← Moonshots with Peter Diamandis
Moonshots with Peter Diamandis
AI Investor Panel: How Will We Fund the Global AI Revolution? | EP 219
00:33:04
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- [00:01]
A
How will we fund the global AI revolution?
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B
All the rules are being rewritten about how you fund growth because we just need all the capital we can get. What is the main thing?
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A
It is AI.
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C
Where does the next Nvidia style growth come from?
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B
The COMPUTE has gotten so expensive they're going to dedicate massive amounts of capital to this space.
- [00:24]
D
I'm the old fashioned stock exchange. I think our common challenge will be to make sure that we find as many ways as possible that we match the capital with the opportunities.
- [00:36]
C
The amount of capital going into the sector way outstrips the venture funds. That trend is now drawing in a huge amount of money, which is why we're talking about it on this stage in Saudi Arabia. The untapped but mobile capital is here in this room. And if it jumps on the opportunity, it's like an opportunity I've never seen before. Now that's a moonshot, ladies and gentlemen.
- [01:07]
A
All right, welcome everybody to our AI Mini Summit brought to you by Linc Exponential Ventures. It's a pleasure to have you. We're going to be having a series of 30 minute conversations that look at AI investing, where the next trillion dollar companies are coming from. We'll be having a session of our moonshot summit and I'd like to open with our first session which is how will we fund the global AI revolution? To enable this conversation, it's a pleasure to bring on stage three leaders in this field. David Blunden. David is my business partner. He's a serial entrepreneur. He is the managing partner of Lynk Exponential Ventures. With 23 startups under his belt, a long track record of a 44% IRR, little over a billion dollars AUM based on the campus of MIT and Harvard. David Blunden, please come on up, Take a seat here. Thank you, David. Next up is Bonnie Chan, CEO of the Hong Kong Exchange and clearing HKEX since March of 2024, bringing over 30 years of global capital markets, legal and listening transformation experiences. Bonnie, please join us. And finally on our panel this morning is Anjane Mehta, partner at Andreessen Horowitz A16Z, investing in frontier AI open source infrastructure. The man who's backed Anthropic on the board of Mistral. Please welcome to the stage Anjane Mehta. So how will we fund Take a load off the global AI revolution, guys. So I mean when I think about it, we are seeing today, at least in the United States, $1 billion deployed per day into AI. The expectations are we're going to see that growing to $3 billion a day by 2030. And I expect it's going to blow through that. In fact, I'm seeing capital flowing to the exclusion of a lot of other things. Let's open with opening thoughts around that. Anjanai. I mean, you're at one of the largest venture funds on the planet. What percentage of a 16Z is flowing towards AI? And what are your, what are your thoughts about, about the capital availability to fund this, you know, infrastructure, you know, what we call on the Moonshots podcast tiling the earth in compute.
- [04:04]
B
Right? How much, how much capital is flowing into AI? Basically all of it. And it's still not enough because, you know, the firm was founded to be a verticalized firm. We have an infrastructure fund, an applications fund, a healthcare fund, and all of those are now AI funds. Right? Because AI is a cross stack thing. Whether you're working with teams that we're training foundation models or building applications. I don't think anybody is not an AI investor anymore. On the other hand, what's also insatiable is that is the need for these for AI businesses, especially ones that generate tokens that leverage the latest generation and reasoning models, which generate 10 times more tokens than traditional Genai models. Before reasoning, we're living through Javon's paradox every day where no matter how much infrastructure build out we do, no matter how many, you know, algorithmic efficiencies there are, we somehow just need more compute, more infrastructure to serve the state of the art demand in text, code, image, video. It's just sort of this insatiable explosion of use cases. And I just don't think we figured out how to change the traditional venture capital stack to fund all this growth. And that's why you're seeing, you know, we try to fund entrepreneurs as much as we can, but then we got to pull in all the friends we can. Whether that's Nvidia as a strategic who invests on the cap tables directly alongside us. It might be a data center provider. There's just, you know, whether it's Satya doing a billion dollar investment into Micro into OpenAI as a nonprofit four years ago, or it's Amazon and Google investing in Anthropic, there's just all the rules are being rewritten about how you fund growth because we just need all the capital we can get.
