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. Both are provided by OpenAI and are software products that use AI to generate art and writing, respectively (and often at astounding quality). 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 fourth quarter of 2025 – 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

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

Alphabet (NASDAQ: GOOG)

The Gemini app now has 750 million monthly active users (was 650 million in 2025 Q3); the Gemini app is seeing significantly higher engagement per user after the launch of Gemini 3 in December 2025; Alphabet offers the most extensive model portfolio in the world and leads across text, vision, and image-to-video model leaderboards; Gemini 3 Pro is state-of-the-art in reasoning and multimodal understanding; Gemini 3 Pro has the fastest adoption of any model in Alphabet’s history; Gemini 3 Pro has consistently processed 3x the number of daily tokens that 2.5 Pro has; Gemini 3 is powering Google Antigravity, a software development platform with more than 1.5 million weekly active users since its launch 2 months ago; Alphabet’s 1st-party AI models, including Gemini, now process 10 billion tokens per minute from direct APIs (was 7 billion in 2025 Q3); Gemini 3 is now integrated into AI Mode and AI Overviews in Google Search; more than 120,000 enterprises are using Gemini today; a sheer majority of the top SaaS companies in the world are using Gemini; management is confident of maintaining the innovation momentum with Alphabet’s 1st-party models; management is not seeing Gemini cannablising Google Search; management is not in any hurry to introduce advertising to the Gemini app; 

Our Gemini app now has over 750 million monthly active users. We are also seeing significantly higher engagement per user, especially since the launch of Gemini 3 in December…

…We offer the most extensive model portfolio in the world and lead across text, vision and image to video LMArena leaderboards. Gemini 3 Pro drives the state-of-the-art in reasoning and multimodal understanding. It has seen the fastest adoption of any model in our history. Since launch, Gemini 3 Pro has consistently processed 3x as many daily tokens on average as 2.5 Pro.

Our latest model powers Google Antigravity, our new development platform where agents can autonomously plan and execute complex software tasks. It already has more than 1.5 million weekly active users after launching just over 2 months ago.

Our first-party models like Gemini now process over 10 billion tokens per minute via direct API used by our customers, up from 7 billion last quarter…

…We have integrated Gemini 3 directly into AI mode in search. Now search can better understand your query, dive deeper on the web and generate interactive UI experiences. And last week, we upgraded AI Overviews to Gemini 3, giving users a best-in-class AI response at the top of the search results page…

…Today, more than 120,000 enterprises use Gemini, including AI unicorns like Lovable and OpenEvidence and global enterprises like Airbus and Honeywell. 95% of the top 20 and over 80% of the top 100 SaaS companies use Gemini, including Salesforce and Shopify. Gemini is becoming the AI engine for the world’s most successful software companies…

…We are obviously improving these models across many paradigms, right, on pretraining, post-training, test and compute and so on. And we are bringing multimodal models into the picture. We are bringing agentic capabilities, the coding area is showing a lot of progress. And obviously, integrating all of this together and offering a great customer experience for our — to our products as well as through our APIs to our cloud customers. To me, it feels like there’s a lot of headroom ahead. And as you’ve seen our trajectory over the past 2 years in terms of how we have been making progress. I think we are in a very, very relentless innovation cadence. And I think we are confident about maintaining that momentum as we go through ’26…

…People are obviously using search, experiencing AI Overviews and AI Mode as part of it and Gemini app as well. And the combination of all of that, I think, creates an expansionary moment. I think it’s expanding the type of queries people do with Google overall. And so overall, some of it all is what we see as a growth opportunity, and we haven’t seen any evidence of cannibalization there…

…In terms of the Gemini app today, we are focused on our free tier and subscriptions and seeing great growth, as Sundar discussed. But ads have always been part of scaling products to reach billions of people. And if done well, ads can be really valuable and helpful commercial information. And at the right moment, we’ll share any plans. But as we’ve said, we’re not rushing anything here.

Google Cloud saw accelerating growth in 2025 Q4; Google Cloud backlog grew 55% sequentially to $240 billion in 2025 Q4 (was $155 billion in 2025 Q3); Google Cloud was able to lower Gemini serving unit costs by 78% over 2025; Google Cloud had double the new customer velocity in 2025 Q4 compared to 2025 Q1; the number of Google Cloud deals in 2025 exceeding $1 billion surpassed the past 3 years combined; existing Google Cloud customers are outpacing their initial commitments by over 30%; nearly 75% of Google Cloud customers have used Google Cloud’s end-to-end vertically integrated AI stack; Google Cloud has 14 product lines that each exceed $1 billion in annual revenue; Google Cloud is offering a wide range of Alphabet’s 1st-party leading generative AI models to customers; 350 customers each processed more than 100 billion tokens in December 2025; revenue from products built on Alphabet’s 1st-party AI models was up 400% year-on-year in 2025 Q4; the integration of Gemini and Google Workspace is driving wins for Google Cloud; revenue from AI solutions built by Google Cloud’s partners increased nearly 300% year-on-year in 2025 Q4

Cloud significantly accelerated with revenues growing 48%, now on an annual run rate of over $70 billion. Backlog grew by 55% quarter-over-quarter to $240 billion, representing a wide breadth of customers driven by demand for AI products…

…Google Cloud’s backlog increased 55% sequentially and more than doubled year-over-year, reaching $240 billion at the end of the fourth quarter. The increase in backlog was driven by strong demand for our cloud products, led by our enterprise AI offerings from multiple customers….

…As we scale, we are getting dramatically more efficient. We were able to lower Gemini serving unit cost by 78% over 2025 through model optimizations, efficiency and utilization improvements…

…We are winning more new customers faster. We exited the year with double the new customer velocity compared to Q1…

…We are also signing larger customer commitments. The number of deals in 2025, over $1 billion surpassed the previous 3 years combined…

…We continue to deepen our relationships with existing customers who are outpacing their initial commitments by over 30%.

Nearly 75% of Google Cloud customers have used our vertically optimized AI from chips to models to AI platforms and enterprise AI agents, which offer superior performance, quality, security and cost efficiency. These AI customers use 1.8x as many products as those who do not, enabling us to diversify our product portfolio, deepen customer relationships and accelerate revenue growth. Our product line has multiple monetization levers spanning infrastructure, platform and high-margin AI-powered products and services with 14 product lines each exceeding $1 billion in annual revenue…

…We also offer our leading generative AI models, including Gemini, Imagen, Veo, Chirp and Lyria to cloud customers. In December alone, nearly 350 customers each processed more than 100 billion tokens. In Q4, revenue from products built on our generative AI models grew nearly 400% year-over-year, significantly accelerating from the prior quarter…

…Our integration of Gemini and Google Workspace is driving wins with global brands like Schwarz Group and public sector organizations like the U.S. Department of Transportation. We are also seeing momentum with independent software vendors. Revenue from AI solutions built by our partners increased nearly 300% year-over-year and commitments from our top 15 software partners grew more than 16x year-over-year.

Google Cloud has the widest variety of compute options, from NVIDIA’s GPUs to Alphabet’s own TPUs; Google Cloud will be the among the first cloud providers to offer NVIDIA’s latest Vera Rubin GPU; Alphabet has been working on its 1st-party TPUs for 10 years; Alphabet’s TPUs are being used by leading frontier AI labs (likely referring to Anthropic) and organisations in financial services, automotive, and public service; management seems to not be willing to sell TPUs to 3rd-party data centers

We have the industry’s widest variety of compute options. That includes GPUs from our partner, NVIDIA, who announced at CES, that we’ll be among the first to offer their latest Vera Rubin GPU platform, plus our own TPUs that we have been developing for a decade…

…We offer leading infrastructure for AI training and inference to our cloud customers with the industry’s widest variety of compute options from our own seventh-generation Ironwood TPU to the latest NVIDIA GPUs. Our 10-year track record in building our own accelerators with expertise in chips, systems, networking and software translates to leading power and performance efficiency for large-scale inference and training. Our cloud AI accelerators serve the leading Frontier AI labs, capital markets firms like Citadel Securities, enterprises like Mercedes-Benz and governments for high-performance computing applications…

…[Question] How should we think about the potential for TPUs to move outside of Google Cloud and into external data centers and develop as an incremental revenue stream?

[Answer] In terms of TPUs, I would think about it as it’s reflected in our overall part of what makes Google Cloud an attractive choice is the wide choice of accelerators we bring to bear here, and we meet customers in terms of what their needs are and the choice as well as other things we bring as part of Google Cloud, the end-to-end efficiencies in our data centers, all of that comes to bear. And that’s what you see in the strong momentum in Google Cloud. And given the overall investment we are making, we expect to be able to drive that momentum there. 

Alphabet’s management recently launched personal intelligence in AI Mode in Google Search; management recently introduced the Universal Commerce Protocol as a new open standard for agentic commerce; Google Search saw more usage in 2025 Q4 than ever before, with AI being an expansionary force; management has shipped 250 product launches within AI Mode and AI Overviews in Google Search in 2025 Q4; Gemini 3 is now integrated into AI Mode and AI Overviews in Google Search; management has made the transition from AI Overview to AI Mode completely seamless; daily AI Mode queries per user doubled in the US since launch; AI Overviews continue to perform well; queries in AI Mode are 3x longer than traditional searches, and a significant portion of queries in AI Mode lead to a follow-up question; people are searching in new ways beyond text, with 1 in 6 AI Mode queries being in non-text format; users of AI Mode can soon use a new checkout experience to buy directly because of the universal commerce protocol

In January alone, we have launched personal intelligence in AI mode in search and the Gemini app…

…And we laid the groundwork for shopping in the AI era by introducing a new open standard for agentic commerce, the Universal Commerce Protocol built alongside many retail industry leaders…

…Search saw more usage in Q4 than ever before as AI continues to drive an expansionary moment…

…We shipped over 250 product launches within AI Mode and AI Overviews just last quarter. We have integrated Gemini 3 directly into AI mode in search. Now search can better understand your query, dive deeper on the web and generate interactive UI experiences. And last week, we upgraded AI Overviews to Gemini 3, giving users a best-in-class AI response at the top of the search results page. We have also made the search experience more cohesive, ensuring the transition from an AI Overview to a conversation in AI Mode is completely seamless…

…First, once people start using these new experiences, they use them more. In the U.S., we saw daily AI Mode queries per user double since launch and AI Overviews continue to perform very well. Second, people are engaging in longer, more complex sessions. Queries in AI Mode are 3x longer than traditional searches. We are also seeing sessions become more conversational with a significant portion of queries in AI Mode now leading to a follow-up question. Third, people are searching in new ways beyond text. Nearly 1 in 6 AI Mode queries are now nontext using voice or images…

…We are building the era of agentic commerce and working with our partners to introduce the universal commerce protocol in our consumer products and across the web. We’ve received tremendous feedback from the industry. Soon, people can use a new checkout experience to buy directly in AI mode in Gemini from select merchants.

Alphabet’s management is seeing strong demand for its 1st-party enterprise AI agents; Alphabet has sold more than 8 million paid seats of Gemini Enterprise to 2,800 companies; Gemini Enterprise managed over 5 billion customer interactions in 2025 Q4, up 65% year-on-year

Leading enterprises are also driving strong demand for our enterprise AI agents. We have sold more than 8 million paid seats of Gemini Enterprise, our enterprise AI platform to more than 2,800 companies, including BNY and Virgin Voyages to streamline knowledge management and automate processes. Gemini Enterprise managed over 5 billion customer interactions in Q4, growing 65% year-over-year for customers, including Wendy’s, Kroger and Woolworths Group. 

Alphabet is Apple’s preferred cloud provider

We are collaborating with Apple as their preferred cloud provider and to develop the next generation of Apple Foundation Models based on Gemini technology.

1 million channels used Alphabet’s new AI creation tools in December 2025 each day; 20 million viewers used Youtube’s new Gemini-powered Ask tool in December 2025

On average, every day in December, over 1 million channels used our new AI creation tools to supercharge their creativity. During that same month, more than 20 million viewers used our new Ask tool powered by Gemini to learn more about the content they watched.

Waymo recently raised its largest investment round to date; Waymo surpassed 20 million fully autonomous trips in December 2025; Waymo is now providing 400,000 rides per week; Waymo recently launched its 6th market in Miami; Waymo will soon expand to the UK and Japan; Alphabet participated in Waymo’s latest investment round

This week, Waymo raised its largest investment round to date and is well positioned to continue its momentum with safety at the core. In December, we surpassed 20 million fully autonomous trips and are now providing more than 400,000 rides every week. Waymo continues to expand its service territory. Its sixth market, Miami, launched 2 weeks ago, and Waymo will soon expand its service to multiple cities across the U.S. and in the U.K. and Japan. The team has made incredible progress on important capabilities, including opening up public service to airports and freeways…

…Alphabet funded a significant portion of the $16 billion investment round that Waymo announced on Monday, which will allow the business to accelerate its global expansion.

Alphabet’s management is investing in AI to drive improvements across all areas of marketing; management thinks AI gives businesses the ability to reach more customers in more places than before; Gemini improves advertising quality, advertiser tools, and new advertising experiences; Gemini helps Alphabet evaluate advertising relevance with greater accuracy than before; Gemini helps Alphabet deliver ads on longer, more complex searches that were previously challenging to monetize; Gemini helps Alphabet improve understanding of non-English languages, thus helping businesses scale globally; Gemini helps businesses generate new advertising campaigns through a conversational experience; advertisers used Gemini to create 70 million creative assets in AI Max and PMax in 2025 Q4; Artizia used AI Max to achieve an 80% incremental uplift in conversion value in 2025 Q4; L’Oreal used AI Max in 2025 to increase revenue for DTC (direct to consumer) brands by 23%; management is in the early stages of experimenting with AI Mode monetization, with an example being Direct Offers, which allow advertisers to show exclusive offers to shoppers who buy directly in AI Mode

We’re investing in AI to drive significant improvements across all areas of marketing. We’re expanding the entire playing field that advertisers can compete on. AI gives businesses the ability to reach more customers in more places than ever before. Gemini uniquely positions us to bring the transformational benefits of AI to ads in 3 critical areas for our customers: ads quality, advertiser tools and new AI user experiences.

First, ads quality. We’ve been deploying Gemini models to improve query understanding at a rate of almost a launch per month for the last 2 years. These improvements drive better query matching, ranking and quality, making search ads even more effective. With Gemini across our ads quality stack, we evaluate relevance with greater accuracy than with previous generations of models. This has significantly improved our ability to systematically deliver more helpful high-quality ads, contributing to a meaningful reduction in irrelevant ads served. Gemini’s understanding of intent has increased our ability to deliver ads on longer, more complex searches that were previously challenging to monetize. Gemini models also have a significant impact on query understanding in non-English languages, expanding opportunities for businesses to scale globally.

Second, we’re building more agentic actions into our advertiser tools. Businesses can now leverage Gemini in conversational experiences within Ads and Analytics Advisor to identify and run recommended actions such as generating new campaigns. Advertisers use Gemini as a real-time partner to assemble creatives. In Q4 alone, they used Gemini to create nearly 70 million creative assets via text customization in AI Max and PMax. For instance, Aritzia, Canada’s premier fashion house used AI Max to find new high-value customers that traditional strategies miss, delivering an 80% incremental uplift in conversion value for Q4. L’Oreal, one of the first alpha testers, used AI Max in 2025 across 800 unique campaigns in 23 countries and 30 brands. AI Max enabled the L’Oreal Group to maximize its presence across the full consumer journey, fuel its consumer growth and increase revenue for DTC brands like NYX by 23%.

The third area is how we monetize new AI user experiences in search. We have significantly increased our focus on AI mode and are in the early stages of experimenting with AI mode monetization like testing ads below the AI response with more underway. For example, we announced Direct Offers, a new Google Ads pilot, which will allow advertisers to show exclusive offers for shoppers who are ready to buy directly in AI mode. This new type of sponsored content uses AI to match the right offer provided by the retailer to the right user.

Google Cloud had 48% revenue growth in 2025 Q4 (was 34% in 2025 Q3) driven by growth in GCP; GCP grew at a much higher rate than Google Cloud’s overall growth, driven by enterprise AI products, which have billions in quarterly revenue; the enterprise AI products included enterprise AI infrastructure (i.e. usage of TPUs and NVIDIA’s GPUs) and enterprise AI solutions; the core GCP, non-AI business was also a meaningful contributor to growth; Google Cloud operating margin was 30.1% (was 23.7% in 2025 Q3 and was 17.5% in 2024 Q4)

The Google Cloud segment delivered outstanding results in the fourth quarter as the business continued to benefit from strong demand for our enterprise AI products. Cloud revenue accelerated meaningfully and were up 48% to $17.7 billion. Revenues were driven by strong performance in GCP, which continued to grow at a rate that was much higher than cloud’s overall revenue growth rate…

…GCP’s performance was driven by accelerating growth in enterprise AI products, which are generating billions in quarterly revenues. We had strong growth in both enterprise AI infrastructure, driven by deployment of TPUs and GPUs and enterprise AI solutions, which benefited from demand for our industry-leading models, including Gemini 3. Core GCP was also a meaningful contributor to growth due to strong demand for infrastructure and other services such as cybersecurity and data analytics. We also had double-digit growth in Workspace, driven by an increase in average revenue per seats and the number of seats. Cloud operating income was $5.3 billion, more than doubling year-over-year, and operating margin increased from 17.5% in the fourth quarter of last year to 30.1%.

