Invest Like the Best: Ben Thompson on Big Tech, China, and the AI Boom Running Out of Money - [Invest Like the Best, EP.487]
My guest today is Ben Thompson, the founder and author of Stratechery. Ben is one of my favorite business thinkers and I love talking to him about everything happening in markets and technology. We
view source ↗Invest Like the Best: Ben Thompson on Big Tech, China, and the AI Boom Running Out of Money - [Invest Like the Best, EP.487]
Sourced by
podcast-ingeston 2026-08-18. Auto-transcribed via AssemblyAI (universal-2,en). Speakers identified by AssemblyAI Speaker Identification using the per-podcasthost/regularshints; the resulting label→name mapping is in the frontmatter. Duration: 1h16m. Episode page: https://colossus.com/episode/winners-losers-ai-era/. Audio: https://traffic.megaphone.fm/CLS4584299095.mp3.
Show notes (from RSS)
My guest today is Ben Thompson, the founder and author of Stratechery. Ben is one of my favorite business thinkers and I love talking to him about everything happening in markets and technology.
We go through every important company, including OpenAI, Nvidia, Intel, Apple, Microsoft, Google, and Amazon. We also discuss why he thinks it would be dangerous for the United States to win the AI race outright, what container shipping and the railroads of the 1870s tell us about the buildout, and why the binding constraint on all of this may be capital rather than compute.
Please enjoy my conversation with Ben Thompson.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe.
Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus.
Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest.
WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel.
Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest.
Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgeline.ai.
Editing and post-production work for this episode was provided by The Podcast Consultant.
Timestamps:
(00:00:00) Welcome to Invest Like The Best
(00:02:16) Winning the AI Race With China
(00:08:28) Timing, Capital, and the Railroads
(00:11:34) Berkshire, Google, and Absolute Profits
(00:14:23) Verifiable and Unverifiable Domains
(00:20:20) Aggregation Theory in the AI Era
(00:22:06) The Real Cost of Inference
(00:25:40) Why Consumer AI Needs Advertising
(00:30:08) Compute Shortages and Commodity Markets
(00:35:46) Memory Cycles and Boom Bust Dynamics
(00:42:08) TSMC, Intel, and Where Risk Goes
(00:44:51) The Best Setups in Big Tech
(00:52:14) The Frontier Model Contenders
(00:54:27) Microsoft's IBM Playbook
(01:00:45) Meta, Attention, and Advertising
(01:07:29) NVIDIA, Commodities, and Power
Transcript
Patrick O'Shaughnessy: Ramp is the only platform built to make your finance team leaner, faster and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp customers grow revenue 3.2 times faster than the average American business. Visa, Vercel, Kersher, Stripe Notion, Elevenlab, Shopify and 70,000 other businesses all now run on Ramp. Mine does too, and so should yours. Learn more@ramp.com invest, OpenAI, cursor, anthropic, perplexity and Vercel all have something in common. They all use work OS to achieve enterprise enterprise adoption at scale. You have to deliver on core capabilities like sso, scim, RBAC and audit logs. Instead of spending months building these mission critical capabilities yourself, you can just use work OS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit workos.com to get started. Felix Byrogo is a personal finance agent that turns a single prompt into finished client ready work using your firm's own templates, context and standards. Send Felix an email like Take these comments and turn them for me or update my tracker with the context of these emails and Felix sends back finished PowerPoint decks, Excel models and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at Rogo AI Felix hello and welcome everyone. I'm Patrick o' Shaughnessy and this is Invest like the Best. This show is an open ended exploration of markets, ideas, stories and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts@colossus.com Patrick O' Shaughnessy is
Ben Thompson: the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit Psum VC
Patrick O'Shaughnessy: so Ben, if you can believe it, how long it's been since we last did this. The world was very different. No AI at the time we talked about aggregation theory. Mostly I thought of fun place to begin since the world has changed so much is to Hear what you think it would mean for the US to win the AI race.
Ben Thompson: I think it would be very problematic for the US to win. Let's say we take the most sort of fantastical scenario where if you control AI, you basically your military is better than anyone else. Somehow it fixes our manufacturing. All these things that I don't think AI is necessarily going to do because they sort of deal with the real world. But in this world, what is the game theory optimal response of China to blow up tsmc? Game theory can get very sort of convoluted and complex. To me, this one actually isn't that complicated. There is a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley, coming out, I think of one of the labs in particular, where if we get to a place where we have a meaningful superiority in terms of a military national security perspective, I think that's very dangerous for the world.
Patrick O'Shaughnessy: But in that state, how much does it extend beyond TSMC being blown up? Because in that state, I would assume we figured out how to build fabs here in the U.S. you know, to some degree, and are less reliant on that one choke point.
Ben Thompson: I think there's a little bit of magical thinking which I just invoked in terms of manufacturing and whether it be fabs, whether that be actuators, all these precursors, I think the degree to which we are dependent on China is underappreciated and is not something that is going to be fixed outside of a conflict just because fixing so many of these things is going to be dramatically dumb. If your competitor is sourcing from China and you're going to start sourcing or getting things from the US you're going to be at such a disadvantage, relatively speaking, that you're just not going to do it. So you do it when you have literally no choice. And that works for very big headline items, like you can browbeat Apple to move some of their iPhone manufacturing to India, for example. But even that is a good example because Apple is not truly moving out of China. They're diversifying to an extent, but it would just cost so much, it's like paying an insurance policy that if you don't have to pay it and it's astronomically expensive, you're just not going to pay it. It's one of those sort of hypotheses that I just have a hard time even grokking because the only world I see where we truly pull out and have no dependency on China, such that if they want to blow Up Taiwan, who cares? There's no impact on us. Seems pretty fantastical to me. And I think there's a bit of facing reality in this regard that is not present these conversations.
Patrick O'Shaughnessy: Put yourself in their shoes. What do you think the motivations are?
Ben Thompson: Everyone can use a good bogeyman. I think from the AI trade perspective, nothing works better than we have to beat China. And I do think we need to be China, we need to be competitive. I despair at the extent to which over the last few years in particular so many of our responses, particularly from a political perspective has been to like, try to be like China. I think we should be going the other direction. More openness, more innovation, less top down control, less restrictions on speech and things along those lines. America succeeds by being on the leading edge and by leading into that.
Patrick O'Shaughnessy: You said probably the US being purely dominant in AI is not the right end state for the world. What is your ideal equilibrium for how this goes worldwide?
Ben Thompson: There's a bit where AI right now is kind of like the Taiwan situation in that the current status quo actually doesn't seem so bad. The question is how sustainable is it? But maybe it's sustainable for longer than we think. The way I think about it right now is I think OpenAI and Anthropic are clearly on the frontier. Who knows what's happening with Google and then GROK and Meta are chasing them. Meanwhile the Chinese are very capable, very smart and also definitely distilling these models to sort of stay about six to nine months behind. And it feels like a pretty good equilibrium that I think is generally favorable to the US. Now the question is how long can it stay this way? And there's lots of questions out there like can the Chinese actually pull ahead? I'm still a little skeptical for various reasons, whether be from chips getting to the leading edge. I think that last six to nine months is very difficult. It's going to be instructive how Meta and GROK do in terms of actually catching up, especially as we get to the world of AI improving itself, using AI to make the AI better, which I think is definitely a real thing. I think you see a real acceleration from both OpenAI and Anthropic recently, which was sort of theorized and it seems to be coming true. And to the extent that's true, can you actually catch up? And I think the other question about this by the way, is to what extent does that apply to cost, to serve, to marginal costs? If you can apply AI to optimizing your stack, to figuring things out, to analyzing all the data is your cost to serve structurally lower than anyone else. This is the thing about the open source models. The talk about them being free is bizarre to me because it's marginal costs. You still have to run inference like GLM or Kimi. Kimi is very expensive to serve. The cost per answer is significantly higher. Everyone referring to these as free. It feels like in the narrative, it's in people's head that free is free. Now I can use AI for free. No, you can't use AI for free. You're not paying necessarily the R and D to create the AI, but you're definitely paying the inference to sort of run it. So right now I kind of like where we are. And the pushback would be that's right now it's not going to stay that way, which I think is fair pushback, but I don't know this way longer than we think.
