Ben Horowitz on AI Infrastructure, Economics and The New Laws of Software

Summary

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AI + a16z Podcast

Episode Summary: Ben Horowitz on AI Infrastructure, Economics, and the New Laws of Software

Date: May 19, 2026
Host: a16z (Alex Rampell interviewing Ben Horowitz)
Duration: ~29 minutes

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Episode Overview

This episode features a16z co-founder Ben Horowitz in conversation with general partner Alex Rampell, delving into artificial intelligence’s impact on the foundational “laws” of software development, company strategy, infrastructure, economics, and venture capital. The discussion draws connections between today’s AI revolution and past technological upheavals, exploring how incumbents and startups alike must adapt to a world where old competitive moats are eroded, bottlenecks have shifted, and the rules for both building and defending companies are fundamentally changing.

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Key Discussion Points & Insights

1. The Bottleneck Economy: Rebuilding U.S. Infrastructure (00:00–02:18, 09:45–14:26)

• AI’s Demand on Physical Resources:
  The U.S. is facing broad infrastructure shortages (rare earth minerals, electricity, memory, manufacturing), all exacerbated by rapidly increasing AI deployment. "America's gotta rebuild its entire infrastructure. ...Almost everything is the bottleneck." (A, 00:00)
• Graph Comparison – China vs. U.S.:
  China’s infrastructure and output are growing at a steeper curve than the U.S., raising concerns about capacity and global competitiveness.
• Supply Chain & Latency:
  "If you go buy a server from Dell, they're like, sorry, we don't have any RAM to sell you. ... They could build a new factory, but that would take us five years." (B, 12:20)
• Historic Parallels:
  Past technology rollouts (e.g., fiber optics) had different bottlenecks vs. today, where “almost everything is a bottleneck.” Expect chip availability to precede electricity sufficiency.
• Industry Response:
  VC is now funding basic power infrastructure, like transformers, as a result: "We invested in a transformer company. Not like an AI transformer, like an actual power transformer company..." (A, 11:45)
• Vertical Demand for AI Capacity:
  "The demand for these tokens is straight vertical. But the ability to build that capacity is absolutely not vertical." (A, 11:45)

2. AI Disrupts The Old Laws of Software (02:18–04:33)

• Mythical Man Month Overturned:
  Historically, you couldn’t “buy your way out” of software problems – hiring more engineers didn’t guarantee faster progress ("Nine women can't have a baby in a month."). Now, with sufficient GPUs and good data, progress can be purchased.

  “That rule no longer holds. With enough GPUs and the right data, companies can now compress years of development into weeks.” (C, 00:34; A, 02:29)

• Erosion of Moats:
  Traditional software moats—customer lock-in, data, UI—are being quickly dissolved. It’s now easy to replicate code or transfer data, and AIs themselves are flexible with interfaces.

  "In Software, possession is 9/10 of the law... Those are pretty much gone." (A, 02:29)

• Resulting Pressure on Businesses:
  Products and pricing are under “tremendous pressure;” any captured value must be derived from distinct, defensible sources.

3. The Existential Challenge for Company Leaders (04:33–08:27)

• Compressed Timelines:
  Innovation cycles have accelerated from decades or years to possibly just weeks.

  "If you have a good product, you might have 10 years to run... now it might be like five weeks." (B, 04:33)

• CEO Dilemmas:
  CEOs of pre-AI or legacy companies face harsh market skepticism and must be radically honest about what value they possess.
• SaaS ‘Apocalypse’:
  "There are doubts on terminal value" (B, 05:05), making valuation and remaining as a standalone company much riskier.
• Nuanced Company Survival:
  Some companies with niche requirements (e.g., travel company Navon needing explicit airline partnerships) still maintain strength despite market pessimism. Survival is now highly context-dependent.
• Key Quote:

  "If you keep looking at it like the old world and it's got completely different laws of physics, you are definitely going to die." (A, 08:26)

4. From Features to Companies: Definitional Confusion in Startups (08:27–09:24)

• Blurred Lines:
  Creating features or products is easier (and faster), but distinction between a feature, a product, or a company is more confusing than ever.
• Quote:

