# AI Power Law: Venture Capital Strategy Shifts

**Podcast:** a16z Podcast
**Published:** 2026-09-10

## Transcript

We've looked at the data of 3,000 venture capital firms in the U.S.
Only 20 have achieved consistent 3x net returns over the last two decades.
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.
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 BrockBot on Sam's side.
He's talked about Astra and some of the long-running capabilities that are going to come out soon.
What do you think is going to be the next $100 trillion market cap company?
It is possible that...
AI isn't just creating faster growing companies.
It may be changing the power law itself.
In this episode, A16Z's Jim Kaa and David George sit down with Accolade Partners Adam Verdian to unpack what that means for technology investing and portfolio construction.
For most startups, too much capital can become a liability.
But David argues that Frontier AI is different.
Dollars can be converted directly into compute.
and compute can make the product better, reinforcing advantages of companies already at the frontier.
They discuss why AI's addressable market could extend far beyond software, why Aram believes AI is becoming a core rather than satellite allocation, and why access, selection, and position sizing matter even more as the power law becomes more extreme.
They also look at what comes next, from robotics and healthcare to energy, chips, and data centers, and why some of the biggest outcomes of the AI era may still be in categories that barely exist today.
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 and venture capital, and now it's systemic throughout, and particularly the three frontier model companies, SpaceX, OpenAI, Anthropik, represent somewhere between $3.5 to $5 trillion 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 Viridian, thank you for joining me.
Great to be here.
Thanks for hanging out.
Thank you for having us here.
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?
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, right?
And they can take different forms, but brand, reputation in the market, the accumulation of resources all provide competitive advantages.
That all still is the case.
But 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.
And this is a thing, like, how do you screw up a startup?
Well, Throw too much money at it and have them hire a thousand people.
And then you create all these coordination issues and overhead issues and dueling priorities.
And it sort of 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 the businesses better.
And so 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.
I think will continue to be the case.
So, Ram, so 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 since you were last year.
I brought this gem back.
Oh, my gosh.
Oh, my gosh.
This is amazing.
How do you still have this?
This is amazing.
So we dug in the catacombs, and we made this extra-large version just for posterity here.
I can't believe that.
We got this from the catacombs.
But incidentally, during the last 10 years, a lot has changed in the world.
And if you remember, at that point in time, people were bellyaching about...
fund sizes being too large back then.
And you had one at a billion.
Yeah, the first one at a billion.
Venture fund tree?
Exactly, exactly.
And so a lot has happened since then.
How do you think about your venture portfolio juxtaposed against your private equity one?
And then just also generally asset allocation, we've talked 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 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 four 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.
And we are at a point where 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.
And so you have the fastest growing technology.
It's hitting on all parts of the GDP.
So as an allocator, it's hard not to make the case.
It's not a satellite position.
You should be core or a super core in some shape or form.
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, I guess, 18 years ago.
This was a cottage industry.
And now it's not.
And I talk about this all the time, but our asset class is five to six trillion dollars of value.
And the dynamics around companies staying private longer, they're not going to reverse.
I mean, 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 revenues.
Yeah, the top test outcomes, I think.
used to be 10 billion and now they're like 40 billion.
Yeah, it's soon to be probably 100 billion by the time Anthropic and then OpenAI come out.
Yeah, yeah, yeah.
And look, this makes sense, right?
The last cycle created 25 trillion in market cap, new market cap, and a bunch of that went to the incumbents.
Yeah.
But a lot of it went to startups and the new startups and each one of these subsequent waves gets bigger than the prior one.
And so our expectation is take the 25 trillion and it's going to be a larger number.
Yeah.
Yeah, and I'm constantly confused about the tame of AI.
Love your thoughts on this.
Take healthcare.
Healthcare spends 60 to 100 billion on healthcare IT per year.
But AI is hitting on actual labor and the value of tasks that are being performed in healthcare.
That's claims, billing, administration.
That's a trillion dollar industry.
So the TAM of AI can be 10x plus bigger than traditional SaaS or healthcare IT.
And what is that value?
Well, what's the economic value of a task that's being performed?
That's the TAM you're looking at.
And then there's some capture rate that the AI company will take.
But we have no idea.
what, how big the 10 can get.
And to your point, you look at every wave, the incumbents are 10x smaller over time.
