Bill Maris: How Google Could Crush AI Competitors, Why Small Funds Win, and AI's Atari Stage
28m 42s
Bill Maris, former CEO of Google Ventures and founder of Section 32, is returning to venture capital with a focus on smaller, highly selective funds. He asserts that smaller funds—averaging $400 million—outperform larger ones due to greater attention to founders, better investment discipline, and higher return potential. Drawing from his experiences at Google, including incubating Waymo and Calico, Maris emphasizes the role of computer science and foundational technologies like physics engines and controllers in enabling the AI revolution. He believes that future breakthroughs in life sciences, particularly through computational biology and in silico modeling, will yield significant value. Maris also challenges the current trend of private firms hoarding value and excluding public investors, arguing that such practices undermine equitable wealth distribution. He warns that venture capital remains inherently bimodal—featuring rare, massive returns alongside most underperforming investments—highlighting the need for transparency and founder-centric investing. His strategy prioritizes early-stage, high-impact innovation over late-stage, capital-intensive bets, challenging traditional venture models and advocating for a more focused, sustainable approach rooted in technological and human capital.
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 invested 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 hundred years and what's about to happen in the next hundred years with the advent of AI, the world's 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 Lex, Calico, and many other projects as well and before that I found that a web hosting at 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 when I 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 in 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 try 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 e-mail 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 e-mail in your closet how many websites and e-mails could I put in my closet so I immediately quit my job because I had kind of glimpsed through a keyhole and through the 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 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 we 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 so I called the landlord and said the roof 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 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 server is getting electrocuted or perhaps I get electrocuted but as an entrepreneur I was willing to take that risk which you know news flash I survived my shoes though are still stuck on that roof in Vermont which takes me to less than 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 in the 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 1989 and there's someone taking a picture that makes sense it's probably a film camera and 2005 is not very different there's still someone back there taking a picture and then let's go just four years later to 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 and this was kind of before our 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 and we look closely this gentleman has decided to a 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 a Google and a challenge was given to me the challenge was Google needs a vendor 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 minor is the co-founder of Android and he became my partner in crime as we conceptualize what what could Google ventures be we went up and down sand hill road and we 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 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 is it's a hundred 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 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 fun size would be and people were excited here's a headline from tech crunch at the time and and people inside of Google were also pretty excited this is 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 uh two thousand ninety two two thousand eighteen top quartile VC returns look like this and top death style looked like this uh using publicly available information I'm not sharing any non public for prior to a 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 uh 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 two thousand and seventeen uh I decide 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 lunches free and the massages are plenty and so forth uh but after the idea you know sunk in I the advice discerned into raise as much money as possible uh you know that's the right way to run a fund you'll get a big management fee you'll be happy things are gonna work out really well for you uh and I thought about that relative to everything I had done up to that point and I decided to to not uh take that advice uh and then over the course of my time at section 32 we've had six funds we've invested in companies like CrowdStrike and Cohear and Coinbase uh and all six of those funds have averaged about 400 million in size and all are performing in their top desial and to the extent there is DPI to measure that's the only measure as far as I'm concerned in venture that counts as DPI uh which takes me to lesson four that uh this is this will be heresy to some but small funds outperform large funds uh this is simply the math this is not uh uh uh
of an opinion I'm trying to convince you of, but there are many reasons for this. Smaller funds, you can have more focus. You have, 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-desile performance of DPI, fund 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-desile 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 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 through, 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 who'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 you guys. Thanks so much. - You guys are old friends. - Yes we are. - We go way back. Well, 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 dollar exit to Monsanto. - What was your multiple? What was the return? - Oh, I don't know. - It was actually good for you guys. - It was quite good, yes. - Yeah, you guys were in the B&S. - Yeah. - And when billion dollars was a lot of money then? - That back then that was a good deal. - That would have been this round. - Now it's like an A round. - Yeah, that would have been your A round. - Now we're gonna do it again with a hollow. So, 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 the data he shared is that you can get significant multiples to get to that next phase. 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 to whatever. 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 experts. - 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 of trillion 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. 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 tokens to 80%. I'm gonna cut them in. What happens to the business models of open AI and the throttpick at that point? What happens, tell us. Actually, 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 that tooth fault 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. Well, it may not be a margin though, to the, they may be burning investor cash, sort of like an Uber type model, grab market share, grow 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 got to have cash generation. So that's 100% possible. It's 100% a problem. Look, I'll just say it's been said before, a trillion four spend commitments on 60 billion dollars 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, so your four, you know, those 401Ks, those retirement plans to get into those companies now, which are getting bizarre exceptions to S&P 500 rules, that 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 three X from here or sure, but they-- So the, 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 profound. My position is, it creates more wealth creation for the people who don't need it. And it makes the people's retire accounts the back holders. 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. Just the public's retirement accounts the back holders. 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 this question, when these dollars get distributed, there's going to be a handful of funds that have enormous returns. I mean, just unbelievably excessive. Founders to, you know, is going to print $100 billion return on $200 million of invested capital. But that's one fund in isolation, right? And there'll be a few. You're funds when you are at GV are going to print an enormous upside. And so if you don't look closely, beyond the averages, venture is going to look incredible. If you look past the averages, venture's still going to look extremely bimodal, a handful of winners at a ton of losers. How does that play out? I mean, one, that's how venture is, right? 75% of funds lose money. But two, in order for founders funder to 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? You've got to make a business case in the public market that can show that this business is worth the discount of value 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. And 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. OK, 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 when AI is everywhere? And there's tumors on one side and the utopia is-- Is it 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 '80s to today is going to happen in AI, but in the next video.
