Aram Verdiyan: We've looked at the data of 3,000 venture capital firms in the US. Only 20 have achieved consistent 3x net returns over the last two decades.

David George: Right now, clearly the power law is more extreme than it has been in the last 10 to 20 years of technology investing. For the first time, you can take capital and throw it at a company and it compounds their advantage.

Aram Verdiyan: AI is attacking every facet of the GDP, transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time. Elon has talked publicly about Grok. On Sam's side, he's talked about Astra and some of the long-running capabilities that are going to come out soon.

Host: What do you think is going to be the next hundred trillion dollar market cap company?

Host: Welcome back to the A16Z podcast. Something fundamental has changed in how value gets created. Power law used to be just a feature of a cottage industry in venture capital and now it's systemic throughout. Particularly the three frontier model companies SpaceX, OpenAI, Anthropic represent somewhere between three and a half to 5 trillion dollars of potential enterprise value. And shockingly before SpaceX went public, a lot of our LPs and also the broader institutional allocator community didn't have a lot of exposure to it. And so we'll talk about today why potentially portfolio construction and asset allocation may have changed, why power law is not just only in the venture capital industry and then particularly where and how value actually compounds today. David George, Aram Verdiyan, thank you for joining me.

David George: Great to be here. Thanks for hanging out.

Aram Verdiyan: Thank you for having us here.

Host: Awesome. Awesome. Awesome. Okay. So DG. So if you add up every venture-backed IPO for the last six years, all of them together, where does it go from here?

David George: Right now clearly the power law is more extreme than it has been in the last 10 to 20 years of technology investing, probably going back to the emergence of the network effect driven businesses. But for the first time, you can take capital and throw it at a company and it compounds their advantage.

Host: So, Aram, first of all, you're not just one of our longtime LPs at Accolade, but incidentally, it's been exactly 10 years since you were actually an employee of A16Z. And so, for your 10 year anniversary, we dug in the catacombs and we made this extra large version just for posterity here.

Aram Verdiyan: Oh my god. Oh my gosh. This is amazing. Why do you still have this? This is amazing.

Host: I can't believe that.

Aram Verdiyan: And you had one at a billion. I remember the first one at a billion. First venture fund.

Host: Exactly. Exactly. And so, you know, a lot has happened since then.

Aram Verdiyan: Yeah, let's take venture today. We've reached 100 billion in revenue in AI. It took SaaS 15 years to get to the same point.

David George: I'm very biased, but if you just think about the shape of the markets and how they've changed since I started my career: it's venture-like outcomes in late stage.

Host: Yeah. I mean, you had that insight in 2019 when you left GA. It's venture-like outcomes in late stage.

David George: So it's no longer just early stage and you IPO when you have 100 million in revenues.

Aram Verdiyan: Yeah. The top decile outcomes, I think, used to be 10 billion and now they're like 40 billion.

David George: Yeah. Or soon to be probably 100 billion by the time Anthropic and OpenAI come out.

David George: Yeah, and look, this makes sense, right? Like the last cycle created 25 trillion of market cap.

Host: Yeah.

David George: But a lot of it went to startups and new startups, and each one of these subsequent cycles, our expectation is that take the 25 trillion and it's going to be a larger number.

Host: Yep. Yeah. And I'm constantly confused about the TAM of AI. Would love your thoughts on this.

Host: Like TAM on compute, you always hear things like a percentage of GDP, which is such a big hand-wavy number. How do you think about that?

David George: Yeah, I think if you think of it purely in the TAM of software, I think it's the wrong way to look at it.

Host: Yeah.

David George: Because software is only 1.5 to 2 trillion dollars of GDP. But if it replaces or augments knowledge work, that's tens of trillions of dollars.

Aram Verdiyan: If you take a step back and think about the evolution of tech: semiconductors, personal computers, internet, cloud, mobile. In those cycles, tech was an enabler taking a tax on IT spend. AI is attacking every facet of the GDP: transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time.

Aram Verdiyan: When you look at the velocity of the leaders: Elon has talked publicly about Grok. On Sam's side, he's talked about Astra and some of the long-running capabilities that are going to come out soon. These are not just tools; they are autonomous systems capable of executing complex multi-step workflows.

David George: The reason you can throw capital at it to compound advantages is the feedback loop of compute, proprietary data, and top-tier talent. The front-runners build an unprecedented moat that widens with every capital infusion.

Host: This brings us straight back to the extreme power law distribution. From the perspective of an LP or allocator, what does this mean for portfolio construction?

Aram Verdiyan: That is the exact thesis we study at Accolade. If you look at Cambridge data across 3,000 venture firms over two decades, only around 20 have achieved consistent 3x net returns. Average venture is not compelling enough given the illiquidity risk. But top 1% access generates outcomes that outstrip any alternative asset class.

David George: Top brands enjoy preferential attachment. The best founders gravitate toward top-tier firms for access, strategic knowledge, ecosystem relationships, and downstream signaling. In AI, this preferential attachment is stronger than ever.

Host: Many traditional allocators ask: If top funds scale to $5B or $10B+, can they still generate venture-scale multiples?

David George: The math depends entirely on terminal market caps. If the ceiling was $50B, giant funds wouldn't work. But if AI creates companies worth $5T, $10T or more, deploying hundreds of millions at multi-billion valuations can still return massive multiples on massive funds.

Host: Let's turn to public markets and traditional software Private Equity. There is widespread debate over whether legacy SaaS is facing an existential AI threat.

Aram Verdiyan: Pre-ChatGPT, PE did $200B-$300B in software LBOs at 15-20x EBITDA for low-growth SaaS. Today, public multiples for decelerating software trade at 2x revenue. If AI-native tools eat into their workflows, PE firms won't just face multiple compression—they might not find any exit buyer at all.

David George: Every buyer looks at a software target and asks: "Will this workflow be automated away by a frontier model or agent in 24 months?" You cannot just parachute in an operating partner and say "let's sprinkle AI on it." You need true founder mentality—like Intercom did—willing to cannibalize legacy revenue to build native AI architecture.

Host: Another pushback from LPs is the timeline to liquidity. Companies stay private longer, and IPO windows have been sluggish.

Aram Verdiyan: The best GPs manufacture liquidity along the way via tender offers and secondary sales while keeping core compounding exposure. Selling compounding generational winners like Stripe or Databricks too early to satisfy a short-term DPI metric destroys long-term alpha.

David George: Exactly. We actively managed liquidity on our Fund I vintages, returning substantial capital while preserving upside in the highest conviction power-law compounders.

Host: To wrap up on that provocative question: What will be the world's first $100 Trillion market cap company?

David George: That might be two tech cycles away. But look at how early we are in real diffusion: Enterprise AI spend per seat is still nascent; Waymo has fewer than 10,000 vehicles on the road; Healthcare and drug discovery are barely scratched. The next SpaceX or OpenAI scale giants will bridge physical hardware, robotics, and foundation intelligence.

Aram Verdiyan: I'll add one critical category: Energy, power, and infrastructure bottlenecks. Transmission, grid approvals, and 10x compute density mean legacy data centers cannot support modern clusters. Solving these physical constraints in next-gen silicon, advanced memory, and dedicated clean power creates $100B+ opportunities. The demand is real; solving supply bottlenecks is where the value unlocks.

David George: It's time for the machine age. Let's bring the machines.

Aram Verdiyan: I love it.

Host: All right, let's close on that. Thank you both so much. It was super fun. Thank you for having us. Awesome. See you.