All-In with Chamath, Jason, Sacks & Friedberg - Bill Maris: How Google Could Crush AI Competitors, Why Small Funds Win, and AI's Atari Stage

Episode Date: June 9, 2026

(0:00) Bill Maris joins the Besties! (0:33) Four critical lessons from a career in technology (5:58) Building Google Ventures with data and machine learning (9:51) Why small VC funds beat big ones on average (14:36) OpenAI's valuation problem and the AI price war (19:09) AI's "Atari Stage": what comes next? (25:23) VC's broken incentives and the future of deep tech Thanks to our partners for making this possible! EY - Agentic AI is introducing a new investment discipline. As AI shifts to consumption-based models, EY connects spend to enterprise value. https://www.ey.com/en_us/insights/ai/agentic-ai-token-costs?WT.mc_id=3501318&AA.tsrc=sponsorship NYSE - Thank you to our partner, the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE. https://www.nyse.com Plaud - Never miss a moment. Plaud, our official wearable AI note-taking partner at All-In Liquidity Summit, captured every insight. https://www.plaud.ai Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg

After saying he was out, now Bill Maris is returning to the investing world. The founding CEO of Google Ventures has raised $150 million for his new fund called Section 32. With a smaller fund, I have the advantage to be very selective in the companies that I invest in, the people that I hire. We're going to invest for a financial return. Any other metric is impossible to measure and therefore won't succeed. Think of the change that has happened just in the last 100 years and what's about to happen in the next 100 years with the advent of AI. The world is going to change by orders of magnitude. Thank you very much for that warm welcome. I am Bill Maris. I'm the founder of Section 32. Prior to that, I was the founder and

CEO of Google Ventures. I was also Google's vice president of special projects where I incubated Waymo and Google X, Calico, and many other projects as well. And before that, I founded a web hosting a data center company, which we're going to talk a little bit about. And today I think I'm going to talk to you about a few of the lessons I've learned on these interesting experiences I've had in life. So we'll start, we're going to have four lessons I'm going to talk about. And we're going to go back to 1997 to start when I was a fresh college graduate. I had a degree at neuroscience.

And I found myself on Wall Street somehow, managed to land a job there. but I was miserable having to wear a suit and trudge to work in the heat. But one good thing came of that, which was I looked in the closet of the office one day, and I saw a server. And I asked, well, what is this thing beneath our jackets? And they said, well, that's where our email and websites live. And as can happen to many of us, I had a moment where I felt like I was bathed in the light of inspiration. And I thought, I think I've glimpsed the future.

I think I can maybe make a business out of this, because if you can have our website and email in your closet, how many websites and emails could I put in my closet? So I immediately quit my job, because I had kind of glimpsed through a keyhole. And through that keyhole, I thought I saw the Internet, and I saw a data center. And it looked something like this.

Or maybe when I say data center, you think of something like this or something like this. But in 1997, a state-of-the-art data center looked almost exactly like this. We had three servers, a small, medium, and large. Business grew. We eventually had five servers. And this isn't a data center at all. This was my apartment where I founded the company with credit cards. and the servers lived in one room, the work happened in the other room, and it would get very hot in that room.

And this was in Vermont, so I opened the windows, and then we get very cold, so cold, in fact, that by noon, if you had a glass of water in your desk, it would ice over. You may think, though, this isn't so bad, but actually this was also my apartment as well. This was the bed, and you may look at that and think, well, you've got a... mattress and a nice pillow and then look at that nice blanket, but this is a rug I got from Home Depot to keep myself warm on those nights. And one day, there was a thunderstorm. The roof started to leak. And I knew I needed to do something because water and computers and servers don't mix well. So I called the landlord and said, the roof's leaking. The landlord said, well, that happens sometimes. But I knew that I needed to do something. So when you don't know what to do, you go to Home Depot. I got a bucket of tar and a mop, and I went up on the roof, and there was lightning, and there was rain,

and I went up there, and I tarred the roof. And I did not glimpse the future in that case, because I didn't know when you're tarring the roof that you should start at the far corner and work towards the door, rather than the reverse, and I tarred myself into a corner, but the choice that I faced was either the servers get electrocuted, or perhaps I get electrocuted, but as an entrepreneur, I was willing to take that risk, which, you know, newsflash, I survived. My shoes, though, are still stuck on that roof in Vermont, which takes me to lesson two, which is to see the future,

sometimes you need to be a little bit insane. It may appear to those around you that you are tarring the roof and a thunderstorm, and to that point, I'm going to share a few slides here, that a friend named Stuart Butterfield was kind enough to share with me. And here's the inauguration in 1989, and there's someone taking a picture. That makes sense. It's probably a film camera. And 2005, it's not very different.

