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Why No Company Will Win the AI War: The "Rebel Alliance" Thesis | Nick Grossman
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- [00:01]
Anthony Pompliano
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Sponsor Voice
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Nick Grossman
we're going to absolutely be living in a multi agent world. Thousands, millions of agents. And that there will be agents that kind of look like people or teammates that you talk to and then there will also be agents that are just embedded in all of our systems.
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Anthony Pompliano
And what's going on, guys? Today we got a great conversation with Nick Grossman.
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He's a general partner at Union Square Ventures.
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And in this conversation we go deep
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down the AI rabbit hole.
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We talk about a thesis that he
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has called the Rebel Alliance. It's the idea that there's going to
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be tons of companies and that the
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AI trend is way, way bigger than you're thinking about on. We get into some of the nuanced details about what's happening in terms of building this technology, where value is going
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to accrue, what are the different use cases, where is the technology going to
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be valuable and what are some of
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the negative trade offs.
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All of that and much more in this conversation with Nick Grossman. All right, Nick, you guys have a great piece that you put out that talks about the Rebel alliance when it comes to how the AI industry is going to evolve. What exactly is the Rebel Alliance?
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Nick Grossman
So the Rebel alliance is our view that AI is too big an opportunity to be dominated by one or two or three companies and that it's actually forming into a massive ecosystem. And we're seeing that happen in real time as agents and agentic approaches start to take hold. And that's really two things. It's like agents that interact with consumers as AI Dr. Agents, AI financial advisor agents. These sort of personified agents are going to be approaching every sector and every use case. And then also under the Hood, it's agents as infrastructure. What we're seeing now is the Agentix stack is forming like the compute stack formed in the prior eras of the web and mobile, where we're not just talking about what are the products that people interact with, but how are every product going to get built. And that's agents under the hood, agents as infrastructure, fleets of agents that are going to get orchestrated in millions of ways. And what that says to us is that agents and the agentic AI approach is taking over and it's going to look more like the fabric of computing and apps than I think, the thing that a lot of us are maybe afraid of, which is everything collapsing into one or two apps, like a chatgpt or a cloud.
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Anthony Pompliano
Now, if you were talking like Peter Thiel, he'd be like, competitions for losers. I want to find the monopoly. He's done a great job at that throughout his career. You're really arguing that that is not going to be the end state here.
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Nick Grossman
Well, there are real network effects in AI in model training and quality. And so far we've seen compounding advantage going to the big labs. And the big question has been how far will that take us and how much of advantage will that accrue, especially as they go farther up into the application layer and get access into more data from more places. So that's the worry that it's going to just compound and that there's going to be an unassailable gravity into the big AI companies. But I think what we're seeing is a couple things. We're seeing real competition among the models. So it's the frontier models from the big labs, but also the leading open source models are really good and they keep getting better and better in lockstep. And developers are experiencing a lot of choice and are pitting models against one another. And then we're also seeing that the world is big and wide. And if you think about the advantage is being able to capture data and use that data to improve systems, that data is going to show up in lots and lots places. And a lot of that data value is going to accrue not just to the model layer, but also the application layer and other layers. So it feels like a huge, huge, huge opportunity for a lot of folks to win.
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Anthony Pompliano
Now, let's talk about a couple of different components. And I was telling you before we started that we've been building Silvia, which is this AI cfo. And so in a weird way, I have half my brain as an investor, half my brain is operating this Business building the technology and seeing a lot of these nuances that I've frankly would not see if I was just sitting in the investor seat. One of the things is you're constantly worried about the big labs who have tens of billions, hundreds of billions of dollars, trillion dollar valuations, thousands of employees saying, that's a nice little business you have there. I'm going to take that for myself. Now, we have both had the blessing and the curse where some of the labs have launched competing products and some of them have chosen to go after enterprise or something different. How do most of the companies in your portfolio think about, is it good when there's competition because that validates a space? Is it bad because they have a lot of assets? Are they distracted? And so maybe they go into a space, but they're not very good at doing it because they're not as focused. What are you seeing?
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Nick Grossman
Yeah, I mean, it's definitely both. And I think any entrepreneur building this space is excited about where they can go and also worried about the shadow of the big labs. We refer to it as the kill zone, the AI kill zone. And where does that land and how big is it and how real is it? I think we don't really know the answer. I think folks who are closest to the core skill set of the models today are probably the most worried. And that's things, you know, language, media, law, code, you know, things that are really natively digital where the core experience of the big products from labs is really good. But I think as you get. So I think there's a lot of worry there. But even within those areas, you know, there are really good companies that are building defensibility and specialization and connectivity into the real world. If you look at like AI lawyers or AI doctors or even AI financial advisors, like there's a lot to build or even in AI music, you know, it's about developing an experience that is special and unique and really resonates. And you know, our bet is that while the big labs are amazing at what they're, what they're doing, the idea of being the best at every use case is, is too much, too much to chew.
