Why Investors Are Rethinking Everything for the AI Era

We’ve looked at the data from 3,000 venture capital firms in the U.S. Only 20 have achieved consistent 3x net returns over the last 2 decades.

Right now, clearly, the power law is more extreme than it has been in the last 10 to 20 years of technology investing.

AI is attacking every facet of the GDP: transportation, labor, services, capital, and coordination. There hasn’t been a technology paradigm that hits $30 trillion in GDP at the same time.

What do you think is going to be the next $100 trillion market cap company?

Elon has talked publicly about Grok bot. On Sam’s side, he’s talked about Astra and some of the long-running capabilities that are going to come out soon.

1. Why Power Law Is No Longer Just a Venture Thing

Something fundamental has changed in how value gets created. The power law used to be just a feature of a cottage industry in venture capital, and now it’s systemic throughout. In particular, the 3 frontier model companies—SpaceX, OpenAI, and Anthropic—represent somewhere between $3.5 trillion and $5 trillion of potential enterprise value.

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. Today, we’ll talk about why portfolio construction and asset allocation may have changed, why the power law is not limited to the venture capital industry, and particularly where and how value actually compounds today.

David George, Aram Verdiyan, thank you for joining me.

Great to be here. Thanks for having us.

2. Every Venture-Backed IPO Combined: Where Does It Go From Here?

Thank you for having us here.

Awesome. Okay, DG: If you add up every venture-backed IPO from the last 6 years—all of them together—where does it go from here?

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 consumer companies. There are many reasons why that’s the case.

Increasing returns to scale have always been a dynamic in our business. Obviously, it’s well covered how a network-effect business can have increasing returns to scale, but so can software businesses. They can take different forms, but brand reputation in the market and the accumulation of resources all provide competitive advantages. That’s still the case.

Right now, especially with the labs, for the first time in my career, you can take capital and throw it at a company, and it compounds their advantage. How do you screw up a startup? Throw too much money at it, have it hire 1,000 people, and then create all these coordination issues, overhead issues, and dueling priorities. It gets messed up because you can’t hire enough people to do enough things fast enough.

Now that’s not the case. You can throw dollars at compute, and compute can make products and businesses better. To me, it’s not terribly surprising that the power law is more extreme right now. Economies of scale are a very real thing in the AI market, and I think that will continue to be the case.

So, Aram, first of all, you’re not just one of our longtime LPs at a16z. Incidentally, it’s been exactly 10 years since you were actually an employee of a16z. For your 10-year anniversary—since it was last year—I’ve brought this gem back.

Oh, my God. This is amazing. Why do you still have this? This is amazing.

We dug into the catacombs, and we made this extra-large version just for posterity. I can’t believe that I got this from the catacombs.

Incidentally, during the last 10 years, a lot has changed in the world. If you remember, at that point in time, people were bellyaching about fund sizes being too large back then.

3. Rethinking Portfolio Construction from a Blank Sheet

And you had one at a billion. I remember.

The first one at a billion. The first venture fund. Exactly.

A lot has happened since then. How do you think about your venture portfolio juxtaposed against your private equity one, and then just generally asset allocation? We were talking about this on the way in. If you were to start from a blank sheet of paper again, knowing what you know now, how would you have constructed it differently?

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. AI did that in 4 years, and we’re not even close to anywhere in terms of the penetration of demand.

The reason DG is saying you can throw capital at it—that’s a function of unlimited demand for inference. We’re at a point where AI is attacking every facet of the GDP: transportation, labor, services, capital, and coordination. There hasn’t been a technology paradigm that hits $30 trillion in GDP at the same time.

You have the fastest-growing technology hitting all parts of the GDP. As an allocator, it’s hard not to make the case that you should be core or super-core in some shape or form. It’s not a satellite position.

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 in private equity and growth equity—that was 18 years ago—this was a cottage industry, and now it’s not.

I talk about this all the time, but our asset class is $5 trillion to $6 trillion of value. The dynamics around companies staying private longer aren’t going to reverse.

You had that insight in 2019 when you left GA. It’s venture-like outcomes in late stage, which is now happening.

So it’s no longer just early stage, and you IPO when you have $100 million in revenue.

The top-decile outcomes, I think, used to be $10 billion, and now they’re like $40 billion.

Or soon to be probably $100 billion by the time Anthropic and then OpenAI come out.

And look, this makes sense, right? The last cycle created $25 trillion of new market cap, and a bunch of that went to the incumbents. But a lot of it went to startups, and each one of these subsequent waves gets bigger than the prior one.

Our expectation is that you take the $25 trillion, and it’s going to be a larger number.

Yep.

I’m constantly confused about the TAM of AI. I’d love your thoughts on this. Take healthcare. Healthcare spends $60 billion to $100 billion on healthcare IT per year, but AI is hitting actual labor and the value of tasks being performed in healthcare. That’s claims, billing, and administration. That’s a trillion-dollar industry.