- [05:44]
A
Bonnie When I see an offering being made by Elon for XAI or by anthropic or by OpenAI, instantly it's filled. I mean, people are fighting to get into these deals and no one's asking is the valuation, is the deal going to make sense? They're just throwing capital at this. How are you seeing it from your perspective?
- [06:08]
D
Well, first of all, I do agree with the comment that Andre made, which is the insatiable demand, right? I mean, just everyone wants to pour money into it. But I must say, Peter, it's very interesting how you put together this panel because, you know, as I see it, I'm sort of, you know, sticking stuck in the middle of these two gentlemen. You represent the private side, shall we say, the VCPE community. I'm the old fashioned stock exchange. I do public offering, I do IPOs. And so, you know, how are we going to fund it? I think there are many different ways. But suffice to say, you know, given that, you know, Hong Kong Stock Exchange, obviously we're in Asia and I would say given the demographics, there is an emergence of a lot of, well, a big population of retail investors. We tend to now call them protel investors with technology now everyone have their own trading theories and strategies and whatnot and they can execute, you know, rather in a rather sophisticated manner. So I must say that, you know, from my vantage point, I still think whatever ways is available which can bring as many different pockets of demand right from different investors at all corners of the world will probably be a good way to support the development of AI on the one hand and really quest that insatiable demand on the other hand. So to put things in context, we've done quite well this year in the IPO space. In fact, Hong Kong is now number one on the global IPO league table. This year we have, we have 300, 300 deals in the pipeline waiting to get done. We have already done about 80 year to date and I would say of the 80 which has been completed and the 300 which is still waiting in line, about probably half of it has something to do with AI. Now there are different manifestation, but I would say especially with the companies in the Chinese mainland these days, if you are not already doing something with AI or being, you know, at the very center of the AI development, you probably quite unable to compete and be successful in your business. So this is sort of my answer to your question. I think really just given how much capital is needed to support the growth, whether it's private, whether it's public, whether it's credit, whether it's equity, does not matter. I think our common challenge will be to make sure that we find as many ways as possible that we match the capital with the opportunities.
- [08:52]
A
David at Lynq you're seeing and investing in companies as the first check?
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C
Yep.
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A
Companies born out of mit, out of the, you know, CSAIL computer science AI Lab and out of Harvard. What are you seeing as the growth of companies going into AI that is feeding the pipeline at the early stage?
- [09:16]
C
Well, I'll tell you, there's a reason Bonnie's on this panel and sandwiched between the startup guys because the amount of capital required coming into these companies, like you said, $3 billion a day coming up, US venture is 200 billion a year. So it's not even close. You know, five times more money needs to come from somewhere. And so as Ange said, you know, some of it comes from Nvidia, some of it comes from corporate venture. But these companies like Mercur, one of the ones in our portfolio valuations, went founding 30 million, 300 million. Two billion. 10 billion.
- [09:49]
A
And how, what time? Two years.
- [09:50]
C
Two years. So first of all, the $10 billion number is unprecedented in eight or 10 years. And what used to be incredibly rare is now incredibly abundant. But the amount of capital going into the sector way outstrips the venture funds. And so what we generally see is the corporate money, the Nvidia money comes in to fill the void. But the people working there, they say, wow, this is really fun, I'm glad I made that anthropic investment, but I'm going to go do my own fund. So the talent tends to eventually come out of the corporations and go into the 2 in 20 private sector to fill the space. So I think that that trend is now drawing in a huge amount of money, which is why we're talking about it on this stage in Saudi Arabia. The untapped but mobile capital is here in this room. And if it jumps on the opportunity, it's like an opportunity I've never seen before.
- [10:45]
A
Can we talk about the two sides of AI? One is the build out of AI infrastructure. Right. And the other is AI applications and the build out of those applications. Where do you see the capital split between those and the attractiveness to venture funds or public markets for those two things?