In terms of Alphabet’s outlook, management notes that Google Cloud is seeing significant demand, and that the demand/supply situation is still tight; management notes that Alphabet’s AI investments have already translated into strong performance in the business; management expects capex of $175 billion to $185 billion in 2026 (nearly double from $91.4 billion in 2025, which was itself up 65% from $55.4 billion in 2024, and 2024’s capex was up 69% from 2023); the capex will be for AI compute capacity to build frontier models, as well as for compute to (1) improve user experiences and drive higher advertiser ROI in Google Services, and (2) meet Google Cloud customer demands; management expects the growth rate in depreciation expense to accelerate in 2026 Q1 and meaningfully increase for the year; Google Cloud’s supply is tight even when it has been ramping up supply, and management expects tight supply throughout 2026; when management makes capex decisions, they go through a rigorous process of assessing the return on the investment; the capex in 2026 will be split 60-40 in terms of servers, and data centers and networking equipment; just over half of Alphabet’s compute capex in 2026 is expected to go towards the cloud business

In Google Cloud, we’re seeing significant demand for our products and services, which we expect to continue to drive strong growth despite the tight supply environment we’re operating in…

…The investment we have been making in AI are already translating into strong performance across the business, as you’ve seen in our financial results. Our successful execution, coupled with strong performance reinforces our conviction to make the investments required to further capitalize on the AI opportunity. For the full year 2026, we expect CapEx to be in the range of $175 billion to $185 billion with investments ramping over the course of the year. We’re investing in AI compute capacity to support Frontier model development by Google DeepMind, ongoing efforts to improve the user experience and drive higher advertiser ROI in Google Services, significant cloud customer demand as well as strategic investments in Other Bets. Keep in mind that the availability of supply, pricing of components and timing of cash payments can cause some variability in the reported CapEx number…

…We’ve been supply constrained even as we’ve been ramping up our capacity…

…I expect the demand we are seeing across the board across our services, what we need to invest for future work for Google DeepMind as well as for cloud, I think, is exceptionally strong. And so I do expect to go through the year in a supply-constrained way…

…We have a highly rigorous framework that we use internally where we look at all the needs for investment, whether it’s from our own organization or from external customers and have an estimate of what that investment could potentially yield, obviously, not just near term but long term as well. So we take that into consideration when we make the following decision. The first one is the total investment that we make across the company. This was, for example, in 2025, the $91 billion we invested in CapEx and our estimate for CapEx investment this year. So what’s the total envelope that we want to invest to ensure that we can drive both near-term and long-term growth for the company. And then the second way we use that framework is to just allocate these funds across the organization, determine where we should make these investments. And throughout the year, as you can imagine, we always look to understand where things are moving, whether it’s external dynamics or internal dynamics, and I’ve mentioned some of the supply chain pressures we’re seeing externally. So we look at this with a highly rigorous framework to make sure that we’re making the right decision.

It was exciting to see the fact that we’re already monetizing and you saw it in the results that we just issued this quarter, the investments that we’ve made in AI. It’s already delivering results across the business. I know it in cloud, it’s very obvious external, but you’ve heard the comments on the success we’re seeing in search, the comments from Sundar and from Philipp and then the Frontier model development that really serves as the foundation for the organization. We then also look at just the cash flow, cash flow generation and the health of our financials and the balance sheet. That’s important as well…

…Approximately 60% of our investment in 2025, and it’s going to be fairly similar in 2026, went towards machines, so the servers. And then 40% is what you referred to as long-duration assets, which is our data centers and network and equipment…

…For 2026, just over half of our ML compute is expected to go towards the cloud business.

In agentic use cases, Alphabet’s management thinks coding is the area where progress was most felt

I’ll take the agentic part first. I definitely think ’25 was more about laying the foundation, getting the models to start being more robust in agentic use cases. And obviously, coding is an area where the progress was the most felt.

The launch of the Universal Commerce Protocol (UCP) in January 2026 has been really well received; management is integrating UCP into all of Alphabet’s AI surfaces; management thinks 2026 is the year where consumers can actually experience agentic commerce; management sees the UCP making it much easier for (1) consumers to complete transactions, and (2) merchants to showcase their offerings

I think the launch of Universal Commerce Protocol at NRF in January with a bunch of partners, founding partners, I think has been super well received. So I’m excited now that we have laid the foundation of interoperability on which agentic commerce can work. And now we are integrating those experiences into Gemini, AI Mode and so on. So I think this is a year where you will see consumers actually being able to use all of this, and I’m excited about the opportunity ahead…

…Part of what’s been good in designing the Universal Commerce Protocol is it makes it much easier for users to complete transactions. But at the same time, it allows merchants to help showcase the range of their offerings, if they want to make promotions, et cetera. So all of that is built into the protocol.

About 50% of code used within Alphabet is written by AI agents that are then reviewed by engineers; Alphabet is employing AI widely within the company

About 50% of our codes are written by agents, coding agents, which are then reviewed by our own engineers. But certainly, it helps our engineers do more and move faster with the current footprint. We look at how we run the business across the organization. So using AI within the business to drive daily operations. It can be all the way from the engineering team to small teams within our back office, even within my finance team, for example, we deployed agents within our treasury organization. We’re deploying agents within how we run — how we pay and reconcile invoice, et cetera.

Alphabet’s management is seeing successful SaaS companies incorporate Gemini deeply into their products and internal processes; management thinks SaaS companies that seize the moment with AI can continue growing; management is seeing very robust token consumption growth by SaaS companies in 2025 Q4

[Question] It just seems like there’s a market belief that the software companies are kind of losing seat power, losing pricing power, and it looks like it could be a really terrible customer base. I can’t imagine that that’s actually going to happen. But could you just talk about it? You’re at the forefront of AI and the impact that that’s having on software companies.

[Answer] in terms of Gemini adoption and how — what this moment means for SaaS, et cetera. Look, at least from my vantage point, I definitely see we have very, very good SaaS customers who are leaders in their respective categories. And what I see the successful companies doing is they are definitely incorporating Gemini deeply in critical workflows, be it on improving their product experience and driving growth or using it to drive efficiency within their organizations. And I think it is an enabling tool, just like it has been an enabling tool for us across our products and services, be it Search, YouTube, et cetera. I think the companies who are seizing the moment, I think, have the same opportunity ahead. And at least we are excited about the partnerships we have there. And the momentum, if I look at it in terms of their tokens usage, et cetera, the growth has been very robust in Q4.

A major concern of Alphabet’s management at the moment is the ability to build AI compute capacity

I think specifically at this moment, maybe the top question is definitely around compute capacity, all the constraints, be it power, land, supply chain constraints, how do you ramp up to meet this extraordinary demand for this moment, get our investments right for the long term and do it all in a way that we are driving efficiencies and doing it in a world-class way.

Amazon (NASDAQ: AMZN)

AWS grew 24% year-on-year in 2025 Q4 (was 20% in 2025 Q3), and is now growing at its fastest pace in 13 quarters; AWS’s run rate has reached $142 billion (was $132 billion in 2025 Q3); AWS’s chips business, including Graviton and Trainium, are over $10 billion om annual revenue rate, and growing triple-digits; AWS is where most companies’ data and workloads reside, and why most companies want to run AI in AWS; AWS’s non-AI workloads are growing faster than expected; management thinks that if companies want to use AI well, their data and applications need to be hosted in the cloud, and this is driving cloud migration; AWS’s backlog is $244 billion in 2025 Q4, up 40% year-on-year (was $200 billion in 2025 Q3)

AWS growth continued to accelerate to 24%, the fastest we’ve seen in 13 quarters, up $2.6 billion quarter-over-quarter and nearly $7 billion year-over-year. AWS is now a $142 billion annualized run rate business, and our chips business, inclusive of Graviton and Trainium is now over $10 billion in annual revenue run rate, growing triple-digit percentages year-over-year…

…We consistently see customers wanting to run their AI workloads where the rest of their applications and data are…

…If you look at the capital we’re spending and intend to spend this year, it’s predominantly in AWS. And some of it is for our core workloads, which are non-AI workloads because they’re growing at a faster rate than we anticipated…

…If you really want to use AI in an expansive way, you need your data in the cloud and you need your applications in the cloud. Those are all big tailwinds pushing people towards the cloud…

…[Question] Maybe a few parts just on AWS. Can you speak to the current state of your revenue backlog as of Q4?

[Answer] I’ll start with the first one, which is on backlog, our backlog is $244 billion. That’s up 40% year-over-year. I think it’s up 22% quarter-over-quarter.

Amazon’s management is seeing that AI applications tend to use multiple models, as different models are better on different dimensions; Amazon Bedrock, AWS’s fully-managed service for companies to leverage frontier models to build generative AI apps, makes it easy to use multiple models for inference; Amazon Bedrock now has a multi-billion dollar annualised revenue run rate, and customer spend was up 60% sequentially in 2025 Q4

Customers are realizing as they get further into AI that they need choice as different models are better on different dimensions. In fact, most sophisticated AI applications leverage multiple models, whether customers want frontier models like Anthropic’s Claude or open models like Mistral or Llama, Frontier Intelligence with lower cost and latency like Amazon Nova or video and audio models like TwelveLabs or Nova Sonic. Amazon Bedrock makes it easy to use these models to run inference securely, scalably and performantly. Bedrock is now a multibillion-dollar annualized run rate business and customer spend grew 60% quarter-over-quarter.

Amazon’s management sees that a lot of work is needed to post-train and fine tune an AI model before it can be used in an application; AWS’s SageMaker AI service, makes it easy for users to post-train and fine tune AI models

Customers sometimes think if they have a good model, they will have a good AI application. It’s not really true. It takes a lot of work to post-train and fine-tune a model for your application. Our SageMaker AI service, along with fine-tuning tools in Bedrock make this much easier for customers.

Enterprises using AI models are currently infusing their proprietary data into the models late in the process through fine tuning or post-training; Amazon’s management believe enterprises will want AI models to train on their proprietary data earlier in the process, through pre-training; AWS’s NovaForge service allows enterprises to mix their own proprietary data into the pre-training phase of Amazon’s 1st party frontier Nova models; NovaForge is the 1st of its kind feature

To date, companies have tried to shape models with their own data late in the process, usually with fine-tuning or post-training. There’s a debate in the industry about this, but we believe that enterprises will want models trained on their own data at an early stage of pretraining if possible. So their models have the best possible foundation for what matters most to each enterprise on which to learn and evolve. It’s a little like teaching a child of foreign language early in their life. That becomes part of their learning foundation moving forward, and it makes it easier to pick up other languages later in their life. To solve for this need, we just launched Nova Forge, which give customers early checkpoints on our Amazon Nova models, allows them to securely mix their own proprietary data with the models data in the pretraining stage and enables their own uniquely customized versions of Nova, what we call Novellas, trained with their data early in the process. This will be very useful for companies as they build their own agents on top of the model. There is nothing else out there like this today and a potential game changer for companies.

Amazon’s management is seeing customers want better price performance from AI chips; Amazon has landed over 1.4 million of its 1st-party AI chip, Trainium 2; Trainium 2 has 30%-40% better price performance than comparable GPUs (likely referring to NVIDIA’s GPUs); Trainium 2 is currently at a multibillion-dollar annualized revenue run rate; 100,000-plus companies are already using Trainium 2 as it is the majority of Bedrock’s usage; management recently launched Trainium 3, which is 40% more price performant than Trainium 2; management expects nearly all of Amazon’s supply of Trainium 3 to be committed by mid-2026; management is already seeing very strong interest for Trainium 4, which is under development; AI start-up Anthropic is training its next cloud model with Trainium 2 through AWS’s Project Rainier; Project Rainier started with 500,000 chips and is continuing to increase; Trainium 2 is currently fully subscribed; Trainium 4 is expected to launch in 2027; customers are already asking about Trainium 5; Anthropic is pleased with Project Rainier

Customers are starving for better price performance. And typically, and understandably, the dominant early leaders aren’t in a hurry to make that happen. They have other priorities. It’s why we’ve built our own custom silicon and Trainium, and it’s really taken off. We’ve landed over 1.4 million Trainium2 chips, our fastest ramping chip launch ever. Trainium2 is 30% to 40% more price performance than comparable GPUs and is a multibillion-dollar annualized revenue run rate business with 100,000-plus companies using it as Trainium is the majority underpinning of Bedrock usage today. We recently launched Trainium3, which is up to 40% more price performant than Trainium2. We’re seeing very strong demand for Trainium3 and expect nearly all of our Trainium3 supply of chips to be committed by mid-2026. And though we’re still building Trainium4, we’re seeing very strong interest already…

…You mentioned Project Rainier, Anthropic is building their next — they’re training their next cloud model on top of Trainium2. And that’s what Project Rainier is. So we talked about 500,000 chips there. You’ll see that continuing to increase. They’re also using a fair bit of Trainium2 for other workloads and their own APIs beyond just Project Rainier. But Trainium is a multibillion dollar annualized run rate business at this point, and it’s fully subscribed…

…There’s very substantial interest in Trainium4, which is coming in 2027. And we’re already having conversations about Trainium5…

…The Project Rainier has gone very well. I think Anthropic is quite pleased with it.

Amazon’s management thinks the primary way companies will derive value from AI will be agents; management thinks companies will use both their own agents and those built by others; management thinks it’s difficult to build agents, so they have launched Strands, which helps users build agents from any AI model; AI agents require a secure and scalable way to connect with multiple elements of a company’s tech stack, and management thinks this is a hard problem to solve; management has launched Bedrock AgentCore to help companies connect agents to the elements; customers are excited about Bedrock AgentCore; Amazon has built multiple agents for customers to use, including Kiro for coding, Amazon Quick for analytics, AWS Transform for software migration, and more; the number of developers using Kiro grew 150% sequentially in 2025 Q4; management is seeing customers get excited about fully autonomous agents and have launched such agents, such as Kiro for coding, AWS DevOps for operational problem solving, and AWS Security Agents for application security

The primary way companies will get value from AI is with agents, some their own, some from others, and there are several customer challenges that we’re well positioned to solve. It’s harder to build agents than it should be. For that, we’ve built Strands, a service enabling agents to be created from any model. Once agents are built, enterprises are apprehensive about deploying to production because these agents need to securely and scalably connect to compute, data, tools, memory, identity, policy governance, performance monitoring and other elements. This is a new and hard problem where a solution has not existed until we launched Bedrock AgentCore. Customers are quite excited about AgentCore, and it’s unlocking deployments.

Customers also want to leverage others’ useful agents, and we’ve built several, including Kiro for coding, Amazon Quick for knowledge workers to leverage their own data and analytics, AWS Transform for software migration and Amazon Connect for call center operations. We continue adding new capabilities and usage continues to grow quickly. For example, the number of developers using Kiro grew more than 150% quarter-over-quarter. 

In addition to agents that customers direct, customers are also becoming excited about agents that require less human interaction. They can be fully autonomous, run persistently for hours or days, scale out quickly and remember context. At this past AWS re:Invent, we launched Frontier Agents to do that. Kiro autonomous agents for coding tasks, AWS DevOps agents for detecting and resolving operational issues and AWS Security Agents for proactively securing applications throughout the development life cycle, and they’re already making a big difference for customers.

Rufus, Amazon’s AI shopping assistant, can now research products, track prices and auto buy; Rufus can now shop tens of millions of items in other online stores and make purchases for customers; Rufus has 300 million customers in 2025; management thinks agentic commerce will be a great experience for consumers; customers who use Rufus are 60% more likely to complete a purchase; management thinks Amazon will eventually have relationships with 3rd-party agents that have commerce capabilities, but the commerce capabilities need to be a lot better than what’s available now; management thinks that consumers will prefer a commerce agent from a retailer they are familiar with, over a horizontal agent that also has commerce capabilities, and this is why management is optimistic about Rufus

Our Agentic AI shopping assistant, Rufus, has rapidly expanded. Rufus can research products, track prices and auto buy, purchasing a product in our store when it reaches your set price. It can also now shop tens of millions of items in other online stores and make purchases for customers using our Agentic Buy for Me feature. Last year, more than 300 million customers used Rufus…

…I’m very optimistic about the customer experience that will ultimately be what customers use for Agentic shopping. And I think it’s good for customers. I think it’s going to make it easier for them…

…Customers who use Rufus are about 60% more likely to complete a purchase…

…We will have relationships with third-party horizontal agents that can enable shopping as well. We have to collectively figure out a better customer experience. It’s still — these horizontal agents don’t have any of your shopping history. They get a lot of the product details wrong, they get a lot of the pricing wrong. And so we have to try to find a customer experience together that’s better and a value exchange that makes sense for both parties. But I’m very hopeful that we’ll get there over time…

…I think you’re going to have to look at as time goes on, which types of — which shopping agents are consumers going to use. And it kind of reminds me in some ways of the early days of kind of all the search engines that were referring traffic to retailers. And it’s still a relatively small portion of the overall traffic and sales. But of that fraction, you have to ask how many consumers are going to prefer using a horizontal agent where it’s kind of a middle person between the retailer and the consumer versus wanting to use a great agent from that retailer that has all its shopping history and that has all the data right there and makes it easy if you’re just spearfishing for something to shop for it right there or if you want to do discovery, you can do it there, and it’s got the best data on shopping. I think a lot of customers are ultimately going to choose to use a great shopping agent from that retailer. Because if you think about what consumers really want in retail in a retailer, they want really broad selection. They want low prices. They want really fast delivery. And then they want a retailer that they can trust and that takes care of them. And I think horizontal agents are pretty good at aggregating selection, but retailers are much better at doing all 4 of those items. And so I’m very optimistic that people will use our shopping agent.

The usage of AI has helped Amazon deliver highly relevant and useful advertisements for customers; Prime Video ads continued to grow and had meaningful contribution to Amazon’s advertising revenue growth; Prime Video had an average ad-supported audience of 315 million in 2025, up from 200 million in early-2024; management recently launched Ads Agent which helps brands to create and optimize campaigns at scale and target effectively; management recently launched Creative Agent, which creates full funnel ad campaigns for advertisers through a conversational interface, shortening campaign creation from a week to hours

Sponsored products advertising in our store continues to be our largest ads offering and the combination of trillions of shopping, browsing and streaming signals with advanced AI and machine learning led us to deliver highly relevant and useful ads for customers…

…We recently announced our Ads Agent, which lets brands use AI to create and optimize campaigns at scale, implement effective campaign targeting and quickly create actionable insights. And our Creative Agent lets advertisers research, brainstorm and generate full funnel ad campaigns from concept to completion using conversational guidance in Amazon’s retail data, transforming what was a week-long process into just hours.

