From the Archive: Can Anyone Catch NVIDIA? | The Future of Chips and Infrastructure

Summary

Podcast Summary: The a16z Show—"Can Anyone Catch NVIDIA? | The Future of Chips and Infrastructure" (July 15, 2026)

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Episode Overview

This episode of the a16z Show is a deep dive into the state of AI infrastructure, the central role of NVIDIA in the hardware ecosystem, and whether competitors can catch up. Host Aaron Price Wright is joined by semiconductor analyst Dylan Patel (SemiAnalysis), a16z’s Guido Appenzeller, and Zahara, as they discuss AI models, monetization strategies, the economics behind GPT-5 and other models, the shift in data center and chip architectures, and the challenges facing NVIDIA and the broader AI hardware market.

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Key Discussion Points & Insights

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1. The New AI Model Economics: Cost, Routing, and Monetization

• GPT-5 Disappointment & Cost Focus
  Dylan Patel discusses how GPT-5 isn’t necessarily a huge leap in capability but signals a shift to optimizing cost and infrastructure utilization.
  • "GPT-5 is not spending more compute per se. The model did get a little bit better, but... the curve of intelligence is all about compute spent."—Dylan Patel [02:03]
• Routing Models Based on Query Value
  OpenAI’s new router system helps control which model is allocated based on query value and user type, with implications for monetization and performance:
  • "If they ask 'why is the sky blue,' just route them to Mini... but if they ask 'what's the best DUI lawyer near me,' now you spend more compute because that can be monetized."—Dylan Patel [06:06]
• Monetizing Free Users
  Patel argues that direct monetization (e.g., taking a cut from action-based AI agents) will outperform ads:
  • "You can't just make the user's result worse by injecting ads... I think with the router, they’re close to figuring out monetization."—Dylan Patel [06:46]

[Key segment: 02:00–07:34]

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2. Economic Pressures: Subscription vs. Usage-Based Pricing

• Cost, Token Usage, and Subscription Gaming
  Users are altering behaviors to maximize value from subscriptions (e.g., changing sleep schedules to beat token limits), creating pressure on usage-based pricing.
  • "I saw the craziest thread on Twitter where this guy said he changed his sleep schedule... just so he can maximize usage."—Dylan Patel [08:02]
• Enterprise vs. Consumer Pricing
  More predictable usage makes flat-fee enterprise licenses more feasible, while consumer use is too variable.
  • "For enterprises we could see more flat-fee pricing because you can average it out more."—Guido Appenzeller [11:07]

[Key segment: 07:35–11:33]

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3. AI Value Creation Exceeding Value Capture

• Explosion of AI Value, But Value Capture is Lacking Despite massive impact, the main AI providers (OpenAI, Anthropic, etc.) aren’t capturing much of the value they create.
  • "OpenAI is not even capturing 10% of the value they’ve created... The value capture is really broken."—Dylan Patel [16:08]
• Commoditization & Capture Dilemma As models become commoditized, the hardware economics shift further.
  • "We’re getting commoditized... thus you should temper your expectations of how much you can spend on GPUs."—Guido Appenzeller [18:08]
• Future Inflections in Value Capture Growth in AI-driven ads, integrated agents, and enterprise use cases may allow better capturing of generated value.

[Key segment: 16:03–18:26]

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4. NVIDIA’s Dominance: Supply Chain, Software, and Scale

• NVIDIA's Moat NVIDIA’s control over supply chain, process node, HBM memory, software, and time-to-market is nearly insurmountable—new entrants cannot offer marginal improvements; they must be 5x better.
  • "They're going to have better networking than you... you have to really leap forward in some other way."—Dylan Patel [00:00 & 31:59]
• Custom Silicon Threat Cloud giants like Google, Amazon, and Meta are increasing custom silicon efforts to chip away at NVIDIA's dominance, but face internal cultural and engineering barriers.
  • "Amazon is making millions of Trainium. Google's making millions of TPUs... Clearly, that's the biggest threat."—Dylan Patel [19:52]
• Software Eats Hardware The ongoing dynamic: whoever controls software (and cloud services) eventually eclipses hardware incumbents—NVIDIA’s investment in software is both defense and growth.
  • "That's why NVIDIA is making all these software libraries... They're trying to commoditize inference."—Dylan Patel [23:07]

[Key segment: 31:59–33:52; also 23:07–24:30]

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5. Barriers to Challengers: Silicon Startups & Hardware-Software Co-design

• The Silicon Startup Wave Startups raise billions on unlaunched chips, riding the AI hype and capital glut.
  • "Etched and Revos... have gotten the amount of funding they've had without even launching a chip."—Dylan Patel [24:44]
• Insurmountable Moats Incumbents hold supply chain, software, and customer integration moats too large for new entrants to breach without “disruptive technology leaps.”
  • "You can’t just do the same thing as NVIDIA... you have to leap forward by 5x."—Dylan Patel [31:59]
• Hardware/Software Co-Design Past bet-the-company chip architectures (e.g., more SRAM for smaller models) were leapfrogged by shifts in model types (Transformers), underlining the difficulty of timing and focus for new entrants.

[Key segment: 24:44–33:52]

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6. Geopolitical & Physical Infrastructure Constraints

• China vs. US: Power, Capital, and Policy
  • In China, power supply is less a constraint for AI than in the US; however, China’s AI growth is currently constrained more by capital allocation than by hardware.
    • "China has such an infinite supply of power... in America, we've got data centers ready to go, but not enough power."—Dylan Patel [34:18]
  • US faces unique infrastructural challenges: labor shortages, power grid constraints, physical build-outs.
    • "Electricians in Texas... your pay is up 2x now... This labor problem is a challenge too."—Dylan Patel [41:32]
• Chips & Export Controls Complex US-China export restrictions and backdoor allegations (re: NVIDIA H20 in China) add uncertainty but do not halt deployments through overseas data centers.

[Key segment: 34:18–44:18]

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7. Data Center Economics: Power, Cooling, and CapEx

• Power & Cooling Less Costly than Chips Most of the data center cost is in hardware (GPUs, networking), not in power or cooling.
  • "80% of the cost of a GPU data center is capital... 20% is land, power, cooling."—Dylan Patel [45:54]
• Speed Kills (and Pays) Spending more to get hardware operational sooner is justified by the overwhelming value in extra compute time (e.g., Tesla/Elon example).
  • "Elon did what seemed silly, spending on generators and chillers to get the DC up 3 months faster... totally worth it."—Dylan Patel [46:14]
• Locating Data Centers Where to build is as important as how much power to use; proximity to power sources is becoming an investment battleground.

[Key segment: 45:54–47:03]

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8. Chip Wars: Intel, TSMC, Samsung, and the Global Supply Chain

• Intel’s Strategic Role & Survival Intel lags in process technology, but is strategically essential as the only real US-based advanced fab alternative to TSMC.
  • "If something happened to Taiwan, Intel would have the most advanced technology in the world (but it’s not economic)."—Dylan Patel [47:13]
• TSMC’s Monopoly Restraint TSMC’s monopoly is real but tempered by their Taiwanese culture of modest pricing.
  • "TSMC is a monopoly... if they were managed by Americans, they would have raised prices a lot more."—Dylan Patel [47:13]
• Intel’s Management & The Path Forward Management challenges, cultural silos, and pressure to restructure hamper Intel’s agility—but splitting the company now would risk bankruptcy.
  • "To properly split the company... would take so long you’d be bankrupt by then."—Dylan Patel [48:42]

[Key segment: 47:13–52:53]

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9. Advice for Industry Leaders: OpenAI, NVIDIA, Google, Meta, Apple, Microsoft, Elon

(Rapid-fire, closing advice rounds, memorable quotes and critiques)

To Sam Altman (OpenAI):

• "Immediately launch a method for you to input your credit card into ChatGPT and agree that for anything it agentically does for you, it'll take X cut. This will make them so much money."—Dylan Patel [11:47]

To Jensen Huang (NVIDIA):

• "Why don’t you get into the infrastructure game somehow?... Reinvest and build a bigger company that controls the infrastructure end-to-end."—Dylan Patel [54:27]

To Sergey Brin/Sundar Pichai (Google):

• "Open up the kimono on TPUs, start selling them, open source the software, and reorg to build much faster data centers."—Dylan Patel [54:52]

To Zuck (Meta):

• "Zuck sees the vision, but he also needs to focus on releasing things faster... have a ChatGPT or Claude Code competitor, release way more products."—Dylan Patel [58:31]

To Tim Cook (Apple):

• "You guys are going to lose the boat if you do not spend like $50, $100 billion on infrastructure... I don’t think they’ve truly realized what happens when the interface to computing is AI."—Dylan Patel [59:56]

To Satya Nadella (Microsoft):

• "They were super aggressive in '23 and '24 and pulled back heavily... They're losing grasp on OpenAI, internal model efforts are failing, chips are bad, they need to shake the crap out of the company."—Dylan Patel [61:32]

To Elon Musk (xAI/Tesla):

