E295: Why AI Agents Will Quietly Replace 80% of Investment Teams

0:00Ali Ansari:At a high level, how do you explain MicroOne?

0:03David Weisburd:MicroOne is the AI platform for human intelligence. So what that means is we vet highly skilled people, mainly PhDs, professors, and industry experts, mainly in medical, finance, and legal, but also many other domains. And we help train frontier large language models. So you could think of the AI labs, the way they're kind of improving their model capabilities is by gathering net new human data for their post-training pipelines. and we help them gather that net new human data. And who are your customers? Customers are the Frontier Labs that build foundational models. And we also have enterprise customers, you know, Mac 7 and kind of Fortune 500 broadly that are building also foundational models, but also they're building enterprise agents that we help them evaluate and kind of get ready for production.

0:55Ali Ansari:One of the reasons I want to chat today is because is upstream of the LMs improving. There's these improvements to the models. Maybe you could unpack on why are LM models improving and how much of that is this recursive AI improving itself and how much of it is the PhDs and these other professionals? It's almost entirely humans that teach the models in some way or another.

1:23David Weisburd:Of course, that started with the pre-training phase where humans taught models by first creating the internet. Of course, that was the largest set of human data that we had initially, which the models kind of took an unsupervised route of training. And that was kind of the initial state to the foundational models. And then afterwards, where the models really got useful is when humans kind of started to do a bunch of preference labeling, choosing which answer is better and so forth based on the model responses. And then once we pass that phase, now we're in this kind of expert data training where humans are creating really complex data from scratch, whether it's doctors, lawyers, finance experts, and investment banking and other areas.

2:12Ali Ansari:2025 was supposed to be this year of AI agents. Some people think it's going to happen in 2026. What needs to happen for AI agents to gain traction in the general market?

2:24David Weisburd:Really, there's just one fundamental bottleneck that needs to be resolved. And that is enterprises need to dedicate large portions of their product budget and really just implement in their product workflow this notion of evaluations. and so what I mean by that is if you think about like what does product development look like in any given enterprise or just any company in general there's usually a phase of design, you design whatever software you're trying to build, there's some approval processes and then you get into development the programmer develops it, there's full stack backend development, frontend development, etc and then you put that into some QA engineering phase where there's usually one QA engineer that goes in and kind of tests The product says, okay, this works.

3:14David Weisburd:And it's kind of a binary thing. Like the software either works or it doesn't. And then it goes into production. And that needs to change. And the part that needs to fundamentally change is the QA part, where there's no more just one QA engineer going in and saying, okay, this software works and we can put it to production. But instead, there needs to be an evaluation framework for each of the actions that the probabilistic software needs to do. in other words, the agent needs to do. Because the agent, there's no notion of the agent works or doesn't work. It's instead, what is the action space of this agent?

3:50David Weisburd:What are all the things that I want it to do? And what are all the things that it should do? And basically what the experts do is they create human data to measure exactly the capabilities of each of those functions. And then once the threshold is met, then the agent can move into production with confidence. What's happening right now is that there's a lot of good demos because if the agent works one out of five times or one out of 10 times, you'll just record that one out of five and it looks really impressive. But then it doesn't work four out of the five times and you cannot have that in production.

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5:49David Weisburd:So another way for AI agents to scale,

5:52Ali Ansari:they need to behave like smart humans or ideally smarter than the smartest humans in order to assess that, you need to have some framework in mind to assess the AI agent's performance versus a smart human. Exactly. Every trend seems to have a killer app in the beginning with social. It was Facebook. With the iPhone, some would argue it was Instagram. What's the killer app for AI agents?

6:18David Weisburd:The obvious example is coding. I would argue actually the only use case that is very useful in production now. But I think that's actually an exciting thing that it's really the only one that's working super well because we've seen the immense amount of speed it's added to programming and really like how productive it's made software engineering in general. And so imagine kind of applying that same thing to essentially every other domain.

6:46Ali Ansari:Sustained alpha is contingent on oftentimes having asymmetric information, having access to information or data other investors don't have. What are some early case studies for how investors are using AI in order to get information edge over the competition?

7:06David Weisburd:So makers are private equity investors. And, you know, they're creating LBO models or they're manipulating them in some way. And models are getting quite good at that. So, you know, the data that we've been helping kind of a lot of foundational model companies create is around these kind of manipulation of spreadsheets generally, which helps investors in their day-to-day work, which allows them to, again, work on the kind of higher level of thinking that any investment requires. What we do at MicroOne is we try to kind of simulate this real world environment that investors usually work in. And so what we try to do is to get the models good at these capabilities, you have to try to replicate the same workflows that investors go through in terms of like the collaboration they go through and the kind of like multi-expert task creation that happens and the overall kind of peer reviews that happen in the process.

8:07and so that's really the goal for us.

8:10Ali Ansari:Now the model could take care of that. Now they could focus on which industries they want to go to, meeting the right people, meeting the right co-investors, selling themselves to the investments themselves if needed like in a venture capital and focus on higher level activities than just being in that model.

8:27David Weisburd:That's exactly right. You're part of this new generation of AI entrepreneurs,

8:31Ali Ansari:these AI native entrepreneurs. How do you look at building a business that maybe the previous generation built differently?