- [11:06]
B
Anj, that's, it's a, it's a really interesting question because the last few years basically three, four years were dominated by the infrastructure build out. Right? So most of the capital that was going into startups was being converted directly to GPUs. What's interesting now is you have a whole category of super exciting application businesses. You know, we got Amjad right here, building one right in the coding space. And to build application businesses like that you not Sometimes you need GPUs, but other times you need tokens from other foundation models. That's now a raw ingredient as well. So the capital stack was just raw cash. Then came you'd convert raw cash to GPUs and then the foundation model teams converted the GPUs to tokens. And that's an input now into application developers, which is, if you think about it, way more of a scarce resource. High quality tokens from foundation models is a much more scarce resource than raw GPUs and GPUs are a much more scarce commodity than raw cash. And so that's the prep stack, I would say.
- [12:08]
A
Do you see the demand for infrastructure build out continuing and accelerating or topping.
- [12:16]
B
Out accelerating and not being able to, not accelerating fast enough? Because now the fundamental constraint is energy. Right? We literally just don't have enough power density in most of the legacy data centers in most regions of the world. And you've got to go retool these data centers for GPUs. If you look at the new Blackwells from Nvidia, all the research scientists I talked to are really excited because it's got the NVL72 networking stack, which means you can do a bunch of great big memory intensive training runs like video models. And then you get down to the brass tacks of where when can that data center actually go live, when can we get it cabled, when can we get the energy permits? And that's way after when the chips can actually get there. And so the infraspan, the infra needs are largely driven by demand forecasting. As we discussed earlier, demand is completely uncapped and meanwhile the compute supply chain is caught up. But the energy constraint hasn't, the energy supply hasn't. And so what we're living through right now is this frenzy for energy contracts where compute providers are trying to outbid each other to buy literally just energy supply. So depending on which part of the infrastack you're talking about, I don't see things slowing down. From a, from a, from a funding perspective like the capex, going into this, into infra is not slowing down, but what we may be faced with a hard wall on is just energy scaling. We just don't have enough electricity to power the chips.
- [13:44]
A
Bonnie, what are you seeing in the public markets in terms of energy data center build out, chip build out, application companies?
- [13:54]
D
Well, it is all of the above. Right. But I do want to make a slightly more nuanced point. I think at the moment the money that has been put into AI, you know, the 2 billion a day, a lot of it is probably put into the, you know, these different opportunities on the premise that there is a promise that somehow it's going to translate into things which are much easier to evaluate, right? So at the moment, people just want a piece of AI. They don't care whether it's infrastructure, applications, energy does not matter. But eventually, I think as the journey continues, I see a point where people will start to be a little more focused in terms of how we put a value on all these different opportunities. So from my vantage point, for example, and I think you raised a very interesting point, the energy bet is the, is a million billion dollar, multibillion dollar question because without that, you really cannot go that far. And therefore, if I look at my pipeline, for example, I think China, as a lot of, you know, has been quite advanced in terms of coming out with new energy solution. And, and it's not only generating that new energy, storing and you know, I mean, China is a massive country, right? So how do you make sure that, you know, you have all the grids talking to one another and then you can generate, you know, with the western and the northwestern part of China, abundance of sunshine, wind and everything, right? You have the geographic or geological conditions to, to help generate that, that green energy. How do you make sure that you can disperse that right into data centers again at every corner of the country so that, you know, you can support all the data center, the infrastructure and all that. Now with that as the building block, you therefore can proceed to the next level and talk about the compute, the, the applications and all that. Again, I would say that China has an advantage because it is still, you know, a very big and dominant manufacturing hub. And, and with that it's actually quite easy to think about possible applications and you know, how you embed AI into production processes. I would also say that where I'm seeing a lot of activity is really the data intensive sectors, right? So just to cite an example, we are now beginning to see a lot of companies, you know, in the drug discovery business, for example, right, embedding AI, which is, as you could imagine, right, the, the traditional way of drug discovery. You have to go through clinical trials, you have to select samples and you know, and, and all of that is data and intensive. But if you can speed it up, right, with AI, you can imagine you are going to accelerate the pace of drug discovery so much.