Amazon’s management expects 2026 capex to be $200 billion (was $128 billion in 2025, and $83 billion in 2024); most of the capex will be for Amazon’s AI needs; management is seeing really high demand for AWS’s core and AI features; AWS is monetising compute capacity the moment it is installed; management has deep experience producing high return on invested capital (ROIC) with AWS capex, and they are confident the AI capex will also generate high ROIC; for 2026 Q1, revenue growth is expected to be 11%-15% and operating income growth is expected to be between -10% and 17%; one way AWS’s ROIC for AI capex is already showing up is in the expansion of its operating margin; the vast majority of AWS’s capex has been for compute capacity that is consumed by external customers; AWS, as well as the other cloud providers, could actually grow faster if they had more supply of AI compute

We expect to invest about $200 billion in capital expenditures across Amazon, but predominantly in AWS because we have very high demand, customers really want AWS for core and AI workloads, and we’re monetizing capacity as fast as we can install it. We have deep experience understanding demand signals in the AWS business and then turning that capacity into strong return on invested capital. We’re confident this will be the case here as well…

…Q1 net sales are expected to be between $173.5 billion and $178.5 billion. This guidance anticipates a favorable impact of approximately 180 basis points from foreign exchange rates. As a reminder, global currencies can fluctuate during the quarter. Q1 operating income is expected to be between $16.5 billion and $21.5 billion…

…On the investments we’re making, as Andy said earlier, we are putting into service with customers all capacity that we’re getting and it’s immediately useful. And we’re also seeing a long arc of additional revenue that we see from other customers and backlog and commitments that people are anxious to make with us, especially for AI services. So you can see that’s working its way into our P&L, both through CapEx and also through our operating margin in AWS. AWS is 35% operating margin through Q4, up 40 basis points year-over-year…

…The vast majority of our — the capital that we spend and the capacity that we have is consumed by external customers. We have — Amazon has always been a very large AWS customer, a very helpful AWS customer because they’re very demanding, and they use the services very expansively and stretch the limits as we launch things. So they’ve always been a very important big customer, but always a very small fraction of the total, and that’s true today in AI as well as the overall AWS business…

…So we’re growing at really an unprecedented rate yet, I think every provider would tell you, including us that we could actually grow faster if we had all the supply that we could take. And so we are being incredibly scrappy around that.

Amazon’s management thinks that inference will be the majority of AI workloads in the long run

I think some of the things that you will see over time in the AI space is you’re going to keep seeing all of the inference services, which is going to be the majority of the long-term AI workloads is going to be inference. You’re going to see the inference keep getting optimized.

It appears that Amazon’s management is willing to bring free cash flow to negative to aggressively invest in AI, as they see it as an unusually large opportunity

[Question] Are there any financial guardrails or governors in place that we should think about around the spend just in terms of operating income growth or positive free cash flow?

[Answer] I think this is an extraordinarily unusual opportunity to forever change the size of AWS and Amazon as a whole. I think it also is an extraordinary opportunity for companies to change all their customer experiences and for start-ups to be able to build brand-new experiences and businesses that would have taken much longer to try to accomplish before that they can do right now. And so we see this as an unusual opportunity, and we are going to invest aggressively here to be the leaders because like we’ve been in the last number of years and like I think we will be moving forward.

Market demand for AI compute currently looks like a barbell to Amazon’s management, with AI labs on one end spending a lot on compute for just a handful of applications, and with enterprises on one end that are using AI for productivity purposes; the middle of the barbell are production AI workloads from enterprises that are under evaluation; management thinks the middle part of the barbell will be the largest and most durable aspect of market demand for AI compute, but it has yet to materialise and it’s only a matter of time

The way I would describe what we see right now in the AI space is it’s really kind of a barbelled market demand where on one end, you have the AI labs who are spending gobs and gobs of compute right now, along with what I would consider a couple of runaway applications. And then at the other side of the barbell, you’ve got a lot of enterprises who are getting value out of AI in doing productivity and cost avoidance types of workloads. These are things like customer service or business process automation or some of the fraud pieces. And then in that middle of the barbell are all the enterprise production workloads. And I would say that the enterprises are in various stages at this point of evaluating how to move those, working on moving those and then putting them into production. But I think that middle part of the barbell very well may end up being the largest and the most durable. And I would put in the middle of that barbell, too, by the way, I would put just the altogether brand-new businesses and applications that companies build that right from the get-go run in production on top of AI…

…When I look at this and what’s happening, it’s kind of unbelievable if you look at the demand of what you’re seeing already with AI, but the lion’s share of that demand is still yet to come in the middle of that barbell. And that will come over time. It will come as you have more and more companies with AI talent as more and more people get educated with the AI background, as inference continues to get less expensive, and that’s a big piece of what we’re trying to do with Trainium and our hardware strategy. And as companies start to have success in moving those workloads to — further and further success in moving those workloads to run on top of AI.

Almost every conversation Amazon’s management is having with companies regarding AWS starts with AI; management thinks that the AI movement will eventually involve many, many more companies than what’s seen today

There’s a number of AI labs, but almost every company you talk to, almost every conversation we have on the AWS side, starts with AI…

…This AI movement is not going to be a couple of companies. It’s going to be thousands of companies over time.

Amazon has over 1,000 AI applications internally that are in production or being developed, and these applications are used in all areas of Amazon’s business

Internally, we have all sorts of ways that we are using AI. We have over 1,000 AI applications that we’ve either deployed or in the process of building, and they range from our shopping assistant in Rufus that we were just talking about to Alexa+, which is a really large-scale generative AI application to applications in our fulfillment network that allow us to have more accurate forecasting predictions to how we do customer service and our customer service chatbot to how we are making it much easier for brands to create advertisements and to optimize all their campaigns across the full funnel of advertising options we have to — in live sports, if you watch Thursday Night Football, you can see defensive alerts, which predict which player is going to blitz or pocket health.

AWS added 3.9 GW of compute capacity in 2025, and that is more than any other company in the world; the 3.9 GW of compute capacity AWS added in 2025 is twice what AWS had in 2022; management expects AWS’s compute capacity to double by 2027; AWS added 1.2 GW of compute capacity in 2025 Q4

In 2025, AWS added more data center capacity than any other company in the world…

…If you look in the last 12 months, we added 3.9 gigawatts of power. Just for perspective, that’s twice what we had in 2022 when we were an $80 billion annual run rate business. We expect to double it again by the end of ’27. We added 1.2 gigawatts of power in Q4, just quarter-over-quarter.

Apple (NASDAQ: AAPL)

The consumer response to AirPods Pro 3 has been amazing; AirPods Pro 3 has a live translation feature; management has been hearing powerful stories of people using live translation to communicate seamlessly across languages

The response to AirPods Pro 3 has been amazing. Customers are raving about the rich immersive sound quality, the unmatched level of active noise cancellation and the noticeably improved comfort that makes them effortless to wear. Features like live translation are also changing the way people can communicate by helping users connect across languages in real time and making everyday conversations feel more natural and accessible…

…And as I touched on earlier, we are hearing powerful stories of people using live translation to communicate seamlessly across languages.

The majority of enabled-iPhone users were using Apple Intelligence in 2025 Q4 (FY2026 Q1); management has introduced dozens of features in Apple Intelligence since launch; Apple Intelligence now supports 15 languages; one of Apple Intelligence’s most popular features is Visual Intelligence; management thinks Apple’s products are the best platforms in the world of AI because of Apple silicon; Apple is collaborating with Google to build the next generation of Apple’s foundation models that will power Apple Intelligence; management determined that Google’s AI technology was the most capable for building Apple foundation models; even with the collaboration with Google, Apple Intelligence will continue to run on-device and in Private Cloud Compute; management sees both on-device and cloud inference as important; a growing percentage of Apple’s overall iPhone installed base is AI-capable

During the quarter, we were excited to see that the majority of users on enabled iPhones are actively leveraging the power of Apple Intelligence.

Since the launch of Apple Intelligence, we’ve introduced dozens of features, including writing tools and cleanup and made it available in 15 languages. These AI experiences are personal, private, integrated across our platforms and relevant to what our users do every day. We are bringing intelligence to more of what people already love about our products so we can make every experience even more capable and effortless. One of our most popular features is Visual Intelligence which helps users learn and do more than ever with the content on their iPhone screen, making it faster to search, take action and answer questions across their apps. And as I touched on earlier, we are hearing powerful stories of people using live translation to communicate seamlessly across languages.

And these are just some of the many powerful AI features that are enabling our users to do remarkable things with our products, which are far and away the best platforms in the world for AI. That’s in no small part because of the extraordinary power and performance of Apple silicon. 

Building on our efforts in the AI space, we are also collaborating with Google to develop the next generation of Apple foundation models. This will help power future Apple Intelligence features, including a more personalized series coming this year. We’re incredibly excited for what’s to come with so many new experiences to unlock…

…We basically determined that Google’s AI technology would provide the most capable foundation for AFM — I’m sorry, Apple foundation models. And we believe that we can unlock a lot of experiences and innovate in a key way due to the collaboration. We’ll continue to run on the device and run in Private Cloud Compute and maintain our industry-leading privacy standards in doing so…

…[Question] When you think about how Apple might manage AI, do you see that evolving towards more edge AI or on device services versus cloud-based AI?

[Answer] We see both being important, the on-device and the private cloud compute. And so we don’t see it as an either/or we see it as both…

…[Question] Can you speak at all to roughly what portion of your iPhone or overall active device installed base is now AI capable?

[Answer] We don’t provide that specific number, but it is a growing number, as you can imagine in our installed base.

Apple’s management will continue with Apple’s hybrid approach when it comes to capital expenditure for AI data centers (of using its own data centers as well as those of 3rd-parties)

Just speaking of CapEx, in general, as you know, we have a hybrid model for CapEx. And so I think that what happens is our CapEx can be volatile, independent of kind of the volume and the performance of our business.

The use of Apple’s own chips in its products provides both strategic as well as direct value, and has impacted the company’s gross margin in a positive way

As far as impact on gross margin, we have been, as you know, investing in core technologies like our own silicon, our own modem. And certainly, while those do provide opportunities for cost savings and can be reflected in margins, they also importantly provide the differentiation that’s really important for our products as well and give us more control of our road map. So I think there’s a lot of strategic value to it, but also we are seeing investments in our core technologies impacting gross margin in a positive way.

ASML (NASDAQ: ASML)

ASML’s management has seen the company’s customers become more positive in their medium-term outlooks, driven by demand for AI; ASML’s customers, for both Logic and Memory (DRAM) chips, are building capacity, and this has translated into orders for ASML’s EUV systems; ASML’s Logic customers are becoming more comfortable about the long-term sustainability of AI demand; ASML’s Memory (DRAM) customers are ramping up capacity for their advanced nodes, and these nodes require more EUV layers; management sees a strong belief in ASML’s customers that AI demand is real, and these customers are adding major capacity, starting in 2026

If you listen to our customers, both what they say publicly, but also what they told us, it’s pretty clear that customers over the past couple of months have actually become more positive in their assessment of the medium-term market perspectives as they see it. I think it’s primarily on the basis of the more robust view that they have when it comes to demand for AI, which seems to be more sustainable from their vantage point. That recognition has led some of our customers to really invest in capacity and gear up their plans for medium-term capacity expansion…

…The market outlook has notably improved in the last few months. This is especially true when it comes to the build-up of the capacity for AI applications, being data centers or other infrastructure. Now, we start to see that this build-up is also translating into need for capacity at our advanced customers. This is true for Logic. This is true for DRAM. This starts to translate also into orders for our most advanced technology, especially EUV. So in the last few months we have seen our DRAM customers, our Logic customers, starting to accelerate their planning-capacity and having this discussions with us. 

If I look at Logic first, so there we see our customers starting to be more comfortable about the sustainability of the long-term AI demand. This means that they are more willing to accelerate their capacity-planning. They are transitioning also from 4nm technology to 3nm technology, which is going to be more demanding in terms of advanced technology. Finally, of course, the ramp of 2nm is going on and I would say is accelerating in order to fulfill the future need of mobile and HPC applications.

When I look at DRAM, there also the demand is very strong for HBM, of course, but also for DDR. This most probably will lead to a very tight supply, at least in 2026 and most probably beyond that. So we see our customer ramping 1b, 1c nodes, which are going to be critical for that demand. And on those nodes, we see them increasing basically the amount of EUV layers. We have talked about that in the past. We see that happening very strongly right now…

…  I think a strong belief that the AI demand is real and a preparation for that, with on the short term a major addition of capacity. This will start in 2026 and will last beyond that. 

ASML’s management is seeing multi-beam inspection becoming more critical as the use of 3D structures in advanced Logic and Memory chips increase; management expects ASML’s E-beam inspection system to gain more traction in 2026

On E-beam inspection, multi-beam is becoming more and more critical. 2025 was also a good year for this product. Allowing us to mature the technology, demonstrate initial value with our customers. We expect also that product to have more traction in 2026…

…With the continuing increase of 3D structures in advanced Logic and Memory, we see more adoption of our multi e-beam inspection system to detect optically non-visible yield-limiting defects.  

ASML’s management continues to see strong growth for the semiconductor market, especially for advanced chips, in the long-term, driven by AI; management sees higher lithography intensity in ASML’s customers’ manufacturing processes; ASML’s management sees AI driving much faster growth in demand for advanced memory and advanced logic chips compared to non-AI memory and non-AI logic chips; AI demand is driving demand for not just more transistors per chip, but more wafers as well

One of the key points we made at our Capital Markets Day, November 2024, was that AI applications will require more advanced technology in DRAM and Logic and will drive basically some of our most advanced products. I think that this is being confirmed as we speak. The last few months have pointed basically exactly to that dynamic. We also see that the progress we continue to make on our cost of technology with EUV is driving for more litho-intensity. And that’s, again, something that has been confirmed in the last few months…

…You see the historical growth of memory logic, which is about 6%, 7% year-on-year…

…What you see with AI is that when we look at advanced logic, when we looked at advanced memory the growth on those segments is going to be more than 20% year-on-year for the foreseeable future. And this is really what is going to drive basically more demand on lithography. Why is that? So we’ve talked in the past a lot about Moore’s low, of course. And Moore’s Law is law that say that every couple of years, we need to double the number of transistors per chips. And that law has been true for many, many years for PC for mobile application. Now when you look at AI and this started to happen in 2010, the curve is far more aggressive. When you look at the most advanced AI product today, NVIDIA products, for example, the request is not to grow 2x every 2 years, but in the last few years to grow 16x every 2 years. So you see a major acceleration basically of the need for silicon. And of course, we provide that in 2 different ways. We provide that with scaling by making transistors small. We can put more transistor per chips. And this has been a good way basically to provide more transistor and follow Moore’s law for many, many years, but that’s not enough anymore. And if you cannot put enough transistor per unit of area per chips, then the only option will be to make more wafers. And that’s a bit what we see happening with AI…

…I pick one example, and I picked it from NVIDIA because all of you are, of course, very much aware of what’s happening there. Today, on the Blackwell system, you need about 2.5 wafers to create the product. If you look at 2027 on the revenue product, this number will go up to 10 wafers. So to provide the same product to their customer, NVIDIA will need 4x more wafer than today.

ASML’s management thinks AI will have a big effect on overall GDP (gross domestic product)

The effect AI can have on the overall GDP is pretty big. In fact, if you look at the U.S., even in 2025, AI was accounting for a very large part of the growth, and we expect that basically to be applied to the entire worldwide GDP.

ASML’s management sees AI demand driving demand for even ASML’s more mature DUV (deep ultra-violet) lithography systems; management continues to drive the roadmap for ASML’s DUV lithography systems

What’s interesting with AI is that this basically touch on all products. Of course, AI is going to require very advanced chips, and this is going to drive EUV, for example. So this year will be a big year for EUV, Roger will talk about that. It’s going to drive advanced inspection tool. But at the same time, AI needs a lot of data generation, a lot of sensor, and this will be still created by the use of more mature technology such as DUV. So AI will have also this effect basically to really drive our entire product portfolio in the coming years…

…We continue to drive the road map both on Immersion, where we have launched our 2150, which basically give us sub-nanometer accuracy and more than 300 wafer per hour. Productivity is important. Productivity, of course, a way to get capacity. So we continue to drive that on immersion, I think the example of the NXT:870B, which is a KrF system is even more spectacular because there we have been capable to achieve more than 400 wafer per hour. And that tool today is creating a lot of interest at our customer because productivity, again, is capacity.

ASML is making good progress in its partnership with AI startup Mistral (reminder that ASML had invested in Mistral in 2025)

Back in the end of the summer when we announced our collaboration but also our investment in Mistral. The rationale there was to get AI in ASML and to get the very best people, the very best competence in ASML in order to be able to first strengthen our core competencies, read putting AI in our product, support the connected market, to offer some of those capability to our customers and also create new opportunity basically moving forward. That’s a project we are going to talk more about in ’26, in ’27. We are making great progress with Mistral, our partner.

ASML’s management thinks memory is more likely to be the bottleneck for AI today

It’s difficult to say if logic or DRAM is the bottleneck for AI today. I will still pick mostly memory at this point of time. And the reason for that is that it comes to memory, the demand for high bandwidth memory, which is the AI memory is extremely high. But the demand for DDR memory, which is for mobile PC is also very high. And as a result, we have seen basically the price of DRAM going up significantly in the last few weeks. Therefore, there’s a need for capacity. And our memory customers are moving very aggressively.

ASML’s management is already planning for hyper-NA EUV systems (this are systems that are even more advanced than high-NA EUV), but there’s a lot of flexibility when it comes to the timeline for introducing hyper-NA

We talk about low NA, we talk about High NA I think we talked about Hyper NA because we see that in the future, there may be a need for even a more advanced litho system. And we could end up in a war, I’m talking 10 years from now where the customer use basically each one of those 3 systems. Now this being said, when you look 10 years ahead, it’s very difficult to know exactly when this will happen. And in order to not have to answer that question today, what we did is develop a program, which we call high productivity platform. So Roger mentioned it as one of the key program in EUV, and that program basically consists in defining an EUV platform that will come to the market early next decade, and that will be able to support Low NA, this major productivity improvement. We look at more than 400 wafer per hour. High NA, also with major improvement and potentially Hyper NA. So we’re designing a platform basically that we’ll be able to receive ultimately low NA optic, high NA optic, hyper NA optic. This give us basically the full flexibility over time to decide exactly when and how we should introduce hyper NA.

