Invest Like the Best with Patrick O'Shaughnessy
Dylan Patel - Inside the Trillion-Dollar AI Buildout - [Invest Like the Best, EP.442]
30 Sep 2025
My guest today is Dylan Patel. Dylan is the founder and CEO of SemiAnalysis. At SemiAnalysis Dylan tracks the semiconductor supply chain and AI infrastructure buildout with unmatched granularity—literally watching data centers get built through satellite imagery and mapping hundreds of billions in capital flows. Our conversation explores the massive industrial buildout powering AI, from the strategic chess game between OpenAI, Nvidia, and Oracle to why we're still in the first innings of post-training and reinforcement learning. Dylan explains infrastructure realities like electrician wages doubling and companies using diesel truck engines for emergency power, while making a sobering case about US-China competition and why America needs AI to succeed. We discuss his framework for where value will accrue in the stack, why traditional SaaS economics are breaking down under AI's high cost of goods sold, and which hardware bottlenecks matter most. This is one of the most comprehensive views of the physical reality underlying the AI revolution you'll hear anywhere. Please enjoy my conversation with Dylan Patel. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. 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. – This episode is brought to you by Ridgeline. 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. Head to ridgelineapps.com to learn more about the platform. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:05:12) The AI Infrastructure Buildout (00:08:25) Scaling AI Models and Compute Needs (00:11:44) Reinforcement Learning and AI Training (00:14:07) The Future of AI and Compute (00:17:47) AI in Practical Applications (00:22:29) The Importance of Data and Environments in AI Training (00:29:45) Human Analogies in AI Development (00:40:34) The Challenge of Infinite Context in AI Models (00:44:08) The Bullish and Bearish Perspectives on AI (00:48:25) The Talent Wars in AI Research (00:56:54) The Power Dynamics in AI and Tech (01:13:29) The Future of AI and Its Economic Impact (01:18:55) The Gigawatt Data Center Boom (01:21:12) Supply Chain and Workforce Dynamics (01:24:23) US vs. China: AI and Power Dynamics (01:37:16) AI Startups and Innovations (01:52:44) The Changing Economics of Software (01:58:12) The Kindest Thing
Chapter 1: What is the relentless focus on leverage in finance teams?
The best operators have a relentless focus on leverage, finding ways to multiply their impact rather than just working harder. But here's what I see happening in finance teams everywhere. Brilliant people getting buried in expense management busywork. If you think about it, you become a finance leader because you love strategic work.
modeling scenarios, optimizing capital allocation, finding the insights that actually move the business forward. But instead, you're chasing receipts and categorizing transactions. It's the opposite of leverage. This is exactly why I'm so bullish on what the team at Ramp has built.
Kareem and Eric understood that every minute spent on manual expense management is a minute stolen from high leverage work. So they automated all of it. Automatic categorization, receipt matching, spending controls that actually work. Check out ramp.com slash invest and see what happens when you eliminate the busy work. Cards issued by Sutton Bank, member FDIC. Terms and conditions apply.
To me, Ridgeline isn't just a software provider, it's a true partner in innovation. They're redefining what's possible in asset management technology, helping firms scale faster, operate smarter, and stay ahead of the curve. I want to share a real-world example of how they're making a difference. Let me introduce you to Brian.
Brian, please introduce yourself and tell us a bit about your role. My name is Brian Strang. I'm the Technical Operations Lead, and I work at Congress Asset Management.
How would you describe your experience working with Ridgeline?
Ridgeline is a technology partner, not a software vendor, and the people really care. I get sales calls all the time and I ignore them.
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Chapter 2: How is Ramp automating expense management?
Ridgeline sold me very quickly. We went from 7 billion to 23 billion and the goal is 50 billion. Ridgeline was the clear front runner to help us scale. In your view, what most distinguishes Ridgeline? They reimagined how this industry should work. It was obvious that they were operating on another level.
It's worth reaching out to Ridgeline to see what the unlock can be for your firm. Visit RidgelineApps.com to schedule a demo. One of the hardest parts of investing is seeing what's shifting before everyone else does. AlphaSense is helping investors do exactly that.
You may already know AlphaSense as the market intelligence platform trusted by 75% of the world's top hedge funds, providing access to over 500 million premium sources from company filings and broker research to news, trade journals, and over 200,000 expert transcript calls. What you might not know is that they've recently launched something game-changing, AI-powered channel checks.