So it's hard to estimate it, but I can tell this for myself, I've been chronically wrong about how big these outcomes can get.
Yeah, same here.
Yeah, labor, I mean, look, if you just look at like how much of dollars are spent in the U.S.
economy on labor versus software, it's something like 40 times more.
Now, that doesn't mean, importantly, that labor is going to go away.
I think labor is just going to get reinvented.
And so we'll end up with sort of reimagination of the tasks that humans do.
But I think that's actually the whole point of AI is you're going after this different thing.
And so to equate it to software and say, oh, it's the next evolution of software is far too limiting.
Our legal counsel says to us often, it's like, I love Harvey.
All my clients think they're lawyers now.
And they could actually spar with me on topics where they would have probably been like, 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 the usage of AI.
And so all those use cases are massively, massively probably underappreciated.
We don't still even know.
Yeah, they're expansionary.
Yeah, that's the whole point.
Yeah.
There was this whole thesis where, well, Frontier Labs are going to cannabize the apps, which layer is going to win.
It turns out everyone is sort of growing.
Yeah, yeah, yeah.
We just, a friend of mine did a podcast where he described everything is going to work.
kind of thing.
And I describe it slightly differently, but we get questions all the time from LPs when they ask us like, you know, which layer in the stack is going to work.
And I'm kind of like, I don't know, the market is going to be so big.
Like I think everything might work.
Now there's going to be a lot of companies that don't work and there may be idiosyncratic categories that don't work.
But I think by and large, it's far too limiting to think, oh, if open source does a good job, it's bad for the labs and vice versa.
So we try and remove ourselves from thinking in a zero sum way like that.
Extract it out, because why do people think it's going to be a winner-take-all?
And we can hypothesize, but, you know, in the last year of technology, it was probably winner-take-all in a lot of categories.
But this feels categorically different because we're re-underwriting a lot of the fundamentals.
So maybe extract it out.
Yeah, look, winner-take-all is an interesting way to describe it, because if you just look at the market cap growth of all the leading technology companies, like, there are many, many, many that were successful.
It wasn't winner-take-all, right?
Now, there's an important distinction.
We very much are believers in the power law within a given category.
So the winners will capture the vast majority of the market share and market cap.
And second place is playing for scraps.
But I think there will be a massive expansion of the amount of categories that we have, right?
And so if you go back 20 years, CRM was not really a category.
I mean, it was small.
It was like Siebel Systems and things like that.
But, you know, now it's a massive category.
And so I think the same thing will happen.
We've seen it in every technology market that we invest in.
Again, our approach is in our business, we can tolerate loss, right?
And if we're not losing money in a given fund on a given amount of investments, we're not taking enough risk, right?
And so 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.
Now 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 will get investments that we make that 10X or more.
in returns.
And so if we're doing a good job, we're backing the leading company in every category that is a credible category.
And if the category works out well, then we do a great job.
And if the category doesn't work out well, that's okay.
That's kind of the risk that we live with.
Yeah.
And embedded in that is also kind of timing because I know around you and I lament on this in that I think a lot of folks oftentimes think that 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 then there's these aberrations in the market where it's probably actually true.
And so I know you advise a lot of your LPs on the importance of consistency in venture capital, probably even more than any other asset class, because you just never know when these technologies can come out.
Maybe walk through that, because there's a lot of institutional allocators out there who actually don't have.
access to a lot of the frontier, certainly models.
Now they're trying to play catch up and in some instances, probably maybe introducing some adverse behavior that is a little bit too reflective of things being a little bit too frothy.
So maybe unpack that for us.
Yeah, I mean, the extremeness of the power law that DG talked about.
If you as an allocator have not had access to the top five to 10 companies over the last five to 10 years, you're significantly behind in terms of returns.
And let's take a step back.
We've looked at the data of 3,000 venture capital firms in the U.S.
Only 20 have achieved consistent 3x net returns over the last two decades.
Sorry, say that one more.
20% have achieved.
Less than 1%.
Wow.
Consistent 3x net returns.
That's incredible.
And it's actually, you don't have, you don't need seven, eight funds in those 20 years.
Do you have three to four 3x net TPPI 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 the consistent ones consistently had access to the category-defining companies every vintage.
Now, there are exceptions.
And by the way, just having the logo is not sufficient enough.
If you're early stage and you have a large fund, you need to own enough.