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Anthony Pompliano
And do you feel like that is, you know, kind of. It feels like there's like general purpose models and approach, then there's this like specialized workflows. And again, I'll just go right to the example I understand, which is when I look at OpenAI launched a competing product, the number one takeaway I had was there was some core functionality that was missing. If you're looking at it through the investor lens. So the AI CFOs trying to serve these investors, and so it's missing some of this core functionality. If you look at it through an engineering lens, it's exactly what you would build because it's like, hey, plaid and snap trade. These things have the connectivity. And so it was like, oh, wait a second here. I've read about in legal or medical or, you know, but I don't understand those sectors to the, to the depth. I understand the investing stuff. And so to me it was almost like, are these engineering problems or are they actually like, almost like customer pain problems that need to be solved? And where does that expertise lie?
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Nick Grossman
Yeah, I mean, I think you're taking the bet that, you know, as someone with experience in that field and knowledge and, you know, a feel for the customer need, you can pull the product in the right direction. And I think a lot of entrepreneurs are doing that not just in categories that are kind of close to the big labs, but in, in other categories too, like factory automation and robotics and all these areas. Like, there's so much expertise to be had in the world, there's so much connectivity to the real world to be kind of implemented. And I think there's a lot of knowledge at the edges. And on top of that, there's just like a lot of different form factors that tools and applications need to take. And I think people are going to pull them in many, many, many ways, I hope.
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Anthony Pompliano
When you look at your portfolio, how much of it is you guys betting today on like general purpose approach versus specialized workflows?
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Nick Grossman
If you had to put percentages, that's interesting. It's definitely a combination of both, you know, where we have investments up and down the stack from, you know, open approaches to model training to, you know, AI applications focused on, you know, medical and music and other things. And so I think in robotics and lots of others. And so what we're. I think one of the things that makes us bullish about the idea of the Rebel alliance is that as we watch our teams build applications across all these spaces to build a really good application, you're using a combination of general models and specialized models. So if you're building a robot that needs to navigate around and do patrols around the outside of a factory or whatever, there's general reasoning, there's also spatial reasoning, there's local mobility, there's a whole lot of functions that you need to have. Some of those can run on the cloud, some of them need to run locally. As you start going out into all the places of the world, you start seeing a real hybrid approach to model usage. Functionality needs to me, which says the world is going to be multimodal and most use cases are going to be multimodal, a combination of general purpose and specialized.
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Anthony Pompliano
Yeah.
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What I see on the model side is we were one of the fastest growing, I think on a percentage basis, customers of Claude. Just month over month, we exploded the wake up call. Actually, you'll find this interesting. I was talking to one of the executives at a public company and he asked me, he said, how much are you spending on compute? And I told him and his eyeballs fell to his head. He goes, you're spending more than us.
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And this is like a really big public company.
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And I was like, oh, that's a problem, right? Like, how is that possible? And so we basically went and almost created like an internal task force, like, let's get the token cost down. So my takeaways were one, the token cost was going to become a topic for us. I started talking to other CEOs and they were like, hey, this is too exp. But the second thing was I still wanted the intelligence and productivity. And it sounded like other CEOs did too. It was just like, I want a lower unit of intelligence per dollar, if you will. And so I thought that was going to have to be. That means we have to go find a degradated performance in order to get the lower cost. Now it seems like the consensus is like, well, this whole idea of model routing and the ability to predict the complexity of a query and which model is best at answering it and is it specialized or not. And you're seeing that proliferate across the companies that you guys are invested in.
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Nick Grossman
Definitely. And it's both cost and quality. I just saw a report that came across last week about compute performance using a bunch of different harnesses on top of a bunch of different models. And the best performing coding harness was the PI open source harness on top of a combination of models that was beating out CLAUDE in Claude code or OPUS or in Claude code. And we're seeing a similar story across pretty much everything in our portfolio, where when you can intelligently use a mix of models, you can not only optimize your cost, you can improve your performance by using the best model for the best task and orchestrating models that way. So I also came at it thinking it's just about cost and everybody just can't be just like with their pedal on the metal for the most expensive cloud model. But it's Also about optimizing for quality. And I think as everybody moves from kind of prototype phase to deployment phase.
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Sponsor Voice
Right.
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Nick Grossman
The whole world has been prototyping with Claude for the last and OpenAI model, GPT models for the last nine months. A lot of stuff's moving into production now and I think we're going to see much more sophisticated orchestration of agents and models, both for cost and performance.
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Anthony Pompliano
Explain that a little bit more in terms of the orchestration. Like my experience is people think they're talking to one model, but actually the underlying kind of technology is a bunch of models doing different things.
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Nick Grossman
Yeah. I'll give you an example from something we're building at usv. So we have an internal platform that tracks deals and companies and people and ideas and all this stuff. Kind of like a CRM, like our platform.
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Anthony Pompliano
Like a Palantir for vc.
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Nick Grossman
Exactly. And we built it, and we built it ourselves over the last nine months, which is crazy. And we just launched something yesterday where every deal, company, idea, potentially person in our platform has an agent attached to it and they're all orchestrated through software. So it's not just a single agent you talk to. It's like thousands of agents that are under the hood in our system. And that's just one way of designing it.
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Anthony Pompliano
But what are like the. So like you as a user of this product, you'll come in and say that you've been tracking a company, you want an update on it.
