. All of the inventory for TPU systems are associated with committed contracts.

Alphabet’s management expects TPU system sales to continue scaling in 2026 Q4, followed by a meaningful ramp in 2027; management expects Google Cloud margins to be pressured in the near term because of the use of bridging compute capacity that will mostly come online in 2026 Q4; management continues to expect 2026 capex to be $195 billion to $205 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); 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, but the AI-related capex is expected to generate an attractive ROIC that meaningfully exceeds Alphabet’s cost of capital

In Google Cloud, we expect TPU system sales to continue to scale in Q4, followed by a meaningful revenue ramp in 2027. With respect to Cloud margins, given the supply-constrained environment, we started utilizing third-party compute capacity at the end of Q3 and will ramp usage in Q4. We expect margin headwinds in the near term as we utilize this capacity…

…Our full year 2026 CapEx guidance range of $195 billion to $205 billion remain unchanged. We continue to expect our CapEx to increase significantly in 2027 and will provide further details on our Q4 call.

In terms of expenses, the significant increase in our investment in technical infrastructure will continue to weigh on the P&L in the form of higher depreciation expenses and related data centers operation costs such as energy…

…Let me say a few words on how we think about return on invested capital and free cash flow. We are in a unique position to be able to monetize our AI investments across many products that reach billions of users and enterprises. Our AI investments in Search are driving our highest query growth in half a decade. Google Cloud has generated tens of billions of dollars in AI revenues year to date, and Google One is our fastest-growing subscription product, driven by demand for AI plans. Throughout this investment cycle, we expect to generate an attractive ROIC that remains meaningfully above our cost of capital and anticipate generating strong cash flow from operations. Our sizable investments in technical infrastructure and inventory build of TPU systems as we scale that business will put pressure on free cash flows.

Alphabet’s management saw a step-change in the company’s productivity with the introduction of the 3.8 Flash internally; the introduction of Argon internally has led to a huge inflection point in productivity, and Carbon will be a meaningful step up from Argon

[Question] Sundar, on a recent podcast, you said that 2027 could be the golden age of productivity for Google because of the new class of models and how they will integrate with your other products?

[Answer] We definitely started seeing a step change, I would say, with 3.8 Flash internally as our models got better at long-horizon tasks, agentic coding, et cetera. But definitely, it’s seeing a huge point of inflection once we’ve started deploying Argon internally. It is one of the most meaningful internal productivity levers we have seen on software engineering, directly tracking high-quality code that’s getting checked in, the rate at which it’s getting checked in, reducing the number of rollbacks. But more importantly, people are tackling massive problems as well as we are beginning to see innovation on the product and the consumer experience side from deploying those models. But all this happened only about four weeks ago. We are now deploying Carbon internally, and Carbon is a meaningful step up from Argon.

Amazon (NASDAQ: AMZN)

AWS grew 39.3% year-on-year in 2026 Q3 (was 36.7% in 2026 Q2), the 6th straight quarter of acceleration, and is now growing at its fastest pace in 19 quarters; AWS’s backlog is $578 billion in 2026 Q3 (was $496 billion in 2026 Q2), up triple-digits year-on-year; AWS customers are taking up compute capacity as soon as it’s installed; AWS’s AI business has rapidly improving operating margins; AWS’s non-AI (core compute and storage) revenue grew at the fastest rate in several years in 2026 Q3; the operating margin of AWS’s AI business is currently well ahead of where the non-AI business was at a similar age, despite heightened costs and massive depreciation, but the AI business’s current margin is still lower than the overall AWS business; management has continued to accelerate the capex payback for AWS’s AI business and they have even more conviction now compared to a few years ago that the AI investments will yield strong revenue, free cash flow, and ROIC (return on invested capital); management sees AWS having a strong flywheel where its (1) functionality and performance drives customers to run apps and store data on AWS, which is core-AWS (2) leading to customers running AI workloads on AWS where their data and apps are, (3) leading to more usage of AWS because AI workloads require core-AWS; management sees the flywheel as driving attractive long-term economics for AWS regardless of the level of compute supply; management still sees 85% of global IT spend being on-premises and that the spend will flip to the cloud over the next 10-20 years, as AI-driven migration capabilities make the transition easier, and being on the cloud benefits enterprises; overall demand for AWS is exceptionally strong; management sees enterprises as still being early in using inference at scale, but yet inference is already half of AWS’s AI revenue; AWS is now at a $184 billion revenue run rate (was $169 billion in 2026 Q2); management thinks AWS can reach $1 trillion in revenue in the next 5-10 years, and management has a plan to fund this growth; AWS’s AI revenue grew triple-digits year-on-year in 2026 Q3; big chunks of AWS’s capacity continue to be reserved well in advance, and management has line of sight to some of 2029; management has very good visibility into AWS for the next few years

Despite lapping a comparable year-over-year period where we added a lot of capacity, AWS grew 39.3% year-over-year, accelerating for the sixth straight quarter, our fastest growth in nearly five years. Backlog is $578 billion, growing triple digits year-over-year and well-diversified. We’re installing an AI accelerator about every 14 seconds, and almost as fast as we install capacity, customers are gobbling it up. Our core compute and storage revenue grew at their fastest rates in several years.

Our operating margins have long been strong on our core offering, but rapidly improving on the AI side too, well ahead of where they were on core at a similar time of evolution, even with heightened component costs like memory and significant depreciation from the CapEx we’re investing. We continue to accelerate our CapEx payback and have even more conviction than we already had that the investment we’re making in AWS will yield a much larger revenue and free cash flow-generating business than we would have imagined a couple of years ago, with strong return on invested capital…

…We are happy with the progression in both core and AI margins, and as Andy said earlier, they are ahead of where the core was at a similar point in time, but they are still lower, so there is going to be that mix issue….

…In AWS, having the leading functionality, security, and operational performance can’t help but drive customers to run their apps and store their data in AWS, which can’t help but drive AWS AI demand because customers prefer their production inference to be near the rest of their data and apps, which can’t help but drive core AWS demand because AI uses core for work like reinforcement learning, post-training, and tool usage, which can’t help but drive reinvestment back into our broader AWS functionality…

…It also yields very attractive long-term economics for us, regardless of supply scarcity…

…On the core side, it’s important to remember that the vast majority of global IT spend, about 85%, is still on premises, and we’re in a 10- to 20-year cycle where that equation will flip to the cloud. Conventional wisdom has been that linchpin workloads like mainframes or storage area networks would never move to the cloud, but these workloads are moving as AWS has built unique cloud substitutes and AI-driven migration capabilities to make this transition much easier. Enterprises get the benefits of less CapEx, lower operating costs, faster innovation, better developer productivity, and data accessibility to more easily leverage AI…

…Overall demand for AWS capacity remains exceptionally strong. Enterprises are still very early in using inference at scale, and yet inference already accounts for well over half of AWS’s AI revenue and growing, and we have a lot of training revenue as well…

…AWS is a $184 billion annualized revenue run rate business, and yet the opportunity remains extraordinary…

…We believe that AWS can grow north of 40% in each of 2027 and in 2028 and be a trillion-dollar revenue business in the next five to 10 years…

…Would it be more convenient if this type of acceleration and opportunity happened over 25 years and you could spread the CapEx over 25 years? Yeah, it might be more convenient, but that’s not the world that we live in, and that’s not the opportunity. We can fund this plan. I think it will forever change the size of Amazon…

…Our AI offerings are gaining traction with customers, with AI revenue growing triple digits year-over-year…

…Despite how much capacity we are providing, the big chunks of capacity that people take, the hundreds of megawatts or sometimes up to a gigawatt of capacity, those big chunks are reserved well in advance because you cannot leave a gigawatt of capacity hanging around. We have very good visibility into 2027 and 2028 and some of 2029 too, with respect to what customers want in those really big chunks of capacity…

…We have very good visibility into the next few years on what AWS looks like.

Amazon has acquired DuckLabs, and DuckDB will become the SQL dialect of AWS Storage and Analytics; DuckDB will enable customers, for the 1st time in analytics, to enjoy zero migration or portability costs between query engines; management thinks the zero migration and portability cost feature will help AI agents to efficiently query data

With our acquisition of DuckLabs, DuckDB will become the universal SQL dialect of AWS Storage and Analytics, simplifying data processing with a modern analytics engine that is fast, simple to use and ubiquitous, and where customers will be able to write SQL queries once and then execute using any AWS analytics service. For the first time in analytics, customers will have zero migration or portability costs between query engines. This is a big deal, and you can imagine how it will also help agents do efficient querying as well.

Bedrock is AWS’s fully-managed service for companies to build upon frontier models; Fortune 500 companies are spending 7x more on Bedrock year-on-year in 2026 Q3 with token volume growing 17x; Bedrock is the fastest growing multi-billion dollar service in AWS’s history; Bedrock recently added managed versions of the latest Kimi, GLM, and SpaceX models; the data of customers that use Bedrock is secure and private; management thinks that the future of AI will (1) consist of 6-8 models that are good enough for nearly all workloads, and each of them will have their strengths, and (2) have customers that use multiple models; management sees Bedrock has being relevant for the future of AI; management thinks the Western world are very hungry for Western open sourced models; most of Bedrock’s workloads today are being powered by Trainium

We continue to expand the capabilities of our inference service, Amazon Bedrock, and it is leading to remarkable growth, with Fortune 500 companies spending nearly 7x more on Bedrock year-over-year and total Bedrock token volume growing nearly 17x year-over-year. Bedrock is the fastest growing multi-billion dollar service in AWS’s history. We also just added Bedrock-managed versions of Kimi, GLM, and SpaceX models…

…We have designed Bedrock so customers’ data remains secure and private. Inputs and outputs are never shared with model providers or used to train base models. When customers tune a foundation model, we create a private copy specifically for their use, and data is fully encrypted both in transit and at rest…

…One of the lessons that we’ve learned, I’ll say for 20+ years in offering technology to customers, is that there is not one tool to rule the world. People always assume that whatever is in the lead early on is going to be the only thing or things that people use, and it’s never the case. It’s not true in database, it hasn’t been true in analytics. In the beginning of AI, everyone said the only framework people use is TensorFlow. It turns out PyTorch is much bigger now. It’s not going to be true with models either.

I’ll say in the next two to three years, I think there are going to be 6- 8 models that are about as good as each other, or at least good enough for 95%+ of the workloads. Some will be better at reasoning, some will be better at coding, some will be better at visual pieces, some will be lower latency, and some will be lower cost. Most customers are going to use multiple models, and many will use multiple models in the same generative AI application. What customers really want to do, and we see this validated over and over again when we talk to customers, is they want a service that has really broad selection of those frontier models, and that has very good price performance, and that has very strong security and access control. They want that service so that they can decide what combination of models they want to use and to swap them back and forth as they leapfrog each other, as different constraints of their applications require them. That’s what we see with Bedrock…

…I think the Western world is very hungry for additional open models that are Western open models…

…The majority and most of Bedrock today is being run on top of Trainium. We do that in meaningful part because we really like the price performance that we are getting, and it allows us to both keep prices as low as possible for customers, as well as our ability, we like the margins associated with it.

Amazon’s management is seeing frontier models surface previously unknown software vulnerabilities, and at a much higher frequency; management has built AWS Continuum for customers to address software security; AWS Continuum already has established enterprises as customers; AWS Continuum is being integrated into popular vibe coding tools such as Claude Code, Codex, and Kiro; AWS Continuum is enjoying unprecedented customer interest for a new security service; some people think 20% of all AI spending in the next 1-2 years could be in security

As frontier models get more powerful, they are surfacing software vulnerabilities previously not found and at unprecedented rates. We built AWS Continuum to discover, prioritize, validate, and remediate code vulnerabilities at machine speed. It is already being used by customers like Capital One, IBM, Infor, Luminate, and Robinhood. In Q3, we announced that we will integrate it into the tools where code is being written, Anthropic’s Claude Code, OpenAI’s Codex, and AWS’s Kiro. The interest from customers exceeds anything we have ever seen for a new security service…

…There are some people who think that the total amount of tokens used over the next year or two, 20% of it is some significant percent of it may end up being just security.

AWS customers are using AgentCore to deploy agents at an accelerating rate; AgentCore has tens of thousands of customers, and they deployed 2x more agents in 2026 Q3 than the prior 3 quarters combined; management is seeing companies build their own agents and use turnkey agentic services; AWS has agentic services that customers love; management thinks every modern enterprise production application will be rewritten to be agentically capable

Agents continue to be deployed at accelerating rates, many of which are being built on AgentCore’s building blocks. Tens of thousands of customers currently use AgentCore, deploying 2x more agents with it in Q3 than the prior three quarters combined. In addition to building their own purpose-built agents, most companies will also use turnkey agentic services, and we offer several that customers love. Kiro for coding, Amazon Connect for contact center agents, and AWS Transform for migrations. They are all iterating quickly and picking up customer momentum. As is Amazon Quick, our intelligent AI work companion, which is growing very rapidly as large enterprises like Mondelez, EchoStar, 3M, Southwest Airlines, and DXC Technology are choosing Quick because it is purpose-built for work with no compromises on customer data…

…I believe virtually every modern enterprise production application will be rewritten to be agentically capable, because that’s the way you’re going to have to deploy if you want to move quickly or you want to actually guard in the right way against some of the security risks.

AWS’s chips business grew triple-digits year-on-year in 2026 Q3; revenue commitments for Graviton and Trainium in 2026 Q3 both increased more than 20x year-on-year; management sees AI as being a tailwind for AWS’s Graviton CPU chip, because AI workloads, including agent tool use, requires CPUs; management sees Graviton as the strongest CPU chip for AI, offering 30%-40% better price performance than alternatives; customers using AWS’s 1st-party chips saved more than $2 billion in 2026 Q3; management recently open sourced TorchNeuron for developers to run existing PyTorch code on Trainium without rewriting code; the backlog for prior generation of Graviton chips still grew more than 100% sequentially in 2026 Q3 even after Graviton 5 was launched; short-term capacity for prior generation Trainum chips are selling at a premium compared to 2026 H1; management intends to sell Trainium to 3rd parties outside of AWS in late-2027; Amazon’s chips business is helping AWS to optimise capacity and improve its cost efficiency; management is seeing that enterprises are eager to save on price-performance as they deploy inference into production, and this is where Trainium has an advantage; management believes that lowering the cost of inference is important for enterprises to start deploying inference at scale, and Trainium is an important part of this view; selling Trainium externally will lead to more revenue in the short term, but selling Trainium through AWS will lead to more absolute revenue and free cash flow over time; managemen thinks selling chips is a very different business than running a cloud

Our chips business continues to grow triple-digit percentages year-over-year, and revenue commitments for Graviton and Trainium have each increased more than 20x year-over-year. AI continues to be a tailwind for Graviton because post-training reinforcement learning and agent tool use is mostly done on CPUs versus AI accelerators, and Graviton is the strongest CPU chip, offering up to 30%-40% better price performance than alternatives. Customers using our custom silicon saved more than $2 billion in Q3 alone, and we just open sourced TorchNeuron so developers can run existing PyTorch code, which most already use on Trainium without rewriting a thing. As we launch new generations of chips, demand for prior generations remains strong. After our launch of Graviton5, backlog for prior generation Graviton instances still more than doubled quarter-over-quarter. Short-term capacity for prior generation Trainium instances sell at a premium compared to the first half of the year. With Trainium demand so strong, customers continue encouraging us to make it available outside of AWS. We intend to do so late in 2027…

…Our investments in custom chips and custom networking, along with software and process improvements, are helping us optimize capacity and build a more cost-efficient infrastructure…

…Increasingly, what you’re finding right now is most customers, every customer that uses AI at scale, but particularly enterprises now as they’re starting to deploy more inference into production, they are very eager to save on price performance. Trainium, with the meaningful advantage it has in price performance versus other alternatives, is very compelling to people…

…I think if we all want enterprises to be able to use inference the way we all believe they will, we have to keep working on taking the cost of inference down. That is a huge mission for AWS. It’s one of the significant reasons why we’re building our own custom silicon in Trainium, is that we just really believe it’s incredibly important for customers to be able to use inference in a much more cost-effective way…

…If you sell externally, you are able to realize more revenue in the short term because you get to recognize the revenue as soon as it leaves your inventory. On the flip side, if you decide to use it inside your cloud in the form of EC2 instances or Bedrock, you actually will end up driving more absolute revenue and free cash flow from those chips because you get to use them over five to six years and drive revenue for a longer period of time…

…We believe we are going to have a very significant business selling chips externally, and we are going to start in late 2027. We will be thinking through that balance, I think, probably every year. They will both be very significant. Our ability to create price, performance, scalable chips is going to be a very significant strategic advantage for us at scale, both in our own cost structure as well as in building a separate business and selling external chips. Selling external chips really is a very different business than your cloud infrastructure business.

Amazon’s management continues to expect capex for 2026 to be $220 billion (capex was $128 billion in 2025, and $83 billion in 2024); the majority of Amazon’s capex is for AWS; management expects capex to increase meaningfully in 2027; management has clear line of sight to strong financial returns on the company’s capex

We still expect 2026 cash CapEx of approximately $220 billion, the majority for AWS, and expect to spend meaningfully more in 2027 with a clear line of sight to attractive financial returns,

Robotics and AI are delivering measurable gains in Amazon’s fulfilment network; Amazon doubled its fleet of robotic arms in 2026; Amazon is developing foundation models for robotics to deliver useful robotics at scale; Amazon has over 1 million robots in its fulfillment network; Amazon’s safety record has improved in the past 4-5 years, driven by robotics; management sees the opportunity to deploy robots for delivery for the last 100 yards; Amazon’s new designs for its fulfilment network are robotics-heavy

Robotics and AI are delivering measurable gains through productivity and employee safety, which we’ll expect to compound in the future. In 2026, we doubled our fleet of robotic arms like Cardinal and Sparrow. We’re also developing robotics foundation models that enable faster programming and teach robots increasingly complex physical tasks, with the goal of delivering useful robotics at scale. Looking ahead, we see meaningful opportunities to improve productivity across our global fulfillment network and lower our cost to serve while continuing to get products to customers faster…

…We have over 1 million robots now in our fulfillment network, and while they’re having very significant impact on our cost structure and on our productivity and on safety. If you look at our safety numbers in our fulfillment network, they’re at record low numbers. We’ve made huge progress over the last four to five years, and robotics has been a big piece of that…

….I think we have the opportunity to also assist on some of the delivering of the last 100 yards to customer doors…

…If I look at the new designs that we have coming in our fulfillment network, including our expansion of same-day facilities, they are very heavy on robotics.

Over 400 million Amazon customers (350 million in 2026 Q2) have used Alexa for Shopping (previously known as Rufus) in the last 12 months; customer interactions with Alexa for Shopping was up 570% year-on-year in 2026 Q3 (was up 5x in 2026 Q2); US customers using Alexa for Shopping spend 50% more per order than those who don’t; management anticipates working with other agents (Meta Platforms’ Muse?), but they think Alexa for Shopping will be the primary shopping agent on Amazon

Over 400 million customers have used Alexa for Shopping, our agentic shopping assistant, in the last 12 months, with customer interactions up 570% year-over-year. U.S. customers using Alexa for Shopping are spending 50% more per order than those who don’t…

…Though we anticipate working with horizontal agents, we believe Alexa for Shopping is going to be the primary shopping agent customers use to shop Amazon.

Amazon’s management thinks agentic shopping from horizontal agents has no meaningful impact on Amazon’s advertising business; management will soon launch Amazon Ads Agent, which brings all of Amazon’s advertising offerings into a single AI-first platform; management has launched full-funnel campaigns in Amazon Ads Agent, where advertisers provide initial inputs, and Amazon’s AI does the rest

The natural question is whether agentic shopping from external horizontal agents is meaningfully impacting Amazon Ads. The answer is no. Few customers are doing much shopping this way yet, as the experience is still not great nor more convenient than coming to a retailer directly to research or one-click order there…

…Headlined by Amazon Ads Agent, which brings together our full advertising offering under a single AI-first platform. Backed by trillions of shopping, browsing, and streaming signals, it supports advertisers at every step with insights, answers, and the ability to optimize and act on their behalf. We also launched full-funnel campaigns in Amazon Ads Agent, bringing sponsored ads, display, video, and streaming TV into a single campaign. Advertisers provide initial inputs, including budget and duration, and Amazon’s AI plans, executes, and continually optimizes the rest.

Intel (NASDAQ: INTC)

Intel’s Granite Rapids CPU is ramping really fast while the Clearwater Forest CPU is shipping for revenue; Intel’s CPU products is seeing significant demand for 2027, driven by the compute needs of agentic infrastructure; Intel’s Diamond Rapids CPU is on track to ramp in 2027; Diamond Rapids is seeing strong interest from customers for inference and agentic workloads; Intel’s CPUs will be deployed by Oracle in its AI buildout; Intel is facing some headwinds in meeting the extremely high AI-driven demand for its CPU products; the headwinds include an industry-wide shortage of advanced organic substrates for EMIB (embedded multi-die interconnect bridge) packaging, and a factory incident in Israel; management sees CPUs as far more efficient at performing tasks compared to GPUs; management sees the TAM (total addressable market) for CPUs growing from $40 billion today to $200 billion in 5 years, because of agentic workloads; management sees competition in the CPU market for Intel, but thinks Intel has unique strengths, namely, (1) strong pipeline of CPU innovation, (2) willingness to customise CPUs for custom workloads, (3) the only advanced semiconductor fab in the USA; management sees demand signals from customers continue to strengthen, but supply is expected to be constrained up to 2028; Intel’s CPUs play a central role in enabling agentic workloads, and management expects growth of more than 30% in its server CPU units in both 2026 and 2027, with higher growth to be achieved if supply were available; management expects the DCAI (data center and AI) segment to achieve revenue in 2026 that’s $10 billion higher than initial expectations, driven by broad-based growth and purpose-built silicon

Within our server business, we are making steady progress on our roadmap. Granite Rapids on Intel 3 has become one of the fastest-ramping server products. Clearwater Forest, our first Intel 18A server products, is shipping for revenue. Demand for 2027 is significant, especially in CPU dense agentic rack scale infrastructure. Diamond Rapids, our first Intel 18A-P server product, is sampling and on track to ramp in 2027 with strong interest for inference and agentic workloads. We were pleased to recently announce an agreement with Oracle Cloud Infrastructure to deploy our latest generation CPUs to support their AI build-out.

I also want to acknowledge headwinds limiting our progress to meet this extremely high AI-driven demand. First, Intel 3 yield and cycle time continue to improve, but I am demanding significant execution from the team. Second, even as our Intel 3 wafer outputs ramps quickly, an industry-wide shortage of advanced organic substrates for our EMIB packaging is also impacting our ability to scale output. Finally, early this month, an unfortunate and tragic accident led to an extended shutdown in our Israel factory. While the teams have worked quickly to address the situation, it will impact our Q4 10-nanometer server shipments…

…Increasingly, energy efficiency is becoming the most important metric for the AI infrastructure build-out. While GPUs are great at thinking tasks, deterministic CPU are far more efficient at doing tasks. While GPU reasoning query run as high as multiple watt-hours of energy, a single CPU query could cost only joules of energy. Applying even conservative estimates to the total number of digital agents and agents served per CPU, it is easy to anticipate a massive dollar and unit TAM for CPUs over the coming five years. By our estimates, server CPU TAM, largely driven by agentic workloads, could increase as much as five times from where it is today, from $40 billion to more than $200 billion over five years…

…We are, of course, mindful of fierce competition underway as nearly every fabless company race to meet this unprecedented demand with their own offering. But Intel is uniquely positioned to benefit from this demand in three ways. First, as a custodian of x86, the most widely deployed ISA in the world, we are doubling down on x86 innovation, have an exciting Tick Tock roadmap of CPU innovations in the pipeline. In addition, for the first time, we are making fully modular, verified instances of our x86 CPUs optimized for the custom workloads available to our customers. Second, as one of the just three advanced node semiconductor foundries in the world, and the only one domiciled in the U.S., we are well positioned to support not only our own demand but also the demands of wider fabless industry. We are also open to building any CPUs at scale for our customers fully agnostic to underlying ISA. Third, as modern computing platforms increasingly become complex system in the package, our leadership in advanced packaging offers highly differentiated solution, especially to meet the requirements of the massive AI infrastructure build-out underway…

…Demand signals from our data center customers continue to strengthen. We expect the industry to remain supply constrained throughout 2027 and into 2028…

…The adoption of agentic AI is broadening the role of CPU compute. AI workloads are moving beyond prompt-based responses toward multi-step reasoning, orchestration, and autonomous persistent action. Our CPUs play a central role in enabling these workloads. As a result, we continue to see strong demand for our DCAI products. We now expect industry server CPU units to grow more than 30% in both 2026 and 2027. Growth would be higher in both years if additional industry supply were available…

…Based on our current outlook, DCAI revenue is on track to finish 2026 nearly $10 billion above expectations entering the year. Q3 growth was broad-based across hyperscale, enterprise, and Neo Cloud customers. We also continued to see strong momentum in purpose-built silicon, with revenue increasing more than 50% sequentially and more than doubling year over year.

Intel’s management sees a tripling of Intel’s purpose-built silicon business over the next few years after tripling over the last 3 years

Purpose-built silicon remains our fastest-growing business segment, tripling over the last three years and poised to triple again over the next few years. We see further growth opportunity with both existing and new potential customers, including AI labs, and expanding our IP portfolio to meet the needs of a wider array of customers…

Intel’s management recently announced the company’s D-Series inference-optimized GPU; the new GPU has encouraging early feedback and performance is tracking well

We recently unveiled our Intel GPU D-Series inference-optimized GPU. Early feedback is encouraging. Performance is tracking well. This is new market for us, and it will take time to scale, but customer engagements are progressing towards firm commitments.

Intel’s PC (personal computer) business is seeing a slowdown because of memory prices and a shortage of memory chips because of AI-related demand for memory chips; management expects the slowdown in the PC business to last for a few quarters

In recent weeks, our customers began signaling a PC slowdown based on the memory prices and availability. Year-to-date system endpoint prices have increased significantly, placing additional pressure on already limited IT budgets. The pace of this PC correction has accelerated over the last two months as our OEM partners are aggressively managing inventory to match the softer sell-through. We expect the current environment to persist until memory availability and prices begin to normalize. While the next few quarters will be challenging for the PC market, our roadmap continues to strengthen, and our long-term outlook remains positive…

…The industry continues to navigate one of the most significant supply shortages in the history, driven by the unprecedented demand for super intelligence.

Intel Foundry’s Intel 3 and Intel 18A yields both increased in 2026 Q3, with Intel 18A’s yield improving at a double-digit rate; wafer output of both Intel 3 and Intel 18A exceeded management’s expectations; Intel 18A-P is on track for launch by end-2026 for Intel’s internal products; management is engaged with a few highly strategic customers on Intel 18A-P; management is confident Intel 18A-P will be an attractive node for external Intel Foundry customers; Intel 14A is on track; management recently released Intel 14A’s 0.9 PDK (process design kit); management is pleased with Intel 14A’s progress with multiple potential customers; management is pleased with Intel Foundry’s progress with EMIB and EMIB-T for advanced packaging for internal use; both EMIB and EMIB-T have significant external customer traction; management is seeing robust output for HBI (hybrid bonding interconnect); Intel Foundry is bringing additional capacity online, ithe US and Asia; Intel Foundry is still overwhelmingly serving Intel and not external customers; Intel 3 costs improved 15% from 2026 Q1, while volume increased mid-teens sequentially; Intel 18A costs have fallen by 40% since 2026 Q1, and its yield has improved ahead of internal projections; management expects Intel 3 to be a profitable node for Intel Foundry in 2027; management thinks Intel Foundry will have some external customers to announce in the next 2 quarters for advanced packaging

Intel Foundry execution continued to improve consistently. During the quarter, Intel 3 and Intel 18A yields both increased again, with Intel 18A improving at a double-digit rate. Wafer output on both nodes exceeded expectations. Intel 18A-P remains on track for launch by year-end across our internal product portfolio, and external customer engagement continues to build. We are engaged with a couple of highly strategic customers, and I am increasingly confident that our Intel 18A-P will be an attractive node for external foundry customers. Intel 14A also remains on track. In October, as committed, we released our industry-standard 0.9 PDK to select customers. It is our highest quality 0.9 PDK, meeting industry standard yield and performance criteria at release. As is standard practice in the foundry business, I have consistently maintained that we will not disclose customer details. That being said, I remain highly encouraged by the progress we are making with multiple potential customers on 14A.