If you're late stage, DJ, I'm curious if you'll agree, sizing is really critical.
Yeah.
So...
Venture-like returns are possible in late stage, but your best company should be 5%, 10% plus 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 now does.
So we have found the right portfolio sizing and the ones that consistently have gotten access are in the top 20 out of 3,000.
So if you don't have them, there is a huge dispersion of returns.
And if you don't have those, you're getting the average venture return.
If you look at Cambridge data, the average venture return over the last 10 years is 1 to 2x net.
You will 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.
Yeah, I just pulled up a tweet from our friend Endowment Eddie who posted actually 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 lifecycle investor, and help land customers slash hires.
LPs have slowly followed along, but most are still dragging their heels because it's actually it's counter to conventional wisdom.
Yeah.
And the outcomes are larger.
So funds can be larger.
There are some exceptions.
In those 20, there are some small firms that are focused on niche vertical markets or they're playing at a stage that's so much earlier than the bigger firms where like there is not a lot of competition with the bigger firms.
Now, the problem with that strategy is you have to stay consistent in terms of fund size and their 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.
Death of the middle.
Death 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.
You know, like, but this death of the middle thing.
How do you define it?
Like, what's the middle?
So I think, like, sort of what you described, like the highly specialized.
you know, some of the ones that were very early to AI with like deep, deep, deep domain experts have done a pretty good job, right?
Like they've done a good job and, you know, sometimes they can move fast, get into things or take shares of deals that we want to do.
And, you know, that is a reality.
Then I think there's like the, you know, whatever, we're, you know, large scale venture, right?
In the sense that we have many product lines and we can scale all the way from.
you know, a seed all the way through to when you go public.
And I think we have some peers who employ a similar strategy, right?
And, you know, there's, I'd like to think that we're the best, but there's a few other folks who do that.
Everything else in between that, I think, struggles to compete a little bit for the reasons that Eddie said, right?
What does the founder care about?
The founder cares about, you know, sort of taking capital from a partner that they think can de-risk the outcome for themselves.
Like that, if you were just to simplify it, that is the simplest way to describe what the founder really cares about.
Now they care about the partner, right?
Like they care about the person.
So you have to, you have to be a good actor and all those things.
But there's a reason why we build up a tremendous amount of resources, right?
Like it's why we have 700 employees.
That's why we take, you know, the management fees that we make on our funds and we invest them in operating resources because we think that it will, one, bend the curve on the outcome and two, help us to win deals.
And so.
You know, when founders select their partners, often, you know, if it's a hot deal, like they'll have many alternatives, like that's the revealed preference.
And, and, you know, as Eddie said, we're doing an okay job with that.
What I agree with him about then is our LPs coming to invest in us is a byproduct of that.
Right.
And so, you know, our business, our business is a flywheel.
The flywheel starts with, you know, are we, are we deep domain experts?
Are we going to have a point of view that is the right point of view?
Um, can we demonstrate to the founder that we, you know, are the right partner for, for her or him?
Um, if so, we win the deal.
If we can help to make the outcome better, that's great.
Um, and then if we do make the outcome better, there's two things that happen.
One, um, our business has persistence of returns partially because, um, you know, the, the new founder wants to be around the winners, right?
Like they want, they, they care about that because there's important brand signaling and that has knock on effects for them.
And then secondly, you know, by being a part of the winners and helping them, you know, in small ways, we create sort of killer references.
And the founders then tell the other founders, hey, you should work with these folks.
And so that's the way the flywheel works in our business.
One theory, curious to get both of your takes, is take pre-seed seats.
So sub-20, 30, $40 million valuation, sub-100 million funds.
They can coexist with the big firms because at inception stage, say there's seven AI companies kind of doing the same thing.
I would think a large firm like Andreessen would want to wait for a round or two until there's more relative certainty.
Because one thing you don't want to do is be in the number two or number three, like you said.
You have to be in the category winner.
So you'd rather wait for that round and actually double down and lead the A or the B.
So the small firms, they can carve out a niche for themselves a clip or two earlier than the big firms and actually have a right to win.
They can do really well and be complimentary to the big firms.
Do you agree with that?
Yeah.
Yeah, and look, we have like very healthy relationships with seed funds across the ecosystem.
We also do do seed ourselves, right?
But pre-seed, for sure, you know, sort of earlier than we took.