Patrick O'Shaughnessy: If you could know anything about the future of how this will go, to be more confident in, like, where the equilibrium will end up. What is it? Is it like the length of the S curve, like how far up the S curve we are? At some point these things presumably will level out. Maybe not. What would be the thing you'd want to know that would give you a better sense of what the future might look like?
Ben Thompson: I am concerned that with the scare around, people freaking out about Mythos and this hugging face incident, that the actual implication of that is not that we reduce these dangers, but we just stop releasing stuff. We on the outside start to lose any sense of, like, where is actually the frontier? What is actually the frontier? And where it is, there becomes sort of a false sense of security. Because right now everyone's basing their understanding of Mythos on fable. But how good is fable actually relative to Mythos? That sort of gap is only going to, I think, increase over time. So I think that's a real question that I'm not sure about. This question of the recursiveness and AI sort of making itself better, does that lead to some sort of takeoff? And at the end of the day, there's timing questions in lots of different ways. I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment. We're working our way down the capital curve. We started with free cash flow. The speed with which the tech companies blew through the debt markets is kind of incredible. It took like a year. And now Google's issuing equity. Nvidia's putting together these. This $500 billion, $500 billion thing to tap into like pension funds and insurance floats and things like that. What's after that? Where's the money come after that? Well, ideally we actually flip back to free cash flow funding this. But if there's a gap there, if we don't get there soon enough, then we could have a big blow up. But at the same time, even if we have this blow up, the AI is not going away, it's not going to stop improving. It's going to keep sort of progressing in a way that we look back on the dot com era, we look back on the railroad era, or we look back on whatever bubbles through history ultimately immaterial in terms of the broad scope of humanity. Even if they were very devastating.
Patrick O'Shaughnessy: What can the railroads teach us? Do you think it's now the last bigger build out, right, in terms of percent of GDP or getting there?
Ben Thompson: I think we might be bigger at this point. Or it's like it was the biggest in the ballpark. The railroads had a real duration mismatch. To build a railroad and make money off it was a decade or multiple decades long endeavor. Whereas you had to issue money to pay for it in the short term and the world ran out of money. Right. And I think that is probably the aspect. I think that's why people reach for the railroads because everyone talks about are we going to have enough compute, are we going to have enough electricity? Maybe the nearest term questions are we going to have enough money? Which is kind of a bizarre thing to think about. That's what happened in the 1870s. The world just ran out of money. The funny thing is the railroads kept operating and they expanded the west. Their contributions to GDP was astronomical. They're still contributing to gdp. Railroad money is what's going into Google right now for Berkshire Hathaway. Like, like, very funny. It's quite literal. Berkshire Hathaway has this problem. To me, this Nvidia deal is very much paired with the Google equity issuance, which I thought was shocking when it happened.
Patrick O'Shaughnessy: Why was it shocking?
Ben Thompson: Because it's Google. They can't raise money. Like why are they issuing equity? Why are they reducing their upside if they believe so strongly in this? But the Berkshire comparison is interesting because to a rough approximation they make, they have See's candies famously. Right? Tremendously high margin business. The problem with a lot of high margin businesses is the percentage profit you can make is very high. But the absolute profit you can make, no reinvestment Runway. That's right. You're just accumulating cash. The brilliance of The BNSF railway thing was basically they took the See's candy profits and said here's another industry whose margins are way worse, but the absolute dollar amounts are so large that those way worse margins result in absolute profits that are much larger. Bnsf in 2025 or something. The amount of free cash they've threw off in one year was more than See's Candies had thrown off its entire lifetime. Even though you're talking about a low margin business compared to a very high margin business, I think there's an aspect from Berkshire Hathaway where once your capital gets so large, you start operating in a world of like absolute numbers as opposed to percentage numbers. And the reason why I thought that was so interesting that story is it seems to capture where Google itself might be going. And so it was very symbolic for them to invest in Google. Google has this unbelievable high margin business of search. One of the most perfect, beautiful business models of all time. And the purest aggregator of them all, like scales in every direction. Doesn't have to invest any money to do it. Everything. Zero marginal cost. It's amazing. Meanwhile there's this AI opportunity which requires just astronomical it just incinerating cash. But you can imagine if AI is intelligence and it's TAM is basically all white collar work and eventually with robotics, everything potentially the absolute profits available here, even if the margins are lower, is so much larger that will we look back and Google Search with See's Candies. It feels like that's what's happening in that world. You use all your free cash flow. They've done that. You tap the debt markets to the tune of hundreds of billions of dollars. They've done that. You issue equity. What does an equity issue do? It dilutes your interest in your interest, your shareholders. So you have a smaller percentage of the pie. Well, you have the smaller percentage of an astronomically larger pie. At the end of the day, no one's going to be complaining. It was very symbolic, Berkshire being the symbol of that equity issuance. And in that are they actually not just an investor in Google, but a model for Google and where they're going.
Patrick O'Shaughnessy: I'm curious, setting aside the commercial and competitive components of this, like you're describing how AI pilled on the pure technology would you say you are relative to other people thinking about this space?
Ben Thompson: I have a view that is both super bullish and less bullish in some respects. Okay, so I am not fully convinced about the generalizable argument. AI is clearly incredible at coding. It kind of blows my mind that People were doing this a year ago, like actually writing out code. It's very good at math, obviously, but the obvious riposte is that these are sort of verifiable domains. What is the evidence or where is the compelling evidence of being very good at verifiable domains? Queenly translates to being very good at sort of unverifiable domains or domains that take have a very long sort of verification loops. I think that's still a little bit to be determined. And it's interesting because I raised this question and there are some people at the labs that were on a panel and I was kind of annoyed at the answer because the answer took me for an AI bear. Oh, well, people thought we couldn't solve chess or we couldn't solve Go. And we solved those easy enough. And I'm like, I thought we could solve chess, I thought we could solve Go because they're knowable domains. Scale was the answer to both of those. But also both of those were bounded. What is the go to example? That's not chess, that's not Go. That is genuinely in a new space. That's sort of an unknowable space where it's doing things that were not possible. That is sort of the I'm not fully convinced sense. However, AI trained at a rough approximation, trained on all the data of the Internet. All the data of the Internet, that's distillation. It distilled all of the end state of human thought. The actual typing on Reddit, it doesn't have the traces. It doesn't actually have the thought, the emotion or whatever that went into typing that comment or typing writing that essay, say neuralink, whatever. What if the actual payoff from neuralink is actually capturing the traces of human thoughts that actually dramatically expands the capabilities of these models in this world? My concerns about verifiability is like, well, we solve verifiability by getting more data. My sense is that a huge number of jobs, a huge amount of economic activity does not exist in these domains that I'm not convinced that AI is good at. Actually, there's a lot of people in the world who are kind of like sentient AIs to a certain extent. They operate very well in verifiable domains. They're given jobs, they do them. And it's almost like a somewhat pessimistic view of humanity to a certain extent. But I think that market is so huge and so large that if the models did not improve at all from where they are right now, the economic opportunity is actually massive. I Wrote an article a while ago. There's the whole like accelerationist movement. What I call myself was a reluctant accelerationist. I think we need to push forward because we can't go back and the worst thing we can do is get stuck where we are. So I'm very AI peeled in terms of its impact on the economy, its sort of upside in terms of monetization. I'm not sure about the timing, what
Patrick O'Shaughnessy: would be like the gradient towards it. Imagine law or medicine where I don't know whether or not you would consider those verifiable. Like law is like a code of some sort. Medicine, we have a certain state understanding of things.
Ben Thompson: I mean I think medicine is by far one of the biggest opportunities. It's both one of the biggest opportunities and also one of the most challenging ones because of all the regulations and all the access. Like if you could turn an AI turn machine learning onto all the medical records, I think the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable. So that is a very optimistic view. On the flip side, like when is that going to happen? Right? I think the optimistic frame I put on humans is our capacity to create needs is sort of unlimited. So I think we'll do a very good job of creating new opportunities and job serving the fullness of time. The sort of more pessimistic way to put it is our ability to create red tape and muck is also fairly unlimited. How much of our economy is actually we've managed to create more and more jobs that is just make busy and make slow to a certain extent.