• "He's losing a lot of talent and axing good projects... He's a magnet for amazing talent but some snap decisions are hurting him... maybe just get off Twitter and focus on the products again."—Dylan Patel [63:19]

[Key segment: 54:18–64:09]

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Notable Quotes & Memorable Moments

• "We've already seen AI's value creation exceed... but the value capture is broken."—Dylan Patel [16:08]
• "Etched and Revos have raised a lot without ever launching a chip."—Dylan Patel [24:44]
• "If you want to beat NVIDIA, you can't be 2x better. You need to be 5x better because their supply chain, software, and speed will close the gap before you catch up."—Paraphrased from [31:59]
• "The chips are 60–80% of the cluster cost... they already bought the chips, can't put them anywhere because the data centers aren't ready."—Dylan Patel [41:45]
• "TSMC is a monopoly, but they're good Taiwanese people, not dirty American capitalists."—Dylan Patel [47:13]
• On Intel: "It takes them five to six years to go from design to shipping... the rest of the industry goes through one to three revisions if they're good."—Dylan Patel [48:39]

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Timestamps for Key Segments

• 02:00–07:34: OpenAI’s new architecture, “router,” economic focus in AI models
• 07:35–11:33: Usage-based vs. subscription pricing, user gaming, stickiness
• 16:03–18:26: AI value creation vs. value capture, commoditization
• 23:07–24:30: NVIDIA’s software approach, API commoditization
• 24:44–33:52: New silicon startups, hardware-software co-design, why it’s hard to catch NVIDIA
• 34:18–44:18: Geopolitical constraints, China/US power, export controls, data center build-out
• 45:54–47:03: Data center economics, CapEx, why speed pays
• 47:13–52:53: Intel’s challenges, TSMC’s role, splitting Intel dilemma
• 54:18–64:09: Advice for industry leaders; rapid-fire closing thoughts

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The Takeaway

This episode delivers a sweeping, candid look at the AI infrastructure race, the economic and technical moats around NVIDIA, and the challenges faced by would-be competitors. Key takeaways include the shift toward cost-centric model deployment, intensity of CapEx and infrastructure constraints, difficulty in capturing AI-created value, and how software and hardware are evolving in lock-step. The “AI gold rush” is real, but the only way to unseat NVIDIA, according to Dylan Patel, is for new entrants to “leap forward by 5x”—marginal improvements are not enough.

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For the most in-depth details and direct attributions, listen around the provided timestamps.

Transcript

[Dylan Patel]

Nvidia is going to have better networking than you. They're going to have better hbm, they're going to have better process node, they're going to come to market faster, they're going to be able to ramp faster, going to have better negotiations with whether it's TSMC or sk, Hynix and the memory and silicon side or all the rack people or like copper cables, everything. They're going to have better cost efficiency. So you can't just like do the same thing as Nvidia. You have to really leap forward in some other way. You have to be like 5x better.

[Podcast Host]

The AI race isn't just about models. It's also about the infrastructure underneath them. Chips, data centers, power networking, and the economics that determine who can keep scaling. In this conversation, semianlysis co founder Dylan Patel joins Aaron Price Wright, Guido Appenzeller and me to discuss the state of AI hardware, why Nvidia remains so difficult to compete with, and how companies like Google, Amazon, Meta and OpenAI are approaching the next generation of AI infrastructure. We also explore custom silicon AI economics, robotics, export controls, and what founders and investors should be paying attention to as the compute race accelerates.

[Aaron Price Wright]

Dylan, welcome to the podcast.

[Dylan Patel]

Thank you for having me.

[Aaron Price Wright]

We've been trying to get you for a while. You're a busy man, but it worked out. Guido, why want to introduce why we were so excited to have Dylan on the podcast and what we're excited to discuss.

[Guido Appenzeller]

I think Dylan, you've done exceptional job in covering what's happening in the AI hardware space, AI semi space, and now more and more data center space as well. And just looking at it, currently the most valuable company on the planet is an AI semi company. Right. The I think biggest IPO so far in AI was an AI cloud company. This is currently where it's happening right. In any gold rush in the early days is the peaks and troubles that make money. And I think this is the stage that we're in. So super excited to have you here today.

[Dylan Patel]

Awesome. Thank you. Happy to talk about my favorite topics.

[Aaron Price Wright]

Amazing. Well, maybe let's start with GPT5. We just had some of the researchers for Christina and Isabella on here last week you said it was disappointing. Why don't you share your reactions or what capabilities you were hoping to see

[Dylan Patel]

or overall, I think it depends on what tier of user you are.

[Zahara]

Right.

[Dylan Patel]

If you're just using GPT5 and before you were $20 or $200 a month subscriber, you no longer have access to 4, 5, which in my Opinion is still a better pre trained model for certain things or you no longer have access to O3 which would think for 30 seconds on average, maybe, right? Whereas GPT5, even when you're using thinking, only thinks for like 5 to 10 seconds on average, right? Which is an interesting sort of phenomenon, right? But basically like GPT5 is not spending more compute per se. The model did get a little bit better on a vanilla basis, right? 40 to 5 is, is actually quite a bit better. But when you think about, you know, what is this curve of intelligence, right? It's like the more compute you spend, the better the model gets. And that's whether it's a bigger model, which GPT5 isn't, right? You can see it's not a bigger model. It's roughly the same size, you know, or you think more, right? But again, like this is something that OpenAI's first thinking models, you know, the first few generations of 0103 would think for a long time and waste a lot of tokens, if you will. And when you look at for example anthropic's thinking models, even when you put them in thinking mode, they think a lot less, right? To get to the same results or better results, right? As OpenAI was. And so OpenAI, I think like optimized a lot of like well, if I ask like I think the silliest one I had asked was like oh, three once is pork red meat or white meat and. And it thought for like 48 seconds I was like what are you doing? Like this should just like tell me the answer. And so like the nice thing is that GPT5 will think a lot less even if you select thinking manually. But more importantly, they have the sort of auto functionality, the router which lets them decide whether or not hey, do I route to the regular model? Do I route to maybe mini if you're at a rate limits or do I route to thinking, right? And how much do I think? But in general the thinking model will think less. So, so there's less compute going into a power user's average query than before.

[Guido Appenzeller]

But is it even more interesting? OpenAI can now control how much computer wants to allocate to you, right? If we're in a high load situation, maybe tune the router a little bit so it's less, right? Maybe scalp. I have no idea what they're doing behind the curtain, but there's this meme out there at the moment that basically all they did which is a meme, right? It's not true, but all they did is take all three, plus a couple of smaller models, put a router in front and offer that at a lower blended price, essentially.

[Aaron Price Wright]

Right.

[Guido Appenzeller]

I think there's a little bit of that.

[Aaron Price Wright]

Right.

[Guido Appenzeller]

Cost suddenly matters. And they figured out a way how they can steer that, I think.

[Dylan Patel]

Yeah. I mean, and they talked about how they've been able to dramatically increase their infrastructure capacity because I myself was just regularly using O3 or 4.5, right? And now I'm forced to use auto, which sometimes gives me the O3 equivalent thinking model, but sometimes gives me just the regular base model, which sucks. But like, I think for the free user, it's actually quite interesting, right? The free user was not getting thinking models pretty much ever, or not using them, or in many cases they just opened the website and asked their query. And now sometimes their query gets routed there. So sometimes they get a way better model. But now sometimes the opening. I can gracefully degrade them if they need to. Right. And I think the router points to the future of OpenAI from a business, Right. Like, you can look at sort of the model companies, right? Anthropic is fully focused on B2B, right? API code, et cetera, Right. Or Claude code, whatever it is, right? OpenAI, yes, they have that business codex and API business, but really their majority of the revenue is consumer. Right? And it's consumer subscriptions. But they have no way to upsell, you know, to make money off of all the free users. Right. In any other application, consumer app, the free user still pays via ads. But this is not compatible with AI, Right. Like, it's a helpful assistant. You can't just make the user's result worse by injecting ads. Banner ads don't really work in AI either. So it's like, how do you now monetize them? And I think with the router, they're getting really close to figuring out how to monetize that user. Right. With the new sea of applications, if you saw her product that she launched at Shopify, I think it was Shopify was an agent for shopping, right? And now this, like, immediately clicks, like, oh, if the user asks a low value query, hey, why is the sky blue? Just route them to many, right? The model can answer perfectly fine. And that is a chunk of queries, right? But if they ask, what's the best DUI lawyer near me? Right? All of a sudden this is like, you know, you're in jail, you have one shot, you're like, screw it. Let me ask ChatGPT what the best DUI law is. And now all of a sudden, the model is not capable of it today, but soon enough it'll be able to contact all the lawyers in the area and figure out what their results are and maybe search their, like, court filings and whatever. Right. Book the best lawyer for you or

[Guido Appenzeller]

an airplane, maybe negotiate a cut as part of that.