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. 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. It wasn't better stories that would make better games. It was controllers and physics engines and GPUs. Those are the parts of the AI cycle that I'm interested in, which is all the platforms that need to be built to-- >> Ginery. >> You're correct. That is going to make this reality real in the next five years. It's not just bigger models. 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. Not as much anymore. >> My interest in life science, I founded Calico, and I've been very interested in that space, and we were investors in Flatiron, 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-- >> You could 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 those areas, which is-- >> It seems if you 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. 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. Do you think that we're about to see this massive-- >> I hope 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 simulation 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 off-shore, versus the US? >> I think the US 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 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 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 to work, and that used to be a scientific pool that we used to access, and we used to recruit. >> We really need the neurological reserves here, and this business with- >> Or brain trust with the other way to say that, but the H1B 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 has 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 and- >> 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 it's going to take to get us there. >> That's good one. I want to bring Sachs and Freebergroom at a time, if it's possible. >> I'm curious to your thoughts on the venture capital business. I think you've did five craft funds or four. >> Well, we've done four venturing, 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, you know, sort of bills? Look at this and do you believe in the $500 million fund suite spot? Or do you think you need to become Andreessen Horowitz when you go back to the private sector? >> Well, I don't think we need to become Andreessen Horowitz, but, you know, I look, I think fund size determines fund 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. They'll determine your check size and that's where the 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 you speak 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 percent tile 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've returned 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, why? Or friends, it's a different model, but giant fund why 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 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. Thank you.
Podcast Summary
Key Points:
Bill Maris is returning to venture capital with a $150 million fund called Section 32, emphasizing selectivity and deep focus over scale.
He believes smaller funds outperform larger ones due to greater founder attention, better investment discipline, and sustainable return potential.
Maris highlights the transformative power of AI and computing, arguing that computer science—not intuition—drives future success in innovation.
He shares personal anecdotes of early entrepreneurial insights, such as discovering data centers in a Vermont apartment, which shaped his path into tech investing.
Maris advocates for investing in foundational platforms like physics engines, controllers, and GPU technology that enable AI advancement.
He is particularly interested in computational biology and life sciences, seeing potential for breakthroughs in human health through in silico modeling.
He warns against the current trend of private companies leveraging exclusivity to exclude broader public participation in wealth creation.
Maris argues that venture capital’s true performance is bimodal—few massive wins amid most losses—making transparency and long-term founder support essential.
Summary:
Bill Maris, former CEO of Google Ventures and founder of Section 32, is returning to venture capital with a focus on smaller, highly selective funds. He asserts that smaller funds—averaging $400 million—outperform larger ones due to greater attention to founders, better investment discipline, and higher return potential. Drawing from his experiences at Google, including incubating Waymo and Calico, Maris emphasizes the role of computer science and foundational technologies like physics engines and controllers in enabling the AI revolution.
He believes that future breakthroughs in life sciences, particularly through computational biology and in silico modeling, will yield significant value. Maris also challenges the current trend of private firms hoarding value and excluding public investors, arguing that such practices undermine equitable wealth distribution. He warns that venture capital remains inherently bimodal—featuring rare, massive returns alongside most underperforming investments—highlighting the need for transparency and founder-centric investing.
His strategy prioritizes early-stage, high-impact innovation over late-stage, capital-intensive bets, challenging traditional venture models and advocating for a more focused, sustainable approach rooted in technological and human capital.
FAQs
Bill Maris has raised $150 million for his new fund called Section 32.
Smaller funds allow for greater selectivity and focus, enabling deeper engagement with founders. Data shows that funds under $750 million have higher top-quartile returns, with a 4.76x average compared to 2.42x for larger funds over $1 billion.
It illustrates that entrepreneurial success often requires taking bold, risky actions—even if they seem irrational or dangerous—such as investing in the future despite immediate risks.
While working on Wall Street in 1997, he saw a server in an office closet and realized it was the heart of email and websites, sparking his vision to build a business around data centers and internet infrastructure.
At Google, Maris used machine learning to design an optimal investment portfolio and determine the ideal fund size, even though the term 'AI' was avoided at the time in favor of 'machine learning.'
He primarily invests in human health and life sciences, including computational biology and longevity research, as well as in foundational technologies like physics engines and GPUs that enable AI advancements.
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