Patrick O'Shaughnessy: Vanta automates security and compliance for over 16,000 fast moving companies like Ramp, Kersher and Harvey, keeping them audit ready around the clock. It's the number one agentic trust platform and it now helps companies like yours watch for the risks that show up between audits or across your vendors, your AI tools and your whole environment. Every new tool your team signs up for every vendor that turns on AI features is an opportunity for something to go wrong. And most security programs weren't built for AI's pace of growth. The Vanta agent works like a 24.7grc engineer in the background finding issues, drafting fixes for you and cutting vendor assessment time by up to 50%. Whether you're a fast growing startup or a global enterprise, Vanta helps you earn and prove, improve trust. Invest like the best listeners. Get a Special offer for $1,000 off@vanta.com invest Ridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation reporting, trading and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation. You can request a demo at Ridgeline AI. If I go back to the early 2010s, maybe the aggregation theory was stewing in your brain and then you published it in 2015. I think it's fair to say, like that theory, that idea, maybe you could just quickly remind people what it is defined the winners and losers of that era of technology. I'm really curious how you're thinking about what theory or principles will define this era of winners from like a financial perspective and market cap perspective.
Ben Thompson: I go back and forth, even just on the question of aggregation theory itself. How much does that apply in the same thing? Yeah, yeah, because I like, like a pushback that people have is one of the key components of addition theory is zero marginal costs. And your marginal cost shows in lots of ways. The one that I focused on the beginning was distribution. And people say, oh, I don't have distribution. I pay Google friends. Like, oh no, you have a website. Your problem isn't that you have distribution. Your problem is you don't have demand. And you're paying for demand when you're paying for ads and things on those. Because the aerators control demand and they control demand. Because in a world of abundance, the hard problem is not distribution, it's discovery. How do you actually find what you're interested in? So the companies that solve discovery in their domain come to dominate that market. They get a virtuous feedback loop. That sort of aggregation theory in a nutshell. And the other thing is transaction costs. There's no transaction costs. Google can scale to the whole world. And they can scale to the whole world not just on the user side, but also on the monetization side. The vast, vast, vast majority of advertisers on Google or Meta never talk to someone at Google or Meta. They just go up and they buy ads. It's all done by computers.
Patrick O'Shaughnessy: The perfect business.
Ben Thompson: Those computers, from a business perspective costs $0. AI, obviously that changes significantly. Inference costs are real. But then again, how real are they? They're real.
Patrick O'Shaughnessy: Right now I don't know are they? Depends on the company, but they're way more real than those prior examples. Well, like if you look at gross
Ben Thompson: margins for sure, but you have this incredible spread. So you have people. I think the vast majority of people who are using AD today are using it as basically a Google substitute or like a recipe maker or whatever it might be. And my suspicion is that the cost to serve those people is extremely low and low indeed. Basically similar to serving them a webpage. I would imagine it's marginally higher, but not that much higher. Then you have on the other extreme people who are actually leveraging test time. Scaling. It used to be we just scale by making the models bigger and bigger. Now you can scale as far as time. How long do you think about the answer? Well, you could think about the answer for days, weeks or months. That is directly marginal cost. Every second longer you're thinking is costing more money. Which speaks to like we think about AI and inference as this one question that's I was pushing back on you. But actually the marginal cost question for the different user, the user using free chatgpt and the user trying to solve a math theorem, they're not even remotely in the same universe. I think you see this challenge actually in the enterprise in a very interesting way. Microsoft recently they are shifting their enterprise plan so they come up with like an E7 plan. A hundred dollars per user per month that includes some amount of usage. But then they also are charging for usage on top of that. I think this is a kind of a fraught position for Microsoft to an extent. Because the positive way to think about Microsoft is, is they do everything you need as a business. Every individual component might not be the best, but you get it all for one price. And they all mostly work together. And if you're particularly a small, medium sized business or even a large enterprise, there is real value. That's right. It makes life easy. The moment you start having to think about how much you're paying, it's not just that. That's a new decision, number one, that is untethered from headcount Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee. And baked in the cost of that employee is $100 a month or $50 a month for their license. It was kind of a thoughtless revenue stream for Microsoft. Now if you think about usage, you have to think every single month. How much do I want to spend? That introduces two problems. Number one, most companies aren't set up to do this they make budgets like once a year. This idea we're going to be thinking about through our budgetary allotment on like a monthly basis doesn't compute. There's an aspect where they're used to thinking about Capex decisions or one time costs. And there's a bit where what I'm talking about this employee like the loaded cost of employee. It's not Capex but it's kind of like Capex. It's like you make the decision up front and you don't think about it anymore. The decision sort of already made. But if you're thinking about usage you have to do it again. The final thing is if you're every month looking at your Microsoft bill and how much did I use? You start thinking about what am I paying for? How good is each of these products? Should I actually just start thinking about and spreading this out? And I think they had to do it because that extreme of user who uses a ton of tokens and is actually leveraging AI costs way more to Microsoft than a hundred dollars a month. They can't support them but they want to hold on to the set cost for the vast majority of employees who can fit in that because they need to ask their customers to think a little bit for those extreme employees but they don't want them to think too much because that breaks the model in very surprising ways.
Patrick O'Shaughnessy: Are you surprised at all that the recipe builder user that is very low cost to serve that there hasn't been a great business model that's emerged around them just yet. Google and Facebook are sort of business perfected in this prior era. They haven't seemed to figure this out at all.
Ben Thompson: I am frustrated but not surprised. This is obviously a market that should be supported by advertising. That is why advertising is always the consumer business model. Consumers don't want to pay. There's two things to understand about consumers that Silicon Valley has to relearn about every 10 years. Number one, consumers do not want to pay for software. And number two, consumers do not care about being productive. We went through this in early SaaS. The canonical company for this in my mind is Dropbox. So Dropbox, unbelievable product like especially when it first came out in business school. I was one of the first people to use Dropbox and that went off like crazy. I have so much storage still like my free Dropbox because I gave out my code to like so many people. So Drew Houston makes this amazing product so easy to use, just absolutely seamless. He was very clear about this. He wanted to build a consumer company. And there's that famous story of him meeting with Steve Jobs. Apple was interested in acquiring Dropbox and they're like, oh, we want to build a company. And Steve's, you know, your feature, not a company, which that plain Jane, just file sync. Apple did make a feature as far as like sort of icloud drive and what Dropbox. They grew very fast and then they had like a two year lull. And in that two year lull, what they had to do was basically completely rebuild the app from the bottoms up because not enough consumers are going to pay for it. Enterprises could see the value they would pay. But if you want enterprise, you need permissions, you need control, you need someone else to be able to set all these sorts of things. And their app wasn't even created to do that at all. So they had to rebuild the whole thing and realize the only way we're going to make money is by selling to companies. Why do companies pay? Because companies are paying employees. To the extent they can make their employees more productive, they're getting a greater return on their investment. It's the complete inverse of a consumer. A consumer's like, I spent all day working. Why do I want to come home and be more productive? I want to sit on the couch and watch reels. But you see that with AI and you also have this overarching, just skepticism of advertising. I've gotten so much traction on trichary by being an advertising appreciator. And I go back and read my early articles about advertising that were kind of directionally correct but also like were not very good at all. But I got so much traction doing it because I was the only person writing about advertising. In a world of everyone want to have a blog and Twitter, no one want to talk about advertising. But even now there's in Silicon Valley this sort of embarrassment about the fact that the valleys in many respects monetized by advertising. And particularly during the last sort of eight years, there was a Facebook's icky.
Patrick O'Shaughnessy: The best engineers don't want to go work on this problem.
Ben Thompson: And so you literally had OpenAI replaying the Dropbox story, but at like 100 exercise being like, no, we're going to sell subscriptions to consumers. They did. They sold a lot, but they didn't sell enough. If you're going to be in the consumer market, you have to be doing advertising. They're doing advertising now. It's a little weird. They finally pivoted to doing advertising. At the same time, they're like, oh crap, we need to Go for the enterprise because Anthropic is kicking our way in. So I'm not quite sure what they're doing there. They have been rolling out ad features very rapidly. Things like capi and the connections with retailers. So you know, if a purchase went through so you can do all the tracking and things like that. I'm very interested to see how that goes. There's a bit where had they leaned into advertising immediately as soon as ChatGPT was a hit, I think they would have a killer ad product right now. I think that Google would be in much bigger trouble. I think Meta would be in much bigger trouble because if you have this flywheel, the thing about advertising with consumers is your ability to monetize.