[Dylan Patel]

Yeah, of course they're going to take a cut. Right. But this is a much better way of monetizing the free user is like, you know, it's like Etsy. 10% of their traffic now comes from chat and OpenAI makes nothing off of that. But they really, really will soon. Right. And partially that's because Amazon blocks chat. But there's a way to make money from shopping decisions, whether it's booking flights or looking for items. And those. You now say free user. I don't care. I'm going to send you to my best model. I'm going to send you to agents. I'm going to spend ungodly amounts of compute on you because I can make money off of this.

[Zahara]

But.

[Dylan Patel]

But if it's a query that's like, helped me with my homework, I'll send you like a decent model.

[Guido Appenzeller]

Right.

[Dylan Patel]

I don't need to spend money on you. And so this is how I think, like, OpenAI can finally make money off of the free user. And I think that's the biggest, like, thing about the router. Right.

[Guido Appenzeller]

This is super interesting. I think this is the first time that we've seen that there's a launch of a new model where to some degree cost is the headline item. Right. I mean, so far I was always like, who has the smartest model, who has the highest MLU score. Now we have suddenly people who use models for coding for eight hours a day and surprised that if you take a large context window and the best model creates thousands of dollars of cost a month. So cost matters. And so to some degree. So where you're on the Pareto frontier between cost and performance is the new benchmark for model competitive. No longer cost alone. Is that what we're seeing here or.

[Dylan Patel]

I mean, I think definitely.

[Zahara]

Right.

[Dylan Patel]

Like OpenAI said, they doubled their rate limits for big amounts of users. They've dramatically increased the number of tokens they're serving from this launch, which effectively says this is an economic release.

[Guido Appenzeller]

Probably also means the tokens are now cheaper, right? Otherwise.

[Dylan Patel]

Yeah, yeah, for sure, for sure. I think the funniest thing is this whole cost thing you mentioned is like, we've seen this in the code space, right? Cursor had to pull away the unlimited Claude code. Initially they have this super expensive plan and it had like unlimited rates and then they were only like a weekly rate limit. Now they have like hour based rate limits. And I saw the craziest like thread on Twitter where this guy said he changed his sleep schedule, right? Modeled after like how sailors in the bay, if you're sailing you can't sleep, right? Like solo sailing. They'll take like power naps when they get to the right spots so that they can like still be safe in

[Zahara]

the morning when it's not very windy.

[Dylan Patel]

Well, but like they can't sleep uninterrupted, right? And so because Anthropic had to put rate limits that are like not just week based but like number of hours based and like he like basically sleeps multiple times a day, but small chunks just so he can maximize the usage. And there's also a leaderboard on Reddit where people are like competing to see how many tokens they're using through their subscription. And there's like a dude spending like $30,000 a month.

[Guido Appenzeller]

So I'm going to find some developer in India that I do pair programming with so I can get the day cycle, he can get the night cycle and we both can maximize together the quota for the account. Is that the future then?

[Dylan Patel]

I mean, but it's clear like people are taking advantage of the negative gross margin, like sort of subscriptions that are offered. I think Anthropic probably makes a positive gross margin off of my subscription. I don't code enough, but there's plenty of people that are definitely losing money. And so as you said, it's an

[Guido Appenzeller]

economic IT push more and more to I think just usage based pricing, right? I think if you have an underlying commodity you're reselling to some degree that has that is that large a part of your cost of goods, right? You need to go to user space pricing.

[Dylan Patel]

How much do you think the like customer capture and stickiness for these code products is? I'm curious what you think on that. Right? Once you use an ide, once you integrate one of the CLI products in like how sticky is it or is it?

[Guido Appenzeller]

That is a billion dollar question. That's a very conservative estimate. Look, Andrew Parthy has this great slide where he basically says if you're building an agentic system today, right? But fundamentally what it is is off this loop, right? Where half of the loop is the model thinking, right? And then trying to do something, the other half is then the user verifying what did the agent do? Is it the right thing, providing feedback and trying to steer it in the right direction? Because we can't run forever. Eventually you need to steer it back. One half of that is the model provider, right? They're trying to build the best model. The other half is really about, I think designing the best possible UI to enable a user to give feedback. And I think there's value in that. So I think there's a certain amount of stickiness in there. Right? So what are all the different tools like in terms of visual. Like say take code editing, right? How can I most easily visualize what the code changes are? How can I most easily visualize what they impact, which files? How can I. I for small changes get very quick feedback versus for complex ones, you don't get complex feedback. There's not some tools that actually draw diagrams for you of what they do. Right? So I think this will be the battle. I think there's stickiness in that, right? How much? Exactly. This is a quick question.

[Dylan Patel]

So in that sense like people should be doing subscriptions to get people locked in, right, Instead of moving to usage based pricing.

[Zahara]

Well, I think it's the customers that don't want to do usage based pricing because it's so hard to guarantee, it's so hard for it to get away from them. And you actually want guarantees and you're willing to commit to pretty high spend in order to not have usage based pricing. I think it's the model companies that want usage based pricing.

[Guido Appenzeller]

I think with consumers it's frankly very hard to not have usage based pricing just because the variability is so massive. Right? If it's us coding versus somebody who does this as their full time job, right? You just have a factor of 20 or so difference in usage that costs a lot of money, right? I think it's just one. I think for enterprises we could see that like more flat fee pricing because you can average it out more.

[Zahara]

You have a developer that's using it all day, you kind of know in a general sense like how many hours a day they're programming and what that

[Dylan Patel]

sort of looks like the vibe quota.

[Aaron Price Wright]

Zahara, before we leave OpenAI, I would ask a broad question, which is if Sam Altman was sitting here and saying, hey Dylan, I'll listen to anything you tell me to do, any advice you have, as long as it makes OpenAI more valuable, would you tell him?

[Dylan Patel]

I would say immediately launch a method for you to input your credit card into chat GPT and agree that for anything it like agentically does for you, it'll take X Cut and then launch that product. Because where it does shopping, right? Because like everyone knows that like Anthropic and OpenAI and all the other labs are buying RL environments of Amazon and of Shopify and of Etsy and of all the different ways to shop on the Internet of of airline websites right now just like, hey, integrate my calendar. I want to fly to there on Thursday. Make sure I don't miss a meeting. Cool book, right? Do that integration like super well. Know my preferences on whether I like aisle or window, all this stuff, right? And just take a take rate. I think this will make them so much money the moment they launch it. And I think they're working on it already. But I'd like to hear how he thinks about it because he shifted his tone massively on like ads over the last six months, right? He used to be like, no way. And now he's like, maybe, you know, there's a way to do it without harming the user. And I think this is how you monetize the free user, right? So I think that's probably what I'd tell him, slash, ask him about like a whole line of questions around this.

[Aaron Price Wright]

Well, he's coming on the podcast in a few weeks, so we'll ask him. I want to shift to Nvidia. Nvidia's having a monster year. They're up almost 70%. What are the possible paths from here? How do you see it playing out?

[Dylan Patel]

Depends like how pilled you are on like the continued growth. But I think you guys have a good vantage point. We have a good vantage point of how fast revenue is growing for a lot of these companies, especially the code companies, but even many other applications. I think we can clearly see the demand side is accelerating, right? And then if you look at the training side, I think the race is on. Meta's upping hugely. Google's upping hugely. If you just look at again, just OpenAI and Anthropic and the compute that they have and are getting this year from Google and Amazon for Anthropic and from Microsoft Core weave, Oracle for OpenAI, 30% of the chips are going to them, just those two companies. But that's actually like, okay, well like 70% of the stuff, like who's making off? Well, one third of it is like ads, right? Whether it be bytedance or Meta or many of the other people who are doing ads. So then it's still like, okay, well where are the rest of these one third of the chips coming from? Well, they're like Mostly uneconomic providers who I don't think it's like an obvious bet that they're going to, you know, keep raising bigger and bigger rounds. So what happens there? I think with the, you know, we talked about like coding, right. Like earlier actually the Quinn Coder 3 model is actually super cheap if you're running it on prem or if you're running it in the cloud with all these inference libraries. And so like there's stuff like that as well. So I think the question is like, how much does it keep growing? Because clearly I think the first third is definitely skyrocketing, right. Of OpenAI anthropic lab spend. The second third of like ads is going to grow. It's not going to grow like crazy. But I think there's definitely an inflection point that could be hit with gen ads. I know Meta has been experimenting with it a lot, but I could totally be convinced that there's going to be a huge inflection and take rate there, right? Where you start showing me personalized ads. Like every person that's an ad is like, looks like me. And I'll be like, okay, yes, except like slightly better. So I like feel better, right? And I'm like, I want to buy it.

[Guido Appenzeller]

Yeah. I have no idea how this is going to going to scale. Right. But if you ask the question, how much could it scale, right? Like how much value value are we creating here? Can we create enough value to actually keep growing for a long time? If you just take AI software development, right?

[Dylan Patel]

Yeah.

[Guido Appenzeller]

We know we can easily get about 15% more productivity out of it.

[Dylan Patel]

I don't think that's right. I think it's way higher.

[Guido Appenzeller]

No, no, with a straight like I talk to a lot of enterprises, like a classical enterprise straight up GitHub copilot deployment, that gives you about 15%. We can do much more than that.