- [16:49]
A
You have a friend of mine going public on your exchange in silica medicine.
- [16:53]
D
In the next I'm not allowed to comment. I know any.
- [16:56]
A
I'm specific.
- [16:57]
D
Yeah, well, anyways. But yeah, I think you see my point there. Any data intensive business will be a darling.
- [17:05]
A
You know, in this regard, David, you're seeing companies at inception, you're seeing entrepreneurs, brilliant entrepreneurs. And I think you've commented that the number of startups coming out of MIT and Harvard in the AI world is like quadrupled in the last few years. Yeah, more than what kind of distribution? What are you seeing? Where are they going into application layers? Compute. What are you seeing as the categories?
- [17:33]
C
The companies coming out of MIT and Harvard are overwhelmingly going into vertical use cases. And then also some foundation model companies like liquid AI will be on stage right after this. So there are a few of those, but many, many more vertical use case companies. And the success rate of those is near 100%. And so they're attracted to first, they're not super capital intense, 100%. Well, so far for us, MIT and Harvard, teams that fit a profile are 100%. I've never seen anything like it before. And it's because the use cases are so abundant relative to the talent pool. So if you have the talent, and you'd have to be crazy to go after a bad use case right now, you can use AI for so many things. It's very, very different from crypto, which was the last wave. More similar to the Internet. The Internet is incredibly flexible. Can use it for many, many things. And you saw, you know, when I started investing in the late 1990s, everything you invested in succeeded. Why? Well, because the Internet can do almost anything. And so unless you're insane and going after something really dumb, you're gonna succeed. So I haven't seen that again in my lifetime until now, and now it's the same thing. And the value isn't enormous and the teams are thriving every single time, but they're really attracted to the vertical use cases because they're not as capital intensive as building out an entire data center. Now, you know, Chase Lockmiller is doing Stargate, so there's one guy who's an exception to that. There's a $500 billion build out, so. But that's relatively rare. Most people go after the use case.
- [19:00]
A
And how quickly are you seeing the valuations in those kind of companies scale?
- [19:04]
C
I mean, it's in like Chase, Locke.
- [19:06]
A
Miller or in the companies. And they're doing the vertical.
- [19:10]
C
In Lynx Studios, typical entry valuations are what they've always been. Maybe 20, $30 million. First funding will be 100 to 300 million. And then within two years, if you're going to be a unicorn, you're going to get there in two years. Now, which means the founders now are still 23, 24 years old. So that's a new thing in the world too. You know, we have a bunch of people that I can name, I think about my entire lifetime of investing, can name like three or four people that I knew or invested in that hit billionaire under the age of 30. Now I can name eight that we've invested in in just the last few years. It's like there's this new class of person roaming around that barely has a driver's license but has a billion dollars in liquidity. So we have to kind of adapt to that.
- [19:56]
A
Used to be a billionaire, being a billionaire was a big deal. Now we're just going to wait for the trillionaires to start.
- [20:01]
D
We're all born in the wrong age.
- [20:03]
C
Yeah, yeah, yeah.
- [20:04]
A
You know, I want to understand what you guys consider the biggest risks over the next year. Is it compute cost inflation? Is it talent scarcity? Is it regulatory intervention? We've been on this incredible inflationary and exponentially growing curve on all things AI. Just like used to be AD.com on the end of your company, now it's like, oh, we use AI. Anj, what are you seeing as the risks?
- [20:38]
B
So on fundamental like progress of capabilities, we already talked about the one energy, which I'm concerned about, but I double click on that.
- [20:46]
A
So will, will these companies have access to sufficient electrons to run the data centers? Is it, Is it what? What is the scarce resource in the.
