Is AI a Bubble? | Gavin Baker on Data Centers, GPUs, and the AI Economy

Summary

Podcast Summary: The a16z Show

Episode: "Is AI a Bubble? | Gavin Baker on Data Centers, GPUs, and the AI Economy"

Host: David George (a16z)
Guest: Gavin Baker (Managing Partner & CIO, Atreides Management)
Date: July 14, 2026

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Episode Overview

This episode delves deep into the popular question facing investors, founders, and technologists: Is AI in a bubble? David George and guest Gavin Baker compare today's "AI boom" to past tech infrastructure cycles (notably the dot-com and telecom bubbles), examine where value is accumulating in the AI stack (chips, models, applications), and discuss the real economic durability of AI investments. Baker offers perspective from his years as a tech investor, including firsthand experience in previous bubbles, and gives candid views on data center build-outs, GPU economics, business model disruption, and where he sees opportunity (and caution) across hardware, software, and consumer AI.

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Key Discussion Points and Insights

1. Are We in an AI Bubble?

• [00:00, 03:53]

  • Gavin Baker strongly asserts: “I do not believe we're in an AI bubble today.”
  • Compares the 2000 telecom bubble (marked by overbuilt "dark fiber") to today, when “there are no dark GPUs”. All available AI infrastructure is heavily utilized, in stark contrast to unlit telecom lines in 2000.
  • Contrasts valuations: Cisco traded at 150-180x earnings at the bubble peak; Nvidia is closer to 40x—much more reasonable and sustainable.

  Memorable Quote:

  “At the peak of the bubble, 97% of the fiber that had been laid in America was dark. Contrast that with today. There are no dark GPUs.”
  — Gavin Baker [03:53]

• [05:53]

  • The internet required building a two-sided network (sites AND users); AI is being adopted much faster due to cloud distribution and API accessibility.

2. Data Center Hype vs. Real Utilization

• [02:45]

  • US data centers represent $1 trillion in value, set to grow by $3-4 trillion. Recent data center spend (last three years) exceeds, in inflation-adjusted dollars, the total cost of building the Interstate Highway System over 40 years.
  • Google saw a 150x increase in tokens processed in 17 months—clear proof of real usage, not just speculation.

• [07:17]

  • Large tech companies funding AI infrastructure (Google, Meta, Amazon, Microsoft, etc.) are uniquely well-capitalized and see this spend as existential:

    “Larry Page apparently internally said I'm happy to go bankrupt rather than lose this race.”
    — Gavin Baker [07:17]

3. Round-Tripping Concerns & Nvidia's Position

• [07:34]

  • Minor “round tripping” exists (e.g. Nvidia investing in buyers of its own chips), but it’s not at an alarming scale and is seen as strategically rational due to the competitive landscape—especially against Google with TPUs.

  Memorable Quote:

  “Jensen [Huang] is one of the two best CEOs, along with Elon, I have ever known. And I think he’s playing a strong hand really well.”
  — Gavin Baker [10:11]

4. The Model Layer and Market Structure

• [10:38]

  • Baker emphasizes humility in predicting winners at the application layer—citing that at the comparable stage in the Internet’s history, Google had not been founded, nor Facebook or Uber.
  • Advantages in AI may favor incumbents more than previous tech shifts:

    "The raw ingredients of kind of data, the capital to buy compute, and distribution... All of today's biggest tech companies have all of those in spades."
    — Gavin Baker [11:29]

• [12:36]

  • ChatGPT’s launch felt like "Pearl Harbor" for Google, prompting a slow but significant response.

5. AI Business Models: Gross Margins, SaaS, and Application Layer

• [13:10, 14:43]

  • Gross margins at AI model labs are structurally lower than classic SaaS:

    “It’s going to be a long time before we see a truly kind of an AI lab, a frontier lab with gross margins anywhere near SaaS or Internet era margins.”
    — Gavin Baker [13:10]

• AI SaaS companies need to embrace lower gross margins as a sign of active product usage and market fit, rather than seeing it as a weakness or failure.

  "Don’t be scared and look at declining gross margins kind of as a mark of success rather than a badge of shame or something to be feared."
  — Gavin Baker [15:36]

• [17:00]

  • Investors’ perception is shifting; high margins can actually signal a lack of product usage.