[Dylan Patel]

But bro, you know how bad GitHub Copilot is? Like how did they, how did they look at their revenue arrow? It's so funny if you look at their revenue ARR chart, it's like quad code three months has surpassed them. Cursor easily surpassed them. And then even companies like Replit are like. And Windsurf Cognition are going to pass them. It's like you appreciate the crowd.

[Guido Appenzeller]

So look, let's assume we can get this to 100% so we can double the productivity of a developer, right? About 30 million developers worldwide, give or take. Let's say 100k value add per developer. It might be a little high worldwide. US is low, but worldwide is high. So it's $3 trillion.

[Dylan Patel]

Yeah, yeah, right.

[Guido Appenzeller]

So we're probably building technology here which adds 3 trillion of dollars of GDP value. In theory we could put that into GPUs because that's the main goal.

[Zahara]

Just from a coding model, like not just from a coding model, ignoring every other use case.

[Guido Appenzeller]

So at least in theory the value generation is here to keep growing. Right now how that translates to the industry is much more complicated.

[Dylan Patel]

I think we've already seen AI's value creation exceed sort of. There's like the whole famous like oh, 300 billion problem or 200 billion problem. Now it's 600 billion problem I'm sure. So Koi is going to put out like the one trillion dollar problem, right? Like, like there is some like reality in that of course, but you know, it ignores that like infrastructure spend today is accounting for five years of revenue, not like one. And the revenue looks like this, not like flatline. But I think, I think the main thing is that AI is already generating more value than the spend. It's that the value capture is broken. Right? Like I legitimately believe OpenAI is not even capturing 10% of the value they've created in the world already just by usage of chat. Right. And I think the same applies to Anthropic and Cursor and whoever else you're looking at. I think the value capture is really broken even internally. I think what we've been able to do with four devs in terms of automation, our spend on Gemini API is absurdly low. And yet we go through every single permit and regulatory filing around every single data center with AI and it's, and we take satellite photos of every data center and we like we're able to label our data set and then recognize what generators people are using. What like cooling towers and the construction progress and substations, all this stuff is like automated and it's only possible because of gen AI, but and we do it with like very few developers. And then like the value capture that I'm able to generate by selling this data by consulting with it is so high. But the company is making it as like, like they get nothing out of it, right? Like I think this like there's a value capture challenge here that far out exceeds the sort of creation. Right. And as you get models like GPT5 or open source models continuing to drive it down, it's like the value capture is just harder and harder and harder for these companies because they're making 50% gross margin on Inference or less in many cases.

[Guido Appenzeller]

In so many words, you're saying we're getting commoditized and therefore you can't capture the value and thus you should temper your expectations of how much you can spend on GPUs.

[Dylan Patel]

Well, no, I think, I think you can. I think there's still ways to like inflect hugely on value capture. Right. Like I mentioned, the ads are a huge value capture, but that needs to

[Zahara]

happen before, before we see a massive increase.

[Dylan Patel]

No, I think, I think the other thing is like, there's a lot of capital that's not been spent, Right. Like the hyperscalers still can grow CAPEX 20, 30% next year, right. From what they're doing this year. In addition, companies like Core Weave and Oracle, because they're tapping capital markets, can raise way more than 20 to 30% CAPEX. And then you go down the list further and it's like, oh, the largest infrastructure funds in the world, like Brookfield and Blackstone. Well, actually they're, they're turning all of their eyes to investing even more into infrastructure AI infra. And then you're like the sovereign wealth funds of the world, like the G42s or you know, the Norway one or GIC and Singapore, like these people have barely started touching AI. And so I think there's a whole lot more capex that can come without it being necessarily like economically motivated. Day one. I'm more so saying like economically motivated capex can only grow like so much, but there's so much other. Like, like, like where it's not clear from. You know, if you have a spreadsheet, you know, and you're basing it on real business that you should actually spend this much. But people will because they believe. I believe, I think you believe like infra. You know, people believe that this will be. You'll get profit out of it. But there's no like 100% certain, like, you know, way to argue it.

[Aaron Price Wright]

Yeah. How threatened at all is Nvidia by. By custom silicon?

[Dylan Patel]

I think that's the biggest thing, right, is when we look at orders from Google and from Amazon, right. Especially their. And, and Meta. Their custom silicon is not, not Microsoft. Their custom silicon kind of sucks. But the other three, they're really upping their orders massively over the last year. You know, Amazon is making millions of Trainium. Google's making millions of TPUs. TPUs clearly are like 100% utilized, right? Yeah, Trainium's not there, but I think Amazon will figure out how to do that. And Anthropic will. So, so I think, I think that's the biggest threat to Nvidia is that people figure out how to use custom silicon more broadly and this sort of becomes the sort of like if AI is concentrated, then custom silicon will do better. And that's not even talking about like OpenAI's silicon team and stuff, right? Like if AI is really concentrated, then then they'll do better custom silicon. But if it gets dispersed broadly because there's all these open source models from China and there's all these open source software libraries from, you know, Nvidia and China and it makes the deployment costs like rock bottom, then potentially hear me out here.

[Guido Appenzeller]

If, if Google TPU is, is able to compete with Nvidia in theory could do it on the, on the open market. Nvidia is worth more than Google these days. Shouldn't Google start selling their chips to everyone? I mean, in theory they should be able to achieve a higher market cap.

[Dylan Patel]

I absolutely think so. I think Google's even discussing it internally. I think it would require a big reorg of culture and a big reorg of like how Google cloud works and how the TPU team works and how the JACK software team and XLA software teams work. I totally think they could. It would just take them like shaking themselves pretty hard to be able to do it. Yeah, but I totally think Google should sell TPUs externally, not just renting, but like physically.

[Guido Appenzeller]

It's kind of funny if a side hobby in theory has a higher company value potential as you than your entire business.

[Zahara]

Especially as you think about the degradation of search for business.

[Dylan Patel]

I mean, yeah, I think, but I think like if you were to ask like Sergey, right, like hey, do you think selling chips and infrastructure is more valuable or cloud or, or Gemini, he'd be like, no, no, no, no, no. Like Gemini is going to be worth way, way, way more. It's just not yet today. Right? And so I think like, like today you say Nvidia is the most. Again, it's like a whole concentration thing, right? If the world is super concentrated in terms of customers, then Nvidia will not be the most valuable company in the world. Right? But if it gets dispersed more and more, which arguably we're starting to see, with a lot of these open source models getting better and better and better and with the ease of deploying them getting better, then you would see, I think you could argue Nvidia will remain the most valuable company in the world for a long period of time.

[Guido Appenzeller]

Historically, no pun intended, software has eaten the world in Most markets.

[Aaron Price Wright]

Right.

[Guido Appenzeller]

I mean like if you look at early networking days, Cisco was the most valuable company on the planet. Right. For a while. It's no longer. Right. The guys that build services on top like, like Google or Amazon or Meta,

[Dylan Patel]

eventually Eclipse, which is why Nvidia is like making all these software libraries. Right. Like that's, that's. And they're trying to commoditize inference. Right. Like you guys don't I think even have an inference API provider investment, do you?

[Guido Appenzeller]

Well, we have with all kinds of model providers.

[Dylan Patel]

Model providers, but I'm talking about a pure API provider investment I think. Right? Is that correct? I think I talked to one of the team members, maybe Rajko or someone about why you guys didn't invest in together or a fireworks and sort of the argument was like, well, we think just serving models alone without making them will sort of be commoditized. Yeah, right.

[Guido Appenzeller]

We have some in the stable diffusion ecosystem like with like.

[Dylan Patel]

Yeah, it's a little bit different dynamics there.

[Guido Appenzeller]

I think they tend to make much more compound models than the LLM folks, I think different.

[Dylan Patel]

But like you guys don't have one of these like, you know, base 10 or any of these like sort of like API investments because you think this is from someone on the infra team that you guys think it'll get commoditized because the software Nvidia is making because VLM and sglang, which is like open source software coming out of Berkeley and now, you know, sort of has their own environments now and supported by many. Like this being commoditized means that like API providers aren't necessarily worth a ton, right? Is sort of your argument maybe. I think that's, that's relevant to this whole thing. Which is, you know, why, right? Like why would you do this?

[Guido Appenzeller]

Shifting gears, what about the silicon startups? What's your take on those? I mean there's, there's a ton of capital flowing into that, right. We've seen not numbers, but probably billions being invested in chip startups.

[Dylan Patel]

Yeah, for sure, for sure. I mean whether you're looking at companies, I think it's pretty impressive that a few companies like Etched and Revos and a number of other companies, Maddox and others have gotten the amount of funding they've had without even launching a chip. Right. In the past, yes, silicon companies would make money or raise money, but they would at least launch a chip before they get a, you know, a big round. But like etched in Revos like have raised, you know, a lot of money without ever launching a chip. Publicly, which I think is, I mean it speaks to. Well, like, yes, silicon is super capital intensive. If you're building a chip, especially an accelerator which has so many moving pieces and there's, there's like, there's like 10 different accelerator companies out there, right, like that are newish in the last few years.