Mastercard (NYSE: MA)

Mastercard’s management sees agentic commerce as being in the early days; Mastercard launched Mastercard Agent Pay in 2025 and has now enabled US card issuers to participate; management will enable Mastercard’s global issuer base to work with Agent Pay by the end of 2026 Q1; management is working with the entire agentic commerce ecosystem across all regions; although agentic commerce is still early, management thinks it will come fast; management thinks agentic commerce can affect tokenisation of payments very positively

For us, Agentic Commerce represents another avenue to enable payment choice with the same trust that we always deliver. It’s early days, but we are ready. You remember, last year, we launched Mastercard Agent Pay, a framework designed to foster trust in Agentic transactions. We have now enabled our U.S. issuers to participate in Agent Pay, and we are working to enable our global issuer base by the end of the first quarter…

…We’re actively working with ecosystem participants to adopt Agentic Commerce across all regions…

…In Asia, we’re partnering with Anthem on card-based tokenized payment solutions for Agentic payments. In the U.K., we’re consulting clients such as Lloyds Banking Group, Elavon and Santander on Agentic Commerce innovations. And in the UAE, we’re piloting Agentic payments with the leading retail and entertainment group, Majid Al Futtaim. And with banks, merchants and digital players, we continue to position them for success in this new era of commerce, whether it be through consulting, security, data-driven insights or new loyalty programs, we are there…

…What an exciting space and might be one of those use cases, AI-driven use cases that meet our reality much faster than other AI use cases out there. So I think Agentic Commerce is going to come fast. So this whole idea of a consumer using an agent to drive — have a better commerce journey, I think that just resonates with people. You get better quality insights, you get better recommendations…

…You could also see that the application of services. For example, tokenization get to see a very different path than it might see without Agentic Commerce. So these are all aspects that make me very excited about this.

Meta Platforms (NASDAQ: META)

Meta rebuilt the foundations of its AI program in 2025 and will soon start shipping new AI models and products; management expects Meta to steadily push the frontier over the course of the year as it ships new AI models; management expects to use AI models developed by Meta Superintelligence Labs (MSL) to build compelling AI products; management has already used MSL’s models to build AI dubbing of videos into local languages; Meta now supports 9 different languages and hundreds of millions of people are watching AI-translated videos daily; the translated videos are driving incremental time spent on Instagram; the AI dubbing tool will support more languages throughout 2026; management is pleased with the current progress of Meta Superintelligence Labs, but it’s a long-term effort

In ’25, we rebuilt the foundations of our AI program. Over the coming months, we’re going to start shipping our new models and products. I expect our first models will be good, but more importantly, we’ll show the rapid trajectory that we’re on. And then I expect us to steadily push the frontier over the course of the year as we continue to release new models…

…We expect to use the models developed by Meta Superintelligence Labs to deliver compelling and differentiated AI products. One area we’re already seeing promise is with AI dubbing of videos into local languages. We are now supporting 9 different languages with hundreds of millions of people watching AI translated videos every day. This is already driving incremental time spent on Instagram, and we plan to launch support for more languages over the course of this year…

…We’re about 6 months into building MSL. I’m very pleased with the quality of the team. I think we have the most talent-dense research effort in the industry and some of the early indicators look positive. But look, I think that this is going to — is a long-term effort, right? We’re not here to do this to ship like one model or one product. We’re doing a lot of models over time and a lot of different products.

Meta’s management’s vision with AI is to build personal super intelligence; management thinks what makes agents valuable is the context they can see, and Meta’s agents can provide a uniquely personal experience

Our vision is building personal super intelligence. We’re starting to see the promise of AI that understands our personal context, including our history, our interests, our content and our relationships. A lot of what makes agents valuable is the unique context that they can see. And we believe that Meta will be able to provide a uniquely personal experience.

Meta’s management is merging LLMs (large language models) with the AI recommendation systems of the company’s social media platforms and advertising system; management thinks the introduction of LLMs to the recommendation systems will significantly improve the performance of the already-powerful recommendation systems, and have a positive implication for commerce activity taking place on Meta’s platforms

We’re also working on merging LLMs with the recommendation systems that power Facebook, Instagram, Threads and our ad system. Our world-class recommendation systems are already driving meaningful growth across our apps and ads business, but we think that the current systems are primitive compared to what will be possible soon. Today, our systems help people stay in touch with friends, understand the world and find interesting and entertaining content. But soon, we’ll be able to understand people’s unique personal goals, and tailor feeds to show each person content that helps them improve their lives in the ways that they want. This also has implications for commerce. Our ads today help businesses find just the right very specific people who are interested in their products. New Agentic shopping tools will allow people to find just the right very specific set of products from the businesses in our catalog. We’re focused on making these experiences work across both our feeds and across business messaging, significantly increasing the capabilities of WhatsApp over time.

Meta’s management has simplified Instagram’s ranking architecture to enable more efficient model scaling and this has led to a 30% year-on-year increase in watch time on Instagram Reels in the USA in 2025 Q4; video time on Facebook grew double-digits year-on-year in 2025 Q4, and views of organic feed and video posts were up 7% as a result of optimisations in ranking; Meta is now surfacing 25% more reels published on the day compared to 2025 Q3; the prevalence of original content in the US on Instagram was up 10 percentage points in 2025 Q4, and 75% of recommendations are now from original posts; Threads saw a 20% lift in time-spent from improvements to its recommendation models; management sees a lot of opportunity for Meta to achieve additional gains in engagement on its apps through scaling the complexity and amount of training data in its models, and the introduction of LLMs to the recommendation systems; management is developing Meta’s next generation recommendation systems and the work includes building new model architectures from the ground up on top of LLMs; the improved engagement in 2025 Q4 across Meta’s platforms was from multiple optimisations

Instagram Reels had another strong quarter with watch time up more than 30% year-over-year in the U.S. Engagement is benefiting from several optimizations we made to improve the quality of recommendations including simplifying our ranking architecture to enable more efficient model scaling. This unlocks the ability for our systems to consider longer interaction histories to better identify a person’s interests.

On Facebook, video time continued to grow double digits year-over-year in the U.S., and we’re seeing strong results from our ranking and product efforts on both feed and video surfaces. The optimizations we made in Q4 drove a 7% lift in views of organic feed and video posts on Facebook, resulting in the largest quarterly revenue impact from Facebook product launches in the past two years…

…On Facebook, our systems are surfacing over 25% more reels published that day than the prior quarter. On Instagram, we grew the prevalence of original content in the U.S. by 10 percentage points in Q4 with 75% of recommendations now coming from original posts. 

Threads is also seeing strong momentum again, benefiting from recommendation improvements. The optimizations we made in Q4 drove a 20% lift in threads time spent.

We see a lot of opportunity to drive additional gains. This includes scaling the complexity and amount of training data we use in our models while continuing to make our systems more responsive to people’s real-time interest. We’re also focused on incorporating LLMs to understand content more deeply across our platform, which will enable more personalized recommendations.

Another big area of investment this year is developing the next generation of our recommendation systems. We have several big bets on this front, including building new model architectures from the ground up that will work on top of LLMs, leveraging the world knowledge and reasoning capabilities of an LLM to better infer people’s interests…

…We launched several ranking improvements in Q4 on Facebook and Instagram that drove incremental engagement. And there isn’t really one single launch that is driving most of the gains. It’s multiple optimizations to our recommendation systems that are helping us make more accurate predictions about what will be interesting to each person…

…We’re going to continue to make recommendations even more adaptive to what a person is engaging with during their session. So the recommendations we surface are more relevant to what they’re interested in at that moment.

Meta’s management thinks AI will enable a surge in new, immersive and interactive media formats, leading to more interactive feeds in the company’s social media platforms

Soon, we’ll see an explosion of new media formats that are more immersive and interactive and only possible because of advances in AI. Our feeds will become more interactive overall. Today, our apps feel like algorithms that recommend content. Soon, you’ll open our apps, and you’ll have an AI that understands you and also happens to be able to show you great content or even generate great personalized content for you…

…Video will continue to be here for a long time. It’s going to continue growing, it’s not going anywhere, just like photos and text in many ways, continue to grow even as the market continues to grow beyond that. But I don’t think that video is the ultimate kind of final format. I just — I think that this is going to get — we’re going to get more formats that are more interactive and immersive and you’re going to get them in your feeds. So you can imagine this, I mean there’s obviously a lot of details to fill in on this, but you can imagine people being able to, easily through a prompt, create a world or create a game and be able to share that with people who they care about and you see it in your feet and you can jump right into it and you can engage in it. And there are 3D versions of that, and there are 2D versions of that and Horizon, I think, fits very well with the kind of immersive 3D version of that.

Sales of Meta’s AI glasses tripled in 2025; the glasses are some of the fastest-growing consumer electronics products in history; management thinks most people who wear glasses today will switch to AI glasses in the future; management is directing most of Meta’s investments in the Reality Labs segment to AI glasses and wearables; management thinks Reality Labs’ losses in 2026 will be similar to 2025, and will be the peak going forward

Sales of our glasses more than tripled last year, and we think that they’re some of the fastest-growing consumer electronics and history. Billions of people wear glasses or contacts for vision correction and I think that we’re in a moment similar to when smartphones arrived, and it was clearly only a matter of time until all those flip phones became smartphones. It’s hard to imagine a world in several years where most glasses that people wear aren’t AI glasses.

For Reality Labs, we are directing most of our investment towards glasses and wearables going forward while focusing on making Horizon a massive success on mobile and making VR a profitable ecosystem over the coming years. I expect Reality Labs losses this year to be similar to last year, and this will likely be the peak as we start to gradually reduce our losses going forward while continuing to execute on our vision.

Meta’s management wants Meta to continue investing significantly in AI infrastructure, and has established the Meta Compute division to deliver the infrastructure for the company; management sees long-term investments in silicon and energy as an important part of Meta Compute’s work; management continues to build AI infrastructure for Meta that is flexible; management expects the cost per gigawatt of Meta’s AI infrastructure to decrease significantly over time; management is deploying a variety of chips in Meta’s AI infrastructure; Meta’s ads retrievable engine, Andromeda, can now run on chips from NVIDIA, AMD, and Meta (MTIA); MTIA is currently running inference  workloads on Meta’s core ranking and recommendation models, but management will extend MTIA in 2026 Q1 to also cover training workloads; management expects Meta to have sufficient cash flow to fund its infrastructure investments in 2026, but they are also looking for external financing that may lead to net-debt on the balance sheet; with Meta’s current and planned compute capacity, management is exploring businesses beyond ads; management continues to have a robust ROI-driven process when planning investments for its AI models 

We will continue to invest very significantly in infrastructure to train leading models and deliver personal super intelligence to billions of people and businesses around the world. I recently announced Meta Compute with the belief that being the most efficient at how we engineer, invest and partner to build our infrastructure will become a strategic advantage. Dina Powell McCormick also joined us as President and Vice Chairman, and she will lead our efforts to partner with governments, sovereigns and strategic capital partners to expand our long-term capacity, including ensuring positive economic impact in the communities that we operate in around the world. An important part of Meta Compute will be making long-term investments in silicon and energy. We will continue working with key partners while advancing our own silicon program. We’re architecting our systems that we can be flexible in the systems that we use, and we expect the cost per gigawatt to decrease significantly over time through optimizing both our technology and supply chain…

…We’re working to meet our silicon needs by deploying a variety of chips that optimally support each of our different workloads. To that end, in Q4, we extended our Andromeda ads retrievable engine, so it can now run on NVIDIA, AMD and MTIA…

…In Q1, we will extend our MTIA program to support our core ranking and recommendation training workloads in addition to the inference workloads it currently runs…

…As we invest in infrastructure to meet our business needs, we continue to prioritize maintaining long-term flexibility so we can adapt to how the market develops. We’re doing so in several ways, including changing how we develop data center sites, establishing strategic partnerships, contracting cloud capacity and establishing new ownership structures for some of our large data center sites.

We have a strong net cash balance and expect our business will continue to generate sufficient cash to fund our infrastructure investments in 2026, which is reflected in our expectations. Nonetheless, we will continue to look for opportunities to periodically supplement our strong operating cash flow with prudent amounts of cost-efficient external financing, which may lead us to eventually maintain a positive net debt balance…

…[Question] It just seems like you’re going to have a tremendous amount of capacity. How do you think about expanding your opportunities beyond ads, things like subscriptions or licensing cloud models?

[Answer] We are focused on things beyond ads, I think the numbers make it so that for the next couple of years, ads are going to be, by far, the most important driver of growth in our business. So that’s why, as we’re working on this, we have a balance of new things that we’re trying to do, while also investing very heavily and making sure that all of the work that we’re doing in AI improves both the quality and business performance of the core apps and businesses that we run there…

…A year ago on this call, I think I talked about the set of investments we were making in 2025. as part of our 2025 budgeting process across our ads performance and organic engagement initiatives. And those investments have generally paid off, and we feel really good about kind of the — the process we ran in terms of using projected ROI to stack rank investments, make sure that we had a robust measurement system funded things that were positive ROI and then tracking how they performed over the course of the year. And we are — we’ve just finished running our 2026 budgeting process, and we have funded a similar set of investments, which we expect will enable us to continue delivering strong revenue growth in 2026.

Meta’s management thinks AI will dramatically change how the company works in 2026; management is investing in AI tooling (i.e. agents) for employees; management is seeing AI helping single persons accomplish projects that used to require big teams; management has seen agentic coding tools help increase Meta’s output per engineer by 30% since the beginning of 2025, with even stronger gains seen in; management thinks AI agents will have a profound positive impact on the productivity of the technology sector and the whole economy

I think that 2026 is going to be the year that AI starts to dramatically change the way that we work…

…We’re investing in AI native tooling so individuals at Meta can get more done, we’re elevating individual contributors and flattening teams. We’re starting to see projects that used to require big teams now be accomplished by a single, very talented person…

…A big focus of this is to enable the adoption and advancement of our AI coding tools where we’re seeing strong momentum. Since the beginning of 2025, we’ve seen a 30% increase in output per engineer with the majority of that growth coming from the adoption of agentic coding, which saw a big jump in Q4. We’re seeing even stronger gains with power users of AI coding tools, whose output has increased 80% year-over-year. We expect this growth to accelerate through the next half…

…There’s a big delta between the people who do it and do it well and the people who don’t. And I think that’s going to just be a very profound dynamic for, I think, across the whole sector and probably the whole economy going forward in terms of the productivity and efficiency with which we can run these companies, which I think — my hope is that we can use that to just get a lot more done than we were able to before.

Meta’s year-on-year advertising conversion growth accelerated through 2025 Q4; management expects further gains in 2026, driven by further integration of AI across all layers of the marketing and customer engagement funnel; management continues to scale the complexity and size of Meta’s models for selecting what advertising to show; in 2025 Q4, management doubled the number of GPUs used to train Meta’s ads-ranking GEM model, and adopted a new sequence learning model architecture to process longer sequences of user behaviour; Meta’s recent initiatives to improve its advertising models drove a 3.5% lift in ad clicks on Facebook, and a 1% gain in conversions on Instagram in 2025 Q4; management launched a new run time model across Instagram Feed stories and reels in 2025 Q4 that drove a 3% increase in advertising conversion rates; Meta has continued making progress with Lattice, its unified model architecture for advertising ranking models; management consolidated models for Facebook Stories and other services into the overall Facebook model in 2025 Q4, and this drove a 12% increase in advertising quality; management expects to consolidate more models in 2026 compared to what Meta has done in the previous 2 years; Meta’s ads retrievable engine, Andromeda, can now run on chips from NVIDIA, AMD, and Meta (MTIA); Andromeda’s compute efficiency has tripled in 2025 Q4; Meta does not typically use GEM for inference because it is costly; management thinks Meta’s larger advertising models can benefit from having more compute; management expects to meaningfully scale up the cluster used for training GEM in 2026; management expects to further improve the transfer of knowledge from GEM to run time models; this is the 1st time management has found a recommendation model architecture that scales with similar efficiency as LLMs, and they are hopeful this architecture can be scaled up while preserving an attractive ROI (return on investment)

We’re seeing very strong results from the ad performance investments we made throughout 2025 with year-over-year conversion growth accelerating through the fourth quarter. We expect the set of investments we’re making in 2026 will enable us to drive further gains as we continue to integrate AI across all layers of the marketing and customer engagement funnel.

The first area is our ad system where we’re continuing to scale the complexity and size of our models to better select which ads to show. In Q4, we doubled the number of GPUs we used to train our GEM model for ads ranking. We also adopted a new sequence learning model architecture, which is capable of using longer sequences of user behavior and processing much richer information about each piece of content. The GEM and sequence learning improvements together grow a 3.5% lift in ad clicks on Facebook and a more than 1% gain in conversions on Instagram in Q4. This new sequence learning architecture is significantly more efficient than our prior architectures which should enable us to further scale up the data, complexity and compute we use in our future ranking models to deliver performance gains.

As we scale up our foundational ads models like GEM, we are also developing more advanced models to use downstream of them at run time for ads inference. In Q4, we launched a new run time model across Instagram Feed stories and reels, resulting in a 3% increase in conversion rates in Q4.

We continue to progress on our model unification efforts under Lattice as well. After seeing strong success with the consolidation of Facebook feed and video models in the first half of 2025. In Q4, we consolidated models for Facebook Stories and other services into the overall Facebook model. This, along with a series of back-end improvements drove a 12% increase in ads quality. And in 2026, we expect to consolidate more models than we had in the prior two years as we continue to evolve our systems towards running a smaller number of highly capable models…

… To that end, in Q4, we extended our Andromeda ads retrievable engine, so it can now run on NVIDIA, AMD and MTIA. This, along with model innovations, enabled us to nearly triple Andromeda’s compute efficiency…

…We don’t typically use our larger model architectures like GEM for inference because their size and complexity would make it too cost prohibitive. So the way that we drive performance from those models is by using them to transfer knowledge to smaller lightweight models used at run time. But I would say that we think that there is room for our larger models to benefit from having more compute. And I think as we scale up the compute available to those models, and the foundational models in different areas that power the different stages of ads ranking and recommendation, we expect that we will see gains coming from that…

…In 2026, we’re expecting to meaningfully scale up GEM training to an even larger cluster, increasing the complexity of the model, expanding the data that we trained it on, leveraging new sequence, learning architecture that we had begun deploying in Q4. And we’re also going to further improve how we transfer the learnings from our GEM foundation models to the runtime models that we’re using…

…This is the first time we have found a recommendation model architecture that can scale with similar efficiency as LLMs. And we’re hoping that this will unlock the ability for us to significantly scale up the size of our ranking models while preserving an attractive ROI.