There's still someone back there taking a picture. And then let's go just four years later, another inauguration. And if we look closely, it's quite a bit different because now everybody's got a camera. Everybody's got a camera. This was kind of before cameras were mushed into cell phones. It was kind of around that time it was starting to happen. But that's not the most interesting thing about this photo, because in this crowd is someone who, to his friends, I'm sure, seemed insane,

who also did glimpse the future. If we look closely, this gentleman has decided to, I don't know, live stream or record the inauguration on his laptop. He knew something that those around him didn't know, which is one of the things that I've always looked for in entrepreneurs is they know a secret about the future that most of us don't believe. Let's fast forward to 2007. I find myself somehow at Google, and a challenge was given to me.

The challenge was Google needs a venture fund. We were starting to make some investments. We didn't have a coherent strategy. There were no budgets. I had to figure out what to do. So I first found a friend, Rich Miner, he's the co-founder of Android, and he became my partner in crime

as we conceptualized what could Google Ventures be? We went up and down Sand Hill Road, and we talked to everyone. Anyone that was willing to talk to us and have a conversation, we were willing to talk to to see what we could learn. We came up with a plan. Our plan was to obtain all the data of venture

that we could find. And being Google, you can imagine, it was a lot of data, historical data, you name it. Then we decided we would, as step two, use AI, but at that time, Google would not let us use the term AI. And this persisted for many years. Bill, AI is science fiction. It's 100 years away if it's ever going to happen. Let's stick to machine learning. By the way, when you say AI, it freaks people out. So stop freaking people out.

So we had to call it machine learning, and we used machine learning to do two things. Design the ideal portfolio construction by running millions and millions of simulations and back testing and all of the things you can imagine that data scientists would do, and to determine what the ideal fund size would be. And people were excited. Here's a headline from TechCrunch at the time, and people inside of Google were also pretty excited. One of the senior execs I later learned had this to say. And, you know, I have to admit, it seemed crazy.

The plan seemed crazy at the time. But let's look at how it turned out. So over this time period of 2009 to 2018, top quartile VC returns looked like this, and top decile looked like this. Using publicly available information, I'm not sharing any non-public proprietary Google information. we would estimate Google Ventures returns at about 4.1x. And I adhered more closely to the strategy

and the investments that I led and the investments that I led turned out like this, which takes me to lesson three, which is don't bet against computer science. I've seen it happen many, many times in many, many fields. If you apply the right kind of computer science at the right time to the right problem, you will get to the right answers. I would not bet against it, even if it looks like you're tarring the roof in a thunderstorm. So let's fast forward to 2017, I decided to start my own fund.

And again, those around me said, you're insane. Why would you do that? You're in the warm womb of Google. Lunch is free and the massages are plenty and so forth. But after the idea sunk in, the advice turned into raise as much money as possible. That's the right way to run a fund. You'll get a big management fee. You'll be happy.

Things are going to work out really well for you. And I thought about that relative to everything I had done up to that point. And I decided to not take that advice. And over the course of my time at Section 32, we've had six funds. We've invested in companies like CrowdStrike and Cohere and Coinbase. And all six of those funds have averaged about $400 million in size and all are performing in their top decile. And to the extent there is DPI to measure,

that's the only measure as far as I'm concerned in venture that counts is DPI, which takes me to lesson four. This will be heresy to some, but small funds outperform large funds. This is simply the math. This is not an opinion I'm trying to convince you of, but there are many reasons for this. Smaller funds, you can have more focus.

I mean, I've already managed a multi-billion-dollar fund, with hundreds of employees. It's distracting. You cannot give the attention to founders that I would like to give. There are many reasons for this. And if we look at top-decil performance of DPI, funds smaller than 750 million, average return of 4.76x, and funds larger than a billion, 2.42x, funds below 750 million across that time period represented 95% of top decile performers with discontinuous return compression above 750 million. Why is this? There's a lot of reasons for this.

