How I Invest with David Weisburd
00:04:32 · David Weisburd, Joseph Coyne
[00:04:32] S0: Something I hear over and over about AI implementation is that it's mostly a cultural issue. Yeah. Sure. There's data. There's processes, but starts at the culture level. How much does culture play a role in terms of enterprises being able to implement AI within the organization? [00:04:49] S1: That's right. And it starts from the top within organizations. Right? If if senior leadership is embracing it, it has to be, in a lot of cases, top down to reach sort of the most mission critical parts of the enterprise. And so, you know, some leaders, especially within tech companies, are thinking about this on a daily basis. And I can tell you for a fact that some companies leading huge banks, huge consumer companies, huge airlines are not native to this. And so there's a lot of education and foundational work that needs to happen for them to really reap the benefits of AI's, you know, power and and and possibility. [00:05:21] S0: This episode is presented by Juniper Square, the operations partner for private markets. So if I'm the CEO of Delta, let's say I just put my foot in my mouth with a Starlink and saying that I'm... Don't want Starlink and and don't like SpaceX, all these things. But let's say I want to rehash my image and I want to show that I'm leaning into technology and leaning into AI. Is that the problem set that you would go into, which is elastic pricing for seats? What would you advise the Delta CEO to do? What's the next steps?
00:05:21 · David Weisburd, Joseph Coyne
[00:05:21] S0: This episode is presented by Juniper Square, the operations partner for private markets. So if I'm the CEO of Delta, let's say I just put my foot in my mouth with a Starlink and saying that I'm... Don't want Starlink and and don't like SpaceX, all these things. But let's say I want to rehash my image and I want to show that I'm leaning into technology and leaning into AI. Is that the problem set that you would go into, which is elastic pricing for seats? What would you advise the Delta CEO to do? What's the next steps? [00:05:53] S1: It's looking at a lot of historical data to understand how they've approached it in the past, but then also having a horizontal view of pricing within the industry. One of the ways you can get a data moat, which is very important these days, is to have horizontal insights about an industry. Meaning, you see hundreds of airlines. The airline you're talking to probably sees mostly its own data most of the time. And so when you bring horizontal insights around best practices and price elasticity and price per mile, what type of seat across different organizations, you can then bring that to bear within large high willingness to pay customers. And so I think it's, a, looking at their own historical data, but, b, gleaning a lot of model insights across thousands of other potential opportunities. [00:06:39] S0: This connects to what we were talking to a couple weeks ago, which is you said that the vast majority of data is not accessible to LLMs. Why is that?
00:06:39 · David Weisburd, Joseph Coyne
[00:06:39] S0: This connects to what we were talking to a couple weeks ago, which is you said that the vast majority of data is not accessible to LLMs. Why is that? [00:06:47] S1: One of our favorite stats, and it actually it actually came through an enterprise that we work very closely with, which is that about 80% or more of the world's data behind firewalls. LLMs have done an absolutely heroic job. Right? Going from infantile phase a few years ago to in the pockets of almost every American and around the world. So would never speak ill of what they've accomplished, but they've accomplished it with the publicly available data, similar to how Google crawls the public web. To get a lens into private data, you have to earn customer trust, you have to bring unique insights, and oftentimes, you need a subject matter expert to be able to have the trust and expertise to enter a market like airlines or banking or insurance. When we see companies that are starting to unlock those types of value, they get access in a highly secure way to proprietary behind the firewall data, the 80%. When they do that, they can start to produce new insights for the company and for the industry they operate in. And that's where really high value opportunities exist. Years ago, we were just pure play enterprise software investors. Now, we always see those companies come with an AI strategy. The best of those companies come with an enterprise software workflow that systematically helps companies to unlock their data and then run AI best practices on it in a highly secure way.
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.