- [20:56]
B
In the chain in the United States? I think that's a direct function of whether the permitting regulations that the current administration is working on end up getting executed on. So there was a big plan that was introduced, the AI Action plan, about two months ago, which I think was a fantastic start. And if you, if you go sort of line by line through that, it really is a very precise, methodically laid out document that says here's what we need to do to unblock progress. And I think if we can operationalize it and execute it, then we should be good. But rarely has that ever happened at scale without a ton of bureaucracy. A ton of bureaucracy. And this is my second actually concern, which is without a ton of, I think, civil blowback. Because the reality is putting these massive data centers down, cabling reallocating parts of our power grid from other things results in tough trade offs we've got to make as a society. And I Just, I want to respond to the previous point a little bit where it is true, we are seeing enormous wealth creation amongst this generation. Right. Anthropic has gone from a company that was, you know, a couple hundred million in valuation just four years ago to $183 billion in 48 months. But I don't think we should be celebrating that as much as we kind of are right now because at the end of the day, the public is not participating in that wealth creation. The vast majority of wealth being created by Frontier AI is locked up inside of private capital like our funds. It's locked up inside a small group of talent that is super mission oriented. But I don't think we've really figured out what happens when the rest of the public goes, well, where's my piece of the future?
- [22:29]
A
Yes.
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B
And I don't think we're ready. I don't think we're talking about it enough. And I don't think governments are doing enough to realize how dire it's about to get when 30% of your IT services GDP sector gets vaporized. By tokens, if you're India, for example, where double digit percentages of your GDP are literally IT services, what do you do when Claude and GPT5 tokenize like vast portions of that flow? I think we love to talk about productivity growth and we don't talk about how to manage the short term transition pains and that's going to be ugly.
- [23:03]
A
So that you're adding that to our risk profile. Civil unrest.
- [23:07]
B
Absolutely.
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C
Well, good example of that too is just a few months ago when Sam Altman said, hey, I'm going to give everybody in the company a $1 million retention bonus. Everybody. And the intention was for that to be cool. The reaction worldwide was that's not cool. And so now you're seeing the AI leaders comes up on the Moonshots podcast a lot. The AI leaders are really downplaying the rate of progress because the people that are picketing outside the door at OpenAI headquarters are lined up a deep now and they're like, look, all this wealth, you guys are all billionaires. What about everybody else out here on the street? And they don't, they don't need that.
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A
And it's, you know, just to put a finer point on that, I know a number of the technology leaders and investors in Silicon Valley who have been getting death threats.
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C
Yeah.
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A
And then they lock down their companies, they lock down their homes. And this is before we're seeing the CPI of electricity going up. But this is before we're Seeing the real layoffs that occur.
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B
Well, so I think this is important. I think AI is going to get blamed for a lot of layoffs that have nothing to do with AI. A lot of the layoffs we're seeing today from big tech companies are really just people correcting over hiring during the zIRP era of 2010-2012.
- [24:26]
A
Also the print money era of, of.
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B
COVID So the easy money is gone and a number of big tech companies that just thought they could keep putting chasing returns by over hiring, which was a fairly rational thing to do then.
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A
In fact the government was paying you to go.
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B
Exactly right. But the incentives have changed. So one I just want to. There will be a lot of boogeymanning around AI that has nothing to do with AI.
- [24:50]
A
Agreed.
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B
Okay. But once we're through that era, what happens is people are going to start asking why aren't my, why isn't my pension fund, my sovereign fund, my retirement plan, participating in the AI wealth creation opportunity? And that's why I think to the point of this panel, which is how do we fund the future of AI? We should be asking how do you connect the frontier AI growth to public wealth creation? And there's a bunch of institutions whose job it is to steward our wealth. Sovereign funds, pension funds, state funds. Why aren't they investing on the cap tables? Why is it family offices? Why is it high net worth individuals when we rent out to raise the seed round for anthropic? I made 22 introductions to them up and down Sandhill road. They got 21 no's. So we had to scrape together 100 million bucks, which sounds like a lot of money, which was a lot more money than back then. Now actually to David's point, it may not be that much, but really that founding round had to be pieced together from angels and height net worth individuals. And I'm still shocked at how often today traditional venture sovereign funds, traditional pension funds are not being aggressive enough in managing the steward. Taking their job as a steward of public capital and exposing it to frontier AI wealth creation, it's just not happening fast enough.
- [26:01]
A
Dave, do you want to add on the risk side?