Meta’s video generation tools have hit a combined revenue run rate of $10 billion in 2025 Q, and the sequential growth was nearly 3x higher than the growth Meta’s overall ads revenue; Meta’s latest incremental attribution feature was rolled out in 2025 Q4 and it drove a 24% increase in incremental conversions; the incremental attribution feature is already at a multibillion-dollar annual run rate just 7 months after launch

The combined revenue run rate of video generation tools hit $10 billion in Q4, with quarter-over-quarter growth outpacing the increase in overall ads revenue by nearly 3x. We are also seeing very good results from our incremental attribution feature, which optimizes for incremental conversions in real time. Our latest model rollout in Q4 is driving a 24% increase in incremental conversions versus our standard attribution model, and this product has already achieved a multibillion-dollar annual run rate just 7 months since launching.

Meta’s click-to-message revenue grew more than 50% year-on-year in the US in 2025 Q4; paid messaging within Whatsapp crossed a $2 billion annual run rate in 2025 Q4; management is seeing good early traction with business AI agents in Mexico and the Philippines, with over 1 million weekly conversations between people and business AI taking place

Click to message ads revenue growth accelerated in Q4 with the U.S. up more than 50% year-over-year, driven by strong adoption of our website to message ads which direct people to a business’s website for more information before choosing to launch a chat. Paid messaging within WhatsApp continues to scale as well, crossing a $2 billion annual run rate in Q4…

…We’re seeing good early traction with our business AIs in Mexico and the Philippines, with over 1 million weekly conversations between people and business AI is now happening on our messaging platforms. This year, we will expand availability of our business AIs to more markets, while also extending their capabilities so they not only answer questions on topics like product availability, but can help people get things done right within WhatsApp.

Meta recently acquired Manus; Manus already has a significant number of businesses paying a subscription fee; management thinks the integration of Manus into Meta’s advertising and business products can have really powerful effects

I don’t think either of us mentioned that the Manus acquisition in the upfront comments, I mean that is going to — is a good example of — you have a significant number of businesses that already pay a subscription to basically use their tool to accelerate their business results and integrating that kind of thing into our ads and business managers, so that way we can just offer more integrated solutions for the many, many millions of businesses that use and rely on our platforms is going to be really powerful, both for accelerating their results using the existing products that we have and I think adding new lines as well.

Meta’s management continues to see the company as being capacity-constrained when it comes to AI compute; management expects the capacity-constrain to last for most of 2026, but there are efforts within the company to mitigate the impacts of the constrain 

[Question] You’ve talked about being capacity constrained internally and not having enough compute to sort of achieve the goals you have on the platform on a product standpoint. I want to know if we can get any update on currently how you think about your own internal needs for compute against that road map?

[Answer] We do continue to be capacity constrained. Our teams have done a great job ramping up our infrastructure through the course of 2025. But demands for compute resources across the company have increased even faster than our supply. So we expect over the course of 2026 to have significantly more capacity this year as we add cloud. But we’ll likely still be constrained through m uch of 2026 until additional capacity from our own facilities comes online later in the year. With that said, I think we have done a good job internally mitigating the impact of compute constraints on our business. I expect that will continue to be the case in 2026. We’re continuing to focus on increasing our infrastructure efficiency in several ways, including by optimizing workloads, improving infrastructure utilization, diversifying our chip supply and just investing in efficiency improvements as part of our core technology development efforts in areas like content and ads ranking.

Meta’s management continues to think that it is critical for Meta to build its own frontier AI models

I think the question was around how important is it for us to have a general model. The way that I think about Meta is we’re like a deep technology company. Some people think about us as we build these apps and experiences, but the thing that allows us to build all these things is that we build and control the underlying technology that allows us to integrate and design the experiences that we want and not just be constrained to what others in the ecosystem are building or allow us to build. So I think that this is a really fundamental thing where my guess is that Frontier AI for many reasons, some competitive, some safety oriented are not going to always be available through an API to everyone. So I think like it’s very important, I think, to be able to have the capability to build the experiences that you want if you want to be one of the major companies in the world that helps to shape the future of these products. So that I think is — it’s going to be, I think, important from a business perspective.

Microsoft (NASDAQ: MSFT)

Microsoft’s management thinks AI is just starting to diffuse broadly into society, which would then lead to substantial growth in the company’s total addressable market (TAM)

We are in the beginning phases of AI diffusion and its broad GDP impact. Our TAM will grow substantially across every layer of the tech stack as this diffusion accelerates and spreads. In fact, even in this early innings, we have built an AI business that is larger than some of our biggest franchises that took decades to build.

When building Microsoft’s AI infrastructure, management is aware of the heterogeneous nature of different workloads, and is optimising for tokens per watt per dollar to decrease total cost of ownership (TCO); Microsoft has been able to increase throughput by 50% in OpenAI inference, which is one of Microsoft’s highest volume workloads; Microsoft recently connected two GPU clusters through an AI WAN (wide area network) to build a first-of-its-kind AI data center

When it comes to our cloud and token factory, the key to long-term competitiveness is shaping our infrastructure to support new high-scale workloads. We are building this infrastructure out for the heterogeneous and distributed nature of these workloads, ensuring the right fit with the geographic and segment specific needs for all customers, including the long tail. The key metric we’re optimizing for is tokens per watt per dollar, which comes down to increasing utilization and decreasing TCO using silicon systems and software. A good example of this is the 50% increase in throughput we were able to achieve in one of our highest volume workloads, OpenAI inferencing, powering our Copilots. And another example was the unlocking of new capabilities and efficiencies for our Fairwater data centers. In this instance, we connected both Atlanta and Wisconsin site through an AI WAN to build a first of its kind AI super factory. Fairwater’s 2-storey design and liquid cooling allow us to run higher GPU densities and thereby improve both performance and latencies for high-scale training.

Microsoft’s AI infrastructure utilises chips from NVIDIA, AMD, and itself (Maia); management recently introduced Maia 200, which has 30% better TCO compared to other leading AI chips; management will be using Maia 200 for inferencing and synthetic data generation for its AI research team, and for production inference workloads; Microsoft has been building its own chips for a long time; Microsoft’s own AI models will all be optimised for Maia 200

At the silicon layer, we have NVIDIA and AMD and our own Maia chips, delivering the best all up fleet performance, cost and supply across multiple generations of hardware. Earlier this week, we brought online our Maia 200 accelerator. Maia 200 delivers 10-plus petaFLOPS at FP4 precision with over 30% improved TCO compared to the latest generation hardware in our fleet. We will be scaling this starting with inferencing and synthetic data gen for our Superintelligence Team as well as doing inferencing for Copilot and Foundry…

…We’ve been at this in a variety of different forms for a long, long time in terms of building our own silicon…

…We’re obviously round-tripping and working very closely with own super intelligence team with all of our models, as you can imagine, whatever we build will be all optimized for Maia.

AI workloads require both AI accelerators (i.e. GPUs) and CPUs; Microsoft’s own Cobalt 200 CPU delivers 50% higher performance compared to the previous version

And given AI workloads are not just about AI accelerators, but also consume large amounts of compute, we are pleased with the progress we are making on the CPU side as well. Cobalt 200 is another big leap forward, delivering over 50% higher performance compared to our first custom build processor for cloud-native workloads.

Microsoft’s management sees AI agents as the new app platform that comes with the platform shift to AI; management thinks customers will need a model catalog, tuning services, harness for orchestration, and more, to deploy AI agents; more than 80% of the Fortune 500 have built active agents with Copilot Studio and/or Agent Builder; management thinks the proliferation of AI agents will create a new, significant growth opportunity for Microsoft; to meet the new growth opportunity, management has introduced Agent 365 for organisations to extend their existing governance, identity, security and management to agents; many of Microsoft’s technology partners are already integrating Agent 365; Agent 365 is the first product of its kind that allows cross-cloud agent cloud

Like in every platform shift, all software is being rewritten. A new app platform is being born. You can think of agents as the new apps and to build, deploy and manage agents, customers will need a model catalog, tuning services, harness for orchestration, services for context engineering, AI safety, management, observability and security. It starts with having broad model choice…

…We are also addressing agent building by knowledge workers with Copilot Studio and Agent Builder. Over 80% of the Fortune 500 have active agents built using these low-code/no-code tools.

As agents proliferate, every customer will need new ways to deploy, manage and protect them. We believe this creates a major new category and significant growth opportunity for us. This quarter, we introduced Agent 365, which makes it easy for organizations to extend their existing governance, identity, security and management to agents. That means the same controls they already use across Microsoft 365 and Azure, now extend to agents they build and deploy on our cloud or any other cloud. And partners like Adobe, Databricks, Genspark, Glean, NVIDIA, SAP, ServiceNow and Workday are already integrating Agent 365. We are the first provider to offer this type of agent control plane across clouds. 

Microsoft’s management sees the company’s customers wanting to use multiple AI models; management thinks Microsoft offers the broadest selection of models among the cloud hyperscalers; Microsoft already has more than 1,500 customers using Anthropic and OpenAI’s models on Foundry; management is seeing more customers choosing geographic-specific AI models

Our customers expect to use multiple models as part of any workload that they can fine tune and optimize based on cost, latency and performance requirements. And we offer the broadest selection of models of any hyperscaler. This quarter, we added support for GPT-5.2 as well as Claude 4.5. Already over 1,500 customers have used both Anthropic and OpenAI models on Foundry. We are seeing increasing demand for region-specific models, including Mistral and Cohere as more customers look for sovereign AI choices, and we continue to invest in our first-party models, which are optimized to address the highest value customer scenarios such as productivity, coding and security. As part of Foundry, we also give customers the ability to customize and fine-tune models. 

Microsoft’s management thinks one of the most important considerations for companies when working with AI is their need to capture the tacit knowledge they possess inside of model weights as their core IP; Fabric’s annual revenue run rate was over $2 billion in 2025 Q4 (FY2026 Q2), and quarterly revenue was up 60% year-on-year; customers spending more than $1 million per quarter on Foundry was up 80% year-on-year in 2025 Q4 (FY2026 Q2); more than 250 customers are on track to process 1 billion tokens on Foundry in February 2026 alone; Foundry is a great on-ramp for Microsoft’s other cloud computing services, as most of Foundry’s customers are using additional Azure solutions

Increasingly, customers want to be able to capture the tacit knowledge they possess inside of model weights as their core IP. This is probably the most important sovereign consideration for firms as AI diffuses more broadly across our GDP and every firm needs to protect their enterprise value. For agents to be effective, they need to be grounded in enterprise data and knowledge, that means connecting their agents to systems of record and operational data, analytical data as well as semi-structured and unstructured productivity and communications data. And this is what we are doing with our unified IQ layer, spanning Fabric, Foundry and data powering Microsoft 365. In the world of context engineering, Foundry knowledge and Fabric are gaining momentum. Foundry Knowledge delivers better context with automated source routing an advanced agentic retrieval while respecting user permissions. And Fabric brings together end-to-end operational real-time and analytical data.

2 years since it became broadly available, Fabric’s annual revenue run rate is now over $2 billion with over 31,000 customers, and it continues to be the fastest-growing analytics platform on the market with revenue up 60% year-over-year. All of the number of customers spending $1 million plus per quarter on Foundry grew nearly 80%, driven by strong growth in every industry. And over 250 customers are on track to process over 1 trillion tokens on Foundry this year…

…Foundry remains a powerful on-ramp for the entire cloud. The vast majority of Foundry customers use additional Azure solutions like developer services, app services, databases as they scale.

Microsoft’s own consumer Copilot agent experiences span a wide variety of domains; daily users of the Copilot app are up 3x year-on-year in 2025 Q4 (FY2026 Q2); users are able to make purchases directly in the Copilot app because of the Copilot Checkout feature; Microsoft 365 Copilot, which is Microsoft’s agentic experience for enterprises, has unmatched accuracy and the quality of its response had the highest sequential increase to-date in 2025 Q4 (FY2026 Q2); Microsoft 365 Copilot’s average number of conversations per user doubled year-on-year in 2025 Q4 (FY2026 Q2); Microsoft 365 Copilot’s daily active users was up 10x year-on-year in 2025 Q4 (FY2026 Q2); management is seeing strong momentum with Researcher Agent and Agent Mode; Microsoft 365 Copilot seat additions was up 160% year-on-year in 2025 Q4 (FY2026 Q2); there are now 15 million paid Microsoft 365 Copilot seats; the number of customers with >35,000 seats in Microsoft 365 Copilot tripled year-on-year in 2025 Q4 (FY2026 Q2); management is seeing strong growth across GitHub Copilot, with Copilot Pro Plus subs for individual developers up 77% sequentially in 2025 Q4 (FY2026 Q2), and paid Copilot subscribers up 75% year-on-year; Microsoft has the GitHub Copilot SDK and recently added a dozen new security Copilot agents; Dragon Copilot is a leader in its category and is serving 100,000 medical providers; Dragon Copilot documented 21 million patient encounters in 2025 Q4 (FY2026 Q2), up 3x year-on-year

In consumer, for example, Copilot experiences span chat, news, feed, search, creation, browsing, shopping and integrations into the operating system, and it’s gaining momentum. Daily users of our Copilot app increased nearly 3x year-over-year. And with Copilot checkout, we have partnered with PayPal, Shopify and Stripe, so customers can make purchases directly within the app.

With Microsoft 365 Copilot, we are focused on organization-wide productivity. Work IQ takes the data underneath Microsoft 365 and creates the most valuable stateful agent for every organization. It delivers powerful reasoning capabilities over people, their roles, their artifacts, their communications and their history and memory all within an organization security boundary. Microsoft 365 Copilot’s accuracy and latency powered by Work IQ is unmatched, delivering faster and more accurate work grounded results than competition, and we have seen our biggest quarter-over-quarter improvement in response quality to date. This has driven record usage intensity with average number of conversations per user doubling year-over-year. Microsoft 365 Copilot also is becoming true daily habit with daily active users increasing 10x year-over-year.

We’re also seeing strong momentum with Researcher Agent, which supports both OpenAI and Claude, as well as Agent Mode in Excel, PowerPoint and Word…

…It was a record quarter for Microsoft 365 Copilot seat adds, up over 160% year-over-year. We saw accelerating seat growth quarter-over-quarter and now have 15 million paid Microsoft 365 Copilot seats and multiples more enterprise chat users…

…The number of customers with over 35,000 seats tripled year-over-year. Fiserv, ING, NASA, University of Kentucky, University of Manchester, U.S. Department of Interior and Westpac, all purchased over 35,000 seats. Publicis alone purchased over 95,000 seats for nearly all its employees…

…Copilot Pro Plus subs for individual devs increased 77% quarter-over-quarter, and all up now, we have 4.7 million paid Copilot subscribers, up 75% year-over-year…

…GitHub Agent HQ is the organizing layer for all coding agents like Anthropic, OpenAI, Google, Cognition and xAI in the context of customers GitHub repos. With Copilot CLI and VS Code, we offer developers the full spectrum of form factors and models they need for AI-first coding workflows…

…And we’re going beyond that with GitHub Copilot SDK. Developers can now embed the same run time behind Copilot CLI, multi-model, multistep planning tools, MCP integration, Ops streaming directly into their applications. In security, we added a dozen new and updated security Copilot agents across Defender, Entra, Intune, and Purview…

…To make it easier for security teams to onboard, we are rolling out security copilot to all our E5 customers and our security solutions are also becoming essential to manage organization’s AI deployments. 24 billion Copilot interactions were audited by Purview this quarter, up 9x year-over-year…

…In health care, Dragon Copilot is the leader in its category, helping over 100,000 medical providers automate their workflows… All up, we helped document 21 million patient encounters this quarter, up 3x year-over-year.

1/3 of Microsoft’s cloud and AI-related capex in 2025 Q4 (FY2026 Q2) are for long-lived assets that will support monetisation over the next 15 years and more, while the other 2/3 are for CPUs and GPUs, driven by strong AI- and Azure-related demand; Azure is still capacity-constrained, and management wants to balance Azure demand for compute with 1st party demand for compute; the ROI for Microsoft’s capex sometimes shows up in increased revenue for Microsoft’s software business (i.e. non-Azure business) too; some of Microsoft’s AI compute is also allocated for R&D; the useful lives of Microsoft’s GPUs continue to be quite matched with the duration of their contracts; Microsoft becomes more efficient with delivery as its GPUs age, so margins actually improve over time

Capital expenditures were $37.5 billion, and this quarter, roughly 2/3 of our CapEx was on short-lived assets, primarily GPUs and CPUs. Our customer demand continues to exceed our supply. Therefore, we must balance the need to have our incoming supply better meet growing Azure demand with expanding first-party AI usage across services like M365 Copilot and GitHub Copilot, increasing allocations to R&D teams to accelerate product innovation and continued replacement of end-of-life server and networking equipment. The remaining spend was for long-lived assets that will support monetization for the next 15 years and beyond. This quarter, total finance leases were $6.7 billion, and were primarily for large data center sites. And cash paid for PP&E was $29.9 billion…

…As we spend the capital and put GPUs specifically, it applies to CPUs, the GPUs more specifically, we’re really making long-term decisions. And the first thing we’re doing is solving for the increased usage in sales and the accelerating pace of M365 Copilot as well as GitHub Copilot, our first-party apps. Then we make sure we’re investing in the long-term nature of R&D and product innovation. And much of the acceleration that I think you’ve seen from us and products over the past a bit is coming because we are allocating GPUs and capacity to many of the talented AI people we’ve been hiring over the past years. Then, when you end up, is that, you end up with the remainder going towards serving the Azure capacity that continues to grow in terms of demand…

…As an investor, I think when you think about our capital and you think about the GM profile of our portfolio, you should obviously think about Azure. But you should think about M365 Copilot and you should think about GitHub pilot, you should think about Dragon Copilot, Security Copilot. All of those have a GM profile and lifetime value. I mean if you think about it, acquiring an Azure customer is super important to us, but so is acquiring an M365 or a GitHub or a Dragon Copilot, which are all by the way incremental businesses and TAMs for us. And so we don’t want to maximize just 1 business of ours, we want to be able to allocate capacity while we’re sort of supply constrained in a way that allow us to essentially build the best LTV portfolio…

…You got to think about compute is also R&D…

…When you think about average duration, I think what you’re getting to is — and we need to remember, is it, average duration is a combination of a broad set of contract arrangements that we have. A lot of them around things like M365 or our BizApps portfolio, are shorter dated, right, 3-year contracts. And so they have, quite frankly, a short duration. The majority then that’s remaining are Azure contracts are longer duration. And you saw that this quarter when we saw the extension of that duration from around 2 years to 2.5 years. And the way to think about that is the majority of the capital that we’re spending today, and a lot of the GPUs that we’re buying are already contracted for most of their useful life…

…To state this in case it’s not obvious, is that as you go through the useful life, actually, you get more and more and more efficient at delivery. So where you’ve sold the entirety of its life, the margins actually improved with time. And so I think that may be a good reminder to people as we see that, obviously, in the CPU fleet all the time.