  “My favorite saying, the best companies have hostages, not customers. Even get some of the data out of the hostage company.” (B, 09:15)

5. Venture Capital’s Shifting Model (09:24–11:42, 19:56–24:06)

• Massive Capital Influx:
  a16z’s evolution from raising $300 million in its first fund (2009) to $15 billion more recently demonstrates the sector’s expansion and globalization (now 35% international money).
• Why So Much Capital? New Role for VC:
  Because AI and tech require foundational infrastructure that’s missing in America; “somebody's got to fund it.”
• Future Scenarios:
  • Oligarchy: Small number of mega companies dominate (JP Morgan/Goldman analogy from Industrial Revolution).
  • Utility Scenario: Big labs become utilities, with most new business built on their platforms.
  • Entrepreneurial Explosion: AI enables billions to try entrepreneurship without “capital gate” or gatekeepers.
• AI’s Potential Impact on VC Jobs:
  The relationship/personal element survives, but only if “entrepreneur” survives as a job.

  “It's kind of hard to be a venture capitalist without some bad venture capital.” (A, 20:44)

6. AI + Crypto: The Infrastructure of Trust (14:26–19:46)

• Credibility Crisis:
  AI can now impersonate humans perfectly, rendering communication channels (email, phone, even Zoom calls) potentially untrustworthy.
• AI as Economic Actors:
  How can an AI have an identity, act as an economic entity, or receive payments? Legacy commerce (credit cards, banks) won’t suffice for non-humans.
• Role of Crypto:
  • Proving Humanity: New urgent need for cryptographic proof of human-ness and identity (signing content, proof of originality).
  • Payment Rails: Bearer internet money (crypto as the solution) may become the medium for AI-to-AI and AI-to-human commerce.
  • Fraud Prevention: During past government stimulus, huge sums stolen due to lack of individual digital addresses/identities; crypto infrastructure could fix this.
  • Source of Truth: Blockchain as trusted arbiter vs. relying on tech giants or governments.
• Notable Quote:

  "Don't believe anything from me unless it's got my cryptographic key on it, all that kind of thing... The only way is you're gonna have to have some cryptographically strong indication, a signed piece of content..." (A, 15:44)

7. Economic & Social Upside: Framing the AI Revolution (24:06–28:31)

• Historical Perspective:
  Technology always induces change and fear, but it raises standards of living and invents entirely new types of work (from farming to knowledge work).
• Universal Entrepreneurship:
  In less-developed economies, everyone is “an entrepreneur” by necessity; AI lets anyone worldwide instantly prototype app ideas, music, or movies with no “gate.”
• Key Quote:

  "Now 8 billion people that might have an idea in their head can get it out of their head. And maybe it's a bad idea. It probably is a bad idea. But there is no longer a gate for them." (B, 24:31)

• John Maynard Keynes’ Prediction Rebutted:
  Humans continuously invent new needs, so abundance never leads to idle leisure as Keynes forecasted.
• Optimistic Prediction:

  "I think in 15 years, ... everybody in America and probably around the world is going to live better than the very best life ... than anybody did in 1980." (A, 27:27)

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Notable Quotes & Memorable Moments

• On Throwing Money at Software:

  “You can throw money at the problem. ...If you have enough money and some good data, you can buy enough GPUs and solve basically anything in software. So that's gone.”
  — Ben Horowitz (A), 02:29

• On Moats & Defensibility:

  “…[T]hose are pretty much gone… So then what is it? Where is your value? What are you delivering?”
  — Ben Horowitz (A), 02:29

• On VC and Infrastructure:

  "We invested in a transformer company. Not like an AI transformer, like an actual power transformer company..."
  — Ben Horowitz (A), 11:45

• On Crypto-AI Synergy:

  "First is just, are you a human or are you a bot? I think everybody is going to really, really want to know that."
  — Ben Horowitz (A), 15:44

• On Fear & Progress:

  "...the history of technology is things have always gotten better... Humans are kind of unbelievable in their ability to come up with new things that they need."
  — Ben Horowitz (A), 25:20 & 27:50