Patrick O'Shaughnessy: The consumer goes up as the volume goes up.
Ben Thompson: Yeah, because the advertiser is bearing the price increase. So there's zero elasticity issues. If you're charging consumers a price, if you want to raise the price, like Netflix, this is their problem with the subscription plan. How much can they raise prices before consumers rebel and drop a tear or give up the service entirely? Charging people money is hard. Giving people things for free is easy. And it's very frustrating that OpenAI did not pursue this sooner.
Patrick O'Shaughnessy: I know you've been spending time with some of the big money firms and sources of capital. What is your sense of their appetite right now and how they're thinking about the future? Because I think this year it's going to be 800 billion or something that we're going to spend in capex. Next year is supposed to be 1.3 trillion. I think is the current estimate. It's going to keep going up from there. We're burning through all the compute that gets installed basically immediately. It's such a strange circumstance that we can use the capacity right away as soon as it's online.
Ben Thompson: Well, that's the thing though. So there's a few timing mismatches that are happening right now. We can't use it right away. All the bulls on Twitter is always like, we don't have enough compute. We don't have enough compute. Well, we don't have enough compute because there was insufficient investment made in 2023 and 2024, which. Yes, absolutely. And by the way, if you think there's not enough computer, TSMC decreased their rate of growth in 2023 and 2024 and 2025. Our shortage of compute is going to get worse in the next few years because a fab. The lead time is even greater than a data center today when we say there's not enough compute. It's not like all the money that the companies are putting in today manifests in compute.
Patrick O'Shaughnessy: Storage.
Ben Thompson: Yeah, no, it all manifests in compute in 2028 and 2029. On the calls you have both Andy Jassy and say Nadella are out there saying, look, we're just building data centers. Like these are the shells. We might not use them now, maybe we'll use them in the future. And we only buy GPUs when we know there's demand for them. That is a great story to tell. I'm not sure that I think is a lot of BS because the reality is if you've built the shell, that money is sitting there. You're not going to let it just sit there. If you invested a fixed cost. And this is the whole logic of commodity markets. I think tech in general doesn't understand commodity markets. Tech is by and large focused on if I produce a highly differentiated product and that differentiation could be like software, it could be a network in terms of developers, it could be a social network sort of thing where peer to peer, where I'm highly differentiated, then my ability to charge higher prices provides sort of my profit margin. So the classic example is like Apple. They have their ecosystem and they have their software and they have third party and all those sorts of things and so they can charge 50% margins on their iPhone. Everyone looks at Apple as like the ideal business model. That's how you run a business. But in a commodity market, the price is set by the marginal supplier. Cost to serve is all that matters. That's right. I had a good friend in Taiwan who is in shipping. Fascinating industry. It's kind of like the airlines do. Another industry that I love to look at. You buy a ship and the cost of that ship is depreciation. Your marginal cost is actually quite low. It's the fuel to run the ship and the cost of the crew. And like your port fees, not that much. What that means is you are going to run that ship.
Patrick O'Shaughnessy: Full achievement.
Ben Thompson: No, you're running no matter what. And you're going to bring down the price of a container as low as it needs to be to cover your marginal costs. Now your paper losses in this situation might be very large because your accounting loss includes depreciation. But the depreciation is an accounting figment. You already paid the money. You're going to run that ship at whatever the market will bear in the container. The beauty of the container. It is a pure commodity. The cost of the market is going to be the marginal Cost. Now, if it gets low enough at some point, people will exit because their marginal costs, they're actually losing money on a shipment. Not just paper money, but like actual real money. They will exit, but then the supply is diminished. So then the price will go back up and you get this interplay of sort of coming in and off. But then let's say the market's very high, like it was during COVID It's like, wow, we're making so much money right now because there's not enough supply. There wasn't enough supply of ships. So containers went from usually being like 3,000, $4,000 to 17,000, $18,000. The amount of money that these shipping companies made in a very short amount of time was insane. What happens though? Well, build more ships. Imagine if we had more ships, right? The problem is it takes two years to build a ship. If everyone makes this decision simultaneously, you suddenly have a lot of ships, price plummets, et cetera. Where we see this is in components in memory, in particular memory, very famous for boom and bust cycles, people entering the market late. But to what extent are, are data centers going to be memory makers where right now everyone can see we don't have enough compute. So everyone's like, we absolutely have to be investing because there's so much money to make. And look at our payback period. The problem is you're measuring your payback period in a time of scarcity. Is that payback period going to hold in a time of abundance? And the sort of. The bold will say there's never going to be time of abundance. AI, we're going to be short forever. Scaling, we're going to be short forever, which maybe we will be. My concern is, even if that's right, we could still have an air gap in that there's so much money going into it right now and not enough has come online to actually make sufficient revenues to handle the situation where we run out of capital. I believe in AI, I think it's a real thing. I think the economic impact is going to be astronomical. I think all the concerns of societal impact are very real and are going to come to bear in a major way. You can believe all that and still be worried about, are we going to make the bridge to this, actually generating the level of returns necessary to continue to fuel this sort of going forward.
Patrick O'Shaughnessy: Can you zoom in on TSMC and the component makers where fabs are involved? And so far at least, my understanding is that they've been quite conservative in their willingness to expand capacity, build new fabs meet the market's demand with similar growth, which they have not done. If that just rate limits this whole thing and prevents us from getting one of these giant overbuilds, we can talk
Ben Thompson: about a few different ones. Like we'll start with memory. Memory used to have tons and tons of memory makers. Every time there'd be a boom, memory makers would sort of re enter the market. New countries would come in like Taiwan used to have, like a memory market. But you would get these exact dynamics. If there's a shortage of memory, there's so much money to be made, you can't bring capacity on immediately. It's the same as shipping, it's the same as what we're seeing right now. That would spur people to come in the market. You get too much capacity, prices would plunge and people would just get blown out. Because the issue is the upfront cost for these is so large, just like buying a ship, like building a fab is even more so. And memory now, like the weeding edges of memory are using things like EUV machines. So the costs are getting into the billions of dollars for these lines. What happens is every time with these bull and bust cycles, some people would enter, more people get washed out. You go through these famous historical moments for these memory cycles. Companies just get blown out. One of the most interesting actually memory stories is how Samsung sort of took over memory was they saw it as an opportunity and they had studied history and, and they realized that actually the way to take over the market is to invest into downturns so that you're ready when the next cycle comes around, which requires a ton of guts and a ton of discipline and a ton of money. But they did that and basically wiped out the Japanese. That's when the South Koreans generally took over the market in a major way. But it got down to three. And the problem is three. It's not a monopoly, but it's kind of an oligopoly. And they all got a lot more discipline about let's not make the mistakes of the past and we're not colluding, but we all are on the same page about let's not do that. And I think that dynamic sort of ran head on to the current moment where it just took a while for them to realize no, there is a secular shift in memory demand that didn't exist for a very long time. I think the memory solution will be solved eventually. The other risk they run is Apple's lobbying to get Chinese memory. What is the number one focus of like algorithmic changes how can we use less memory? I think the memory makers probably screw themselves in the long run by creating such a massive target on their back. I have analogized memory makers to Iran. The issue with the Strait of Hormuz is it's very effective. It's more effective if you don't use it, because then it's always hanging out there as something you could do. Now they did. It turns out it worked. But the uae, Saudi Arabia, they're going to build pipelines, they're going to build new ports. They're not going to let this happen again. It's very painful right now, but say Iran wants to close the Strait of Hormuz in 2035, it's not going to have any effect because it will have been built around. My concern for the memory makers is they might have done the same thing. No one's going to let themselves get in this situation again. As far as memory goes, TSMC is arguably worse because there's only one. There is one company on the leading edge. Obviously intel and Samsung are trying to get there. It's the same thing. All markets carry risk. And a lot of the question is who ends up holding the risk. What I think a lot of the tech companies didn't fully appreciate is the extent to which TSMC has offloaded risk onto the big tech companies and the way they've done that is the risk that TSMC is worried about is, is over capacity. If we build too much, it's not just that we built too much and we have all these fixed costs that are not being fully utilized, but if we build a fab, we expect that fab to run for 30 years. We've like baked in too much capacity into the system for years and years and years. So they are very biased towards being much more conservative. There's a little bit of a culture component to this too. One of the most interesting TSMC stories, it's kind of analysis, that Samsung story was Morris Chang retired in like the late 2000s. New leadership took over. There was the Great Recession and so they pulled back their planned spending. He comes in, fires everyone, and he's like, the iPhone just launched. This is the biggest opportunity we've ever seen. We need to be investing, not cutting. And they invested through the Great Recession and through that downturn. That's what laid the foundation for them taking over, sort of weeding edge semiconductors in that time. Morris Chang is a one of one on the Mount Rushmore, in my mind, of the greatest and most impactful tech executives of all time. The Entire fabless model is so critical to what tech is and what it does, and also just the guts to do that at that time, particularly in someone who lived there, a culture that doesn't necessarily tend to make those sorts of bets. Tsmc, they were pretty conservative, to be totally honest. So what happens though? Where'd the risk go? TSMC is like, we don't want to take the risk. Risk doesn't disappear, it just moves. The risk is right now where you have every single big tech company realizes if we had more compute, we could be making more money. So there's lots of foregone revenue and foregone profits. That is the manifestation of the risk that TSMC handed off to them. Risk doesn't disappear, it just gets handed off. And sometimes that risk doesn't manifest in losing money. It manifests in not making money. And there is money not being made right now because what happened was they were very excited about 5G. They did a big wave of like, investment, expanding their fabs in around 20, 20, 21, 22. And they're like, oh yeah, we're good. Like I said, 2024 chat should be consult. 2022, big thing in tech in 2023. In 2024, their growth rate went down. In 2025, their growth rate went down. In 2026, it's up now. It was very funny because I was writing about this a while ago and then I think it was like one or two earnings calls ago. Suddenly CC way the CEO and chairman is talking about like use cases for AI, the whole earnings call in a way he never had before. This is why the merrymakers are scared. Usually there's like a bullwhip and they're worried about being at the end of the bullwhip where the demand happens, it works its way down the chain and they're at the end and then they double down. It's already too late. They're wasting all their money. And I think the thing with AI is if it's a bullwhip, it's like the longest bullwhip of all time. There's still so much to be built and it just took a while for Asia to get the message. Where these sort of companies are, I think they've by and large gotten it, but them getting the message, it then takes several years for that to actually materialize.