You can use your own numbers, but I'll just do a little thought experiment. If you have a $500 million fund, and let's say on average these days you can own 10% of a company, you need $5 billion of exits to get your money back. Let's just remind ourselves that the 75th percentile of venture loses money, and there is persistence of performance of the top quartile. So if you need $5 billion to get your money back, and if you want to be in this business for the long term, let's say you set your goal at 3X,

you need to return $15 billion of exit value in your companies. Now, if you have a $7 billion fund, and we do the same math, you know, you've got to return $210 billion, 7 billion to 70 times 3x is 210 billion, which exceeds the total venture-backed M&A and IPO exit value in most years. This year may be an exception, but that is something I'm looking forward to talking about when we sit down. For those of you, we've crunched the numbers, we've done all the math. Those are bills for lessons for today.

I hope that they're somewhat useful. There's a lot of stories behind all this, and I'm looking forward to talking about them for a few minutes with the guys. Thanks so much. You guys are old friends. Yes, we are. We go way back. Well, Bill's, when he started Google Ventures, I was the first ex-Google company you invested in.

That's correct. How did it go? Climate Corp. A billion dollars is it to Monsanto. What was your multiple? What was the return? I don't know. It was actually good for you guys.

It was quite good, yes. Yeah. You guys were in the B and the C. Yeah. It was when billion dollars was a lot of money then. That back then, that was a good deal. That would have been the C round. Now it's like an A round.

Yeah, that would have been in your hand. A route. Now we're going to do it again with O'Hollo. Now we're going to do it again with O'Hollo. So, you know, I just want to juxtapose what you said with what Thomas shared. They've got a very large kind of capital base that they invest. And they're investing significantly in these later stage rounds of these well-proven companies where it's, you know, the data he shared is that you can get significant multiples

to get to that next phase. You know, you're more likely to go from a billion to 10 billion, and then you're more likely to go from 10 billion to 100 and 100 to a trillion. trillion of whatever, you know, doesn't that justify an alternative strategy to what you're saying of having smaller funds focused on venture that you can maybe barbell it, have smaller vehicles focused on venture, and then very large vehicles that bet on the sure things that have that durability and that compounding advantage, and you can kind of have the two together, both be three X returns.

So my observation on that would be, one, I haven't seen the data science to support that second conclusion of the late-stage companies, that that can be an ongoing trend other than this one moment, this weird moment in time with these multi-kind-tillion-dollar exits that are coming. That would be kind of observation one. Two would be at a certain point, and this is not a negative, it's just an observation. If you're an RIA and you're collecting assets, that is not venture. You know, venture, as I practice it at least, is a different craft where you are, making concentrated bets of your time and capital on entrepreneurs and helping them build a business.

And there's nothing wrong with late stage investing. However, I also have an observation that a bit of an objection to companies that wrap themselves up in public benefit language and then keep the value creation to themselves and an elite group of investors through a big part of the curve and then say, well, we're here to benefit humanity. Well, what humanity needs is money. So it might be better to go public sooner because we'll see how these multi-trillion dollar IPOs go. However, if I'm Google, and I don't speak for Google, and I decide to arbitrarily cut the cost of, you know,

tokens to 80%. I'm going to cut them in. What happens to the business models of open AI anathropic at that point? What happens? Tell us. Actually, what, yeah, what does happen? Well, if you're a company and you can go to Google and Gemini, and you can pay 80% less for that basically identical product, why wouldn't you do that? And then the compression and the pressure on those other businesses goes super critical. What are the chances they... You don't think that she was fall. That might happen? If I were Google, that's what I'd do. Walk us through the scenario where Google decides with their war chest, with their money printing machine. You know what? Their margin is my opportunity. I'm going to give tokens out

20 cents on the dollar. Every time they lower their price, I lower our price. What happens on the playing field? Walk us through that. Would that not be the rational thing for? It's clear they're going to do it. It may not be a margin, though. To the, it. It may not be a margin, though. They may be burning investor cash, sort of like an Uber-type model, grab market share, capital as a weapon, tokens as a weapon. Token as a weapon, grab market share, grab an install base on consumer and enterprise. But fundamentally, at some point, you've got to have cash generation. So that's 100% possible.