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Important Timestamps

• 00:00–02:18: State of U.S. infrastructure and the “bottleneck economy”
• 02:19–04:33: AI breaks the rules: old laws of software no longer apply
• 04:33–08:27: CEO existential dilemma in a fast-moving, AI-first world
• 09:24–11:42: The evolution of a16z and venture capital for infrastructure
• 11:42–14:26: The supply chain & historic bottleneck parallels
• 14:26–19:46: The AI-crypto intersection: trust, identity, and payments
• 19:56–24:06: The future of VC—consolidation, utilities, or entrepreneurial democratization?
• 24:06–28:31: AI’s social impact, historical comparisons, optimistic future outlook

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Tone and Takeaways

The conversation is both pragmatic and optimistic, acknowledging real dislocations and risks (infrastructure, fraud, decreased moats) but ultimately expressing confidence in human adaptability, entrepreneurship, and technological progress. The speakers use candid language, historical analogy, and humor (“Bernie Sanders interviews Claude: old man yells at cloud!”) to ground the discussion, while maintaining an investor’s forward-looking perspective.

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For listeners seeking a deeper understanding of how AI is rewriting the rules for business, infrastructure, security, and venture capital, this dialogue delivers a nuanced, high-level overview with practical insights and plenty of food for thought.

Transcript

[A]

America's gotta rebuild its entire infrastructure. Like right now, we don't have enough rare earth minerals, we don't have enough electricity, we don't have enough manufacturing capacity. Nvidia will make enough chips, but then we won't have enough memory. Almost everything is the bottleneck.

[B]

The China graph is like this and the US graph is like that. How do we make this seem less scary?

[A]

The history of technology is things have always gotten better. Humans are kind of unbelievable in their ability to come up with new things that they need.

[B]

Now. 8 billion people that might have an idea in their head can get it out of their head. Head.

[A]

I do think what's going to happen

[C]

is for 50 years, one rule in technology was almost sacred. You cannot buy your way out of a software problem. Hire a thousand engineers and you still won't catch a faster competitor. Fred Brooks called it the mythical man month. And every engineering leader believed it. That rule no longer holds. With enough GPUs and the right data, companies can now compress years of development into weeks. But a disruption cuts both ways. The same forces that let startups move faster are dissolving the moats that protected incumbents, customer lock in proprietary data, switching costs, all eroding at once. So in a world where the old defenses no longer work, what actually makes a company worth building, funding, or keeping? Ben Horowitz, co founder and general partner at a16Z, speaks with a16Z general partner Alex Rampel at FinTech Connect conference in Deer Valley.

[B]

So you've been doing this for a long time and I thought maybe I'd start off and it's funny, we actually didn't rehearse this at all because I thought that way would be more real. Right. More unique. But let's talk about. You have this book where you talked about how hard it is to be a CEO and everything that you went through at loudcloud and opsware, that was a giant shift where it's like the market kind of collapsed, the financial market collapsed and you had a really pivot and just change the company. And there are new age companies that are popping up right now. AI first, it's like they hopefully have their shit together. They're off to the races, building something new. But like a legacy company or five or ten years ago where there's this great opportunity but also great challenge. What does a 5 or 10 year old CEO do where it's like they're pre AI.

[A]

Yeah, they gotta figure out what they do. Financial markets hate them.

[B]

Yes, yes. So there's the financial markets Ha.

[A]

Doesn't matter who you are.

[B]

Yes. So I don't know, maybe. Hey, riff on that. I'd love to hear your thoughts.

[A]