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Nick Grossman
Yeah. So we have, so we have this example makes sense. We have a single agent that's like our deal analyst agent, lives in email, lives in our messaging, whatever. You can ask that agent, whatever questions you want. It feels like a team member. Right.
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Anthony Pompliano
And the types of questions you would ask this agent is like, hey, what's the latest on this deal?
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Yeah.
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Nick Grossman
Or like research this company for me, tell me more about the founders, who are the competitors, what are some risks, what should I ask them in the next follow up, in the next meeting I have with them, or whatever. So that's like our main analyst agent that kind of knows about everything under the hood. We have a sub agent that is only focused on that deal. And so we all go to bed at night and that agent wakes up and goes out and listens to Twitter and does web searching and sees the notes that have come in from our system during the day and reads them, kind of dreams about them, thinks about them, goes and does a bunch of work. And then that work gets pushed back down into the main shared context memory data layer. So all the Other agents can benefit from it. So I don't know that this is the ultimate architecture for agents, but what it says to me is that we're going to absolutely be living in a multi agent world, thousands, millions of agents. And that there will be agents that kind of look like people or teammates that you talk to and then there will also be agents that are just embedded in all of our systems. And we're building a version of that. I'm sure you're building a version of that in Sylvia.
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Anthony Pompliano
How much of let's take this internal product that you guys built is you're generating some action, task question, whatever versus it has become, you know, somewhat sentient. I'll use sentient, not scare everybody, but like it's almost thinking about, here's the thing that he's not asking that he should be asking.
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Nick Grossman
Yeah, well I think there's two, two parts to that. One is you want to prompt them to always be questioning and thinking and you know, like reasoning and being proactive. And then the second piece is like what are the triggers that they're working off of? Right. So our fleet of worker agents trigger off a bunch of things. Some of them have like a daily, you know, wake up, think about this company, you know, do some work on sort of a timer, like a chronic job. Others of them are responding to things that are happening whether a meeting gets pushed into our notes database or an email comes across our investment team list or some other trigger that happens. So there's sort of triggers that are firing agentic work within our system all day long. And you know what? We I'm sure where this is going is because as the agents get smarter and more capable, they can do more long running tasks, they can make more decisions on their own. You're seeing that today in coding agents where you can give a coding agent like build me this whole app and they can just go do it like Fable is really amazing at that. So as they can do more long running tasks, they can also be more proactive across each step. So I think we're moving from a kind of a chat based call response paradigm to more of a proactive to like a trigger based paradigm where you have cloud agents that can trigger off of things that are happening in your system to more of a kind of proactive model. And we're already seeing that, you know, move that way.
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Anthony Pompliano
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Now, if we go and let's just use the venture capital example right now, I would say that this sounds like it's a system that you guys built. It's human, you know, generated or kind of human initiated. Then there's a bunch of automation and AI and stuff that's happening. The information is coming back. But like you as a human, you make the decision, right?
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You're the final press? Yes.
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No, you know, Caesar type type thing. There have been many people, 10 years ago, I was like, why don't the software just do it at the earliest stages? You know, there's not a lot to look at as you're kind of just like betting whatever. Can investing get there?
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Nick Grossman
I think certain kinds of investing can get there. Okay, explain. Well, we do venture investing where so much of what we're doing is really episodic and you have to spend time with humans in real life to get to know them and know their company. And it's a very human process today. And also we're not trying to have our agents make investment decisions or kind of execute on those investment decisions. Although there are people who are trying to build agentic vc, like fully agentic vc. We have a portfolio company that's working on that too. So I think for certain types of deals it could happen that way. And then Outside of venture capital, I mean already I think the first fleet of investing agents were like trading crypto and things like that, where it's really more programmatic, the market's accessible, this is kind of a separate conversation. But I think one of the things that's exciting about where the world is going is agents having wallets and making payments and being more autonomous financial actors. And the places where that's going to be the easiest are the markets that are the most open and accessible, either via API or because they're on crypto rails. And so definitely see autonomous investing as a thing that is already happening, going to keep happening. And then will it happen to vc? I think certainly parts of it and maybe eventually all of of it, but I think it'll be a little, probably
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Anthony Pompliano
a little slower some of it. I mean as much as I think we all love and respect yc, like there is somewhat of a joke of like, you know, it's a 10 minute meeting or whatever it is and so there's a bunch of work that gets done that again, it's not fully automated, but you could easily see maybe the human component is being compressed. And so the question is like the last mile, right? How much of that is necessary? And I do wonder, it's almost impossible to know. But how much of that meeting is less about making this decision versus it is establishing the relationship to open the opportunity for the next round or something? It's both.
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Nick Grossman
I think when you think about venture capital deals, the style of deal you tend to focus on most is the lead investor who's really building the relationship and crafting the deal and taking a big role within the company. But in every venture round there's also lots of follow on dollars. And so one area where I could totally see more automated capital coming into venture deals is filling in edges of venture rounds where there's 200,000 or a million or whatever it is left. And maybe there's some signal that a vc, a pool of capital agent, could follow parameters and act pretty autonomously and pretty quickly.
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Anthony Pompliano
So a firm that did something like this, I forget the name of them.