6. Consumer AI, Browsers, and Platform Power

• [18:53]

  • Google’s dominance via Chrome could allow it to overtake AI-native browsers if the company acts.
  • AI browser companies may have moved too soon; major incumbents with scale and distribution (like Google) could quickly subsume their efforts.

• [20:42]

  • The importance of user scale and data is accentuated by new AI training paradigms; the algorithm and product improvement flywheel is starting to manifest in the AI space.

• [22:00]

  • Open source AI models in China could help US competitors keep up with the leading labs.

7. Chips, TPUs, and Market Structure

• [23:03]
  • The real fight is Nvidia vs. Google’s TPUs, with AMD and Broadcom potentially playing important “second source” and networking roles.
  • The complexity of AI hardware:
    • Nvidia is now more than a chip company (“now arguably a data center level company”).
    • Broadcom offers open Ethernet fabric to challenge Nvidia’s closed ecosystem.
    • Expect cancellations of high-profile ASIC programs, especially if Google sells TPUs externally.

8. Business Model Shifts and Outcome-Based Pricing

• [25:53, 27:05]

  • Major disruption opportunities arise when a platform shift is coupled with a business model shift—for example, in customer service, pricing can move to “pay for outcome.”
  • Bullish on outcome-based AI service models, especially where outcomes are easy to verify (support, code resolution, etc).
  • Affiliation and referral fees will likely become prevalent in consumer applications.
  • Some inefficiency in customer acquisition will be squeezed out (Google’s ad model will gradually give way to more outcome-based fees).

  Notable Moment:

  “Why did Google never start a Marketplace? Because people overvalue systematically their ability, once they've acquired a customer through Google, to keep it as an organic customer.”
  — Gavin Baker [28:18]

9. On Timelines: AGI and Robotics

• [29:39]

  • Optimistic about the arrival of AGI (as per Karpathy’s “10 years” comment) but views this as not skeptical at all:

    “Are you kidding? That's wild. Yeah. Sign me up. Shorter timelines, please.”
    — Gavin Baker [29:39]

• [29:48]

  • Robotics is very real and will be “Tesla versus the Chinese,” just like in electric vehicles. Humanoid platforms will likely win for their ability to learn from video/human demonstration.

  Memorable Quote:

  “There's a giant debate, is it going to be humanoids or not humanoids? I think that debate is over because humanoids can learn from watching YouTube videos.”
  — Gavin Baker [30:02]

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Notable Quotes & Timestamps

• "There are no dark GPUs." — Gavin Baker [03:53]
• "It is definitionally impossible, given what we just discussed, to succeed in AI without gross margin pressure." — Gavin Baker [15:20]
• “Jensen is one of the two best CEOs, along with Elon, I have ever known.” — Gavin Baker [10:11]
• "Pearl Harbor for Google." — Gavin Baker, on ChatGPT’s launch [12:36]
• "I think the round tripping concerns are pretty overblown... What Nvidia really needs is for Meta to get their act together, or another American open source player to emerge." — Gavin Baker [08:03]
• "The prize is clearly really big. I see Martin is skeptical... I'm saying there's a chance!" — Gavin Baker [18:21]
• "Work would become optional." — Gavin Baker (channeling Elon Musk) [28:18]
• "Every roboticist I know is extremely impressed [by Tesla Optimus]." — Gavin Baker [30:02]

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Timestamps for Important Segments

• Are we in a bubble? [00:00–05:53]
• Data center and GPU spending [02:45–07:09]
• Round-tripping and competitive landscape [07:34–10:11]
• AI application/model layer and humble forecasting [10:38–13:10]
• Gross margin discussion, SaaS, and software [13:10–18:53]
• Consumer AI, distribution, Chrome, and browsers [18:53–22:56]
• Chips: Nvidia vs. Google, AMD/Broadcom roles [23:03–25:53]
• Business model shifts, outcomes, and affiliate models [25:53–29:39]
• Futurism: AGI, robotics, and who will win [29:39–30:40]

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Overall Tone & Takeaways

Gavin Baker brings a thoughtful, pragmatic, “pattern recognition” lens to the AI economy—skeptical only of shallow analogies to prior bubbles, and notably bullish (though cautious) on the fundamentals of current AI investment. The discussion is rich with industry anecdotes and grounded quantitative context, blending history and present-day dynamics. For founders, technical operators, and investors, the episode offers a framework for where value might actually accrue in the AI stack, why current market signals are (so far) healthy, and sober reminders to focus on real usage, business model innovation, and the unique strengths of today’s tech giants.