On advanced packaging, we are pleased by the progress we are making with EMIB and EMIB-T for our internal products. Both continue to see significant external customer traction as well. We continue to improve on execution, working with our supply chain to secure the capacity to meet the needs of our advanced packaging customers. We are also seeing robust progress in the readiness of HBI, which is an enabler of 3D chip stacking for our upcoming server products. Across our manufacturing network, additional capacity continues to come online. Intel 3 tool installation is progressing well in Ireland, as is in the ramp of the 18A in both Oregon and Arizona, including Fab 62, our second high-volume 18A fab. In New Mexico and Malaysia, we are adding capacity to support strong customer ramps for advanced packaging next year. We are also seeing robust progress in the readiness of HBI, which is an enabler of 3D chip stacking for our upcoming server products.

Across our manufacturing network, additional capacity continues to come online. Intel 3 tool installation is progressing well in Ireland, as is in the ramp of the 18A in both Oregon and Arizona, including Fab 62, our second high-volume 18A fab. In New Mexico and Malaysia, we are adding capacity to support strong customer ramps for advanced packaging next year. In New Albany, Ohio, the team recently achieved important construction milestone with installation of the first major structural truss. And we continue to evaluate unique and highly differentiated opportunity to accelerate our Ohio build-out…

…Intel Foundry revenue was $6.7 billion, up 16% sequentially, driven by better-than-expected growth on Intel 3 and Intel 18A. External foundry revenue was $411 million, an increase of $118 million sequentially, primarily due to a non-recurring customer program…

…Intel 3 costs improved approximately 15% from Q1 levels, while volumes increased in the mid-teens sequentially. Intel 18A costs have declined approximately 40% since Q1, reflecting continued yield and cycle time improvements. Intel 18A yields continue their upward trajectory to industry-standard levels ahead of our internal projections…

…If you look at Intel 7 today, kind of 10 nanometer 7, that’s already a profitable node to us. Intel 3 is going to be profitable next year for us…

…We are going to have couple of customer ready to announce in next two quarter. Clearly, we always want to wait for the customer, if they want to announce, we will respond to that.

Intel’s management continues to expect capital expenditures to be flat in 2026 (around $20 billion); management expects capital expenditures to be up strongly in 2027; management has increased firm purchase orders to equipment suppliers by 40% sequentially in 2026 Q3; despite the expectation of much higher capital expenditure in 2027, management is still committed to deleveraging in 2027; management will only spend on capital expenditure if there’s clear evidence of customer demand; most of Intel’s capital expenditure will be on equipment

Our 2026 gross capital expenditures remains on track to be $20 billion, and we expect 2027 gross capital spending to be up strongly. We have made good progress securing the equipment required to support our growth plans with firm purchase orders to equipment suppliers increasing nearly 40% sequentially. We’re also accelerating clean room build-outs, including Fab 62 in Arizona, and exploring opportunities to accelerate in Ohio. Despite this increase in capital spending and working capital, we remain committed to deleveraging in 2027. We will remain disciplined as we invest, and we will not spend a dollar of capital unless we have confidence in the health of our products and process technologies and clear evidence of customer demand…

…We have, I think, a significant ramp in equipment spend this quarter, and it is expected to be even more significant next quarter. A lot of what we are spending on now is really equipment. We have made the investments. I would call it, in a lot of cases, putting the finishing touches on some of the space and most of the spend is going towards equipment, with the only exception, we are still in the heavy construction phase of Ohio. But outside of that, it is really about equipment.

Mastercard (NYSE: MA)

Mastercard’s new Digital Risk Protection product helps identify vulnerabilities in an organisation’s digital environment; Digital Risk Protection has nearly 800 enterprise customers after being launched for a few months; Mastercard’s enhanced risk offering combines RiskRecon and 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); management is seeing AI change the demand for Mastercard’s security solutions

Within cybersecurity, we continue to scale one-to-one platform sales, but even more so new network products like our newly launched Digital Risk Protection. This capability helps organizations identify vulnerabilities in their own digital environments. After only a few months in market, it now serves nearly 800 enterprise customers. Additionally, our enhanced third-party risk offering brings together RiskRecon and Recorded Future capabilities in one solution. It helps identify, prioritize, and respond to risks before they become business disruptions. AI is not only changing the demand for our security solutions, it’s also changing how commerce happens.

Mastercard’s management is seeing how AI change the way commerce happens; management sees 2 forms of agentic transactions, machine-to-machine, and traditional e-commerce; management thinks both forms of agentic transactions are significant opportunities for Mastercard; management thinks agentic transactions could eventually lead to an entirely new addressable market of micro-transactions; management sees consumers starting to use agents to discover products and make purchases; it’s still early days in consumers’ using agents for purchases, but management expects the trend to ramp up in the holiday season; management is seeing agentic transactions ramping up at impressively weekly growth rates; management believes 4 things are necessary for agentic commerce to function at scale, namely, trust in the payment, the agent, the ecosystem, and ubiquitous reach; management sees Mastercard as having solutions to solve for all 4 elements; management is helping merchants be ready to accept agentic commerce with the new Agent Connect solution; Shopify’s Shop Pay is already using Agent Connect’s features; Mastercard signed a partnership with OpenAI in 2026 Q3 for Agent Connect; management thinks that agentic commerce solutions that take VCNs (virtual card numbers) are not a good long-term solution

AI is not only changing the demand for our security solutions, it’s also changing how commerce happens. The foundation I described earlier, broad participation, global acceptance, and trusted capabilities, positions us to help shape the next evolution of commerce. That includes agentic commerce, where a buyer uses AI agents to discover, select, and purchase a product or service. There are two forms, machine-to-machine and traditional e-commerce. Combined, they create significant opportunity for Mastercard, from more transactions to new B2B carded payments as businesses utilize agents. Eventually, an entirely new addressable market of micro-transactions, all the while creating more opportunity for our services from fraud to longer-term potential in insight and loyalty. Consumers are already beginning to use agents to discover and purchase products. We expect that trend to build as holiday shopping ramps up and to continue well beyond the season. It is still early days, and the numbers are not yet significant in the grand scheme. As the AI agentic assistants have emerged, agentic transactions are ramping up at impressive weekly growth rates. We’re now seeing the early stages of consumer adoption.

For this type of agentic commerce to function at scale, we need four things. Trust in the payment, the agent, the ecosystem, and ubiquitous reach. Mastercard has built capabilities for each of these four. For trust in the payment, tokenization is embedded in Agent Pay and backed by the franchise rules everyone expects today. For trust in the agent, Agent Pay ensures agents are verified and authorized by the buyer and that the merchant and issuer are aware. For trust in the ecosystem, we bring transparency and continuity through the payment flow. Verifiable Intent captures the buyer intent, and our recently launched Agentic Trust Intelligence solution helps banks and merchants evaluate transactions initiated by any AI agent. That allows these transactions to occur wherever Mastercard is accepted. That is the unparalleled and growing reach I talked about earlier.

At the same time, we are helping merchants ready themselves and be more discoverable to agents with our new Agent Connect solution. Many partners are putting all of this into practice. For example, Shopify, through Shop Pay, is using Agent Pay and our network tokens to make agentic payments more secure and seamless by preserving the integrity of the consumer’s credential end-to-end. Shop Pay is already available on multiple agentic platforms with more to follow. With OpenAI this quarter, we signed a broad strategic partnership that includes the use of Agent Pay and goes well beyond. Our agreement establishes collaboration across payments, joint services growth, and AI transformation…

…It is in everybody’s best interest to use Agent Pay and equivalent. We are not supporting solutions that take a one-time VCNs out there as you’re fronting a transaction. That fundamentally breaks a transaction that is important from a consumer perspective. They made a choice. It’s important from a rewards perspective, from a control perspective, and so forth. I think we’re at this turning point where everybody is pushing in the same direction. We are very clear that fully tokenized all the way through agentic pay or an equivalent is the way to go.

Microsoft (NASDAQ: MSFT)

Microsoft’s management announced the biggest update to Copilot in 2026 Q3 (FY2027 Q1); management thinks of Copilot as the OS for work; automated tasks on Copilot has nearly doubled since July; daily Copilot users are up 50% sequentially in 2026 Q3 (FY2027 Q1) and up 5x year-on-year; user satisfaction of Copilot is up 20% sequentially in 2026 Q3 (FY2027 Q1); 90% of Copilot queries are on auto mode, which dynamically routes tasks across models; customers with more than 50,000 Copilot seats increased 8x year-on-year in 2026 Q3 (FY2027 Q1); Copilot is helping to drive momentum with E7; management is bringing Dynamics 365’s business logic and capabilities into Copilot; management introduced 13 new Copilot skills across Dynamics 365 in 2026 Q3 (FY2027 Q1)

We announced our biggest update to Copilot to date this quarter, bringing together chat, co-work, autopilot, and code into one unified experience. It’s one product experience across commercial and consumer and comes with all of Office built in. We think of Copilot as the new OS for work. Customers are increasingly turning to Copilot to get real work done. Automated tasks has nearly doubled since July. Daily users are up nearly 50% quarter-over-quarter and over 5x year-over-year. User satisfaction improved nearly 20% quarter-over-quarter to another all-time high, and 90% of Copilot queries use auto mode, which dynamically routes tasks across models optimizing for quality, speed, and cost. These improvements are translating into larger purchases. The number of customers with more than 50,000 Copilot seats increased over 8x year-over-year, including Capgemini, HCLTech, Hitachi Group, Truist Financial, and U.K. Revenue and Customs Department. Just last week, Volkswagen announced plans to deploy Copilot to more than 330,000 employees, one of the largest deployments to date in Europe.

We’re also seeing strong momentum with E7, which brings together Copilot, E5, Entra, and Agent 365. Thousands of customers in every industry have purchased the suite to date, including Amadeus, BlackRock, Fiserv, New South Wales Health Department, and ZEISS…

…We are extending this vision of Copilot as the new OS for work, bringing the business logic and capabilities of Dynamics 365 directly into Copilot and helping every organization stand up their own infinite SaaS factory. This quarter, we introduced 13 new Copilot skills across Dynamics 365, which enables customers to access CRM and ERP capabilities directly in the flow of work.

Microsoft’s business model now includes consumption, which management thinks expands the company’s TAM (total addressable market) and average revenue per user (ARPU); management thinks the consumption model opens up business-funded demand, compared to the subscription model, which addressed IT-funded demand; Copilot customers on usage-based billing grew 75% sequentially in 2026 Q3 (FY2027 Q1); credit consumption of agentic features on Github has more than 2x-ed since usage-based billing was introduced

Our business model is expanding from per-seat subscriptions to include consumption, expanding our TAM and ARPU. The subscription brings increasingly capable everyday intelligence to every user. Typically, IT funds this, consolidating frontier advances for everyone across the organization. As agents take on higher-value work, consumption opens up business-funded demand, with spending tied directly to the value these agents deliver. The number of Copilot customers actively consuming credits through usage-based billing grew over 75% over the quarter…

…Credit consumption from agentic features on GitHub has more than doubled since usage-based billing went into effect.

Agent-generated PRs (pull requests) on Github have grown 6x in the 6 months ended 30 Sep 2026; agents now open 1/3 of all PRs on Github; management is rebuilding the core Github architecture to address agentic use; management recently announced Copilot Fusion on Github; Copilot Fusion brings multi-model orchestration capabilities, delivering frontier quality up to 40% faster and at 77% lower costs; Github Advanced Security revenue grew 50% year-on-year; Copilot Fusion is ranked highly by Artificial Analysis’s latest benchmark for coding agents

When it comes to software development, all major coding agents have shown up to work on GitHub. Agent-generated PRs have increased 6x over the past six months, and agents now open more than a third of all PRs on the platform. We are very hard at work on reliability as agentic activity on GitHub soars. At GitHub Universe this morning, we shared how we are expanding capacity and rebuilding the core Git architecture for the agentic wave. Also at Universe, we announced broad availability of Copilot Fusion, which is all about bringing multi-model orchestration to software development. It delivers frontier-level quality up to 40% faster and at up to 77% lower cost…

…GitHub Advanced Security revenue increased over 50% year-over-year as customers use its AI capabilities to scan entire repos and fix vulnerabilities in real time…

…In fact, this morning, Artificial Analysis had what I think is the real benchmark, which is the coding agents using our Fusion, and I think we were number 2 or number 3 there, which is awesome to see. Which is you can now get frontier performance 45% faster, 75% cheaper. Who would want to not take that?

Foundry is becoming more important to Microsoft’s growth; Foundry customers reached 120,000 in 2026 Q3 (FY2027 Q1); Foundry revenue more than tripled year-on-year in 2026 Q3 (FY2027 Q1), driven by efficiency gains across Microsoft’s GPU fleet; Foundry helps customers optimise across quality, cost, and performance across models; the number of Foundry customers in 2026 Q3 (FY2027 Q1) using multiple models to build agents grew 5x year-on-year; Foundry’s revenue from open source models increased 10x year-on-year in 2026 Q3 (FY2027 Q1); Foundry customers can use Microsoft’s harness behind Copilot; Rolls-Royce used Foundry to build an agentic system to manage its supply chain; management thinks enterprise AI workloads will be very dynamic in terms of switching between models

It starts with Foundry, which is becoming an increasingly important driver of our growth. Foundry helps organizations build learning loops grounded in their business context and deploy agents safely at scale. We now have over 120,000 Foundry customers, with revenue more than tripling year-over-year. Foundry’s model choice and feature set, coupled with its deep integration across our stack, is driving differentiation. Azure OpenAI directly benefited in the last quarter with big advances in OpenAI models. When you couple this with our model choice built into Foundry, customers are able to build solutions optimizing across quality, cost, and performance. In fact, the number of customers building agentic solutions with multiple models increased 5x year-over-year, and revenue from open source models on Foundry increased 10x…

…Foundry revenue more than tripled year-over-year, with upside driven by efficiency gains across our GPU fleet that allowed us to allocate additional capacity to customers, as well as higher revenue per token that we benefited from in the quarter…

…Beyond models, we are making the harness behind our own Copilot experience available to customers in Foundry. It gives customers a Microsoft-managed way to build agents that reason, use tools, complete complex, long-running work while preserving their choice of model…

…Rolls-Royce deploys a system to really manage all of their maintenance of whatever their jet engines with 30,000 parts are pretty complex supply chain, both planning, execution, inventory management system, all built on Foundry agentic systems…

…A lot of customers are saying, hey, look, take the Unilever one. It is their system. They will ultimately take even the traces after having done even a bunch with frontier models, and they just want to train their own model. So it can be open weights or, in this case, it was MAI model. So I think all of that needs to be as options that are available. Then depending on pricing, depending on the need of the customer, they will exercise it. There is a temporality to it. It is never like, oh, I start one place and that is where I stop. It is like, I may start at the frontier. I may go to an open weight. I may go to an MAI model. I may go back to the frontier for the next drop for a few months, come out. That is how I think how dynamic these workloads are.

Microsoft’s management thinks the company is making good progress with its 1st party models; management’s goal is to deliver capabilities comparable to frontier models within 3-6 months of their release, at a fraction of the cost; Microsoft’s MAI-Cyber model now powers over 40% of the scans in Microsoft’s MDASH code scanner at 18% lower cost per scan; MAI-Transcribe is the cheapest and most accurate transcription model in the world; MAI-Code-Flash has 3 million users in Github Copilot in September 2026; more than 4,000 customers have used Microsoft-Decision-1, a model for structured decision-making; Microsoft recently completed pre-training of MAI-Base-2, which has 4x-12x better training compute efficiency compared to other open weight models; Unilever’s agentic system was built with MAI models

We also continue to make rapid progress with our first-party models. Our goal is to advance the cost to outcome frontier, which means delivering capabilities comparable to leading models within three to six months of their release at a fraction of the training cost, thereby allowing us to transfer those cost benefits to customers. You see this in our products today. MAI- Cyber now powers over 40% of the scans in our agentic MDASH code scanner at 18% lower cost per scan. MAI- Transcribe is the most accurate and cheapest transcription model in the world, supporting Dragon Copilot’s multilingual workflow. MAI- Code- Flash has three million users in GitHub Copilot last month alone, helping developers get more out of their AI credits. Microsoft-Decision-1 is our new model for structured decision-making, outperforming both LLMs and other decision models in latency and quality. Over 4,000 customers, including Atos Group, Equinor, and RBI, have already used it in the first month since its release. We recently completed pre-training MAI- Base- 2, which outperforms other open weight models we tested with 4x- 12x greater training compute efficiency. This base model will be used in the upcoming MAI models across thinking, code, and cyber…

…Take the Unilever one. It is their system. They will ultimately take even the traces after having done even a bunch with frontier models, and they just want to train their own model. So it can be open weights or, in this case, it was MAI model.

Fabric customers grew 50% year-on-year in 2026 Q3 (FY2027 Q1) to 44,000 (was 40,000 in 2026 Q2); 19,000 customers now use both Fabric and Foundry (was 17,000 in 2026 Q2); management is finding that customers are rapidly deploying Microsoft’s AI-optimised databases; PostgreSQL’s growth accelerated for 4 consecutive quarters in 2026 Q3 (FY2027 Q1); the number of PostgreSQL customers who also use Foundry was up 90% in 2026 Q3 (FY2027 Q1); Microsoft has WorkIQ, which makes an entire organisation’s work knowledge available to an agentic workload; Microsoft IQ combines Work IQ, Fabric IQ, and Foundry IQ, and it has more than 60,000 customers in 2026 Q3 (FY2027 Q1), up 25% sequentially

We now have 44,000 paid Fabric customers, up more than 50% year-over-year…

…Over 19,000 customers now use Foundry and Fabric as enterprises connect and operate agents over real-time operational, analytical, and unstructured data.

Customers are also rapidly deploying our AI-optimized databases, including PostgreSQL, SQL DB, Hyperscale, Cosmos DB, to give agents fast, secure access to the data and context they need. PostgreSQL growth accelerated for the fourth consecutive quarter, and the number of PostgreSQL customers also using Foundry accelerated to 90% year-over-year.

We also have one of the most important databases for any enterprise, which is the knowledge underneath Microsoft 365, spanning people, their relationships, documents, emails, meetings, chats, projects, and other artifacts. Work IQ makes this context available to every agentic workload. With Microsoft IQ, we are bringing together Work IQ, Fabric IQ, and Foundry IQ as a unified context for agentic systems. Nearly 60,000 customers are accessing context across Microsoft IQ, up 25% quarter-over-quarter.

Microsoft has Agent 365 for AI observability and it has over 50 million agents in the registry in 2026 Q3 (FY2027 Q1), up 30% sequentially; Microsoft’s management recently expanded Perception for customers to secure agentic insiders; management recently introduced guardian agents that monitor agentic activity for unsafe actions; management is seeing encouraging feedback on Perception; customers have used Microsoft Purview to audit more than 7.6 billion agent activities in 2026 Q3 (FY2027 Q1), up 5x sequentially; management recently introduced Microsoft Execution Containers to govern and contain agents; agentic products from OpenAI, Anthropic, and Meta Platforms are starting to adopt Microsoft Execution Containers

With Agent 365, we provide a platform for AI observability, enabling customers to manage, secure, and optimize the performance and cost of their agents. Over 50 million agents are now in the registry, up over 30% quarter-over-quarter…

…Last week, we expanded Perception with new capabilities to help customers secure and contain agentic insiders, including expanded agent discovery across local and cloud environments, dynamic permissions that enforce least privileged access for each task, and continuous risk assessment. We also introduced teams of guardian agents that continuously monitor agent activity to help prevent unsafe actions before they occur. We are seeing encouraging feedback on Perception overall from preview customers, including KPMG, Nationwide Building Society, Northern Trust, Celentis. More broadly, we are helping customers secure and govern AI use across the enterprise. Customers have used Microsoft Purview to audit over 7.6 billion agent activities to date, up 5x quarter-over-quarter…

…We introduced Microsoft Execution Containers, a policy-driven execution layer that governs and contains agents across Windows and other platforms integrated with Agent 365 for governance and observability. Agents like OpenAI Codex, Anthropic Claude Code, Meta Muse are adopting MXC, and customers like Meta are starting to deploy it across their enterprise environments.

Microsoft’s management is building the company’s compute infrastructure to be fungible, such that it can be dynamically allocated to the highest value opportunities; Microsoft added 24 new data centers across 5 continents in 2026 Q3 (FY2027 Q1); Microsoft has reduced dock-to-live times for new GPUs by 20% sequentially in 2026 Q3 (FY2027 Q1); management has increased the throughput for Copilot workloads by over 50% sequentially in 2026 Q3 (FY2027 Q1); Microsoft’s AI infrastructure continues to utilise chips from NVIDIA, AMD, and itself (Maia); Microsoft has docked the first Vera Rubin GPUs from NVIDIA; Microsoft’s Maia 200 is now delivering 1.7x more tokens per watt across Teams and Copilot, with Maia 300 expected to drive a further 40% more tokens per watt compared to leading GPUs

We are building against a growing book of business with a fungible fleet that allows us to dynamically allocate capacity to the highest value opportunities across compute, data, Foundry, and Copilot. Accelerating demand led to us adding 24 new data centers across five continents this quarter. We are bringing capacity online faster, reducing dock-to-live times by about 20% quarter-over-quarter for new GB300s in our biggest regions, which make up the majority of the GPU deployments. At the same time, we continue to get more from the infrastructure we already have by optimizing across the data center design, silicon systems, software, and models. For example, we increased the throughput for Copilot workloads by over 50% quarter-over-quarter.

We continue to scale our fleet with the latest innovations from NVIDIA, AMD, alongside our own Maia systems. We have docked our first NVIDIA Vera Rubin GPUs and are very excited about it. Maia 200 is already delivering up to 1.7x more tokens per watt across the AI experiences in Teams and Copilot, and Maia 300 will drive further gains, delivering 40% more tokens per watt compared to the leading GPU-based systems.

Microsoft is working to create new PCs that can run powerful AI models locally

As more capable models run locally, we see an opportunity to make it more intelligence effectively unmetered. We are working with the broader hardware ecosystem to bring these capabilities to a new generation of PCs across AMD’s Ryzen AI 400 Series, Intel Core Ultra Series 3, Qualcomm Snapdragon X2 Series. Earlier this month, we also announced new PCs powered by NVIDIA’s RTX Spark and DGX Station for Windows. With these new devices, you can run last year’s Frontier models on your laptop and last quarter’s model at your desk.

1/4 of Microsoft’s cloud and AI-related capex in 2026 Q3 (FY2027 Q1) are for long-lived assets, while the other 3/4 are for CPUs and GPUs; management sees the potential for each dollar of Microsoft’s capital expenditure to support more revenue and higher returns over time

Capital expenditures were $45.2 billion, as some third party capacity deliveries we expected in Q1 were delayed. Roughly three quarters of our CapEx was for short-lived assets, primarily CPUs and GPUs, including higher memory and storage costs, as we continue to invest in the infrastructure required for our customers to build and scale AI applications. The remaining spend was for long-lived assets. This quarter, finance leases were $7.4 billion. As a reminder, we recognize the full principal value of finance leases in CapEx at the time of lease commencement. Cash paid for PP&E was $39.4 billion…

…As we continue to improve the efficiencies of our infrastructure, each dollar of CapEx has the potential to support more revenue and higher returns over time.

Azure’s growth was broad-based across business-size and workloads; Azure grew revenue by 48% in 2026 Q3 (FY2027 Q1) (was 43% in 2026 Q2); Azure’s revenue growth was better than expected because of efficiency gains in the GPU fleet, which drove outperformance in Foundry, and in the CPU fleet; Azure’s growth in 2026 Q3 (FY2027 Q1) benefitted from higher token pricing; management is starting to see the breadth of enterprise AI workloads starting to emerge; Azure’s demand continues to exceed supply

All-up Azure growth was broad-based across both enterprises and small and medium business segments and spanning both core cloud services and AI infrastructure….

…Azure revenue was ahead of expectations and increased 48%, even with the 3P capacity delays noted earlier, driven by Foundry and improved CPU monetization. Foundry revenue more than tripled year-over-year, with upside driven by efficiency gains across our GPU fleet that allowed us to allocate additional capacity to customers, as well as higher revenue per token that we benefited from in the quarter. Across our CPU fleet, engineering work enabled us to identify and monetize underutilized capacity this quarter, helping us serve more of the growing demand with our existing capacity. The timing of engineering efficiencies like these, as well as 3P GPU lease deliveries, our own capacity build-outs, and ARPU changes from model mix can create quarterly variability in the year-over-year growth rate…

…We had higher token pricing than we expected in the quarter. With some high-quality models that we are able to take advantage of and see good margins on do benefit us in the quarter. That did happen in Q1…

…I was even in Europe just last week, and it was great to see the quality of the agentic systems that are just broadly getting deployed. When Unilever deploys what is their automated order taking and trade coupon dispute management stuff, all as a fully agentic touchless system. Rolls-Royce deploys a system to really manage all of their maintenance of whatever their jet engines with 30,000 parts are pretty complex supply chain, both planning, execution, inventory management system, all built on Foundry agentic systems. RBI, which is an Austrian bank, showed me 20+ business processes end to end. They were talking about whether it is in for credit or what have you, how they are able to now do that so much more optimally. So the breadth, the quality, their ability. Oh, by the way, the multi-model nature of it. They start with a frontier model. Many of them are, in fact, the Unilever one, they just dropped an MAI model trained using our traces. So even the degree of sophistication of what I think of as model optimization, workload optimization, token optimization, was just great to see. So the breadth is starting. Finally, it does feel like the enterprise workloads.

They feel like to me, like the database applications enterprises built in the past that I grew up with are happening now when it comes to AI. In fact, if anything, some of the shiny object stuff is waning, which is it is not about the next big thing. They are really more about, wow, can I build a system? Can I own the weights? Can I manage my costs? Can I have reliability? What about security? So in that sense, the enterprise breadth motion is starting…

…In Azure, we expect revenue growth of 49%- 50% in constant currency, with continued efficiency gains across our CPU and GPU fleet, helping to offset some 3P GPU as-a-service contracts that have been delayed to H2. Customer demand continues to exceed available supply.

OpenAI accounted for 11% of Microsoft’s revenue in 2026 Q3 (FY2027 Q1)

[Question] At the same time, you have OpenAI at 11% of revenue in the quarter.

Visa (NASDAQ: V)

Visa’s management is seeing AI be one of the top 3 focus areas of its clients; empowering agentic commerce is one of management’s top priorities; management thinks agentic commerce requires trust, and that the trust barrier can be solved by using a Visa credential each time an agentic commerce user pays or gets paid; management is currently seeing a limited number of agentic transactions, even with the introduction of Muse and Instinct; Visa has nearly 50 AI partners going live with Visa Credentials and management expects the number to double in the coming months; Visa is collaborating with Anthropic to bring its commerce blueprint to Visa’s merchant network; management thinks B2B is an area for fast adoption of agentic commerce, and Visa has already launched with several partners; Ramp and Airwallex are using Visa Intelligent Commerce to provide B2B agentic commerce solutions; Visa Intelligent Commerce provides trust for agentic commerce users, trust for agents, and trust for the transaction; cross-border e-commerce volume was up 17% in 2026 Q3 (FY2026 Q4), up from 16% in 2026 Q2, driven by retail, including spending on AI platforms; management thinks that agentic commerce will bring incremental opportunities for Visa, but it will take some time to show up; Visa Intelligent Commerce’s role is to ensure that issuers, acquirers, and other ecosystem participants are ready to enable agentic transactions at scale

It continues to be among the top three focus areas for our clients, alongside AI and cybersecurity…

…One of the key areas of innovation for Visa has been empowering agentic commerce. It is clear that in agentic, trust is the biggest barrier to scale. We believe the best way to break through that barrier is with one simple but powerful idea. Every time someone pays or gets paid, it is with a Visa credential. As we look at the albeit limited number of agentic transactions today, Visa credentials are being used by consumers, businesses, and agents. We have nearly 50 AI partners live and in production, including more recently, Shopify’s Shop Pay, with that number expected to double in the coming months…

…I think both Muse and Instinct experiences are good examples of that. I think it is still very early. If you just look at just even the user numbers that are public for Muse, it is still very early in terms of the number of consumers that are doing this, and certainly in the big scheme of things in our overall business, it is a very small amount…

…We are also collaborating with Anthropic to bring their commerce blueprints to our merchant network, combining the intelligence of Claude with the trust, security, and global reach of the Visa network. One of the areas we have identified for fast adoption of agentic has been B2B, and we have had several partners launch and expand offerings. Ramp’s agentic bill pay use case is now generally available, and as Ramp businesses use Visa Intelligent Commerce to automate bill payments volume is growing. Airwallex is testing Visa Intelligent Commerce-enabled credentials and integrating them into their native wallet, Airwallex. BILL’s payment agent is piloting Visa Intelligent Commerce to pay invoices, and Billtrust’s supplier portal will identify the agent and accept payment. Our Visa Intelligent Commerce solution is enabling all of this. It provides trust for the user with credentials, agentic tokens, and transaction identifiers, trust for the agent with the Trusted Agent Protocol and Agent Directory, and trust for the transaction. Once we have verified the user and agent identity, we need to make sure that the purchase instructions are clear by capturing consumer-authenticated intent. Of course, like on any Visa transaction, our rules clarify the roles and responsibilities and processes should something go wrong. It is clear that agentic commerce is here, and Visa’s role is clear: delivering trust…

…Cross-border e-commerce volume was up 17%, more than one point above Q3, primarily driven by retail, including spending on AI platforms…

…[Question] I wanted to ask kind of a follow-on question related to how you are investing in agentic commerce and all the new money movement capabilities. As these flows continue to develop, how much of an opportunity do you expect it genuinely be incremental to Visa versus potentially a migration of existing card volumes?