Yeah, well, I think the first thing you have to recognize in a kind of huge dislocation like this is some very basic axiomatic, like laws of physics are different. And the two that are really different with AI compared to how companies have been built in kind of technology forever is one. It used to be very well known that you cannot throw money at the problem. So for example, if I had a product and I was two years behind, I could not hire a thousand engineers and catch my competitor like it's a mythical man month. Nine women can't have a baby in a month. Everybody knows that it never works. No problem. That's no longer true. You can throw money at the problem. If you have enough money and some good data, you can buy enough GPUs and solve basically anything in software. So that's gone. The second thing that we knew for sure is like in Software, possession is 9/10 of the law. So if you have the customer, you have multiple lock ins, you, you have the migration pane lock in, you've got the data lock in, you've got the user interface lock in. Those are pretty much gone, right? So it's very easy to replicate the code, it's very easy to move the data. And then it's not even going to be a human talking to your software, it's going to be an AI. And AIs are really flexible on how they use user interfaces. So that mode is gone. So I think that's just like the first thing you have to recognize as a CEO that like, okay, that's going away. So then what is it? Where is your value? What are you delivering? And it turns out there are many things that are of value, but if you're trying to get good pricing through any of those things, you're going to be under tremendous pressure. Your price has to be a function of some other value that's much more distinct that you provide.

[B]

Got it. And the other thing that we've talked about this a lot internally as a firm is that once upon a time, like, if you have a good product, you might have 10 years to run with that product, maybe five years, and now it might be like five weeks. Well, we also talk about this like in terms of going public. So companies are staying private a lot longer, which probably is good. If you're going through an existential crisis, you'd much rather do that as a private company. Than a public company. But also the reason why the SaaS apocalypse is happening is because there are doubts on terminal value.

[A]

Yeah, right.

[B]

So everybody who starts a company, they're doing it because they want to create economic value. They're capitalists. They're trying to actually benefit from this equation financially. But if you wait too long, maybe your company is worth zero. That's kind of scary. And that was always a risk, but it would play out over decades.

[A]

Yeah, it's not as fast a risk.

[B]

So I guess if you were. I mean, Loud Cloud's around today, you're the CEO. And again, bad example. Sorry, sorry to give you very scary. I know.

[A]

Although actually Loud Cloud would be actually data.

[B]

Yeah, exactly. You would be very well positioned. But I guess what is it that a CEO should do? Potentially differently. I mean, obviously move faster, cut faster, be more efficient, throw money at them, like all these things that we've talked about. But it's like, shit, if I go public and I get disrupted, then I have this terrible life of I'm going to be a penny stock. If I just wait, there's this chance that I get eviscerated. And this kind of like roadkiller success equation is kind of scary, right? I mean, it's always scary, but you would have a. You would have time. And now it feels like you don't.

[A]

Yeah, I think you do have to be honest with yourself on, like, what it is you have really. And like, there are companies that get thrown under the bus correctly and ones that don't. And then if you take a lot of these ideas to their logical conclusion, then you know, nothing is worth anything because there are no people at companies. And if there are no people, who's going to buy your shitty software? So, da, da, da, da. But like, it is more like subtle and it just tends to take much longer than, than we think for some of these things to play out. So then the question is, are you getting stronger in that meanwhile, or are you degenerating? So is what's happening nobody's buying, like, the money just shifted. The customers are buying other stuff, they're not buying yours. In that case, you have a huge problem. You probably have to cut deeply and pivot. On the other hand, look, there's companies that have been slaughtered in the valuation game but are pretty strong. So I'm on the board of this company, Navon, right? Like their travel. So obviously the SaaS apocalypse, like, they're dead. No way you're doing travel. But then you look under the covers and you go well, it actually is a little more complicated than that because on travel like you actually need explicit relationships to. If I'm providing your travel and you're any kind of company that's important at all, you need to travel globally. So now I need a relationship with every single airline in the world, every single hotel in the world, every train, everything. You got to deal with that. You've got to kind of connect back to their budgeting systems and all these things. And then the second thing that's like nobody wants to do, including OpenAI or Anthropic, is sell to the damn travel manager. Like nobody has a channel to the travel manager. It's not something. And you can't even imagine that being a good idea. You want to keep advancing. You want to kind of do the things that Intuit is doing where like, okay, like turn ourselves into more of an AI company and then kind of hold the customer. And by the way, like the AI, like the agentic travel experience turns out to be much more complicated than one would think. And I don't know if it stays that way, but that's the way it is today. So I think it's very company dependent. Like I don't think it's all one thing, but I do think brave new world. And if you keep looking at it like the old world and it's got completely different laws of physics, you are definitely going to die.

[B]

Yeah, well, maybe. Let's talk about venture capital.