[Guido Appenzeller]

I think there's a lot more that

[Dylan Patel]

are like, yeah, yeah, yeah, that's fair. And, and then, and then there's the old guard which continues to raise money, right? Like Groen Cerebras and, and Samanova and, and Tenor and so on and so forth. Right. Like, or Graph Core getting bought out by SoftBank and SoftBank dumping money into this effort as well, right. There's, there's a lot of capital being invested to Disperel sort of Nvidia's top dollar or top position. But it becomes challenging, right? It's like, how do you beat Nvidia, right? Like the hyperscalers I think are like kind of lucky in that they can, they can do mostly the same thing as Nvidia, right.

[Guido Appenzeller]

They have a captive customer which is themselves, right. It's a huge asset and it's, they

[Dylan Patel]

can, they can just win on supply chain, right? Like I'm using cheaper providers.

[Guido Appenzeller]

It's a margin compression exercise essentially.

[Dylan Patel]

Yeah, yeah. And maybe, maybe for certain workloads like metaphor recommendation systems, they'll have a better, you know, they can specialize more. But for the most part it's like, no, we're targeting the same workloads. We can just simplify supply chain or in house a lot of it and compress margin and it'll be fine. But in the case of these other companies, it's like, well, they don't have a captive customer, so now you have to contend with, well, I'm using the same ecosystem and either I can use some custom silicon provider who's going to take a margin anyways on top of. And that's going to compress my, like what I can sell for or I can try and in house everything. But then it's like this is really hard, right? Like I'm going to do all the software design. I mean I'll do all the silicon design. I'm going to build all this different ip, I'm going to manage the supply chain on chips, on racks, on everything. Right? Ends up being a huge effort in terms of team size. All in the end, like, hey, I make a 75% gross margin. As Nvidia AMD sells their GPUs for 50% gross margin. And they have a hard time out engineering Nvidia. And they're great at engineering, right? Like they're, they. But yet they still take more silicon area, more memory to achieve the same performance. And they have to sell for less. So their margin gets compressed.

[Guido Appenzeller]

That makes sense. But look the, I think historically if you look at it, typically new entrants in markets didn't win by marginally improving on something existing. That happens sometimes, but, but more likely they jumped on some of the, some kind of disruptive technology leap. Right. Whereas we have a different approach, we have different technology. Is that possible here? I mean to some degree maybe the Zoboff is simplifying a little bit. But I think part of the reason why the transformer model won was because it runs so incredibly great on GPUs. Right? Like a recurrent neural network is similarly performant, it looks like, but it runs terribly on a gpu. So did we sort of pick the model for an architecture? And now it's hard to come up with an architecture that you know really well.

[Dylan Patel]

It's, it's hardware, software, co design, right? Like there's like, there's all this hype about neuromorphic computing, right? Like theoretically it's amazing and super efficient. It's like okay, great, like there's no ecosystem of hardware, there's no ecosystem of software. It would take like you know, tens of thousands of people who are the best AI today, focusing on that to even prove out if it's worthwhile or not. Right. On a hardware side, on a software side, on a model side. And so like you look at like Grox or Bris Samanova, they all like sort of over index to the models that were leading at the time when they designed their chips. And so they made certain trade offs, right? They put a lot more memory on chip and Nvidia was like, well, we're

[Guido Appenzeller]

not going to do that faster at least. Right?

[Dylan Patel]

Well, more like, like if you compare the amount of memory of SRAM on Nvidia's chips, it's much, much lower.

[Guido Appenzeller]

Yes, correct. They went as RAM instead of dram, but right then they usually have less dram. So there's a trade off there as well, right?

[Dylan Patel]

There's less dram, there's more sram and because there's more SRAM on the chip, you have to have less compute on the chip. And so they ended up losing, right. Because the model sizes got too big and all this. Right. And so you have this like super weird dynamic where they bet on something that was actually better Right. Like, I have no doubt that Cerebras would run certain types of models better than Nvidia or Grok or, hey, Dojo, right? Dojo runs certain. You know, in Tesla's, Dojo would run certain types of models way better than Nvidia's chips because they're optimized to that. But then it's like, oh, well, actually, even in vision tasks, you use Vision Transformers now. So it's like, okay, cool. Because model sizes grew and all these things. So it ends up being a catch 22 in that you optimize for something. And so now today you have this new age of AI. Accelerator companies are like, okay, we're going to optimize for Transformers. But the time they started designing, they're like, okay, Transformers are dense models that are this big. What's the best? The hidden dimension is 8K and your batch sizes are this big and your sequence points are this big. So let's just make a super large systolic array so you can create the maximum efficiency. And that turns out, oh, look at Deep Seq or go look at what the Labs are doing. Actually, their shapes are much smaller. Actually. You need to do a bunch of small matrix multiplies, not massive, massive, massive, singular matrix multiplies per layer. And then it ends up, oh, well, that chip you're designing for that is actually not super effective for that. And so the software is evolving constantly because of what works best on Nvidia. And you see that with, you know, whether it be what Deepseek's doing or Alibaba is doing or what the labs are doing internally. And you even see this like for Google, right? Like their open source Gemma models make different decisions because the shapes of a TPU are different than a gpu. And those, the GPU and the TPU are actually not that far apart, right. Like you would say, yes, they're very different, but like Blackwell and TPUs are very, very, they're converging on similar designs, actually. Whereas like to beat Nvidia, you can't just have the supply chain, you know, win, right? You don't have this captive customer. So now you need to do something, you know, that will give you 5x advantage, right, in hardware efficiency for a certain type of workload and then pray the workload doesn't shift, right? Because Nvidia is also optimizing their architecture generation. They've added a lot of stuff to make their model, their chips way better for the existing models. But it's like they're taking, you know, large steps Every year, every two years towards something. Whereas you have to like go way over there in left field and hope that models stay over there. Right. Because you have to win by 5x because Nvidia is going to have supply chain efficiency over you. They're going to have time to market over you in terms of like a new process node or new memory or you know, whatever, whatever technology. Right. Even AMD, right. They got to 2 nanometer before Nvidia. They had higher density HBM. They use 3D stacking all these things on supply chain that should be better than Nvidia. And yet they still lose.

[Guido Appenzeller]

They're still the software angle and Nvidia is fantastic.

[Dylan Patel]

Yeah. And then there's software as well. Right. But it's like Nvidia is going to have better networking than you. They're going to have better hbm, they're going to better process node, they're going to come to market faster, they're going to be able to ramp faster and have better negotiations with whether it's TSMC or sk, Hynix and the memory and silicon side or all the rack people or like copper cables, everything. They're going to have better cost efficiency. So you have to be like 5x better.

[Guido Appenzeller]

But to be fair, if somebody had a viable competitor which would even be marginally cost competitive, if my guess is many of the big consumers of GPUs would immediately shift some revenue there just to have a number tool. Right? Just to, just to.

[Dylan Patel]

Well, that's AMD today, right? And Microsoft somewhat. Yeah, I mean like there's still pretty

[Guido Appenzeller]

limited traction though, right?

[Dylan Patel]

Sure. But Meta continues to buy from them and Microsoft did buy a bunch and then they stopped because it's like, well, yes, they're, you know, AMD is giving you all these advantages but it ends up still not being better on a performance per watt basis. And they have a way bigger software team. They're somewhat competitive on like all these dynamics that I mentioned. Right. So you can't just like do the same thing as Nvidia. You really. And, and do it better. Right. Or try and execute better like amd. Like you have to really leap forward in some other way. But that's this. The design cycle takes so long that models will shift. Right. Because they're like, oh, what's the next generation TP and GPU look like? Okay, let's optimize for that. And the research path is, you know, like great. Like yes, neuromorphic computing could be the most optimal thing for us to do. But no one's Working on that. Because you have to advance in the tech tree you've chosen, right? If you restart the tech tree, you're going to be like, well, this sucks. And so like, if it branches this way and you're over here, you're screwed. Because you have to be. There's a mode because the supply chain stuff means that 5x actually turns into a 2 1/2x and then Nvidia can compress their margin a little bit if you're actually competitive. And then that two and a half X becomes like a 50% better. And then. Yeah, so it's like it ends up being way too difficult to. And the software stuff, right, Everything like takes your 5x and makes it like, oh, you're actually only 50% better.

[Zahara]

And defense supply chain, for sure.

[Dylan Patel]

Yeah, defense supply chain. And then like, they get, they get that, right? Like, so it's like. And Lutnick himself said we had to do this for rare earth minerals. I was like, interesting. China. There's like provinces in China that have like, rules that say the H20 is not efficient enough to be deployed. Which is like super bizarre because it's clearly the best AI chip China has. Huawei is still a little bit behind.

[Zahara]

Well, what's interesting is that, you know, efficiency is just not. Is so much less of an issue in China than here because they just have the power infrastructure to be able to support. So even if they're running less powerful chips, you know, you would imagine that it doesn't really matter because China has just such an infinite supply, infinite supply of power that, you know, they'd sort of be okay with it.