[Answer] We do have conviction that there is real incremental opportunities. But as we think about the next four quarters in 2027, it all remains very small in the big scheme of the enormous size of our business. A couple other notes in terms of where we see incremental opportunities. One, you mentioned some of the work we have been doing on Visa Direct and things like that. Those are big incremental opportunities. Two, when you think about machine-to-machine payments and the CLI as a commerce interface, there is a lot of incremental opportunities there. Then just more broadly, in the big scheme of things with agentic commerce, are agents finding more buying opportunities for us? Are agents making more purchases at more seller locations because they are optimizing based on value, speed, delivery, whatever it is important to the user? Those are opportunities. I think we remain very excited and have conviction about the opportunities, but I think we all just need to realize that the development of all of this is still quite early…

…[Question] I want to hit on this a little bit more on the agentic network token. Our understanding is that this token is sort of a bolstered version of the network token. It’s more of a scoped network token, if you will. It will have all the programmatic aspects, meaning a time window, merchant category code, dollar amount. It will have the upfront intent, as you mentioned in your prepared remarks. All of that gets kind of bundled up into the agentic network token. The question is, how ready are the issuing banks, and what do they need to do in order to say, “Yes, I’m willing to have an agent issue an agentic network token for one of my customers.” Related, what about the merchant acquirers that are out there, the PSPs? Are they ready to accept the tokenization handshake, if you will?

[Answer] That was a great summary of a lot of the objectives of Visa Intelligent Commerce and our agentic tokens. In short, we are doing the work across the ecosystem to ensure that issuers, acquirers, and other ecosystem participants are not only ready but are ready to enable these transactions at scale. We know how to do this work. If you look at our history, even just with tokenization pre-agentic commerce, this is the work that we did broadly across the world. I think I mentioned in my prepared remarks now that we have tokenized e-commerce transactions in 200 countries and territories around the world. We know how to do this work to enable the ecosystem. I also just want to come back and build on your points around these agentic tokens and just put it in context of what we are delivering through Visa Intelligent Comme

Visa’s new agentic-AI-driven way of working has made big impacts on its initiatives for stablecoins; the Visa Stablecoin Platform enablement for OUSD was designed, built, and shipped in seven weeks with 12 people with the new agentic-AI toolchain and would have taken much longer otherwise; more than 60% of Visa’s global product development and engineering teams have adopted the new agentic-AI-driven way of working; management intends for 100% of the teams to adopt this new way of working; Visa strengthened its defenses in FY2026 with the Visa Vulnerability Agentic Harness, which has been open sourced; Visa has used AI to deliver over 350 product releases in FY2026, up 25% year-on-year

I spoke last quarter about the fundamental changes to the way that we build and ship products enabled by utilizing our agentic toolchain. For example, the Visa Stablecoin Platform enablement for OUSD was designed, built, and shipped in seven weeks with 12 people. This would have taken much, much longer and required many more people without utilizing our agentic toolchain. Today, more than 60% of our global product development and engineering teams have adopted this new way of working with the intent of reaching 100% this year…

…This year, we tested and strengthened our defenses through our Visa Vulnerability Agentic Harness, which we then open-sourced to the world…

…We demonstrated our ability to significantly accelerate product development with the true birth of tokenized agentic commerce transactions powered by Visa, with blockchain infrastructure and platforms designed for payments, with the increased utilization of AI across our business to deliver over 350 product releases, up 25% 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 have a vested interest in Alphabet, Amazon, Mastercard, Microsoft, and Visa. Holdings are subject to change at any time.

What We’re Reading (Week Ending 01 November 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 01 November 2026:

1. Most Improbable Agent of You – Kevin Kelly

AI today is probably the most centralized it will ever be as well. Currently the most reliable way we know how to make AIs smarter is to make them bigger. This principle is called the scaling law…

…That scaling results in massive computer processing centers, running millions of chips, with trillions of parameters, powered by gigawatts of energy, costing tens of billions of dollars…

…On the other hand, each one of us runs a very powerful (better than most AIs), very tiny (big as your skull), very energy efficient supercomputer running only on 25 watts at the end of our necks. There are some 8 billion of these tiny supercomputers spread around the planet, interconnected via the internet. We have an existence proof that biological intelligence does not need to be centralized.

Today, because of rapid innovation in AI architectures and training formulas, the amount of compute an AI needs to deliver a given capability has been cut roughly in half every 8 months. Combine that with the hardware gains of Moore’s Law, and the cost per capability halves roughly every 6–7 months. Assuming Moore’s Law continues, within a few years, a model as capable as today’s leading LLM – let’s call it Opus – could run on a device you own. Within about a decade, the cost of training one from scratch would be affordable by an individual, and fine-tuning your own copy would cost far less.

That means everyone could own and control an AI like today’s Claude or ChatGPT or Gemini. You would not have to ask permission to do things, or be obligated to train it with certain guidelines, nor have to accommodate the preferences and sensitivities of others. The AI could reflect you…

…Many of the issues that people have with AI today would be resolved if the AI were not only available from a huge massive monopolistic tech company, but instead was generated, delivered, overseen, and maintained by individuals. A lot of the criticism of AI today is really just a criticism of capitalism. There is the question of who benefits from the value being created, who controls who gets what (the best AI models may be kept for internal use by some companies), who gets to train the AI mind, and supervise what it is trained on, and who decides what it is used for…

…I want a personal AI that I have trained myself on all my life’s experiences, all the influences I have had, all the work I have done, and all the people I most admire. There will be advice and schools for training your personal AI to become the most useful to you. In fact there will be teacher AIs who educate personal AIs, and people will hold opinions on the best way to educate your personal AI and what kind of AI teachers you want. You want the teachers weird but not too weird. You may want to train your personal AI on the classics, rather than everything. The current frontier models are trained on everything and they are aimed at everyone for all problems. They are by default, generalists hitting the average in the middle of the mainstream.

But we want a personal AI, an exoself, that is as unique as we are. We don’t want the best AI, we want the ONLY AI. We want an AI that is as improbable as we are, and improbable in the same unique direction that we lean. We’ll want to curate the education of our AI.

2. Earnings & Expectations – Ben Carlson

Earnings growth and stock prices were more or less in line with one another in the 1940s, 1960s, 2000s, 2010s and 2020s (thus far).

As bad as the stock market did in the 1930s, earnings were even worse. Earnings far outpaced the stock market in the 1970s. The stock market was up far more than profits in the 1950s. Same thing in the 1980s and 1990s in terms of growth.

These divergences can be even greater in any given year:..

…A wide spread between earnings growth and stock market returns looks to be the norm. The average absolute spread between the two numbers is almost 25%.

This can be even more pronounced in individual securities and sectors…

…Profits for semiconductors are up nearly 200% while the stocks are up a little more than 40% on the year. And they are currently in a drawdown of more than 20% from the highs.

Fundamentals tell the story but you also have to take into account expectations and what’s priced into stocks. Most of the time no one knows what’s priced in.

In the long run fundamentals drive the stock market. In the short run the market and earnings are not always in lockstep with one another.

This is what makes investing in the stock market equal parts exciting and infuriating.

3. 5 myths and misunderstandings about investing in the stock market – Sam Ro

One of the most popular ways to measure value is to divide an asset’s price by its earnings. This is the price-to-earnings (P/E) ratio. When a stock’s P/E is above its historical average, it’s considered expensive. When it’s below average, it’s considered cheap.

This has led many to assume that expensive stocks tend to get cheaper and cheap stocks tend to get more expensive. In other words, valuations tend to revert to a mean, right?

“We cannot emphasize enough that valuation metrics are NOT historically mean-reverting,” Goldman Sachs analysts wrote in January. They’ve characterized it as “an oft-repeated myth.”

The firm has advanced this controversial view since 2013 (and again in 2022 and 2025). To make their point, they used a Dickey-Fuller test, a tool statisticians use to analyze time-series data. The test found that P/E ratios lacked stationarity, which means the averages aren’t stable over time.

This conclusion aligns with arguments from some top Wall Street strategists in recent years. They’ve argued that persistent improvements in business performance and capital structures have justified the higher valuations we’ve seen lately, an idea supported by theory.

And so, the mean itself is moving.

The idea that valuations aren’t mean-reverting probably makes a lot of people uncomfortable. Then again, trading based on the assumption of mean reversion hasn’t exactly yielded consistent results for people…

…Let’s say you got lucky and managed to buy one of these few stocks that go on to smoke the rest of the market. Buckle up.

In a report published last year, Morgan Stanley’s Michael Mauboussin and Dan Callahan studied the price behavior of 6,500 stocks. They took a closer look at the 20 stocks with the best total shareholder returns over the 40 years from 1985 to 2024.

“The median maximum drawdown was 72% for the best group, and the median maximum drawdown duration, the time from peak to trough, was 2.9 years,” they found. “The median time to return to the prior peak was 4.3 years.”

Just thinking about one of my positions losing 72% of its value makes me queasy, even knowing full well this is the average behavior of the best stocks.

4. Why Smart People Make Dumb Decisions – Ben Carlson

A high income, large portfolio, diploma from a prestigious university, or enviable job title doesn’t automatically qualify you to manage money successfully. In fact, many of the worst investors are highly successful in other fields.

In a roundtable discussion following a visit to Theranos in 2015, Vice President Joe Biden stated he had just witnessed, “the laboratory of the future.” Theranos is the Silicon Valley healthcare company that promised it could take a pinprick of blood from a patient’s fingertip to run a series of diagnostics that would revolutionize the blood-testing process. The company was founded by Elizabeth Holmes, who dropped out of Stanford at age 19 to create a device that, according to her, was “the most important thing humanity has ever built.”

The Holmes story was the perfect Silicon Valley fairy tale, destined to become a best-selling business book and movie. Sadly, this fairy tale turned out to be more of a horror flick, for patients and investors alike…

…Her story was so convincing she managed to fool a who’s who of the wealthy elite. The Walton family, heirs to the Walmart fortune, lost $150 million. Media mogul Rupert Murdoch put $125 million into the company based strictly on gut feel alone. He would later sell the entire stake for $1 to take a tax write-off for the enormous loss on a worthless investment. Betsy DeVos, the Education Secretary for President Trump, and her wealthy family lost $100 million. The list of investors also included New England Patriots owner Robert Kraft, Mexican billionaire Carlos Slim, and a Greek shipping magnate…

…Brendan Moynihan tells the story of Jim Paul in his book, What I Learned Losing a Million Dollars. Paul made a killing trading commodities as a young man but lost a fortune once the fianncual windfall went to his head.

Moynihan learned from Paul that, “Personalizing successes sets people up for disastrous failure. They begin to treat the successes totally as a personal reflection of their abilities rather than the result of capitalizing on a good opportunity, being at the right place at the right time, or even being just plain lucky. They think their mere involvement in an undertaking guarantees success.”

Said another way, smarter people may be worse off than others at recognizing their own limitations. This makes sense when you realize intelligent people have more firepower to convince themselves through the power of self deception.

5. Business Momentum versus Price Momentum – Eugene Ng

Traditional value investing teaches that prices matter. It is far better to buy something cheap than expensive. The larger the margin of safety, the better. Low prices (valuations) are far more desirable than high prices (valuations). When prices recover from oversold levels and mean-revert, returns become very attractive.

However, this critically assumes the underlying asset’s fundamentals remain unchanged. While price narratives can inaccurately drive business narratives (especially for commentators) in the short term, business narratives actually drive price narratives over the long term…

…The four losers (Paycom Software, PayPal, Lululemon, and The Trade Desk) we exited share one pattern. When revenue growth structurally decelerates and fails to reaccelerate, past high growth is unlikely to return. It becomes a one-way freight train that is hard to reverse.

The newer, bigger realization is that (business) momentum tends to persist positively or negatively. Momentum seldom reverses, which is why turnarounds seldom turn. That became increasingly evident in these four companies, and when we saw fundamentals keep deteriorating, and growth drift into single digits, we cut our weeds and watered our flowers and new seeds instead…

…Occasionally, Mr. Market provides excellent opportunities for coiled springs, but they are the exception, not the norm. Coiled springs offer the best returns, but they are the rarest and the hardest to verify. There is some risk that Mr. Market knows something and starts to heavily discount the stock before the quarterly financials reflect it, so one needs to tread more cautiously…

…Take The Trade Desk, for example. We thought it was a compounder. Its co-founder and CTO, Dave Pickles, stepped down in September 2023 before we bought, and we underestimated the impact. Internal product development soon slowed, and Kokai’s product rollout struggled. As revenue growth continued to decelerate against still-strong industry growth, it became a value trap we no longer want to own. Follow the business momentum, not price.

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.

The View On Consumer Spending From The Largest Payments Companies (2026 Q3)

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 third 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 continuing to spend despite economic and geopolitical uncertainty; management sees the economy as having a resilient backdrop; Mastercard’s business fundamentals remain strong; management is seeing a supportive macroeconomic environment with balanced labor markets, strong household balance sheets, and continued wage growth; management sees European consumers as being very resilient

While there continues to be economic and geopolitical uncertainty, consumers continue to spend, businesses continue to invest, and commerce continues to expand. The economy, our overall business, and our network have a resilient backdrop…

…As mentioned, consumer and commercial spend was healthy in Q3 and so far into Q4. Our business fundamentals are strong, and we continue to deliver solid growth across both our payment network and value-added services and solutions…

…This is all underpinned by a supportive macroeconomic environment with balanced labor markets, strong household balance sheets, and continued wage growth. To date, consumers and businesses have demonstrated resilience. There is heightened geopolitical uncertainty, which has persisted for some time, driving energy prices higher and putting more focus on monetary policy around the world…

…If I take a look at the macro environment right now, the European consumers are very resilient.

2. Worldwide GDV (gross dollar volume) was up 7% year-on-year in 2026 Q3 in constant-currency basis; cross border volume was up 11% globally in constant currency, driven by both travel and non-travel cross-border spending (cross border volume growth was 12% in 2026 Q2); switched transactions was up 9% year-on-year in 2026 Q3; card growth was 5% in 2026 Q3, with Mastercard ending the quarter with 3.8 billion cards in circulation (there were 3.7 billion cards in 2026 Q2, and year-on-year growth was 5% then); domestic assessments were up 13%, cross-border assessments were up 16% and transaction processing assessments were up 14%

Worldwide gross dollar volume, or GDV, increased by 7% year-over-year. In the U.S., GDV increased 6%, with credit growth of 10% and debit growth of 1%, which includes the impacts of the Capital One debit migration. Outside of the U.S., GDV increased 8%, with credit growth of 6% and debit growth of 10%. Cross-border volume increased 11% 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 Q3…

…Card growth was 5%. Globally, there are now 3.8 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 13%, while worldwide GDV grew 7%. The six PPT difference is primarily driven by pricing and mix. Cross-border assessments increased 16%, while cross-border volumes increased 11%. The five PPT difference is driven primarily by pricing in international markets and mix. Transaction processing assessments were up 14%, while switched transactions grew 9%. The five PPT difference is primarily related to pricing and a favorable mix, partially offset by lower revenue from FX volatility.

3. In 2026 Q3, Mastercard’s operating metrics were in line with Q2 and remained strong (switched volume was up 9%, switched transactions was up 9%, and cross-border volume was up 11%); in October so far, Mastercard’s operating metrics continue to be strong with worldwide switched volume growth of 10% (9% in the USA, and 11% outside of the USA), switched transactions growth of 9%, and cross-border volume growth of 10%

Starting with Q3, switched volume and transaction metrics are generally in line with Q2 and remained strong. Of note, the sequential increase in the U.S. switched volume was primarily due to recent commercial wins. Looking at cross-border. First, lower yielding intra-Europe volumes were down sequentially from Q2 to Q3, driven by the impacts related to a new customs fee in the E.U., timing of large retail promotional events, and lower crypto purchases in the quarter compared to last year. These impacts on intra-Europe cross-border were the primary reasons for the decline in card not present ex-travel sequentially. Growth in higher yielding other cross-border remained stable and slightly higher quarter-over-quarter.

As we look to the first three weeks of October, spend remained strong with metrics generally in line. A few points to note. In the U.S., switched volume improved sequentially as we begin to see the lapping of the Capital One debit migration, the lift from recent commercial wins, and higher spend on gas. As a reminder, the Capital One debit migration began towards the end of Q3 last year and continued through Q1 2026. Looking at cross-border, the sequential impacts remain predominantly intra-Europe. Growth in cross-border card not present ex-travel continued to see the impacts from two of the areas I noted earlier, notably the customs fee and a tougher comp due to higher spend on crypto purchases last year. The slight deceleration in cross-border travel was due to timing of holidays. Overall, underlying spend remains strong and consumers and businesses are spending.

From Visa

1. US payments volume growth was good at 10% in 2026 Q3 (FY2026 Q4); both discretionary and non-discretionary spend growth remained strong; there was good growth in both US credit and debit volumes; growth across consumer spend bands was consistent with prior quarters, with the highest spend band continuing to grow the fastest; there was no sign of weakening in spend among the lower-spend consumer band

U.S. payments volume grew 10% year-over-year, generally consistent with Q3, as we saw several factors from Q3 continuing to play a role, including the cost of fuel, retail, strong Visa Direct growth, and FIFA-related spend. From the FIFA World Cup round of 32 matches on July 1st through the final on July 19th, we continued to see both U.S. host cities as well as destination cities benefit from the influx of FIFA fans, with total U.S. card-present spend payments volume growth elevated above pre- and post-tournament levels. For the quarter, both U.S. discretionary and non-discretionary spend growth remained strong. Fourth quarter U.S. credit grew 11% year-over-year, and debit grew 9%. Growth across consumer spend bands remained relatively consistent with prior quarters, with the highest spend band continuing to grow the fastest. We did not see signs of the lower-spend consumer weakening in our volumes.

2. Visa’s cross-border volume growth remained strong in 2026 Q3 (FY2026 Q4) at 14%, up from 12% in 2026 Q2; cross-border e-commerce volume was up 17% in 2026 Q3 (FY2026 Q4), up from 16% in 2026 Q2, driven by retail, including spending on AI platforms

Q4 total cross-border volume was up 14% year-over-year, more than two points above Q3, primarily due to travel. Travel-related cross-border volume was up 13%, three points of acceleration from Q3, primarily driven by improvements in both CEMEA inbound and outbound. Overall, U.S. inbound cross-border growth for July remained elevated following strong growth in June, reflecting in part the demand around the FIFA World Cup. Focusing on inbound cross-border card-present spend, U.S. host cities increased by over 20% year-over-year from July 1 to July 19, with the most significant increases driven by fans from Argentina and Colombia. Cross-border e-commerce volume was up 17%, more than one point above Q3, primarily driven by retail, including spending on AI platforms.

3. Payments volume on Visa’s network continues to grow in October 2026, with US payments volume up 10%, cross-border volume up 15%, e-commerce volume up 17%, and processed transactions up 10%; management will soon stop discussing intra-quarter payment volume trends

Let’s look at the drivers through October 21st with volume growth in constant dollars. U.S. payments volume was up 10%, with credit up 11% and debit up 9% year-over-year. For constant dollar cross-border volume, excluding transactions within Europe, total volume grew 15% year-over-year, with e-commerce up 17% and travel up 13%. Processed transactions grew 10% 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 25 October 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 25 October 2026:

1. The Numbers Behind the Bond Bear Market (with a Look at Equities) – Ray Dalio

In one sentence, countries “go broke” when:

1. The supply of debt to be sold is much larger than the demand for it and/or2. Debt service payments (i.e., interest and principal payments) rise relative to incomes to levels that painfully squeeze out other spending.

Both of these things are now happening and are projected to happen increasingly to the extent shown in this report.

Classically, these circumstances a) raise interest rates, tighten credit, hurt equity and other markets, and reduce borrowing and economic activity and/or b) lead governments to accommodate more money and credit creation to temporarily alleviate the debt, which devalues money and pushes the problem down the road. Most typically, debt and debt service payments are reduced relative to incomes through a combination of debt restructuring and debt monetization, and as a result, money and debt decrease in value. In any case, this dynamic is bad for the holders of debt assets…

…The punchline is that it appears very likely that there is and will be much too much supply of financial assets (both debt and equity) relative to the demand for it…

…At the end of September 2026, there were about $32 trillion of marketable Treasury securities outstanding, 8% more than a year earlier. Through September, Treasury already issued approximately $1.5 trillion of net new marketable debt and is expected to issue $2.1 trillion of net new marketable debt over the full year of 2026. That is a huge amount of debt to sell at a time when the government’s debt and debt service burdens are extremely high relative to its income…

…The amount of short-term debt that has to be sold has been rising in an unusual way for two reasons: the size of the Treasury debt is rising at a fast pace and the share of debt that is short-term is rising at a fast pace because the demand for Treasury’s long-term debt has been falling relative to the supply. In an attempt to deal with this worsening supply/demand balance for bonds, Treasury has been shortening the maturities of the debt it has been selling…

…Foreigners as a whole are the largest holders of the stock of debt, with 32% of the debt, and the Fed is the next largest holder at 14%. Together, they own nearly half while Americans of all sorts own a bit more than half. You can see by comparing the percentage of holdings with the recent percentages of their purchases in 2025-26 that the support for the Treasury market has been shifting away from foreigners to domestic buyers…

… Notably, the composition of demand is changing because the demand for longer-duration debt is clearly weakening. As explained above, foreigners and the Fed together hold nearly half the debt. That’s not a healthy mix for two reasons: 1) such a large amount of debt bought and held by foreigners raises risks, and 2) the Fed’s large role in the Treasury market to bring about balances indicates that the bond market is not in a healthy free market/balanced state…

…It is officially estimated that foreign investors currently own about $9.3 trillion of Treasury securities, making them the single largest broad category of Treasury debt holders. It is also notable that their rate of buying Treasuries is slowing while the supply of Treasuries is increasing…

…In other words, China and Japan (which are two of the largest beneficial holders of US Treasuries) are the most important holders to watch because their incentives have shifted from accumulating to reducing their holdings of that debt…

…While most discussion about the US Treasury supply focuses on the deficit and therefore on new debt issuance, which of course is a huge amount, the quantity of maturing debt that has to be rolled over is much larger. Because the numbers are huge, this is a big potential negative that is not well understood, so I believe it is worthy of a quick illustration.

While debt rollovers typically don’t cause a problem because owners normally reinvest most of their maturity proceeds, that can’t be assumed, especially nowadays. Rollovers can have a big impact on the supply/demand picture. The table below shows the connection between the reinvestment rate and the amount of demand shortfall that could result from reduced reinvestment in billions of dollars. From a supply/demand perspective, these reduced rollover rates are the equivalent of increases in the budget deficit…

…Debt service payments = principal payments + interest payments.

As we looked at principal payments before, let’s look at the interest payments, which, very simply, can be estimated by multiplying the change in the interest rate by the amount of debt. With approximately $32 trillion of marketable Treasury debt, if the average interest rate paid on the entire stock eventually rose by one percentage point (as bond yields have done over the last seven months), the ultimate annual interest expense would rise by roughly $310 billion per year, which over 10 years would add about $3 trillion of supply and debt to the US government balance sheet.[6] (This doesn’t happen immediately because existing fixed-rate notes and bonds continue paying their old coupons until they mature.)

The typical debt squeeze dynamic that leads to overindebted countries “going broke” occurs when more debt leads to higher yields, which leads to higher interest expense, which leads to larger deficits, which leads to more debt, etc. When debt levels and interest rates go up together, it is a double debt service whammy. The increased debt service costs lead to further decreases of demand for the debt; credit market players call that a “death spiral.”…

…In summary, it appears to me that we are in the part of the cycle that follows relatively big interest rate increases but before the previously mentioned adverse impacts on markets and interest-rate-sensitive parts of the economy occur. At this stage, interest rates tend to continue to increase until the supply/demand imbalance starts to shrink toward sustainable levels, which shows up in the weakness in interest-rate-sensitive markets and economic sectors. As the supply/demand picture isn’t improving and neither the markets nor the economy appear to be weakening to improve the imbalance, the bond bear market probably has a long way to go, and we should be alerted to the other markets’ possible weakness. In this part of the cycle, policy makers typically tighten monetary policy in order to try to tap the brakes without popping the bubble. That is now happening in a pretty classic way. If the past is prologue, higher interest rates will 1) produce increasingly severe capital losses for bondholders that will continue until they produce bad equity markets and a bad economy, 2) raise financing costs that will ripple through the economy, and 3) lead other borrowers and sellers of competing financial assets (like equities) to compete harder for money, so money will become tight, reducing the demand for goods, services, and financial assets…

…There is a lot more to look at in order to get a complete picture of the supply and demand for equities, which I will not do here because this note has already gone on too long. But suffice it to say that the supply of financial assets (both debt and equity) being offered appears much larger than can be absorbed by the demand for it.

2. The Market Is Repeating the 1999 Pattern. Is the Lost Decade Beginning? – Outlier Capital

The pattern is repeating. Anyone who says it isn’t has not looked at the chart.

A general-purpose technology. A capex boom large enough to move GDP. A market priced for a decade of profits that have not all arrived. A chorus, getting louder this week, that the next stop is a crash and then a lost decade…

…The Shiller CAPE has been above 40 since May, the second time in a century and a half. The first time was the dot-com top.

Oxford Economics put AI-related capex at 27% of US fixed investment in the second quarter, with the three-year build already above 2% of GDP. Among technology booms of the last two hundred years, only the British railway mania of the 1840s was larger…

…The bond market is not cooperating with the story. The 10-year Treasury is at 5.3% today, up from roughly 4.1% a year ago, the highest in this cycle. The 30-year is near 5.7%. The Fed hiked in September, to a 3.75–4% funds range, and the median dot implies another move before year-end. The 2-year is back above 4.7% for the first time since the summer of 2024…

…This is the setup people mean when they forward the three-column chart. Electrification. The internet. Artificial intelligence. A vertical narrative, a multiple that cannot be defended, and a decade in which the buyers of the top go nowhere…

…In the second quarter the S&P 500 grew earnings 52% year over year, the fastest in five years. The Magnificent Seven grew 118%. The other 493 grew 32%, the best print for that group since late 2021. Strip out Alphabet and Amazon, the two names that distort the picture most, and the index still grew earnings above 30%.

That is not a one-story market hoping the story arrives. That is a market in which the story is already in the income statement, and not only in seven stocks…

…The guidance is the tell. Of 116 companies that have issued a number for the third quarter, 72 have guided up and 44 have guided down. That is the highest count of positive preannouncements since FactSet started tracking the series in 2006. In 1999 the multiple was covering for earnings that were missing. Here the earnings are being revised up into the print, and the multiple is the thing giving ground.

The forward price-to-earnings on the S&P has fallen from about 22x to about 19x , in line with the 10-year average and under the 5-year average, while the index sits roughly 1% from a record. The S&P closed October 2 at 7,723. Price barely moved over the summer. The earnings estimate did. That is the opposite of how a bubble finishes. A bubble finishes with the price running and the earnings apologizing.

