How I Invest with David Weisburd
00:08:05 · David Weisburd, Joseph Coyne
[00:08:05] S0: You made a very important but subtle point, which is the reason 80% of the data is behind the enterprise firewalls is because it comes from the customer. In order to get that customer data, you must provide a good product. So everything is still downstream of having great product. The difference in AI world today is that your data moat starts to be one of the most sustainable forms of competitive advantage. So, again, 95% of enterprises according to Deloitte fail in terms of their pilots. The ones that are succeeding, what are some early wins that you're seeing in enterprise that are maybe not not as obvious to the outside? [00:08:39] S1: Enterprise software that is creating workflows that unlock new data feels like the oil rig company pulling new oil out of the ground for enterprise. And what we're seeing [00:08:51] S0: Give me an example.
00:08:52 · Joseph Coyne
[00:08:52] S1: From inside the enterprise. So one of my favorite ones to talk about is Inspiren, which is a portfolio company. Just closed a $170,000,000 from Insight and and NewView Ventures. And they are in the healthcare space, which is a space that generally speaking, if you asked me ten years ago, Joe, are you gonna invest in the healthcare space? I probably would have said no. But their business model, their workflow, the data that they extract is perfectly aligned with our thesis. And the reason for that is that they have a very low cost device that they place across senior living facilities. And it uses computer vision and other sensors to extract real time data 20 fourseven about residents. That dataset prior to this device was primarily anecdotal. So it's what is the nurse saying? What is the doctor saying? What have we logged? Right? This is a human problem. Humans are not great at this organizational level of detail. Nor can they go as granular as twenty four seven. So what does twenty four seven monitoring unlock? Well, first of all, you can do it in a very anonymized, safe way. So that's... That has to be table stakes. Right? And then the second thing is you can unlock insights about patients, doctors, nurses, staff members over time. And so that helps you do things like prevent falls. If you're monitoring patients twenty four seven and you see somebody that you... Has a known fall risk, right, that can be built into the into the AI. You can monitor them in the middle of the night. If they stand up or they look like they're about to stand up, you can send a staff member minutes before they otherwise would have arrived to help that person with whatever whatever they need in the night. Could be a question, could be a run to the restroom, but either way, if that person is standing up under medication in the middle of the night and they're 85 years old, the negative outcome is a fall, a broken hip, a lawsuit. That's a tens of millions of dollars problem eliminated. So Inspiren, one of the first use cases, was both in hospital and senior living, was they were able to show they could eliminate sixty to seventy percent of falls that would happen oftentimes in the night. The second thing is you have longitudinal data now. So you're watching seniors or occupants over time and how their activity changes.
00:10:59 · Joseph Coyne, David Weisburd
What what that can do is actually do predictive analytics and start to predict things like dementia and Alzheimer's based on activity levels, behavior levels, and things like that. We can also predict if somebody starts exhibiting certain behaviors, things like infection, which is incredible. And lastly, we're now creating a a sort of time series global view of an of an entire operation. So, you know, senior living facility may have hundreds of rooms, you know, dozens of staff members, dozens of doctors and nurses. Now they get to see in real time who's doing what and where. So where are the resources being deployed? [00:11:34] S0: Logistics, essentially. [00:11:35] S1: Right. And so that was anecdotal before. What... And so, you know, you would ask a staff member how their day was, they'd say, oh, it was just... I was so busy. Way understaffed. Right? Classic response. Maybe they noted it in their logbook. Maybe they put it in EHR. Maybe not. Now we can actually say, wow. Joe, the staff member, was supposed to visit 10 rooms today, but he got pulled into 25 rooms. That's a major problem. By the way, Joe, as a staff member, is likely to leave if we're working two and half [00:12:02] S0: times harder.... Burnout.
00:12:02 · David Weisburd, Joseph Coyne
[00:12:02] S0: times harder.... Burnout. [00:12:03] S1: Yeah. And so we're also eliminating burnout among staff because we can load balance according to the needs of the patients. And lastly, you know, over time, patients and and occupants start to exhibit signs of additional needs. Right? Like, they tend to degrade, especially in senior living. And so you wanna appropriately staff up based on the patient's needs. So if my father went into senior living tomorrow, he might need one visit a day. But in two years, he might need 10 visits a day. That is a different different value prop. That's a different staffing mechanism. And my father might need to be moved to a different floor to achieve that where he can get the level of care that he actually needs. So it results in better staff satisfaction, better patient outcomes, better profitability at the end of the day, and an all new dataset that was previously anecdotal at best is now digitized moment by moment across the entire facility. So that's that's oil, as I said, being pulled out of the ground. It was sitting there. It's always been there. But we've... Inspiren has pulled that data out of the ground and created a very sophisticated model to help multiple decisions, whether it's tactical decisions like preventing falls or operational decisions like staffing decisions and care level decisions.
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.