"The Cognitive Revolution"
00:41:36 · Nathan Labenz
[00:41:36] S0: Are there any like rules of thumb or like an example from the ambiance Cleveland Clinic partnership was Cleveland Clinic required that every doctor in their system, I think, use the AI scribe once. And I thought that was really interesting because obviously these AI tools, you know, have a... Have an error rate. So that sort of projected confidence, it was like a minimal ask. It says a lot about like how reliable, you know, you might need your product to be if you're gonna make those kinds of relatively minimalist demands on your team in terms of, like, trying it out. In that case, they... From that one interaction, they report that they now have 75% of their 4,000 doctors using it all the time across 60 specialties. So presumably, that's like a product excellence translated to like this is amazing sort of story. I assume one of the advantages of being like more tightly integrated is like you don't necessarily have to get to that level before you can, like, kind of push the adoption. But are there any, yeah, like, mental models, rules of thumb, frameworks for how to think about how far the product has to be along versus how much to push people to use it even if it's imperfect? What what are the sort of managerial takeaways that you've seen?
00:42:57 · Marc Bhargava
[00:42:57] S1: I think a big learning for us is to find the assets and the companies that have a pull factor. So if you're purely underwriting on a pre... PE style, maybe you're putting in a new management team, maybe you're cutting costs, you're doing all these things. You... That's kinda what you're looking for in the asset. For us, it's really important. Does the company... We've now incubated a company. We're going to buy one of our clients. Does this client really want the technology transformation? Have they tried different products? Have they tried hiring people? Have they really pushed through at the leadership level that they want technology and AI? And we will pay a premium or our companies pay a premium in the acquisition to have the right partner and the right partner mindset. And so that's one thing that's extremely important is from the top, from the leadership position, this is something they've been trying. And then there are all kinds of techniques once the acquisition has been made to of course have it not just leaderships buy in but the entire company. And that honestly is not changing up the workflows too much, keeping it similar. So like in HOA management, you know you can email saying, hey create a version of the HOA board deck for me and it will create it and send it to you via email. So we're not trying to onboard you to some new software tool. In that case, know, you're using your existing text or email workflows. So earlier question you had is like do we totally reimagine it? Not really. We wanna keep a lot of the same workflows but we've already identified behind the scenes a huge part of it, 30% plus can be automated. And so we're trying to keep folks' workflows very similar but use AI with sometimes them not even knowing that you know what's the large language model doing behind the scenes and how is it writing this copy and all that stuff. It feels like they're just interacting with an agent that is able to send them a board deck that they need for tomorrow's meeting, etcetera. So we're generally mapping over those same workflows and so it's not a big change many times for the workforce to actually learn and adopt these toolings because our companies are building the tooling to be very similar to what
00:44:49 · Nathan Labenz
[00:44:49] S0: they do day to day. Yeah. That makes a lot of sense. So there's not a... I mean, this sort of speaks to I think another major question right now, which is like when trying to get value from AI, should we be creating what I traditionally refer to as smart workflows where you have a sort of step by step process that is human designed and each step may or may not be an AI step, but the AI steps, obviously you can optimize your performance and make sure they're working well. One way I characterize those is like, you'll know you're successful when you don't have to check every single output anymore. And I think for a lot of tasks you can get there today. And then there's like this new paradigm of AI that is the more agentic, like the AI chooses its own adventure, you know, maybe within some constraints, but right, like, Claude code, go write this thing or, you know, some of these calling agents like call this person and try to sell them a subscription or whatever. These sort of like open ended, you don't know how many rounds it's going to be, you don't know what the tool calls it's going to choose at any given time are going to be, don't obviously don't know what the environment is going to give How much of that are you seeing deployed in your companies?
00:46:02 · Marc Bhargava
[00:46:02] S1: So on the services side, I think it's going be more of the first case that you outlined where there are seven or eight steps and some of them will be automated and maybe a AI agent or does step one, but a human does step two. And so it's gonna be these hybrid AI enabled workforces where there are multiple steps and some are fully automated, some are not. But in services, think there's still gonna be a huge portion of lawyers, accountants, to your example, doctors, and instead there'll just be different steps that are automated but there'll be key people and it'll be this hybrid AI enabled workforce. That is where I view the world ending with services for many reasons like one, the technology is not there but maybe even more important, many of these spaces are highly regulated so you can't just have an AI doctor even if it was great. And many of them are very human and emotional and it's, you know, a lot of it is talking through learning medical news versus just what's the diagnostic and the prescription, etcetera. So I think in most services industries, it will be the hybrid approach, and that's the one we're employing. I do agree with you though that in certain industries, it could be the full agent automation approach. Go and here's Airbnb, build me Airbnb but for cars instead of for locations, and a swarm of agents will be able to code it up and you'll have sort of an Airbnb for cars that's built for you. So there will be I think some industries that are maybe fully automated. Certainly the holy grail right now is the automation of coding. And you have obviously OpenAI and Anthropic working on that but also Microsoft and Google and then Cognition and Factory and even I think Cursor and others are moving to that trajectory. And so coding is a great example of obviously a massive industry where maybe building websites and products and platforms will be very close to fully automated using agentic swarms.
00:47:45 · Marc Bhargava
But in the industries I'm focused on in applied AI, especially a lot of the services industries which we discussed is really a large part of the global economy, I think it will be a hybrid approach because of the human element, because of the regulatory element, because of the complexity and sort of grey areas of tasks Writing code or doing a math problem generally doesn't have a kind of got to make a gut decision on something while filing your taxes and how you classify something, what's the risk you're willing to take in an audit, etcetera does. And so we're pretty clear on here are the industries where we think this hybrid approach makes sense and we want to incubate companies, maybe they have a role component, maybe they don't. And then also investing in traditional software and agentic companies that we think could maybe fully automate an industry as well.
00:48:33 · Nathan Labenz
[00:48:33] S0: Yeah. One other question I had that prompted is how much do you build versus buy? Because I could imagine like a lot of, you know, another episode coming up with the CEO from Intercom. I could imagine, like, a lot of these companies might ought to just, like, buy Intercom and, you know, have them do, you know, a a big chunk of stuff. Like, it seems like there's a bit of a tension between we want to focus on automating the sort of auxiliary, you know, somewhat less core tasks from the verticalization or vertical specific approach, it feels like the more, you know, true to the vertical you are, the more you're kind of focused in on the core and the more you're kind of trying to do the stuff that, you know, the the pros don't wanna do, the more you're kind of entering into more of a horizontal, like, intercom type scenario. So maybe there's just enough middle ground there that there's still like plenty left to bake, but how do you think about that spectrum?