Commercial RPO (remaining performance obligation) is now $625 billion, up 110% from a year ago (was $392 billion in 2025 Q3); the weighted average duration of the RPO is 2.5 years; the RPO has significant customer-concentration risk with OpenAI, as OpenAI accounts for 45% of the RPO; the non-OpenAI part of the RPO was up 28% year-on-year in 2025 Q4 (FY2026 Q3); the average duration of RPOs for Azure are much longer than the average duration for M365 contracts

Commercial remaining performance obligation, which continues to be reported net of reserves increased to $625 billion, and was up 110% year-over-year with a weighted average duration of approximately 2.5 years. Roughly 25% will be recognized in revenue in the next 12 months, up 39% year-over-year. The remaining portion recognized beyond the next 12 months increased 156%. Approximately 45% of our commercial RPO balance is from OpenAI. The significant remaining balance grew 28% and reflects ongoing broad customer demand across the portfolio…

…When you think about average duration, I think what you’re getting to is — and we need to remember, is it, average duration is a combination of a broad set of contract arrangements that we have. A lot of them around things like M365 or our BizApps portfolio, are shorter dated, right, 3-year contracts. And so they have, quite frankly, a short duration. The majority then that’s remaining are Azure contracts are longer duration. And you saw that this quarter when we saw the extension of that duration from around 2 years to 2.5 years. And the way to think about that is the majority of the capital that we’re spending today, and a lot of the GPUs that we’re buying are already contracted for most of their useful life…

Azure grew revenue by 39% in 2025 Q4 (FY2026 Q2) (was 40% in 2025 Q3); Azure’s revenue growth was slightly better than expected; Azure was capacity-constrained in 2025 Q4 (FY2026 Q2) and management wants to balance Azure demand for compute with 1st party demand for compute

In Azure and Other Cloud services, revenue grew 39% and 38% in constant currency, slightly ahead of expectations with ongoing efficiency gains across our fungible fleet, enabling us to reallocate some capacity to Azure that was monetized in the quarter. As mentioned earlier, we continue to see strong demand across workloads, customer segments and geographic regions, and demand continues to exceed available supply…

…Our customer demand continues to exceed our supply. Therefore, we must balance the need to have our incoming supply better meet growing Azure demand with expanding first-party AI usage across services like M365 Copilot and GitHub Copilot, increasing allocations to R&D teams to accelerate product innovation and continued replacement of end-of-life server and networking equipment.

Netflix (NASDAQ: NFLX)

Netflix started using new AI tools in 2025 to help advertisers create custom advertising based on Netflix’s intellectual property; Netflix started using AI models in 2025 to speed up advertising campaign planning; management continues to invest in sales and go-to-market for the advertising business

In 2025, we began testing new AI tools to help advertisers create custom ads based on Netflix’s intellectual property, and we plan to build on this progress in 2026. We also introduced automated workflows for ad concepts and used advanced AI models to streamline campaign planning, significantly speeding up these processes. 

Netflix is using AI to improve subtitle localisation; Netflix is using AI to help with merchandising

In content production and promotion, we’re using AI to improve subtitle localization, making it easier for our titles to reach more viewers around the world. Additionally, we’re implementing AI-driven tools to help with merchandising, which improves our ability to connect members with the most relevant titles for them to watch.

PayPal (NASDAQ: PYPL)

PayPal’s management’s vision with agentic commerce is to create a universally trusted catalog for AI agents to access, discover, and transact with; PayPal recently connected with early-adopter merchants for agentic commerce; PayPal recently went live with agentic purchasing through Perplexity and Microsoft Copilot; PayPal will acquire Cymbio for its Store Sync technology; management does not expect agentic commerce to move the needle for PayPa in 2026

Let me quickly share some of our latest developments in agentic commerce. Our vision is to create a universally trusted catalog that AI agents can access, discover and transact with safely and securely. Through our Store Sync offering, we are already connecting early adopters like Abercrombie & Fitch, Fabletics, PacSun and Wayfair with agentic chat platforms to allow consumers to discover, evaluate and purchase items within the chat. We went live with agentic purchasing through Perplexity ahead of Thanksgiving, and we are now also live on Microsoft Copilot…

…Store Sync is enabled through a partnership with Cymbio, which we have agreed to acquire to bring this technology in-house. Agentic won’t materially impact 2026 growth. But as AI-powered shopping scales, our aim is to become the default payment option. This is only the beginning, and we are collaborating closely with the major AI platforms as we build agentic commerce capabilities together. 

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s management expects capex for 2026 to be US$52 billion to US$56 billion, up 27%-37% from 2025 (2025’s capex was US$41 billion); most of the capex for 2026 will be for advanced process technologies; TSMC’s capital expenditure is always in anticipation of growth in future years; management now thinks a long-term gross margin of 56% and higher is achievable (previously was 53% and higher); TSMC’s capex in the last 3 years was ~US$100 billion, and the next 3 years is expected to be much higher; management thinks TSMC can earn a high-20% ROE through the cycle; management expects TSMC to shoulder greater capex for its customers; management has raised the revenue growth forecast for AI accelerators for 2024-2029 to mid-to-high-50% CAGR (previous guidance was mid-40%); management now expects 25% revenue CAGR in USD-terms for 2024-2029 (previously was 20% CAGR), driven by all 4 technology platforms; the 25% revenue CAGR projection is conservative

At TSMC, a higher level of capital expenditures is always correlated to the high-growth opportunities in the following years. With our strong technology leadership and differentiation, we are well positioned to capture the multiyear structural demand from the industry megatrends of 5G, AI and HPC. In 2025, we spent USD 40.9 billion as compared to USD 29.8 billion in 2024 as we began to raise our level of capital spending in anticipation of the growth that will follow in the future years. In 2026, we expect our capital budget to be between USD 52 billion and USD 56 billion as we continue to invest to support our customers’ growth. About 70% to 80% of the 2026 capital budget will be allocated to advanced process technologies. About 10% will be spent for specialty technologies and about 10% to 20% will be spent for advanced packaging, testing, mask making and others…

…As a result, in the last 3 years, our CapEx dollars amount totaled USD 101 billion, but is expected to be significantly higher in the next 3 years…

…We believe a long-term gross margins of 56% and higher through the cycle is achievable, and we can earn an ROE of high 20s percent through the cycle. By earning a sustainable and healthy return, even as we shoulder a greater burden of CapEx investment for our customers, — we can continue to invest in technology and capacity to support their growth while delivering long-term profitable growth to our shareholders.

…We expect 2026 to be another strong growth year for TSMC and forecast our full year revenue to increase by close to 30% in U.S. dollar terms…

…We raised our forecast for the revenue growth from AI accelerated to approach a mid- to high 50% CAGR for the 5 years period from 2024 to 2029, underpinned by our technology depreciation and broad customer base, we now expect our overall long-term revenue growth to approach 25% in U.S. dollar terms for the 5-year period starting from 2024. While we expect AI accelerators to be the largest contributor in terms of our incremental revenue growth. Our overall revenue growth will be fueled by all 4 of our growth platform, which are smartphone, HPC, IoT and automotive in the next several years…

…I think that fundamental thing position TSMC to be very good future growth, let me say that, 25% CAGR as we projected, and we used to be conservative. You know that.

TSMC’s management thinks Foundry 2.0 was up 16% in 2025, and is expected to grow 14% in 2026, supported by robust AI demand

Concluding 2025, the Foundry 2.0 industry, which we define as all logic wafer manufacturing, packaging, testing, mask making and others increased 16% year-over-year…

…We forecast the Foundry 2.0 industry to grow 14% year-over-year in 2026, supported by robust AI-related demand.

TSMC’s management thinks recent developments in the AI market are very positive; AI accelerator revenue accounted for high-teens of total revenue for TSMC in 2025; management sees increasing AI adoption in consumers, enterprises, and sovereigns; management has received very strong demand signals from TSMC’s customers and the customers’ customers; management’s conviction in the AI megatrend remains strong; management is disciplined when planning for capacity; TSMC’s lead-time has now increased to 2-3 years; management is very nervous about AI demand, but they have talked a lot with TSMC’s customers and customers’ customers in recent months to understand AI demand, and management is satisfied with the evidence they show of AI helping their businesses; a hyperscaler active in social media (most probably Meta Platforms) achieved very positive ROI from AI; TSMC is using AI internally to improve productivity and even just 1%-2% of productivity improvement would have paid off for TSMC’s AI investments; management sees AI growing into people’s daily life and they think there’s a real long-term trend

Recent development in the AI market continue to be very positive. Revenue from AI accelerator accounted for high teens percent of our total revenue in 2025.

Looking ahead, we observe increasing AI model adoption across consumer, enterprise and sovereign AI segment. This is driving need for more and more computation, which supports the robust demand for leading-edge silicon. Our customers continue to provide us with a positive outlook. In addition, our customers’ customers who are mainly the cloud service providers are also providing strong signals and reaching out directly to request the capacity to support their business. Thus, our conviction in the multiyear AI megatrend remains strong, and we believe the demand for semiconductor will continue to be very fundamental.

As a foundry, our first responsibility is to fully support our customers with the most advanced technology and necessary capacity to unleash their innovations. To address the structural increase in the long-term market demand profile, TSMC was closely with our customer and our customer and customer to plan our capacity. This process is continuous and ongoing in addition as process technology complexity increases the engagement lead time with customers is now at least 2 to 3 years in advance. Internally, as we have said before, TSMC employs a disciplined capacity planning system to assess the market demand from both top-down and bottom-up approaches. We focus on the overall addressable megatrend to determine the appropriate capacity to build. Based on our assessment, we are preparing to increase our capacity and stepping out our CapEx investment to support our customers’ future growth…

…Whether the AI demand is real or not. I’m also very nervous about it. You bet because we have to invest about USD 52 billion to USD 56 billion for the CapEx, right? If we didn’t do it carefully, and that would be big disaster to TSMC for sure.

So of course, I spend a lot of time in the last 3, 4 months talking to my customer and end customers’ customer. I want to make sure that my customers demand are real. So I talked to those cloud service providers, all of them. The answer is that I’m quite satisfied with the answer. Actually, they show me the evidence that the AI really help their business. So they grow their business successfully and healthy in their financial return. So I also double check their financial status. They are very rich. That sounds much better than TSMC. So no doubt, I also asked specifically that what’s application, right? I mean that’s — for one of the hyperscalers, they told me that, that helped their social media software. And so the customer continue to increase. So I believe that. 

And with our own experience in the AI application, we also help to our own fab to improve the productivity. As I mentioned, 1 time say that 1% or 2% productivity improvement, that is free to the TSMC…

…I believe in my point of view, the AI is real, not only real, it’s starting to grow into our daily life. And we believe that is kind of — we call it AI megatrend, we certainly would believe that. So you — another question is can the semiconductor industry to be good for 3, 4, 5 years in a row, I’d tell you the truth, I don’t know. But I look at the AI, it looks like it’s going to be like an endless, I mean, that for many years to come.

All of TSMC’s AI customers in the US are asking for a lot of support from TSMC’s Arizona fab; TSMC’s capacity in the US is very tight, probably going into 2027, and management is working hard to narrow the gap

All my customer and AI customers in the U.S., so they ask a lot of support from the U.S. fab. So because of that, we have to speed up our fab expansion in Arizona…

…The capacity is very tight. We work very hard to narrow the gap so far. Probably this year, next year, we have to work extremely hard to narrow the gap, okay? We just bought a second land in Arizona. That gives you a hint. That’s what we plan to do because we need it. We are going to expand many fabs over there and this giga-fab cluster can help us to improve the productivity, to lower down the cost and to serve our customers in the U.S. better.

TSMC’s management has seen the hyperscalers solve power constraints when building AI data centers through long-term forward planning; TSMC’s customers are telling TSMC that chips are their bottleneck when building AI data centres

[Question] We see that the AI semiconductor growth has seen very strong growth. And I believe all of your customers and customers’ customers very desperate to add more capacity support from TSMC. But I’m just wondering, how does TSMC evaluate the potential power electricity supply for data center?

[Answer] Talking about build a lot of AI data center all over the world, I use one of my customers’ customers I answer because I ask the same question. They told me that they plan this one 5, 6 years ago already. So as I said, those cloud service providers are smart, very smart. If I knew that, I will — anyway. So they say that they work on the power supply 5, 6 years ago. So today, their message to me is silicon from TSMC is a bottleneck and ask me not to pay attention to all others because they have to solve the silicon bottleneck first.

TSMC’s management thinks it will be 2028 or 2029 when the company can match demand and supply for AI chips

[Question] My question is really on AI. I mean, TSMC has been supply constrained for your AI customers, I think, since 2024, and it sounds like 2026 is another year where we’re going to see challenges. Do you think the CapEx you’ve laid out for this year. TWD 52 billion to TWD 56 billion, could that mean that we start to see supply and demand more in balance in 2027?

[Answer] If you build a new fab, it takes 2 and 3 years — 2 to 3 years to build a new fab. So even we start to spend the TWD 52 billion to TWD 56 billion, the contribution to this year almost none and to 2027, a little bit. So we actually are looking for 2028, 2029 supply. 

Tesla (NASDAQ: TSLA)

Tesla’s management thinks the growth of AI and robotics will usher in an era of universal high income

With the advent or with the continued growth of AI and robotics, I think we actually are headed to a future of universal high income, not universal basic income, but universal high income. I mean there’s going to be a lot of change along the way, but that is what I see as the most likely outcome.

Tesla will be investing heavily in capex in 2026 as it builds out vehicle autonomy and increase production of Optimus at scale, along with investing in its own AI chips; Tesla’s capex for 2026 is expected to be higher than $20 billion (was slightly below $9 billion in 2025); management’s planned capex amount of $20 billion or more does not include the 1st-party semiconductor fab that they are thinking of developing; management thinks Tesla will be in an investment cycle for some time; Tesla has sufficient cash resources to fund the capex, and it also has recurring-revenue services such as robotaxi that will help grease the wheels with banks when it comes to financing

This year is going to be a huge investment year from a CapEx perspective. And at the moment, we are expecting that CapEx would be in excess of $20 billion. We’ll be paying for 6 factories namely, the refinery, LFP factories, Cybercab, Semi, a new Megafactory, the Optimus factory. On top of it, we’ll also be spending money for building our AI compute infrastructure, and we’ll continue investing in our existing factories to build more capacity. And then also the related infrastructure along with it. And we’ll also further expand our fleet of Robotaxi and Optimus…

…Just keep in mind that we’re not — none of these numbers, which I shared of $20 billion factors in anything to do with the solar fab or the semiconductor chip fab…

…I think we’re getting into this investment phase because we have big aspirations. And when you look at it, some of these aspirations are — I call them as infrastructure play, especially if you have to do a chip fab, and we have to do a solar cell manufacturing fab, those are infrastructure plays and that funding takes a little bit longer. And you would be in an investment cycle for a little bit longer…

…How are we going to fund it? Initially, obviously, we have over $44 billion of cash and investments on the books. So we’ll use our internal resources, but there are ways where we can fund it, especially when we look at the Robotaxi fleet because any time you have a consistent stream of cash flow, you can go and get money from the banks. And we have had conversations with banks about it. And that is something how we’re going to do it.

Tesla will soon be stopping the production of the Model S and X and shift their production spaces to the production of Optimus, with the long-term goal of 1 million units annually; management will unveil Optimus 3 in a few months; management expects the manufacturing ramp for Optimus to be longer than for regular products because the supply chain for Optimus has to be built entirely from scratch; Optimus 3 will be a general-purpose robot that can learn by observing human actions; management thinks Optimus 3 will have a significant positive impact on US GDP; the Optimus robots are currently not used in Tesla’s factories in a material way, and any usage is for learning purposes for the robot; management expects significant volume of Optimus production to come only towards the end of 2026; management thinks that Optimus’s form factor – it looks like a human – will make it very easy to teach Optimus how to handle human tasks; management believes Tesla’s biggest competitors in the humanoid robot market will be from China; management believes Optimus far exceeds the capability of any robot under development in China; management thinks designing a hand with the required dexterity is the hardest engineering challenge with a robot; other major engineering challenges are a real-world AI model, and scaling production; management thinks Tesla is the only company in the world that can solve all 3 engineering challenges

We expect to wind down S and X production next quarter and basically stop production of Model S and X next quarter. We’ll obviously continue to support the Model S and X programs for as long as people have the vehicles. But we’re going to take the Model S and X production space in our Fremont factory and convert that into an Optimus factory, which will — with the long-term goal of having 1 million units a year of Optimus robots in the current S, X space in Fremont…

…We’ll probably unveil Optimus 3 in a few months. And I think it’s going to be quite surprising to people. It’s an incredibly capable robot…

…There’s really nothing from the existing supply chain that exists in Optimus. Everything is designed from physics first principles. So that means the normal S-curve of manufacturing ramp will be longer for Optimus than it is for products that have at least some portion of an existing supply chain. Like when everything is new, the production rate will be proportionate to the least lucky, least confident part of the entire supply chain. And if there’s 10,000 things that need to go right, it’s — it only takes one to be slow to lag that. But — so it will be sort of a stretched out S-curve. But I’m confident that we’ll get to 1 million units a year of — in Fremont of Optimus 3…

…Optimus 3 really will be a general-purpose robot that can learn by observing human behavior, so you can like demonstrate a task or literally verbally describe a task or show it a task, even show it a video and it will be able to do that task…

…I think, long term, Optimus will have a very significant impact on the U.S. GDP, like it will actually move the needle on U.S. GDP significantly…

…We have had Optimus do some basic tasks in the factory. But as we iterate our new versions of Optimus, we deprecate the old versions. And so it’s not — I wouldn’t say it’s like — it’s not in usage in our factories in a material way. It’s more so that the robot can learn. We wouldn’t expect to have any kind of significant Optimus production volume until probably the end of this year…

…It looks like a human. People could be easily confused that it’s a human. And this helps our strategy for the AI too because you can learn from how humans do these tasks and it’s very easy to teach the robot in the same way as opposed to previous robots…

…I do think that the — by far the biggest competition for humanoid robots will be from China. China is incredibly good at scaling manufacturing, actually quite good at AI, as you can see from the open source — or not the open source, but the sort of — I guess, some of them are open actually. But basically, the models that China is distributing for free are actually quite good and they keep getting better. So China is very good at AI, very good at manufacturing and will definitely be the toughest competition for Tesla. We — to the best of our knowledge, we don’t see any significant competitors outside of China…

…We think Optimus will be much more capable than any robot that we are aware of under development in China. So we think we’ll be ahead in terms of the real-world intelligence, the electromechanical dexterity, especially the hand design, which is by far the hardest thing in the robot. And in fact, I’d tell you there’s really 3 hard things about humanoid robots. Building an incredible hand that has the same degrees of freedom and dexterity as a human hand is an incredibly difficult engineering challenge. Then there’s the real-world AI and scaling production. Those are the 3 hardest problems by far for humanoid robots. I think we’re — Tesla has — is the only company that actually has all 3 of those components.