The seed you would do, I think, is like chunkier, bigger seed, right?
It's like a serial entrepreneur.
They used to be A's, yeah.
That definitely is our speed.
With that said, you know, we have our speed run program, which we just came from actually earlier this morning.
And I don't know, I think the market is evolving.
And because founders have such a preferential attachment, as DG was saying, to the brands, we get the look at everything.
And sometimes it does make sense for us to do the pre-seed and seed.
And so, you know, you kind of want that flex of capability.
And for the founder, to your point, they kind of don't really care where your focus is.
They just want to be in that orbit.
And, like, you'll find the funds to kind of match to it.
And so I think we can coexist in this world, but I think it's also...
very important, like our business is principally an early stage business.
We have to be first to the poll and we may not actually do the investment, but we have to at least understand the landscape and market to be able to actually make the informed decisions later on.
I spoke to a few founders at Speedrun today and they very much are hoping to stay in the orbit.
Yeah, right, right.
And so this is, you know, a new phenomenon that worked 10 years ago.
Again, this is starting to really take shape as...
more of the early stage folks started doing later stage and then extending process tech, but not quite in the way that it is today.
And DG, I don't know if you would agree with that.
Like, it's just virtually impossible to have this sort of mid-stage business effectively without having the early stage and then also the late stage to 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 said that all the time, like our business starts and ends with early stage.
And so...
you know, that provides us a tremendous amount of advantages at the growth stage in terms of access, information, knowledge, relationships, et cetera.
And I would think that, you know, 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, totally.
And you can't, there's both sides.
You can't only do the early.
You can't also only wait to the late too.
And so your point earlier around, it looks like increasingly that firms are converging to a handful of names.
Like that's probably true because, you know, again, this power law dynamic, but also at the same time.
almost majority of those logos, so to speak, we have to get at the early because that's the only way we maintain the ball control and also participate in the pro rata and then some.
And so that's obviously the business that we have to play.
We're big believers that the strongest late stage franchises have a huge early stage franchise attached to them.
Like the ability to win is multiplied when you have an early stage franchise.
Yeah.
And we talk about sizing in late stage where you can, whatever the size of your late stage fund is, if you can...
at scale, put five to 10% of your fund in 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.
Yeah, yeah, no, it's very hard.
Yeah, I've lived that world.
How do you think about, from the LPC, how venture is fundamentally perhaps a structurally different job than maybe when you started your career also as well?
And, you know, how do you think about, also asset allocation within venture, because there's actually subclasses within venture as you think about portfolio construction as well.
Yeah, I mean, in a very simplistic way, in our mind, there's four ways to do venture.
Pre-seed seed, so think sub-150 funds.
There's 1,000, close to 2,000 today in the U.S.
alone.
Messy middle, we talked about.
And then there's a lot of firms there, but there are thousands, not thousands, hundreds of firms.
And then the big firms.
And then dedicated late stage.
So there's four 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.
The traction that's happening in the industry is confusing.
And here's why it's confusing.
You can have a company, I'm actually really curious to hear this from you because we hear this a lot.
Company comes out of Pick Your Accelerator.
I went from zero to five minute ARR in a month.
Yeah.
There's no renewal cycle yet on that company.
Yeah.
And they're raising off of that traction at huge multiples.
Yeah.
And a lot of times they're song to each other in a cohort potentially.
And it's not even ARR, but they're multiplying by 12.
So, but for every, for nine companies like that, there's one really special one.
that's doing a couple of million in ARR, actual ARR, that is a huge valuation that will go on to be the next cursor.
So it is really, really tough actually today to parse out like what's real traction, 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, then 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.
And for how many months were people saying cursor is dead?
Oh, even the morning of the acquisition announcement, people were still saying that cursor's dead.
I'm like, they just announced that they were going to be acquired by SpaceX for 60 months.
Tell me if I'm wrong.
Say it in an ARR, $400 million round, or somewhere maybe around there.
Yeah, yeah, something like that.
A lot of people would say, like, that's crazy.
Why did they do that deal?
Yeah, yeah, yeah.
Well, look, okay, so, like, founder judgment is a very important thing, right?
And so, you know, getting to know founders over time, spending a lot of time with them.
seeing how they think, like, I'd like to think that, you know, especially my early stage partners are pretty good at that.