The TAM of AI can be 10x-plus bigger than traditional SaaS or healthcare IT. What is that value? What’s the economic value of a task that’s being performed? That’s the TAM you’re looking at. Then there’s some capture rate that the AI company will take, but we have no idea how big the TAM can get.

To your point, you look at every wave, and the incumbents are 10x smaller over time. It’s hard to estimate it, but I can tell you this for myself: I’ve been chronically wrong about how big these outcomes can get.

Yeah, same here. Labor—I mean, if you just look at how much of the dollars spent in the U.S. economy go toward labor versus software, it’s something like 40 times more.

That doesn’t mean, importantly, that labor is going to go away. I think labor is just going to get reinvented, and we’ll end up with a reimagination of the tasks that humans do. But I think that’s actually the whole point of AI: You’re going after this different thing. To equate it to software and say it’s the next evolution of software is far too limiting.

Our legal counsel says to us often, “I love Harvey. All my clients think they’re lawyers now, and they can actually spar with me on topics where they would have probably said, ‘I don’t really understand this. I’m just going to defer to you.’”

So my billable hours have only gone up with the advent of AI. All those use cases are massively, massively underappreciated, and we still don’t even know—

They’re expansionary. That’s the whole point.

And then there was this whole thesis that frontier labs were going to cannibalize the apps. Which layer was going to win? It turns out everyone is sort of growing.

A friend of mine did a podcast where he described it as, “Everything is going to work.”

I describe it slightly differently, but we get questions all the time from LPs. They ask us which layer in the stack is going to work.

Which layer in the stack?

Which layer in the stack is going to work? I’m kind of like, “I don’t know. The market is going to be so big. I think everything might work.”

There are going to be a lot of companies that don’t work, and there may be idiosyncratic categories that don’t work. But by and large, it’s far too limiting to think that if open source does a good job, it’s bad for the labs, and vice versa.

We try to remove ourselves from thinking in a zero-sum way like that.

Yeah, exactly. Why do people think it’s going to be winner-take-all? The last era of technology was probably winner-take-all in a lot of categories, but this feels categorically different because we’re reunderwriting a lot of the fundamentals.

4. Why This Era of AI Is Categorically Winner-Take-All

So maybe extract it out. Yeah, look, “winner take all” is an interesting way to describe it because, if you look at the market cap growth of all the leading technology companies, there are many, many that were successful. It wasn’t winner take all.

There’s an important distinction: We very much believe in the power law within a given category. The winners will capture the vast majority of the market share and market cap, and second place is playing for scraps.

But I think we’ll see a massive expansion in the number of categories that we have. If you go back 20 years, CRM was not really a category. I mean, it was small. It was Siebel Systems and things like that.

Now it’s a massive category. I think the same thing will happen. We’ve seen it in every technology market that we invest in. Again, our approach is that, in our business, we can tolerate loss. If we’re not losing money in a given fund on a given amount of investments, we’re not taking enough risk.

If you look at our best-performing venture funds over time, I think the loss rate is 60%—

Or so.

—on early stage.

Yeah.

On early stage. At the growth stage, the loss rate will be lower, but it’s probably going to be in the 10% to 20% range. And that’s appropriate because—

With that, you’ll get investments that we make that return 10x or more. If we’re doing a good job, we’re backing the leading company in every category that is a credible category. If the category works out well, then we do a great job. If the category doesn’t work out well, that’s okay. That’s the risk that we live with.

5. Why Consistency Matters More Than Ever in Venture

Yeah. Yep. Yep. Embedded in that is also timing because, Aram, you and I lament this. A lot of people think things are overheated in that moment in time, and then you look back in retrospect and it turns out everything was actually quite cheap. But there are aberrations in the market where it’s probably actually true.

I know you advise a lot of your LPs on the importance of consistency in venture capital, probably more than any other asset class, because you just never know when these technologies can come out. Maybe walk through that, because there are a lot of institutional allocators out there who don’t have access to a lot of the frontier models now. They’re trying to play catch-up, and in some instances they may be introducing adverse behavior that’s too reflective of things being too frothy. Unpack that for us.

Yeah, I mean, the extremeness of the power law that DG talked about is important. If you, as an allocator, have not had access to the top 5 to 10 companies over the last 5 to 10 years, you’re significantly behind in terms of returns.

Let’s take a step back. We’ve looked at the data on 3,000 venture capital firms in the US. Only 20 have achieved consistent 3x net returns over the last 2 decades.

Sorry, say that one more time. 20 firms?

Less than 1%.

Wow.

Consistent 3x net returns.

That’s incredible.

You don’t need 7 or 8 funds in those 20 years. You need 3 to 4 3x net TVPI funds over a 20-year period. We found only 20 firms that have done that.

Wow.

Consistency in venture is really, really hard.

But what’s interesting is that the consistent ones consistently had access to the category-defining companies every vintage.

Yeah.

