Why Investors Are Rethinking Everything for the AI Era

Key Takeaway

Run a 30-minute AI workflow audit today: list the repetitive tasks your team performs, estimate the labor or delay each creates, and test one AI tool on the highest-value task. Do not judge success by whether the tool exists or by a flashy demo; measure whether customers or colleagues repeatedly use it and get materially better outcomes. The episode’s central lesson is that durable AI value comes from real demand, deep workflow integration, and accelerating usage—not superficial “AI-enabled” features.

Episode Overview

A16Z’s David George and Accolade Partners’ Aram Verdian discuss why AI may be intensifying venture capital’s power-law dynamics. They argue that frontier AI companies can convert capital into compute and better products, while AI’s opportunity extends beyond software into labor-heavy sectors, robotics, healthcare, energy, chips, and data centers. The conversation also covers concentrated portfolio construction, evaluating genuine AI traction, and the difficulty of modernizing legacy software businesses.

Full Transcript

Transcript of Why Investors Are Rethinking Everything for the AI Era from A16Z. Auto-generated from episode audio; may contain minor errors.

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 Rokbot 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 Ka and David George sit down with Accolade partners Aram 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 in venture capital, and now it's systemic throughout, and particularly the three frontier model companies, SpaceX, OpenAI, Anthropic, 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 1,000 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, and 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 your last year, I brought this gem back. Oh my gosh, this is amazing. How do you still have this? So we dug in the catacombs, and we made this extra large version, just for posterity here. I can't believe it. 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, I remember. Yeah, the first one at a billion. Venture fund three. 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 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 $5 to $6 trillion of value.

And the dynamics around companies staying private longer, they're not 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 revenues. Yeah, the top decile outcomes, I think, used to be $10 billion, and now they're like $40 billion or so. It's going 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 of market cap, new market cap, and a bunch of that went to the incumbents. 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. Yep. Yeah, and I'm constantly confused about the TAM 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 tasks 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 how big the TAM 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 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, and we don't still even know. Yeah, they're expansionary. Yeah, that's a good point. There was this whole thesis where, well, frontier labs are going to cannibalize 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, 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. Yeah. Extract it out, because why do people think it's going to be a winner-take-all?

And we can hypothesize, but, you know, the last era of technology 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, 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, 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 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, Aram, 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.

Yeah. 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.

But 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 US.

Only 20 have achieved consistent 3x net returns over the last two decades. Sorry, say that one more. 20% have achieved? 20. 20. 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. It was, do you have three to four 3x net TPPI funds over a 20-year period? We found only 20. Wow. 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.

Yeah. 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. Yeah. If you're late stage, DJ, I'm curious if you 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.

Yeah. 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's 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. Yeah. 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 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 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. Yeah. 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, but this death of the middle thing. How do you define it?

What's the middle? I think, sort of what you described, the 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, and 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, 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 the what is 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 a person so 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 in the curve on the outcome and to 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. Can we demonstrate to the founder that we you know are the right partner for her him if so we win the deal. If we can help to make the outcome better that's great.

And then if we do make the outcome better there's two things that happen one our business has persistence of returns partially because you know the new founder wants to be around the winners right like they want 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 you should work with these folks.

And so that's the way the flywheel works in our business. One theory curious to hear both of your takes is take precede seats so sub 20 30 40 million dollar valuation sub hundred million dollar funds. They can coexist with the big firms because at inception stage say there's seven A.I. 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 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. Yeah. Do you agree with that. Yeah yeah yeah. And look we have like very healthy relationships with with seed funds across the ecosystem. We also do seed ourselves right. But precede for sure you know it's right earlier than we typically.

The seed you would do I think is like a bigger seed right. Yeah. They used to be A's. Yeah. Yeah. Yeah. That definitely is our seed. 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 where because founders have such preferential attachment as DJ was saying to the brands we get the look at everything and sometimes it does make sense for us to do the precede 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 speed around 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 were 10 years ago. Again this is starting to really take shape as more of the early stage folks are doing later stage and then extending across the stack but not quite in the way that it is today and DJ I don't know if you 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 etc. 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 till 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'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.

And we talked 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 ten percent 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 relation 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 five hundred million dollar 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 there's I mean in a very simplistic way there's in our mind there's four ways to do venture pre-seed seed so thinks up 150 funds there's thousand thousand close to two thousand today in the U.S.

alone messy middle we talked about and then there's a lot of firms there by their thousands or 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 the traction at a huge multiples and a lot of times they're selling to each other in a cohort potentially and it's not even ARR but they're multiplying by 12 so but for every nine companies like that there's one really special one that's doing a couple of million in ARR actually 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's a lot of certainty when you guys said cursor I don't think there was a lot of certainty and how many for how many months were people saying cursor is dead. Oh even the morning of the acquisition announcement people were still saying that cursor is dead I'm like they just announced that they were going to be acquired by SpaceX for 16 months.

Tell me if I'm wrong. So you're doing an ARR 400 million dollar round or somewhere maybe around there. Yeah something like that. Yeah. Like that okay so a lot of people would say like that's crazy why did they do that. 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.

But certainly like it is hard to parse out real verse 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 yeah. But like I don't know I come back to you know is the market demanding more of your product That is always the question that's supposed to know to my on my computer screen.