[A]

There's a lot of cope going on now too. So like you got to be careful with that.

[B]

Well, that's the thing. It's like there are some things that really are features. And before it would take a long time to build a feature. So you might as well, you know, it's comparative advantage. David Ricardo I could weld my own seal, I could grow my own food, but I'm just going to not do that because I can do things that actually produce more economic value for me. But now it's just becoming not that hard to go create features. But features are not products or not companies. And we've always had this distinction. There's feature, product, company, but it's a little bit confusing figuring out which one is which right now. Just because the ability to create a feature and create a product and even get all of the data. My favorite saying, the best companies have hostages, not customers. Even get some of the data out of the hostage company.

[A]

Yeah.

[B]

So it's a very, very confusing world in terms of figuring out which one is which is kind of maybe a good segue to venture capital land.

[A]

Yeah.

[B]

Of how do you think. I mean, when you started this firm in 2009, big financial crisis, actually. Very, very big financial crisis. Global financial crisis going on. The biggest. The world has changed a lot since then. I mean, how much of what's happening today kind of fits within the mental model of back then and how much is kind of brave new world? Maybe riff on that a little bit.

[A]

Yeah. So it's really different. So our first fund was $300 million. And we raised it from all the traditional kind of LPs, endowments, you know, charitable foundations, et cetera, fund of funds. We just raised $15 billion. Four of the seven funds. Four of the seven funds. So like, not even the whole complex. And you know, we raised it from very, very different kinds of investors. So we, you know, like basically none of our LP base was international when we started. And we're at like 35% international money. And it's from all kinds of places. So. And just tech has gotten more. Like tech has gotten so much more important. I think that, you know, we have to think in terms of the world in a way that we just didn't think. So, for example, like with. Why did you raise so much money? Which by the way, I'm kind of mad at myself. Cause I don't even think I articulated internally well enough. Cause we could have raised even more money next time. Don't worry. We had more money on the table. But the way I was thinking about it is, look, America's gotta rebuild its entire infrastructure right now because we don't have enough, you know, we don't have enough rare earth minerals. We don't have enough electricity. We don't have enough manufacturing capacity. We have the wrong chips. Like they take way too much damn power. They were built for games. We don't have enough anything kind of to be in this future world. And somebody's got to fund it. And clearly that's going to take a lot of money. So all that is brand new. And I would say it's fairly overwhelming in a sense, but it is really, really important. We're pretty much out of electricity down in the United States. Like not. Not 12 months from now. Like right now.

[B]

The China graph is like this, and the US graph is like that.

[A]

Yeah. And the demand for these tokens is straight vertical. But the ability to kind of build that capacity is absolutely not vertical. So we need new everywhere. I mean, like, we need. We invested in a transformer company. Not like an AI transformer, like an actual power transformer. Company because you need kind of better. Easier to manufacture. Manufacture more efficient transformers. And the transformer hasn't changed since. Really. We invent electricity. So like, these kinds of things?

[B]

Well, I guess. How so? There's an old saying, the cure for high prices is high prices. But the problem is there's a lot of latency involved. So right now there are computers that show up with no ram. Like, if you go buy a server from Dell, they're like, sorry, we don't have any RAM to sell you. Because all of that has been gobbled up. Because, yeah, they could build a new factory. Or you and I could decide to go build a DRAM factory. That would take us five years.

[A]

Yep.

[B]

So how do you. I mean. And we don't believe.

[A]

You gotta start now.

[B]

Yeah, you gotta start now. But this is. This is actually, if you remember, which you obviously do, 1999. It's like, well, we have to build more fiber, right? We have to build more capacity. But it's obviously very different because all the GPUs are hot. They're all lit right now. And back then, most of the fiber was dark. Yes, but how do you get like.

