How I Invest with David Weisburd - E295: Why AI Agents Will Quietly Replace 80% of Investment Teams

Episode Date: February 2, 2026

Why are humans — not models — still the biggest bottleneck to AI progress, and what happens when that bottleneck becomes a business? In this episode, I talk with Ali Ansari, Founder and CEO of micro1, about the hidden layer powering today’s AI breakthroughs: high-quality human intelligence. Ali explains how micro1 pivoted from an AI recruiting startup into a critical data infrastructure company for frontier AI labs, why expert-generated data is now the limiting factor in model performance, and what needs to change for AI agents to actually work in production. We also explore how focus, market timing, and ruthless prioritization enabled micro1 to scale more than 30× in a single year.

At a high level, how do you explain Micro One? Micro One 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, you know, 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. One of the reasons I wanted to chat today is because 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. Of course, that started with the pre-training phase where humans taught models. by first creating the internet. Of course, that was the, you know, the largest set of human data that we, that we had initially, which the models kind of took an unsupervised route of training. And, you know, 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, kind of 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. 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? 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 front development, et cetera.

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 products. It says, okay, this works. 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 it doesn't work. It's instead, what is the action space of this agent? what are all the things that I wanted to do and what are all the things that it should do? And basically what the experts do is they, you know,

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 ten times, you'll just record that one out of five. and it looks really impressive.

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What's the killer app for AI agents? 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. 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 an information edge over the competition? So makers and private equity investors and they're creating albial 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, around these kind of manipulation of spreadsheets generally, which helps investors in their, 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 Micro One is we try to kind of simulate this real world

environments 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. and so that's really the goal for us. 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. That's exactly right. You're part of this new generation of AI entrepreneurs, these AI native entrepreneurs.

How do you look at building a business that maybe the previous, generation built differently. 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 work, but kind of operated with humans running agents versus just humans doing the job on their own. 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. 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 kind of

systems that actually change the domain of any given function. So what I mean by that is is, you know, 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

kind of 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. And the rest will be taken by that, that agent, which the investment making human kind of, you know, helps manage. Support for today's episode comes from Square. The all in one way for business owners to take payments, book appointments, manned staff, and keep everything running in one place. Whether you're selling lattes, cutting hair, running a boutique, or managing a service business, Square helps you run your business without running yourself into the ground. I was actually

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to do work every day. I mean, obviously part of it is like to make sufficient cash and so forth. But but in most cases, the other part of it is that it's like actually like pretty meaningful. Like you're, you're doing something, uh, that you care about and, and you're impacting the world in like some, you know, cool way. And so like, I don't think humans are going to want to just stop that. They instead will do more of that because those functions will actually be, because what

they do is will actually be. even more impactful because of agents. 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. You know, what this means is that there's essentially going to be net new functions created pretty rapidly by humans in every domain. 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 and also have the ability to kind of, create versions of themselves and, you know, 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 efforts spend towards safety evaluations and red teaming and so forth,

then I think we will be just fine. 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 not slowing progress in AI in any way. I think they're accelerating it really nicely. But I would say 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 science and engineering to come up with good frameworks here that needs to be updated basically every day. 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? One thing that I've actually realized quite recently is market matters a lot.

And I think 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, you know, the growth that we had was by far last year when we decided to only focus on this application of human data and build this data infrastructure. for laps. We were kind of split into these like a bunch of different markets and long story short, we decided to focus on the application of the AI recruiter agent that we built, which was just human data and only focused 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 like there was a lot of demand in the market. The company, you know, more than 30x in one year, which was, which was last year. And of course, like previous years to that, we, you know, 3x, 5x, you know, whatever, like these numbers were still good, but but 2025, we literally more than 30x. And so this made me realize that we had focused on one specific application where the market really had demand and things blew So the lesson is like really focus on, don't neglect focusing on the right market.

And what was upstream of that? You had to fire your customers and focus the team. 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 on the AI labs and, you know, 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 the branding of our company as well. Like we were able to freely explain on our websites and, overall kind of like sales materials that we are data infrastructure for for labs versus we're building a recruitment engine. And this, you know, this allowed us to actually close the labs like pretty quickly because of it. So it just goes back to the innovator's dilemma. How in the world can a startup compete against a $10 billion company?

And the thing that the startup always has is focus as the most finite resource. And if they could focus on one thing, then. And downstream of that, you could just drop a $10 billion, $100 billion trillion industry. Exactly. And I think in these cases, focus is like the industry we're in is, it's interesting because the reality is we actually have to balance how we focus. The focus is we are all in on data, you know, as I've said earlier, but we also can't actually focus on any one data niche because of how fast these data niches change.

and how many different structures there are. Like, for example, if we focused on just finance data or just coding data, it actually wouldn't make so much sense because the same customers have so many different needs that they want to use a very small amount of vendors for. And if you focused on, like, one modality, you would actually not be a good vendor.

So naturally, we have to build the product in a kind of paradoxically focused way where the focus is actually to be able to vet all types. types of skill sets and and built this like data platform that can actually handle all data modalities. Running an AI company today is a practice in truly first principles thinking. How do you become a better CEO with such uncertain terrain in front of you? It's a good question. I, uh, I am, uh, you know, asking that every day. And what, one thing I do every single day is I, I try to cancel as many meetings as I can the next day.

Like I look at my calendar and I question every meeting. like from the ground up, doesn't matter when it was set, maybe it was set a few weeks ago and it actually is not relevant anymore. And so I actually end up canceling like roughly 30% of meetings every single day by just questioning it. And this saves me, you know, many hours a week.

And so there's sort of like this notion of constantly questioning what I spend time on is probably the most important. The best CEOs are always trying to get to ground truth. There's structural ways to do that, Elon basically removes all the middle layers. So there's an organizational structure, but also just getting to ground truth really means talking to the customers.

And ultimately, even more important than whether the product is good or not is whether the customer is happy or not. It works best when those things are together. But getting to ground truth, which is the customer feedback, seems to be one thing that every single CEO that's scaling fast has in common. There's no alternative than the CEO and really the whole exact team talking to customers very frequently. I haven't been practically an account executive at Micro One and it needs to stay this way for a while, especially because we have such a small amount of customers.

It's the clients that we have, but it's also the experts that we have that actually help us kind of train these models and so forth. And we look at the experts also as customers. And so, you know, I try to be very close to our expert community. And these sort of things I think are important to really understand, like, what in this case both of our customer types really want and what really makes them stay with the micro-wam. Ali, this has been an absolute masterclass. Thanks so much for jumping on.

Yeah, thank you, David. Thanks for having me. That's it for today's episode of How I Invest. If this conversation gave you new insights or ideas, do me a quick favor. Share with one person your network could find a valuable or leave a short review wherever you listen. This helps more investors discover the show and keeps us bringing you these conversations week after week.

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