Like how do you know that when the company's been only operating or selling for a couple of months you're not gonna be able to do it with financial analysis you'll have to do it by really understanding the customers talking to customers and then you know it's like one of the things that I said about Harvey over time as an example right. They did a really good job commercially early days like because they were they were smart they were 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 and I from a retention standpoint not retention no just 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 when we did at Harvey. But you know that's the kind of signal that we look for. You got to go you got to layer down like everyone can do court 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 stage 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 many other things. And what's the biggest pushback? I mean you're the most prolific fundraiser I know. So what's the pushback you get from LPs? Thank you. I'm 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 etc. And so we talked a lot about this at the outset you know around valuations and what the potential 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 that the LP historically has not been incentivized to actually embrace change in some respects. Right. So 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 super right. Like that is the error of omission and like that is 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 GP. 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. And so if you miss the frontier models back to your first question as an LP but you kind of work along the benchmark right maybe slightly below the benchmark you're keeping your job.

Right. Right. That's fascinating. And also for most folks and I'll leave fund to funds out of the equation because it's a different different piece but 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 incentive misalignment. It's actually quite direct. So where do we go from there in terms of the LP kind of role and see it like it. I think we also have an important role and function we oftentimes talk about in the context of our job as the 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. But of those 20 firms out of 3,000 like you are not going to see consistency.

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 I'm going back to the average in venture that's just not what one should do. It's not actually 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 1.5 to 2x net without the lockup without the risk you're taking in venture. You talk about 6% loss ratio, P doesn't have that. LP has other problems we can talk about today when it comes to AI software. 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 it, it returns 10% of your fund, it does not move the needle at all. 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. appropriate percentage of overall capital allocated to venture and growth compared to private equity or public markets or real assets, credit, whatever?

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'd like to answer that. Look at the public markets today. We vibe coded something that created a great way for us to assess AI resiliency. 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, et cetera. 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 way to play it is 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, Silver Lake potentially 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 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 exclude the IPOs, like Cursor, the M&A sale to SpaceX was way bigger than that. The problem with that is you look at the pre-Chad 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. 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. 21, it was the inverse. Inverse, exactly. It's basically like fully aligned with risk, right? Exactly. It's correlated with risk in the public markets. Exactly. Yeah. 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. 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.

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. Yeah, he did. This is the thing that 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. 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 a 2016 through 2021 pre-chat GPT vintage software company that was fine, but doesn't have that AI native features anymore.

It's not accelerating. It's growing like 30%. On the venture books, it's at like 10, 20 times revenue. It can't go public anymore. 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. Yeah. What happens to that company? We have a lot of exposure to those companies. Yeah. 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?

And he actually said to me, don't give me a podcast answer, which is ironic. And 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. Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy. So coding, I think if you were to paint the bullish scenario for regular software and for slower pace of change, you would say coding hit, but that's kind of a head fake.

Coding is perfectly documented, right? So it has perfect data, it's verifiable, and it's simulatable, right? And so most tasks in business do not share those three 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. And then some of them will evolve and have AI solutions, and they'll change their business models. And 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, 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 if you look at our portfolio, we have some of those companies put about 95% of our nav is not in those companies, right? It's in the companies that are growing very fast, accelerating, et cetera. 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 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, we're super early, right? 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 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 end. They're making up for lost time now that they've converted their portfolio from 91% to 58 and venture and 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 started joking around about Bendix, but that was probably a great outcome for Airtable outside of the fact that 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've talked about venture and growth, we've talked about private equity, we've talked about public markets, and by the way, the composition of all of those have 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 two 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 CESpocalypse, 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 competitors are 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. So 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. Yeah. You need to completely... And 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. So obviously this is a group that is very pro, you know, venture and growth, this 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, it 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 so... And an IPO isn't actually a distribution. It takes, it could take 12, 24 plus months before you actually get liquidity out of an IPO.

Like, especially if you own 10, 15% at IPO, like 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, 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?

The answer is unanimously no. Now could some of these companies go public earlier than not? Sure. Entropic was really first funded in 2021. It's about to go public five years later. Cursor from first acquisition, from first fund of 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. So 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 Stripe that we invested at the seed stage. And we asked all of our LPs, like, hey, do you want liquidity out on this? We recognized, 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, it's 17 years in, we've got to get this liquidity out, we've got to wrap up the fund, etc.

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, you know, 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 is an interesting question that you and Gavin were tossing back and forth, EG, 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 $100 trillion market cap? Oh, my God. There 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 skeuomorphic version. We're gonna have a native version. It's gonna be proactive. It's gonna do work on our behalf. It's gonna create a ton of value for consumers.

And we're kind of nowhere on that. I mean, yeah, there's, you know, a billion chatbot users, but, you know, like, that's like scratching the surface. We are nowhere on robotics, but I think robotics is gonna be bigger than the language stuff. And I think it's gonna happen in the next 10 years. We are almost nowhere on autonomy, right? Like, there's fewer than 10,000 Waymos live in the US, 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 gonna be massive. And then, you know, we're in this interesting era of reimagining 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 gonna 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 gonna 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 onward. The VC ecosystem supports that well. I think the new fund you have is really gonna 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, 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 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 the concern. I've heard LPs say, this is like the dot-com where 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, the next generation chip companies, memory, et cetera, that's a huge opportunity.

It's time for Machine H. 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. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z, and subscribe to our Substack at a16z.substack.com.

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