How I Invest with David Weisburd
00:10:16 · David Weisburd, Chaz Englander
[00:10:16] S0: time we chatted, you gave me this shocking statistic that you believe that companies will experience 55% productivity gains in the next twelve to eighteen months on the backs of AI. Where are these productivity gains specifically gonna come from? [00:10:30] S1: So I was saying, well, what even qualifies us for making that statement? A lot of what we're doing if... As we're selling into firms is we're building business cases. So we have to deeply understand where AI is applicable today. The big thing that really no one saw is the progress that happened with reasoning models. Reasoning is effectively a technique. It's very difficult to forecast progress in underlying techniques. Now forecasting progress in the underlying models is theoretically that's a lot more predictable but techniques are more difficult to predict clearly. And so, as we look at that and we look at all the data, really looking at where the time is going more importantly, particularly the more junior members of the team, we're looking at a 50% efficiency, certainly before the end of twenty twenty seven, but that could be a considerable amount of time before that. And and I don't think that's because of the technology. I think that's because of the adoption. [00:11:29] S0: Part of your business processes is you go into organizations and you look for productivity gains that they could have using AgenTeq AI. What's the exercise that you go through? How can an organization figure out where they have the most productivity gains?
00:11:29 · David Weisburd, Chaz Englander
[00:11:29] S0: Part of your business processes is you go into organizations and you look for productivity gains that they could have using AgenTeq AI. What's the exercise that you go through? How can an organization figure out where they have the most productivity gains? [00:11:42] S1: We do an entirely free, you don't have to sign up, discovery phase. So we will work with a customer and we will identify where we think AI is most applicable today beyond just a chat based interface. As I said, workflow automation. So we do that over a two, three or four week period. And really come the end of that period, we're targeting a number of... And we don't really look at workflows unless it's a 60 plus percent efficiency gain on a single workflow level. The reason we do that is because we really wanna focus on short term ROI to deliver value, you know, quickly. But we do that now with with all of our customers. [00:12:19] S0: Perhaps this is a dumb question, but is this a soft... Is this a software process that's running on machines? Are these virtual machines? How do you actually implement these systems? The user just logs in, presses a button, and the report is generated. How does that mechanically work?
00:12:19 · David Weisburd, Chaz Englander
[00:12:19] S0: Perhaps this is a dumb question, but is this a soft... Is this a software process that's running on machines? Are these virtual machines? How do you actually implement these systems? The user just logs in, presses a button, and the report is generated. How does that mechanically work? [00:12:33] S1: So if you kinda picture it, the way it would work is we have an Excel type interface. So think of, you know, just Excel but powered by AI. You upload a document and what we're doing is we're deconstructing that document into its individual components and we're making... You know, the model is making the best guess as to where you would get that information from, from documents, from fax set, from web filings, from, you know, wherever you may normally get that information from. The user would come in and confirm, would click save, and then that workflow would exist. Right? And they can deploy that workflow to just themselves, to the rest of the team, to the whole group, and then they can obviously, as you go, you can make alterations and then someone can make a copy and, you know, adapt that workflow to them as a specific user if they want to. [00:13:06] S0: Is it fair to compare you as an enterprise OpenClaw of sorts? And how do you look at open source projects like OpenClaw competing with what you're doing? Quite important. That has become very clear in
00:13:06 · David Weisburd, Chaz Englander
[00:13:06] S0: Is it fair to compare you as an enterprise OpenClaw of sorts? And how do you look at open source projects like OpenClaw competing with what you're doing? Quite important. That has become very clear in [00:13:19] S1: the legal AI space, think. I think it's like 80, 90% of the top 100 law firms now use either LEGO or Harvey, and I think finance is about twelve months behind. So I think we can learn a lot from what happened in that market. And if you look at what happened there, it's really similar with us, is the entire product, you know, that's the bottom in the AgenSys system through to the UI, is designed with the customer in mind. Right? And that last mile delivery to the customer, really from what we're seeing is where the impact is. [00:13:46] S0: So it's not what the agents are necessarily going and gathering, it's how that's presented to the customer and it's very specific to that customer base.
00:13:46 · David Weisburd, Chaz Englander
[00:13:46] S0: So it's not what the agents are necessarily going and gathering, it's how that's presented to the customer and it's very specific to that customer base. [00:13:54] S1: Exactly. And it's just... It's the design of the overall system. Right? So it's like, you know, if you think about the agents, it's what data you integrate with. The way that the agents communicate, what language do they communicate in. Do they understand financial concepts well? Where is the context coming from? So from an agenda perspective, there's lots of finance specific things that go on that are really really important. Again, you've seen that in the legal AI world. But then it's the UI. How is a user interacting with these systems? You know, coming back to this point of can you build a 200 page PowerPoint presentation exactly as you would have done before, graphs, tables, charts, logos. You know, think about the complexity that goes into these types of outputs, like FEDs and CDDs and so on. Right? Can you do that on your phone in ChatGPT or in Claude? No. And so you have to... It's the agent system all the way through to the UI and with user interacting with these systems that becomes really really important.
00:14:45 · David Weisburd
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