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This summary covers the core content, arguments, and memorable moments of the episode for listeners seeking in-depth understanding without replaying the full conversation.

Transcript

[David George]

Are we in an AI bubble?

[Gavin Baker]

I do not believe we're in an AI bubble today. I was depending on how you look at it, the privilege and the misfortune of being a tech investor during the year 2000 bubble, which was really a telecom bubble. And I think it's really helpful to compare and contrast today to the year 2000. The year 2000 Internet bubble, or telecom bubble, was defined by something called dark fiber. At the peak, 97% of the fiber that had been laid was dark. Contrast that with today, there are no dark GPUs.

[Podcast Host/Announcer]

The headlines change. The underlying questions don't. As AI investment continues to reshape the technology landscape, founders and investors are still grappling with the same core issues. Are we building too much infrastructure? How durable are today's economics? And where will the long term value accrue? David George sits down with Atreides Management's Gavin Baker to unpack the AI boom. From GPUs and data centers to frontier models, software, and robotics. Whether you're an investor, founder, or simply trying to understand where AI is headed, this conversation offers a thoughtful framework for thinking about one of the greatest technology shifts in decades.

And that brings us to our opening Fireside Chat. We're going to start with a taboo question right out of the gate. Are you ready for it? If AI is the biggest trend in the world right now, where is the evidence for it? Why is it only just beginning to show up in the economy? And as Andrej Karpathy asked, are agents really just ghosts? To kick this off and to help us answer this question, please join us in welcoming Gavin Baker, Managing partner and CIO of Atreides. Now, some of you may know Gavin as that really thoughtful guy on Twitter. Anytime some big piece of AI news comes out, I know more than a few people who count on Gavin to explain why what the F is really going on. So a huge thank you to Gavin for being with us today. Joining him is our very own David George, general partner at A16Z.

[Gavin Baker]

Who knows what that music was from.

[David George]

Glad they got our pump up music right?

[Gavin Baker]

Yes. Battlestar galactica. The original 1977 one. In case we have to all fight Cylons in a few years.

[David George]

Yeah, good segue into the topic, I guess. So thank you for being here. I always love talking to you.

[Gavin Baker]

Same. Really grateful to you for inviting me. Grateful to your colleagues for having me here. I really look forward to the next two days. I think I'm gonna learn a lot, so thank you.

[David George]

Yeah. Okay. All right, so the big topic is AI bubble, kind of macro view of things. So maybe just to start with a couple stats to set the stage and then I want to get your take on where we're at. So we have about a trillion dollars of data centers in the US. The plan is to add 3 to $4 trillion in the next five years. Over the past three years, we have already built out in data center capacity a larger amount of dollars than the entire US interstate highway system, which took 40 years, just in terms of dollars. And that's inflation adjusted. OpenAI alone, I think has more than a trillion dollars of deals set up that they've committed to and we can talk about that, but at the same time. So those are all like big numbers on infrastructure and they're scary and they say, oh, bubble. And Google released a stat recently that they have seen a 150x increase in the amount of tokens processed in the last 17 months. So on the one hand you've got this crazy scary sounding build out, on the other hand you actually have a bunch of usage that's happening. So are we in an AI bubble?

[Gavin Baker]

I do not believe we're in an AI bubble today. I had, depending on how you look at it, the privilege and the misfortune of being a tech investor during the year 2000 bubble, which was really a telecom bubble. And I think it's really helpful to compare and contrast today to the year 2000. First, I think Cisco peaked at 150 or 180 times trailing earnings. Nvidia's at more like 40 times. So valuations are very different. Most important, however, is that the year 2000 Internet bubble or telecom bubble was defined by something called dark fiber. And if you're a veteran of the year 2000, you'll know what that was. But dark fiber was literally fiber that was laid down in the ground and not lit up. Fiber is useless unless you have the optics and switches and routers that you need on either side. So I vividly remember companies like Level 3 or Global Crossing or WorldCom would come in and they say, we laid 200,000 miles of dark fiber this quarter. This is so amazing. The Internet's gonna be so big, we can't wait to light these up. At the peak of the bubble, 97% of the fiber that had been laid in America was dark. Contrast that with today. There are no dark GPUs. All you have to do is read any technical paper. And one of the biggest problems in a training run is that gpu' melting. And there's a very simple way to kind of cut to the heart of all of this. It is the return on invested capital of the biggest spenders on GPUs who are all public. And those companies since they ramped up CAPEX have seen, call it a 10 point increase in their ROICs. So thus far the ROI on all the spending has been really positive. It's an interesting and open debate about whether or not it will continue to be positive with the quantum of spend we're going to have on Blackwell. I personally think it will. But there's no debate that thus far the ROI on AI has been really positive and valuation wise we're just not in a bubble.