- [26:03]
C
Yeah, I completely agree with what Anj said. And I'll give you another parallel risk which is that the core AI companies that do things like customer support, white collar automation, just killing it. I mean adding immense amounts of value and the investment community coming in has started to extend that to oh, tech is a good place. Let me put 200 million into fusion energy. You're like, well that's not A.I. well, but it's going to create the electricity about four or five, six years from now to fund to. So it's related to AI. I'm like, well, okay, but that's very capital intensive and you're not sure it's going to work. And so I think it will work and I think it's a good area to invest. But if it doesn't work, that's where you're going to have this. What happened to the Internet in 2000? The Internet was very real and if you waited long enough, it came roaring back. But everybody lost confidence in 2001. Why? Because there's some really bad peripheral investments and now we're seeing that. And I don't want to throw too many things under the bus, but some things, like robotics is very capital intensive. Fusion energy is very capital intensive. It's not the obvious win of AI. It's a peripheral investment. Some of those will be good. Some of them are going to consume a ton of money and turn into losses. And that may scare off the entire investment community. And so that would be tragic because if you look at things like just if you look at AI voices doing sales and customer support, that's half a trillion dollars of payroll worldwide today. The AI does it better than anyone on the phone already. Like existing tech. We just need to deploy that half trillion. If you invest in that, you cannot go wrong. But if you get sold an investment in something that's kind of like, well, quantum computing also might work, maybe, maybe it will, maybe it won't. But much more speculative and very, very capital intensive, please.
- [27:50]
D
And I do want to chime in there, I think listening to all this, right? I mean, one part of me is saying I want to democratize these investment opportunities, right? Let more people partake in the, in the party. But on the other hand, right, just given how the current ecosystem has built out, the valuation has already hovering somewhere up here, opening the door for investors, especially retail guys, to partake in. And the public markets also caused me concern, right? Because I mean, for all I know, they could be the last one being handed the, you know, being the last ones at the party before the whole thing collapses. So I think, you know, I would call that as a risk. How do we actually find that new equilibrium where these opportunities are not just monopolized by a very small group? How do we make more sense out of the valuation which we are seeing, which is again being established by a very small and rather opaque, in some instances, price discovery mechanism. But you know, to your Original question about risk. I do see the energy piece as one which is very difficult to solve because, I mean, even at my company, right, we're exploring with, you know, what we can do with AI and we have come up with a few cases, right. That we were experimenting with. And the next thing you know, I come, you know, the electricity bill arrives and you started scratching your head. Right. I thought is going to help me with productivity and make things faster, easier, you know, more accessible. Yes, but there's always a cost there, right. So I guess, you know, people just need to.
- [29:34]
A
We have a minute left for closing thoughts from each of you. Anjane?
- [29:39]
B
I think the answer lies in institutions who represent the public. Sovereign funds, wealth funds, you're right. Opening up the markets to retail investors who may not understand what's going on may not be is not the answer. But I think institutions who represent the public are the answer. And it's our job to educate them and make them more aggressively. I think take a position in the wealth creation opportunity that's happening. Otherwise the public will get left behind.
- [30:01]
A
Bonnie, closing thoughts on who's going to fund.
- [30:03]
D
No, I agree, I agree with that. I really think the, you know, everyone in this ecosystem need to work together to find that new equilibrium. It shouldn't be wealth creation for a tiny fraction of the world's population. And we need to find the right, right way to get it done.
- [30:21]
A
All right, Dave.
- [30:22]
C
I think the most important thing that I heard on this stage today was what Andre and saying the story of how Anthropic got funded. So many people are not getting in the game and Silicon Valley investors that are just walking down the street and investing each other are killing it and running away with all the, all the gains because it's just not that hard. You just need to get into the loops, get into the places that are making these investments and get in the game. And then the pro rata rights on that deal alone would have allowed you to invest a follow on of probably what, 4 or 5, $10 billion of follow on. But you just had to be there in the game at the outset.
- [30:57]
E
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- [31:09]
A
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- [31:12]
E
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- [32:06]
F
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