[A]

Yeah, well, there were bottlenecks when we were building fiber. The bottlenecks were kind of in different places. So, you know, we, like, the servers weren't capable of putting like, bits out even fast enough to do video. Right. And like, this software was really. We didn't have load balancers, we didn't have application service, we didn't have anything. And so you had all this fiber and all this bandwidth, but, like, you couldn't actually build the applications. And then most of the end users weren't. It's a network too. So, you know, people weren't connected on the other end, so it just didn't work. And then we had the dot com crash and all these things. So now we're in a little different place. Cause almost everything is a bottleneck. I do think what's gonna happen is like, we'll probably have enough chips long before we have enough electricity. So Nvidia will make enough chips, but then we won't have enough memory and we won't have enough electricity. So we're in that kind of. So I think you really have to study where we are at each point in the supply chain and figure out how to alleviate those bottlenecks. And by the way, God bless Elon the Tarifab. That's the idea. He's going to just go deal with all the bottlenecks himself, which is how he does things. Which is why we need him.

[B]

Indeed. So I feel like you're an expert in three things. Hip hop, AI and crypto. And I don't know anything about hip hop, but I, you know, I've gone to your, your. I've heard a lot from you, but let's talk about the other two.

[A]

Yeah.

[B]

In particular crypto and AI. So I actually just wrote about this. I mean, you remember the origins of crypto was hash cash.

[A]

Yeah, yeah, yeah, yeah.

[B]

And the scariest thing right now from my perspective is that everybody with Claude or with ChatGPT can actually, like go super deep and personalize a phone call, an email. Like, it seems like all communication is going to be completely unusable. Yep. I don't know if you agree with me. I 100%, because it's like, normally I can just delete, delete. I get some email yesterday. Dear Alan at Index Ventures, it's like, well, I'm not Alan. I don't work in Index Ventures. Delete.

[A]

Yeah.

[B]

I'm very grateful that this person messed up my name because I could just delete that.

[A]

Yeah.

[B]

Whereas if I get a thousand emails, like, the best way of thinking about an email inbox is it's a to do list that has write access for the public.

[A]

Yeah.

[B]

Right. It's like anybody can get in. And now anybody can personalize. Same thing for phone calls. Like, what do we do? And then it seems like there's a lot behind crypto. That's why I mentioned hashcash, because it was originally intended to stop spam.

[A]

Yeah.

[B]

So do you think there's overlap between AI and crypto? I know you do. So tell us about that.

[A]

Yeah. So I do think it starts with the problems that AI causes. And actually one of the first thing, I woke up in the middle of the night one day and I was like, oh, my God, somebody's going to go on a zoom. It's going to be AI me, and they're going to tell my finance team to wire, you know, $500 million to Nigeria. And that's going to be a problem. So, you know, and then we're like, okay, everything's hardware, root of access. Don't believe anything from me unless it's got my cryptographic key on it, all that kind of thing. So I knew these problems were coming. They're coming so fast now. So I think there's kind of several categories of things. First is just, are you a human or are you a bot? I think everybody is going to really, really want to know that. Be it, social media, a dating app, a zoom call, like, anything you want to know. Am I talking to an actual human? Like, okay, can I prove that I'm a human being? And then can I prove that I'm me? And then can I sign content? Like, how do I know it's true? There needs to be a distinction between. I get so many AI videos sent to me from my family that they think are not AI videos. And they're like, did this really happen? And I'm like, no, you could actually ask Grok. And it's pretty good at that right now. But. But I think, you know, like, Grok's getting to the point where it can barely figure it out. And I think at some point it won't be able to figure it out, or AI will not be able to tell what's an AI. So the only way is you're gonna have to have some cryptographically strong indication, assigned piece of content that says, okay, yeah, I made this. Or this is like, really a video of me, Marco Rubio, giving a speech. This isn't something that somebody faked. And then there needs to be a source of that truth. And who are you gonna trust for the truth? Are you gonna trust Google? Are you gonna trust Meta? Are you gonna trust the U.S. government? I think you wanna trust the kind theoretic properties of the blockchain. So I think that's going to be just a very, very important part of the infrastructure. And then you get into, like, fraud, and how do you know somebody's a citizen to get them money? Everybody's talking about, well, let's do ubi. Well, great. But when we did the stimulus program, we found out that the government is very bad at getting money to people. I don't know. It's like, depending on the numbers you read, it's somewhere around $450 billion got stolen. So what you really need is everybody needs an address where you can send them money. And so I think that's a crypto problem. And then finally, how does an AI become an economic actor? Like, how do I make money as an AI? How does somebody send me money? Can I be a merchant, a credit card merchant if I'm not a human? I don't think so. I think that's actually kind of. And, you know, and it's probably not the kind of right infrastructure anyway. And so you need a bearer instrument on the Internet. You need Internet money for these AIs to be economic actors. And I think that's very likely to be crypto. So. So I Think it's a. There are many opportunities in the crypto space that have been generated by AI.