There are exceptions, and by the way, just having the logo is not sufficient. If you’re early stage and you have a large fund, you need to own enough of it. If you’re late stage, DG, I’m curious if you agree: sizing is really critical.

Yeah.

Venture-like returns are possible in late stage, but your best company should be 5% to 10% or more of your fund. That way, you can actually return the fund on a single company. Fund-returning math in late stage didn’t exist before. It does now.

Yeah.

We’ve found that the right portfolio sizing and the firms that consistently have gotten access are in that top 20 out of 3,000. If you don’t have them, there’s a huge dispersion of returns. If you don’t have those companies, you’re getting the average venture return. If you look at Cambridge data, the average venture return over the last 10 years is 1x to 2x.

You’ll do better in private equity. You’ll definitely do better in the public markets. You don’t need to lock up your money for 10 years.

Yeah, for sure. I just pulled up a tweet from our friend Eddie, who posted this morning. He said, “Interest in big VC funds has been driven by founders, not LPs. Founders, more often than not, want the brand that can scale, be a life-cycle investor, and help land customers and hires. LPs have slowly followed along, but most are still dragging their heels because it’s counter to conventional wisdom.”

Yeah. The outcomes are larger, so funds can be larger. There are some exceptions among those 20. There are some small firms that are focused on niche vertical markets—

—or they’re playing at a stage that’s much earlier than the bigger firms, where there isn’t a lot of competition with the bigger firms. The problem with that strategy is that you have to stay consistent in terms of fund size and strategy. If you start getting bigger over time, then you bump into the big firms, and I think it becomes really, really hard to stay consistent.

Yeah. Yep. Death of the middle.

Death of the middle of the middle.

We said we were going to drink every time we said “death of the middle.”

I’m very complimentary of many of our peers in the venture ecosystem. But this “death of the middle” thing—

How do you define it?

What’s the middle? I think what you described—highly specialized funds, some of the ones that were very early to AI with deep, deep, deep domain experts—have done a pretty good job. They’ve done a good job, and sometimes they can move fast, get into things, or take shares of deals that we want to do. That’s a reality.

Then I think there’s large-scale venture, in the sense that we have many product lines and can scale all the way from seed through to when you go public.

I think we have some peers who employ a similar strategy. I’d like to think that we’re the best, but there are a few other folks who do that.

Everything else in between struggles to compete a little bit, for the reasons that Eddie said. What does the founder care about? The founder cares about taking capital from a partner they think can de-risk the outcome for themselves. If you were to simplify it, that’s the simplest way to describe what the founder really cares about.

The founder cares about the partner, right? The governance person. You have to be a good actor in all those things. But there’s a reason why we built up a tremendous amount of resources. That’s why we have 700 employees.

That’s why we take the management fees that we make on our funds and invest them in operating resources, because we think it will, one, bend the curve on the outcome, and two, help us win deals.

When founders select their partners, often, if it’s a hot deal, they’ll have many alternatives. That’s the revealed preference. As Eddie said, we’re doing an okay job with that.

What I agree with him about is that our LPs coming to invest in us is a byproduct of that.

Our business is a flywheel.

The flywheel starts with: Are we deep domain experts? Are we going to have a point of view that is the right point of view?

Can we demonstrate to the founder that we are the right partner for her or him?

If so, we win the deal. If we can help make the outcome better, that’s great.

If we do make the outcome better, there are 2 things that happen. One, our business has persistence of returns, partially because the new founder wants to be around the winners. They care because there’s an important brand, and that has knock-on effects for them.

Secondly, by being a part of the winners and helping them in small ways, we create killer references.

The founders then tell the other founders, “You should work with these folks.”

That’s the way the flywheel works in our business.

One theory—I’m curious to get both of your takes—is to take pre-seed bets. At sub-$20 million, $30 million, or $40 million valuations, with sub-$100 million funds, they can coexist with the big firms because, at the inception stage, say there are 7 AI companies doing roughly the same thing.

I would think a large firm like Andreessen Horowitz would want to wait for a round or 2 until there’s more relative certainty. One thing you don’t want to do is be in the number 2 or number 3. As you said, you have to be in the category winner. You’d rather wait for that round and double down and lead the A or the B.

Mhm.

The small firms can carve out a niche for themselves a round or 2 earlier than the big firms and actually have a right to win. They can do really well and be complementary to the big firms.

Yeah. Do you agree with that?

Yeah. And look, we have very healthy relationships with seed funds across the ecosystem. We also do seed ourselves, right? But pre-seed, for sure—earlier than we typically do.

The seed you would do, I think, is a chunkier, bigger seed, right? Like for a serial entrepreneur.

Yeah, that definitely is our sweet spot. That said, we have our Speedrun program, which we just came from earlier this morning.

I think the market is evolving because founders have such a preferential attachment, as David George was saying, to brands. We get to look at everything, and sometimes it does make sense for us to do the pre-seed and seed. You want that flexibility of capability, and for the founder, to your point, they don’t really care where your focus is. They just want to be in that orbit, and you’ll find the funds to match it.