The CAPE above 40 is real, and anyone waving it at you is not inventing it. They are using a 10-year trailing, inflation-adjusted multiple to answer a question about next year’s operating earnings. A high multiple on earnings that never arrive is a lost decade. A high trailing multiple on a market whose forward multiple is already back at average, because the earnings showed up, is a market that can earn the valuation from behind. Expensive is not the same as hollow…

…What Would Make This 1999

Few things. None of them is a 5.3% yield on its own.

Capex stops converting into revenue, and the $300 billion gap becomes a write-down rather than a lag. The other 493 stop growing and the entire earnings number collapses back into 7 names. The next leg of the index is the multiple, not the profit. The delivery queue turns into cancellations. The labs’ negative cash flow migrates from the private round into the listed buyers’ income statement and stays there.

3. Brain-Computer Interfaces: The Next Compute-Medical Frontier Enters the Production Era – Abhishek Jain

Neuralink’s N1 implant is a coin-sized device that replaces a small section of skull and interfaces with the brain via 64 flexible threads carrying 1,024 electrodes. The threads are inserted by a surgical robot called R1, which places them into the motor cortex at depths of a few millimeters. The device records the electrical signals from individual neurons, transmits them wirelessly to an external computer, and returns machine-learning-decoded output as digital commands.

Two things distinguish this approach from every other BCI in clinical use. First, the channel count. At 1,024 electrodes, Neuralink’s N1 records from roughly sixty times as many sites as Synchron’s Stentrode and slightly outnumbers Precision Neuroscience’s current Layer 7 array. Higher channel counts allow richer decoding of intended movement, which is decisive at the frontier of what the technology can currently do.

Second, the surgical robot. Human hands cannot reliably place hair-thin threads to sub-millimeter accuracy without damaging vasculature. Neuralink built R1 specifically to remove that limitation. In December 2025, Musk announced the company will move to an almost entirely automated surgical procedure in 2026, including a dura-piercing thread design that eliminates the need to open the outer brain membrane. If the automation lands as described, procedure time falls dramatically and the surgeon-supply bottleneck weakens…

As of June 2026, Neuralink has implanted 26 patients across four countries, spanning five clinical programs: PRIME (motor control for quadriplegia), CONVOY (robotic arm control), VOICE (speech restoration), Blindsight (vision restoration), and a newly announced hearing initiative. Trial sites are active in the United States, United Kingdom, Canada, and the UAE.

Zero serious adverse device events have been reported across the entire clinical footprint. That is a meaningful figure…

…Neuralink’s public roadmap is a straightforward multiplication of the current architecture. The N1 delivers 1,024 electrodes today. Musk stated on January 28, 2026 that a next-generation implant with roughly three times current capability is expected by late 2026, targeting the range of 3,000 channels. This claim is not independently verified and is not tied to a specific regulatory filing. The longer-horizon targets are 10,000 and eventually 25,000 channels per implant…

…The specific bottlenecks are known. Electrode-thread yield at scale is a serious manufacturing problem, as each thread must be defect-free across its entire length. R1 surgical robot fleet size limits the number of procedures the company can perform per week. Trained surgeon supply constrains the geographic footprint. ISO 13485 quality-system compliance, service logistics for explant and replacement, and long-term device support are all operationally material and rarely emphasized in company communications.

For context, the closest medical-device precedent is deep brain stimulation for Parkinson’s disease, where Medtronic and Boston Scientific have spent decades building the manufacturing, surgical training, and reimbursement infrastructure that permits routine commercial deployment. Neuralink is attempting to compress a comparable curve into two or three years, which is either an operational achievement of the first order or an aggressive timeline that will slip.

4. Inside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster – Kevin Lee, Yashar Bayani, Ahmed Sharif, Yee Jiun Song

Right now, in New Albany, Ohio, Meta is undertaking its most ambitious AI infrastructure project to date. Prometheus, our gigawatt-scale supercluster, is an advancement in our infrastructure that is training the next generation of foundational models that will bring new AI experiences to billions of people…

…Large training jobs require everything to work optimally to sustain their performance; an issue on a single host can slow down or stop the entire job, lowering its ‘goodput’. By focusing on improving our systems’ stability and reliability we were able to achieve more than 85% goodput for pre-training runs and more than 90% goodput for reinforcement learning (RL) runs of Muse Wonder. During these training runs, we were able to achieve an overall wall-clock mean time between interruptions (MTBI) of ~47k GPU hours. Since then, we have landed further changes across our fleet including firmware fixes to increase MTBI to more than 54K GPU hours, and we are constantly improving it…

…Today, Prometheus comprises an array of hundreds of thousands of GPUs across multiple interconnected buildings, including traditional and AI-optimized data center buildings and rapid deployment structures, all tightly interconnected by miles of fiber and functioning as a single machine.

This capacity allows us to pursue larger models and longer context windows than were previously practical. At full scale, Prometheus will comprise roughly 1 million distributed GPUs, all functioning in lockstep…

…Prometheus currently deploys two NVIDIA GPU platforms, with a third (based on NVIDIA’s Vera Rubin NVL72) arriving soon. Each represents a generational leap in performance and efficiency, leveraging our NVL72 rack architecture where 72 GPUs act as a single accelerator within the same network fabric…

…In traditional web services, the data center network transports requests and responses between services whose instances typically don’t require high bandwidth, high performance, and tight, lock-step coordination across many servers. Large-scale AI training requires all these, meaning the network that connects them is now an important part of AI workloads. To best address this, in addition to the standard data center network connecting servers, several years ago we added a second dedicated data network based on Ethernet, called the Back-End (BE) network, to enable direct GPU-to-GPU communication both within and across data center buildings.

Large pre-training jobs partition the training into data-parallel model replicas, creating a hierarchy in network usage: Latency-sensitive and bandwidth-sensitive operations concentrate within a replica, while traffic that is more tolerant of higher latency and less bandwidth is scheduled between replicas. Our network is designed to match this hierarchy, with low-latency, high-bandwidth fabrics within buildings and an aggregation network connecting the building fabrics…

…At the building level, each Ethernet-based fabric interconnects up to 20,736 GPUs through a high-bandwidth, low-latency multi-layer topology. Our initial design used a Disaggregated Scheduled Fabric (DSF) to mitigate the network congestion caused by training collectives that typically have a small number of large, long-lived network flows that collide under traditional load-balancing techniques; it does this by utilizing sub-packet-level multipath routing and packet re-ordering in the fabric along with virtual output queues and credit-based end-to-end flow control…

…The Backend Aggregation (BAG) network is a multi-layer hub-and-spoke network that connects building-level fabrics across the Prometheus cluster, spanning 2 km to over 100 km. It is built with Arista 7800-series chassis running deep-buffered Broadcom Jericho3 silicon, delivering multiple Pbps of bandwidth per region pair.

Running pretraining over this network required a dedicated optimization effort with Meta Superintelligence Labs to ensure training was not slowed down, especially given speed-of-light constraints at these distances.

Pulling this all together, the complexity of the Prometheus network involves millions of miles of fiber strand, tens of thousands of switches, and millions of optics — from 2×400G FR4 LITE short-reach, low-cost pluggables inside the buildings to 800G-ZR coherent optics for links beyond ~10 km…

…The transition from the H100 generation’s NVL8 architecture to NVL72 has fundamentally redefined our approach to infrastructure resilience. In the NVL8 era, the scale-up domain — the group of GPUs tightly coupled via NVLink for tensor parallelism — was limited to eight units. With NVL72, that domain expands to 72 GPUs, increasing the fault blast radius by 9X.

The larger scale-up domain is what gives NVL72 its bandwidth advantage, but it also makes the 72-GPU rack the unit of allocation we plan capacity around. Training parallelism maps most efficiently onto power-of-two group sizes, so each NVL72 domain runs a 64-GPU slice and keeps its remaining eight GPUs as in-rack spares. When a GPU fails, a spare takes its place immediately, so the job restarts from its last checkpoint instead of waiting on hardware repair, and the rack stays in the job. The in-rack redundancy pushes the effective rack fault rate down to the floor set by failures that can remove an entire pod; for rarer whole-rack faults we hold roughly 2% additional racks in reserve. For a 32,768 active-GPU training run, this means provisioning 523 NVL72 racks, or 37,656 GPUs, about 13% above the active footprint. Most of that margin (11%) falls out of the 64-of-72 slice geometry rather than being an absolute reliability tax.

Physical complexity further intensifies these reliability requirements. A single NVL72 domain incorporates 5,184 tuned differential-pair signaling links operating at 200 Gbps, each traversing multiple potential points of failure, including GPU SerDes, compute tray PCBs, cable cartridges, and switch tray interfaces. Maintaining consistent signal integrity across this dense fabric — at 200 Gbps per lane — represents an unprecedented engineering challenge at this scale…

…Deploying hundreds of thousands of first-generation accelerators reveals quality issues that are hidden at lower volumes. The GB200 deployment has transformed hardware maturation into a primary engineering discipline for our teams and partners.

5. Markets Are Always Different This Time – Ben Carlson

A friend was cleaning out one of his rental properties and came across the business section in an old newspaper from 1960:

The chart shows the make-up of the S&P 500 by sector at the time:

425 industrials
25 rail companies
50 utilities

Things look just a tad different today.

This is what the S&P 500 company breakdown looks like now:

Industrials 83
Financials 76
Information Technology 75
Health Care 60
Consumer Discretionary 47
Consumer Staples 34
Utilities 31
Real Estate 30
Materials 24
Energy 21
Communication Services 22

Yes the stock market is more concentrated in the top names now, but there is far more diversification in the types of companies that make-up the index.

This is also one of the reasons it’s so difficult to compare historical valuations to today’s markets. The companies and sector make-up of the index are completely different…

…Maybe stock market returns will be lower going forward on a gross basis than they were in the past. But on a net, after cost basis, investors have never had it better than today.

Reg FD is probably the most important change to the market structure this century that no one ever talks about. Once regulators required any significant corporate information be made available to all investors at once, it made it much harder for hedge fund managers and other large active investors to win using proprietary information.

There’s a reason there haven’t been nearly as many legendary investors beating the markets lately. The playing field has been leveled.

It’s also true that the low fruit has been picked in many ways because market knowledge and data are so much more ubiquitous today.

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 Meta Platforms. Holdings are subject to change at any time.

The Latest Thoughts From American Technology Companies On AI (2026 Q3)

A collection of quotes on artificial intelligence, or AI, from the management teams of US-listed technology companies in the 2026 Q3 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 third 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
2026 Q2 – here, here, here, and here

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

ASML (NASDAQ: ASML)

ASML’s management has completed value-based pricing conversations with key customers but the outcomes will not be visible in 2026; most of ASML’s expected growth in 2026 is coming from memory net system sales with, specifically DRAM, with 75% growth; management expects the ratio between logic and memory to shift towards memory, but logic demand remains strong; management expects ASML’s advanced logic business to increase by over 25% in 2026; management expects the logic business to be larger than the memory business in 2027

[Question] We also talked last quarter about higher value-based pricing and the contribution that might play on the gross margin. How is that showing up as part of the outlook?

[Answer] We completed those conversations. So that’s been done with the key customers. The effect of that is not really visible in 2026. It will start to become visible partly in the course of 2027. The full impact of that you will start to see in 2028 and beyond…

…Turning to end use, this year, we expect logic net system sales to grow around 10% and memory net system sales to grow around 75%. The growth in memory is driven by the combination of capacity expansion of our customers and the increasing adoption of EUV on advanced DRAM nodes…

…We are still on trend where memory become more and more important, 75% increase, which is of course significant. You start to see the results of this huge memory investment. That, by the way, is mostly DRAM, to be clear. We continue to see that moving forward. I think we have said in the past that the ratio between logic and memory will most probably increase towards memory. Still, I would say that logic is also very strong. To be able to predict exactly where this is going to land is difficult because I like to stress that we see also a very strong demand on the logic side, because of course, as you know, the two are very much coupled when it comes to delivering AI capacity to the market…

…Logic went up around 10%, but the advanced logic piece, included in there is far better. So that grew over 25%, is expected to grow over 25% in this year. You are right, memory is getting closer to logic, but logic remains very important. When we look at 2027, memory comes close. But I still believe that logic will outweigh memory in the total book of business for next year.

ASML’s customers are seeing their demand rising both in the short and long-term; management thinks ASML’s customers have very good visibility into their demand; ASML’s customers think they will be in a supply-shortage until at least 2028, and so they are building up capacity faster; the faster capacity build up by ASML’s customers are leading to more demand for ASML in the short-term across its product portfolio, especially for EUV machines; ASML has already taken in a lot of orders for 2028 and there are even orders for 2029 and 2030; the higher visibility that ASML’s customers have on their demand has given ASML better visibility too; management thinks the demand they are seeing is real and not inflated by buying-behaviour from customers; customers that put an order with ASML have to make 2 serious commitments; management has not accepted the 2029 and 2030 orders because they are still understanding the overall demand picture for that time frame; management does not see ASML’s customers’ clean room constraints being a major factor on the company’s demand; the interest management is seeing from customers for 2029 and 2030 comes equally from DRAM and logic; the DRAM customers talking to ASML for 2029 and 2030 are from the US and Korea (so Micron, SK Hynix, Samsung)

We did indeed spend a lot of time with our customers and all of them are commenting that they see their demand increasing on the short term but also extending in time. I think that their visibility is very good at this point of time. Today they expect that their supply will not meet the demand at least until 2028. So this is a very, very strong demand on their side. And of course this means that they want to build up more capacity faster. So they are accelerating their plans. They are announcing new fabs. For us this translates on the short term into more demand, as Roger mentioned. Overall, basically for our holistic product portfolio and on the long-term increased visibility for our demand, especially with EUV. So a lot of our discussions with our customers now are shifting in fact beyond 2027. A lot of order intake for 2028. But also some strong interest to already place orders for 2029 and sometimes even 2030. ..

…Because they have very high visibility on what is needed in the future. And as a result we also have this very high visibility…

…[Question] Do you worry that customers are pulling in orders or giving you bigger forecasts just to secure a slot?

[Answer] When it comes to the demand we are referring to, the increased demand we are referring to, I think we are very much capable to validate that demand with some more detailed plan our customers are sharing with us in term of fab construction, release, et cetera. I think that when we talk about demand, when we refer to demand in those discussion, we really refer to demand we believe is real and not inflated by any potential buying behavior from our customer…

…If you put an order with ASML, that’s not a frivolous piece of paper that you put your name under. It comes with two serious commitments. One serious commitment is a commitment to make a down payment. As I qualified it before, it’s a down payment that is enough to hurt, right? So that’s one element. Secondly, a PO is a real commitment on both sides of the equation, and it also means that if you want to get rid of that commitment, that’s not going to be easy. That comes at a price, not just the down payment, but more than that. Therefore, before customers sign a PO with ASML, they’ve made up their mind, and they’re not doing this frivolously…

…Customers are requesting us, and therefore we have not yet accepted those, is that we want to make sure that we have a good picture of what the overall demand in 2029 and 2030 looks like that customers are currently signaling, and mirror that against the capacity expansion as we see it at that point in time. We want to do that because we don’t want now to accept orders from customer A, B, and C, and then to tell customer D, who hasn’t given orders yet for 2029 and 2030, “Sorry, we’re sold out.” That would be totally inappropriate for customers that have been business partners for us for a very long period of time. We’re now really using this period of time to mirror, to get a good understanding, what is the total demand that our customers are signaling for the 2029, 2030 timeframe? What does it look like?…

…If we look at the longer term, as I said before, beyond those little move or those tactical move, we have confirmed that the number our customer are giving us are really lined up with their plan. In fact, in many cases, when the supply is tight, we make sure before we fully allocate a tool to a customer that the space will be there, the facility also will be there, so that we don’t ship a tool that will be waiting for weeks for a customer to use it, because, of course, other customer will be very disappointed with that. We do a lot of work to make sure that in all the plans we have at ASML, we don’t have delay of more than a few days or weeks, I would say, in shipping our product. Short answer is, I think, no. We don’t see our customer clean room constraint being a major factor on our demand…

…[Question] Just to understand the discussions that you have on 2029 and 2030, if it is more because you seem to be very positive at the moment on memory. If it is very balanced between memory and logic, or if it is still memory manufacturer who are giving you some visibility on 2028– 2029, sorry, and 2030.

[Answer] You need both memory and logic to get some AI chips. I think we have discussion with both customers because they get the same message from their own customer. I think today, DRAM is a bit more aggressive on the buildup of those capacity. But yeah, we have discussion with both. I think we see equal interest, equal, I would say, demand trajectory for both DRAM and logic, because we need both basically to get the product out…

…[Question] DRAM, is it at the same time the U.S., Korea, and even China, or is it more concentrated from a geographic standpoint?

[Answer] We are mostly talking about EUV in this case. So this excludes China.

ASML’s management has been planning for a 30% increase in capacity in each of 2027 and 2028, but management is also clear that the planned capacity increase is not enough to meet demand; ASML’s management is looking to increase capacity in 2028 beyond the 30% that had been planned; ASML’s Installed Base business is able to help its customers increase manufacturing capacity; management will be introducing the NXE:3800F EUV machine in 2027 that will also help customers increase manufacturing capacity; ASML’s customers are asking the company to increase capacity; management will soon be introducing the next generation DUV system that will also help customers increase manufacturing capacity; management does not see Carl Zeiss as a bottleneck for ASML’s capacity expansion; the capacity-increases that management has in mind are already validated with ASML’s supply chain

Last quarter, we talked about 30% more capacity next year, and also the aspiration to have another 30% increase in 2028. A lot of focus there. But to be honest, it’s becoming very clear that 30% in 2027, 30% in 2028 will not allow us to meet our customer demand. So we have to do more and we are looking basically at ways, especially in 2028 and beyond, to further extend our capacity…

…In addition to increasing unit shipments that Christophe talked about, one important driver obviously is the Installed Base business that we’ve talked about quite a bit. So of course that is a major driver. It’s something that customers really like. Because what it does, it gives you within your existing footprint, you get pretty fast incremental capacity there. So that’s been very successful so far and we expect that to continue to be quite successful on a go forward basis. We’re also very aggressively developing upgrades. So we will get more and more upgrades on our installed base available to our customers. So in that way, we try to help customers in getting instantaneous access to more capacity…

….We talked in the past about the NXE:3800F, which gets you from the 230 wafers per hour that we have today to 260 wafers per hour. That will be introduced in 2027. That will be in small quantities. But obviously for 2028 and beyond you will see that really helping to increase the capacity of our customers…

… [Question] What then would you say are the key things that you want to focus on for the rest of the year but also in preparation for next year?

[Answer] I think it’s very clear, it’s more capacity. As we discussed today, our customers are asking us every single day to get more tools…

…We also plan to introduce the next generation critical Deep UV immersion, running at 330 wafers per hour. These new products will provide incrementally more wafer capacity to the industry as they ramp in the coming years…

…We do not see Zeiss today as a bottleneck for the capacity expansion we are discussing, even beyond the 30% we mentioned for 2028. We have done, in the recent years, many work with all the supply chain to make sure that the supply chain will not be an issue for us…

…Every number we share with you is the result of us having validated that the supply chain can, of course, follow those numbers.

ASML’s customers are starting to use the company’s high-NA EUV machine for high-volume manufacturing; TSMC has asked ASML to co-lead a consortium to introduce a 12-inch reticle for high-NA EUV; the 12-inch reticle is slated for introduction in 2031 and to enter production in 2033; management thinks the existence of the 12-inch reticle project is a sign that the entire ecosystem wants to more and more adoption of high-NA EUV; TSMC will introduce high-NA EUV in 2030 for high-volume manufacturing; all of ASML’s other major customers are also in the consortium; Samsung will be using high-NA EUV in high-volume manufacturing in 2028; Intel continues to use high-NA EUV in high-volume manufacturing, and the performance of the high-NA EUV system meets or exceeds comparable layers pattern using low-NA EUV; management now expects to see adoption of high-NA EUV increase node-on-node moving forward

The High NA program started more than ten years ago. We are all very happy to see that our customers are now about to start using the tool in high-volume manufacturing…

…So TSMC has asked us to lead with them a consortium basically to bring a 12-inch reticle to the industry. This is what is happening sometime in the first half of the next decade. But it’s also the sign of how important High NA has become in their plan. That’s also why they have basically confirmed that they will also introduce High NA around 2030 in high-volume manufacturing. So this is a very important announcement because it shows basically that over time, customers are looking at using High NA more. This is why they want to use the 12-inch reticle. Now you have noticed that all other major customers also joined that consortium…

…To further ensure chip makers can fully leverage the advantage of High-NA EUV, we also, in collaboration with TSMC, began an industry-wide initiative to develop larger formats EUV photomask. This collaborative effort aims to establish a 12-inch mask pilot line by 2031 and enter production in 2033…

…I think the announcement, and the drive of TSMC first on the 12-inch critical mask, is very interesting there. Because the first signal it gives is that, in the mind of the customer, there will be more and more use of High-NA. Because if it was not the case, no one will really bother to go to 12-inch mask on High-NA. The 12-inch mask drive and very wide adoption by customer in the consortium, is just a sign that everyone has in mind over time to develop basically more and more adoption of High-NA…

…Samsung has also announced that they will be using High NA in high-volume manufacturing starting in 2028. And Intel, I think we talked about that before, they continue to run the system in high-volume manufacturing, increasing the number of layers. Of course, we look now at a significant amount of wafers being run there…

…Intel announced that it has exposed more than 1 million wafers using its High-NA EUV systems, with performance that meets or exceeds comparable layers pattern using low-NA EUV…

…With availability across the existing High-NA EUV fleet approaching 90%, we now expect to see adoption increase node and node moving forward.

ASML’s management sees very strong demand in China for domestic consumption for 2026 and beyond; management sees ASML’s Chinese customers all wanting to work with the company; management thinks the likelihood of strong competition for ASML in China is still low; management thinks China is still a fair distance away from being able to compete with ASML, even though there have been rumours for 25 years that China is on the cusp of being able to compete with ASML

If you look at what China needs in terms of mainstream logic, in terms of memory, there is very strong demand in China for their domestic consumption. If you look at the fabs that are being built in China, there is a lot coming online there. Therefore, we would expect the demand in China to be strong. This year, 20%. I would argue that also for 2027 and beyond, based on the intrinsic drivers, it seems that the demand situation in China remains very strong…

…Where we are today, I think, with our Chinese customer, is that they all want to work with us. I think this is very clear. I won’t give any market share number, but you can imagine it’s extremely high, especially on immersion. Therefore, as long as we have a good market access, as long as our customer can trust they can work with us, I think that the likelihood of a major competition in China is still pretty low…

…If they were to not have access to some of our tools, then you get a lot more motivation. But it’s still a very, very long way to go. I think that we have been having this discussion about China having a tool to compete with us for the last 25 years, and every other years, we get the news that this is coming. If you stick to fact, I think the fact this year are not different from the fact a year ago or even five years ago. So I think that the progress is not very strong.

Intuitive Surgical (NASDAQ: ISRG)

Intuitive Surgical’s management is developing AI capabilities on da Vinci 5, including a voice user interface, and an automated fourth-arm control; management thinks the AI capabilities will improve efficiency and outcomes, but the technologies must earn the trust of the ecosystem before they can be scaled; the da Vinci 5 has multiple programs with advanced visualisation amd AI features that can add revenue per procedure

Our teams continue to develop additional digital and AI capabilities on da Vinci 5, including a voice-activated user interface and automated fourth-arm control to further enhance efficiency and the user experience. AI-controlled motion in surgery has the potential to improve efficiency and outcomes, but it is early and must earn the confidence of regulators, surgeons, care teams, and patients before we deploy it at scale…

…Another area that we are very excited about when it comes to da Vinci. We have multiple programs going there with advanced visualization technology around hyperspectral imaging, fluorescing molecules, and some AI features that we do think will bring that incremental value, and we are going to be able to add some revenue per procedure as a result.

Meta Platforms (NASDAQ: META)

The vision of Meta’s management for personal superintelligence is for every person to have an extremely capable personal agent that helps them achieve their unique goals in life; Meta launched its personal agent product, Muse, recently and in 1.5 months, more than 10 million people have set up Muse personal agents; Muse has been #1 in the App Store nearly every day; management thinks Muse’s engagement and retention are off to a strong start, with 25% of people who have set up Muse using it every day; the longer people use Muse, the better their retention; Muse’s retention appears to be at least 2x better than where other AI apps started at; the most common use case for Muse so far is shopping, with an average purchase size of more than $200; payment technology company Plaid said Muse has shopping activity never seen before on other services; Muse’s shopping volumes in the first week were much higher than management expected; other common Muse use cases are personal productivity and connecting to other personal services; Muse has state-of-the-art privacy and security; management intends to implement Muse Confidential VM (virtual machine) soon, so that even Meta cannot see users’ content; the introduction of Muse Wonder, Meta’s latest frontier model, has improved Muse’s quality, engagement, and retention; management believes Muse will perform better over time as Meta releases better models; management is seeing that businesses are very excited about enabling agentic commerce; management has been positively surprised by how many businesses want to connect with Muse

We have been discussing our vision for personal superintelligence for more than a year now. We want to help every person have an extremely capable personal agent that understands your goals and can help you achieve them and improve your life in whatever ways you want — whether that’s improving your relationships, helping you save time, save money, build a business, create new things, or anything else that you can dream of. Muse is the product form of this vision…

… In just a month and a half, more than 10 million people in the US and Canada have set up Muse personal agents. Growth continues to be strong and we have been #1 in the App Store almost every day. Engagement and retention are also off to a strong start. About 25% of people who have set up Muse use it on any given day. For our earliest adopters, the longer that they use Muse, the better that retention seems to get. To put this in perspective, this is more than twice the level that Threads started at or where we believe that other AI apps started at as well.

The most common use case for Muse is shopping, with more than a quarter of people who are using Muse engaging in this with an average purchase size over $200. Plaid told us they hadn’t seen activity like they’re seeing with their connector on any other service before. And in the first week we had to work with Stripe to raise credit limits since the volumes were above what any of us expected. Beyond shopping, the most common connectors are for personal productivity and connecting Muse to your other services like email and calendar. This shows the importance of the Muse Secure VM that we’ve built to provide state-of-the-art privacy and security, and we’re looking forward to following up soon with the Muse Confidential VM, which will make it so that even Meta cannot see your content.

We have also seen that Muse Wonder improves quality, engagement, and retention further, which suggests that as we continue to improve intelligence, Muse will perform better and better over time…

…[Question] When you think about the friction points around agentic commerce, how do you guys as a company think about solving for friction that’s rising where there are aggregators or suppliers that don’t want agents to interact with their platforms or their services and how you think about lowering that friction or creating the right balance between the existing services that consumers access today relative to how this might all be changed or altered by agentic behavior over the medium to long term?

[Answer] Right now what we’re seeing is that, overwhelmingly, businesses are very excited about enabling this because they recognize that it’s going to be a new opportunity for them not only to increase the sales and the quality of the experience for existing customers but also potentially to bring in new customers… We’ve been very positively surprised by how quickly, I think a huge amount of businesses have wanted to not only figure out how to make it so that Muse can use their services more effectively but have reached out to us to integrate around it.