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Nick Grossman
I know what you're talking about and I can't remember the name either. We have a portfolio company, but they
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Anthony Pompliano
basically just followed, they were like, hey, there's, you know, here's the Tier 1 VCs, if they like lead an up round and they're the lead and it's done in some time, whatever, the program matters.
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Correct.
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Nick Grossman
That looks like programmers.
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Anthony Pompliano
The human still said yes, but it was pretty much done you could imagine
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Nick Grossman
more and more and more of that getting automated. And could you go into other parts of venture maybe? Probably.
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Anthony Pompliano
But now when it comes to agentic investing, one of the things I've been somewhat surprised by is digging a little into the details of like how some of these systems work. There is like automated execution and then there is like full on agentic decision making and automated execution. I don't think anyone has a problem yet or is concerned about like automated execution because there's still some element of like oversight or parameter set or you know, if then type statements, whatever. The AI models I have seen very disperse outcomes. I've seen some people be like tell me a stock to buy that's going to double by the end of the year.
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Right. And right goal pretty crude.
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Just general prompting. I haven't seen too many success stories from that. I've also seen we see it on Sylvia, we actually have one engineer who every couple of days he'll post I made X dollars and he'll explain what he did. And it's some combination of stock screen plus maybe he's got some unique view on an industry or something. But really what he's using is again it's kind of his idea that he's then using to find the idea the full like here's 100 grand, like knock yourself out. I feel like we're all excited about it. I just don't know if anyone's fully there yet.
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Nick Grossman
Yeah and I can't say that I've heard too many actual stories of that actually happening.
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Anthony Pompliano
I know one person who is building this bought what he has been doing is less like high frequency trading type stuff because obviously those guys are excellent at what they do. And this is almost more like index creating. And so it's like less frequent trading and it's more I think just like you can kind of look at hey what's going to happen for the next 90 days type stuff. Right. That to me feels like okay. And in his case specifically performance is like amazing. But the average person just prompting the model. I haven't heard too many.
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Nick Grossman
Yeah, I've heard some. I know a team that is using agents to trade frontier markets. So corners of prediction markets, other kind of obscure places where maybe there's some early dislocations that maybe it's largely retail and maybe an agent can see patterns before the big institutions get there and arb everything out. I don't know. So I do think there are probably existing corners of real markets where you could probably just set an agent Free and have it. Give it a wallet, load it up.
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Anthony Pompliano
Those guys are making money.
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Nick Grossman
They're making some money. Yeah.
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Anthony Pompliano
This is like, it's.
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It's quote unquote, working on a.
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Nick Grossman
Working on a scale early.
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Anthony Pompliano
Got it.
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Okay.
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Nick Grossman
Yeah.
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Anthony Pompliano
The other aspect of this that I find fascinating is if you know that the agents start to become more involved in financial markets. Yep. Well, like every company in the world, every founder in the world starts saying, how are the decisions made? What are the things that matter? And we know this because humans do it. If they know that a bunch of VCs are all looking to invest in physical AI, all of a sudden every company's got physical AI in their deck. So there's always this cat and mouse game of the signal gets arbed away. That feels like where AI is really good at dealing with the complexity, identifying some of the stuff, et cetera.
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Sponsor Voice
Right.
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Nick Grossman
And arbing that away faster. Is that what you mean?
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Anthony Pompliano
Yeah, yeah.
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It's just.
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Okay, where's the trend shifting? I don't know if you know who Jordy Visser is, but I do this episode with him every Saturday, and one of the things he told me, the best idea he's ever told me, he
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said to me, a lot of the
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AI leaders are more public in terms of interviews and stuff than any business leaders ever have been. Like, Jensen gives like five interviews a week. Right. People want to know what he's thinking. If you take those and you take the transcripts and you put them into the systems and you say, hey, based on what Jensen is talking about, what are the companies that are going to benefit over the next 12 months or something. Like, scary accurate.
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Nick Grossman
Yeah, I bet. Right.
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Anthony Pompliano
And so, you know, he tells me, this is like a cool idea. Gavin Baker on stage at selling conference. So what I do is he's talking about Trainium being like a big H2 trend.
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Nick Grossman
Okay.
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Anthony Pompliano
Okay, take it. I go ask the model. So. Well, if Trainium is the thing, Listen to this interview.
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Whatever.
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What do you think? A full risk on portfolio, 30% allocation. Marvell. 60 days later, Marvell's up like 100% right now again.
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Nick Grossman
Did you trade that?
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Anthony Pompliano
No, dummy me.
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Jordy had told me about Marvell then.
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Right. And so I'm like, oh, of course I can't now. Fomo, Right, Right. But what I started to realize is that is almost exactly what great investors do. They listen to a bunch of complex data points and information and then come up with, here is how I'm going to express this view. And that seems like a perfect AI. Use case, for sure.
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Nick Grossman
And then the question is, how much of that capability is just generally available to everybody? And then if it's available to everybody, then everybody has it, and then the market goes away. And then the question is, what's the difference between having general intelligence and specific intelligence?