Secondly, like, it is hard to parse out real versus, you know, kind of misleading interaction and misleading in the sense that, like, you can't take market signal from it, not that anybody's doing any misleading.
There's probably some of that too.
Yeah, there's probably some of that too, but like, I go back to...
I love every now and then people are like helping to redefine what ARR actually means.
I'm like, this is a very helpful PSA for the industry.
This is always helpful, yes.
Yeah, but like, I don't know, come back to, you know, is the market demanding more of your product?
That is always the question that's a post-it note on my computer screen.
But how do you know that when the company's been only 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, talking to the customers.
And then, you know, it's like one of the things that I said about Harvey Overtime as an example, right?
They did a really good job commercially early days.
Like, because they were smart.
There was like a research plus lawyer combo.
You know, they got some momentum and some, you know, high-profile law firms to sign up early days.
But, you know, the usage was not very good, right?
And so, like, if you looked at the actual deployment of it, it just, it looked like mediocre compared to some other software firms, you know, in AI companies.
From a retention standpoint?
Not retention.
No, it's actual usage.
Usage of it.
Now, fast forward, post-reasoning models, like, that totally flipped.
And you could see, you know, absolute takeoff of adoption, right?
And so a bunch of different things happen at the same time.
Lawyers got way more value out of the product.
You could see it in usage and engagement.
And then it became, because it was like high utility usage and engagement, it almost became a flip from what was previously like, oh, we're scared of things like hallucinations to no, no, no.
Every client is actually demanding the law firms use the product.
And so, you know, I think we look for markets like that.
We try to catch them early.
Like we try to catch them earlier than we did at Harvey.
But, you know, that's the kind of signal that we look for.
You got to go, you got to go layer down.
Like everyone can do cohort analysis.
Everyone can look at renewal data.
But like understanding the texture of the market and what the customers actually want, need and their alternatives.
I think, you know, that's how you make the decision.
That's why it's so important to have the early state business because they're the deepest in the technology and the products.
And they, you know, 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.
So what's the pushback you get from LPs?
They keep me very gainfully employed at this firm.
I agree that you are a prolific fundraiser.
What's the biggest pushback you're getting from LPs on, like, the state of AI, the state of venture?
So a lot of it is worries around, you know.
are we catching a falling knife here?
Just like the timing of the market where we are, like, are things overheated, et cetera.
And so we talked a lot about this at the outset, you know, around valuations and what the potential of the market is.
But I do get a lot of sentiment from the LPs that their job is also about to fundamentally change as well.
And you mentioned earlier one aspect of it around AI making it more challenging to evaluate opportunities and funds.
But the other aspect of it as well is the LP.
historically has not been incentivized to actually embrace change in some respects, right?
You know, this is very much a job where the end goal is actually somewhat diametrically posed with the risk tolerance of the GP.
And this is just the mechanics of the industry, but oftentimes say, you know, a GP can get fired for missing out, you know, the next, you know, Facebook, the next Uber, right?
Like that is error of omission.
And like that's fireable.
But LPs on the flip side only get fired if you invest into a mentor.
So in some respects, like the incentive outcomes are actually completely opposite of the GDP.
You don't get fired for investing in IBM if you're an LP.
Exactly.
Yeah.
And in fact, like you don't potentially even get fired for not investing at all.
Yeah.
And so if you miss the frontier models, back to your first question as an LP, but you kind of were along the benchmark.
Right.
Maybe slightly below the benchmark.
You're keeping your job.
Right.
Right.
And so.
That's fascinating.
And also.
for most folks, and I'll leave fund-to-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 the next potential of generation.
It's incentives for London.
It's actually quite direct.
So where do we go from there in terms of the LP kind of role and see it?
I think we also have an important role and function.
We oftentimes talk about in the context of our job as a leader of the venture capital.
industry is we have to help folks understand where the future is going.
And part of that is understanding how to infiltrate not just within their venture capital allocation, but across their entire portfolio.
And that, I think, is way more interesting than just saying, hey, like, you might miss out on this next generation returns or the optimization of, like, the next frontier model or, you know, one or two power law companies, etc.
Access selection sizing is 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, like you are not going to see consistency, right?
Maybe half of them are consistent.
But the LP's job is also to find the next gen firms as well as continue accessing that.
So one is you access, two is selection, three is portfolio construction sizing.
And it'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, 20 firms pretty consistently.