I think we can coexist in this world, but I also think it’s very important that our business is principally an early-stage business. We have to be first to the pole. We may not actually make the investment, but we have to at least understand the landscape and the market to be able to make informed decisions later on.

I spoke to a few founders at Speedrun today, and they very much are hoping to stay in the orbit long term.

Right. And so this is a new phenomenon. Ten years ago, this was starting to really take shape as more of the early-stage folks started doing later stage and then extending across the stack, but not quite in the way that it is today. David, I don’t know if you would agree with that, but it’s virtually impossible to have this sort of mid-stage business effectively without having the early stage and then also the late stage come behind it as well.

Yeah, I mean, look, I’m very biased, but I think the reason that we’ve been successful as a growth fund is because of our early-stage business. I say that all the time: our business starts and ends with early stage.

That provides us a tremendous amount of advantage at the growth stage in terms of access, information, knowledge, relationships, and so on. I would think that our early-stage partners would probably say that the growth business provides them benefits too, because it allows us to scale up and deepen partnerships with founders over time, and that helps to win deals at the early stage.

Yeah, totally. There are both sides. You can’t only do the early stage, and you can’t only wait until the late stage, either. Your point earlier was that it looks like firms are increasingly converging around a handful of names. That’s probably true because of this power-law dynamic, but at the same time, the majority of those logos, so to speak, we have to get at the early stage. That’s the only way we maintain ball control and participate in the pro rata and then some.

We’re big believers that the strongest late-stage franchises have a huge early-stage franchise attached to them. The ability to win is multiplied when you have an early-stage franchise.

We talked about sizing in late stage. Whatever the size of your late-stage fund is, if you can, at scale, put 5% to 10% of your fund into one of the category-defining companies, the way you could do that is because you had an early-stage franchise that developed that relationship with the entrepreneur and the management team early on. It’s really hard to come in as a de novo late-stage firm and write a $500 million check.

6. How Venture Has Structurally Changed Since the 2000s

Yeah. No, it’s very hard. I’ve lived that world.

How do you think about, from the LP seat, how venture is fundamentally, perhaps structurally, a different job than when you started your career? And how do you think about asset allocation within venture? There are actually sub-assets within venture as you think about portfolio construction as well.

In a very simplistic way, there are 4 ways to do venture. There’s pre-seed and seed, so think sub-$150 million funds. There are 1,000—close to 2,000 today—in the U.S. alone.

Then there’s the messy middle, which we talked about. There are a lot of firms there—hundreds of firms. Then there are the big firms, and then there’s dedicated late stage. So there are 4 ways to play it.

We have done the larger firms for decades now. We’ve done the seed firms. We’ve selectively done a few in the messy middle, and we haven’t done dedicated late stage for the reasons we talked about.

How is it changing?

AI is actually making our jobs harder than ever before. It’s making it harder because rounds are larger in general, they’re faster, and the traction that’s happening in the industry is confusing.

Here’s why it’s confusing: You can have a company come out of—pick your accelerator—and say, “I went from $0 to $5 million ARR in a month.”

There’s no renewal cycle yet on that company.

And they’re raising off of that traction at huge multiples. A lot of times, they’re selling to each other in a cohort, potentially. It’s not even ARR, but they’re multiplying it by 12.

For every 49 companies like that, there’s 1 really special one doing a couple of million in actual ARR that has a huge valuation and will go on to be the next Cursor.

Yeah. It is really tough today to parse out what’s real traction and what’s not. Valuations are really high. This is why the big firms do well. I actually think they can wait, or they have enough relative certainty in the next round to lead that round.

But even then, there isn’t a lot of certainty. When you guys did Cursor, I don’t think there was a lot of certainty. For how many months were people saying Cursor was dead?

Even the morning of the acquisition announcement, people were still saying that Cursor was dead. I was like, “They just announced that they were going to be acquired by SpaceX for $60 billion.”

Tell me if I’m wrong: $3 million ARR and a $400 million round, or somewhere around there?

Yeah, something like that.

A lot of people would say, “That’s crazy. Why did they do that deal?”

Yeah. Well, look, founder judgment is a very important thing. Getting to know founders over time, spending a lot of time with them, seeing how they think—I’d like to think that, especially, my early-stage partners are pretty good at that.

Secondly, it is hard to parse out real versus misleading traction. And misleading in the sense that you can’t take a market signal from it—not that anybody’s intentionally misleading anyone.

There’s probably some of that too.

Yeah, there’s probably some of that too. But I go back to—

I love it when people help redefine what ARR actually means. This is a very helpful PSA.

This is always helpful, yes. But I come back to this question: Is the market demanding more of your product? That is always the question. I have that posted on a note on my computer screen.

How do you know that when the company has only been operating or selling for a couple of months?