[David George]

I couldn't agree more. The other thing that I would say is you can contrast the actual adoption and usage of the technology from then. Right. The Internet was actually really hard because you had to build a two sided network like you had to build websites and then you had to get users and it's much more difficult. And in the case of the AI tools, all you have to do is kind of light them up via API or turn on your website chatgpt and everybody has access to them. Right. Built on top of cloud computing on top of the Internet and you can get to instant distribution a billion people right away.

[Gavin Baker]

Absolutely.

[David George]

So the other thing is the counterparty, so you mentioned this, they happen to be the best companies in the history of the world. Right. I think collectively the people who are coming out of pocket the writing checks for this Capex, I think they collectively generate like $300 billion of free cash flow a year, is that right? Something directionally round numbers. Yeah. And they have $500 billion of cash on the balance sheet. So whenever people are like oh my God, it's a bubble, is it going to pop? I'm like, I think it's kind of fine. I mean it costs like 40 or $50 billion to light up one gigawatt.

[Gavin Baker]

Yeah. If you're on Nvidia chips.

[David George]

On Nvidia chips, yeah. Yeah. So you know there's kind of like an $800 billion buffer growing. $300 billion every year.

[Podcast Host/Announcer]

Yeah.

[Gavin Baker]

I mean free cash flow at some of them has begun to maybe.

[David George]

Well this goes to your point on return on invested capital.

[Gavin Baker]

We should see that next year. Down a little bit, a little bit of a mismatch of the build out. But Larry Page apparently internally said I'm happy to go bankrupt rather than lose this race. And I think that is the mentality for sure at Google and perhaps Meta, it's just seen as Existential. And you have to win.

[David George]

Okay, so lots has been written about these round tripping deals because round tripping is a very scary concept from the Internet build out. That was a big problem. What do you make of it?

[Gavin Baker]

Here it is objectively happening. Money is fungible. So Nvidia, if they sign a deal with OpenAI, they can say, hey, you can't use our money to buy our chips. But money is fungible, but it's happening at a very small scale. Yes, yeah. And I think, I know this is

[David George]

like a crypto or blockchain.

[Gavin Baker]

Exactly, yeah. And I think what is driving this isn't the need to finance GPU or data center purchases, but it's actually competitive dynamics. So Nvidia's biggest competitor, it's not amd, it's not Broadcom, it's certainly not Marvell, it's not intel, it's Google and more specifically it is Google because Google owns the TPU chip. And this is by far maybe perhaps today the only alternative to Nvidia for training and maybe the best inference alternative. And Google's a problematic competitor because they also own a company called DeepMind and they have a product called Gemini. And I think you could argue that they are the leading AI company today. I think they've taken 15 or 20 points of traffic share in the last two or three months. And that's just traffic to Gemini. It does not include search overviews. I suspect on a actual traffic basis, Google is bigger than OpenAI, anthropic anyone today. And that business is going to run on TPUs. And then we have three other labs that are relevant today. There's Anthropic and that's in Amazon and Google Captive. You know, Anthropic is really going to run on TPUs and trainiums. And so you're left with Xai and OpenAI at the forefront. And if Google is going to a lab like Anthropic and saying I'm going to help you fundraise and give you chips for competitive reasons, it's very hard for Nvidia not to respond. And as Jensen said, he thinks it's going to be a good investment. So I think the round tripping concerns are pretty overblown. Yeah, I mean what Nvidia really needs is they need Meta to get their act together or another American open source player to emerge or maybe some sort of detente with China and AI.