[Dylan Patel]

So it's interesting, which is, it's a big challenge in America, right? Like, there have been, there have been companies that were like, they would, they've like, you know, Jensen keeps saying he couldn't give away H20 in America for free, but I've literally like heard companies like, say, like now say, like, yeah, no, I mean, I wouldn't because, like, I only have this much power. How am I going, you know, in data centers ready to go over the next year? If I bought an H20, I'd literally have less compute capacity and then I'd lose. Right. Even if it was free, like, it doesn't make sense. Whereas China doesn't care. They can build these things. They have the muscle. I'm curious how this all shakes out. You know, China's posturing really hard. They even like, put out something. I was like, we're investigating to see if there's backdoors in the H20, it's like there's no backdoor in the age 20. Like, chill, you know, it's like, you know, GPU. GPUs are usually like firewalled from the public Internet anyways. Like you, you step through stuff before you get to the GPU clusters. So like a back wall, back door wouldn't even matter. I don't know. I think, I think it'll be, it'll be interesting to see because China can definitely deploy way, way, way more power to AI the moment they decide to. Um, but there's these, like, there's like competing interests, right?

[Zahara]

Like, because they want Huawei to be better than Nvidia.

[Dylan Patel]

Yeah. And then this is how Nvidia argued to the administration. They're like, if we don't do this. Actually, I think it's like a very like, powerful argument that like, like, for example, within Triton, which is a common ML library anyway, like, like ByteDance has open sourced some stuff that plugs into this that is like super awesome. And there's like all these other libraries. It's not just models that China open sources. That's like software for Nvidia that Chinese companies open source in a sense. Like By Nvidia Selling GPUs is Nvidia's argument again, like, was like they were able to, you know, stop Huawei from building up a software ecosystem and the Western ecosystem is better. But then in the flip side, it's like, again, if you believe the models deliver more economic value to society than the hardware, which. Which I actually think they do. It's just there's a value capture problem today then you're giving China way more by giving them H20s and soon a version of Blackwell that's cut down like, like Trump said. Right. Versus. Versus, you know, selling on the chips. Right. The economic value derived from selling the chips is not as large as, you know, being able to somehow sell them AI services.

[Zahara]

So is. Is China gatekeeping power for AI?

[Dylan Patel]

I don't think so. I think, I think again, like, there's a lot of, like, what we see is that like, even with H20 being sold to China into China and future versions of the chip, the H20E and other chips, we still see like Chinese companies like Alibaba renting GPUs outside of China because the GPUs they can get outside of China are just so much better on a dollar spend per performance basis, renting them or even like going through sort of like a Singaporean company that is effectively a Chinese company. And building data centers and putting chips in them. So it's like I don't think China's limiting the power per se, it's that it's, you know, you can only if you can spend like Chinese companies are growing their capex way more than US companies on a percentage basis next year. The dollar, absolute dollar number is, you know, obviously the US companies are spending more still on AI the percentage basis. Chinese companies are growing more next year and you still have the problem of like well dollars spend to AI output in tokens or in whatever is going to be lower because these chips are worse. So power is not the gating factor. It's always capital, right? At least today, right now, China can spend a lot more capital if they wanted to. They're subsidizing the semiconductor industry to the tune of like 150, $200 billion a year through SOEs, through capex. That's not generating revenue, etc. So it's not like they couldn't do this to the AI ecosystem, right. Given you know, Meta's CapEx is like 60 billion, right. And Google's CapEx is like 80 billion, right. Like they could totally spend way more than that on a single effort. They just haven't decided to. And I just think for the US our build outs are constrained by power, right? Like Google has a ton of TPU's sitting waiting for data centers to be powered and ready as does matter with GPUs. Right. We posted about how Meta is now building these like effectively Tense.

[Guido Appenzeller]

Is this to some degree also coupled to their unwillingness to sell them to a broader ecosystem? I mean if they want to be confined in their own data centers and they're, you know, didn't ramp data center build out for their own hyper, for their own sort of hypersecular use cases quickly enough, right. Then yes, that constrains them, right. If, if they were on the open market, will we still be constrained?

[Dylan Patel]

Yeah, yeah, for sure. Because like companies like Core Weave, you know, why is Core we valuable is really because they build infrastructure really fast, right. And their software is nice I think but like a lot of their customers are bare muddle, right? Just, just replace the GPUs whenever they're broken and network they glue more aggressively

[Guido Appenzeller]

and I think they'll go anywhere. Jensen likes them as well to be fair.

[Dylan Patel]

Yeah, they'll go. Yeah, yeah, that's very important as well. But they'll like go because it's unconcentrates the ecosystem which is better for Nvidia. Right.

[Guido Appenzeller]

Having worked at Intel. I know exactly what's going through his mind. Yeah.

[Dylan Patel]

So I think what's really important is that like Core Weave doesn't care, right. They're like, oh, crypto data center. I will convert it to AI Data Center. Right. They bought a company for like $10 billion that's doing crypto mining, which is worth like $2 billion like a couple years ago. And it's not because their bitcoin mining business is growing, it's because they have power data centers, Right? Like anywhere and everywhere people are trying to build power data centers and companies like, like Core Weave and Oracle are moving to the. Actually today Google just didn't bought 8% of a crypto mining company called Terawulf. Right.

[Zahara]

Not because they're getting into crypto mining.

[Dylan Patel]

No, because they need the data center, they need the power. Right. And it's like all the hyperscalers have like said screw off to my sustainability pledges because they need power as fast as possible. Right. They're, you know, they're, they're doing things that are, not that they take a little bit longer to move the ship, but like even if you didn't do it in your own self built data centers, there's still a lot of challenges in the open market. There's a deficit. Right. And that's, that's constraining American chip build outs heavily. Yes. Others could maybe do it a little bit faster like Core Weave or others. Right. Oracle's got an open mind as well, but it's still constraining us build outs heavily even even though the capital has been spent. Right. The chips are, you know, 60 to 80% of the cost of the cluster depending on what chips you're getting. So it's like they've already bought the chips, they just can't put them anywhere because the data centers aren't ready. This applies to Google, applies to Microsoft, applies to Meta, applies to a lot of folks.

[Zahara]

I mean it's really hard to build infrastructure, power infrastructure in the US So

[Dylan Patel]

power grid, interconnections, transmission substations, all of this stuff like, like electrical contractors, electricians in Texas, if you're willing to like be a travel electrician. It's like, it's like oil pay, right? Like it used to be that like if you're physically adept, you could go make, you know, 100 grand in West Texas, but like who the fuck wants to do that now? It's like, well you could, you could go like 200 miles away from Dallas and what's still a reasonable town and, and build A data center and work on the wiring within the data center and all this other stuff, the transmission stuff. And your pay is up like 2x now versus what it was just a few years ago. This labor problem is a challenge too. And it's, it's. Yeah, I think in China they don't have any of these problems but they just haven't spent the capital yet. But capital is an issue as well because of the scale of what's being spent. Right. Like, like Nvidia's revenue this year is going to be like over $200 billion and next year expects over 300 billion plus. Google is going to spend like $50 billion on TPU data centers. Right. And it's like, and Amazon's going to spend tons and tons on trainium data centers. It's like the scale of dollars is quickly growing to nation state level stuff. And what's more important is being able to decide to spend the dollars and what's cost effective. And so to some extent China's still constrained by that. But they can smuggle chips in, they can build data centers outside of China, they can rent data centers outside of China and be more and have the most cost effective, you know, Blackwell chips or whatever. Right. ByteDance is, you know, either the biggest or the second biggest customer of Google Cloud for a reason. Right. And they're getting, you know, over, you know, they're getting many, many Blackwell from them. Right. And the same with Oracle and the same with Microsoft and all these other companies are renting tons of chips to China anyways because it's more cost effective to do that than build it yourself. So it's not like China has this mentality where we only have to. Well, the government does, but the infrastructure Companies don't like Alibaba, 10cent, ByteDance, etc.

[Guido Appenzeller]

So what's the end game for data centers? I mean we need more power, we need more cooling. Will at the end be every. All data centers would be next to a nuclear reactor or lots of solar, next to deep level, like deep seawater that we use for cooling or something like that. Or what's.

[Dylan Patel]

I think that cooling is the physical cooling of a data center. There's this whole narrative about oh, AI uses so much power and it's not really, you know, farming. Alfalfa uses like 100x the water of, of AI data centers. Even by the end of the decade it'll be the same and it's like alfalfa is like worth very little. So it's like anyway, there's like, it's like cooling is like, not that, you know, people have like experimented with like you know, undersea data centers to reduce

[Guido Appenzeller]

the cooling cost, but that doesn't make sense.

[Dylan Patel]

It's like 5, 10% savings. But then like if you want to

[Guido Appenzeller]

get the water out of the ocean then, then to put the data center into the ocean, I think it's like

[Dylan Patel]

if you want to service it, like you're screwed. Right. So like the same with power. It's like we talk a lot about like the power is not actually that expensive. It's just hard to build. Right.

[Guido Appenzeller]

Hard to get to the right place

[Dylan Patel]

and to get to the right space and convert it down to the voltages and all this stuff that chips need.