You’re not going to be able to do it with financial analysis. You’ll have to do it by really understanding the customers and talking to them.

One of the things that I said about Harvey over time, as an example, is that they did a really good job commercially in the early days because they were smart. They were a research-plus-lawyer combination. They got some momentum, and some high-profile law firms signed up early on.

But the usage was not very good. If you looked at the actual deployment, it looked mediocre compared with some other software firms.

From a retention standpoint—

Not retention—usage of it.

Now fast-forward to the post-reasoning-models period, and that totally flipped. You could see the absolute takeoff in adoption. A bunch of different things happened at the same time: Lawyers got much more value out of the product, and you could see it in usage and engagement.

Because it was high-utility usage and engagement, it almost became a flip from what was previously, “We’re scared of things like hallucinations,” to, “No, no, no, every client is actually demanding that the law firms use the product.”

I think we look for markets like that. We try to catch them early—earlier than we did at Harvey. That’s the kind of signal we look for. You have to go lay it down. Everyone can do cohort analysis, and everyone can look at renewal data, but understanding the texture of the market, what the customers actually want and need, and what their alternatives are—that’s how you make the decision.

That’s why it’s so important to have the early-stage business, because they’re the deepest in the technology and the products. They obviously saw it in Cursor, and they’ve seen it in many other things.

Jen, what’s the biggest pushback? I mean, you’re the most prolific fundraiser I know.

You could be very gainfully employed at this firm.

I agree that you are a prolific fundraiser.

7. Are We Catching a Falling Knife? LP Sentiment Today

What's the biggest pushback you're getting from LPs on the state of AI and the state of venture?

A lot of it is worries around whether we're catching a falling knife here: the timing of the market, where we are, whether things are overheated, and so on. We talked a lot about this at the outset, around valuations and what the potential of the market is, but I do get a lot of sentiment from LPs that their job is also about to fundamentally change.

You mentioned earlier one aspect of it: AI making it more challenging to evaluate opportunities and funds. But the other aspect is that the LP historically has not been incentivized to actually embrace change in some respects. This is very much a job where the end goal is somewhat diametrically opposed to the risk tolerance of the GP, and this is just the mechanics of the industry.

But I oftentimes say a GP can get fired for missing out on the next Facebook or the next Uber. That's the error of omission, and that's fireable.

But LPs, on the flip side, only get fired if you invest in a bad manager. So, in some respects, the incentive outcomes are actually completely opposite of the GP's.

You don't get fired for investing in IBM if you're an LP.

Exactly. And in fact, you don't potentially even get fired for not investing at all.

Yeah.

If you miss the frontier models, back to your first question, as an LP, you're kind of along the benchmark, right?

Maybe slightly below the benchmark, and you're keeping your job.

Right, right. And that's fascinating.

And also, for most folks—and I'll leave fund of funds out of the equation because it's a different piece—but for a lot of folks, the upside actually is not that interesting for them. So, the pitch of, like, “Hey, you're going to miss out on this next potential generation”—the incentive misalignment is actually quite direct.

So, where do we go from there in terms of the LP role? I think we also have an important role and function. We oftentimes talk about, in the context of our job as leaders of the venture capital industry, how we have to help folks understand where the future is going.

Part of that is understanding how to infiltrate not just within their venture capital allocation but across their entire portfolio. That, I think, is way more interesting than just saying, “Hey, you might miss out on this next generation of returns,” or “the optimization of the next frontier model,” or one or two power-law companies.

Access, selection, and sizing are what LPs do. So, access—you could argue you have the data to figure out who has done well historically. Out of those 20 firms out of 3,000, you're not going to see consistency. Maybe half of them are consistent.

But the LP's job is also to find the next firms, as well as continue accessing those. So, 1 is access, 2 is selection, and 3 is portfolio construction and sizing. That's critical from an LP standpoint, because if you have an asset class where 20 firms out of 3,000 do well, you should concentrate in those 15 to 20 firms pretty consistently.

When I see a portfolio with 50, 60, or 70 venture capital firms, it's very hard for me to imagine that the overall portfolio can generate better than the average. And again, I'm going back to the average in venture. That's just not compelling enough for the illiquidity relative to any of the other asset classes—public markets, private equity.

Private equity can probably get you 1.5 to 2x net without the lockup, without the risk you're taking on. You talked about a 60% loss ratio. PE doesn't have that, right? PE has other problems we can talk about today when it comes to AI software, but portfolio construction and sizing for an LP is critical.

I've seen too many times an LP or an allocator find an interesting fund, actually get it right, and put 1% of their fund into it. Great. You 10x-ed it. It returns 10% of your fund. It does not move the needle at all.

Yeah, yeah, yeah, yeah. Where do you all think we are today in that evolution Jen was talking about, in terms of the appropriate percentage of overall capital allocated to venture and growth compared to private equity, public markets, real assets, credit, or whatever it is?