[David George]

Yeah, when people ask me about Nvidia and all the moves and the round tripping, my reaction is everything they've done is completely rational, 100% rational. Yeah, long term.

[Gavin Baker]

Yeah, sure, long term.

[David George]

Things they do may not have as high of a return on capital as other things, but strategically I think they're all kind of the right moves.

[Gavin Baker]

Jensen is one of the two best CEOs, along with Elon I have ever known. And I think he's playing a strong hand really well.

[David George]

Yeah. All right, so you started getting into the model companies. Let's just talk about the model so we can come back to chips and memory and networking, because I want to get your take on that. But, you know, since we're on the model side, what do you think happens with market structure? Who wins where? Who are you most optimistic about? Where do you have concerns?

[Gavin Baker]

So I think humility is an important virtue for an investor. And I'm just. If we're going to make an analogy and say that ChatGPT is to AI as Netscape Navigator was to the Internet, at this point in the Internet boom, Google had not been founded. Mark Zuckerberg was in middle school, Travis Kalanick was in kindergarten. So it's just very early. So I think it's important to be humble about making high confidence predictions at the application layer. It's one reason I think the infrastructure layer is often maybe a safe place to be at the beginning of one of these new technology waves.

[David George]

We'll actually talk about the role they play at the infrastructure layer because there's a piece of them that obviously they serve as an infrastructure layer powering other application providers and then they also have their own applications. So I think I would draw the distinction.

[Gavin Baker]

Yeah, I mean, that's most true of Google, but I think it's hard to have high conviction other than to observe the Internet was a very disruptive innovation. I think there's reasonable arguments that AI could be a sustaining innovation because the raw ingredients of kind of data, the capital to buy computer and distribution, which is what you need. All of today's biggest tech companies have all of those in spades. So as long as they execute well, hire good people and have a sound strategy, I think you could see it be a sustaining innovation for a lot of members of the MAG 7. On the other hand, I do think it's existential. And if you don't execute, IBM might be a good fate.

[David George]

Yeah, yeah, that's.

[Gavin Baker]

That's tough. Yeah.

[David George]

Data distribution, compute dollars, talent.

[Gavin Baker]

Yeah. And like have every right to win.

[David George]

Yeah, they have every right to win. And it seems now more than before, they're taking it quite seriously. Yeah, maybe Google in particular, but oh, no, no. Obviously MET is making the dramatic moves they're making too.

[Gavin Baker]

No, to me, ChatGPT was Pearl harbor for Google and we're going to see how they responded and they're slowly starting to respond.

[David George]

Yeah. And then what do you think? What's your forecast for that? Sort of the platform piece of their business, infrastructure piece. What do you think? How do you think it shakes out in terms of like business model, market structure? So do you think they end up as high margin businesses like the Clouds or like aircraft manufacturers? Or do you think they end up very competitive in low margin businesses like airlines?

[Gavin Baker]

I don't think they'll be airlines, but anybody can just look at the P and l of a SaaS company circa 2021 and 2022 and you see 80, 90% gross margins. The nature of AI because of scaling laws. Richard Sutton's the Bitter Lesson. They're just more compute intensive, so their gross margins are structurally going to be lower. But that doesn't mean they can't be great businesses. I think it's going to be a long time before we see a truly kind of an AI lab, a frontier lab with gross margins anywhere near SaaS or Internet era margins. Now their opex can be a lot lower and maybe that's how you square it, but just the gross margins are fundamentally different. And until scaling laws change and the importance of test time compute, things like that change, which I don't see happening. They are going to be lower margin.

[David George]

Yeah. Okay, so let's talk about application layer. So you just kind of got into it a little bit with the SaaS businesses and I don't know if you've waded into this fight on Twitter, but it's sort of every few months it comes up and it's like SaaS is terrible and it's dead and it's all going to go away. And then with Andre's Darkesh interview he just did, it's, you know, like the market's reacting positively to it and it's like a whipsaw reaction. So what do you think happens with SaaS and software?