It's 100% problem. Look, I'll just say, it's been said before, a trillion for spend commitments on $60 billion of revenue. And now you're going to go to the public and hope that retail is going to pick that up. Yeah, tell us about companies staying private longer and how unfair that is to the bottom half of society who don't get to participate in it. Well, let's speak for those 99% who are mostly not us, right? So your 401Ks, those retirement plans to get into those companies now, which are getting bizarre exceptions to S&P 500 rules. All of the rules are being broken. the passive funds, the ETFs are going to have to pick them up,

and where do you think we are on that curve of value creation? Could they go 3x from here? Sure. So just to say it as plainly as possible, we're going to force overpriced products on the 401K holders of America who didn't get to participate early. This is your position, that this is profoundly unfair, and creates more wealth creation for the people who don't need it, and it makes the people's retirement.

accounts, the bagholders. There's a lot of risk in that. And my objection is, don't say you're doing this for the benefit of humanity and do the other thing. Make the public's retirement accounts, the backholders. Or just say this is how we're running our business and this isn't for the benefit of humanity. Bill, do you think that what happens to venture, I asked Thomas's question, when these dollars get distributed, there's going to be a handful of funds that have ginormous retirement I mean, just unbelievably excessive. Founders 2, you know, is going to print a $100 billion return on $200 million of investment capital. But that's one fund in isolation. Right. Right. And there'll be a few. Your funds when you were at GV are going to print an enormous upside. And so if you don't look closely though at beyond the averages, venture is going to look incredible. If you look past the averages, venture is still going to look extremely bimodal. A handful of winners and a

a ton of losers. How does that play up? I mean, one, that's how venture is, right? 75% of funds lose money. But two, in order for founders fund or pick any fund to get that $100 billion out, they have to sell that stock to someone else. Otherwise, it's just on paper. So who's the buyer for that? Is it retail? Is it, you know, you've got to make a business case in the public market that can show that this business is worth, this countervalue of its future cash flows. And so whether it's SpaceX or Anthropic or so forth, like, can that case be made? We'll see six months after or so. I know they're playing with the lockups to kind of drag that out, but we'll see what the public market thinks of that.

Okay. So we have this one set of companies, and then there's everything else. What do you like in the everything else bucket as a venture investor? So I'm going to make an analogy to the gaming industry. We all get asked and we all think about, well, what does the future look like? like when AI is everywhere. And there's Dumers on one side and Utopians. Is this Zork?

On the other. That's Zork. I'm going to get to that. Just to give me, bear with me 30 seconds. It's probably not as bad or as great as everyone says. So let's look at the gaming industry. So I used to play this game. Zork, there's one called Planet Fall back in the 80s.

And it was very brittle. It was turn response, turn response. Grab the lamp. Oh, it's a lantern. I should have said lantern. Go north. And you wait for the computer to respond. Let's show the most sophisticated retail available AI system out there today on the next slide.

And tell me how different it looks. So what's happened to the gaming industry from the 80th to today is going to happen in AI but in the next like five years. So that will be compressed in terms of how quickly that change happens. But we would all agree games are better today than they were then. They're photorealistic. You can like inhabit them. They're moving very quickly. On the AI side, there will be ambient computing, there will be, the problems that Zork had will be solved for AI,

lack of memory, lack of consistency, session resets, and so forth. How did we get there? To answer your question, I don't plan on investing in kind of larger models, right? Just like it wasn't better stories that would make better games. It was controllers and physics engines and GPUs, and those are the parts of the AI cycle. that I'm interested in, which is all the platforms that need to be built. Machinery. Correct.

That is going to make this reality real in the next five years. And it's not just bigger models. I think we're at the Atari command line stage of AI, and we're going to get to the PlayStation 10 stage in the next five years. You also used to do a lot of stuff in life sciences. Yeah. Not as much anymore. My interest in life science, I founded Calico and been very interesting.

in that space and we were investors in flat iron and veer and lots of other companies. I'm very interested in that space because it is a dual benefit of helping people and also do good, do well. Correct. However, the therapeutic space that requires human clinical trials is a specialist investment area that we're not spending a lot of time on. I'm very interested in computational biology and in those areas. It seems if you're just look on X that there's a renaissance happening in human health. I don't know if that's true, whether it's cures for pancreatic cancer, cancer vaccines, peptides, obviously. There's just an explosion. And a lot of it seems to come back to computation. But this class of winners so far is not really

computationally driven. It was just really good science 10 years ago. Yeah. And so do you think that we're about to see this massive? I hope so. So I started Calico and again, it was like fringe science, longevity at the time. And now we're investors in New Limit, which is Blake Byers and Brian Armstrong's company and a number of other companies in that space, which doesn't seem so crazy anymore. However, because of the human biology and the FDA, if you find a compound and you think you've got something, that's like 5% of the work. There's still all kinds of titrating and safety testing that needs to go on. And so I don't think it's going to go quite as exponential as we would all like it to. However, if we can achieve a realistic stimulation of a human