Yeah, this is hard for me to answer because all I do is venture growth, so I would be biased to say it should be supersized.

We like that answer, though.

Look at the public markets today. We vibe-coded something that created a great way for us to assess the AI resiliency of public companies, and now we're doing that on the private side, and it's helping us hugely in our growth equity portfolio.

But in the SaaS public markets, there are only 15 to 20 companies, max, trading above 10 times revenue, which is an insane number because it used to be dozens and dozens a few years ago. Every one of those companies, for the most part, is showing acceleration of growth from AI. You're either in monitoring, security, deployment of agents, and so on.

So, it goes back to the same principle: if you are in some shape or form tied to AI, which is the fastest-growing facet of all the elements of GDP, then you should supersize it in your portfolio. That will have impacts in the public markets.

Even private equity today, when they're doing a new investment, they're looking for something that's AI-native. They're not looking to buy a workflow software company growing 10% that's seat-based. That's just not happening. They're looking for the system of record that can show acceleration with an AI-native management team.

Yeah. So, that connective tissue of AI is actually across every asset class today.

Yeah. And one of the strongest ways to play it is probably through venture.

Yeah.

Well, even the exits, though. We were talking about Silver Lake potentially buying Workday, for example—doing more provocative things in private equity when you have the capabilities to potentially infuse them and bring them into the future as part of that.

The other version of it is that exits in venture now way exceed private equity. I was looking this up last night. In private equity this year, the biggest exits are the buyout of EA, which was around $50 billion, and Medline, which is around $50 billion.

Cursor—let's exclude the IPOs—the M&A sale to SpaceX was way bigger than that. And so, the problem with that is—

You look at the pre-ChatGPT vintages in private equity: you would have paid, I don't know, 15 to 20 times EBITDA for a software asset that's growing 10% to 20% max.

Yeah.

If you look at the public markets today, that asset is trading at 2 times revenue.

Yeah.

And the problem is not just that the valuation might be low. There might not be a buyer for that company, because if you're looking at a software company today, the first thing you think about is: What is the terminal value? Is it resilient from AI?

The best way to show that is organic growth acceleration. Our data shows 1 percentage point of growth in the public markets is equivalent to 3 percentage points of EBITDA.

Yep.

So, by the way, it's funny because in COVID everyone was like, “We need to be profitable.” It was the inverse, and now it's the opposite.

No, no. In 2021 it was the inverse. Post-2021, it's basically fully aligned with risk, right? Correlated with risk in the public markets.

Exactly. Unfortunately, a lot of those private equity deals aren't growing fast enough. They're not showing that acceleration, and they may not have the management teams to revamp the business.

What Intercom did is a great example: bring the founder back, revamp the whole business, create an AI-native product, scale it, and then sell. It's almost like you're suiciding your existing business, which in private equity is really hard to do.

It's really hard to do. I just spent a little time with the founder, and I went up to him at an event and gave him a big high five. I said, “You did it, man.”

You did. This is the thing that's really, really hard to do. You know, it's an N of 1 right now.

8. The Legacy SaaS Problem: What to Do with the Old Book

There are a bunch of really good founders who are capable with those businesses, in the public and private markets, who I think are going to take a crack at it. So, we'll see.

Yeah. Maybe on that thread, though, DJ, we also sometimes get the pushback that folks who have been in venture and allocated to venture might have a similar problem: they have legacy SaaS businesses as well. What's the balance between how you think about the historical stuff?

Let me ask you that question. I'm going to piggyback off of Jen's question to you. You've got to pick a 2016 through 2021, pre-ChatGPT vintage software company that was fine but doesn't have those AI-native features anymore. It's not accelerating; it's growing at around 30%. On the venture books, it's at 10 to 20 times revenue. It can't go public anymore. No one cares to take that public.

Yeah.

Silver Lake has no interest in that company anymore. They would have a year ago, but they don't.

Yeah.

What happens to that company?

You know, look, it's like—

We have a lot of exposure to those companies, too. So—

I would say it's very TBD, right? I was with one of our CEO founders this weekend, and he was like, “Give me the straight scoop. What do you actually think is happening?” He actually said to me, “Don't give me a podcast answer,” which is ironic.

I think both things can be true: AI is the biggest generational change that we've ever seen, and it's going to transform industries. There will also be some enduring value in software companies that are able to adapt.

Part of the thing that we're monitoring, which makes us extremely bullish about AI, is actual diffusion into the real economy. If you were to paint the bullish scenario for regular software and for a slower pace of change, you would say, you know, coding's been hit, but that's kind of a head fake, right? Coding is perfectly documented. It has perfect data, it's verifiable, and it's simulatable.

Most tasks in business do not share those 3 attributes, and so maybe the diffusion into other knowledge work beyond coding will take a lot longer. That would be the case to make for the software companies. Some of them will evolve, have AI solutions, and change their business models. I think that's a must, but that would be the case for why maybe it's a little bit overblown.