[Zahara]

So it's less the magnitude of power and more where it is and how it moves.

[Dylan Patel]

Well, the magnitude too. Right. Like it's going to be in terms

[Guido Appenzeller]

of total worldwide energy consumption, AI data density is still. Yeah, it's a fraction of a percent.

[Dylan Patel]

It's not even by the end of the decade, you know, the US will be like 10% of our power will be a data centers, which is still like electricity of our electricity.

[Guido Appenzeller]

And in terms of energy that's even a smaller fraction, right?

[Dylan Patel]

Oh yeah, yeah. Because you think about shifting to electric

[Guido Appenzeller]

vehicles also, you can probably make a bigger swing than, than you know, with all the AI data centers we can build.

[Dylan Patel]

But outside like it's like in Europe, like that number's not moving up that fast. And like all these other countries, I think we need to build a lot more power. But it's not like some, some crazy, crazy like amount. It's just like doing it properly is the hard thing. And, and, and again like the cost of power. Like you go look at like these deals people are signing, they're still signing like even though the prices skyrocketed from like a few cents a kilowatt hour for these massive, massive purchases to like 10. It's still, you know, when you think about the full TCL of a cluster, you know, the GPU cost of networking, all of that stuff far outstrips the power.

[Guido Appenzeller]

Yeah.

[Dylan Patel]

And same with cooling.

[Guido Appenzeller]

What percentage is power from like if you do a four year AM, a GPU data center, what percentage will be power?

[Dylan Patel]

80% of the cost of a GPU data center if you're building Blackwell is capital. Yeah, right. It's the GPU purchases, it's the networking, it's the, it's the physical data center conversion, power conversion equipment. All of this stuff is like 80% of the cost. And then 20% is going to be your land and your power and your cooling and your cooling towers and your backup power and your generators and all this stuff. It's like nothing. Which is why it doesn't matter if you spend, you know, 10% or 50% more on that. Because at the end of the day the expensive thing, right, like this is why what Elon did would seem silly, right? They spent a lot more money on, you know, generators outside the data center and these mobile chillers to cool the water down for their liquid cooling instead of like the more cost effective option because it got the data center up three months faster. And so like that three months of additional training time is worth way, way, way more on a TCO basis. Right? The performance you got out of the chips and the time to market and all this is way, way faster. And therefore it was the right decision. Even though this part of the data center ballooned at cost, everything else is still there and you're still paying for the chips. And if they were sitting idle, it's not worth it, right?

[Zahara]

Just by like bypassing the grid, bypassing anything to do with interconnect, anything to do with public utilities.

[Dylan Patel]

Exactly, exactly.

[Guido Appenzeller]

What's your take on Intel? Where is intel going?

[Dylan Patel]

I think the world, well, the US needs Intel. I think the world needs intel because like Samsung is doing worse than intel on leading edge process development, in my opinion, based on, even on various customers in the industry having done test chips at like intel versus Samsung. They think, I think industry generally agrees that intel is further along, you know, the sort of the 2 nanometer class process technology than Samsung is. But both are way behind tsmc and TSMC is, is a monopoly in some extent. The number one question always people ask is like, why is TSMC not making more money? Why are they only raising prices, you know, next year, you know, 3 to 10% depending on what it is. It's like TSMC is a monopoly. Like they could raise a lot more, but they're, they're good Taiwanese people rather than like dirty American capitalists. If TSMC was owned by or was managed by Americans, I think most ownership is actually American in terms of the stock markets on the New York Stock Exchange and all this. Like, you know, they would have raised prices a lot more. And so like there is this like difficult, difficult thing to be done that like, hey, there's one island that controls all leading edge semiconductors and not just all leading edge, like the majority of trailing edge production as well. Yeah, something needs to be done. Intel is behind, but not like, not like absurdly so, right. Like if something were to happen to Taiwan, intel would have the most advanced technology in the world. Right. It's just, it's not economic.

[Guido Appenzeller]

Can you keep intel as one company if you want. I want them to be competitive.

[Dylan Patel]

I think the process of splitting it would take so much executive time and so much executive effort that you would have been bankrupt by then. Right. And that's the big challenge. Like I think intel should be separate, right? But to properly split the company and for all the management time that's needed, it was like absurd. And instead like what you need is like you need Lip Bhutan who's the CEO, who's CEO of Intel. You know, there's a lot of drama going around about him because he's, he's one of the greatest semiconductor investors ever, right. He's invested in so many different companies. First, you know, he was on the board of like smic, which is China's tsmc effectively, which is like a big like drama or like the big, the, some of the biggest tool companies in China is the first investor in them because you know, it was a multipolar world there and he was making good investments. But like, you know, now like people are getting mad about that. But it's like no, he, he, he recognizes he, he, the companies like he understands the supply chain. He needs to not spend his time on splitting the company because then he never actually fixes the company. Right. Intel's problem is that like it takes them five to six years to go from design to shipping the product, in some cases more. And when they tape out a chip, right, like you know, you send the design to the fab. The fab brings back the chip. They go through 14 revisions in some cases. Whereas like the rest of the industry goes through like one to three right. Revisions if they're good of like send the design in, get the chip back, test it, send the design in, right. For a public launch and they'll launch a chip in three years.

[Guido Appenzeller]

So but if you look at intel today, right. They still don't have a competitive entry on the AI side and they won't.

[Aaron Price Wright]

Right.

[Guido Appenzeller]

Can you. So but what does it mean for their offering? I mean they're still doing great on CPUs. They don't have a good AI chip product. Is it long term sustainable positioning. Right. I mean as a standalone chip company,

[Dylan Patel]

IBM still makes more money every launch off of mainframes. So it's not like, it's not like x86 is dead. It's like you don't get the growth rates but like you could totally run this as a very profitable enterprise. And I think the same with PCs, right? There's, you know, there's some turmoil, there's some ARM entry, there's some AMD competition but like, I think it's a very, it can be a very profitable business if it had like one third the people or half the people working on it. And so like Lip Bhutan, to fix intel needs to go into both the design company and lay off a shitload of people but like keep all the good people and make sure that they're designing fast and they're launching from design conception to launches, two to three years, not five to six. And, and that's on the design side and make that profitable. And then on the fabs he has to do the same thing. There's all these people. Like one of the heads of fab automation at Intel, I explicitly told Lip Bhutan because, you know, we have a couple ex intel people who are actually good in the company that worked on the fab side and we're like, they were like, who's the worst people and friends. It's like, oh, this guy sucks. Explicitly told Lip Bhutan he had never talked to the guy because it was like four layers down. The company has like absurd amounts of hierarchy. It's like four layers down. He goes and talks to the guy and he's out, right? It's like, like he figures out like who's bad, right, and who's good and he has to go in and he's still like, hey, the vast majority of the team at intel is the one who led the world in production and process technology for 20 years, right? But there's a lot of like built up crap. So he has to go figure this out, right? He can't waste his time on like, oh, all this like structuring to split. Like I think it would be better if the company split. I just don't think he can spend the time to do that. And if the design side of the company is, you know, you're not really going to get into AI, you're not really going to, you have to make some money there. But the fabs I think could truly become a competitor. But they're going to go bankrupt by the time anything, you know, can happen. So they have to figure out how to get capital. So he has to figure out how to get capital, he has to figure out how to clean up all the crap, make the, you know, yields go up, right? Make the product ship way faster. Like all of These things are basically,

[Guido Appenzeller]

I think the goals are completely correct. I mean, I think the big challenge, just reflecting back on my time there, right. I think the big challenge is that right now if you look at intel, right, they have essentially software sort of the chip design making and then there's of, you know, the core manufacturing part, right. And they have three very different cultures and it's very hard to get everything under one umbrella, right. And so I think that is the big challenge.

[Dylan Patel]

I think you get you should run the company separately, right, like, but like you can't physically separate them entity wise because it's going to take so long to sever all these things because he doesn't have time, right. Like intel is literally going to go bankrupt if they don't have a big cash infusion or they lay off like half the company, right? Which some could argue you need to lay off like 30% of the company anyways. But there's a lot of bad things that happen if that happens, right. And they need to spend a lot more on building the next generation fad, even if they fix the fab and they don't have money for that, right. So there's like, there's like a lot more, more important problems than like physically separating the company. Even though I think long term, yes, the fab has to be separate from the chips design software, right? Like our chip design part of the company. Just like that's going to make each company much more accountable, be able to service their customers better, etc. It's just, that's going to take too long and they're going to go bankrupt by then.

[Guido Appenzeller]

Awesome.

[Dylan Patel]

But I think, I think, I hope, I hope, I pray someone does something right, like you get a big capital infusion. I don't know, the big hyperscalers are like muscled into like, oh, okay, wait, if TSMC eventually grows their margin to 75% because of the monopoly, plus they intake all this stuff like CO package optics and power delivery and all this, like all of a sudden the cost is going to spike. So we should actually just throw $5 billion at intel each, right? Screw it. And that could actually give intel enough of a lifeline to potentially get to something and maybe be competitive. That's the hope.

