How I Invest with David Weisburd
00:13:13 · David Weisburd, Joseph Coyne
[00:13:13] S0: Inspiren is a fascinating case study. It's kind of like minority report, but it's actually helping the patients. And also in many ways, it's this gateway drug to physical AI. Jensen Huang predicts that physical AI would be a $50,000,000,000,000 market. A lot of the top VCs I know right now are trying to invest into physical AI because they see it as a new frontier once kind of all the software and all the data based AI gets integrated. And most of that application is actually building from scratch, so humanoids or something sensors and factories, things like that. But here, it's actually going into existing systems and getting existing insights from systems versus kinda building these brand new technologies from scratch. [00:13:58] S1: Yeah. And all the new technologies you just mentioned live and exist in the future based on ingesting the data we're capturing now. So imagine that instead of having a staff member, you had a robotic assistant. Well, how is that robotic assistant gonna learn over time? Well, wouldn't it be great if you had a twenty four seven dataset of video capture for thousands of patients over thousands of days? That sounds like the perfect training desk dataset for that type of use case. And so, obviously, the impact right now at Inspiren is is tangible, but the dataset they're creating is... Hasn't even been monetized. And that's one of the things that's exciting for us.
00:13:58 · Joseph Coyne, David Weisburd
[00:13:58] S1: Yeah. And all the new technologies you just mentioned live and exist in the future based on ingesting the data we're capturing now. So imagine that instead of having a staff member, you had a robotic assistant. Well, how is that robotic assistant gonna learn over time? Well, wouldn't it be great if you had a twenty four seven dataset of video capture for thousands of patients over thousands of days? That sounds like the perfect training desk dataset for that type of use case. And so, obviously, the impact right now at Inspiren is is tangible, but the dataset they're creating is... Hasn't even been monetized. And that's one of the things that's exciting for us. [00:14:36] S0: I imagine they're gonna be monetizing not per seat or not per device, but if you are... I think you mentioned sixty to seventy five percent of falls are prevented with with the device. If you're able to keep these large, you know, multi $100,000 lawsuits from happening over and over, that's extremely profitable to the health care centers. And if you monetize off of the losses that you're preventing, that that could be quite a sizable business.
00:14:36 · David Weisburd, Joseph Coyne
[00:14:36] S0: I imagine they're gonna be monetizing not per seat or not per device, but if you are... I think you mentioned sixty to seventy five percent of falls are prevented with with the device. If you're able to keep these large, you know, multi $100,000 lawsuits from happening over and over, that's extremely profitable to the health care centers. And if you monetize off of the losses that you're preventing, that that could be quite a sizable business. [00:14:59] S1: Yeah. Today, we actually do have kind of a SaaS pricing model, so, like, a per room deployment pricing model. But you're right. I think in the selling process, that is one of the most tangible aspects to get in the door in the first place and then to win a pilot. But what keeps us there over the long run is that the staff, the administrators, the owners all become addicted to this new data. Once you've pulled that new oil out of the ground, putting it back would be pretty difficult. Right? You've already realized these staff retention benefits, these profitability benefits, and these patient improvement benefits in addition to those those first use cases, which is, hey. If we could prevent falls, would that be helpful? Right? That gets us in the door. What keeps people is that that long term reliance on this new dataset. [00:15:45] S0: It's fascinating example of physical AI. In terms of physical AI, maybe you could double click on additional use cases you're seeing today in your portfolio.
00:15:53 · Joseph Coyne
[00:15:53] S1: There's certainly some things to be fearful of in AI. But when you see tangible impacts helping people's lives, it starts to... It... It's the other side of the coin. Right? It's like you can't get all the positives without the negatives. The negatives can be mitigated with cybersecurity and hardening and and data protection and things like that. Obviously, personal personal protections as well. But when you see the positive impact in something like health care or a company in our first portfolio called Bark, which protects kids online using natural language processing. So we're ingesting about 7,000,000 kids data real time across multiple apps and across the OSs to establish emotional baselining for those kids. We can then predict with very high accuracy when somebody's experiencing depressive episodes or specifically has flagged, even if they're using tons of slang, you know, self harm, harm to others, or if a child predator has entered the chat. And so the impact coming out of that company, as somebody who has three kids especially, gives me shivers every time I open it. Right? The the the VC muscle memory tells you to open up a a company in... Company update and say, what was the revenue? What was the growth? But in this case, I go straight to the impact. And that's what gives me shivers. It's dozens of school shootings, hundreds of suicides prevented, child predators escalated to the FBI, dozens every year. And so doing something like protecting kids with a complex natural language processing engine, plus predictive analytics and AI, is just another use case where I think you can get behind AI if you see use cases like that. But again, there's plenty to be fearful of, and I think there's plenty of companies being built to protect against some of the the dark side of AI. And that's a thesis that we're leaning into going forward. And then on your physical AI question, I would cite a company called Evercoast, which does four d, meaning in motion capture of primarily human beings. And they use a camera array, which is pretty much off the shelf cameras with very sophisticated processing capabilities in the background to reconstruct human beings or objects in motion. So effectively video in four d. And those... The early use cases for that business were primarily in the media space.
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.