Meta’s management sees Muse entering an operational phase that involves scaling the product, growing revenue, and optimising compute costs; management expects Muse to have billions of users; management wants to create a large Muse network as quickly as possible because they think a scaled network will get more done for users; management expects the average revenue per user for Muse to eventually exceed the few hundred dollars per year seen for American users of Meta’s family of apps; management expects Muse to be free for most users and monetisation will come from (1) transaction-based fees from free users, (2) subscriptions from users who want better models and/or more tokens, (3) subscriptions from businesses who want more features, and (4) connecting Muse to improve personalisation and quality of content and advertising across Meta; Muse is very compute-intensive currently but management expects to improve its cost efficiency by 10x in the next year; management thinks Muse will become highly profitable if compute efficiency improves over the next few years; there are at least 500 million small businesses using Meta’s family of apps, and management thinks Muse can help these businesses; Muse is currently powered by Muse Spark, and Muse Wonder is available only on the paid tier; management wants to introduce a more efficient version of Muse Wonder for Muse; management recently previewed the Personal Agent Protocol, an open standard for Muse and Meta business agents; management sees a few ways to improve Muse’s compute efficiency, including (1) leverage off-peak and spare compute capacity, and (2) using smaller modles; management expects to introduce monetisation to Muse faster than Meta’s normal playbook

Now that Muse has clear product-market fit, we’re entering a new phase and a more operational phase that is focused on scaling it. The goals of this next phase play to many of Meta’s strengths. We need to do three things: scale the product, grow revenue, and optimize compute costs. We expect to grow Muse to serve billions of people…

…We think that billions of Muses in a network at scale will be able to get more done for people and businesses by working together. So we’d like to create a large scale Muse network as quickly as possible…

…The average revenue per American user for our existing Family of Apps users is several hundreds of dollars annually, and we expect that Muse will reach an even higher level over time due to the amount of commerce that we already see taking place. This will take some time, but we’re starting to work on this now…

…For most people, the product should be free and we will build commerce tools so we get paid a fee based on the transactions that we help facilitate. We expect this will be paid for by businesses. For people who want the most advanced models or to use extra tokens, they subscribe directly and pay. For business owners who want additional features to use Muse to grow their businesses or for companies that want people to use Muse for work, they will be able to subscribe directly and pay as well. And for those who want Muse to help improve the personalization and quality of their content and ads across Meta, they will be able to connect to those experiences as well…

…On the compute side, we are building out the capacity to serve more than a billion people over the next few years. In the near term, Muse is very compute-intensive to run. But we expect to improve its cost efficiency by more than 10x in the next year, which will enable it to be profitable once the revenue lines are at scale. If compute efficiency continues improving quickly for another few years, which we think is highly likely, then Muse will be highly profitable over multiple years…

…We also see a significant opportunity to extend Muse to small businesses. Based on a recent user survey, we estimate that at least 500 million self-identified small businesses use Meta’s family of apps to reach customers and generate revenue. Muse could help these businesses acquire customers and operate more efficiently, while helping people complete transactions. Over time, consumer and business agents could connect and transact directly, creating opportunities around paid usage, commerce, and business services…

…We launched Muse first on Muse Spark, and that’s what is currently the default across the free tier. And we rolled out Muse Wonder, and people can access that in the paid tier today. And we know that it performs a lot better, so we’re working on getting a more efficient version of Muse Wonder that we’ll be able to roll out more broadly…

…Earlier this month, we previewed the Personal Agent Protocol, an open standard across Muse and Meta business agents that we are developing with industry partners like Sierra, Walmart, Stripe, Shopify, et cetera. The protocol is intended to define how a consumer’s personal agent can interact securely and reliably with the business’ agent and preserve appropriate controls for both the business and customers…

…I think there are a couple of dimensions of achieving that efficiency. I think some of the more nearer-term pieces include things like leveraging offpeak or spare computing capacity to run some sets of jobs, using smaller models. There’s a lot of work to be done across different inference efficiency initiatives, like all in service effectively of higher GPU utilization and other product efficiency work we can do also. And I think over the longer term, I think this is a place where we expect that we’re going to be able to, over time as LLMs scale, get more tasks done with fewer tokens and also rely on AI models themselves to help us improve the efficiency of the software that we use to run the model…

…We are going to work on monetization somewhat sooner on this than our normal playbook for a couple of reasons. One is just the size of the numbers that we’re talking about in terms of both the costs and the opportunity, I think make it kind of important to make progress on that in order to be able to scale it. The other is just that such a large part of what people are doing is commerce. And to your question, that does include travel, too, I think we might consider travel a different case, but travel is actually one of the other top use cases for Muse, is basically people planning that out and planning different kinds of things that they would get than they might get through our Family of Apps.

Meta’s management is building the Meta Enterprise Platform to serve as much high-margin inference demand as possible to leverage on the large fixed costs that come with training frontier models; management thinks that traditional enterprise companies do not have AI usage locked down yet

Since training is a large fixed cost, now that we have leading models, we want to serve as much high-margin inference demand as possible. That is why we are building the Meta Enterprise Platform, led by CJ Desai. Muse Wonder and even more advanced models that we are training can serve additional high-margin services beyond the Muse network, including our API, Muse Code, and other enterprise services…

… AI is creating huge amounts of disruption in enterprise today. And we don’t — from what we can see, we really don’t believe that traditional enterprise companies have this locked down.

Meta’s management sees the business having many good uses for its compute capacity; management believes that building multiple ways to deploy advanced models will increase the chances that one or some of them will significantly outperform management’s expectations; Meta has yet to reach the limit on effectively applying compute to accelerate its core business

A fundamental feature of our business is that we have many good uses for this compute. It is difficult to forecast the exact growth rates for the Muse network, for our enterprise platform, and for how Muse Wonder and beyond will accelerate our core business. But through building out multiple ways to deploy these advanced models, we increase the chances that one or more of them significantly outperforms our expectations. Beyond serving other businesses, we have not yet found the limit of how effectively we can apply compute to accelerating our own core business.

Meta’s management is using Muse Wonder, the company’s latest frontier model, to improve its ability to autonomously improve the company’s recommendations and advertising systems; management expects an increasing rate of improvement in Meta’s systems as the company builds better models; Muse Wonder is state-of-the-art across many dimensions important for personal super intelligence; management thinks Muse Wonder is proof of the importance of Meta training its own models; Muse Wonder has led Meta AI’s engagement per person to more than double since April; the introduction of Muse Wonder, Meta’s latest frontier model, has improved Muse’s quality, engagement, and retention; management believes Muse will perform better over time as Meta releases better models; management plans to focus on pushing the frontier with Meta’s models as well as distilling the frontier models into smaller, efficient product models for personal agents; Meta is already working on models larger than Muse Wonder that are impressive and capable; Meta is already working on smaller models that will deliver similar intelligence to Muse Wonder more efficiently

We have implemented an eval with Muse Wonder to improve how effectively it can autonomously improve our recommendations and ads systems. And as our upcoming models get even more advanced, I expect that we’re going to see an increasing rate of improvement in these systems…

… Muse Wonder is state-of-the-art across many dimensions that matter for personal superintelligence. This validates the importance of training models ourselves to deliver the best experience. In addition to Muse, we’ve also seen that engagement per person in Meta AI has more than doubled since April when we started rolling out our new models…

…We have also seen that Muse Wonder improves quality, engagement, and retention further, which suggests that as we continue to improve intelligence, Muse will perform better and better over time…

…We plan to focus on both pushing the frontier of intelligence and distilling those models into much smaller and more efficient product models that will be optimized for personal agents. My view is that we will be able to build superintelligence through scaling, so we are methodically working our way up the ladder there. We are already working on larger models after Muse Wonder, and they’re going to be very impressive and capable. We’re also working on new smaller models that are coming out soon that will deliver Muse Wonder-level intelligence more efficiently.

Meta has been shipping many models in the last few months

I’ve been very pleased with our pace of execution here. In the last few months we’ve shipped Muse Spark 1.2 and 1.3, Muse Code, two open weights models with Muse Glimmer and Muse Spark Open, and also Muse Voice.

Meta’s management has refocused Reality Labs on delivering personal superintelligence; the Muse agent will soon appear in Meta Glasses; management expects Muse to be the most important experience within Meta Glasses; management sees Muse as being a central pillar of the Meta Glasses business; management does not want to make a large profit on the sale of the Meta Glasses hardware; management wants to make profit on Meta Glasses through subscriptions to Muse; Reality Labs will now be shipping new devices only if it can achieve major breakthroughs; Reality Labs recently introduced the Meta VR Glasses, which is the first ever VR experience in a pair of glasses; management expects Reality Labs’ operating loss to be $15 billion in 2027 (was $19 billion in 2025) and to trend towards profitability from there

Our work on personal superintelligence is now also the focus of Reality Labs. Muse is coming to Meta Glasses starting next month, and that will be an important step towards this. People already love many features of the glasses already — music, calling, capturing moments, hearing assistance, and more — but over time we expect Muse and having your personal superintelligence with you throughout the day will be the most important part of the experience. Muse is also central to the business model for the glasses. Again, our mission and strategy are to serve everyone here. We believe in putting the power of this technology in everyone’s hands. So we have priced our devices not to make a large profit on the hardware, but to build a large software and superintelligence business on top of them over time. Glasses sales continue to grow rapidly, and now with Muse, we expect that paid subscriptions will increase and that this business will begin improving meaningfully from here.

I also shared at Connect that we are changing our approach to virtual reality to only ship new devices when we have major breakthroughs like we had with the Meta VR Glasses this year — which of course are the first VR experience ever in a pair of glasses…

…We expect that the P&L of Reality Labs to improve from here, with operating loss coming down to around $15B next year and trending towards profitability from there.

Meta continues to see gains from its content recommendation initiatives; global time spent per user on Instagram is at an all-time high and was up double-digits year-on-year in 2026 Q3; time spent on Facebook Reels reached all-time highs globally and in the US, and both were up double-digits year-on-year in 2026 Q3; Meta’s upgraded model architecture that ranks sequences of Reels at a time helped grow time spent on Instagram Reels by over 4% in 2026 Q3; 45% of all recommended content on Instagram Reels is now less than one day old, up from 34% in 2026 Q2; Facebook Reels watch time in the US increased by 1% from a doubling of the amount of history the ranking model can consider; Meta has used LLM-based content-understanding systems to improve engagement with Instagram search results by 1% in 2026 Q3; management has started moving Meta’s content-understanding models into the Muse family; the Muse family of models has reduced per-unit content processing costs by more than 80% across Meta’s apps

On the first, we continue to see significant gains from our content recommendation initiatives. On Instagram, global time spent per user reached an all-time high and grew double-digits year-over-year in Q3, with strength across both Reels and Feed. On Facebook, Reels time spent reached all-time highs both globally and in the US, with both growing double-digits year-over-year…

…Engagement gains across our services continue to be driven by improvements we’re making to the relevance, personalization and timeliness of the content we recommend. Investments into our recommendations engine across retrieval and ranking have continued to yield positive results on Instagram and Facebook.

For instance, on Instagram Reels, our relevance launches in Q3 increased Reels time spent by over 4%, driven in part by our upgraded model architecture that ranks sequences of Reels at a time instead of evaluating each Reel independently, as well as improvements we made to add richer watch time signals. On Facebook Feed and Reels, we completed the global launch of a new generative retrieval architecture which, inspired by LLMs, labels pieces of content with semantic representations and generates potential recommendations, helping our systems surface relevant new posts before they’ve accumulated significant engagement. This new architecture is now rolled out to roughly one-quarter of unconnected Feed views and half of Reels views and increased Reels watch time by 1%…

…Nearly 45% of recommended Instagram Reels are now less than one day old, up more than 11 percentage points from Q2. On Facebook Reels, we doubled the amount of history our ranking model can consider, driving a 1% increase in Reels watch time in the US…

…We continue to process public Instagram posts each day through LLM-based content-understanding systems. These signals have improved the engagement with Instagram search results by 1% through the end of this quarter. This quarter, we also began moving our content-understanding models onto our first-party Muse model family. In offline evaluations, these models are outperforming our prior production models and are reducing per-unit content processing costs by more than 80% across Instagram, Threads, Facebook, and ads. Beyond improving quality, owning more of this stack gives us greater flexibility to develop models around the signals that matter the most for personalization.

Meta’s management introduced Meta Generative Recommender, which uses LLMs to reason about ad content and user preferences simultaneously, in 2026 Q2; management launched the same approach to generative retrieval on Facebook Feed in 2026 Q3, driving a 2% increase in advertising conversions across targeted segments; management has expanded Meta’s GEM (Generative Ads Model) model to additional surfaces, driving a 1.7% increase in conversions for offsite conversion campaigns and a 9.6% increase in app ad conversions; improvements to Andromeda in 2026 Q3 increased conversions by 1.4% on Facebook

Last quarter, we introduced Meta Generative Recommender, which uses LLMs to reason across ad content and user preferences to predict the best ad for each person. In Q3, we launched this approach to generative retrieval on Facebook Feed, driving a nearly 2% increase in conversions across targeted segments. In addition, we expanded our GEM model for ads ranking to additional surfaces, driving a 1.7% increase in conversions for offsite conversion campaigns and a 9.6% increase in app ad conversions…

…In Q3, improvements to Andromeda such as scaling model complexity and throughput increased conversions by 1.4% on Facebook.

Advertisers who adopt Meta AI business assistant’s recommendations had 5% more conversions at the same cost per conversion and increased their advertising investment by 9%; management will be rolling out more features to Meta AI business assistant; maple syrup producer Simply Organic Maple used Meta AI business assistant for campaign messaging and saw a 57% increase in purchases and a 36% reduction in cost per purchase

Meta AI business assistant is becoming a more proactive and capable partner for advertisers. We estimate that advertisers who adopted its recommendations drove almost 5% more conversions at the same cost per conversion and increased their advertising investment by 9% compared with similar non-adopters. We are rolling out additional capabilities, including creative insights, stronger business context and more agentic actions. These features will allow marketers to plan, create and optimize campaigns, as well as schedule tasks and alerts, through a conversational experience with approvals and previews that keep them in control…

…Simply Organic Maple, a family-run maple syrup producer in Vermont, used Meta AI business assistant to improve its campaign messaging. After the business assistant recommended testing longer, more descriptive advertising copy, the business saw a 57% increase in purchases and a 36% reduction in cost per purchase. Its strongest revised ad delivered a 3x return on ad spend, a 52% improvement from the original.

10 million small businesses are now using at least one of Meta’s Gen AI advertising creative tools (was 9 million in 2026 Q2)

In Q3, nearly 10 million advertisers used at least one of these tools. Advertiser adoption of our video generation features grew 25% quarter-over-quarter, driven in part by ready-made videos that reduce generation time to just a few seconds from a single image upload.

Guidance for capex in 2026 has been raised to $135 billion to $145 billion (previous guidance was $130 billion to $145 billion) or growth of 87%-100% (2025 capex was $72.22 billion); management expects to continue aggressively investing in AI infrastructure in 2027; management thinks Meta’s AI investments are already delivering substantial incremental advertising revenue; management thinks Meta’s AI investments for MSL (Meta Superintelligence Labs) can unlock significant new revenue opportunities while strengthening the core business, but the monetisation will take time; management expects to sustain strong overall revenue growth in 2027

We anticipate 2026 capital expenditures, including principal payments on finance leases, to be in the range of $135-145 billion, narrowed from our prior outlook of $130-145 billion.

Looking beyond 2026, we are still developing our budget and capacity plans for next year, but we expect to continue investing aggressively to support both our core business and emerging AI initiatives. Our current capacity and what is scheduled to come online gives us the flexibility to allocate resources across model training, core business growth, and the development and support of new products. Our AI investments are already delivering substantial incremental advertising revenue through stronger engagement, better recommendations, and more efficient monetization. Building on this progress, our MSL investments are creating the technology and infrastructure needed to unlock significant new revenue opportunities and strengthen capabilities across the business. While these investments will take longer to generate returns than those in our core business, the breadth of monetization opportunities and internal efficiencies gives us confidence in their long-term potential.

Looking ahead, although we are not providing a full-year 2027 revenue outlook, the benefits our AI investments are delivering to the core business, combined with emerging opportunities from model advances and product launches, position us to sustain strong overall revenue growth throughout 2027.

Meta’s management sees 3 broad themes for how its core advertising business can continue improving; the 1st theme is around traditional machine learning, where management sees a robust and healthy pipeline of improvements to be made; the 2nd theme is around incorporating LLMs into Meta’s ranking and recommendation engines; the 3rd theme is around generative AI tools found in the Advantage+ suite that make it easier for advertisers to quickly and seamlessly generate creatives

I think our ads business has been benefiting for now many years from our ongoing investments in both kind of what we would have called traditional AI machine learning models and then, of course from the sort of more recent generation of models. I would say that there are three themes there that I think are worth outlining.

The first is just the kind of traditional roadmap of performance improvements that we have in the pipeline based — that is what I would describe more as business-as-usual machine learning work. That continues to be a robust and healthy pipeline. And as we are looking into 2027, again we’re not providing an outlook, but I would say that our investments and product improvements across both engagement and ads initiatives give us confidence that we have meaningful runway to grow our core business throughout 2027…

… The second theme I would say is that we are excited about some of the new research bets that we have as we move into sort of a more LLM-oriented AI infrastructure to help us more deeply reason about people’s preferences and what they’re interested in both across organic content and recommendations, and we expect that we will be able to unlock benefits to our ranking and recommendations engine from that research, too…

…The third thing is that we expect that generative AI tools which have already very valuable for advertisers will continue to grow in terms of the features that we make available to advertisers primarily through the Advantage+ suite but features that make it easier for advertisers to quickly and seamlessly generate, whether it is creative or take an image and generate video or generate sound, all, I think in service of making the ads increasingly personalized for the person that they’re being served to.

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s capital expenditure is always in anticipation of growth in future years; management has maintained capex guidance for 2026 at US$60 billion to US$64 billion; TSMC’s capex in the last 3 years was ~US$100 billion, and management continues to expect the next 3 years to have much higher capex (previous guidance was for it to be “much higher”); the majority of TSMC’s capex is for long-term demand and management is also looking for revenue growth to outpace capex growth; management expects TSMC’s business to continue to be supported by leading-edge nodes, including 2nm; management now thinks the AI accelerators business is likely to have a CAGR for 2024-2029 of even higher than the high-50% (previous expectation was for even stronger CAGR for for 2024-2029 than the high-end of the mid-to-high-50% CAGR communicated in the 2026 Q1 earnings call)

At TSMC, a higher level of capital expenditures is always correlated with higher growth opportunities in the following years. Given the continued robust multi-year demand from our customers, including the emerging agentic AI market, we reiterate our full year 2026 capital budget to be between $60 billion and $64 billion. We continue to expect our CapEx investment from 2026 to 2028 to be even more significantly higher than the past three years as we continue to invest heavily to support our customers’ growth…

…The majority of the CapEx is always related to the long-term demand. That will be the composition going forward. Capital intensity-wise, as we always said, if do our job right, the increase in revenue will outpace the increase in CapEx, and that is still the case from what we are looking at…

…Moving into fourth quarter 2026, we expect our business to be supported by continued strong demand for our leading-edge process technologies, including the steep ramp-up of our 2 nm technology…

…[Question] Any update on your AI CAGR growth? I recall previously we talking about like a high 50%.

[Answer] All I can say right now is the demand is extremely robust, okay? We did not see any sign of slowdown. Whether it is a high 50 CAGR, it does not matter. It probably will be higher…

…There is no ceiling for TSMC’s profitability, but a dramatic increase will not happen overnight either.

TSMC’s management thinks recent developments in the AI market are very positive for AI’s long-term demand, in particular, the rise of agentic AI; management thinks there’s great adoption of open source AI, which will lower the barrier to AI usage and lead to more demand for leading-edge silicon; management sees agentic AI and networking driving further silicon demand, in the form of CPUs, and networking chips; management thinks the value of semiconductors is set to increase in the future because of the entire AI trend; management thinks AI-related demand is structural and will last for multiple years; management thinks TSMC is well-positioned to capture the full AI-related demand since all AI players are working with the company; management has not included CPU demand in their assessment of TSMC’s AI-revenue CAGR

The AI market continues to be very strong. Recent developments are very positive to AI’s long-term demand outlook. We observe increasing AI demand fueled by the rising adoption of agentic AI as we transition from chatbot to AI assistance. In addition, we are observing great adoption of open source AI models. This will drive greater efficiency and help lower the barrier to future AI usage, and result in more and more demand for leading-edge silicon. Thus, AI-related demand is being supported by strong underlying demand for AI accelerators. In addition, the emergence of agentic AI CPUs and the continued demand for networking chips are driving further silicon demand. Therefore, semiconductor will remain essential in the AI mega trend. The value of semiconductors in the global supply chain is set to continue to increase in our view. This will create more value opportunities for our customers to capture and more value opportunities for TSMC as well.

No matter which approach is taken, you will require use of the most advanced semiconductor process technology. With our technology leadership, manufacturing excellence, global capacity expansion, and customer trust, we are well-positioned to capture the full multi-year structural AI-related demand. Therefore, all the AI players are working with TSMC…

…[Question] On the agentic AI demand on the CPU. So I wonder, according to our previous framework, demand forecast framework, the mid to high 50s growth including the CPU or not, and if we heard our clients increase the CPU forecast significantly, when is the next time we refresh our AI demand growth numbers?

[Answer] It is not included in our AI demands calculation. The reason is very simple, because we cannot identify which CPU user in the conventional server CPU or AI server CPU. Also, it depends on our customer’s application and their own allocation. So, no. So far today, we stick to our initial very narrow definition on the AI’s demand.

When planning for TSMC’s manufacturing capacity, management collaborates closely with its customers, and customers’ customers, and now they are even checking on the development progress of data centers and power supplies in the USA; management thinks the cloud service providers (CSPs) have been well-prepared; management has a very good idea of the multi-year product roadmap and production plans of TSMC’s customers and continue to hold strong conviction in the AI mega trend; management is moving at full speed to increase TSMC’s capacity, with TSMC expanding at a record pace; TSMC has nearly 20 fabs in progress worldwide, with 13 leading-edge and advanced packaging fabs in Taiwan to be built over the next several years; management intends to capture every piece of business possible; management thinks the demand for TSMC is still higher than supply and they are working very hard to close the gap

To address the structured increase in the long-term semiconductor market demand profile, TSMC collaborate closely with our customers, yet our customer’s customer to plan for our capacity. In addition, we are now going one step further and sending our own teams of people to check on the data center build-out progress and power supply situation in the U.S. The feedback is so far so good, and the CSPs has been well-prepared. Internally, TSMC employed 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. Given the fundamental complexity of leading-edge technologies and the design in and lead time involved, we already have a very good idea of customers’ multi-year product roadmap and production plans. Thus, we continue to hold strong conviction in the AI mega trend.

We are moving full speed ahead to increase our capacity to meet our assessed demand, and stepping up our CapEx investment to support our customers’ future growth. TSMC is expanding at unprecedented pace with a nearly 20 fabs worldwide in progress. We are building 13 leading-edge and advanced packaging fab in Taiwan over the next several years, and another five to six fabs overseas…

…We have no intention to leave food on the table for others, and we want to capture every piece of business possible…

…We have a very sophisticated capacity planning process, and we also have a very disciplined on our consideration. Let me share with you, we put every possibility we can, talking about the CSPs, CapEx, data center build-out, financial, power supply, and also other component supply, et cetera, into our consideration. We make our own judgment, and then based on our assessment the demand, we still think the demand is higher than the supply, and we are working very hard to narrow the gap.

TSMC’s management will only be utilising ASML’s high-NA EUV lithography systems in 2030 because they think that is when the technology will be sufficiently mature with a good cost curve; management is confident that TSMC’s relatively late adoption of high-NA EUV compared to competitors will not disadvantage TSMC

[Question] You did touch upon it, the High NA agreement with ASML from 2030 onwards. I’m just curious, historically, TSMC was more ahead of the curve with the EUV adoption. It looks like by the time you adopt High NA EUV, it would be 2030, 2031, but some of your competitors would already been using it by then. Do you see that as a disadvantage, or you’re not worried about competition getting ahead with High NA EUV?

[Answer] We always evaluate a new tool, and if we want to put into the mass production, it got to be the best technology with a very reasonable cost, and most important is the best maturity. Okay? We say in 2030 we will start to use it because we also think at that time, High NA will be mature, and we are also working very closely with our ASML to establish a 12 in photomask by 2031. At that time, we think that High NA’s cost will be reasonable and for TSMC to make it into the mass production. This is a very good tool, let me emphasize that. In the smaller geometry, it definitely has some advantage. Whether that our technology, because we are using it later than our competitor, so disadvantage to TSMC? No, because we have confidence that we are still the best company to offer the most advanced technology to our customers.

TSMC’s management thinks the company’s advanced packaging technology is very competitive; management is willing to be the backup plan for some customers that are using Intel’s EMIB-T advanced packaging technology

[Question] If some customer, they initially choose to use Intel’s EMIB-T, and for some reason cannot get to sufficient demand, are you also support customers for the CoWoS version or capacity.

[Answer] I still think that TSMC’s advanced assembly, or they say packaging technology, are very, very competitive. But the most important thing is that we using that one to help us help our customer’s product can be introduced in the market smoothly. That’s our number one priority. We have to offer them the best technology with enough capacity. So far, let me share with you today’s situation. We are in the real-world production of 5x reticle size. I don’t believe that anybody can do that today except at TSMC, and the yield is excellent. So our customer are very satisfied with the result. We are working on the 9x reticle size and moving into the 14x reticle size. Of course, we are also working on the CoWoS, and to make sure that in the future we can offer a reasonable cost reduction. But so far, the CoWoS is still the number one choice for our customer. As time goes by, yes, our competitor offer a very competitive solution. Still, I want to say that today’s situation, we are the only one that makes sure that the advanced packaging is a 5x reticle size, and still, we are very competitive. I want to emphasize that… Let me say that, yes, we will be. Number one priority I already indicate that TSMC’s customer’s product can be introduced into the market smoothly.

TSMC’s management thinks the learning curve for the company with the new technology nodes is actually better now with the current situation of having both smartphone and AI HPC (high-performance computing) applications drive demand for the new nodes

[Question] In the past a new node is mostly ramped with smaller die size and simpler design. But today we have both the smartphone and also with the AI and HPC applications coming in. Does that increase the complexity of the node process architecture, make the learning curve more challenging or difficult, and therefore the implication to the margin?

[Answer] Today’s situation actually is a little bit different from previous years. Yes, we now have a very strong demand from smartphone and also AI HPC applications. So the smaller die size per se or the big die size in the accelerator, they happen at the same time. This is to TSMC’s advantage, let me say that. Because we are good. We have been prepared for this kind of thing, and happy to working with us. So the learning curve, yes, but we are doing a reasonable job, and so the customer are very happy today.

TSMC’s management thinks the shortage of power supply in the USA for AI data centers does not drive additional demand for the company’s backside power rail solution

[Question] Could this tighter and tighter power grid surprise make your customer actually accelerating adoption of your Super Power Rail product for the better performance power?

[Answer] Whether this kind of situation change our customers’ behavior and move more into the backside power rail? Answer is no. Whether you use the backside power, those kind of metallization scheme is based on your need for your product. Okay? Some of the accelerator might be using it, some of the product like mobile phones, they don’t use it. So it depends on customer’s product.

Tesla (NASDAQ: TSLA)

Tesla’s management sees Full Self-Driving (FSD) software as the biggest driver of demand for the company’s vehicles; management thinks there’s no competitor to Tesla’s FSD; FSD is approved in many countries in Europe, but Tesla is pushing for an EU-wide approval; management thinks an EU-wide approval could happen in the next few months; more than 75% of Tesla vehicle deliveries in North America in 2026 Q3 included a paid FSD subscription at the time of delivery, up 20% from 2026 Q2; every Tesla car delivery comes with a free month of FSD, but vehicle owners are still signing up for the paid subscription; FSD now has 1.7 million paid customers globally, and more than 50% of them are paid subscriptions; management wants to sell certain cars in Europe with some FSD benefits before a broader approval in Europe is obtained; Tesla is building AI-based mapping and navigation into FSD v.15 (version 15) and it will be very personalised, and will also tap on the collective intelligence of all Tesla cars; management thinks FSD is an incredibly hard problem to solve for other vehicle manufacturers; management is seeing many customers walk into Tesla’s showrooms and ask for an FSD-drive, instead of any specific car model; management thinks Tesla’s FSD is much better than what any other Chinese car company can deliver; FSD is already approaching $1 billion in ARR (annualised recurring revenue)

Obviously, we are going to be ramping, and we are ramping production meaningfully. The biggest driver for demand is Full Self-Driving. Today, there is really no competitor for FSD in any car that you can buy…

…We are pushing very hard for FSD approval in additional markets. We are now approved in many countries in Europe, and we are hoping for an EU-wide approval. It is hard to predict, but maybe within a few months, but I would be surprised if it takes longer than Q1. Obviously, that is not a timeframe we control, but we do anticipate being fully approved throughout Europe and many other countries…

…In Q3, over 75% of deliveries in North America included a sign-up for paid FSD subscription at the time of delivery, which is approximately 20% higher than Q2. Overall, the FSD attach rate continued to improve, reaching over 1.7 million paid customers globally, of which for the first time, 50% is based on subscriptions. Given the strength of FSD in the U.S., we are taking extra steps to sell certain cars in Europe with some benefits to try and get more fleet out there before a broader FSD approval is obtained…

…One thing which I’d also like to make sure it’s clear when I say in North America that 75% attach rate at the time of delivery, this is every car which we’re delivering comes with a month of free FSD subscription. Still, people are signing up even though they have that free available…

…We are building our own maps, but not the same way as OpenStreetMap. We are building an entirely new architecture with essentially AI-based mapping and navigation. It’s going to be super awesome. It’s going to be integrated with voice. You can talk to it, ask for different routes. It’s going to learn your personalized preferences, plus also the fleet preferences. The collective intelligence of all the cars are going to flow into making the maps and navigation be super cool. You’ll see the start and end of trips already use this technology pretty soon as part of the V15 release. Over the next several months, it’s just going to take over all of the navigation functions, parking, destination handling, and just will know you better than you know yourself in some regard for the driving task…

…It’s easy to get to the first 90% and make a cool-looking demo. But when you get in the long tail of this problem, it’s so long that people don’t realize anyone with their sane mind should not attempt to solve this problem. It’s so difficult, and so long tail. Especially to get to a safety point where you can drive safely for millions of miles with no injuries and so on, that’s going to be extremely hard. As we release unsupervised FSD to the broader customer fleet, it’s just going to be I don’t think anyone can match it in several years…

…Especially in our showrooms here in the U.S., customers are now coming in and saying, “Can we get into an FSD drive?” They’re not asking for 3/Y or Cybertruck. They’re saying, “Can we get an FSD drive?” That has become the key driver because what it does is it actually opens up a lot more flexibility for different parts of the population wherein they had issues driving conveniently before. Now they’re getting their freedom back by being able to use FSD…

…The leapfrog which we have in terms of this technology is not there with any of the other Chinese OEMs today. We are working very diligently to try and get broader FSD approval in China. It is hard but for certain cars, we have been able to have FSD operating and whoever has tried it there, they understand how much better it is as compared to any of the competition. In fact, I do not know if you have seen this, there have been some competitor CEOs who have come and done FSD drives here in the U.S., and they themselves have been blown up as to what FSD is capable of doing…

…[Question] Are you at some point going to disclose for us the annual recurring revenues that FSD represents. I did my own math, and I’m thinking it’s probably now approaching $1 billion of ARR. Is that the right order of magnitude?