Patrick O'Shaughnessy: Do you have a sense for how long you think it will take given the extreme shortage of compute?
Ben Thompson: The interesting thing is what this means for intel and Samsung sort of logic business. I've been writing about the problem of this dependency on TSMC for years. One of my first articles in 2013 was exhorting intel, like, say you have to build a fab business. You're not going to be a designer anymore. There's a huge business in manufacturing chips. I thought I was late writing it. Then their stock goes to the moon throughout the 2000 and tens as they're riding the sort of cloud wave. And it wasn't until 2020 where they finally realized we fell behind. By the way, there's this huge opportunity, we're totally unprepared for it. We don't have a customer service mindset or culture organization or all the IP building blocks and all these things that TSMC has and they need a customer. They need customers to help them actually build a real foundry business. So I would write about this a problem. I write about the China issue like you're dependent on a company that is 60 miles offshore of our greatest ubiquitous opponent who thinks it's theirs. So these are big problems. That's where I came to appreciate this insurance issue for a big tech company to go to intel and say, intel, you make our chip. And by the way, the biggest benefactor of this is going to be you because you're going to learn how to work with a partner and the biggest pain is going to be us because we're going to have to figure out how to work with you. We could just go to tsmc. They are awesome. They are so great to work with. We know they're going to do a good job. It just never made rational sense for anyone to go work with Intel. That was their fundamental problem. In a unchanging world, TSMC would just win forever. But this is where TSMC in some respects made the same mistake as the money makers, made the same mistakes as Iran. If I can continue the analogy because they didn't invest the last few years, the shortages are going to be so acute. Big 10 companies that we're foregoing so much revenue and so many profits because we don't have enough compute. We will go through the pain of getting intel up to speed, of getting Samsung's logic up to speed. The scarcity is what ultimately saved Intel. I expect at some point that they're going to announce some major partner for the first time. It's going to be a big deal. But ultimately TSMC brought it on themselves.
Patrick O'Shaughnessy: It's the cure for high prices is high prices thing where we're going to route around them.
Ben Thompson: There's all these things as like an analyst sitting on the side, you can write these things. And one of the things I sort of learned no one's going to pay insurance that they don't need to pay when that insurance expected value is negative. The way to solve the geopolitical problem of dependence on TSMC is to come up with a compute use case that is so massive that everyone is economically incentivized to bring other people up to speed and then we get the sort of geopolitical insurance for free.
Patrick O'Shaughnessy: If you think about the, let's say, top 10 or 15 technology companies, which ones do you think have the most interesting setups today for their business?
Ben Thompson: The answer is always Amazon. The reason Amazon is so compelling is the extent to which they build for them. They are their first best customer. They provide the scale to get basically anything off the ground which they then sell to other people. AWS is the most obvious example. Aws, contrary to sort of popular thought, was not spare Amazon capacity. Actually it took a long time to get Amazon.com onto AWS. What it drove was the understanding that we can't be having so many meetings. Like we need to have just compute that you can plug in purely API surface. You don't need to talk to anyone, it's just there and oh, by the way, if we do that for our internal retail teams, we could do that for anyone. Turns out the retail is so big we have to start with everyone else. AWS actually started serving external customers before it served internal ones, but now it serves them all. You got other products like say the logistics, where it was the opposite. Right now we're using external providers for logistics, UPS and FedEx and USPS. We need to build this up ourselves. And now they built up themselves. They're offering it to third parties. Other people can use their delivery services. You see this in market after market they're talking about some of their AI products or their chip products. What's the beauty of the Graviton or the Trainium, particularly the early versions? The early versions were terrible. But if you're on Amazon and you're using some of their managed services, like say the redshift database service, they don't tell you what the processor is underneath that. You're just buying a managed service so they can put all their crappy processors underneath the services they're selling and that gives them the volume and the capacity to iterate them and get better. And they get to the point where they can actually sell them externally. Because they were the first best customer for Graviton. Graviton got better because they were the first best Customer for Trainium. Trainium got better and now Trainium is obviously running anthropic and AI products. We'll see if any of them take off. They have call center software. Their call center or their customer experience is going through AI. By the way, it's pretty good.
Patrick O'Shaughnessy: I haven't tried it.
Ben Thompson: Moving back to America, I've been buying lots of stuff every summer I'd buy lots of stuff in a very brief amount of time. Sometime like the last year or so. You can go on and you're clearly talking to a chatbot. But the chatbot does a great job and it actually does take care of the problem. So you could see that actually starting to work in that regard. But they're building up these AI services for their own business that they're going to make broadly available. And some of them will work, some of them won't. It's such an elegant approach given they have so many investments in the real world. Their core business feels so impervious to AI. For the model version of AI, it will benefit from AI. But their moat feels deeper than anyone as far as their core business. And their ability to just sort of generate new business lines organically is very compelling.
Patrick O'Shaughnessy: What about Apple? They sat this whole thing out, it seems.