Tesla is now able to do its first robotaxi rides in Austin, Texas without a safety monitor; management thinks the amount of fully autonomous rides from Tesla will be increased dramatically every month going forward; management thinks there’s substantial economic opportunity for Tesla in the form of existing Tesla vehicle owners (for those who own vehicles with AI4 hardware versions) adding their vehicles to an autonomous fleet; depending on regulations, management expects Tesla to have fully autonomous vehicles in dozens of cities to half of the US by the end of 2026; revenue and cost per mile metrics for the robotaxi business are still not meaningful at the moment; management thinks autonomous vehicles will significantly change the global market size for automobiles; management is using Tesla’s vast network of charging and service centers that only the company has to prepare for the demand for robotaxi and autonomous vehicles; Tesla now has over 500 vehicles in the robotaxi fleet between the Bay Area and Austin in the US

We’re able to do our first rides with no safety monitor in the car in Austin. These are paid rides. So these are just sort of randomly selected paid rides with no safety monitor. And I think maybe — as of maybe yesterday or so, we actually don’t — we don’t even have a chase car or anything like that. So these are just cars with no people in them and no one is following the car in Austin. So we obviously are being very cautious about this because we want to have no injuries or serious accidents along the way. So I think it makes sense to be very cautious, but you’ll see the amount of autonomy increased dramatically, I think, every month essentially…

…There will also be an opportunity, something we’ve talked about for a long time for existing owners of Teslas to add or subtract their cars to the fleet, kind of like how Airbnb works where you can add or subtract your house to the Airbnb inventory. And I think probably the value of the Tesla — the sort of partial — a few people adding or subtracting the cars to Tesla autonomous fleet is probably a little underweighted by a lot of people because we’ve got millions of cars with AI4 that can do this. So that — it might potentially — I think it will provide an opportunity for a lot of customers to earn more by lending their car to the fleet than their lease cost to Tesla, yes, which is kind of — it’s kind of like you get — in that scenario, you basically get paid to own a Tesla…

…We expect to have fully autonomous vehicles in probably, I don’t know, somewhere between a 1/4 and 1/2 of the United States by the end of the year, pending regulatory approval. A big factor would be if there’s some kind of federal preemption for autonomous vehicles. In the absence of that, you kind of have to go on a city-by-city or state-by-state basis. But nonetheless, even if it is city by city, state by state, we expect to be in, I don’t know, dozens of cities, dozens of major cities by the end of the year…

…We’re still in the early phase of our fleet deployment and are still doing a lot of validation testing, the revenue and cost per mile metrics are not meaningful to discuss at the moment…

…[Question] Today, there are approximately 90 million cars sold globally each year. Does Tesla have a view based on its Robotaxi ambition what this number will be in 5 or 10 years?

[Answer] Obviously, autonomy and Cybercab are going to change the global market size and mix quite significantly. I think that’s quite obvious. General transportation is going to be better served by autonomy as it will be safer and cheaper…

…We’re using our vast network of charging and service centers that really only Tesla has in this space to jump start our infrastructure build-out needs to get ahead of Robotaxi and autonomous vehicle demand. And we expect that because of this network, we are the only company capable of scaling at the rate that is needed for the tsunami of autonomy that is coming…

…One other thing people forget that we’ve been deliberate on all this in the sense that we have the supporting infrastructure already being in place, whether it’s service centers, charging. Yes, we’ll have to augment as the fleet grows, depending upon the density of where the demand is and whatnot. But it’s not something like we just stumbled upon it and we’re starting to do. We’ve been at it for years. Yes, not every city is designed the same way. Same thing. Our infrastructure is also not the same in every city. But you have to give us credit that it’s been a journey.

…In terms of Robotaxi vehicles carrying paid customers, I think we’re well over 500 at this point between the Bay Area and Austin.

There are many countries where Tesla is selling vehicles where the latest version of FSD (Full Self Driving) software is not available; FSD now has nearly 1.1 million paid customers globally, and 70% of them paid upfront; in 2026 Q1, Tesla transitioned fully to a subscription model for FSD; a variant of the autonomous software used for robotaxi was recently shipped to customers of Tesla’s consumer vehicles with v14 (version 14) of FSD, and there was a lot of happy feedback from customers

We saw increase in demand leading to record deliveries in smaller countries like Malaysia, Norway, Poland, Saudi Arabia and Taiwan, while continued strength in the rest of APAC and EMEA. We, therefore, ended 2025 with a bigger backlog than in recent years. Note that none of these countries have the latest version of FSD supervised available yet…

…FSD adoption continued to improve in the quarter, reaching nearly 1.1 million paid customers globally. Of these, nearly 70% were upfront purchases. It is important to note that beginning this quarter, we are transitioning fully to a subscription-based model for FSD. Therefore, net additions to this figure will primarily be via subscription model and in the short term will impact automotive margins…

…A variant of the software that’s used for the Robotaxi service was shipped to customers with V14, and customers saw a huge jump in performance, like a lot of happy feedback from customers. So — and since then, we have improved the software significantly as well.

Tesla’s Cybercab vehicles for the robotaxi fleet are designed to accommodate just 2 passengers or less because 90% of vehicle miles travelled are with that number of passengers; the Cybercab model will not have a steering wheel or pedals, so it’s fully autonomous; management expects to start production of Cybercab in April 2026, with a typical S-shape curve for the production ramp; in time, management expects to be producing several times more Cybercabs per year than all of Tesla’s other vehicles combined; management thinks that 1%-5% of miles driven in the future will be performed by humans; the Cybercab has a different design to traditional passenger vehicles and it is super optimised for minimum cost per mile and a much higher duty cycle; management expects a Cybercab vehicle to be used 50-60 hours a week compared to 10-11 hours a week for a human-driven car; management is designing larger vehicles for the Cybercab in the future

And over 90% of vehicle miles traveled are with 2 or less passengers now, which is why we designed Cybercab that way…

…The Cybercab, which is a dedicated 2-seater, dedicated Robotaxi. It’s a little confusing with the terms, Robotaxi and Cybercab, sorry about the confusion. But — and in fact, in some states, we’re not allowed to use the word cab or taxi. So it’s going to get even more strange. It’s going to be like cyber vehicle or something, cyber car. But the Cybercab, which is a specific vehicle model that we’re making, does not have a steering wheel or pedals. So this is clearly — there’s no fallback mechanism here. It’s like this car either drives itself or it does not drive. And we expect to start production in April. As always, it’s an S-curve of — the production rate is an S-curve. So it starts off very slowly and then grows exponentially, then you hit the linear and then ultimately, it asymptotes at whatever your target volume is. So — but we would expect over time to make far more Cybercabs than all of our other vehicles combined. Given that 90% of distance driven or distance being — distance traveled exactly, no longer driving, is 1 or 2 people. I think it’s like 80% is just one. So it would mean that long-term Cybercab — we would make several times more Cybercabs per year than all of our other vehicles combined…

…The vast majority of miles traveled will be autonomous in the future. I would say, probably less than — I’m just guessing, but probably less than 5% of miles driven will be where somebody is actually driving the car themselves in the future, maybe as low as 1%…

…The whole design of Cybercab was to optimize the fully considered cost per mile of autonomous driving. And it’s a different design problem than if you’re trying to design cars for people who will be driving versus being driven. And — so Cybercab is, like I said, super optimized for minimum cost per mile and also for a much higher duty cycle. So we would expect Cybercab to be used probably 50 or 60 hours a week instead of the 10 or 11 hours a week that a driven vehicle is used. So typically, people might drive their car for 1.5 hours a day on average, so it’s like 10 hours per week out of 168. But I think an autonomous vehicle is likely to be used probably 5x as often, which means that you need to design the vehicle for much more wear and tear per unit time and much more resilience…

…We will have larger vehicles in the Cybercab in the future that are designed for full autonomy. And we’ve actually shown pictures of this, and in fact, have shown prototypes. So this is not exactly a secret. In fact, we’ve given people rides in them. So we’re not keeping this — hiding this light under a bushel here.

Tesla’s management thinks getting the design for Tesla’s AI5 chip right is the most important thing for the company at the moment; management is confident that AI5 will be a very good chip; management expects AI6 to follow AI5 in under a year, and for AI6 to be a much better chip than AI5; management’s priority with Tesla’s chips is for internal usage as they believe that chip production will be the key limiting factor for Tesla’s growth in the next few years; Tesla is currently using its own AI4 chips in its data centers and is conducting training of its AI models with both NVIDIA chips and the AI4 chips; management thinks Tesla needs to build its own fab to (1) solve production constraints at the major leading-edge fabs, and (2) reduce geopolitical risk; if Tesla were to build its own fab, it will be in the USA and will include logic chips, memory chips, and advanced packaging capabilities; management thinks that some people are underestimating the geopolitical risks related to advanced fabs (likely referring to the situation involving TSMC, Taiwan, and China); management thinks memory chips will be a bigger limiter to Tesla’s growth than logic chips; there are currently no advanced memory fabs in the USA; management will be making a big announcement on Tesla’s fab in the future; management has sufficient plans to solve for Tesla’s chip supply for the next 3 years, but anything beyond that is fuzzy

I tend to spend time on whatever the most critical issue is for the company and completing the AI5 chip design and having it be a great chip is arguably the #1 most critical thing to get done, which is why I’m spending more time on that than currently anything else at Tesla…

…I do think AI5 will be a very good chip. And I feel quite confident about the design at this point. And then AI6, which will follow that, it will be — aspirationally would follow that in under a year will be yet another big leap beyond AI5…

…In terms of selling it outside of Tesla, we first need to make sure we have enough chips for all of our vehicle production and all of our Optimus production, and then we will actually use the AI5 chips in our data centers…

…When I look ahead at, let’s say, what’s the limiting factor for Tesla growth, if you go, say, 3 or 4 years out, I think it actually is chip production. Is there enough AI logic and enough AI — enough memory, enough RAM for our volume. And right now, I see that as being the thing that probably limits our growth in 3 or 4 years, which probably imply that we’re not selling chips outside of Tesla because we need them.

…We already use the AI4 chips in our data centers. So when we do training, it’s a combination of the AI4 chips and NVIDIA hardware primarily that we do training with…

…This is definitely going to be a sort of a controversial thing, but I think Tesla needs to build a Terafab. And I mentioned this at the shareholder meeting. But even when you look at the output of — the best case output of all of our key suppliers and I would say even beyond suppliers like strategic partners like Samsung, TSMC and Micron, and we say like what’s the most you could possibly make, then it’s not enough. So we — I think in order to remove the constraint, the probable constraint in 3 or 4 years, we’re going to have to build a Tesla Terafab, a very big fab that includes logic, memory and packaging domestically. And that’s actually also going to be very important to ensure that we are protected against any geopolitical risks. I think people may be underweighting some of the geopolitical risks that are going to be a major factor in a few years…

…I think if we don’t do the Tesla Terafab, we’re going to be limited by supplier output of chips. And I think maybe memory is an even bigger limiter than AI logic. So for example, we have chip supply deals with TSMC in Arizona and Samsung in Texas. But currently, there are no advanced memory fabs at scale in the United States. They are zero, literally zero. Hopefully, Micron will have something going in a few years because they’re all headquartered in Idaho, where they make a lot of potato chips, but we need to make computer chips, too…

…Quite frankly, it would be crazy not to try the Terafab. We’ll have a bigger announcement on this in the future…

…We do have a solution for logic and memory for, let’s say, the next roughly 3 years. But if you start going beyond 3 years, we look at the scaling plans and how many fabs are getting built and especially if you factor in geopolitical uncertainty, there’s always risk that maybe the best chips don’t arrive that people were expecting to arrive.

Tesla recently invested in Elon Musk’s AI startup, xAI; Tesla is collaborating with xAI on AI technology and in fact, Tesla vehicles are already utilising xAI’s model, Grok; management believes Grok will be very useful for managing Tesla’s potentially massive robotaxi fleet; management sees Grok as a model that could be useful for also managing a fleet of Optimus robots 

On January 16, 2026, Tesla entered into an agreement to invest approximately $2 billion to acquire shares of Series E Preferred Stock of xAI as part of their recent publicly-disclosed financing round. Tesla’s investment was made on market terms consistent with those previously agreed to by other investors in the financing round. As set forth in Master Plan Part IV, Tesla is building products and services that bring AI into the physical world. Meanwhile, xAI is developing leading digital AI products and services, such as its large language model (Grok). In that context, and as part of Tesla’s broader strategy under Master Plan Part IV, Tesla and xAI also entered into a framework agreement in connection with the investment. Among other things, the framework agreement builds upon the existing relationship between Tesla and xAI by providing a framework for evaluating potential AI collaborations between the companies. Together, the investment and the related framework agreement are intended to enhance Tesla’s ability to develop and deploy AI products and services into the physical world at scale. This investment is subject to customary regulatory conditions with the expectation to close in Q1’2026…

…Even today, if you look at Tesla vehicles, we are using Grok in there…

…Grok will be very helpful in, say, maximizing the efficiency of the management of a large autonomous fleet. So I mean, if you’ve got an autonomous fleet that’s in the future 10 million vehicles or tens of millions of vehicles, then optimizing the efficient use of that fleet, Grok will be, I think, way better than any heuristic solution or sort of manually managed solution.

And if you say you’re managing, say, a large team of Optimus robots to build a factory or build a refinery — say hypothetical — it’s a hypothetical example, a rare earth or refinery, which we do desperately need in America, then you say, well, like what’s going to organize the Optimus robots to build that ore refinery that would — you need — kind of need an orchestra conductor. And so then Grok would be kind of the orchestra conductor for the Optimus robots to build the — hypothetically and — it might not be hypothetical in the future. I’m just saying it’s not currently in our plans.

Tesla’s management believes that Tesla’s AI model has the highest intelligence density per gigabyte, by far, in the world

I think one of the metrics one to consider for any given AI model is the intelligence per gigabyte, especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte. So you could say like, for a given number of gigabytes, how much functionality can you get out of it? I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more. Like this is going to sound like a pretty bold statement, but I kind of know what the intelligence efficiency of the big models are like Grok and like to be honest — and a bunch of the other models. And Tesla AI is like in terms of its memory efficiency, more than an order of magnitude better.

Visa (NASDAQ: V)

Visa’s Intelligent Commerce solution uses Visa Tokens as the foundation for agentic payments; Visa is working with more than 100 partners in the global commerce ecosystem to enable agentic commerce and over 30 partners are already building in Visa’s sandbox; Visa recently expanded into B2B agentic payments with Ramp; Visa recently entered into an agreement with AWS for Visa Intelligent Commerce to help developers build agentic commerce solutions; Aldar is integrating with Visa Intelligent Commerce to provide recurring payments services; Visa’s Trusted Agent Protocol helps bring trust to agentic commerce; Visa recently partnered with Cloudflare and Akamai on Trusted Agent Protocol; Visa is currently building interoperability between Visa Intelligent Commerce and Google’s Universal Commerce Protocol; Visa’s agentic solutions are already live in the US and CEMEA (Central Europe, Middle East, and Africa); Visa’s agentic solutions are currently in pilot phase in Asia and Europe, with Latin America and the Caribbean (LAC) soon to come

One of those [ capabilities ] that is enabled with Visa Tokens is an important area of innovation, agentic commerce. Our Visa Intelligent Commerce solution utilizes tokens and their configurability as the core underlying foundation for agentic payments. We’re working to enable agentic commerce with more than 100 partners across the commerce ecosystem globally. Over 30 partners are actively building in our sandbox with multiple agents and agent enablers running live production transactions and more partners expected in the future.

Just this quarter, we expanded into B2B agentic payments with Ramp, streamlining corporate bill payments, enabling their business customers to capture cash back on card payments and optimizing working capital. We also reached an agreement with AWS to make Visa Intelligent Commerce available on AWS marketplace to support developers building agentic commerce solutions connecting secure, automated payment workflows at scale through blueprints for workflows such as travel bookings or retail purchases. In our CEMEA region, Aldar, leading real estate developer, investor and manager is integrating Visa Intelligent Commerce to make reoccurring payments such as property service charges on their Live Alder app.

Our Visa Trusted Agent Protocol continues to help define the connectivity and data elements required to bring trust to the agentic environment. In Q1, we announced partnerships with leading Internet security players, first Cloudflare and then Akamai, who collectively serve millions of businesses globally, including 9 of the world’s top 10 retailers. In addition, we are building interoperability between key elements of Visa Intelligent Commerce and Google’s new Universal Commerce Protocol as part of our global effort to help ensure that Visa transactions are securely supported as different protocols evolve. Our agentic solutions are live in the U.S. and CEMEA and we are initiating pilot programs in Asia Pacific and Europe. LAC is soon to follow where we have already begun token enrollment for agentic commerce with issuers. We believe that we are well positioned to be the infrastructure provider and key enabler in agentic commerce so that every agent interaction is trusted and secure.