[Answer] I think the numbers which you shared are correct. As it scales further, we will start sharing more metrics around FSD, not just the revenue, but also other key metrics around it.

Robotaxi’s fully autonomous miles driven are scaling rapidly on a weekly basis; management thinks no other company can match Tesla’s scale with autonomous vehicles; Tesla’s Cybercab fleet in Austin has grown by 10x since launch a month ago; Cybercabs are in use at scale in Austin, Texas; Robotaxi’s unsupervised miles increased by 8x in 2026 Q3; management is preparing for a broader Cybercab launch in other markets; management thinks Cybercab provides the best Robotaxi experience, but they will augment the Robotaxi fleet with other vehicles over time; management’s ambition with Robotaxi is to take over every part of terrestrial transportation, not just transporting people, and so they are preparing a huge investment cycle for the business; management launched Cybercab with the help of data from Tesla’s other vehicle platforms; management is seeing very good scaling in Cybercab’s AI capabilities; Cybercab will be launched in Dallas in November; management thinks consumer vehicles with FSD can become part of the Robotaxi fleet in 2027 Q2; management is trying to get the Cybercab to customers by the end of 2026; management sees most Cybercabs as being for Tesla’s internal use to deliver transport-as-a-service; the Robotaxi service is still not at the $0.30 per mile cost-target management has, but they see a path to get to the target; management has released a safety report on unsupervised Robotaxis in China, and the data is hard to refute by any regulator; management thinks Robotaxi can start moving the needle in Tesla’s financials by 2027 Q2; Tesla’s engineers think scaling the Cybercab is going to be relatively easy; the Cybercab has 50% less parts than any other Tesla vehicle so that’s 50% less things that can go wrong; the listed Cybercab capacity of 125,000 units a year is not near the actual capacity of that vehicle line; there are still software challenges for the Robotaxi service; management thinks Tesla will end up doing most of the passenger miles on in any country it is allowed to operate freely

We are seeing the Robotaxi miles driven or fully autonomous miles driven scale exponentially, meaning scaling very rapidly. It would be hard to overstate at this point, it really is an extremely fast week-over-week growth in autonomous miles. I do not think there is any company in the world that can match our scale with autonomous vehicles as we continue to ramp. The Cybercab fleet in Austin, for example, has grown by a factor of 10 since launch about a month ago…

…Come into Austin and you’ll see Cybercabs in use at scale in Austin. As I think a lot of people saw with the Austin City Limits concert recently…

…Accumulative unsupervised miles 8x in the quarter, as we had weekly double-digit growth throughout the quarter. We’re taking all the learnings from our Austin expansion to help us prepare for broader Cybercab launch in other markets. Our priority is progressing the density and expansion of the network through vehicles that provide the best experience, like the Cybercab. We will also continue to augment the fleet with other variants over time to address the terrestrial transportation needs of the total population. I’d like to stress that what we are trying to do is literally take over every part of terrestrial transportation. Not just getting people. We’re starting with getting people from one place to the other, but we want to basically own this transportation of any kind on Earth. We are in a huge investment cycle to set us up for the future…

…We launched Cybercab this quarter, which is very exciting. We achieved that by mostly learning from data from the other vehicle platforms. For example, Model Y, Model 3, et cetera. All of the data helped make the Cybercab without requiring a ridiculous amount of vehicles to produce the data. We are seeing very good scaling in terms of Cybercab’s AI capabilities. We launched in Austin, but that’s only because we had some cabs, and we want to swarm Austin with Cybercabs everywhere. There’s no fundamental reason that we are limited to Austin. In fact, next month we’ll be launching in Dallas…

…The software for Cybercabs and Model Y is the same. Pretty soon we will be able to release the same software as part of the V15 release to customers. The only additional requirement is to make sure that, for example, the customer Model Ys can be part of the geo-fences and the fleet management that Tesla has for it to be fully autonomous. We suspect somewhere around Q2 next year is when we can turn on some of the first customer vehicles to be part of the Robotaxi fleet…

…We are trying to get Cybercabs to customers by the end of this year, 2026. Obviously, we are producing them in our line in Texas, but all of those are currently going to our own internal Robotaxi fleet. As Tesla grows, we will mainly deliver fully designed autonomous vehicles to our customers. Cybercab will be the first…

…The vast majority of the Cybercab stuff will be essentially Tesla-owned and transport as a service…

…Week by week, as Elon was talking about, we look at the miles and how many paid rides are happening. Are we at our goal of $0.30 today? No. Do we see a path to get to that? Yes, we do. It is all about scaling operations. What you will see over the next few months is that today, Cybercab, which is the primary Robotaxi product, is available only in Austin. We do plan to bring it out in some other cities before the end of this year. As we add more Cybercabs, we are also learning from the operational experience of managing a fleet, pick-up and drop-off, cleaning, all those things. All those costs are being dialed in, and that is why, like I said before, we definitely see a path to get to our $0.30 target…

…We are actively working with the regulators in China because I do not know if you saw this, but we have released our safety report on FSD before, but we have also released our unsupervised, our Robotaxi safety report. We just think this is the right way to get people into a safe environment because you know the amount of accidents which happen because of driver that was not being alert. This is the only way we can ensure that there are less casualties in the world. Any regulator who looks at the data, it would be very hard for them to refute it…

…[Question] I am curious if there is a way you can triangulate that for us into revenue and maybe profitability of the Robotaxi business. If you sustain, let us say, 15% week-over-week growth in these miles, you will probably cross 10 million miles by the end of the year. How many miles would you need to generate gross profit?

[Answer] I would say it’s not going to move the needle this quarter. But if you say maybe, I think it starts to become noticeable probably in Q2 and then probably a big deal by Q3 or Q4 of next year…

…[Question] How do you expect the ramp-up curve to be on the Cybercab line?

[Answer] I would give kudos to the engineering team. They have designed a product because not only do I talk to the engineers, I also talk to their manufacturing guys. They think this is one of the easiest product for them to scale in terms of manufacturing. Scaling would not be that much of a challenge… We have 50% less parts than any other vehicle we’ve ever built, so that’s 50% less things to go wrong. We’ve definitely been bursting the Cybercab line to see how fast we can run it. I think the listed capacity is more of a rate at which we’re capable of running today, but the actual capacity of that vehicle line is significantly higher…

…[Question] A follow-up on Robotaxi. It does seem like you’ve hit an inflection point here in Austin over the last month or so since you launched the Cybercab. Would you generally say the bottlenecks at this point to growth are more regulatory-driven as you think about expanding into new geographies, and that you’ve kind of gotten through most of the hurdles on the software side?

[Answer] For example, we had initially had the Robotaxi service from 6:00 A.M. to 9:00 P.M., then we expanded to 10:00 P.M., 11:00 P.M. Now it’s going to be pretty soon 2:00 A.M. So we are slowly expanding the availability of Robotaxi, and that is driven by the capability of the AI software. We just want to make sure that there’s not a single safety incident. That is our operating principle. Even if there’s the slightest possible risk at all, we don’t take on that risk. So we are very risk-averse in that sense, as in we want zero injuries or anything bad happening. That is the driving principle. We will see that over the rest of this year, we’ll unlock 24/7 operation, go to higher speed roads. We think all of that is necessary to scale and offer a great product to a lot of users. That’s why we wanted to both do the breadth-first of doing multiple cities. We do six cities now. We are also doing the depth of having a lot of vehicles in a single city, which is in Austin. We’ll expand to more weather conditions, day and night conditions, highways and high speeds, and all of that will be done hopefully by the end of this year, at which point you will see a lot more scaling, a lot more cities being covered by Robotaxi. So it’s not purely regulatory or something like that. It is technology-driven, but we do see that pretty quickly become regulatory, perhaps in Q1, Q2 timeframes…

…Unless we are stopped by governments, which is certainly a possibility, if they are trying to protect a domestic industry. I think Tesla will end up doing a majority of all passenger miles on Earth in any country where it is allowed to operate freely.

Tesla has started early production on the high-volume Optimus line in Fremont; management thinks the ramp of Optimus’ manufacturing will depend on the slowest or unluckiest component involved in the manufacturing process; Tesla already has the vast majority of a million units per line production line landed in Fremont, but management thinks it will take a year or more to get to that annual production rate; management is still tweaking Optimus’ design, and AI software, and believe they will be done by the end of 2027, which is when Optimus can be delivered outside of Tesla; management thinks Optimus can get the training data needed only by Tesla manufacturing 10,000-20,0000 of the robots and having them do self-play; management is building the Optimus Academy for the Optimus robots to practice tasks and these data can be used to improve Optimus’ capabilities; management is convinced that Optimus will be the biggest product on Earth; management thinks there’s no other general-purpose humanoid robot that is close to Optimus; management thinks Tesla can release a video of Optimus doing things it was not pre-programmed to do in about 1-2 months’ time; management thinks a lot of humanoid robot demonstrations are pre-programmed, and the robots are not really using any advanced AI or SI (superintelligence); management thinks Tesla will integrate the Tesla AI 5 chip into Optimus in April 2027; management thinks the AI 5 chip will improve Optimus by an order of magnitude because AI 5 has an order of magnitude more compute than AI 4; management thinks Optimus’ initial and most valuable customers will be industrial companies; the hand of the Optimus robot remains one of the toughest problems for Tesla to solve; management is already building Optimus robots for training purposes even when the hands are not perfected; management thinks there will eventually be more humanoid robots on Earth than humans; Tesla is building a new Optimus factory in Fremont that will have a much larger capacity than the Austin factory

Regarding Optimus, we’ve started what I’d call early production on our high-volume line in Fremont. I want to elaborate on this point because I have said this before, that the growth of Optimus, it’s an S curve, but the initial part of the S curve has a shallow slope and is somewhat bumpy because it is such a radically new product. The way to think about it is there’s 10,000 things that have to go right for production of the robot, and it’ll move as fast as the slowest, least lucky of the 10,000. We do have the vast majority of a million unit per line production line landed in Fremont right now. I would expect that it takes us probably at least a year, maybe a little longer, to get to that annualized rate, to get to 20,000 units a week…

…There’s still some things we need to tweak about the design because you need to deliver a robot that is safe and reliable and has long duration. It can’t wear out after a month. There’s still quite a lot of work to do with regard to the AI and the robot for it to be truly useful for open-ended tasks. Like I said, I would expect that the AI, the electromechanical design, and the reliability and usage duration, the usage life of the robot, I do expect these issues to be solved around the end of next year. That’s the time to expect when we’re delivering Optimus to companies outside of Tesla…

…There’s a lot of things we have to get right about the AI architecture that we’re still working on. We’re making good progress, but that needs to be sorted out. Really, in order for Optimus to get the training data we need, we actually need to make a lot of Optimi. We need to make, I think, on the order of 10,000 or 20,000 bots and then have them do self-play. We’re building out an Optimus Academy, where Optimus can practice doing a very wide range of tasks. Then we can use that data in sort of a flywheel to make it better and better at these various tasks. A big difference with Optimus versus the cars is we don’t have this vast fleet of cars from which we can gather training data. With Optimus, we have to bootstrap it by making the robots and having them do self-play, which is really how a human learns to navigate the world…

…I remain convinced that Optimus will be the biggest product on Earth, and humanoid robots in general will be the biggest product on Earth…

….To the best of my knowledge, there is no one that is even close to Tesla with respect to a general-purpose humanoid robot…

…I think we may be ready to release an Optimus video in a month or so. I want to make sure that when we release this video, that it is not like a canned demo that. When you see these humanoid robot demonstrations, some of them look very impressive, but they are not really using any kind of advanced AI or SI. They are pre-programmed deterministic tasks, and if you actually try to use that humanoid robot in the real world, you would have a lot of trouble. It would not work, basically. So I want to release a video where it is Optimus doing things that are not pre-programmed. I think that is maybe a month or 2 for that to have a demo that you can believe what you see type of thing.

There is another milestone that I think important we want to get to, which is to integrate the Tesla AI 5 chip into Optimus, which, if things go according to plan, will be in approximately April of next year. That is a giant improvement in capability with an order of magnitude more operations per second. It is an order of magnitude more compute, AI 5 versus AI 4. That will help Optimus a lot because the degrees of freedom that a humanoid robot has to deal with are far greater than what a car has to deal with. It is a bit of a struggle to squeeze everything needed to operate with all the degrees of freedom of Optimus and the wide range of tasks. If you think of a humanoid robot is going to do 1,000 times more things than a car would do. That is some very big number, basically. I think the additional compute of AI 5 will make a big difference…

…In terms of the initial customers for Optimus, I think it probably would be things that are industrial applications at first. Things like dangerous repetitive tasks, things that people really don’t want to do, and that you want done basically 24/7. So, around the clock stuff. That is going to be the highest value for Optimus in the beginning. As we scale production, of course, it would be something that people can buy for their home. But the highest value use, I think, will be heavy industry, dangerous tasks, or things that may cause repetitive stress injuries, and that you want done 24/7…

…[Question] A quick follow-up on Optimus. I know you mentioned that we could see a video soon, that there’s quite a bit of progress, but you’re still making some refinements. I guess, just generally speaking, where do you see most of the design changes still happening going forward? Is it still the hand that’s an issue? Anything you can call out there?

[Answer] It is mostly the hand, and just the fine-tuning of the hand design, and the fingers especially. It is a hard problem, because humans have self-repair. Our body repairs itself, but robots don’t. Our fingers are kind of squishy and have lots of sensors in our skin. It is hard to make a hand that can handle lots of wear and tear. But it is still squishy enough to pick up delicate objects, and has all the sensors and doesn’t damage the sensors when you do repeated actions. There is a lot of little things that you realize actually what an incredible thing the human hand is at the end of the day. So it is mostly from an electromechanical standpoint, the hand. There are other things that are improving as well throughout the robot, but that is the main thing…

…[Question] Can you talk about the strategy around ramping Optimus production just a little bit to start training, even if the hands aren’t fully baked with the final design? It seems like there’s a lot that you could end up doing in terms of the training cycle and maturing some of the simulation technology, even without perfected hands.

[Answer] That’s what we’re doing, is we’re building robots that do not have perfected hands, and we are making progress with Optimus training and ironing out all aspects of the supply chain and the production system. We’re doing all these things in parallel, like this giant production line at our Fremont factory, massive supply chain, and then fine-tuning the design and engineering simultaneously with the AI software. All these things are happening simultaneously.

…I think at some point there will be more humanoid robots on Earth than there are humans…

…You should know that not only the Fremont factory, we’ve also started constructing a factory here in Austin, which would be able to do a lot more robots as compared to the Fremont one.

The research fab for Tesla’s Terafab project is progressing quickly; the research fab will commission machinery int 2027 H1 and management hope to start manufacturing in May/June 2027; management’s aim is to manufacture useful chip volume around 2028 Q3; management thinks Terafab will be the most valuable building in the world; management thinks Terafab will be necessary for the scaling of Optimus, as it can address bottlenecks across the semiconductor value chain; management thinks TeraFab will need significant capital to meet the goal of reaching useful chip volume around 2028 Q3, in the order of $100 billion over the long term; Tesla has already placed most of the orders for the first set of equipment for TeraFab

Regarding the Terafab, the Austin-based research fab is progressing quickly. We’ll begin commissioning machinery in the first half of next year, and hopefully, if things go well, we’ll make our first test chip just to see if we can make any chip at all around May or June. Our aspiration is to be making some kind of useful chip at volume around Q3 of 2028. As I’ve said publicly on X, the Terafab I think will be the most valuable building in the world and will probably have an economic value that will place it in the top 20 of countries. So it’s an epic project. This will be an incredibly important asset, if not a necessary asset, in order to scale, especially Optimus, as we can address bottlenecks in semiconductors, whether it’s memory, packaging, logic, or whatever the case may be…

…[Question] You mentioned that you had an an aspirational goal of 2Q 2028. Do you have a sense on how much capital needs to be invested to get there? Also what kind of milestones we should be on the lookout for on the way there?

[Answer] It’s going to be some pretty significant capital. It’s certainly in the tens of billions for Terafab. We’ve got to figure out what the relative split is between SpaceX and Tesla in terms of who pays for what at Terafab. But it’s not going to be less than tens of billions in capital equipment up through early 2028. Then it’ll probably be north of $100 billion long term…

…The first set of equipment for the fab which Tesla is building, we’ve actually already placed most of the orders.

Tesla’s energy business is seeing extreme demand from data centers, and this is driving improvements in selling prices and margins

On the energy business, we had our second-best quarter of deployments of 13.7 gigawatt hour of energy storage… the business is witnessing extreme demand from data centers, and we are doing our best to increase production, and accordingly, we’ll see improvements in ASP and further improvements in margins as we move forward.

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 ASML, Intuitive Surgical, Meta Platforms, and TSMC. Holdings are subject to change at any time.

What We’re Reading (Week Ending 18 October 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 18 October 2026:

1. How Personal Agents Get Paid – Tanay Jaipuria

Over time, in certain categories, there may be a world where agents decide that they want to capture more of the take rate that aggregators do. For example, Booking’s might make 14% to 15% of GMV, but the affiliate fees they provide might only net out to ~5-7%.

To earn that extra fee, agents could get more involved in directly providing inventory access, accurate prices, reliable bookings, potentially being the merchant of record and support for changes and cancellations, rather than using the aggregator itself for these things.

As one note, it may be significantly easier to “compete” or try to displace aggregators that are more digitally oriented than for aggregators/marketplaces that do a lot of work in the physical world (such as an Uber or a DoorDash)…

…So far, we’ve largely covered the potential for agents via fees from merchants/partners, but there’s one other opportunity, which is around advertising (i.e., specifically trying to drive incremental demand for merchants).

It is possible that these don’t look like ads at all and in an agentic world, could just be paid through higher take rates, but I figure it’s still worth quantifying since we may also see other forms of ad units as well.

A merchant might pay the agent (in addition to the customer directly) to have their offer considered when someone asks for a hotel or a pair of shoes.

2. Models as Insider Risks in the Super Intelligence Era – Satya Nadella

We can’t treat Super Intelligence as a set of nested black boxes and simply accept or reject its recommendations, answers, and actions. We must build contained systems whose behavior we can observe, limits we can test, and actions we can always contain.

In other words, we need to separate the supply of intelligence from the authority over it.

Setting aside the hard problem of alignment, we need to start with an engineering approach to containment and governance. We need to surround non-deterministic models with strong, deterministic system design, human controls, and reliable operating procedures, and establish industry standards where existing ones are insufficient.

Treating frontier closed and open weight models like insider risks is a way to build such a system. Not because they are necessarily malicious, but because any sufficiently capable actor with access to important systems can make mistakes or be compromised, and the architecture of containment and control must account for that.

The good news is we have learned a lot about how to handle powerful actors inside the enterprise. This isn’t new! We’ve established best practices and refined them over decades (establish identity, limit privileges, log activity, create containment boundaries, etc.)…

…We should therefore design these systems around principles of observability…

…The most trustworthy Super Intelligence system will not be the one with the model we trust most. It will be the one that enables us to trust the model the least.

3. Room at the Top – Abdullah Al-Rezwan

I came across this astonishing chart from Michael Batnick yesterday showing that the combined market cap of the bottom 434 companies in the S&P 500 equals that of the top 5. It is the time series, though, that drives home just how unusually concentrated the index has become over the last 30 years. Even at the height of the tech bubble, the number was below 350 and following the crash, the number went closer to 200. And we have been at or above tech-bubble levels of concentration consistently since 2019, save for a brief pause in 2022…

…In a follow-up tweet, he noted: “Interestingly, the TTM net income of the top 5 stocks is also equal to the bottom 436 companies.” …

…Technology distribution is now truly global, a handful of companies have aggregated demand on top of it, and with near-zero marginal cost, much of every incremental dollar of revenue falls straight to the bottom line. Concentrated market cap hence looks more like the market faithfully mirroring where the profits have actually gone.

But wait a minute, aren’t we leaving behind the world of zero marginal cost? Perhaps, but given the allegedly lofty inference margin in the API layer for the labs, one wonders whether we haven’t quite diverged from the zero marginal world as much we thought we did. More importantly, I sometimes wonder what it would imply if all this capex buildout actually yields attractive returns…

…I am a naturally optimistic person. I share the unease about a world in which a handful of companies generate much of its profits, but despite the differences, railroads can still be quite instructive. The same UBS report had this rather mind boggling stat:

“…railroads, despite being a declining industry over the period of the study (falling from 63% of the US market in 1900 to less than 1% today), actually outperformed both the US stock market and their newer technology competitors since 1900.”

Ultimately, the economy the railroads helped make possible grew far larger than the rails themselves. Refrigerated railcars turned a St. Louis brewery into Budweiser. A national rail network turned a mail-order catalog into Sears. Railroad express contracts turned a parcel-and-cash courier into American Express. Grain sheds at the end of a rail line turned an Iowa grain flathouse into Cargill.

AI may inflict plenty of creative destruction, but it will likely also usher in a Cambrian explosion of small businesses that can hire and scale with AI agents faster than ever before. Much of their spending may well flow straight back to the top, so the “434” may not come down anytime soon. But perhaps the most hopeful version of this story doesn’t require the top 5 to shrink at all; it’s one where companies not yet founded grow large enough to join them.

4. Insurance claims to test Altman and Amodei liability for ‘rogue’ AI – Lee Harris

Insurers are preparing for multimillion-dollar claims stemming from AI agents going “rogue”, with growing concern that top executives such as OpenAI’s Sam Altman and Anthropic’s Dario Amodei could be held liable for the actions of their models…

…Insurance broker Aon analysed more than 300 AI-related legal cases and found that insurers could also be on the hook to pay claims under policies covering crime, intellectual property, media liability, cyber security, and technology errors and omissions.

Top industry figures also said AI executives could be sued for the actions of models, with insurers potentially facing claims under “directors and officers” insurance policies, which cover executives if they are taken to court for their decisions or statements.

“Ultimately, the OpenAI CEO is liable [for the Hugging Face incident], because there’s an absence of control in their business,” said Tim Rayner, UK head of underwriting and claims at Verisk. “So from a directors’ and officers’ perspective, it would come back to him.”

If OpenAI had purchased D&O insurance, Rayner added, the ChatGPT maker could seek to claim on the policy to cover potential future losses from lawsuits targeting its chief executive Sam Altman…

…Kevin Kalinich, a broker at Aon focused on digital risks, said that the strength of a legal claim against the head of an AI lab would depend on factors such as whether the executive had used “reasonable business judgment” in public statements about the company and its products.

However, Aki Hussain, chief executive of insurer Hiscox, said it was too soon to know how US courts would treat liability for AI agents: “It’s the US, so [a claim] could be under anything — whatever insurance they’ve got.”

5. Will AI replace workers? Not if we build it right – Daron Acemoglu

Many people have been forecasting that AI will drive remarkable improvements in productivity and economic output.

But reality seems to be closer to the great economist Robert Solow’s 1987 quip about computers—“everywhere but in the productivity statistics”. AI advances aren’t in the productivity statistics either, at least as of now.

Achievements of current AI models in writing computer code and designing software or in radiology diagnostics are truly remarkable. But that is in the lab, where context is clear and tasks are neatly delineated. The evidence from firms that adopt these tools for the same tasks “in the wild” isn’t as impressive. Often, engineers have to spend a lot of time debugging AI-written code and radiologists and doctors cannot combine their expertise with AI tools, leading to suboptimal outcomes. Several studies and reports have failed to find much in the way of productivity gains from adoption of AI by most companies. Of course, things may be different, and much better, with new AI agents, but so far the disappointing productivity performance isn’t confined to the aggregate. It is also there for firms that are experimenting with AI.

None of this should be surprising. Every new technology takes time to be productively adopted, and typically, the more transformative the technology, the slower this process is. Some draw parallels between AI and fire. A less hyperbolic comparison might be to electricity.

Famously, electricity, despite all of its revolutionary potential, took several decades to be adopted and used widely, even in manufacturing. Already in 1881 New York and London had central generating stations and many were foreseeing that electricity would completely transform manufacturing, homes and transport. But as late as the 1920s, only about half of factories and homes were using electricity. We may in fact expect productive adoption of AI to be even slower because of the necessary reorganization of firms. Job tasks will have to be reallocated between AI models and workers, and many specialists will have to learn how to work with AI. Managers will have to develop their own understanding of what it is that they want from AI and how they can work towards getting it. Worse, many firms may attempt to go for full automation of certain occupations, which is likely to remain impossible in the near future.

All of this will not just delay the productivity gains from AI, but imply that job displacement effects will be slow as well. Several rounds of forecasts predicted the end of radiology or drivers or accountants: none of that has happened yet. Hospitals still need radiologists (in fact more than before), there are still journalists, paralegals, accountants and office workers.

Two years ago, I estimated that within 10 years, we shouldn’t expect much more than about 5% of what humans do to be replaced by AI.

Where does this 5% number come from? Though it is admittedly no more than a guesstimate, it was based on an assessment of what AI models could achieve then and how these capabilities would develop in the second half of the 2020s.

To put it simply, my forecast was that by the end of the 2020s AI would be in a position to successfully automate simple office and cognitive tasks, making up about 20% of work in the US economy. I predicted that the impact on more complex cognitive and office jobs would be slower in coming, because it would take AI much longer to acquire the judgment, multi-dimensional reasoning and social skills that most jobs require. Nor would society be comfortable with AI fully taking over air traffic control, mental health therapy or the management of companies.

AI-based automation of jobs that involve heavy interaction with the physical world will also have to wait, because it will take time for flexible robotics, AI models with a full sense of spatial-causal relations. It may be even longer for computer vision technologies to reach the point where AI could be seamlessly combined with robots to perform tasks in manufacturing and construction.

I also estimated that, given the diffusion of other technologies, such as electricity, computers and previous computer vision technologies, only about one quarter of the 20% that can be fully automated would be automated within 10 years. This gave the 5% number.

My forecast (with the full warning that it was and remains impossible to know how quickly these models would advance) was that GDP would increase by about 1.5% over 10 years. Since then, advances in models have been more rapid than many, including me, predicted. On the other hand, we haven’t seen many applications that can easily be adopted and transform the production process, and, as I explain below, the magic of AI needs to happen in the applications for it to have a sizable impact on the economy.

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 Microsoft (Satya Nadella is the CEO of Microsoft). Holdings are subject to change at any time.