[B]

Yeah, because it feels like it's this old Yogi Berra saying it's so crowded, nobody goes here anymore. Like we're kind of, we're entering that era because number one is, are you a real person? But the problem is that cowork is so good right now. Or open claw. I just say you are a real person. You were a real person, but now your addresses are being used by a machine. Right. So captchas don't make any sense. Captcha is an anachronism. What is a captcha?

[A]

Right.

[B]

So it feels like the solution lies in kind of economics somehow and game theory.

[A]

Yes. Yeah. And that too. Right, right, right. Like are you going to just have to. Well, maybe like I think has is kind of a relevant idea again.

[B]

Yeah, no, totally. So maybe why don't we talk about where you think venture capital is going? And I mentioned this because Mark got some crap for saying all the jobs will go away except for one job of venture capital, which was seen as a self serving comment. But in his defense, I will say it's partially because it's a non deterministic problem. It's like, all right, you're betting on an entrepreneur first and foremost and you want to know that this entrepreneur, as I like to say, can materialize labor capital and customers. And you can't just like, you know, run an algorithm on. I mean maybe you can, but there's just not a lot of data out there. It's very, very hard to do. So that's the logic by which. And also just personal relationships in general will probably survive AI but if there's

[A]

a venture capitalist, then that kind of assumes there's an entrepreneur job. No?

[B]

Yes, yes, that is true. It takes.

[A]

It's kind of hard to be a venture capitalist without some bad venture capital.

[B]

Yeah. If you're very bad, you could just raise money and never alloc. I guess, but I guess what do you think the world of venture capital looks like kind of today? We've obviously done a lot of things internally as a firm to try to embrace AI very, very fully. But now, five years, 10 years from now, just given what's potentially going to happen to white collar work.

[A]

Yeah, it's really tricky because you kind of go back to the last transition like this, which was kind of the transition to the industrial revolution. So the venture capitalists of the railroads and the automobiles and so forth ended up becoming JP Morgan Chase, Goldman Sachs et CETERA So they ended up becoming banks. And some of the reason for that was just how fast that materialized. So I think in the 30s, like 20% of American workers worked for the auto industry, which is like spectacular compared to what it is today. And so things in the Industrial revolution kind of started out very much like we are today in venture capital, where there were whatever, 300 auto companies and so forth, and then it consolidated very hard into, you know, in the US a big three and so forth. And then the kind of venture capitalists went upstream with the companies. I think that's one scenario where like, okay, there's going to be a small number of very gigantic companies and they're going to own everything and so forth. There's another kind of future where it's like, okay, they got really big and then we've kind of finally hit the asymptote on this intelligence idea. And like they're as smart as they're going to be or whatever. And we're either going to nationalize the big labs and their utilities, their electricity plus, plus like fu, if you're going to think you're going to collect all the money and then everybody's just going to build on this utility set of things and then that's a very different venture capital world. So I would say, you know, as I'll quote Yogi Berra, like the problem with the future, you know, the problem with predictions, they're very hard, especially about the future. And I think this future is particularly hard because it's so dynamic and it's really hard. And then like, how does the electricity shortage play into? Does it make the big companies all powerful because they suck up all the electricity and nobody else can get it and nobody else can get any GPUs or does like that push all the computing out to the edge? And then the models just get really good and small and everybody's like, well, I got enough in my phone and what they're going to charge me for their mega GPU farm is just outrageous and I'm just going to do that. So there's many ways it could go and I don't know, I guess, I don't know. But I could see venture capital being much bigger and much more exciting because everybody in the world is an entrepreneur. Or I could see it being more like what happened in the Industrial revolution and like new companies are just harder.