[Aaron Price Wright]

Can we finish by finishing this game that we started when we gave Sam Altman advice if Jensen was here. What advice would you have from.

[Dylan Patel]

Hmm. If Jensen was here? You know, I think, I think he has a massive, massive balance sheet, right? Jensen does. He's, he's cash free. Cash flow is like you know, ridiculous. The tax cut, the new, the new Trump, you know, tax bill Institute, something really incredible which is that you can depreciate all of the GPU cluster cost in year one, which we put out like a note about how like the tax implications to like meta are like $10 billion a year and across each of the major hyperscalers it's like massive. It's like, well, Nvidia is going to spend tons and tons of cash or they're going to spend like, you know, tens of billions of dollars of taxes. Why don't you get into the infrastructure game somehow? Now this is obviously going to be like crazy because like now they're buying their own GPUs and putting them in data centers and doing stuff and they're competing with their own customers, but they're already doing that anyways because their customers are trying to make chips. But they should like accelerate the data center ecosystem with investments, right? Because really we think we can have very high degree of accuracy on what they're going to do next year in terms of revenue because it's just the number of data center watts that are being built, right? This is harder thing to shift up and down. Now there's a little bit of share difference between how much is TPU versus gpu. But it's like you have to accelerate the infrastructure and you need to spend all of this capital that you're building, right? Like, okay, do you want to go the route of doing buybacks and dividends? Great, you're a loser if you do that, right. You can make more money by reinvesting and building a bigger company that's not just chips into the ecosystem or servers into the ecosystem, but actually controlling the infrastructure end to end somehow. So I think there's something he could do there with this massive war chest and there's a reason like Nvidia's done some buybacks and they've done some dividends and increasing, but the cash on their balance sheet keeps growing and they're going to have north of $100 billion of cash on their balance sheet by the end of this year, I think. So it's like, what are you going to do with that? I think, I think there's something moving into the infrastructure layer. Much more that they could do. If he really wants to be the king of the world, right, Which I think he does as Sergey and Cinder, who I think I think they should open up the kimono on TPU's, right, like start selling them, open up the software, open source a lot more of the XLA software because there's open XLA and there's xla but the vast majority is closed source. Really, really open up the Komodo on that and be a lot more aggressive. Right. They're still pretty not aggressive on data centers. They're pretty not aggressive on a lot of elements of the company. The TPU team's next gen designs are pretty non aggressive partially because a lot of the TPU team has left to go to OpenAI. The best people that I knew, it was actually really annoying. I knew like four people or five people and they all went to OpenAI and it's like fuck, like now I don't get as much. I met some other people. Right. But it's like, you know, I think they could be a lot more aggressive in many ways across the company. They don't have to be right, but they could because AI, you know, like this ChatGPT take rate. The shift of search queries, the monetizable ones, especially from to to purchasing agents is going to really screw Google long term if they don't, you know, get their act together. I think they've gotten their act together on DeepMind. There's still some inefficiencies but Sergey works on, works within DeepMind a lot and they're driving hard. They're still a little bit behind but like I think like physical infrastructure, TPU and how much money they could make and how much they could take the wind out of everyone else's sails if they start selling TPU's externally and reorg around like building data centers much faster so that they do have the most compute in the world. Because they did. But now there's certain companies that are going to surpass them potentially over the next few years if they don't really get their act together. So I think that's what I would say for them.

[Zahara]

Yeah.

[Dylan Patel]

And also like, like learn how to ship product.

[Aaron Price Wright]

Zuck.

[Dylan Patel]

I think, I think Zuck. You know, it remains to be seen what goes on with superintelligence but like they're trying to move super fast with the data centers. You know, like screw it, we'll build tents instead of like physical data centers because we only need these for five years anyways. You know, the superintelligence moves. You could, you could say whatever you want but like you know, trying to buy like thinky for like 30 billion or SSI for 30 billion didn't work out. So then they spent you know, not even that much on hiring, not 30 billion on hiring all these people. So I think that he recognizes the urgency with the models, with the infrastructure. So I really think he needs to like, you know, if you read his website post about like AI, like, I think, you know, he sees the vision, right? There's the wearables, there's integrating AI into that, there's being your AI assistant to do all this purchasing and stuff. I think he sees the vision, but I think he also needs to focus on like actually like releasing that faster. But also like the products that they do outside of their core ip every time they launch, something is kind of middle, right? You know, Meta Reality Labs is doing well, but I think they should like go more explicit, like have a chat GPT competitor, have a Claude, like Claude code competitor, like just start releasing way more products because they're really just focused on their individual gardens rather than like branching outside of it.

[Aaron Price Wright]

Do you think Apple should have that same sense of urgency or if Tim Cook was here, what would you tell them?

[Dylan Patel]

The funny thing is like some of their best AI people are now like at Super Intelligence, they're building an AI accelerator. They're going to, they're, they have AI models, but they're just like way slower. They did mention on the last earnings call they're going to allocate more capital this, but it's like, guys, Apple, like you guys are going to lose the boat if you do not spend like 50, $100 billion on infrastructure.

[Guido Appenzeller]

You don't think the Grand Siri will cut it?

[Dylan Patel]

I think, I think like more and more you'll see people like, you know, great Apple has this walled garden, but like they could only do so much to protect it, right? Idfa, like they shut down ads to, or data sharing to Meta, but Meta made better models and now they have way more data and way more power over the user than they ever did before. Kind of it was good that Meta kicked the crutch off of them or Apple did. But the same applies to like AI. Like yes, they have access to the text and they have access to this. But like I think other people are going to be able to integrate user data and agents will be able to integrate all this user data and they'll start to lose control of what the user experience is as more and more gets disintermediated by AI being interfaced rather than touch, rather than, you know, touchpad and keyboard. And I don't think they've truly realized what happens when the interface to computing is, is AI. Like they, they market it but like that's going to shift computing really heavily. They have great hardware and their hardware teams are working on awesome stuff and form factors. But like I just don't know if they get what is actually going to happen to the world in the next five years. Truly well enough and they're not building fast enough for it.

[Aaron Price Wright]

What about Microsoft to that end?

[Dylan Patel]

Microsoft has the same problem. I think they were super aggressive in 23 and 24 and then they pulled back heavily. Right now OpenAI is slipping through their grasps. There's that whole thing there. They cut back on data center investments heavily. They were going to be the largest infrastructure company in the world by like a factor of 2x which would have been, you know, you could argue maybe that was like too much and maybe it wouldn't have been economical. But like they're losing grasp on OpenAI. Their internal model efforts are failing spectacularly. Like they're on LLM arena right now and they're pretty decent there. But it's like that's just like a sycophantic model. Like it's a code name but like whatever. Like Mai is like failing. Azure is like losing a lot of share to Oracle and Core Weave and Google and so on and so forth. Right. Their internal chip effort is by far the worst of any hyperscaler. Like they're just like misexecuting like GitHub. How is GitHub not the highest ARR software code model?

[Guido Appenzeller]

They only had the best IDE, the best source code repository, the best enterprise, Salesforce, the best model company as a relationship and they were the first to market.

[Aaron Price Wright]

Right.

[Guido Appenzeller]

So they had everything going for them

[Dylan Patel]

and like there's just nothing. Right. It's like, like GitHub Copilot is failing. Microsoft Copilot is like still crap. Right? Like yeah, it's unusable. It's like what, what is going like you know what is going on? Like you need to shake the crap out of the company. Like I think they win a lot because they have the best business to business relationship with so many enterprises that's

[Guido Appenzeller]

like worse on the planet.

[Dylan Patel]

Yeah. But like they end up like not having the actual product to sell them which is like really scary. So they need to really work on product. Yeah, Satya has done great on sales and stuff but like, yeah, if Elon

[Aaron Price Wright]

was here, what advice would you give him?

[Dylan Patel]

A lot of people at XAI are mad about the porn models. Like porn, porn stuff. It's fine. Like you're going to make a ton of money off of this. This is how you accelerate the revenue of that company. But like he's, he's losing a lot of talent and axing a lot of good projects. But Elon has a, is a magnet to amazing talent and building stuff, so I won't bet against him. But it seems like since he left the administration and focused on stuff again. But I think, I don't know, I think he's focused on a lot of things and I think like Robo Taxi starting to look good actually. Again, like, I haven't written one yet, but I have some friends who've ridden one. It's like, looks pretty decent. He could like not make these snap decisions, which often are the reason why he's amazing. But like some of these snap decisions are hurting him. Yeah, I'm not sure if I can give Elon that much great advice because I think maybe it's just like get off Twitter and focus on like the products again. Right. More. But he is working on that stuff a lot.

[Aaron Price Wright]

Yeah, I think that might be a good place to wrap.

[Guido Appenzeller]

Awesome.

[Aaron Price Wright]

This is a great discussion. Dylan. Thanks so much for joining us.

[Dylan Patel]

Thank you for having me.

[Zahara]

Thank you.

[Podcast Host]

Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to, like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on x1.6Z and subscribe to our [e-mail] thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.