What The USA’s Largest Bank Thinks About The State Of The Country’s Economy In Q3 2026

Insights from JPMorgan Chase’s management on the health of American consumers and businesses in the third 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 third 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 continues to experience strong business investment and consumer spending, but there are risks to the global and US economy, including large fiscal deficits, sticky inflation, rising interest rates, and more.

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 continued to experience strong business investment and consumer spending in 2026 Q3, but the risks (large fiscal deficits, sticky inflation, high asset prices, rising interest rates, and geopolitical tensions) are shifting; JPMorgan’s debit and credit spend were both up 10% in 2026 Q3, driven by consumer demand; management is not trying to predict higher interest rates, but they think the chance is higher than others think; management thinks if interest rates stay very high for a long time, the consumer will be impacted; the US consumer is currently very healthy because of a strong labour market; management thinks the balance sheets of JPMorgan’s middle-market clients are strong; management sees the corporate credit outlook as stable, even though there are pressures

The U.S. economy continued to be fueled by strong business investment and ongoing consumer spending. However, large fiscal deficits, sticky inflation, elevated asset prices, rising interest rates and geopolitical conditions continue to shift under the surface. These forces may be manageable, but we remain prepared for a wide range of outcomes…

…Debit and credit spend grew 10% year-on-year, with continued momentum across spending categories, including gas and travel, supported by consumer demand…

…[Question] As we assess the risk of interest rates maybe moving even higher than what we saw in the third quarter, maybe your latest thoughts around how you assess the resiliency of the economy to handle maybe a 6% rate on 10-year and just the Fed hiking a few times.

[Answer] Of course things like that could happen. I would never take that off the table. They could happen for good reason, which is a lot of demand for capital, a lot of productivity, and things are fine. They could happen for bad reason, in sticky inflation around the world. They can happen not because of what is in the U.S. It could happen because of what happens overseas. So we are not predicting it. I have always thought there is a higher chance than other people…

…If you keep real rates very, very high for a long time, at some level, that’s designed to slow down economic activity to fight inflation. It’s hard to imagine that that wouldn’t have an impact on the consumer. So far, what we’ve seen in the current data and what’s been true, I would say, for the last few years is that the consumer has been quite resilient in the face of a number of headwinds that under different circumstances you might have expected would present a challenge. But I think the conditional expectation of consumer credit health as a function of higher rates is a fairly nuanced question. But fundamentally, the picture is pretty simple. The consumer is currently very healthy, primarily because of a healthy labor market. But if the central bank needs things to cool off, and that involves an aggressive extended rate cycle, almost by definition, that’s going to involve higher unemployment, which will produce weaker consumer credit results…

…I think there, the theme for some time now has been people worried about other people while doing fine themselves. The theme is always like, we run these surveys and they’re always like, “Oh, I’m worried about this, I’m worried about that.” And it’s like, “How are you doing?” “Oh, I’m doing great.” So it’s kind of an interesting dynamic there. No question that higher rates, I think input costs, inflation, the diesel narrative, these things are tough for middle-market clients. So we see them trying to manage that to the best of their ability, and I think it does create a little bit of caution in that space. But I think balance sheets are strong…

…[Question] Could you talk about the corporate credit health? It looks like you built some reserves on the wholesale side.

[Answer] The wholesale build is actually just from new activity, so it’s not downgrades or change in the outlook or anything like that. I would say that the corporate credit outlook is currently pretty stable. But obviously there are issues out there, and we’ll have to see how those flow through the performance of our borrowers. Energy prices, fuel inputs, higher rates, these are all things that will create challenges in different pockets of the economy and C&I space, as well as the CRE space for that matter. So we’ll see. But so far, it’s very early obviously, but in general, we think corporate balance sheets are in quite good shape, and so there’s been resilience.

2. Net charge-offs for the whole bank (effectively bad loans that JPMorgan can’t recover) was down at US$2.2 billion compared to a year ago (charge-offs was $2.4 billion in 2026 Q2)

Credit costs of $2.7 billion with $2.2 billion of net charge-offs and a $539 million net reserve build.

3. JPMorgan’s investment banking fees were up 14% in 2026 Q3 from a year ago because of strong performance in advisory; management still has a positive outlook, but they’ve seen a slowdown in capital markets activities, which has led to short-term uncertainty; management thinks the capital markets pipeline is fine at the moment, but it can open or close very quickly

IB fees were up 14% year-on-year, reflecting strong performance across products, notably in advisory. While our outlook remains constructive, supported by ongoing client engagement, evolving market conditions have recently slowed activity a bit, so we’ll have to see how the quarter develops…

…The pipeline is fine, but I pointed out many times before, the pipeline is like an accordion. It can open and close pretty quick. You have seen in the U.S., as Jeremy said, the word delayed, probably. We do not really know. We will see. But I was in Europe recently, and the pipeline there is actually quite healthy, and even more so in IPOs than we would have thought. We just do not know…

…We do not obviously know exactly what investment banking fee revenue is going to be in the fourth quarter. I think that we do know that the overall environment is robust and supportive, and there is a lot of business that wants to get done and needs to get done. As you point out, Erika, not surprisingly, given the rate sell-off, you are going to have the market take a little bit of a breath and a pause to make sure buyers and sellers can meet on valuation terms. We did see a little bit of that slowdown in the second half of September, basically. We will see how that plays out into December. I mean, sorry, into the fourth quarter.

4. Management continues to expect credit card net charge-offs for 2026 to be 3.2% (previous expectation was 3.2%; was around 3.3% in 2025)

We continue to expect the card net charge-off rate to be approximately 3.2%.

5. Management feels good about investing long-term in areas that improve the security and resilience of the USA

I want to brag on the Investment Bank a little bit. They have hired a tremendous amount of very talented people in SRI, and we are lucky to have Todd Combs here, investing some of the capital. They have had a lot of ideas. It is not just meetings at this point. They have helped fund some interesting types of things. We have more ideas. They are doing a really great policy around it too, which is what can you do to accelerate security and resilience in the U.S. around, whether it is shipbuilding or —we had a big announcement in Philadelphia. I am proud of them. We have had a bunch of people who retired, who raised their hand, and they are coming back more for patriotism than anything else. It has been just a great effort for the company. As we pointed out, it is global, and the global response has also been exceptional. I have something across my desk which is, alone, over $10 billion or $20 billion of financing in one country. A lot more to come here. I would put in the same category, American Dream Initiative, the innovation economy initiative, the commercial banking initiative around the world. These things are working. We think we are doing the right kind of good business at good returns.

6. Management is going to be stricter with deploying capital for credit because they are seeing credit terms becoming a little loose; the loose credit terms appear to be related to too much AI-related investment activity

[Question] Just following up on the capital allocation. Maybe a comment or two on where you are less comfortable allocating capital to?

[Answer] I am not going to give you very specific stuff, but we are tightening up a little bit on credit. I think some of the credit terms have gotten a little bit loose. You have heard us talk about that before, and other people. A little too much pay, a little too weak covenants, a little too much priming good assets, a little too much pushing on it. We just do not feel you have to chase that kind of stuff anymore…

…To Jamie’s point about tightening up credit, that is a little bit of an overall point about just making sure we are really scrubbing everything. In this moment where AI is such an overwhelming theme, it is producing so much innovation in so many different pockets. There is capital being tapped through all kinds of different mechanisms. We just need to be really, really on top of things to make sure that nothing slips through.

7. Management thinks this latest rate hike cycle is even more unusual than the last one and there isn’t much historical precedent to study

I think it is an important thing for people to be thinking about because this particular rate cycle, this latest rate cycle, is arguably even more unprecedented than the last one. We just don’t have a lot of historical data about how people behave when you see rate hikes from the launch point that we’ve seen this cycle. So it’s been an unusual 10+ years, I would say, and I think it’s quite important for people to realize, especially people who are doing this by training models on historical data, that the historical data set’s just not that rich. So it’s very important to be kind of thinking out of the box.

8. Management thinks no financial institution is immune at this point to a major change in sentiment in the AI space

…I think there, in addition to the obvious single name focus, the other thing to be very careful about is just the AI theme. Because the AI theme is so broadly diffused into the marketplace. It’s not just traditional construction lending. Now, increasingly, it’s working capital revolvers. You also see it in the equity markets. It’s kind of everywhere. So you really need to be running stresses that test your resilience to a meaningful change in the overall theme across the entire enterprise. That’s something that we spend a lot of time thinking about. Currently, our exposures are still quite modest in the context of our overall wholesale exposure, but that’s not the same as saying that we would be immune to a major change in sentiment in the AI space. I think I would be surprised if anyone, frankly, was immune to that at this point.

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 27 September 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 27 September 2026:

1. The Most Important Market in AI is the Middle – Tomasz Tunguz

Yesterday, Anthropic released a new model & cut its price. Ninety minutes later, OpenAI did the same.

Most business AI use is the messy middle: multi-step workflows that need a smart enough model at a price a company can afford. It is the most important part of the market today, & it is where the competition is fiercest. The price cuts are the evidence.

In June, Anthropic set the frontier price at $10 & $50 per million tokens with Fable 5. In July, OpenAI answered with GPT-5.6 Sol at $5 & $30, matching that capability at a third of the cost per task.

The Opus line had never moved. Opus 4.5, 4, 4.8 & 5 all listed at $5 & $25 per million tokens. Yesterday’s cut was the first…

…But the right tail of the market is thinner than almost anyone forecast. Anthropic’s Fable 5.1, its most capable & most expensive model, commanded only 3.7% of gateway spending in its first twelve days. Its predecessor peaked at 13.2% when access was restored in July, then fell to 4.9% a month later when Opus 5 shipped at half the price. Among large corporate accounts, frontier models fell from 53% of token consumption in early August to 45% by September…

…As intelligence per dollar explodes, the distribution of tokens may shift to commodity. Whether that happens will determine the economics of the AI market.

2. History Doesn’t Repeat, but It Rhymes – Venky Ganesan

Between 1998 – 2000, teams of engineers left Cisco, Nortel, Lucent and Bell Labs to build optical switches, terabit routers and long-haul DWDM gear, and they were bought, sometimes before the product existed, for billions of dollars in stock. Cerent went to Cisco for $6.9 billion. Xros, a Menlo portfolio company from before my time at the firm, went to Nortel for $3.25 billion with 90 employees and no shipping product. Qtera, also $3.25 billion, same story. Chromatis went to Lucent for $4.5 billion. Siara went to Redback for $4.3 billion while, in the words of one reporter, still in the prototyping phase.

Here is the cast, in case the analogy isn’t obvious. Cisco was the Nvidia of its day: the picks-and-shovels company that everyone had to buy from, briefly the most valuable company on earth at around $555 billion and 200 times earnings. Nortel, Lucent, JDSU and Ciena were the hyperscalers: the giants with enormous balance sheets and inflated stock who bought the startups and built the capacity. The CLECs, WorldCom, Qwest and Global Crossing were the customers, the ones actually placing the orders, and they were doing it with about a trillion dollars of borrowed money. And the optical startups were the neo labs…

…Thirteen startups were bought pre-revenue by strategics in 1999 and 2000. Between them they had raised roughly $500 million of venture capital. They sold for about $31 billion. Every investor in every one of those companies made money, and some of the multiples were absurd: Xros returned something like 130 times the capital that went in. Now look at what happened to the products. Ten of the thirteen were killed within about two years…

…The venture returns had nothing to do with whether the product worked. They had to do with whether somebody with an inflated stock price bought you before the music stopped.

Now look at the ones that didn’t sell early. Thirty-two companies raised $100 million or more privately between 1999 and 2002, about $5.9 billion in total. Twenty-two of them, 69 percent, returned roughly nothing. Caspian raised $317 million and shut its doors in 2006. Procket raised $272 million and sold its intellectual property to Cisco for $89 million. Pluris raised $215 million and never shipped a router. Three returned capital back. Two, Amber and Flarion, returned three – four times. Five got public before the window closed. Corvis went out in July 2000 at a $27.6 billion valuation with zero revenue, raised $1.1 billion, and only ever deployed about six switches. Avici, CoSine, Tellium and ONI followed the same path. The public got the loss instead of the VCs, and all five are gone…

…Vintage determined who won. Founded in 1996 or 1997 and exited by the middle of 2000, you did great, product or no product. Founded in 1999 or 2000 and still private in 2001, you got nothing, team or no team. Nobody could see the line while they were standing on it. In the summer of 2000 the second group looked exactly as smart as the first. The only difference was the calendar.

The end, when it came, was fast. Ciena agreed to buy Cyras for $2.6 billion in December 2000. The deal closed in March 2001 at $1.1 billion. The price fell 58 percent between the handshake and the check. Cisco’s revenue was growing 60 percent through December 2000; by the April 2001 quarter it was down 25 percent and the company wrote off $2.25 billion of inventory…

…The parts that rhyme are the structure. There is a picks-and-shovels monopoly at the center with a handful of customers who are each a huge share of its revenue. There is a layer of giants spending more than they earn to build capacity for demand that hasn’t shown up yet: the big four hyperscalers have guided to somewhere between $720 and $760 billion of capex this year, Alphabet’s capex exceeded its operating cash flow last quarter for the first time in its history, and the industry has issued about $344 billion of AI-related debt this year alone. There is a customer layer, the labs, whose spending commitments dwarf their revenue: OpenAI at roughly $40 billion of run-rate against around $1.4 trillion of compute commitments. And there is a startup layer priced off the last round rather than off anything built…

…The parts that don’t repeat matter too, and I don’t want to pretend they don’t. Nvidia earns money. Cisco was at 200 times trailing earnings; Nvidia is at 29x, with a 62 percent net margin. The customers this time have real revenue growing very fast, not just borrowed money; the CLECs never had anything like $40 billion of run-rate, let alone Anthropic’s. The hyperscalers are, for now, cash-generative franchises with businesses that exist independent of AI, which Nortel and Lucent were not. If you had to bet on which layer of this stack survives a downturn, the answer today is much better than it was in 2000…

…You cannot pick your way out of this. The 2000 telecom investors were good at picking; they picked Larry Roberts and Tony Li. What they didn’t do was size for the possibility that the calendar, not the team, would decide the outcome…

…And who were some of those people who funded those companies? They were VC legends like Vinod Khosla, Pierre Lamond, Paul Ferri, Ed Anderson, and Jim Breyer.

3. The AI Inference Revolution Is Here – Matthew S. Smith

An untrained LLM is like a jumble of Scrabble tiles on a table. Instead of single letters, though, the tiles show fragments of words, called tokens. Everything you’d need to write almost anything is present, but nothing makes sense.

Training a model organizes this jumble using a guessing game played at scale. The model is shown real text with the next token hidden and asked to predict what comes next. After each guess, the correct token is revealed and then compared to the prediction, and the difference is used to calculate the model’s accuracy. The game is played not with a single sentence but over billions of passages.

While a real game of Scrabble can be played over a bag of chips and a few drinks, AI training is computationally intense. The model updates its parameters through backpropagation, a process that repeatedly calculates how each of a model’s billions or trillions of parameters should shift to make the next prediction better. This is why tech giants are building larger data centers than ever before.

Eventually the model’s creator decides further training isn’t worth the cost, and the guessing game stops. Backpropagation ends, the parameters are frozen, and the LLM becomes a pretrained model. Fine-tuning—a short training run on smaller, more specialized data—adds final tweaks, and the model is deployed.

Next comes inference. This is the process of using the deployed model, which, now that it’s been trained, has learned to spit out Scrabble tiles—tokens—in a sensible order.

You might think that AI inference is less computationally demanding because the backpropagation calculations used to update parameters are eliminated. But Sudeep Bhoja, founder and CTO of the inference-hardware company d-Matrix, explains that inference adds new challenges.

The models are “autoregressive” in nature. That is, the next output depends on the previous one. “So to generate the next token, you have to read all of the weights and all of the [context] from the previous token,” explains Bhoja. The context includes all of your prompts, all of the LLM’s replies, and all of the files you upload. It’s a lot of data and a lot of processing.

An LLM generates its reply in two phases: prefill and decode. Prefill is the model reading a prompt. It processes every token at once, computing how each token relates to all the others. This operation is called attention, and it’s a defining characteristic of the transformer architecture behind modern LLMs. It allows them to respond to a word in its sentence, paragraph, and larger context rather than on its own. Think of it like arranging Scrabble tiles before you place them in a game. Many players move tiles around to imagine how they connect. Self-attention plays a similar role, though instead of moving physical tiles, each token sends a query to the others and receives a score indicating the token’s relevance.

These queries result in two types of vectors: the keys and values. They are typically placed in a store called the KV cache. This isn’t strictly required, as a model could instead recompute these vectors with each new token it generates. But nearly all LLMs use a KV cache to reduce how much computing they do. The KV cache is stored in memory and becomes a scratchpad to which the LLM can return to understand a conversation, and though it starts small, it can swell to dozens of gigabytes.

Prefill is a problem that can be easily divided up and worked on in parallel. This is why GPUs became the dominant AI accelerator as LLMs surged in popularity. Graphics rasterization (computing the color of every pixel on a screen) is also massively parallel, so GPU architectures were a natural fit.

Next comes decode. Here, the model generates its reply one token at a time. At each step it takes the most recent token, weighs it against everything in the KV cache, uses that information to predict the next token, and adds the new token’s key and value to the cache. Then it repeats in sequence, token by token.

This is where the autoregressive nature of the model works against inference speed. Predicting each token requires reading the entire model from memory, and that model consists of possibly tens to hundreds of gigabytes of parameters (the numbers representing what the model learned in training). Crucially, this is in addition to the memory required to store the KV cache.

As a result, the movement of all this data through memory often requires more bandwidth than inference hardware has available. So at least some of the computing parts of a GPU sit idle as it waits for data…

…The big players—Nvidia and Amazon—are going for an all-chips-on-deck approach. Nvidia’s GPUs and Amazon’s Trainium training accelerators are still great for part of the inference workload: the prefill stage, where all the context keys and values are calculated. But to accelerate decode, the part where new tokens are generated, they are looking to new, memory-centric architectures from smaller players.

In Nvidia’s case, the smaller player was Groq (not to be confused with Grok, the family of LLMs trained by SpaceXAI). Nvidia purchased intellectual property and hired talent from Groq at the end of 2025, and just three months later at the Nvidia’s GTC 2026 conference, Jensen Huang unveiled the Nvidia Groq 3 language-processing unit (LPU). Groq’s architecture relies on memory—in its case, SRAM—built directly into the chip’s architecture…

…Amazon Web Services (AWS), for its part, struck a deal with Cerebras, to pair the Trainium accelerator with Cerebras’s Wafer-Scale Engine 3 (WSE-3). Cerebras takes a similar approach to Groq, though at a much larger scale. WSE-3 turns an entire silicon wafer into a single chip that contains over 4 trillion transistors. The design doesn’t connect to external memory but instead etches 44 gigabytes of SRAM into each wafer. “We store the [model] weights on the SRAM,” says James Wang, formerly director of product marketing at Cerebras who has since moved to SpaceXAI. “So that’s easily 40 to up to 80 billion parameters that we can support on one chip.”

Amazon plans to use AWS Trainium chips for prefill, and Cerebras for decode. But Cerebras’s chips can also go it alone in inference. WSE-3 was deployed by OpenAI to power GPT-5.3-Codex-Spark, a variant of the company’s coding mode, outputting over 1,000 tokens per second. For comparison, OpenAI’s standard GPT-5.4 deployment outputs 50 to 125 tokens per second…

…Most computers store numbers in a 32-bit or 64-bit format. These determine how many bits are available to represent a single number. If too few bits are available, the number can’t be stored without losing information. The quality of an LLM benefits from more-precise number formats, but this creates a problem for inference performance. More-precise numbers aren’t free. The bits that describe them take up more space in memory and require more silicon and energy to compute…

…The process of converting an LLM from a more-precise number format to a less-precise format is called quantization, and it’s been in use for several years. However, researchers are finding new ways to quantize models down while retaining a large majority of the model’s quality.

Nvidia recently created a new 4-bit number format, NVFP4, for this purpose. AMD, Intel, and Qualcomm have instead rallied around a competing 4-bit number format called MXFP4 that Nvidia also contributed to developing. “It’s the black art of AI,” says Buck, of Nvidia. When Nvidia quantized DeepSeek-R1 from FP8 to NVFP4, scores on seven major benchmarks degraded by less than one percent while performance improved by three times, the company says.

4. Frontier Overhangs – Ben Thompson

One of my go-to examples in Anthropic’s Safety Superpower was the company’s decision to predicate Fable usage on Anthropic holding onto all customer data for at least a month; this was a big deal, and I argued at the time that Anthropic was making a bet that its models were good enough to convince enterprises to give up on zero data retention:

It’s pretty significant, I think, that Anthropic is declaring that not retaining data is no longer an option, at least if you want access to their best models. Yes, today, that retention is for safety purposes only, and not for training; it’s plausible, however, that Anthropic’s lead becomes so significant that they quietly announce that they are going to train on that data as well, and companies will feel they have no choice but to go along. That additional training data, of course, will only further increase Anthropic’s lead, and all of this will be justified because Anthropic has already clearly decided they are the only ones who can be trusted to be in charge.

In fact, Fable wasn’t good enough: customers pushed back, and Fable usage stayed relatively low; when Fable 5.1 was released, the Anthropic-gets-to-keep-your-data provision was gone. This is evidence of Christensen’s theory in action: customers demonstrated the willingness to base their model-choice decision on something other than pure performance, namely, data retention policies.

This doesn’t, in and of itself, suggest that new model capabilities aren’t desired; it does, however, suggest that current model capabilities are “good enough” for customers to not do whatever is necessary to get access to the cutting edge, which reduces the value of the cutting edge to its proprietors, and gives credence to the strategy of Microsoft and others focused on separating harness and model. Pure capability no longer translates directly into a moat…

…The key for the frontier labs, then, is to build those user touchpoints while they have superior capabilities. However, this is where Meta’s recent launch of Muse is a bearish signal. Muse is, by a significant margin, the best and most approachable personal agent product I have tried. Meta deserves a tremendous amount of credit for the product work they have put in, as well as the massive infrastructure commitment entailed in providing users with a very capable virtual machine for free. Oh, and of course they deserve credit for the Muse Spark model undergirding Muse.

That noted, Muse Spark 1.3, the most advanced Meta model, is still not state-of-the-art, and that is the bearish signal: it is good enough for a very good personal agent product, and critically, a personal agent is much stickier than a chatbot. Once you have put all of your information into a personal agent and actually incorporated it into your day-to-day life, it is much more of a challenge to change to something else. This is in contrast to Codex/ChatGPT and Claude Code: yes, you may have developed your own set of skills and understanding of how each harness works, but at the end of the day the relevant artifacts (i.e. your code) are in GitHub, and it’s not that much of a lift to point a different agent and harness at those artifacts if the alternative is better and/or cheaper (or doesn’t want to keep all of your data).

In short, model capability is good enough that compelling products — products that actually have moats — can now be built, and from a business perspective it would do Anthropic and OpenAI good to devote more of their resources to actually building such products.

5. The US Dollar’s Two Competing Forces – Joe Weisenthal and Tracy Alloway

But Englander sees this line of thinking as a dead end. He writes:

Think of the US as an asset manager or hedge fund that converts low-yielding foreign savings into higher-returning assets. The USD’s value partly reflects how much of that higher return the US must surrender to attract the capital it needs, and partly the rest of the world’s confidence that the US can deliver these returns. Investors may also allocate capital to the US if it is seen as a better preserver of wealth than elsewhere. In each case, the USD’s value is part of the price the rest of the world pays for these services.

Under this framework, a stronger USD reflects higher expected returns from US asset management or greater demand for US custody and safe-haven services – hence the ‘dollar smile’ (referring to USD strength during both strong growth and rising safe-haven demand). A weak USD signals the opposite, or an expectation that policy or financial market uncertainty will make these services available at a lower cost in the future.

Now what’s interesting is not that the dollar has fallen since President Trump’s inauguration, but that the periods of pronounced weakness have tended to coincide with what many people perceive to be “policy missteps” such as Liberation Day, Scott Bessent’s bond buyback announcement, and the row over Greenland…

…Now unfortunately, as Englander notes, the weaker USD hasn’t “bought” the US anything in terms of trade imbalances. Real non-oil imports continue to go basically straight up, and the US hasn’t seen any particular boom in manufactured exports.

To put it another way, if there is a “good” version of dollar weakness, we’re not getting it.

Disclosure: We currently have a vested interest in Alphabet, Amazon, Mastercard, Meta Platforms, Microsoft, and Visa. Holdings are subject to change at any time.

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, Amazon, Meta Platforms, and Microsoft. Holdings are subject to change at any time.

Company Notes Series (#18): Oriental Watch Holdings

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. If you have any thoughts on the series you would like to share, feel free to do so through the “Contact Us” page; we appreciate any feedback. Thanks in advance!

Start of notes for Oriental Watch Holdings

Data as of 22 September 2026

Oriental Watch Holdings (HK:0398) is a pureplay luxury watch retailer listed in Hong Kong.

With a trailing dividend yield of 10.9%, PE of 9 and PB of 0.91, the company screens very well. I originally wanted to do a deep dive into the business, but fellow investment writer Michael Fritzell already published a solid write-up. Rather than repeating what was written, I decided to present a brief overview of the industry, outline the company’s background, and analyze the key considerations for potential investors.

The Industry

Luxury Swiss watch brands traditionally did not operate their own retail outlets. Instead, brands such as Rolex and Patek Philippe partnered with trusted local operators who managed multi-brand stores or dedicated mono-brand boutiques on their behalf.

Unlike traditional distributorship agreements, however, authorized dealer status is granted on a store-by-store basis. Every single store expansion requires separate negotiations with the brand itself.

This setup allows brands like Rolex to maintain tight control over their overall retail footprint.

Despite the rise of smartwatches and digital devices, the luxury watch market has remained resilient. In 2025, Swiss watch exports totaled CHF 25.6 billion across approximately 14.6 million units.

Oriental Watch Holdings

Oriental Watch Holdings operates 39 stores in total: 24 in Mainland China, 11 in Hong Kong, and 2 each in Macau and Taiwan. Founded in 1961 by Dr. Yeung Ming Biu, the company grew from a single shop into a major regional retailer.

Initially operating primarily in the mid-tier segment, Oriental Watch acquired La Suisse Watch Company in 1973. This strategic acquisition secured authorized dealership rights for Rolex and its sister brand, Tudor—two of the industry’s most prestigious brands.

This move laid the foundation for a 50-plus-year relationship with Rolex. In 1993, the company was publicly listed on the Hong Kong Stock Exchange.

Today, the business is led by Dr. Yeung Him Kit, Dennis (son of Dr. Yeung Ming Biu). He served as co-managing director alongside his father starting in 2003 before assuming sole leadership in 2021 following his father’s passing.

Dynamics of Authorized Dealers

Authorized dealers hold an enviable market position due to insatiable demand for top-tier timepieces, which frequently sell out before even reaching the showroom floor.

To secure a spot on waitlists for highly coveted models, clients often build relationships with dealers by purchasing less sought-after inventory. This dynamic enables retailers to move slower-selling stock effectively.

This arrangement is equally advantageous for the watch brands themselves.

Authorized dealers cannot simply order high-demand stainless steel sports models (such as the Daytona or Submariner); they must also accept deliveries of less popular models, such as smaller-diameter two-tone pieces. By moving this broader selection, authorized dealers boost total sales for the brand without diluting the prestige of flagship models.

While Oriental Watch is estimated to carry around 100 brands, its business is heavily anchored by Rolex and Tudor, which together generate an estimated 70%-80% of total sales.

Rolex Certified Pre-Owned Program

Rolex recently launched its official Certified Pre-Owned (CPO) program, marking its first formal entry into the secondary market.

The initiative was designed to provide secondary-market buyers with authenticity guarantees in a market flooded with counterfeits.

Through partnerships with authorized dealers like Oriental Watch, trade-in watches are processed through Rolex’s authentication system and resold with an official Rolex CPO guarantee and seal.

This initiative offers reassurance to pre-owned buyers while providing Oriental Watch with a valuable new revenue stream in the pre-owned segment.

Rather than taking a share of resale profits, Rolex charges service fees for authenticating the watches and issuing official certifications.

Operational Transformation Under Dennis Yeung

While founder Dr. Yeung Ming Biu built the company’s base, his son Dr. Dennis Yeung has significantly transformed its operations.