Ben Thompson: It feels like it might be a situation of better be lucky than good. To a certain extent. Apple has their whole ecosystem. At the end of the day, they do own access to customers so they can get suppliers. This is the classic aggregator play. If you own access to customers, suppliers come to you, not the other way around. So they can get suppliers for their AI as needed. And by the way, to the extent it's true that people don't want to be productive, they just want a sort of a chatbot. Not only can they serve them a chatbot and finally getting a Siri that works, but you can see a future where this absolutely can work on device. And they actually don't even need to pay for inference costs either because they're using the customer's electricity. I don't think we're quite there. There's a reason they're using Google Cloud and Nvidia chips, but you can certainly imagine a future where that's the case and they're in physical goods. Actually making phones is hard. Having retail, having distribution for physical goods, they're more insulated. The smartphone is so perfect, it's small to fit in your pocket. It's big enough to watch basically anything on it. You can run your whole life on it. All your entertainment is there. When we Talk about customers want to be entertained. The TV is now an accessory. It's all on your phone. I don't see anyone taking over the phone. The question is, is the phone always going to be the center or is there a bit where particularly in the home. This is where OpenAI's efforts here are very interesting. Where you want sort of an ambient AI where you just talk to the AI and it tells you what you need. Apple is the best position to provide that. But can they provide that without having leading edge models? Can they provide that if they're so phone centric? Or is it like a Microsoft situation? Microsoft didn't miss mobile. They were very early to mobile. The problem is their mobile was a small PC. They assumed the PC would always be the center and their phones were going to be something that was off that Apple realized, no, we need to reset. The phone is not going to be accessory to the Mac. The phone is going to be the phone. The ipod helped them realize that and going with Windows and all that. But will they fall into a Microsoft like trap? Assuming the phone's so good, it's always going to be the center and then let's figure out around it. Or is this finally the time when actually ambient the cloud just in general, AI being everywhere, it can manifest through your phone, it can manifest through a device, can manifest on your computer is actually better and is actually disruptive to them. I think it's possible. I also think it's totally valid for Apple to double down on what they do. The other thing about the AI stuff is on what basis should we expect Apple to be good at this? At the most crude level, AI is this probabilistic endeavor. Apple is the king of deterministic products. A physical product, you ship that iPhone, you ship it once and it's gotta be good. If it's bad, it costs you billions and billions and billions of dollars. Apple's never had an iPhone, recall. It's amazing that care and decision making and diligence and fierceness in terms of your supply chain and making hard decisions is very, very different than everything that goes into like making great AI. I generally prefer companies to do what they're good at. So from my perspective, I'm fine with Apple not doing AI. I want them to keep making great devices.
Patrick O'Shaughnessy: Of the five potential Frontier AI winners so OpenAI, Anthropic, Gemini, SpaceX, AI, Grok and Meta, which of those firms do you think has the Most interesting setup?
Ben Thompson: OpenAI and Anthropic obviously are the riskiest, but also have the Biggest upside, never discount number one, the power of belief. They think they're creating God. The most impactful things in history have usually been fueled by religion. The two religious organizations in Silicon Valley are open ads, kind of like mainline. They go to church every Sunday. This are like evangelicals. That's anthropic. They're all in it is core their belief. That goes a long way. The fact you need to make a business work for you to survive goes a very long way. Google just needs search to not die too quickly. Meta has the huge advertising business. In a world where Meta was run by anyone other than Mark Zuckerberg, they would not be on the leading edge. That is one of the purest manifestations of founder energy. For better or for worse. Their business is so amazing. You see them just easily sort of doubling down on that Google. There's a bit where they had Google cloud, they have TPUs, they've been doing research in this. It makes sense why they're pursuing this. Meta being like, actually we're going to hire a completely new team and we're going to start from scratch. And this all again is pretty insane. Credit to Mark Zuckerberg in that regard. Again, you could decide whether that's a good idea or not. And then SpaceX AI, data centers in space, the theory is there. Do they have to own their own model though, to do that? They'd get better margins if they do. Then again, if we actually run out, whether through political opposition or power or whatever it might be, if we run out of data centers on Earth, they can run whatever model they want. As we're seeing with selling their capacity to anthropic right now, they're all pretty interesting. Probably the case for SpaceX AI is probably the weakest because the data center and space play is so highly differentiated. If that plays out, I'm not sure to what extent they need to even have their own model. So why are you wasting billions and billions of dollars in the meantime? That's a fair question from a tactical perspective. I love the cursor acquisition that makes so much sense for both companies and so I've been intrigued to see what they do. Meta is probably the most interesting.
Patrick O'Shaughnessy: You've written a lot about this recently.
Ben Thompson: I think there's a very good case to make that it is more reckless to not be on the frontier. If you're a digital company. The counter to Meta is actually Microsoft. Microsoft is not on the frontier. The reason why Microsoft has $40 million of free cash flow. Last quarter, Microsoft paid a $10 billion dividend, last quarter, there's some money. But their play is, oh, we're going to play all these off each other. We're going to provide middleware. We're by the platform that enterprises will build on us and we're going to sort of disintermediate the models. I think it's a rational play. It's the IBM play of the 90s. History echoes. Everyone talks about Google, Google, like following Microsoft, Microsoft follows IBM. And you can see that to an extent.
Patrick O'Shaughnessy: What did IBM do? What's the analogy?
Ben Thompson: Well, so IBM had this dominant, we talked about in the 70s, and then you fast forward to the 90s and IBM is this very distressed asset. And the thought was IBM needed to break up into all these different pieces they had. So Lou Gerstner comes in and takes it over. Gerstner's real key insight to IBM is we're pretty mediocre at everything. It's kind of like what I talked about Microsoft before. And that's the price of monopoly. Once you've been a monopoly, you kind of lose your capacity to be good because you didn't need to compete anymore. And I think a lot of tech incumbent companies have this problem. It didn't matter what they did, they were going to rake in money. And if you don't have the pressure, if you don't have the incentive, if you don't have the fear of death or the fear of God as we talk about these model companies, then you don't do your best work. And the problem is that once you lose that muscle, it's gone. You're just sort of fat and flabby. So what Gerstner realizes, actually the worst thing IBM could do would be to break it up into component pieces, because all those component pieces are actually not very good. Our biggest asset is that we're big. It's like, what? No, what does it mean? We're big. It's the 90s. This Internet thing's coming along. There's all these companies that kind of know they have to figure out the Internet and they don't know what to do. They need someone who can come in, understand their business and help them get online. That's basically what IBM did. So they built out and this is an echo of what's happening now. Huge consultant force. And they put all their time into building. Basically it was middleware where they would go in and they'd put this layer between a company's old school mainframe, which all these companies had, and then modern web services. On the other hand, so they could have websites and E commerce sites and all this sorts of thing. And it gave IBM a 30 year lease on life. Yes, in theory you could go get point solutions from all these hot Silicon Valley startups, but you understand that you don't know how to do that. You know us, we'll come in, will create all this middleware, build this big consulting force to help you implement it and you'll get online. And IBM basically brought all of corporate America online. That's Microsoft's playbook. Microsoft will help you figure out AI. It will help you figure out in a way where you're not giving away the crown jewels to these companies. We're going to build this platform, this harness this sort of middle layer. We're dependable, we're stable. You know us, we have backwards compatibility to the 80s. You can build on us and then we'll manage all the changing models and what's updating and do all those sorts of things. And does that mean you'll get the absolute best experience? No, middleware saws off the sharp edges. You sort of get a lowest common denominator capacity. But if you value, and this is the oldest enterprise sales motion. How did Oracle go to market? Oracle went to market in the 1980s. Larry Ellison with another technology taken from IBM or just IBM didn't want it, relational databases. And they're like, you don't want to be locked into IBM relational database. You could run anywhere, come with us. The reason this is a joke is because Oracle, everyone's locked in more than anyone, right? But all of enterprise sales is companies whose long term goal is to lock you in, getting you on board by trying to make you scared of being locked into somebody else. All the cloud companies are like, oh, portability, whatever you do, whatever. They're like, oh, just use our service that only runs on our cloud. And now you're locked in. That's Microsoft's playbook. It's a very rational playbook and I think it makes sense. That's why they have extra money, because they're not on the frontier. They are building massive data centers, but they're building data centers for inference, they're not building it for training. And their story about investing in time and response to customer demand is more believable in that regard. They're not having to tell a fungibility story where we're building big data centers for training that will be used for inference down the road.
Patrick O'Shaughnessy: Maybe go back to this notion that it's reckless to not be in the front.
Ben Thompson: The reason why that's concerning though is at the end of the day, why are we using Microsoft products again?
Patrick O'Shaughnessy: Because we did before.