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Anthony Pompliano
How do you think it plays out? We know that obviously the model labs, for some period of time, they have access to the best model that isn't yet publicly released. Forget for a second the government even, like, approving who gets it, who doesn't.
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Right.
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Just like, in general, they got to test it before they release. We also have heard reports of maybe some of the less popular large models, like, basically trading in the market with these models. Does that seem like a path for these guys, is like, to hold back the best models for themselves?
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Well.
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Nick Grossman
And to do what with them? Is that. Yeah. And, like, trade the market with them. That you could just turn the lab into ssi.
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Anthony Pompliano
Ssi, I think, is the one that is supposedly rumored to have done this.
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And I'm kind of torn. Right.
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On one hand, if you can navigate the market, that is the ultimate sign of intelligence, for sure. So, okay, that would mean the model's really valuable. But if everyone's just trading against each other with models, maybe there's not as much alpha. Maybe you should go sell to cybersecurity companies or whatever.
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Sponsor Voice
Right?
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Nick Grossman
Yeah. And I guess a point you're making is like, I think we're just at the beginning of understanding how superintelligence goes to market in what package. Right. Because right now the labs are selling the models as APIs or as consumer applications, but you can also internalize them. Right. So you could, in theory, develop a frontier model and not sell it and just turn it into a trading fund or something else. It's a little bit like Google back in the day, channeling all of its data into making its own product better. And we haven't seen any of the big model companies go this way. But it's not inconceivable that there could ultimately be a better business model for developing superintelligence than the ones that we're familiar with today.
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Anthony Pompliano
I also think there's another way to come at this. The model companies are. Their business right now is making the model. Right. If you look at. In the crypto world, like Robinhood, the Robinhood chain now is all the rage. Everyone's talking about that. If you look at Revolut, Revolut went and they actually trained a foundation model using a bunch of data they had. Like, it does feel like everyone is always talking about vertical integration as, like, model up, you know, to the application layer. But like revolut, getting into the model game was not something I think a lot of people were thinking about before they announced that. That's like the opposite direction going down
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Nick Grossman
from the application layer, correct? Yeah.
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Anthony Pompliano
Like, they just have unique data.
- [28:34]
Nick Grossman
And if you have that big of a footprint with that much user coverage and that much data, I think it's potentially possible to do that depending on what type of model you want to train. I don't know how how broad their model is versus how narrow, but presumably they have enough data to train. And I think any really scaled application company is going to have enough data to train models to do certain things. And then there's a question back to your point at the beginning, about the pace of generalized models versus specialized models and how those continue to shake out. But I think we'll continue to see application layer companies go down to the model layer and also the sort of big labs, like go up to the application layer and we'll see where it all lands.
- [29:15]
Anthony Pompliano
When you're talking to founders, one of the big things I hear people talking about is I don't want to use XYZ model because I'm like, giving away my data. I'm giving them whatever the zero data retention policies, like, do they work, not work. There's a lot of questions right now about are you essentially handing over this really valuable resource in exchange for the model? What is the conversation you're having with founders? And do you guys have a specific point of view?
- [29:42]
Nick Grossman
Yeah, I mean, this blew up in the news over the weekend with Alex Karp and Palantir and everything else.
- [29:46]
Anthony Pompliano
And he has a tendency to be able to get attention. He does.
- [29:49]
Nick Grossman
He's really good at that. I think, certainly, for starters, everybody is using the enterprise plans with the best retention data policies and assuming those are the best they can get. And I think a lot of founders are parallelizing across different models and not necessarily putting all of their data into any. All of their experience or their data streams and traces into one place. And then of course, there's a whole stack of data that never reaches the actual model itself, which application layer companies can uniquely see and learn from. So I think at the stage that we invest at, which is the seed and the a, like very early stage companies are, most founders are mostly focused on capability, performance, user experience, can they deliver something that works. And then I think as they scale, they tend to worry more about these structural problems of, you know, Making sure that they're sort of protected as best possible. Which is why you hear more about it from the biggest companies and are less about it from the early stage startups who are just trying to get something that works going.
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Take it a step further. Like one of the things we've always been tempted to is like, oh, we'd be an amazing data labeling company, right? Like, you know all these people, you, you could have them opt in, you could pay them some percentage, whatever. We're probably not going to go down that path. But you could see that all these companies, they're like, wait a second, I'm generating tons of data. And especially if I can get my users to almost like a YouTube Rev share, hey, if I go monetize this data, you get a piece of whatever. What is the conversation with some of these businesses that are like they're building AI products, but I know the model labs are calling companies saying, hey, we'll pay you a ton of money. Is that a business?
- [33:58]
Nick Grossman
I think it depends. You saw the first wave of this happening with all the UGC content, like the Web2era UGC content companies seeing selling data to the labs as a potential adjacent business model. And I think that can work. This is like Reddit and Stack overflow and folks like that. And then today there were the first round of big data generation companies like Scale AI and others who uniquely built a business model around selling data to the labs. And I think that has worked pretty well for a category of companies. Within that category, there's a question of how much it becomes commoditized and flattened as a business model versus really defensible. We have a portfolio company that does DNA synthesis and uses a really patented chemical process to generate data for folks building biomodels. And that's an example to me that feels more defensible and specialized versus just doing work out in the world. And then I think we see all the time companies in the middle, like new startups that are getting out and building something. And the question is, should I take my product direct to consumer? Should I internalize and verticalize or should I sell data or something else as a service to either one of the big labs or some other incumbent? And I think we're generally just as USV less excited about that category because it feels like you should be looking to capture the most value you can and depending on how defensible your spot is, selling data to the labs probably may not be the best option. So I think it really kind of depends on where it sits.
