How I Invest with David Weisburd
00:18:13 · Joseph Coyne
So we worked with the Olympics and captured every Olympian in Paris, for example, doing their specialty with a four d camera array around them. That created digital humanoid reproducible assets from those gymnasts or from those from those gymnasts and and other athletes that NBC could use in other media generatively. And so that was the initial use case. We've done that now tens of thousands of times with hundreds of customers, including Apple, Netflix, and others, and have a really nice business doing that capture processing asset management and dovetailing it with the production workflows. That business is interesting. Right? They're already making millions of dollars in revenue. What's really interesting is they have the world's largest dataset of human four d captured assets. And why does that matter? Exactly what we said before. Robotics training. Right? Generative humans for robotics training. Real world applications. And so to do that at scale, you could imagine a dataset like Evercoast becoming a pipeline to create generative human beings at scale to further train robots, self driving cars, you know, the digital nursing example we gave earlier, home care, etcetera. And so that's sort of step two of unlocking the dataset there. And, you know, notably, that dataset is very valuable to a lot of folks. And so we have to be very careful about, like, who do we partner with? Do we sell the data? Do we build our own internal models? And so that's a conversation we have almost weekly with the founder there.
00:19:45 · David Weisburd
Ad[00:19:45] S0: One thing I've learned from talking to hundreds of investors is that great investment firms aren't built on investment returns alone. The firms under are great at the things most people don't see, their operations, their relationship with LPs, and the quality of information they use to make decisions. And here's what AI has changed. Every firm now has access to the very same models, so the intelligence isn't the edge anymore. The edge is what you could feed it. A firm with its fund operations and data in one connected record can actually put AI to work. A firm running on disconnected systems simply can't. That's why thousands of GPs run their funds on Juniper Square. Juniper Square puts your fund operations, data, and administration together in one connected record. That means less time managing disconnected systems and more time investing, working with LPs, and building your firm. This episode is brought to you by Juniper Square, the operations partner for private market GPs. Learn more at junipersquare.com/howiinvest. That's junipersquare.com/howiinvest. A lot of people have trouble thinking second order effects or third order effects. But as the LMs continue to get better and the inference continues to be more and more sophisticated, the moat will continue to be the data. Right. Just think about Tesla, for example, with their self driving car and their self driving technology. I think it took a decade for them to gather the information just driving on the streets of Austin, San Francisco. And that was the bottleneck. It was the ten tens of year... Ten years. And then, obviously, they had to build the models, which was nontrivial at the time. This was pre chat GPT. Now I'm sure they're using a lot more LMs. But in a post LLM world where you have all the models, where you have basically genius level physics, PhDs, you know, 10,000 of them that could work on your problem set, the data... The moat actually becomes the data.
00:21:33 · Joseph Coyne, David Weisburd
[00:21:33] S1: We hear this word a lot in our kind of investment committee, which is, you know, either flywheel, which is people use a lot, but we also talk about, like, procyclical advantage. Meaning, once you're Tesla and you get a lot of data, then your model becomes better and you get more users, and then you get even more data. [00:21:51] S0: That goes back to the customers being the source of the data. [00:21:53] S1: Right. You have to prime the pump. Sometimes that's costly. In Tesla's case, it was building a car that had autonomous driving features years ago. Now it's a procyclical event where every incremental user often chooses Tesla because of its self driving, and so they further cement their data lead. So that's a procyclicality we talk about a lot where that becomes part of your defensibility over time. A new entrant starting at zero or even even above zero will still be so far behind a Tesla or an Evercoast, in this example, to catch up. And so incremental users choose those companies again and again and further deepen their data moat. [00:22:32] S0: I wanna go back to this question of business models. I don't think there's been a technological revolution that has not led to significantly new business models, but yet people are continuing to charge in this kind of SaaS SaaS model. What do you see in terms of business models and how they evolve in a post AI world?
00:22:49 · Joseph Coyne, David Weisburd
[00:22:49] S1: Yeah. I think you have to meet your customer where they're comfortable, especially when you're a startup. So will SaaS modeling be the traditional or most frequent use case in the future? Possibly, possibly not. But right now, that's where large enterprises have been trained to purchase and been... You when they think about unit economics, going back to the Inspiren example, you know, how much is our customer paying us per month? How much does this cost us per month? What's our cost savings? What's our cost savings per user? Unit economics leads you to think about it in terms of a cost per unit. Oftentimes, that rolls up to a SaaS pricing model. Now in more avant garde tech, you might see usage based models, which we're already seeing a lot of. You may see even outcomes based models, right, where if we can produce this for you, large enterprise, you'll give us this. Right? Those are a little bit harder sells. I mean, I think that they're very aligned with outcomes driven and ROI driven implementations. So don't get me wrong. I kinda love that. But the buying behavior is new. And so there's some education and some comfort level, particularly with large enterprises, some of whom have been around for a hundred years. Right? And certainly over the last twenty years, they've been habituated to to price based on SaaS per seat or per month. So getting them out of that will take some time. Don't I think it's impossible, but I I think it'll... We'll see a slow ramp away from it. [00:24:10] S0: What comes next?
00:24:10 · David Weisburd, Joseph Coyne
[00:24:10] S0: What comes next? [00:24:12] S1: I think the usage based, consumption based, token based models are all things that are in the conversation right now. What we've seen is some hybrid approaches, so some SaaS plus utilization. We've seen some pure play utilization or token based models. Those can be scary for the CEO, CFO of a large enterprise because you have some opacity around, what am I gonna pay? You know, I know what my budget is. We've all bought into the budget. We told our board, our budget's x. What if we exceed that? Right? That can create problems. And so understand that and creating visibility is important for enterprises to really get comfortable with that buying behavior. I think you can cap it. You can tier it. That creates more hybrid approaches that look sorta like SaaS plus utilization. That may be the on ramp process that gets them comfortable over time to say, look, we've been doing this for two years now. Our utilization is x. We've paid a million dollars a year for the last three years. This year, we'll probably use it 20% more. So we've budgeted 1,200,000. That'll take some time. Right? They need to have the data behind that and the comfort level to make that type of decision. But, yeah, it's how most markets develop over time, and I don't think it'll be different here.
00:25:21 · David Weisburd, Joseph Coyne
[00:25:21] S0: Sitting where you're sitting, couple weeks ago, had David Breach, the Vista Equity president. They've deployed over a $100,000,000,000 in software. So they certainly thought long and hard on on this issue, and he believes that the future of software is not just selling software, but selling solutions. It's kind of do it yourself versus done for you model. Do you see that as the evolution in software itself? [00:25:42] S1: In the early days, we're seeing a lot of that. Consultative selling. I think we've seen portfolio companies with very powerful technology enter Fortune 500 companies, take the covers back, or open the hood and be surprised by how disparate and unruly the data is. And that very quickly turns into a data consulting project. For a VC, that's not what I love to hear. Right? We wanna hear [00:26:07] S0: like doesn't sound scalable.