So when I see a portfolio with 50, 60, 70, venture capital firms.
It's very hard for me to imagine that the overall portfolio can generate better than the average.
And again, going back to the average in venture, that's just not what one should do.
It's not compelling enough for the liquidity.
Relative to any of the other asset classes, public markets, private equity.
I mean, private equity can probably get you like one and a half to two X net without the lockup, without the risk you're taking in venture.
You talked about 60% loss ratio.
P doesn't have that.
Right.
P has other problems we can talk about today when it comes to AI software.
But so.
Portfolio construction 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'd it, it returns 10% of your fund.
It has not moved the needle at all.
Yeah, yeah, yeah.
Where do you all think we are today in that evolution that Jen was talking about of how you would, I know you're biased, but the appropriate...
percentage of overall capital allocated to venture and growth compared to private equity or public markets or real assets, credit, whatever.
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 like something that created a great way for us to assess AI resiliency.
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 like 15 to 20 companies max trading above 10 times revenues, which is quite an insane number because it used to be dozens and dozens a few years ago.
And 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, etc.
So it goes back to the same principle where if you are in some shape or form tied to AI, which is the fastest growing facet of all the elements of the GDP, then you should supersize that 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 seed-based.
That's just not happening.
They're looking for the system of record that can show acceleration with an AI native management team.
So that connective tissue of AI is actually across every asset class today.
And one of the strongest ways to play it is, I mean, it's probably through venture.
But that's where selection access and portfolio construction is really critical.
Yeah.
Well, even the exits that we were talking about, you know, Silver Lake potentially, you know, buying Workday, for example, just like doing more provocative things, for example, in private equity land when you have the capabilities to potentially infuse and bring them into the future as a part of that.
The other version of it is also...
exits in venture now way exceed private equity.
Like, you know, like I was looking this up last night.
Private equity this year, the biggest exits are the buyout of EA, which was like around $50 billion, and Medline, which is around $50 billion.
Cursor, let's not, let's exclude the IPOs.
Like, Cursor was, the M&A sale to SpaceX was way bigger than that.
And so, The problem with that is, like, you look at the pre-chat GPT 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, 20% max.
If you look at the public markets today, that asset is trading at two times revenue.
And the problem is not just the valuation, 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, what is the terminal value?
Is it resilient from AI?
The best way to show that is organic growth acceleration.
Our data shows one percentage of growth in the public markets is equivalent to three percentages of EBITDA.
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, 21, it was the inverse.
Inverse, exactly.
It's basically like fully aligned with risk, right?
Exactly.
Correlated with like risk in the public.
Exactly.
So, unfortunately, a lot of those private equity deals, they're not growing fast enough.
They're not showing that acceleration.
And they may not have the management teams to revamp the business, like what Intercom did.
is a great example.
Bring the founder bank back, revamp the whole business, create an AI native product, scale it, and then sell.
Like, 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 I was like, I just gave him a big high five.
And I'm like, you did it, man.
He did.
This is the thing that like is really, really hard to do.
Yeah, really hard.
It's an end of one right now.
But, you know, there's a bunch of really good founders who are capable of those businesses, public markets, private markets, who I think are going to take a crack at it.
So we'll see.
Yeah.
Maybe on that thread, though, DG, because we also sometimes get the pushback as well, like for folks who have been in venture and allocated to venture, they might also have a similar problem where they do have the legacy SaaS businesses also as well.
Like, what's the balance between how you think about the historical stuff?
Let me ask you that question.
So I'm going to piggyback off of John's question to you.
You got a pick at 2016 through 2021 pre-chat GPT vintage software company that was fine, but doesn't have that AI-nated features anymore.
It's not accelerating.
It's growing like 30%.
On the venture books, it's at like 10, 20 times revenue.
Eastside can't go public anymore.
Like no one cares to take that public.
Silver Lake has no interest in that company anymore.
They would have a year ago.
They don't.
What happens to that company?
We have a lot of exposure to those companies too.
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, that AI is the biggest generational change that we've ever seen and it's going to transform industries.
And also there will be some enduring value of software companies that are able to adapt, right?
Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy, right?
So coding, I think, if you were to paint the bullish scenario for regular software and for slower pace of change, you would say, you know, coding hit, but that's kind of a head fake, right?
Like coding...
Coding is perfectly documented, right?
So it has like perfect data, it's verifiable, and it's simulatable, right?