Ben Thompson: Like to what extent does it actually make sense to have all these artifacts, all these documents, all these email inboxes? Can't AI just do that? There's a real threat here. Where to Microsoft software business. The whole systems of record thing is funny because one reason why systems of records are so powerful is it's so hard to move them to somewhere else because it's a very tedious, repetitive job. Oh, AI is actually surprisingly good at that. I'm not sure how good the system is Record Microsoft isn't so much systems of record. They do have some of the dynamics business. It's user interface. It's like where you actually interact with the computer. That's the part when you see Codex quad coworker or whatever. It is aimed like an arrow to the heart of what Microsoft has in the long run. All digital companies are But Microsoft is very much their strategy is sound. It's also desperate in a existential way and also in a they might pull it off because they're desperate sort of way. Meta is not threatened immediately, but this is where my bullish view of AI comes in. I think all digital companies are threatened and Meta is a digital company. They have software now. One worry is AI takes up more and more time. Time ultimately is meta's currency. We saw OpenAI tried the Sora thing, didn't really take off. Social networks are actually pretty hard. Also cost a lot of money. It's kind of really interesting. So this came up with the creator payment stuff. So YouTube very famously as paid creators kind of from the beginning and that's a much bigger drag on the business than people appreciate because YouTube has marginal costs to their content now, unlike a Netflix, they don't have to pay that cost upfront, they pay it after the fact. So their sharing revenue is a better model than a Netflix model. Netflix has to pay upfront for content and then ideally make more money. YouTube pays along the way. But Facebook or Meta pays nothing. They pay nothing. Instagram is this unbelievable product that generates all this money for which Facebook pays $0 for content. It's unbelievable. It's funny because you could see a world where for YouTube, AI generated content could theoretically be a positive because the inference cost of generating content could be less than what they're sharing with creators For Meta, AI generated content, to the extent they're the ones generating it is actually a worse margin profile than what they have today. Because what they have today is free. So they have attention. There is a bullish world where Meta is actually very well placed because in a world where we're interacting with the AI all the time, the desire for a human connection becomes greater and it's sort of like a meta going back to their roots. Meta, one of their biggest mistakes actually. Meta was always a social network company. They killed Snapchat or stop Snapchat growth by realizing Snapchat is a great product. Let's layer it onto our network. They brought their network to bear to kill Snapchat. The reason why TikTok was a blind spot for them is TikTok is classified as a social network and it's not a social network at all. TikTok is an entertainment product. It doesn't matter who you follow on TikTok. What you see on TikTok is a function of what you watched and you're going to get more of the same. It's a user generated content network. And the insight from TikTok was the way to get the best content to limit it to your social network is an artificial constraint. We're going to give you the best content from across the whole network and the vast majority of content is going to be crap. But this is like the absolute question before. You don't think about margins, you think about absolute numbers. The absolute amount of great content, even if the margin for great content is infinitesimal, if we have a ton of content, the absolute amount of great content is going to be very large. Then Meta's like we're a social network. Meta's serving you content from your network of people you know and TikTok serving you the best content from around the world. That's why they took a huge chunk out of them. Meta had to shift. That's what's happened with Instagram with Reels is it's not really a social network. It is a entertainment product that pulls from the entire network. And social networking is like the group chat. It's possible in AI. Actually, social network is important again because like we actually want humans. We want to have some sort of connection to them. That'll be interesting to see how that plays out. But the other thing with the models is they're so impactful in advertising. The biggest impact of the models, the biggest monetization right now is probably not anthropic or OpenAI. It's the incremental gain that is happening for Google and Meta. Most of the stuff is pre LLM, but we're getting to LLMs whether it be generating advertising content what do we want? We want verifiable domains. How do you verify if a generated image is good for an ad? Does the ad sell or not? They actually can validate their image creation and their text creation in a way no one else can. And their validation is the ad marketplace running a gazillion a B tests on all these different things. See what works, see what doesn't. Most ads are a throwaway. It's fine. The vast row of ads don't convert. They have this massive advantage, this huge liquid market that is a verification machine where the verifiers are humans deciding whether they click on that ad and make a purchase or not. But they're doing it at global scale that can actually have a feedback loop to make their products better. You're also going to get a world where ad matching is actually still fairly crude. Here's the qualities of the person, here's the qualities of the ad. And it's like you create an embedding, like a vector calculation and see what numbers match, and then you sort of match an ad to the person. What do LMS do? LMS predict. We're going to move to this world where Meta is going to look at people and say, this person probably wants to see this next, and they're going to go find that thing and show it to them. The potential upside in terms of showing people better ads that are more relevant to them, they only need to increase a few percentage points for the returns to be billions and billions of dollars. This alone is worth them investing in being on the leading edge, in having these amazing models. I think a big problem Metta has is they don't tell this story. It's weird, but Mark Zuckerberg has the same problem Sam Altman does. He doesn't love ads. They have the best ad business in the world. They have an ad business that I think is a societal positive. You and I have set up these little content businesses that make great money. Content is you get a ride on social media. I grew up on Twitter, people sharing my links. It was amazing. If you're selling some product, the beauty of the Internet is there is a niche out there that wants that product. The question is, how do you find the niche? Facebook advertising. That's what it does.
Patrick O'Shaughnessy: You.
Ben Thompson: It helps products find the people who didn't even know they wanted that product, but when they get it, they're so happy they got it. That's a huge societal positive. You have new business from a new entrepreneur making a new product. You have customers who are happy. They Got something that they didn't know they would get otherwise. Those customers, by the way, got lots of free entertainment and they'd have to pay for it along the way. And Meta made a bunch of money for themselves and their shareholders, which is basically everywhere in the world. This is why advertising is great. And Meta's advertising in particular is awesome. And I get frustrated that Meta doesn't talk about that. Mark Zellberg has never really talked about the societal benefits of advertising, except in passing. In 20 years, he's handed it off to other people to take care of. And maybe there's a bit where him not paying attention is why. There is a certain grit and grind that goes in building an advertising business. People get frustrated or have questions about as far as data and all those sorts of things. And maybe there was a bit where he didn't want to be involved in it and wipe his hands of it. But you saw this when Apple passed att App tracking. Transparency was one of the worst antitrust violations in the history of technology. Apple unilaterally obliterating all these business models while they're simultaneously building their own. As far as advertising goes and doing all this tracking, why trust us? And meanwhile they're running these advertisements. I remember that advertisement of people on the bus, like overhearing everyone around them, what they're saying. That was such a dishonest representation of how advertising works on the Internet. You had Tim Cook in Congress talking about companies selling data. Facebook's not selling your data. That's value to them. Why would they sell the data? Meta was not prepared to respond. I think you got this with Sheryl Sandberg back in the day. She wouldn't. Every call would talk about advertising, how great it is, and have a bunch of case studies, people who are benefiting from advertising and these new entrepreneurs. And then she left. And it's kind of like that hole never got filled. It feels like it's a company that's kind of like embarrassed. We make a lot of money from ads, but we got glasses and we're doing AI. It's like you have ads and ads are awesome. I think if they had communicated that more consistently, they would be in a better place generally from a PR perspective, they would have been a better place relative to Apple. And I think they would have an easier time right now convincing Wall street that let us invest. The other problem is they've spent cumulative hundred some billion dollars on Oculus, which I hated all along. And so there's a bit where, why should we let you spend money Again,
Patrick O'Shaughnessy: the one major player and company that we haven't talked about much is Jensen and Nvidia. And I'm curious how you would tie this back to the notion of not understanding commodity markets in Silicon Valley. Whether or not you think compute ultimately is a commodity, I'm curious whether or not you think intelligence will ultimately be a commodity. It's interesting that intelligence and compute, which seem to be by far the most interesting and important topics in tech, but both might be commodities and less differentiated than products.
Ben Thompson: The most interesting thing about the Internet is free distribution. Bandwidth is a commodity. The fact that I can pull out my phone right now and connect to any information source in the world for free, free on a marginal cost basis is because it's a commodity. It changed the world. Commodities change the world. There is a aspect of differentiated products by definition have lower tams because there's a elasticity aspect to it. Not everyone can afford to pay for it. People's willingness to pay is going to differ. Your market is going to be constrained. Apple's never going to serve the whole world by selling a device, whereas a Google can because it's free. That matters. You're paying for a commodity, but to the extent it is available to everyone is the extent it is impactful. The Internet is a commodity. It changed the world. So I don't think it'd be weird that intelligence ends up a commodity and changes the world.
Patrick O'Shaughnessy: Commodities often are not thought of as as good of businesses as these differentiated harm engine products. Curious for your thoughts on Jensen and Nvidia specifically.