I think if you look at the way that a lot of the public SaaS companies have reacted over the last few months, there's a little bit of a growing realization in that. All that makes me super, super, super bullish on AI, though. If you look at our portfolio, we have some of those companies, but about 95% of our NAV is not in those companies. It's in the companies that are growing very fast, accelerating, et cetera.

The median company in the U.S. is spending $12 per employee on AI per month. The top 1% of the data set that we've seen is spending $7,000 per employee on AI per month. Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early.

The most cutting-edge banks are probably doing 1% of headcount costs on AI tools. The reason this makes me very bullish is that these are the fastest-growing companies we've ever seen, like, of all time. Again, they're adding more revenue per month than the megacap tech companies, and yet it's probably on the back of adoption by 10 million users, maybe 20 million, maybe 30 million max.

There are 150 million workers in the U.S., and I think AI is going to transform the way we do a lot of work.

Wow.

CalPERS famously lost out on billions of gains by not investing in their backyard. They're making up for lost time now. They've converted their portfolio from 91% public to 58%, and venture and growth from 9% to 43%. There's probably some balance in between those things, but they're leaning hard into it.

There's going to be a lot of value still that's going to be accreted in some of these historical companies. I was sort of joking around about bending. That was probably a great outcome for Airtable, outside of the fact that they're going to actually spin off the hyperagent piece of the business and do really interesting things with that. But in the scheme of things, I think there are going to be a lot of homes for a lot of things. This zero-sum thinking is probably the pitfall that we would advise against.

9. Why "AI Private Equity" Isn't a Panacea

So we've talked about venture and growth, private equity, and public markets. By the way, the composition of all of those has radically changed over the last 10 years. We've also had the emergence of entirely new categories that are available in the private markets, like private credit. Do you have a view on the outlook for those on a relative basis—

In software in particular?

Yeah, I'd say in technology.

The vantage point we have is looking at private credit, which resides in a lot of private equity software portfolios. That's hundreds of billions.

Mhm.

I'll give you 1 statistic. You look at 2021 and 2022: about $200 billion to $300 billion in LBO software transactions happened, with over $200 billion in debt taken out. The average valuation for those software deals was over 25 to 32 times EBITDA. Those companies today are worth probably half that.

The reason you're seeing redemptions in the credit markets in private credit is exactly that. They're looking at the public markets. You've had the SaaS apocalypse. It's been a massive correction in software, and you can see a contraction in valuations, which means the leverage ratios have gone up dramatically.

If you are a software company that is somewhat not resilient to AI, I think you're challenged both in terms of your equity position and credit as well.

By the way, even the AI version of private equity is not completely insulated. We oftentimes talk about how just because you put Sears on a website, it doesn't make it Amazon. You have to have the benefit of building Amazon from the studs, logistically, to make it Amazon. It's not just the website.

In a lot of instances with the private equity-backed companies that are now just infusing AI, we've seen it in some of our companies as well. The peer competitor is like, “The first thing I'll do is, of course, hire AI customer service agents,” because that's an easy, low-hanging fruit.

It turns out that if you don't actually build it into the workflow, you start to churn customers very quickly if they're used to talking to a human. For every drop in NPS, there's a direct correlation with a drop in revenue, and then you start to spiral, especially if you have debt layered on top of it.

Sometimes we hear from folks, “I'll just do the AI version of private equity.” It's not a panacea for generating returns, especially when it's so categorically different from a technological perspective to actually infuse that throughout the company.

Yeah, you can't just throw an operating partner at the company and say, “Let's put AI on it.” It just doesn't work.

You need to do it completely differently. If you do have a founder mentality on the management team, it is possible, but the board has to be aligned, and all the investors have to be aligned—

And you do have to make some really hard decisions, the way Intercom did.

Yeah. Yep.

Yeah. So obviously, this is a group that is very pro-venture and growth in this category. Let's talk about the legitimate opposition to it. What is the case for why maybe the risk that you're taking, or whatever it may be with venture and growth, doesn't justify it?

The pushback we get a lot is timeline to liquidity. The average unicorn is private for 10-plus years, typically, and then you've got all these follow-on rounds that are happening pretty quickly, one after the other. You see maybe the same logo in 5 or 6 different funds, and the question is, how do you get out of it?

And so—

An IPO isn't actually a distribution.

It could take 12 to 24-plus months before you actually get liquidity out of an IPO, especially if you own 10% to 15% at IPO. It's going to take a long time if you're in a generational company. So we get that pushback a lot in terms of timeline to liquidity.

Mhm. And then what is the counter to that pushback?

The counter to that pushback is going back to 3,000 firms: 20 do well consistently. If you're in the top 1% of those firms and you have a category winner, you want to make sure that compounds, actually.

Would you have wanted to sell Stripe, Databricks, or any of those other companies 3 or 4 years ago? The answer is unanimously no. Could some of these companies go public earlier? Sure. Anthropic was first funded in 2021, and it's about to go public 5 years later.