And so like most tasks in business do not share those three attributes.
And so, you know, maybe the diffusion into other knowledge work beyond, you know, coding will take a lot longer.
That would be the case to make for the software companies.
And then some of them will evolve and have AI, you know, solutions, and they'll change their business models.
And I think that's a must.
But that would be the case for why maybe, you know, 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 months, like that's, I think there's a little bit of a growing realization in that.
All that makes me super, super, super bullish on AI though, right?
So like if you look at our portfolio, you know, we have some of those companies, but about 95% of our nav is not in those companies, right?
Like it's in the companies that are, you know, growing very fast, accelerating, et cetera.
You know, the average, I think it was 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.
So not only have we had like limited diffusion beyond coding, but if you just look at diffusion.
of the shape of who is consuming tokens and actually getting real value out of AI today were super early.
The most cutting-edge banks are probably doing 1% of headcount cost on AI tools.
And so the reason this makes me very bullish is these are the fastest growing companies we've ever seen, like of all time.
Again, they're adding more revenue per month than the mega cap tech companies.
And yet it's probably on the back of adoption of like 10 million users, maybe 20.
maybe 30 max.
And, you know, there's one and a half billion knowledge workers in the U.S.
And I think it's going to transform the way we do a lot of work.
Yeah, CalPERS famously lost out on billions of gains by not investing in their back.
And they're making up for lost time now.
They've converted their portfolio from 91% to 58 and venture in growth from 9% to 43%.
There's probably some balance in between those things, but, you know, they're leaning hard into.
But there's going to be a lot of value still that's going to be accreted in some of these historical companies.
And I was sort of joking around about Bendings, but that was probably a great outcome for Airtable, you know, outside of the fact that, you know, they're going to actually spin off the hyper-agent piece of the business and actually, I think, do really interesting things with that.
But in the scheme of things, I think there's going to be a lot of homes for a lot of things.
And this zero-sum thinking, I think, is probably the pitfall of which we would advise against.
So we talked about...
We've talked about, you know, venture growth.
We've talked about private equity.
You know, we've talked about public markets.
And by the way, the composition of all of those have like radically changed over the last 10 years.
We also have had the emergence of entirely new categories that are available in the private markets, like private credit.
Do you have a view on sort of outlook of 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, which is hundreds of billions.
I'll give you one statistic.
So you look at 21-22, about $200 billion to $300 billion in LBO software transactions happened, with over $200 billion in debt taken out.
The average valuation for the software deals were 25 to 32 times EBITDA.
Those companies today are worth probably half that.
The reason you're seeing redemptions in the credit markets and private credit is exactly that.
They're looking at the public markets.
You've had cesspocalypse.
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.
So if you are a software company that is in somewhat not resilient to AI, I think you're challenged both in terms of your equity position, also credit as well.
By the way, even the...
AI version of private equity is not completely insulated.
We oftentimes talk about, like, you know, just because you put Sears on a website didn't make it Amazon, right?
You have to have the benefit of building Amazon from the studs logistically to make it Amazon.
It's not just the website.
And in a lot of instances with the private equity-backed companies that are now just infusing AI, we've seen it actually in some of our companies as well.
where the peer competitor is like, oh, the first thing I'll do is, of course, hire AI customer service agents because that's like an easy, low-hanging fruit.
Like, it turns out, if you don't actually build on the workflow, you start to turn customers very quickly if they're used to talking to a human.
And for every dollar, every drop in NPS is like a direct correlation with drop in revenue.
And then you start to spiral, especially if you have debt laid on top of it.
Oftentimes, sometimes we hear from folks like, well, I'll just do the AI, you know, kind of version of private equity.
It's not a panacea for generating returns, especially when it's just so categorically different from a technological perspective to actually infuse that throughout the company as well.
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 completely, and if you have, by the way, if you do have a founder mentality at the management team, like it is possible, but the board has to be aligned.
All the investors have to be aligned.
And you do have to make some really hard decisions the way Intercom did.
Yeah, yeah.
Yeah, so obviously this is a group that is very pro, you know, venture and growth as a category.
Let's talk about the legitimate opposition to it.
And what is the case, you know, for why maybe the, you know, the risk that you're taking or whatever it may be with venture and growth, you know, doesn't justify it.
I mean, the pushback we get a lot is timeline to liquidity.