Channel checks give you a real-time, expert-driven perspective on public companies weeks before they show in earnings or consensus revisions. AlphaSense uses an AI interviewer to run thousands of expert calls with real human experts every month, asking consistent questions across experts so the signals are clean, comparable, and useful.
You get live updates as interviews come in, full transcript access and coverage across every major sector. Instantly compare insights across experts and analyze quarter over quarter trends in sentiment and key performance indicators. For investors trying to stay ahead of the fast moving markets, it's already table stakes. 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 Review, our quarterly publication with in-depth profiles of the people shaping business and investing.
You can find Colossus Review along with all of our podcasts at joincolossus.com.
To learn more, visit psum.vc.
My guest today is Dylan Patel. Dylan is the founder and CEO of Semi Analysis. At Semi Analysis, Dylan tracks the semiconductor supply chain and AI infrastructure build out with unmatched granularity, literally watching data centers get built through satellite imagery and mapping hundreds of billions in capital flows.
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Chapter 3: What insights does Dylan Patel provide on AI infrastructure?
All these commercial real estate people are trying to get in this. There's some way where they'll be able to figure out other people to front the capital and then come up with a deal much like it is Oracle, but OpenAI has to do more of the work in terms of setting up the cluster, the software, the networking, etc.
The nice thing for NVIDIA is, of that 50 billion they capture, maybe 35 billion of that is CapEx that goes directly to NVIDIA. So year zero, OpenAI slash its partner spends $50 billion on the data center. The timing is not exactly that, but they spend $50 billion on the data center, 35 goes into NVIDIA, NVIDIA's gross margin is 75%. So, you know, again, I'm gonna make it simple numbers.
Let's say it's 10 and 40, 10 billion COGS, 40 billion revenue, $30 billion of gross profit. If we fix the numbers, it's effectively like half their gross profit from that deal is going directly to OpenAI in the form of equity investment. The 25% that's COGS, NVIDIA is paying for that.
And then they keep the other half of the gross profit on their balance sheet or do buybacks, whatever they want to do with it. So NVIDIA is not necessarily like, they are like round tripping some of this. What effectively is happening is OpenAI gets the opportunity to pay for a big chunk of it in equity. And NVIDIA is lowering their prices without lowering their prices effectively.
And they're getting ownership of a company. But NVIDIA comes out great because they're getting the CapEx dollars up front. So all they're really doing is they're saying half of my money that's in this, sure, it does make its way to me somehow. But in reality, I still made half of that gross profit.
And the other half is equity in a company that may or may not be worth something, a company that may or may not be able to pay hundreds of billions of dollars of compute deals that they've signed, in which case they'd be bankrupt.
It's about the highest stakes like capitalism game of all time. And it's so interesting to think about when it might run out. You mentioned like if we hit that final point and we don't see the return, we're kind of toast in a big hole. But I'm also curious about the other side of ability to serve and just demand for like today's models by inference.
The stat I last saw is token demands doubling every two months or something crazy. Obviously, there's all these reasoning tokens that are really exciting for some of the longer thinking models. How do you think about the growth of the pool of demand for inference tokens themselves, even in today's models?
Like even if we just like stop things and fix things and we'll leave that other side of the equation just for a second. What's your model for thinking about that today?
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Chapter 4: What are the implications of the US-China competition in AI?
What most interests you?
So the thing I like to call it is tokenomics. I stumbled upon the word, actually, it's like a crypto world. You kill off crypto, finally, once and for all. So I'm trying to make tokenomics SEO direct to, you know, us talking about tokenomics and then hopefully you talking about tokenomics. Hopefully, like, everyone's using the word tokenomics.
I'll say tokenomics 20 more times.
Yeah, yeah, yeah. It's got to be in the title now, right? You've got some powerful SEO. We kill the crypto bros for this. But tokenomics, economics of the tokens, right? How much compute is being spent? How much is the gross profit? What's the value being created by these tokens? That's the end of the day what's relevant here, right? NVIDIA keeps saying AI factory, which produces intelligence.
That intelligence has value. Let's say you have a gigawatt of capacity. What can I serve? Well, I could serve... a thousand times of a model that's really shitty. I could serve one times of a model that's good, and I could serve like 0.1 times of a model that's amazing. Now, multiply that by whatever factor, how many users, what's the number of tokens outputted.