[Gavin Baker]

You know, I think I, you know, first said probably in early 24 that I thought all of application SaaS might be a zero different than infrastructure SaaS. I would say I have a more nuanced view now and I think there could be some really big application SaaS winners, especially if you serve like a more fragmented SMB customer base. Google is making it really easy if you're a customer of theirs to use your data and essentially make any SaaS app you want, and then your data isn't shared with anyone else. But the critical mistake that I think a lot of retailers made in dealing with Amazon is, is they looked at Amazon's margins and they said, we don't want to be in that business. And that was obviously a terrible mistake. And here we are 25 years later, and, you know, Amazon has really healthy retail margins. And I worry that application SaaS companies are trying to preserve their existing gross margin structures because they believe that if their gross margins go down, their stocks will go down. It is definitionally impossible, given what we just discussed, to succeed in AI without gross margin pressure. And I do not know why they have concerns because we have an existence proof that a software company can deal well with declining margins. In Microsoft and Adobe to. The whole AI thing came along, it used to be that companies were scared to go from on premise to the cloud because margins were lower. Cloud margins are lower, they're still good. And Microsoft, they transitioned from on premise perpetual licenses with maintenance to a cloud model, and it was a pretty good stock for 10 years. So if you're an application SaaS company, what I would just say is, don't be scared and look at declining gross margins. Kind of has a mark of success rather than a badge of shame or something to be feared.

[David George]

It's actually so funny you say that because whenever we have these discussions about companies, basically every company that comes to present to us is like, we're an AI company and we always look at the gross margins. And it's become like a badge of honor for them to actually have low gross margins because they're like, oh my God, people are actually using your AI stuff.

[Gavin Baker]

Yeah.

[David George]

But if you show up and you're like, I'm an AI company, and it's like, I got 82% gross margins. You're like, I don't think anybody's really using it. So, yeah, it's, it's interesting. Yeah. If you're, if you're one of these public companies, would you rather have like 10 bucks of revenue with 90% gross margins or 50 bucks of revenue with 60% gross margins?

[Gavin Baker]

Not hard.

[David George]

Like, it's not that. Yeah, not that complicated. It's hard to do in the public market.

[Gavin Baker]

It's hard to do in public. But if you communicate it, you draw parallels to the cloud transition. I mean, I'm an investor and I would be excited about it, you know, and I don't think I'm alone in the world. And then the big advantage, these legacy application SaaS companies have is they do have these really profitable existing businesses. And so you can run your new AI products at break even and, you know, catch up to the leaders, et cetera, et cetera. And I'm just surprised more people have not done that. Like, why are none of the public coding companies even trying to compete with Cursor? And the reality is Cursor now they have a trillion tokens and, you know, there will be a point where they have enough coding tokens that it's tough to catch them. But I think today, if you're a public coding company and you said, I'm gonna lean in, I'm gonna run at breakeven, I have an existing business, I'm going to attach it to everything. Hey, you have a chance. And, you know, the prize is clearly really big. I see Martin is skeptical.

[David George]

Martin, Martin Shigen said, you have a chance.

[Gavin Baker]

I said a chance. So he said, I said a chance.

[David George]

It's like a dumb and dumber. You're telling me there's a chance, not

[Gavin Baker]

like a real chance.

[David George]

You're telling me there's a chance. It's like, yeah, exactly. I totally agree. Yeah, we actually saw, I mean, you know, we see it. You know, we may if we, if we, you know, Figma, for example, like when they went out, they are extremely high gross margin and they're like, hey, we're going to, you know, pretty aggressively distribute our AI tools and our gross margins are going to go down. And, you know, investors asked a few clarifying questions and then they were like, oh, that actually would be a good thing. And so surprised more people in the public markets aren't doing it.

[Gavin Baker]

It worked out okay for them.

[David George]

It's working out well. Long game to play. What about on the consumer side at the application layer? So obviously Google was the portal to the Internet, is kind of still is the portal to the Internet. And the whole business model was predicated upon taking some intent and directing you to someone else's website where they would do stuff with you. It's kind of not going to be that way. It already is not that way with, with AI. Although I tried the browser today and I tried to do some pretty basic shopping stuff. And it's, you know, still, still some work to do, but I think it will get there. So what do you actually think happens with the sort of market structure of the consumer Internet companies? Do they get subsumed into a component of a chatbot interface or do you think it's something else?