- [35:45]
Anthony Pompliano
I saw somebody online say something. I think it was this guy Justin Welsh said if you only get paid a flat salary so you have no equity, no upside, no anything, somebody has figured out how to monetize your skills better than you have in a way. The data, if you're selling it to the model lab, have they figured out a better way to monetize it than you? There's a parallel there totally.
- [36:08]
Nick Grossman
And then you could also argue one layer above that, that those labs are selling inference to developers who are building applications. Maybe those developers are capturing the value on top of the inference. So it's kind of crazy.
- [36:22]
Anthony Pompliano
Maybe, maybe.
- [36:23]
Nick Grossman
And it depends. Yeah.
- [36:24]
Anthony Pompliano
How long do you think we need until we have some of these answers? Because I think you're very, I think open eyed about like as you guys are working through this, just in talking to you for a little bit, there's a lot of problems. It could go a couple different ways. I'm assuming as smart venture capitalists, you're going to make bets in multiple potential outcomes. Do you guys have a sense like, hey, five years from now we'll likely have answers to some of these questions? Ten years, one year.
- [36:47]
Nick Grossman
I mean I think if one way, I think it's going to be a little while. I think five years is a decent time. Hide. Things are changing so, so, so fast. But what would we want to see in five years, five years from now? I think we will see a number of massive public companies in the, in the consumer AI or the business AI space, whether that's health, legal, finance, that aren't the big labs.
- [37:15]
Sponsor Voice
Right.
- [37:17]
Nick Grossman
Or industrials or whatever at the application layer basically. And so many of those companies are being built right now. A lot of them have really robust medium sized valuations. I think we're going to see a lot of those mature then I think there's an enormous category of what you might think of as middleware companies, like companies in the middle of the agentix stack. Whether that's memory or harnesses or orchestration or payments or whatever. A lot of those are kind of just getting going as agent orchestration is becoming more of a thing. They're all going to become more important. How many of those become like really big important companies? We're going to see that happen over the next five years and then even a layer below that. What does the landscape of actual models look like is it OpenAI anthropic plus some open source Chinese models and that's it? Or can we develop open models here in the. In other ways? We have an investment in a project that's trying to do decentralized model training in an open way. Like there are a bunch of projects that are trying to do some versions of that. So I think what I'm saying is in five years we'll have a clearer picture of the model layer, a clearer picture of the middleware layer, and a clearer picture of the application layer. And the Rebel alliance thesis posits that there'll be big important companies at all three layers that aren't the big labs. And this is not to say the big labs are not amazing companies building incredible products. What they're doing is magic and it really is just insane and inspiring. It's an and not an or.
- [38:50]
Anthony Pompliano
Now you talked a little bit about open source versus closed source. I think there's also this America versus China component to feels like to me and maybe I'm missing some, but there's a performance component. So whenever an open source model comes out, immediately people are like, which one's better? There's a cost component and then there is like a data security component of like I don't want to give my data or I can self host or whatever. The fourth one though, which I have not heard a lot of people talk about, is what I'll just kind of call like the philosophy of the model.
- [39:22]
Nick Grossman
The personality.
- [39:23]
Anthony Pompliano
Yeah, and just like, and maybe if I just make it really concrete. And as somebody who's building something in finance in particular, you can almost think of like the west is built on capitalism and the east is built more on like a socialist, you know, communist style kind of thought about this Again, huge generalization, but let's just use those to illustrate the point. If you have an American model, whether it's open or closed, it kind of comes with certain ideals, certain perspective, certain risk taking is part of the culture. The east model may not have that. It may be more of a socialist type approach. And so even if you have all the security, the performance, the cost, if the answers, the weights are different for sure. So how do you think about that type of stuff?
- [40:03]
Nick Grossman
I think it's yet another vector of competition and choice.
- [40:06]
Anthony Pompliano
Yeah, and this is like in the
- [40:07]
last week we started to think about this.
- [40:09]
Right.
- [40:09]
This is not something I've been thinking about for a long time. So it feels like it's like a wide open thing that people just haven't really come To a conclusion.
- [40:15]
Yeah.
- [40:15]
Nick Grossman
And it's going to take us a while to understand these nuances. We have another portfolio company called Cradle that does these very nuanced model challenges to try and tease out the personalities of the models.
- [40:29]
Anthony Pompliano
Oh, interesting.