I could do X number of tokens, X times 100, X times a million tokens, right, depending on the model quality. This is sort of where the whole GPT-5 thing comes around. OpenAI had a challenging thing, right? They're like, hey, we have a couple gigawatts of capacity, effectively. By the end of this year, roughly a couple of gigawatts of capacity too.
How do they maximize their serving capacity with this? One avenue is we continue to serve big models and we make bigger models and the tokens are more expensive, but this log log scale is really challenging because yes, the value is way more, but the cost is way more. And then the real whammy is the user experience is way worse. If I serve a massive, massive model, it's slow and users are fickle.
You need the response to be faster than they can read. Hard to calibrate, yeah. Yeah. So there's this like user experience challenge, but really in the end, it's like for a given model level, I think there's a saturation point of how much demand of intelligence there is. You can only have such large child army, right?
Of like people digging trenches or like Kony 2012, whatever it is, like this is very cancelable, but you know. But you can have a much larger army of, or business of like the larger level of intelligence. When you think about what could I have done with GPT-3? GPT-3, even if we paused there, pause the model capabilities, right?
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Chapter 5: What are the implications of the US-China competition in AI?
Like that's, they can't pay for it. At least had to go through their head when they cut back. And so they paused a bunch of data centers. They said, we don't need to be the exclusive compute provider. You can go to Oracle. It's fine. And they relinquished this power. Now, Oracle has that deal. OpenAI sends like 20% of their revenue to Microsoft or API revenue or something like this.
And then Microsoft has this like 49% capped profit structure on OpenAI. And then there's like this whole like IP sharing, like this deal. It's like really hard to understand the mechanics of the OpenAI and Microsoft deal even. So you have this whole power dynamic and they're trying to renegotiate this. The whole deal is like, oh, when we have AGI, you no longer have IP rights.
And it's like, if you ask someone 20 years ago and you put them in front of Chad GPT, it's like, this is fucking AGI. Like, it knows everything. And it can have a conversation. I can't tell it's not a human. Actually, I can tell it's way smarter than a human. But now it's like, ah, whatever. It can't do X, Y, Z. So the thing, the bar always moves, no matter what the level of intelligence is.
And for me, it's going to be like, when the thing puts its hand in its mouth and it's like, yeah, this is me. I'm a human, right? Like, you know, that's sort of like- Consciousness. The sentience, the consciousness of it all, right? That's one power dynamic that's like crazy. Another power dynamic is the one around NVIDIA and the hyperscalers. NVIDIA is the king.
All of the gross profit is going to them today. Pretty much all of it.
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Chapter 6: How is the AI infrastructure evolving in terms of power dynamics?
Sure, TSMC makes some. Sure, SK Hynix makes some. But they have to invest a ton in CapEx. Sure, Broadcom makes a bunch. And Broadcom makes a ton of gross profit off of these companies. But NVIDIA makes by far the most gross profit in the industry. It's not even close.
they're king and they want to continue to be king and they want to make sure GPUs continue to be most used, but also they can't buy anything. They weren't even allowed to buy Arm when they were like a nobody. They were like pretty much a nobody on the grand scheme of things and they weren't allowed to buy Arm. They totally could not buy any major companies.
They'll buy startups, like they bought a startup that I was like a seed investor in and like an advisor in, but like they can't buy a real company. So what do they do with all this cashflow? Sorry, but you're a loser if you just do buybacks. That's admitting you can't get higher returns on your capital, which is fine. Like Meta, Apple, Google, they were mature companies for a while. Guess what?
Those companies aren't going to do buybacks ever fucking again, right? Or not like ever again, but for a while. They think there's better ROI for their capital now. In NVIDIA, if you look at Jensen, he's always flirted with buybacks, but mostly he's been reinvesting in the business. But you can't reinvest that much into the business.
Yeah, he's doing demand guarantees. He's doing all this crazy stuff now.
Yeah, right, right. He's using his balance sheet to- Win. Yeah, try and win more, which is an interesting dynamic. I don't know if there's ever been anything like this in terms of the anti-competitive nature of this, where you backstop clusters. Corey recently got a deal with NVIDIA where it was like they backstopped a cluster.