Cursor—from first financing to acquisition—is a short timeline. The best venture firms actually have fund-returning liquidity pretty quickly, maybe even quicker than private equity. But that subset of firms is tiny.

Yeah. Yeah. Yep. Actually, very famously, a year and a half ago or so, we went to our Fund I LPs. At that point in time, Fund I was 16 years old, and we had this position in Stripe that we invested in at the seed stage. We asked all of our LPs, “Hey, do you want liquidity out on this?”

We recognized that the job we came to do was now done, 16 years in. “Do you want liquidity back on this?” Every single one of those LPs said, “No, we'd rather let this continue to compound.”

Ultimately, a year later, we decided to make that exit because it was 17 years in. We've got to get this liquidity out. We've got to wrap up the fund, et cetera. But so many LPs—I think it's very specific to certain categories, right? Endowments would prefer to let it run.

Family offices, quite frankly, don't want the money back because they don't want to pay taxes on it. They'd rather have it continue to compound. And so there's specific nuance with each LP group, where it's very hard to paint a broad brushstroke across the board on everyone wanting the same thing. But I also think, to your point—

The very best GPs have manufactured liquidity along the way, particularly in 2021, when a lot of folks didn't take money off the table. I think that was a good sign—the first indicator—and now, in this next cycle, can you actually get some early liquidity out through M&A and then let potentially the winners IPO over time, with the fullness of compounding as well?

Yeah, exactly.

I'm going to end on this note because I thought this was an interesting question that you and Gavin were tossing back and forth on, DG. He didn't want to answer the question of what will be the next $10 trillion company, but he had certainty around what will be the next $20 trillion market-cap company. So I'm going to ask both of you: What do you think is going to be the next $100 trillion market-cap company?

Oh my gosh, here we go. I can't even think in those terms. That's probably 2 tech cycles away, not just 1. It is possible that we have entirely new companies that get created. And I think a lot of the market-cap creation that would drive a $10 trillion outcome or more is in new product areas that haven't yet been touched, right? So—

Yeah.

Like what I talked about: the sort of diffusion of the technology into the enterprise—we're nowhere, right? A year ago, everyone talked about consumer AI all the time. No one even talks about consumer AI anymore. But the end-use case for consumers is not going to be a chatbot interface. That's the skeuomorphic version. We're going to have a native version. It's going to be proactive. It's going to do work on our behalf. It's going to create a ton of value for consumers, and we're kind of nowhere on that. I mean, yeah, there's a billion chat users, but we're scratching the surface.

We are nowhere on robotics, but I think robotics is going to be bigger than the language stuff. I think it's going to happen in the next 10 years.

We are almost nowhere on autonomy, right? There are fewer than 10,000 Waymos live in the US, and way fewer robotaxis. There's a lot of open space for others to build in that area, too. Healthcare is 18% of GDP.

We've done nothing to scratch the surface either on care delivery or drug discovery yet. I mean, there are some companies that are working on it, showing some early signs of progress, but I think the progress that we make there in the next 10 years is going to be massive.

And then we're in this interesting era of reimagining all things in the physical world, from defense to manufacturing to data centers. I look at the confluence of all these trends and I'm like, yeah, it may feel like we've done a lot with AI already. But 10 years from now, we're going to look back and say, “Oh my gosh, those other major areas created a ton of value.”

And so I'm excited. I think the next SpaceX AI or OpenAI are probably going to get created. They'll probably be in those kinds of domains.

Yeah.

I'll add one category that, to me, is both a concern and a huge opportunity. This is a plug for your new fund—the opportunities fund that you did. I think very simplistically about the bottleneck in AI today: It's not demand; it's on the supply side. You've got energy—the grid, data centers—then you've got chips, then frontier models and apps. The US is amazing at the right side of that, so chips and onward. The VC ecosystem supports that well.

The new fund you have is really going to help on the left side as well, because the US doesn't have a problem with energy generation; it has a problem with speed to power. That's permissioning, transmission, and regulation. Other countries are putting out 10x more renewable capacity a year.

10. The Real Bottleneck: Data Centers, Chips & the Machine Age

So that is a real bottleneck, and that means reimagining the data center. You talked about the density being 10x-plus. Well, you can't just repurpose an old data center for a new AI facility. So this is where the new fund you have can create not $10 billion, $50 billion, but $100 billion-plus opportunities as well that can really solve the bottleneck.

And I think that is a real concern because demand is not the concern. I've heard a lot of LPs say, “This is like the dot-com, or this is COVID.” It's not, because the traction is real, and it's not ephemeral revenue like—

COVID.

The bottleneck could be supply, but if you have the right inputs, like the fund that's now backing those companies—next-generation chip companies, memory, et cetera—that's a huge opportunity.

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

I love it.

All right, let's close on that. Thank you both so much. It was super fun.

Thank you for having us. Awesome. See you.