[Gavin Baker]

So one, Humility. Hard to say. Two, I would just say, I think the AI companies that have launched these AI browsers may come to regret it because there's something called Chrome that has, whatever it is, 5 billion users. And if you're Google, you can just go look at what happened with Google Buzz. They are very cautious. They're currently in litigation with the government and they could easily do this and probably do it even better, but they didn't want to be first. So now you have two AI native companies with their own browsers. Let them run for three to six months, get a little head start and then, wow, here we are. We had to do this. And I don't know how that's going to work. Maybe for the companies other than Google who don't own Chrome.

[David George]

Yeah, data and distribution is pretty powerful.

[Gavin Baker]

Yeah, hindsight's 2020 and the one thing I would say is I do think it's tough to bet against the companies with large existing user bases today. And I also think reasoning has fundamentally changed the economics of these frontier models. You know, pre reasoning, I often said if you are a frontier model without access to unique valuable data and Internet scale distribution, you're the fastest depreciating asset in history. I think reasoning really changed that because the way RL works during post training, having a big user base now kind of unlocks that flywheel that was at the center of every great consumer Internet company where you have a good product, you get a lot of users, the users make the algorithm better, the algorithm makes the product better and it just spins and that it's not quite spinning yet in AI, but you can squint and see it. And so I think that fundamentally changes economics for anthropic, for xai, for OpenAI. But I mean, Mark Zuckerberg's trying hard. We'll see.

[David George]

Yeah, a lot of smart people in there now.

[Gavin Baker]

Yeah, for sure. I think the worry is, and I think this is another interesting thing is if you don't like, in a strange way, the Chinese open source model ecosystem is a godsend to any American company that's trying to catch those four leading labs. Because the problem is if you don't have Gemini 2.5 Pro or a later checkpoint of it, or a later checkpoint of GROK that we don't see, or a later GPT checkpoint training the next model, you're at a big disadvantage. By the way, one thing I just want to say that drives me crazy is all these people who say that GPT5 is the end of scaling loss, GPT5 is a smaller model. It was not Designed to be better. It was designed to be more economical for OpenAI and Microsoft to run. Any reference to GPT5 and scaling laws is crazy. Yeah, sorry. Rant, rant over.

[David George]

We got the pedestal up here if you want.

[Gavin Baker]

Yeah, exactly.

[David George]

Shaking your hand. Yeah, that'd be good. Do you want to talk about chips?

[Gavin Baker]

Sure.

[David George]

So, okay. I know you love Nvidia. Talk about your view of Nvidia, AMD, TPUs, ASICS and how do you think sort of market structure shakes out their competitive advantage that the various players have?

[Gavin Baker]

Yeah, I think it goes, I think it is really, it's a fight between Nvidia and the Google tpu. And then something that I don't think is broadly appreciated is the extent to which Broadcom and AMD are effectively going to market together. Nvidia is no longer just a semiconductor company, as I'm sure you'll hear from Jensen tomorrow. It was a semiconductor company, then a software company with cuda, now systems company with these rack level solutions and now arguably a data center level company with the level of architecting they're doing with scale up, scale across and scale out, scale across networking. So the networking, the fabric, the software, it's all important. And what Broadcom is saying to companies like Meta is, hey, we will build you a fabric that can theoretically compete with Nvidia's fabric, which is a mixture of NVLink and either InfiniBand or Ethernet. It will build it on Ethernet, it's going to be an open standard. And hey, we'll make you your version of tpu, which by the way took Google three generations to get working. And you know what, if your ASIC isn't good, you can just plug AMD right in. But I personally believe most of those asics are going to fail, particularly if

[David George]

it's in the fullness of time, like over a period of time or in

[Gavin Baker]

the fullness of time in the next three years, I think you'll see a bunch of high profile ASIC programs canceled. Especially if Google starts selling TPUs externally, which has been all over X. And then who knows exactly how that would work. Because if you're anthropic, it's just rumored, Anthropic wants to buy tens of billions of TPUs. If you're anthropic, maybe you don't want Google seeing your secret sauce, but there's ways around that. So I think this is really a battle between Google and its TPU enabled by Broadcom for now. And Google can take the TPU away from Broadcom whenever they Want now they can't do the Ethernet networking that Broadcom is doing, but they control the tpu. So it's really Google and the TPU versus Nvidia with Amazon. That's a very talented team, arguably the most talented silicon team at any hyperscaler, the Annapurna team. I think the Trainium 3 will probably be a much better chip than the Trainium 2. It took Google three generations to get the TPU right. And then AMD will always be kind of the second source. And you need a second source.