Now, Corby would have never built this cluster because it's for like short-term demand and renting GPUs on short-term is like a terrible business model.
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Chapter 7: What challenges do AI data centers face regarding power supply?
You want to do long-term contracts and you want to do long-term contracts to people with balance sheets. That's the golden goose, but that doesn't exist so much. So you do long-term contracts with people who don't have a balance sheet, like OpenAI. And if you can't do that, then you'll do short contracts with people who do have a balance sheet, right?
Like there's this whole matrix of like who you rent GPUs to. But from NVIDIA's interest, it's like, You know what I really love is when venture capitalists fund a company and then 70% of the round is spent on compute. I fucking love that, right? And that's what's happening with all these companies.
Whether it's physical intelligence, they're spending a lot on like robot arms and shit too, but they're also spending a lot on compute. Or it's like any other startup that's raising, Cursor, whoever, right? And even if it's not directly, it's indirectly going to GPUs. They love when people spend their entire round on GPUs.
What would be really good is if it wasn't like a two-year deal or a three-year deal for that compute. If it was, you can spend 70% of your round on one training run. Leave a company with these ideas, gather the data, do the training run, and then you have a product. And you show how good the model is and then you try and raise again.
That's what would be really great for NVIDIA, but no one wants to build a cluster who's predicated on that as the business model. That's crazy. So they have to backstop a cluster to do that. Or, hey, open AI... might go to their own chip. They might go to some ASIC from another company. They might even buy TPUs. They might even like go to Amazon. They don't really care.
They're not beholden to Microsoft anymore.
Trying to serve a product to a customer.
And they want to build the digital god and they want to serve a product, right? And make revenue, right? So they don't have to go to NVIDIA. NVIDIA is the best option. But you know what would be really, really helpful is if I could, going back to the earlier part in this discussion is I get the compute up front and I don't have to pay for the compute for the first year.
It'd be really good if I could do that because then for a full year, I can do training. I can subsidize inference. I can do all these things that build up a user base and then I can actually pay for it. I have a year of a gig a lot to figure out a business model, whether that is serving free tokens and then implementing this purchasing, purchasing stuff for the free user.
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Chapter 8: How does the conversation conclude regarding the future of AI and its impact?
And like, that's a lot of the stuff we focus on at my company. But at the end of the day, if the models don't improve, we're absolutely screwed. And if this lasts another year and then it happens, like the U.S. economy will go into a recession straight up because of this and probably Taiwan as well and probably Korea as well.
Because there's so much buildup and revenue flowing through to us for this. But when you look at these other things, like the bubbles of the past, some of them were just silly nonsense, right? Like tulips. Silly nonsense, right? Crypto. Complete Ponzi scheme, right? But then there's other stuff that's like... This was real, right?
Like the UK like spent like some absurd percentage of their GDP on railroads for like a decade.
6% or something crazy.
Yeah. We're nowhere close to 6% of our GDP. Like, holy shit. That was like, okay, there's tangible, but it's like, oh, well, we did overbuild because like how many goods are there to transport? You must build these railroads to reduce the cost of transport so much because you have no clue when the demand stops and you've overbuilt.
And because there's 10 people trying to do it at once, you're obviously going to overbuild at some point. Same thing with fiber. And like a lot of the argument against this is like, well, no, but this time it's the strongest balance sheets in the world. It's the world's most profitable companies. They can all pull the plug at any point.
Microsoft pulled the plug at one point before they're like, oh, shit, no, no, plug it back in. They recently plugged it back in. They're like, oh, wait, we're starting. We're restarting this. We're going out into the market. We're signing deals with Nebius for GPUs. Like, I don't remember how big the deal was. It's like $19 billion for Nebius.
It's like if they had just not pulled the plug on their data centers, they wouldn't have had to do that. They wouldn't have to pay those gross profit dollars to Nebius. But Nebius made the bet that the demand is there, and they were right. When you think about this, it's like, what is the level of demand where this stops, right? If scaling laws continue, of course, there's an adoption curve.
There's a pace. There's realities with capital. There's realities with supply chains. Things take time. adoption for businesses takes time. But if you like boil it down to it, it's like your demand for 30 year old senior engineers at Google who know how to make and program anything is effectively like, I don't want to say infinite, but it's $2 trillion of value.
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