Ben Thompson: Nvidia's position is, I think, definitely unnatural. You look at Nvidia, they've maintained all their margins. Isn't that amazing? It's 2026 and everyone's coming for them and they're still charging however much money for a chip, but they're actually not maintaining their margins. Because this whole question of circular financing is people talk about Lucent and things like that and you know, this whole deal, Nvidia is providing a 25% backstop. But if you actually ascribe a value to that to Nvidia's taking equity in the Neo clouds or whatever, they guarantee they're going to buy all their compute to 2030. Why do they do that? So that the entity in question can get a lower cost of capital, so they can buy, etc. But implicit in that, why do they get a lower cost of capital? They get a lower cost of capital because Nvidia assumed risk. This is my point before. Risk never disappears. It just appears somewhere Else taking on risk has a price. There is a world where AI takes off, it never stops and everything is fine. And Nvidia captured all the upside of their risk. But there's also a world where, say that this is Neocloud, that backed up a ton of compute comes to market. The hyperscopes have plenty of compute. They don't have enough compute. Nvidia is paying for a compute that no one wants. They just lost a bunch of money. If you think about it, there's an expected value of that investment. That expected value, it's not zero, it's not 100%, it's somewhere in the middle. But that is a diminution of Nvidia's profitability. If you actually look at their business holistically, what that is is a price cut. Now, the price cut didn't show up in margins, it didn't show up in what they're offering. But a lot of what Nvidia is doing is how can we maintain our margins even if the wide view, sort of discounted cash flow, expected value, holistic view of our company, people do discount cash flows. But are you actually considering all these pieces? The reality is, is that moving stuff off the balance sheet, by and large works. But they're doing all these deals to maintain what feels somewhat unnatural. We have seen price cuts. They're just manifesting in these very bizarre sort of ways. Now, in the long run, I think the challenge is their ultimate competitors are the hyperscalers, particularly Google and Amazon. So Google and Amazon aren't just building their own chips, but they're also looking to sell those chips externally. Google already made a deal to sell like 20% of their TPUs Gentropic. On the last earnings call, Andy Jassy practically confirmed that they'll be selling Trainium 3s or maybe training 4, or they'll be on training chips sort of eventually externally, which makes sense. That gives them a long term buy into these companies. There's a huge amount of R and D that goes in developing chips. They get more leverage on their spend. It all makes sense. And by the way, they're not selling their chips on differentiation, they're selling their chips as commodities. Nvidia is the one selling a differentiation. People aren't going to Amazon to use Trainium, so they're not cannibalizing the attractiveness of their cloud by selling Trainium outside. So they're Nvidia's biggest problem. Because what's the number one advantage that the hyperscalers have? Scale, Lower cost of capital. It's a capital fight. They have a lower cost of capital than the Neo clouds do. The Neo clouds are. They'll buy Nvidia left, right and center. And by the way, it also makes total sense that why SpaceX satellite Elon's out there. We will always buy Nvidia because they're the best. No, you'll buy Nvidia because they're the most fungible. Nvidia is true. It is the most fungible. Cuda's moat is dramatically diminished because the models don't care what they run on. And that's what actually matters. Was built on top of the models. But it still matters. It's still something of a mode. It's. If you want to play the game spacexi is doing where we're going to build a lot and rent it out, but reserve the right to pull it back. Of course you're going to be on Nvidia because the easiest way to rent it out is to be on Nvidia. You saw this very early, by the way. You go back to 2024, 2023, Nvidia starts talking about all these sovereign clouds. They start talking about. They tried to call these neutron models. They had this thing in 2024. I remember it was the first one where it was like the Rockstar GTC at San Jose and like the Hughes Coliseum and just one comes out. It was a very boring GTC. The old ones used to be Nvidia demonstrating like 50 gazillion things as they're throwing stuff at the wall. They knew they had something with GPUs and. And they're trying to like find people excited about him. Yeah, Once LM showed up, it's like, oh, we have the use case. But they were coming up with all these enterprise offerings. I can't remember what they were called, but they're like these modules basically that of course they were free, but they only ran on Nvidia. And you could see what they were doing is they were trying to lock people in. Intel is a good example here. AMD cleaned them out in hyperscaler sales because the hyperscalers are putting the effort to get stuff working on AMD versus Intel, there are still small differences. Even though they're x86 because they're buying at such scale, the investment to do it is worth it to get a better chip or lower price or whatever it might be. The part of Intel's business that never floundered was selling to government and selling to enterprises. They don't have the resources of a hyperscaler they're not buying at that scale. They're just going to keep buying what they had before. That's why Nvidia talks about selling to sovereign clouds. That's why they talk about selling to enterprises. Because they want to get in these markets where they're not going to be balancing this chip versus that chip. The hyperscalers have always been the threat to Nvidia. For that reason, they're actually bigger. So you have this issue where the hyperscalers are the threat. The hyperscalers have a better cost of capital than the other companies. If he wants to buy them, that's how you get this deal. This week I see this deal as a response. That's why it goes with the Google deal. Google can just issue equity. The shareholders don't love it, but their monetization capacity is much higher than Nvidia or Nvidia's customers are. I think what Nvidia is hoping for. Maybe they would say this in so many words, but if we get to a world where we actually run out of power, that's probably good for Nvidia because in a world where we're totally constrained on power, everyone want the best. We have to get the best efficiency, the best token efficiency. And I think Nvidia is still the most token efficient. So that is a good world for them. Probably the biggest problem for Nvidia over the last couple of years is I think the US has actually brought a lot more power online than expected. It surprised me, whether it be what Elon did, sort of behind the meter which has been replicated, or West Texas and natural gas. But even like restarting nuclear plants, you
Patrick O'Shaughnessy: love how the US responds to these things.
Ben Thompson: It's awesome. It's actually one of the biggest encouraging signals about the US is I was writing early on, assume this is a bubble. You want there to be a long term payout. The.com, we got fiber in the ground. And by the way, Google's played this game before. Google built its business by buying up dark fiber. They had the killer search engine but so much of the power, what they do is because they bought up all this dark fiber that was basically free. After the dot com era, our core Internet still runs on WorldCom Fiber. That was a lasting benefit. The railroads, BNSF is throwing off money that's going to Google from Northern Pacific and Jay Cook selling bonds to retail investors. You want a bubble that produces something that lasts. And very what's going to last from AI? The GPUs don't last that long. Data centers. Okay, fine, but what is it going to be? Power. It has to be power. If we're in a world where this all blows up and we have way too much power, that is an amazing world to be. We've always been energy constrained. Energy undergirds everything. What would it be like to live in a world of energy abundance? It's hard to even imagine because our minds are so constrained by the fact we've actually always been in energy scarcity. I think we've done an unbelievable job. Power for sure is a constraint. It's going to be a constraint, but I think it has taken longer to become a constraint than anyone expected. And I wouldn't be surprised if that includes Jensen Huang. I think he thought insufficient power was going to be Nvidia's moat sooner than it happened. It turns out that the longer we have enough power, the more time Amazon has to make Trainium better, the more time Google has to make TPUs competitive from an efficiency standpoint. And if we get in a world where this is a world where those margins seem very hard to sustain, I
Patrick O'Shaughnessy: love hearing your takes on just everything going on. It's the most interesting time I've ever observed in this world that you love so much. So thank you so much for your time.
Ben Thompson: Thank you very much.
Patrick O'Shaughnessy: If you enjoyed this episode, visit colossus.com, you'll find every episode of this podcast, complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital and private audio publication featuring in depth profiles of the founders, investors and companies that we admire most. Learn more@colossus.com subscribe.
Ben Thompson: Foreign.
Patrick O'Shaughnessy: You know how small advantages compound over time. That's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default. Better data, better decisions, better economics over time. See how@ramp.com invest as your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk. Learn more@vanta.com invest the best AI and software companies, from OpenAI to Cursor to Perplexity. Use work OS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline is redefining asset management technology as a true partner, not just a software vendor. They've helped firms 5x in scale, enabling faster growth, smarter operations and a competitive edge. Visit ridgelineapps.com to see what they can unlock for your firm. Every investment firm is unique and generic. AI doesn't understand your process.
Ben Thompson: Rogo does.
Patrick O'Shaughnessy: It's an AI platform built specifically for Wall street, connected to your data, understanding your process and producing real outputs. Check them out at Rogo AI Invest.