[B]

Yeah, well, it's kind of a good, it's a good follow up or a good parallel question which is like, how do we make this seem less Scary, because I don't know if you saw.

[A]

It's a lot of change. You know. It is.

[B]

Well, but yes and no. I mean, 98% of Americans were farmers.

[A]

Yeah.

[B]

1789.

[A]

Yeah.

[B]

I'm pretty sure they're not farmers right now. I mean, you made this interesting point where if you go to, like, a third or fourth world, if there is a such thing, country, everybody's an entrepreneur.

[A]

Yeah.

[B]

100%. Like the guy. Like, I sell bananas by buying them here and selling them there. Everybody's an entrepreneur. There are no organized companies.

[A]

Yeah.

[B]

And the cool thing is that now 8 billion people that might have an idea in their head can get it out of their head. And maybe it's a bad idea. It probably is a bad idea. But there is no longer a gate for them. There's no capital gate. There's no, like, idea. It's just like, boom. And it's not just for code. It's like, you know, I can write music. Right. I can make a movie. Like, this is super exciting. So that feels like a very, you know, we. If you're trying to make this not look dystopian, I don't know if you saw Bernie Sanders interviewing Claude. Like, this is literally. Old man yells at cloud. Like, you know, metaphor. No metaphor. Right. It's just like, he's yelling at the cloud. And, like, that's the dystopian view, and it's wrong. I feel very passionate that that's wrong. But we need a better narrative.

[A]

So the history kind of. I would say, if you look at it, a macro standpoint. Right. The history of technology is things have always gotten better. Like, would you like to live in the world before electricity? You know, probably not. It doesn't sound that you can if you want, but, like, nobody seems to opt into that. And I think we're very much in kind of a period like that. But it is. The transition is always scary because it's a different world. Like, the jobs. Everybody was a farmer. Like, everybody was a farmer in, like, 1750. I think it was, like, 93 or 94% of America was farmers. And then, like, almost all those jobs are gone. Just like, you know, like, the jobs that we think are jobs that they would have thought were ridiculous. Like, ridiculous. If you were a farmer, you would think, what I do is the dumbest thing in the world. Or a product marketing manager. Any of this stuff. It's like, that's not a job. You're not making any food. Like, you're not building a house. Like, how could that be a job. So I do think it's very hard to see to the other side of that, but I think it's very, very likely to be, like, way, way, way better for everybody. Just, like electricity ended up being way better for everybody. And to me, like, the biggest, kind of the most salient, like, wrong idea was from John Maynard Kean. So he wrote a paper that wasn't that famous, but the great economist of the Depression, where he said, look, things are gonna be so abundant, and everybody's needs are gonna be met. Everybody's gonna have a house or, like a shelter, and everybody's gonna have enough food to eat. And then once you have your needs met, you're gonna work way less, like 15 hours a week max. Cause your needs are met. But what he didn't realize was, well, we're not just gonna need one car. We're gonna need a car for every person. We're gonna need, you know, computers and television sets and this and that and the other and awesome vacations and, like, food that takes, you know, like a chef 10 hours to prep and all this kind of thing, which did not exist then. Like, there was no, like, whatever foodies and tasting menus and all that bullshit that we have now. But, like, that's all a need. Like, that want goes to a need very fast. And, you know, humans are kind of unbelievable in their ability to come up with new things that they need. And, you know, and then you have to make those and so forth. And I think it's going to be, you know, look, I think in 15 years, the truth is, everybody is going to in America and probably around the world is going to live better than, you know, the very best life, you know, from just luxury, access to information, et cetera, et cetera, than anybody did in 1980. So, like, that's the world that we almost certainly are going to get to. So you shouldn't be so mad about it. But it is disconcerting. All right. Well, especially if you're trying to teach little kids, you know, they're like, what should I do? I don't know.

[B]

That's a hard one. Well, on that note, Horowitz at Andreessen Horowitz, thank you very much. I appreciate it.

[A]

Thanks, Alex.

[B]

All right, thank you.

[A]

Thanks.

[C]

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