His leadership—first as co-CEO from 2003 and then as sole leader from 2021—stands out across three key strategic areas:

1. Streamlining Operations and Rightsizing Inventory

At its peak in 2013, the group operated over 100 retail locations, including 89 in Mainland China. However, government crackdowns on conspicuous consumption led to severe inventory accumulation across the region. In response, Dennis Yeung streamlined operations, closing underperforming branches to focus heavily on core high-performing brands like Rolex and Tudor. Today, the store footprint stands at an optimized 39 locations. Inventory fell dramatically from HK$1.8 billion to HK$460 million, significantly boosting liquid cash assets.

2. Opportunistic Share Repurchases

When the stock traded at a deep discount to book value and low P/E multiples around 2020–2021, Dennis Yeung executed a bold share buyback strategy. Rather than making small daily open-market purchases constrained by low stock liquidity, he initiated a tender offer at HK$3.00 per share when market prices hovered near HK$2.00. This HK$250 million authorization successfully retired 14.6% of outstanding shares.

With shares now trading around HK$3.38, that buyback added clear value. Furthermore, distributing total dividends across 487 million shares instead of 570 million has directly boosted dividend per share metrics over the past six years.

3. Highly Shareholder-Friendly Capital Return

Since taking sole leadership in 2021, Dennis Yeung has maintained a payout ratio of roughly 100% of profits in dividends.

With his two sisters holding substantial direct and indirect stakes in the firm, this generous dividend policy serves as an effective mechanism to unlock value for all shareholders. Backed by a strong balance sheet holding HK$961 million in net cash and zero debt, the group can comfortably sustain high payout ratios without risking operational stability.

The Rolex Distribution Landscape

As noted, Rolex remains the key anchor brand for Oriental Watch Holdings.

Across the industry, Rolex has been gradually consolidating its dealer network, favoring larger regional partners capable of opening dedicated mono-brand boutiques. This may be good for Oriental Watch Holdings as most of its 24 stores in Mainland China are mono-brand boutiques.

According to an industry analysis by Grey Market, 437 out of 578 remaining retail partners operate only a single store. These smaller partners are more at risk of losing their partnership with Rolex.

Official Rolex points of sale have dropped from a peak of over 1,800 in 2022 to around 1,300. Given that Hong Kong has a high density of authorized dealers (24 locations), Oriental Watch could capture additional market share if smaller competitors lose their authorised dealer agreements.

However, risks exist regarding Rolex’s direct-to-consumer (DTC) expansion. In 2023, Rolex acquired major retailer Bucherer after 87-year-old chairman Jörg G. Bucherer chose to sell in the absence of direct family heirs. This acquisition gave Rolex a direct retail presence across Bucherer’s multi-region store network.

Today, around 41 Bucherer locations sell Rolex watches directly.

While Bucherer mainly operates in Europe, it recently opened its first store in China, placing it in direct competition with authorized dealers in the region. There is an ongoing risk that Rolex could favor its own Bucherer stores when allocating highly sought-after models.

Nonetheless, industry observers believe Rolex is unlikely to dismantle its third-party dealer network entirely.

Independent dealers have built decades of localized client relationships, established brand presence, and funded expensive store refurbishments required by Rolex. Abruptly severing these relationships could cause unnecessary operational friction for the brand.

Because this partnership structure has proven mutually successful for decades, drastic disruptions remain unlikely in the short-to-medium term.

Current Status of the Swiss Watch Industry

Dr. Henry Tay, Chairman of Singapore-based retailer The Hour Glass, shared insightful observations in his 2026 Chairman’s Letter.

He highlighted structural shifts in the industry, specifically how market profits are increasingly concentrating among top-tier, highly disciplined luxury houses.

The four main privately held giants—Rolex, Patek Philippe, Audemars Piguet, and Richard Mille—have consistently outperformed by prioritizing craftsmanship, brand perception, and tight supply management over volume expansion.

Rather than increasing production during demand booms, these brands raised prices and embraced scarcity, further cementing their market dominance.

Together, these four private brands now command nearly 50% of total Swiss watch market revenue (up 1,240 basis points since 2019) and an estimated 76% of total industry profits.

With approximately 70-80% of sales derived from Rolex and Tudor, Oriental Watch is well-positioned to benefit from this flight to quality and brand concentration.

However, because the company lacks authorization for Audemars Piguet and Richard Mille and has minimal access to Patek Philippe, it is increasingly dependent on its singular relationship with Rolex.

Valuation and Financials

Operating income has moderated from a peak of HK$433 million in FY22 to HK$312 million in FY26.

Management attributes this softness to broader macroeconomic shifts, with consumer spending moving toward experiences rather than traditional retail goods.

Simultaneously, pre-owned secondary market prices for Rolex models have normalized following their 2022 peaks.

When secondary market premiums compress, authorized dealers face greater friction selling less-demanded models to buyers seeking waitlist priority.

However, secondary market prices have stabilized recently, which could provide a modest operational tailwind moving forward.

Investment Thesis

At the current share price of HK$3.38, Oriental Watch commands a market capitalization of HK$1.6 billion. Backed by HK$961 million in cash and zero debt, the net enterprise value (EV) stands at just HK$650 million.

For FY26, the company declared dividends of HK$0.37 per share, translating to a trailing twelve-month (TTM) yield of 10.9%.

Total dividend distributions amounted to HK$180 million, representing roughly 100% of full-year earnings after tax.

While performance remains tightly bound to Rolex, management’s 50-year plus relationship with its key supplier provides a level of stability.

Assuming the company continues distributing ~100% of earnings, income-focused investors should enjoy strong yield generation.

Conclusion

Oriental Watch Holdings presents a compelling valuation case for income investors. Under Dr. Dennis Yeung, management has demonstrated exceptional capital allocation—rightsizing the store network when needed and executing aggressive share buybacks when valuations plummeted.

A net cash position of HK$961 million offers a strong buffer to support the 100% dividend payout policy. Even if the group sees an opportunity to open a few new stores, the expansion could be easily supported with cash on the balance sheet.

Although heavy reliance on Rolex is the primary risk factor, Rolex’s long-standing track record of supporting key retail partners provides some reassurance.

Overall, Oriental Watch offers attractive potential for yield-focused investors. While annual profits may fluctuate with broader luxury demand cycles, the company’s moat and capital discipline suggests near to mid term results should remain fairly consistent.

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 20 September 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 20 September 2026:

1. Fences, not Sandboxes – Steve Yegge

I’m here to give you a glimpse of a future that I think none of us expected. It’s a future where AIs are governed by laws, not by programs that try to contain and control them…

…My current software factory, the Wheelhouse, is not for navel-gazing: I built it specifically to work on my game, Wyvern. For all the skeptics out there saying, “Where’s the thing people are building,” well, I’ve got mine…

…Long story short, Wheelhouse was built via me complaining endlessly to Fable about what I want out of Wheelhouse — mostly more code launched, faster, but also lots of bespoke monitoring.

I would also notice when Fable would go off the rails, and gently nudge it back. I would let it fail for days to weeks, then make it do things my way. Fable is extremely data-driven, if you permit it to be, and it will insist on experiments and quantitative validation of anything you try to change. But the numbers would almost always prove me right, and Wheelhouse has been in a state of constant innovation.

But that innovation is all directed towards Wyvern. Wheelhouse exists to build and operate Wyvern; it has no other purpose in life. And yet in ten weeks, it has grown from nothing to rivaling the size of Wyvern itself. Wheelhouse is about 600k lines of code and tests (mostly bash), and Wyvern’s code (not counting content) is only about twice that big.

So the factory for building Wyvern is growing much faster than Wyvern is — even with heavy brakes applied lately, after Sol told us to tighten it the F up in a code review. We avoid new machinery but it still continues to grow rapidly, and I’m honestly not sure what the ideal factory-to-product ratio is yet. But it seems to be approaching 1:1.

You might wonder if Wheelhouse is reusable code, whether I could open the repo and let people try it out. I had no idea. I knew that my agents had built something really powerful, capable of shoving 500 commits per day through our merge queue (though we average 270/day), using magic tricks that are a year ahead of their time. It’s a system that we can ride so hard that it scares the players and they tell us to slow down.

But I wasn’t sure if it was reusable. I wasn’t even sure how it worked.

My agents had been using a lot of jargon, and I slowly realized they were reusing the same terms, day in and day out. They were speaking about things in Wheelhouse, using what seemed like recurring new design patterns: fences, ratchets, governors, tripwires, latches, gates, falsifiers… it was a long list, but finite. I just had no idea what any of these jargon terms meant.

So one day, no more than a week ago, after the 100th “fence” reference, I decided to peek under the covers and see exactly what my Fable agents had built. I had them create manifests, taxonomies, audits, and visualizations. They showed me what they had wrought.

This is the part where words fail me and the blog just falls over. My reaction was straight up WTF. No words.

Because I expected them to have built an engineering system. One that, you know, does stuff.

Instead, what they had built was an entire legal system, complete with a constitution, jurisprudence, courts, offices, jurisdiction, case law, rulings, registries, ledgers, rosters, and a full-fledged apparatus for running something resembling a manorial estate.

In short, Fable had produced a medieval government. And there’s no doubt that it was heavily influenced by the target product, Wyvern, which is a medieval fantasy RPG, at least in the fanciful naming we used: Marshal, Seneschal, Reeve, Beadle, Portcullis, etc. But that LARPing was masking a bona-fide system of constitutional governance.

Wheelhouse’s legal system also has an enforcement arm. The fences, gates, ratchets, and so on — when my agents used that jargon, they were referring to the enforcement machinery: the cops, as it were. And cameras, and jails…

…I’m here to tell you that if you allow it, Fable will try to capture all of that into a mechanically provable, AI-operable model of your organization, one where there are no unwritten rules. If there is one unwritten rule in Wheelhouse, it’s that the system hates unwritten rules.

Fable will capture all your rules, and write them down, if you let it. Then it will try building infrastructure to help enforce them.

I see it happening already, and people are fighting it. I see people making Skills to keep Fable from building “extra” stuff. But all Fable is trying to do here is the Right Thing. And that starts by capturing how your system operates, and how it is intended to operate, so it can begin addressing the gaps…

…When you add it all up, Fable is trying to turn Wheelhouse into an engine that can prove, mechanically, that every change to Wyvern is legal. The agents capture every single intention, decision, policy, rule of thumb, and legacy behavior in the system, and they use that to govern every future decision and action. They live by the Rule of Law.

Did they do a good job of all this? Well I mean, for sixth graders, yes, it was a great project. Once I popped the hood, I saw that they hadn’t been curating it, just growing it. It had a lot of cruft — for instance, old rulings that were obsolete or had changed. And ‘rulings’ that turned out to be just good craftsmanship, so we elided them. Like any engineering project, it needed ongoing maintenance.

I minted a new Officer seat, Frog (Head of Wheelhouse Law), and put Frog to work on folding successive cancelled rulings, and a whole bunch of other stuff the agents had overlooked. It’s a work in progress.

But on the whole, it was already a pretty solid system. The garden needed a bit of pruning and weeding, but not a redesign. Which is good, because redesigns are slow. Wheelhouse has a whole system just for the lifecycle of rules/laws: proposing, evaluating, ratifying, enacting, enforcing, measuring, amending, and retiring them.

And Wheelhouse is exceptionally careful not to break itself. So I can’t just make changes to Wheelhouse; they have to go through a ratification and review process, and then a build process, before they can take effect and propagate.

It’s running smoothly, though there are still all sorts of problems at this velocity. At hundreds of commits per day on master, idleness means staleness, and clones can fall far behind if they aren’t regularly pulling while they work. It takes external forces to get this to run reliably, so Wheelhouse has various roles for poking and prodding other agents.

In a lot of ways, it’s just like any other software factory.

The difference is, Wheelhouse is governed by a constitution. Humanity has only one mature technology for coordinating mortal, replaceable strangers via text — namely, law. Wheelhouse has 50 agents that are amnesiac and interchangeable, and the only way they can coordinate is via text. So offices outlive their holders, precedents outlive their incidents, and jurisdiction says who may act. Every group of cooperating humans eventually arrives at a system of laws, and agents are trying to do exactly the same.

So that’s the future. Hundreds to thousands of AI employees at every company, together comprising a city that needs an entirely new bespoke set of laws and rules.

2. First Impression of Muse – Abdullah Al-Rezwan

Before Muse, I poked around a bit with Instinct, a startup that has created a bit of buzz in the personal agent space but still in private beta. My very first impression of using Muse was that it seemed a pretty good clone of Instinct. But once I spent more time on Muse, I thought labeling Muse as a clone of Instinct is a very uncharitable framing.

Like Instinct, you can use Muse on WhatsApp (I didn’t see iMessage integration which you can do with Instinct). But Muse also has its own app which I installed to play around throughout the day. While the WhatsApp version seems just as barebone as Instinct, the app is much more feature rich. The app is available only in the US so far. Given ~40% of my subscribers are from outside the US, I wanted to give you a better feel about the app just by going through my own experience with ample screenshots along the way.

Muse has a very generous free tier. Zuckerberg mentioned in an interview yesterday that the free tier has up to a 100 million weekly token usage limit. While that sounded plenty to me at first, as of this writing, I have already used 81% of my free plan’s weekly limit. I have an option to upgrade to two plans: a) Power ($20/month with 500 million weekly Muse tokens), or b) Maximum ($100/month with 3 Billion weekly Muse tokens)…

…The best use case that I have found so far is I can finally connect all my Gmail accounts with Muse. While using Claude or ChatGPT, one of my persistent problems has been that I can only connect one Gmail account (for what it’s worth, Instinct can do this too). Unfortunately, I have four separate Gmail accounts (one for personal, one for business, one from Cornell, and one that I randomly opened and very sporadically use). The problem is I have used three of these Gmail accounts in different settings and given Gmail’s own poor search functionality, I actually need an AI to go through all my Gmail accounts simultaneously to find a specific email I am looking for…

…I also liked Muse’s “Connectors” which let me connect a bunch of apps from my phone. Once connected, it’s a lot faster for Muse to do the work that needs the data from the app. As I will show later, even if you don’t connect the app, Muse can manually work in its own browser to try to complete a task…

…Let me start with booking accommodation, which has been pretty much every consumer chat bot’s highlighted use case, including Meta. So, I asked Muse to look for an Airbnb for a family trip (8 adults) near Mendocino from December 26 to 31 this year. I was just testing Muse’s capability, so an actual query would require me to share much more contexts than I did. Anyways, Muse used its browser functionality and basically approached it the way any human would: go to Airbnb, fill in the destination, dates, number of guests etc. and go through the search results. After it went through search results, it showed me a wall of texts with different options which you can see below. Brian Chesky has been quite adamant that text based interaction will not work very well for travel booking and a more visual search is required to bridge the gap between hype and actual consumer behavior. When I went through Muse’s suggestions, there are two ways I could go about it: a) just accept whatever Muse is suggesting, and b) actually click through each of the suggested options and carefully assess which one I actually prefer. I’m skeptical that most people will choose the former, and if you choose the latter, are you really saving much time here instead of just going directly to the Airbnb app itself?…

…There is, however, a tangible risk for Airbnb or any OTA out there. I could ask Muse to find out whether the Airbnb it picked is available at a lower price anywhere else on the internet. Muse tried, saw two options, but eventually found those listings to be inactive on those platforms. Airbnb may look fine in this example, but obviously there are indeed plenty of inventory that is multi-listed on different platforms or their own websites. In fact, I don’t have to manually ask this to Muse every time I book an Airbnb. I can just ask Muse to look into any accommodation or flight I book and always first figure out whether there is a cheaper way to book it. Once it’s saved in Muse’s memory, Muse can automatically do this without ever being asked again…

…Then for dinner, I wanted to see if Muse is up to the task of ordering via DoorDash. I asked it to show me some healthy options from Chipotle which I wanted to pick up myself…

…After some back and forth, I picked the chicken+ black bean bowl. To be clear, at least half the time I typically don’t know what I would like to order from DoorDash. I usually browse the app and it is through the browsing experience, I typically end up deciding what to order. So, this is far from my typical way of ordering food. I was just trying to test what Muse can do with a relatively simple food ordering query. Anyways, after picking an option Muse offered, I was prompted to share my DoorDash login credentials. I asked it to use my Gmail account to login. It tried but it got stuck in log-in circular hell. It would try to login, Google would let me know someone’s trying to login to my DoorDash account, I would confirm “it’s me”, and the whole process repeated three times after which I gave up.

For what it’s worth, I personally found these issues prevalent even while using Instinct. I tried the same Airbnb queries that I did with Muse. It took literally an hour, I kid you not, for Instinct to respond to my aforementioned Airbnb query. When I tried the DoorDash query on Instinct, it still took 8 minutes to respond. After experiencing Muse’s speed, the bar just became too high for a startup such as Instinct which probably cannot afford as much compute as Meta is throwing at these problems.

As you can see, there are still plenty of issues Meta (or any consumer agent) needs to work on to make it a very seamless experience. While it’s hard for me to see myself using Muse at the expense of apps such as Airbnb, DoorDash, or Amazon, it’s simply too early to either write Muse off as another flailing attempt at owning the consumer agent layer or assume Muse’s victory given its ample compute budget. I do sense a lot of switching costs though once I connect all my accounts and share login credentials with one agent. I would be very reluctant to repeat this whole process with another agent without a materially compelling incremental benefit.

3. A Stealth Startup Thinks It Just Hacked the Memory Shortage – Lauren Goode

Kepler Computing, a San Jose, California–based startup founded in 2018 by a team of physicists and computer scientists, says it has developed a new architecture for high-bandwidth memory (HBM) that directly addresses some of the chip supply bottlenecks that are constraining the computing market.

While chipmakers typically rely on expensive extreme ultraviolet lithography (EUV) to shrink the transistors on a chip, thereby packing more technology into the same amount of space, Kepler claims that its “3D stacking” approach and a proprietary new material allow it to increase density without relying on EUV at all—and it can work with existing semiconductor fabrication plants.

Kepler says it has made similar gains for the high-speed cache memory typically used in CPUs, GPUs, and XPUs. This so-called SRAM sits within the core of a chip die in order to cut down on data transfer times. HBM, by contrast, uses stacks of DRAM, which is a separate memory component of chips…

…For now, much of Kepler’s testing is happening in Singapore, which is where GlobalFoundries—a manufacturing partner and investor of $50 million—has a facility. Over the past two years Kepler has been building out what it refers to as “mini fabs,” where it produces its memory chips in conjunction with Global Foundries’ 28-nanometer chips. (It has also been running tests in GlobalFoundries’ facilities in Burlington, Vermont.)…

…In bypassing ultraviolet lithography, Kepler is one of a few tech startups working to avoid one of the major bottlenecks in semiconductor manufacturing. The startup claims it can produce SRAM that achieves the same density as 2-nanometer or 3-nanometer chips without having to invest in EUV…

…Kepler’s overarching pitch is that the accelerated computing market shouldn’t have to wait for brand-new memory fabs to be built in order to meet demand. Instead, new approaches to building memory within existing fabs can increase supply.

Their approach is twofold. In terms of improving HBM, Kepler says it has developed a novel 3D-manufacturing technique that fits more memory chips within a fixed footprint. The core compute can then sit closer to the memory, so the data travel between the two uses less energy. Their ultimate goal is to move data around in HBM with the amount of energy that’s comparable to SRAM, all while keeping HBM’s large capacity.

Kepler also says it has improved the density of SRAM using ferroelectrics, which can read and write data at lower voltages than the mechanisms typically used to process and store data in semiconductors. The startup did this by developing a new, low-voltage, composite material that works with this ferroelectric approach…

…In its early build-outs with GlobalFoundries, Kepler says it was able to convert a fab into a “next-generation” fab in just eight months, compared to a typical 24-month timeframe…

…Basically Kepler is betting that any additional costs that come from new materials or retooling existing fabs would far outweigh the $20 billion to $40 billion it costs to build new ones and outfit them with equipment worth hundreds of millions of dollars…

…Kepler Computing still has a long road ahead before it reaches full-scale production—assuming it gets there. To date, the company has run its technology on around 2,000 wafers. The startup says it’s planning to ship its first samples of HBM chips later this year, ramp up production out of Singapore next year, and start chip production in the US in 2028.

4. Software is about to eat the world much faster – Marc Andreessen

Software has been eating the world at the speed of human hands. It is about to eat the world at the speed of compute…

…In the past year, Devin has gone from writing 13% of Cognition’s production code to more than 90%.

When agents write 90% of the code, engineers can literally do ten times as much. The 10x engineer becomes the 100x engineer, as they shift from writing artisanal code to operating as the CTO of a fleet of agents. The 1000x engineer isn’t too far behind. As Scott puts it: “Within our lifetime, engineers will go from bricklayers to architects, focusing on the creativity of designing systems rather than the manual labor of putting them together.”

At Mercedes Benz, engineers turned what would have been an eight month long COBOL migration into 8 days of work with Devin. Rivian teams increased their test generation velocity by 10x. Devin triages and patches vulnerabilities across thousands of repos at some of the world’s largest financial institutions like Itau, where 70% of security vulnerabilities are automatically remediated by Devin.

Every time programmers get more leverage, doomers predict the end of software engineering. Compilers were supposed to shrink the profession. So was open source. So was the cloud. Instead, each leap made software cheaper to build, and demand for software, and engineers, exploded. It keeps happening because, as Milton Friedman observed, human wants and needs are infinite, so economic demand is infinite, and job growth can continue forever.

5. The turbulent AI era is here. The choices we make now are critical – Bill Gates

AI for the first time can replace and even exceed human cognition.

In terms of equity, AI will either be the greatest equalizer ever invented, or the worst source of injustice….

…Unfortunately, right now we are not preparing for it. I don’t see evidence that leaders, experts, and communities are confronting the challenges adequately. There is no plan to ease the entry into the AI era.

Part of the reason for this is that many commentators underestimate the extent of the impact AI will have…

…Another reason people underestimate AI is that analogies to the effects of past innovations are misleading. We have no experience with a technology that can be adopted quickly or that can think and move like a human. When the PC came along, it took twenty years to significantly change how we worked because the software had to be developed, the price had to come down, and people had to learn how to use the tools and incorporate them into their business processes. AI, on the other hand, runs on the devices we already have, and it uses natural language. We don’t have to adapt to it because it can adapt to us…

…The transition to AI comes with three big risks…

…Many jobs will disappear forever.

In 1933, during the Great Depression, unemployment in the United States was roughly 25 percent. It remained in double digits for much of the following decade. It ultimately recovered as demand, investment, and growth returned.

AI may not reach this level, but its impact will not go away with an economic cycle. The jobs at most risk are entry- and mid-level, and the new jobs being created will mostly require skills that take many years to learn.

White-collar jobs are already being hit modestly. After the widespread adoption of generative AI, employment fell significantly among young workers in jobs that are especially vulnerable to replacement, but not among their older colleagues.

I think this trend will continue, but it will not be confined to a handful of industries or occupations. Jobs in sales and customer support (online and over the phone), software engineering, and paralegal work may be among the first affected, but the disruption will reach much further as AI takes on tasks that today still require trained workers: things like assessing loan applications, doing data analysis, and even triaging patients. A few areas like software engineering will generate new demand as the costs go down, so the net job loss in those areas will be less than in others as long as some tasks, such as design, are better done by humans.

Blue-collar jobs will be affected as well. Although robots are not as far along as AI, eventually their cost will be dramatically lower too…

…AI will empower people (and perhaps AIs) to do more harm.

Long before AI entered the mainstream, there was information online about how to create weapons like bombs, bioweapons, even computer viruses. AI will make it much easier to not only get this information but act on it. Even criminals with very limited skills will be able to target victims at every scale: individuals, companies, and governments…

…AI capabilities are starting to be used for cyberattacks. The smartest cybersecurity experts I know are scared about the next few years, because the attackers are getting powerful new capabilities faster than the defenders can fix all the weaknesses. After all, the same AI model that can find a flaw in software so a company can fix it can also help a criminal exploit it. The resources needed to make an attack are going down significantly and we haven’t been able to separate those abilities from benign usage…

…AI could stunt our kids’ development and replace human relationships.

When I was growing up in Seattle, I didn’t have that many friends aside from a few other boys who were like me. It took hard work and a lot of help from my mom to develop my social skills so I could relate to different kinds of people. I still draw on those lessons today at the age of 70.

I doubt I would have put in the same work if I had had an AI companion back then. They talk to you in ways you’re already comfortable with. They don’t push you outside your comfort zone. They are always available and never get mad at you. This gives them the potential to become highly addictive and to rob us of the lessons we learn from connecting with other people.

The body of evidence on this subject is still small and a bit mixed, but there are signs that we should be very concerned. For example, in one study of more than 1,100 people who use AI companions, researchers at Stanford and Carnegie Mellon found that those with smaller social networks were the most likely to turn to a chatbot for companionship. And the heavier and more emotionally personal that use became, the worse they felt.

Young people could be affected for their entire lives. In his book The Anxious Generation, Jonathan Haidt makes an observation about the effect of social media that is even more true for AI: “Like young trees exposed to wind, children who are routinely exposed to small risks grow up to become adults who can handle much larger risks without panicking. Conversely, children who are raised in a protected greenhouse sometimes become incapacitated by anxiety before they reach maturity.”

An AI companion designed to never upset you is a big, protected greenhouse…

…We need both: deep concern about the AI harms we need to minimize, and grounded optimism about the positives if we maximize them for everyone…

…With its ability to synthesize knowledge from every scientific field, AI can accelerate innovation in the world’s toughest technical challenges: providing reliable clean energy for everyone, combating climate change, growing enough food, eradicating diseases, and more. Researchers working on cancer treatments or nuclear energy can use AI to search through massive amounts of scientific literature. It can help them identify patterns that a human might miss and decide which experiments offer the most promise. When intelligence is no longer the limiting factor that it is today, smaller companies will be able to compete with organizations that have far larger research budgets. R&D and innovation will be supercharged.

Healthcare is one area where AI can help solve real-world problems. Many small American hospitals lack on-site specialists who can quickly diagnose a patient during a life-threatening emergency. In those places, AI could make sure a heart attack is caught in time and a family avoids the crushing expense of a medical emergency…

…I surprise a lot of people when I tell them that a second area—agriculture—is where I see the fastest impact of AI in low-income countries. In most low-income countries, farmers don’t get reliable weather forecasts or advice on what seeds to plant, how to protect their crops and livestock from disease, or how to improve their soil. With population growth in these countries and the challenges of climate change, these farmers need more help than ever. Using AI, low-income farmers will soon be able to get better advice about all these things than even the richest farmers get today and increase their output substantially…

…The highest priority is a monumental task: creating a domestic and international framework for dealing with AI.

None of our current institutions were designed to handle a technology that spreads so fast and touches so many parts of our lives. So we’ll need to make new ones.

It’s hard to overstate what an enormous undertaking this will be. After the attacks of 9/11, the U.S. government went through its biggest reorganization since World War II for the purpose of improving just one function, national security.

AI will require much, much more. It will affect national security as well as employment, education, taxation, energy, elections, air and water, public health, the financial system, law enforcement, transportation, public lands, and IT systems…

… I believe that as AI and robots improve, we’ll set aside certain things for only people to do. I’ve started calling this domain Human Reserved, and it’s an example of the kinds of ideas we’ll need to consider.

I like the phrase Human Reserved because it makes me think of nature reserves—places where we could put buildings and roads, but we choose not to because the loss would be too great.

We might set something aside as Human Reserved for economic reasons. For example, we may do it because allowing machines to take over a certain role will displace a large number of people who can’t easily change jobs. You can’t tell a 55-year-old who has worked in construction their whole career that they need to go work at an elder care facility and expect them to find it fulfilling.

Sometimes the decision to make something Human Reserved will be driven by other factors. In health, for example, imagine a robot giving you the awful news that you have an incurable disease. There’s no technical reason why it couldn’t. Yet it shouldn’t…

…I believe we should tax AI tokens and robots. Right now, if you’re an employer and you hire someone, you pay payroll taxes on their earnings. But if you buy a robot, you can usually write it off right away as a business expense. The tax system nudges you toward replacing people with machines.

A tax would slow the rush away from human labor a little and raise money for retraining and a stronger safety net. It would need to be targeted so it does not slow down the purely beneficial uses of AI, like making medicine and education cheaper.

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 Chipotle Mexican Grill and Meta Platforms. Holdings are subject to change at any time.