cell in silico, then you will see that accelerate as well. We're not quite there yet. But generally we're seeing, some might say a flight of capital to India and China right now. Are you seeing that, that the biotech path to market is faster if you invest in firms that are based offshore versus the U.S.? I think the U.S. has always indexed on human safety over speed to market, and that has cost us in some ways. However, some other countries are indexed in the opposite direction, which costs lives. So there's a balance there, but there's certainly, there's research going on in China and other places, experiments and cloning and all sorts of things that, as far as I know,

aren't happening here. So, yes. And I think the gutting of the CDC and the NIH and the, and, and, and, and, and, you know, An anti-science vibe that has now pervades this country has driven a lot of mine share elsewhere as funding is drying up for basic research. I mean, China's got their own paperclip model now. They're recruiting some of the best scientists from Europe and India, and they're all immigrating to China to go do work.

And that used to be a scientific pool that we used to access and we used to recruit. We really need the neurological results. reserves here. And this business with... Or brain trust would be another way to say that. Yes, that as well. But the... The pushing out of H-1B holder, there's so much happening now that it's causing, it's just easier to go elsewhere. That's not good for science. What's your view on what's been called deep tech for the last decade, these traditionally

long investment cycle, capital-intensive, high-risk? Like Elon is one of the few entrepreneurs that have successfully tackled deep tech business model with SpaceX and Tesla. Is this becoming a more tractable area for entrepreneurs to activate and for investors to invest in because of AI enablement and physics engines? Absolutely, because things are moving so much faster. What areas like that are you focused on investing in? I mean, human biology and healthcare, it's probably the largest TAM in the world. So super interested in that.

And then all of the others, I mentioned that, kind of underlay the AI revolution, which are the physics engines and the controllers and the GPUs and everything that is going to take to get us there. I want to bring Sacks in Freiburg before we're out of time if it's possible. Sacks, I'm curious your thoughts on the venture capital business. I think you've did five craft funds or four? Well, we've done four venture and two growth. I'm assuming you're going to be going back into the venture business. But I'm curious your take on when you started in venture and when we started as entrepreneurs 25, 30 years ago,

this was a much different playing field. What are your plans based on sort of bills look at this? And do you believe in the $500 million fund sweet spot? Or do you think you need to become Andresen Horowitz when you go back to the private sector? Well, I don't think we need to become Andreessen Horowitz. But, you know, look, I think fund size to turn around. funds strategy and the size of your fund, because you're going to divide your fund size by 20 to 25 names to achieve some portfolio diversification and construction, that'll determine your check size

and that sort of determines where you play in the market. The thing that's spinning through my head after Thomas's presentation is, you know, are you better off just focusing on, you know, let's call it what used to be called, I don't know, late venture early growth, you know, you're writing $50 million checks. You just kind of wait for the breakouts as opposed to playing in this really noisy, super early stage game. I think the problem with that is we have to look at the incentive structure of venture. So a $5 billion venture fund that returns 1.01x gets to say that they earn the 75th percentile and can raise their next fund.

and no one at the Stanford Endowment is going to get in trouble for writing that check. They need to put two or 500 million into a fund multiple times. So I understand that dynamic. So now let's look at the GP dynamic. Well, if I have a $5 billion fund, I return 1.01x, I'm going to make more money than Bill with his $500 million fund that returns 3x. Okay? So that's also a strange incentive.

So now let's look at the entrepreneur side. I am researcher X from OpenAI. I'm going to start a company. Bill says, I'll give you $20 million at $100 million valuation. I want to buy 20% of your company. Giant Fund Y, or friends, it's a different model. But Giant Fund Y says, well, we have this giant fund. We need to put $250 million in.

And then an entrepreneur says, well, my company's valuation is $100. No, your valuation is now $4 billion. And we'll give you $250 million for a profit. percent of your company, they're going to take that deal every day, unless you're a seasoned entrepreneur who has kind of been down the road and knows the pitfalls of that. And so the incentives are broken in all those ways. And the pendulum will swing back. So I don't think just staying late stage and waiting to sniper at larger companies will be

a long term. The data would suggest that's not going to work in the long term. Okay. Let's thank Bill. Amazing job. Thank you. Bill. Thanks, man.