The AI-powered Visa Account Attack Intelligence solution has scored 60 billion transactions and identified 600 million suspicious transactions in the last 12 months; Visa Account Attack Intelligence has prevented more than $10 billion of fraud in LAC (Latin America and the Caribbean) in the last 6 months

Another AI-powered solution, Visa Account Attack Intelligence was announced in 2024 in the U.S. to help clients prevent enumeration attacks which are when bad actors systematically initiate e-commerce transactions to obtain valid payment credentials. The results of this solution in the U.S. have been impressive, with over 60 billion transactions scored and nearly 600 million suspicious transactions identified in the last 12 months…

…In LAC, for example, in just 6 months, we have almost 90% of clients already activated and have prevented more than $10 billion of fraud.

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

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 fourth quarter of 2025 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.

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From Mastercard

1. Mastercard’s management sees consumer and business spending remaining healthy, supported by a balanced labour market, although there remains geopolitical and economic uncertainty; management remains positive about Mastercard’s growth outlook

As we enter 2026, geopolitical and macroeconomic uncertainty persists. We will continue to monitor and work to navigate just as we have successfully done in the past. But for now, we remain optimistic and confident in our execution and the fundamentals of our business…

…The fundamentals of our business remain strong. The macroeconomic environment remains supportive with balanced job markets across the globe, underpinning healthy consumer and business spending. That said, there continues to be ongoing geopolitical and economic uncertainty. We maintain a disciplined capital planning approach and have levers to pull if needed…

…We remain positive about the growth outlook and our base case for 2026 continues to reflect healthy consumer spending.

2. Worldwide GDV (gross dollar volume) was up 7% year-on-year in constant-currency basis; cross-border volume was up 14% globally in constant-currency, driven by both travel and non-travel cross-border spending (cross-border volume growth was 15% in 2025 Q3); switched transactions was up 10% year-on-year; card growth was 6% in 2025 Q4, with Mastercard ending the quarter with 3.7 billion cards in circulation (there were 3.6 billion cards in 2025 Q3, and year-on-year growth was 6% then); on currency-neutral basis, domestic assessments were up 8%, cross-border assessments were up 17% and transaction processing assessments were up 14%

Let’s first look at some of our key volume drivers for the fourth quarter on a local currency basis. Worldwide gross dollar volume, or GDV, increased by 7% year-over-year. In the U.S., GDV increased by 4% with credit growth of 6% and debit growth of 2%. The growth of our debit portfolio was impacted by the Capital One debit migration, which continued through Q4. Outside of the U.S., volume increased 9% with credit growth of 9% and debit growth of 9%. Overall, cross-border volume increased 14% globally for the quarter, reflecting continued growth in both travel and non-travel related cross-border spending…

…Switched transactions grew 10% year-over-year in Q4… 

…Card growth was 6%. Globally, there are 3.7 billion Mastercard and Maestro-branded cards issued…

…All growth rates are described on a currency-neutral basis, unless otherwise noted. Looking quickly at each key metric. Domestic assessments were up 8%, while worldwide GDV grew 7%. The difference is primarily driven by pricing, offset by mix. Cross-border assessments increased 17%, while cross-border volumes increased 14%. The 3 ppt difference is driven primarily by pricing in international markets, partially offset by mix. Transaction processing assessments were up 14%, while switched transactions grew 10%. The 4 ppt difference is primarily due to favorable mix and pricing, partially offset by a decline in revenue from FX volatility. Towards the end of Q4 and month-to-date January, we saw FX volatility well below historical norms.

3. In 2025 Q4, Mastercard’s operating metrics had good year-on-year growth but there were sequential declines; in January 2026 so far, Mastercard’s operating metrics continue to be strong with worldwide switched volume growth of 9% (5% in the USA, and 12% outside of the USA), switched transactions growth of 10%, and cross-border volume growth of 13%; US switched volume was flat sequentially in January 2026 as the migration of debit volume by Capital One was offset by easier comps from weather impacts a year ago; in all, management continues to see healthy consumer and business spending; consumer spending did not change in 2025 despite what surveys may say; US tariffs that were implemented in 2025 has not affected consumer spending in a noticeable way; consumer spending remains healthy across the world

Starting with Q4 and looking at the metrics on a sequential basis. U.S. switched volume growth declined primarily due to the migration of the Capital One debit portfolio. Worldwide less U.S. switched volume saw a slight deceleration, driven primarily by tougher comps, including the lapping of portfolio wins in Europe. Switched transactions were in line with Q3. Cross-border volume remained strong. Of note, we saw a sequential decline in cross-border card-not-present ex travel, primarily driven by tougher comps from the lapping of share wins in Europe and higher growth from crypto purchases a year ago.

As we look to the first 3 weeks of January, our metrics continue to remain strong, generally in line with the fourth quarter. Of note, U.S. switch volume was flat sequentially as the Capital One debit roll-off was mostly offset by easier comps due to weather impacts in the prior year. We saw a decline in cross-border travel volumes, primarily due to weather-related impacts in Europe this year. Cross-border card-not-present ex travel continued to be impacted by higher growth from crypto purchases a year ago. Overall, we continue to see healthy consumer and business spending…

…When you look back over 2025 over the whole year, and we just take soft data like headlines or consumer confidence data that comes out. On one hand, consumers fill in surveys. At the same time, their spend behavior hasn’t actually changed. So that’s a pattern that just continues. We see — just taking 2025, it hasn’t changed quarter-on-quarter. We see a truly savvy and intentional consumer…

…There is question on how the consumer was affected or not by some of the tariff changes that we’ve seen last year. And that doesn’t show up in our data either. So it’s not coming through. Somewhere across the ecosystem between importers and big brands, it’s all been adjusted in a way that it hasn’t really affected consumer spending, at least we cannot tell that…

…If you zoom out and you look across the world, these patterns are different by region here and there, but the aggregate top line is that consumer spending remains healthy, is the same.

From Visa

1. US payments volume growth was good at 7%, with e-commerce growing faster than physical spend, and it reflected resilience in consumer spending; US credit and debit volume were up 7% and 6%, respectively; the slight step down in US payment volume (credit and debt both grew 8% in 2025 Q3) were partly the result of a Visa Direct customer shifting volumes to its own solution, and Capital One migrating its debit volume; growth across consumer spend bands remained relatively consistent with FY2025 Q4 with the highest spend band continuing to grow the fastest; management did not see a deterioration in spend in the lower bands; both discretionary and non-discretionary spend remained strong; consumer spending in the holiday period of 2025 grew from a year ago in both the US and other key countries globally

U.S. payment volume was up 7%, with e-commerce growing faster than face-to-face spend, reflecting resilience in consumer spending. Credit was up 7% and debit was up 6%. The slight step down in U.S. PV throughout the quarter was driven by debit primarily as a result of a Visa Direct client moving the remainder of its volume to its own solution and a number of other small factors, including the loss of some Interlink volumes to the Capital One debit migration and severe weather that affected certain spend categories.

Growth across consumer spend bands remained relatively consistent with Q4, with the highest spend band continuing to grow the fastest. We did not see a deterioration in the lower spend band and across our volume, both discretionary and nondiscretionary spend remain strong.

Honing in on the holiday season specifically, which we define as the period from November 1 to December 31. I would note a few items. In the U.S. consumer holiday spending growth was in line with last year, reflecting continued strength in retail, an improvement in fuel and some moderation in other spend categories. Focusing on retail. Holiday spending growth was slightly better than last year, driven by strong growth in e-commerce, which continues to take on a greater share of consumer retail spend. In several key countries around the globe, we saw similar trends with consumer retail holiday spending growth up from last year, led primarily by e-commerce growth.

2. Visa’s cross-border volume growth remained strong in 2025 Q4 (FY2026 Q1) at 11%, and was the same as in 2025 Q3

Q1 total cross-border volume was up 11% year-over-year, consistent with Q4. Cross-border e-commerce volume was up 12%, slightly below Q4, primarily from lower growth in cryptocurrency purchases. Travel-related cross-border volume was up 10%, consistent with Q4. We saw continued strength in commercial volumes, and we started to see improvement in U.S. inbound from Canada.

3. Payments volume on Visa’s network continues to grow in January 2026, with US payments volume up 8%, cross-border volume up over 11%, e-commerce volume up 12%, and processed transactions up 9%

Now let’s look at drivers through January 21, with volume growth in constant dollars. U.S. payments volume was up 8% with credit up 9% and debit up 6% year-over-year. Our constant dollar cross-border volume, excluding transactions within Europe, total volume grew 11% year-over-year with e-commerce up 12% and travel up 10%. Processed transactions grew 9% year-over-year.

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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.

JPMorgan Chase (NYSE: JPM) is currently the largest bank in the USA by total assets. Because of this status, JPMorgan is naturally able to feel the pulse of the country’s economy. The bank’s latest earnings conference call – for the fourth quarter of 2025 – was held earlier this week and contained useful insights on the state of American consumers and businesses. The bottom-line is this: the US economy remains resilient, but long-term risks remain.

What’s shown between the two horizontal lines below are quotes from JPMorgan’s management team that I picked up from the call.

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1. The US economy remained resilient in 2025 Q4; the labour market, though soft, has not worsened; consumers continue to spend and businesses remain healthy; management thinks the good conditions could last; management thinks markets are underestimating risks; consumer sentiment is weak, but spending-trends are not deteriorating; debit and credit sales volume were up 7% in 2025; management thinks the short-term macro outlook is positive, but there are longer-term risks, including fiscal deficits in the US and other countries; management sees the Federal Reserve’s ongoing purchase of T-bills as a tailwind for the economy

The U.S. economy has remained resilient. While labor markets have softened, conditions do not appear to be worsening. Meanwhile, consumers continue to spend, and businesses generally remain healthy. These conditions could persist for some time, particularly with ongoing fiscal stimulus, the benefits of deregulation and the Fed’s recent monetary policy. However, as usual, we remain vigilant, and markets seem to underappreciate the potential hazards—including from complex geopolitical conditions, the risk of sticky inflation and elevated asset prices…

…Despite weak consumer sentiment, trends in our data are largely consistent with historical norms and we are not currently seeing deterioration. Across income groups, debit and credit sales volume continued to perform well, up 7% year-on-year…

…When you’re guessing what the macro environment is going to be, if you ask me, in the short run, call it, 6 months to 9 months and even a year, it’s pretty positive. Consumers have money. There’s still jobs, even though it’s weakened a little bit. There’s a huge — there is a lot of stimulus coming from the One Big Beautiful Bill. Deregulation is a plus in general, not just for banks but — banks will be able to redeploy capital. But the backdrop is also important, but the timetables are different. Geopolitical is an enormous amount of risk. I don’t have to go through each part of it. It’s just a big amount of risk that may or may not be — determine the state of the economy. The deficits in the United States and around the world are quite large. We don’t know when that’s going to bite. It will bite eventually because you can’t just keep on borrowing money endlessly…

…The Fed, they don’t call it QE but they’re talking about doing $40 billion a month of buying T-bills. That adds $40 billion a month into bank — all things being equal, to bank reserves. And most of that initially shows up in wholesale deposits and then maybe gets redeployed. So we’ll see how that plays out too. But it does create more liquidity in the system, which I should have mentioned is another tailwind for the economy.

2. Net charge-offs for the whole bank (effectively bad loans that JPMorgan can’t recover) rose 4% from US$2.4 billion a year ago

Credit costs of $4.7 billion with $2.5 billion of net charge-offs and a $2.1 billion net reserve build…

…Net reserve build of $2.1B, reflecting a $2.2B reserve established for the forward purchase commitment of the Apple credit card portfolio.

3. JPMorgan’s investment banking fees fell in 2025 Q4 from a year ago because of a tough comparison-period and some deals that were pushed into 2026; management sees a strong pipeline for capital markets activities

IB fees were down 5% year-on-year, reflecting a strong prior year compare and the timing of some deals that were pushed to 2026. In terms of the outlook, we expect strong client engagement and deal activity in 2026, supported by constructive market dynamics, which is reflected in our pipeline.

4. Management is mindful of risks in non-bank financial institution (NBFI) lending; management sees NBFI lending as having structural protections for lenders, and losses in the category will generally occur only in the event of fraud or a deep recession

In light of the growth and the novel elements of some components of this activity, we are quite mindful of the risks. But given the structural protections, you would generally expect losses in this NBFI category to appear either as a result of additional instances of fraud-like problems or as a result of a particularly deep recession that erodes all the credit enhancement. In that scenario, losses associated with traditional lending to end borrowers would likely be the greater concern for the industry.

5. Management’s current assumption is 2 interest rate cuts for 2026

As usual, the outlook follows the forward curve, which currently assumes 2 rate cuts.

6. Management expects credit card net charge-offs for 2026 to be 3.4% (was around 3.3% in 2025) 

On credit, we expect the 2026 card net charge-off rate to be approximately 3.4% on favorable delinquency trends driven by the continued resilience of the consumer.

7. Management thinks that if caps on interest rates on credit cards are implemented, a lot of people will lose access to credit, especially those who need credit the most, and that will have a negative impact on the economy

For the purposes of this call, given how little we know at this point, the way I would prefer to talk about it is, just assume for the sake of argument that something in the general mode of price controls on credit card interest rates goes through, what would be the consequences of that.

And I think the first thing to say, which you obviously know very well, is that the card ecosystem is an exceptionally competitive ecosystem. It’s among the most competitive businesses that we operate in. And that’s true for all levels of borrower credit score, from high FICO to low FICO. And so in that context, when you — just basic economics, when you start with that as your starting point, the right assumption about what the response of the system is going to be to the imposition of price controls is not that you will simply compress the profit margins, which are already at their sort of competitively optimal level, and thereby pass on benefits to consumers. What’s actually simply going to happen is that the provision of the service will change dramatically.

Specifically, people will lose access to credit, like on a very, very extensive and broad basis, especially the people who need it the most, honestly. And so that’s a pretty severely negative consequence for consumers and frankly, probably also a negative consequence for the economy as a whole right now.

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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.

It has been three years or so since artificial intelligence (or AI) leapt into the zeitgeist with the public introduction of DALL-E2 and ChatGPT. As AI technology develops, I have been tracking how companies are thinking about and using it. 

The latest earnings season for the US stock market – for the third quarter of 2025 – recently ended and there were a number of companies that I follow or have a vested interest in, where the management teams discussed the topic of AI and how the technology could impact their industry and businesses. I shared the commentary from US-listed technology companies recently here. For commentary from non-technology companies, they are below.

Costco (NASDAQ: COST)

Costco’s management has integrated AI into Costco’s pharmacy inventory system and this has improved in-stocks to more than 98%, leading to mid-teen growth in pharmacy scripts filled, higher margins, and lower prices for members; management is deploying AI into Costco’s gas business, which is expected to improve inventory management and drive higher sales; management sees many tangible areas in Costco’s business to implement AI; management sees 2 concurrent phases for AI-implementation, namely, the member-facing phase, and the business-basics phase

An early use case has involved integrating AI into our pharmacy inventory system. This system now compares prescription drug pricing across vendors and autonomously and predictively reorders inventory, improving our in-stocks to more than 98%. This change has played an important role in helping us achieve mid-teen growth in pharmacy scripts filled and has improved margins while lowering prices to our members.

We’re now in the process of deploying AI tools in our gas business, which we expect will improve inventory management and drive incremental sales by ensuring we are always delivering the best value to our members…

…On the AI front, we’re extremely excited about what the future holds for us. I mean we see many opportunities that are really business-driven and tangible — have great tangible business value for us and you look at things like our procurement system as we are a global retailer and we buy from around the world as well as supply chain, what it can do there. And just the tools that we’ve seen that this has improved our employees’ work abilities and their skill sets as well as they do their day-to-day work…

…We look at it in a 2-phase approach that concurrently, we’re going to be focusing on member-facing, how do we improve the experience for the member through AI and then business in basics, how do we continue to focus on the business basics. Our mantra is to bring goods to market at the lowest possible price. And we think AI has a great asset to that, and it really can help us become a much better merchant out there.

Tractor Supply (NASDAQ: TSCO)

Tractor Supply is deploying AI in 3 buckets, namely, (1) off-the-shelf software, (2) custom-built software, and (3) AI agents; in off-the-shelf software, Tractor Supply’s software vendors are increasingly infusing their products with AI capabilities; in custom-built software, Tractor Supply has Hey GURA, Tractor Vision, and Quorso, as examples of AI-powered custom-built software; in AI agents, Tractor Supply recently integrated with OpenAI to enable 1,500 users within Tractor Supply to access AI agents to improve operational efficiency; an example of how AI agents have helped Tractor Supply is in providing automated approval to team members after completion of a manual task, when the task previously needed manual approval

On AI. We’ve got a lot of exciting things going on, on that front. And I’m going to break it into 3 buckets: enterprise-level software, the second is custom-built — now we call off-the-shelf enterprise software. Second, I’d call custom-built enterprise software. And then the third, I would talk about is around agents and automation.

First off, on the enterprise kind of purchased software. All of our vendors that we work closely with are now rolling in AI modules, AI analysis, AI capabilities, whether that’s in ERP systems, whether that’s in replenishment systems, marketing, et cetera. So we are fast adopters there where appropriate, obviously, with clarity of understanding of functionality and security.

The second one, in terms of custom-built, we talked about that several times in the past. Those software systems applications that we built out, we continue to scale, we continue to refine and they continue to — and they’ve become more and more key parts of just how we operate every single day. So whether that’s Hey GURA, which is increasing in its use, whether that’s Tractor Vision, in terms of our customers calling out when customers need help in areas that our team members might not have visibility to them or whether that’s in Quorso, which drives day-to-day operational tasks. So those are just 3 examples of custom-built applications that are scaled out now and continue to ramp in their impact in use by our team members.

On the third one around kind of automation and agent build-out. Over the last 6 months, we’ve done an enterprise integration with OpenAI. We now have over 1,200, I think 1,500 users that now have OpenAI enterprise accounts that’s integrated with our Snowflake Data Lake. And what that allows us to do now is to start really across the organization, building agents to automate and make things simpler and faster. An example of that would be in, say, our Fast team, where in the past, when a Fast team member would finish a planogram reset, they would take a picture, they would send it to their District Manager — District Fast Supervisor, they would review it and provide manual feedback. We’ve now built up the capability where when that picture is taken, AI assesses the picture and gives immediate feedback to the team member and our District Fast Supervisor only has to get involved with escalations. And so it just makes everybody’s job more efficient and allows us to execute faster. And kudos to the team across really all dimensions of our organization for embracing it and driving that productivity enhancements that it can provide.

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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 Costco and Tractor Supply. Holdings are subject to change at any time.