[David George]

All right, exciting. What do you think happens? Okay, so I want to go back to business models. So one of the big things that is widely discussed is like, you know, source of disruption. And Most of the CEOs in this room are CEOs of startups who are trying to go beat some incumbent or find, you know, some new market opportunity. And the most ripe opportunities tend to come when you have a big platform shift that is also accompanied with a business model shift. And so there are a couple of areas where I can see it, I feel like in an obvious way, so you know, we're investors in decagon customer support. Like you can pretty easily see a business model that is priced on the resolution of a task because it's so measurable. You can see, you know, like encoding like a lot of the business model has now shifted to consumption and you know, obviously especially for developer facing things like that's comfortable and pretty well known. What about the rest of the industry? Because I feel like there's sort of this hand wave thing that is going on which is like we're going to go get all of services but it's like, okay, so how do you actually go do that? It's going to be pretty hard. So do you have any prediction on how that plays out?

[Gavin Baker]

Well, I think what you're seeing in customer service, which is kind of like an easy first example, we have a lot of textual data, the LLMs are good at text. You can kind of probably really easily run some RL to make sure that they get a good verified reward. Verified reward being a happy customer, first call resolution or whatever it is. But I do think you will see that played out. Humans were fundamentally paid based on outcomes. And a lot of AI will be augmenting humans, but probably also replacing some humans. And that will involve being paid, paid for outcomes. You know, going back to the consumer business model, you know, everybody's talking about affiliate fees and for sure I'm going to have, you know, my own AI. It will be A version of Grok, because we're both XAI shareholders, it will be a version of Grok that knows me and it likes me. And, you know, when I. When I want to. You know, the next time I want to go on vacation, it will know the hotels that I like to go to, and it'll say, hey, three hotels. I have Gavin, you know, I have Gavin coming. Who's got the best price and the best room.

[David George]

It's going to massively upgrade the gifts that you give to Becky.

[Gavin Baker]

As Becky's in the audience, she really appreciated your Dumb and Dumber reference, I'll have you know. But, yeah, and then there will probably be some sort of affiliate fee. And again, that's just being paid for an outcome and kind of closing that loop, which will be probably a little bit of a business model degradation. Because the great. Why did Google never start a Marketplace? Because people overvalue systematically their ability, once they've acquired a customer through Google, to keep it as an organic customer. So they systematically overpay and they continue doing that. That's why Google never went to Outcomes or Marketplace, because advertising leads to the advertisers systematically overpaying, so that inefficiency will be squeezed out. But, yeah, we'll go to Outcomes. And I think Elon tweeted today that work would become optional. Like, instead of buying your vegetables at a supermarket, you can grow your own garden if you want. Now, who knows how long it takes us to get there, but that doesn't sound wildly implausible to me for how powerful this technology is. I was just struck karpathy. Whatever. Two days ago. Is being painted as a skeptic for saying AGI is 10 years away. Are you kidding?

[David George]

Insane. 10 years.

[Gavin Baker]

That's wild. Yeah. Sign me up. Shorter timelines, please.

[David George]

Yeah, well, so, okay, so. No, that's awesome. While we're on the topic of very exciting futuristic things, robotics, do you have a view on.

[Gavin Baker]

Yeah, very real. And it's going to be Tesla versus the Chinese. In the same way it's Tesla versus the Chinese in cars.

[David George]

Electric cars. Yeah.

[Gavin Baker]

I would just say cars. Not electric cars.

[David George]

Yeah, cars.

[Gavin Baker]

Yeah.

[David George]

Do you have a sense of timeline?

[Gavin Baker]

I mean, you can all watch the optimist videos. Every roboticist I know is extremely impressed. There's a giant debate, is it going to be humanoids or not humanoids? I think that debate is over because humanoids can kind of learn from watching YouTube videos. And then it's easier for a human being to put on a suit and show the robot how to do it. It's kind of crazy to watch the video of all, you know, the 50 octopus robots doing 50 different tasks and then it's very simple, you know, did you put the glass in the dishwasher correctly or not?

[David George]

This is so fun. Gavin. I always love chatting with you. Let's give a hand to Gavin.

[Gavin Baker]

Thank you. David thank you.

[Podcast Host/Announcer]

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