- [40:30]
Nick Grossman
They just published a report a couple weeks ago about which models lie to you the most. Whether it's Grok or Fable or whatever. Turns out Fable in their first test lied the most. Anyway, interesting. So it's just I think these kinds of questions are nuanced and are not covered by your standard software engineering benchmarks. Where I think so far it's just like how well can the agents code? That's more or less been the most important question. As we have agents doing more different kinds of things up and down the stack, these questions of its personality or philosophy are going to come up and we're going to have to figure out ways to test them and understand those contours. And then depending on what you're building, you may want to want a model that's more eastern leaning or more western leaning or more woke or less woke or whatever. And I think we're just starting to figure that out.
- [41:22]
Anthony Pompliano
When you start thinking about different use cases for different things. In a weird way, I was having this debate with a friend of mine. Think of therapy. My general framework I think is if you want a child to learn, you get one on one tutoring. If you want somebody to be healthy, like personal, private healthcare, whatever we think finance, you get personalized experience through Sylvia. But therapy is on one hand you can make a strong argument actually people just need tough love and shut the hell up and go out there. Which I think again it's not a one size fits all solution. But there's plenty of people who'd make that argument. There's also a huge population of people who'd be like, hey, we need to
- [41:59]
be more empathetic and all stuff.
- [42:02]
Nick Grossman
The model you choose definitely may have different, well, the model and the application on top of it model and the prompt and the system.
- [42:10]
Anthony Pompliano
But you could help somebody or in
- [42:12]
that case you could hurt somebody.
- [42:13]
Nick Grossman
I mean, listen, this is why competition and experimentation are good things. Because as we. And also it speaks to the concept of the rebel alliance. Right? Like I don't want there to be a single AI therapist called ChatGPT. Like that's not a good world for us to land in. We should want to live in a world where there are are lots of them with different philosophies and different approaches and different underlying frameworks and models and people can try them all out and move to what feels right and see the results and have the results speak for themselves. And so I think that's a core piece of our point of view on all this. And it's about the models themselves and it's also about how we build.
- [42:56]
Anthony Pompliano
On top of that, because of the Rebel alliance perspective, are you less interested in the large models? Are you less interested in public heights? I know you guys are venture capital, but how do you think about if the Rebel alliance is the right thesis, which I agree with you. Does it box you out from certain areas and maybe other people are investing?
- [43:16]
Nick Grossman
No, I don't think so. The whole point of it is that this is so big, it's just unimaginably big. That's really the core idea. And the answers are going to come from everywhere at all layers. And the big labs are hugely important because they're innovating in the models, they have massive consumer reach and also they're going to be the first to land in the public markets and they're going to get valued on fundamentals before anybody else. They matter a ton. And when OpenAI and Anthropic go public, we'll start to get real analysis of their business models and their margins and their ambitions. And that's going to ripple back through the whole venture ecosystem, which it always does. As far as where it lets us play, I think it lets us play kind of everywhere. And I mentioned the model layer, the middleware layer and the application layer. We're looking at all three. We're also investing at the energy layer because energy is an input across the board for all. And we've been investing in energy for the last six or seven years now as part of all this. And so no, I think there's no shortage of areas for us to invest. And the big labs and the big models make up a really important part of what I think of as the infrastructure of the next Internet. And then we're going to build everything next to them and around them.
- [44:45]
Anthony Pompliano
Now the last part that I always think is like, okay, let's keep kind of going down this path is there's an entire group of people, social belief that this stuff is negative for humans. So we see data centers, local communities, we see the job displacement concerns. I can go on and on about all the different arguments. Yes, are they right, are they wrong, is there nuance? How do you think through maybe some of the non technical impacts if this is as big as you think it is?
- [45:20]
Nick Grossman
Well, there's no question that it's as big as bigger than we all think it's going to be. And so that makes it hard to reason about. And I think the other thing that makes it hard to reason about is the rate of change is extremely fast. And so humans are not good at reasoning about exponential change, myself included.
- [45:40]
Anthony Pompliano
Many humans may not be good at reasoning at all.
- [45:43]
I'll put myself in that bucket.
- [45:44]
Nick Grossman
Right? And so I do think it's going to be really big. And I think it's going to be really big in lots of unexpected ways. And I am, as somebody like yourself who's close to using the technology today, you can feel the magic in it. And so to take the optimistic side of things, are we going to get exponentially better at solving hard problems? Whether that's making investment decisions or finding, discovering new drugs, discovering new forms of energy, doing things in ways that unlock efficiency and make things cheaper and more accessible? I think all of that's going to happen. And those are all things that we want as humans. And at the same time, it's going to be massively disruptive. It's going to be massively disruptive. In terms of jobs already, the sort of data center energy thing is both a trade off. I think data centers are the biggest new consumers of energy. They're putting a lot of strain on the grid. Communities are revolting around them. There are also real economic questions around that, which is what's the right way for not just on data centers, but in AI broadly, if you believe that a lot of historical jobs are going to go away, what's the right way for humanity to benefit from this in some sort of ownership model or some other way? These are questions that we don't really have good answers to right now. And I am an optimist because I just am. And my job is to imagine the good possibilities and invest behind them. But I am absolutely also fully real around the unknowns and the scary things that can happen. And also the, I think with any new technology, whether that's the steam engine or the automobile or the Internet or AI, there are these waves of implications. If you just look at something like, like the automobile, you know, you think of it as a way to get from point A to point B, but you don't think about it as a way that land is going to get developed and, you know, business are going to develop and energy in the environment and the Internet, same thing. You know, there was sort of the optimistic first phase of the Internet and now we have, you know, all the implications of, you know, social media on mental health and politics and all these. Towards all this instability that can kind of come from it.