So it takes...
The average unicorn is private for 10 plus years, typically.
And then you got all these follow-on rounds that are happening pretty quickly, one after the other.
You see maybe the same logo in five, six different firms.
And the question is, how do you get out of it?
And an IPO isn't actually a distribution.
It could take 12, 24 plus months before you actually get liquidity out of an IPO.
Especially if you own 10, 15% at IPO, it's going to take a long time if you're in the generational company.
So we get that pushback a lot in terms of timeline to liquidity.
And then what is the sort of counter to that pushback?
Well, 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 three, four years ago?
Like, the answer is unanimously no.
Now, could some of these companies go public earlier than not?
Sure.
Anthropic was really first funded in 2021.
It's about to go public five years later.
Cursor, from first financing to acquisition, is short.
So 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.
Actually, very famously, a year and a half ago or so, we went to our fund one LPs.
At that point in time, the fund one was 16 years old and we had this position in strike that we invested at the seed stage.
And we asked all of our LPs like, hey, do you want liquidity out on this?
We recognize, you know, the job that we came to do is now done 16 years in.
Like, do you want liquidity back on this?
And every single one of those LPs said, no, we'd rather let this continue to compound.
And then ultimately, a year later, you know, we decided to make that answer because it's 17 years in.
We've got to get this liquidity out.
We've got to wrap up the fund, et cetera.
But, you know, so many LPs, I think, is 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 is specific nuance with each LP group.
where it's very hard to paint a broad brush stroke on, like, across the board on everyone wanting the same thing.
But I also think, to your point, the very best LPs, excuse me, the very best GPs have manufactured along the way liquidity, and particularly in 2021, when a lot of folks didn't take money off the table.
You know, I think that was a good sign of the first indicator.
And now in this next cycle, it's can you actually get some early liquidity out through M&A and then let...
potentially the winner's 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, DG.
He didn't want to answer the question on what will be the next $10 trillion company, but he had a 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 hundred?
trillion dollar market cap.
Oh my gosh.
Here we go.
I can't even think of those things.
Yeah, we're, that's probably two tech cycles away, not just one.
It is possible that we have entirely new companies that get created.
And I think a lot of the market cap creation that you would talk about that would drive a $10 trillion outcome or more is in new product areas that haven't yet been touched, right?
So, like, what...
I talked about the sort of diffusion of the technology into the enterprise.
Like we're nowhere, right?
You know, a year ago, everyone talked about consumer all the time.
No one even talks about consumer AI anymore.
But that is going to, like, the end use case for consumers is not going to be a chatbot interface.
Like that's the skewmorphic 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, you know, a billion Chattuckity users, but, you know, like, that's like scratching the surface.
We are nowhere on robotics, but I think robotics is going to be bigger than the language stuff.
And I think it's going to happen in the next 10 years.
We are almost nowhere on autonomy, right?
Like, there's fewer than 10,000 Waymos live in the U.S., way fewer robo-taxis.
And then, you know, a lot of open space for others to build in that area too.
We, you know, healthcare is 18% of GDP.
Like we've done nothing to scratch the surface either on care delivery or on drug discovery yet.
I mean, there's 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, you know, we're in this interesting era of re-imagining.
all things physical world from defense to manufacturing to data centers.
And so I look at the confluence of all these trends and I'm like, yeah, it may feel like we've, you know, we've done a lot with AI already.
But 10 years from now, we're going to look back and say, oh my gosh, like those other major areas created a ton of value.
And so I'm excited that I think the next SpaceX AI or OpenAI are probably going to get created.
And they'll probably be in those kinds of domains.
Yeah.
Yeah, I'll add one category that to me is both a concern but a huge opportunity.
It's a plug for your new fund on the opportunities fund that you did.
I think very simplistically about like the bottleneck in AI today is not demand.
It's on the supply side.
So you got energy, the grid data center, then you got chips, then you got frontier models and apps.
The US is amazing at the right side of that.
So like chips and onwards.
The VC ecosystem supports that well.
I think...
the new fund you have is really going to help on the left side as well.
Because the U.S.
doesn't have a problem with energy generation.
It has a problem with speed to power.
That's permissioning, transmission, that's regulatory.
Other countries are putting out 10x more renewable capacity a year.
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, 50, 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 a concern.
A lot of, I've heard 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 you're 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 ya.
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