8:39David Weisburd:We pretty religiously follow this notion at Micro One, which is we have to try to get every function within the company to eventually have some AI agent that a human helps operate. And of course, there's a lot of functions where that's not remotely possible yet, but we have to still kind of strive towards it. And the company's overall velocity will be very much defined by this idea of how many agents exist within the company and whether almost every function is not automated. Automated is not the right word, but kind of operated with humans running agents versus just humans doing the job on their own.

9:21Ali Ansari:Let's say you're a private equity fund or venture capital fund in 2028 or 2030. Give me an example of how a day-to-day might look like where humans are working next to AI agents completing tasks.

9:38David Weisburd:Often this is kind of explained as co-workers. And I would actually kind of disagree with this notion of co-workers. I don't think AI agents are going to be co-workers. I think instead what AI agents are going to be are systems that actually change the domain of any given function. So what I mean by that is investment bankers are not going to have the same set of functions as the investment banking agent. Instead, the investment banking agent will take, you know, the investment banker humans do currently. And what will happen is the investment banker, you know, human will only focus on that kind of 10 % that really requires human creativity and focus.

10:39David Weisburd:And the rest will be taken by that agent, which the investment banking human kind of, you know, helps manage.

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12:00Ali Ansari:Get more with Northwest Registered Agent at northwestregisteredagent.com slash invest free. In that future where AI is doing the work, what should humans be focused on and how should they prepare for that future?

12:15David Weisburd:It's going to be a really, a really nice future. And the reason is humans are naturally going to find new things to do. What's going to happen is the human is just going to make their job more fun and come up with new things to do within their job. If you think about like, why does a human choose to do work every day? I mean, obviously part of it is like to make sufficient cash and so forth. But in most cases, the other part of it is that it's like actually like pretty meaningful. Like you're doing something that you care about and you're impacting the world in like some cool way. And so like I don't think humans are going to want to just stop that.

13:02David Weisburd:They instead will do more of that because those functions will actually be –

13:12David Weisburd:So I think what humans will do is they'll basically figure out ways to continue expanding on what they love doing, which will be their work in most cases. What this means is that there's essentially going to be net new functions created pretty rapidly by humans in every domain.

13:31Ali Ansari:One of the concerns humans have is this fear of losing meaning through their work. The second one is this Terminator case where the AI becomes sentient and becomes basically self-acting. What probability do you prescribe to that or do you think it's complete science fiction? It's a very unlikely case where models become completely, you know, have the ability to completely learn on their own.

14:05David Weisburd:and also have the ability to kind of create versions of themselves and in some way reproduce, it's very, very unlikely that those two things become true to the extent that is true for humans. And without that sort of positive feedback loop existing, it's really hard for these systems to really get out of hand truly. So I think that's a very unlikely case. But it doesn't mean that it's a case that we should kind of ignore. Safety evaluations is a very important part of what model providers do, what enterprises do and should continue doing. But I think it's really just that. Like if you have sufficient budget and kind of care and effort spent towards safety evaluations and red teaming and so forth, then I think we will be just fine.

15:04David Weisburd:And in fact, I think this is actually a really good area for the government to focus on. The Trump administration is doing a great job of like not slowing progress in AI in any way. I think they're accelerating it really nicely. But I would say like one area that the government should probably focus on is actually this exact notion of coming up with a safety evaluation framework that requires a lot of like science and engineering to come up with good frameworks here that needs to be updated. basically every day.

15:36Ali Ansari:What's one piece of advice you wish you could go back four years ago and give a younger Ali on how to better run Micro One, how to maybe avoid mistakes or scale faster?

15:48David Weisburd:One thing that I've actually realized quite recently is market matters a lot. And I think, you know, being a very product-oriented, you know, entrepreneur and really just caring about building a good product and sort of assuming the rest will come, which is sort of true. And I like to believe that that continues to be true. But I've come to a pretty important realization where the market you're in really matters. And the growth that we had was by far last year when we decided to only focus on this application of human data and built this data infrastructure for labs, we were kind of split into these like a bunch of different markets.

16:39And long story short, we decided to focus on the application of the AI recruiter agent

16:46David Weisburd:that we built, which was just human data and only focus on that, which of course meant we had to develop a lot of other things. It didn't, you know, it didn't stop at the AI recruiter. We had to build the data platform and a bunch of other things that came afterwards. But once we'd made that decision of just focusing on this kind of one application where the market was really hot and there was a lot of demand in the market, the company more than 30x in one year, which was last year. And of course, like previous years to that, we 3x, 5x, whatever, like these numbers were still good. But 2025, we literally more than 30x.

17:23David Weisburd:And so this made me realize that we had focused on one specific application where the market really had demand and things blew up. So the lesson is like really focus on, don't neglect focusing on the right markets. And what was upstream of that?

17:41Ali Ansari:You had to fire your customers and focus the team.

17:44David Weisburd:So unfortunately, we had to stop serving the customers in terms of startups that would hire engineers from us and other types of customers that we had. We had to stop serving them and slowly phase them out in terms of being customers and only focus on the AI labs and the Mac 7 that are building foundational models. and then we also started to focus on building our product around exactly what the AI labs need and so that kind of changed the product roadmap a good amount and then I would say that the third thing is we made this decision to go all in on data it really changed like the branding of our company as well like we were able to freely explain on our website and overall kind of like sales materials that we are data infrastructure for labs versus we're building a recruitment engine.