- [48:24]
Sponsor Voice
So.
- [48:24]
Nick Grossman
So that's the pattern that keeps happening. I think that's a pattern that's likely to happen here too, possibly even faster. And so I am honestly equal parts excited and scared
- [48:37]
Anthony Pompliano
when I think about some of the job stuff. Personal experience I had is went to the doctor with my wife, she was pregnant at the time. Doctor told us some information, we basically double checked it with the AI, Turned out the doctor was wrong. Went back, the doctor was like, oh my God, I made a mistake. I'm so sorry, whatever. So there was this very personal experience that I had and it made me think that one, somebody should create doublecheckyourdoc.com just like, you know, dollar per check
- [49:03]
or something and like, you'll have a great business.
- [49:05]
But second was, I am not of the belief that like, doctors are going to be replaced by AI. Sure, maybe there's like, you know, reading X rays or something.
- [49:13]
Nick Grossman
Right.
- [49:14]
Anthony Pompliano
But I do think that there's something about, like, not augmenting just the doctor, but like augmenting the patient who's talking to the doctor.
- [49:23]
Nick Grossman
Sure.
- [49:23]
Anthony Pompliano
And I think people kind of already do this, right. They're like, I'm going to go buy a car. Let me go and talk to the AI about like, what are the things I should care about or whatever. And that feels very underexplored as to like there's like a displacement argument, but there's also like a consumer protection argument.
- [49:38]
Nick Grossman
Yeah, well, it's like personal superpowers. Right. So everybody gets super intelligence, whether whether that's entering the doctor's office or entering the car dealership or, you know, dealing with a legal, you know, situation. You know, we use a lot of AI to support our legal decisions, but we still work with, with really talented. You guys use lawyers outside counsel. And I think we're going to keep using both. But because of at least today's AI that I have in hand, I can go to our attorneys a little more prepared and a little faster. That's just the same way that you can go to your doctor a little more prepared. So that's like a today thing, I think, and I do agree that that's an optimistic, empowering view of the world. And maybe in a couple years we have have eyeglasses or an earpiece or neural implant that gives us that augmentation in real time. And I don't even know how many what to make of that happen.
- [50:32]
Anthony Pompliano
It's one of my favorite questions to
- [50:34]
ask friends right now how many people have to have successfully gotten the neuro implant and safely it's understood it was safe. Would it take for you to do it? Yeah. 10. 100? A million? 10 million?
- [50:49]
Nick Grossman
Less than a million.
- [50:50]
Anthony Pompliano
Less than a million.
- [50:51]
Nick Grossman
Okay, probably, yeah.
- [50:53]
Anthony Pompliano
More than 100, more than 100, less than a million. But like, it's kind of interesting to think through.
- [50:58]
You're like the reason I started thinking about this a long time ago was just like, you know, okay, here's the first one. How many?
- [51:04]
Nick Grossman
Yeah, I saw a piece of news
- [51:06]
Anthony Pompliano
recently that Neuralink supposedly done 26 of them. That was a bigger number than I thought.
- [51:11]
Nick Grossman
I wouldn't have been able to guess that number. That is a real number.
- [51:13]
Anthony Pompliano
Right. So like, okay, we're at 26 again. I'm not going to go down it. But then you start thinking like, well if I was like a chess master and give me some advantage again, then
- [51:25]
they're going to have to outlaw.
- [51:26]
Nick Grossman
This is the new steroids, right? It's going to be the new.
- [51:28]
Anthony Pompliano
The enhanced games. Yes, exactly. The mental games.
- [51:31]
Nick Grossman
I'd feel better and I don't know, this is not something I've really gone deep on, but I'd feel better with a try before you can buy, try before you buy. Approach 100% if possible. But it's got to be coming. And I think I, you know, I'm not really a sci fi guy, but you kind of have to go that far now and that can take you both to really exciting, you know, places and really dark places and everybody's going to both of them, you know.
- [52:00]
Anthony Pompliano
Yeah, you need both. You have to get both. In a weird way, you don't get the benefit without the scary potential downside. And I think a lot about similar to how you have to be an optimist in the private market. It's like impossible to make money. As a pessimist, you also as anyone involved in technology, you have to understand there's these trade offs and do your
- [52:16]
Nick Grossman
best to kind of bend it positive if you can.
- [52:19]
Anthony Pompliano
Yeah. All right, where can we send people to find you or read more about the Rebel Alliance.
- [52:23]
Nick Grossman
Yeah, sure. I'm Nick Grossman XYZ and you can follow usv@usv.com and then that's where they
- [52:30]
Anthony Pompliano
can read Rebel Alliance.
- [52:31]
Nick Grossman
There's blog usv.
- [52:32]
Anthony Pompliano
Com. Amazing. All right, thanks for doing this.
- [52:33]
Nick Grossman
All right, thanks, Anthony.