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

Summary

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Podcast Summary: How I Invest with David Weisburd

Episode: E295 – Why AI Agents Will Quietly Replace 80% of Investment Teams
Date: February 2, 2026
Guest: Ali (Founder, Micro One)

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Episode Overview

In this episode, David Weisburd sits down with Ali, founder of Micro One, to discuss how AI agents are transforming investment teams by automating complex workflows, why the biggest leaps in AI still depend on human expertise, and how tomorrow’s investment landscape will fundamentally restructure around human-AI collaboration. Ali shares his inside perspective on building AI data infrastructure, reveals practical case studies already improving investor alpha, and candidly addresses common fears and misconceptions about “AI takeover.”

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Key Discussion Points & Insights

1. What Is Micro One and Who Are Their Customers?

• Ali introduces Micro One as “the AI platform for human intelligence” — sourcing highly skilled PhDs, professors, and industry experts to generate high-quality training data for frontier large language models.
  • “We vet highly skilled people...in medical, finance and legal, but also many other domains. And we help train frontier large language models.” — Ali [00:02]
• Customers include:
  • Leading AI labs building foundational models
  • Fortune 500 enterprises developing AI agents for production use
  • “Customers are...frontier labs that build foundational models and we also have enterprise customers...building enterprise agents that we help them evaluate.” — Ali [00:35]

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2. Human-Centric AI Training

• AI models aren’t self-improving: Despite hype, human expertise is still essential to improving model performance—especially as models advance from internet-scale data to specialized, expert-labeled data.
  • “It's almost entirely humans that teach the models in some way or another. Of course that started with the pretraining phase...afterwards where the models really got useful is when humans...preference labeling...now we're in this kind of expert data training where humans are creating really complex data from scratch.” — Ali [01:17]
• Stages of AI learning:
  • Pre-training via internet-scale data
  • Human preference labeling
  • Direct expert-generated data and workflows

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3. The Core Bottleneck Blocking AI Agents from Broad Adoption

• Enterprise evaluation frameworks are the missing piece: To move AI agents from impressive demos to production-ready tools, rigorous, granular evaluation is needed—far beyond traditional binary QA testing.
  • “There needs to be an evaluation framework for each of the actions that the probabilistic software 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?” — Ali [03:15]
• Why demos don’t equate to production:
  • “If the agent works 1 out of 5 times or 1 out of 10 times, you...record that 1 out of 5 and it looks really impressive. But then it doesn’t work four out of five times, and you cannot have that in production.” — Ali [04:01]

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4. The “Killer App” for AI Agents

• Coding is the breakout vertical:
  • “The obvious example is coding...the only use case that is very useful in production now...we’ve seen the immense amount of speed it’s added to programming....Imagine applying that to every other domain.” — Ali [06:19]

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5. How Investors Are Using AI for Competitive Alpha

• Automation of financial modeling (e.g. LBO models):
  • “Makers and private equity investors...creating LBO models...models are getting quite good at that...allows them to work on the higher level of thinking that any investment requires.” — Ali [07:06]
• Human-in-the-loop workflows:
  • AI gets trained in simulated collaborative investment environments (multi-expert, peer-reviewed tasks) to mirror actual investor workflows

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6. Rethinking the Investment Team of the Future

• Not “coworkers,” but new systems:
  • "I don't think AI agents are going to be coworkers...AI agents are going to be systems that actually change the domain of any given function...the human will only focus on that 10% that really requires human creativity and focus. And the rest will be taken by that agent." — Ali [09:38]
• Example for 2028/2030:
  • Humans focus on industry targeting, relationship-building, and high-level decision-making
  • AI handles repetitive modeling, diligence, and operational tasks

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7. Preparing for an AI-First Future

• Work will be more meaningful, not less:
  • "It's going to be a really nice future. ...What this means is that there's essentially going to be net new functions created pretty rapidly by humans in every domain." — Ali [12:12]
• Humans will create new value on top of AI-automated foundations

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8. Addressing Fears: Loss of Meaning and the “Terminator” Scenario

• Low probability of runaway AI:
  • “It’s a very unlikely case where models...completely learn on their own and also have...the ability to kind of create versions of themselves...it’s very, very unlikely.” — Ali [13:49]
  • Focus on constantly updated safety evaluation frameworks
  • Government (e.g., Trump administration) is accelerating AI development, but must invest more in safety science

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9. Founder Lessons: The Importance of Focus and the Right Market

• Key to scaling Micro One:
  • “Market matters a lot…Once we made that decision of just focusing on this kind of one application where the market was really hot...the company more than 30x in one year.” — Ali [15:45]
• Firing customers and focusing the team:
  • Directing all resources toward lab-specific data infrastructure
  • "We had to stop serving...startups...and only focus on the AI labs...and that kind of changed the product roadmap." — Ali [17:41]
• Paradox of focus:
  • Must balance absolute focus with handling many data domains for key customers
  • “We have to build the product in a paradoxically focused way where the focus is actually to be able to vet all types of skill sets.” — Ali [19:04]

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10. CEO Practices Amid Uncertainty

• Cancel meetings ruthlessly:
  • “I try to cancel as many meetings as I can...I actually end up canceling like roughly 30% of meetings every single day by just questioning them.” — Ali [20:15]
• Talk to customers directly and often:
  • "There's no alternative than the CEO and really the whole exec team talking to customers very frequently. I am practically an account executive at Micro1, and it needs to stay this way for a while." — Ali [21:26]

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Memorable Quotes & Timestamps

• "It's almost entirely humans that teach the models in some way or another." – Ali [01:17]
• "There needs to be an evaluation framework for each of the actions that the probabilistic software needs to do..." – Ali [03:15]
• "The obvious example [for AI agent killer app] is coding...that's actually an exciting thing." – Ali [06:19]
• "I don’t think AI agents are going to be coworkers...they’re going to be systems that actually change the domain of any given function." – Ali [09:38]
• "Once we made that decision of just focusing on this kind of one application...the company more than 30x in one year." – Ali [15:45]
• "There's no alternative than the CEO and really the whole exec team talking to customers very frequently...I am practically an account executive at Micro1." – Ali [21:26]

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Important Timestamps

• [00:02] – Introduction to Micro One
• [01:17] – How humans train AI models; stages of AI learning
• [03:15] – The QA bottleneck and new evaluation frameworks
• [06:19] – Coding as the AI killer app
• [07:06] – Case studies: how investors use AI for alpha
• [09:38] – Why AI agents won't be "coworkers," but new types of systems
• [12:12] – Preparing for an AI-first future
• [13:49] – AI existential risk: why it’s unlikely, but safety matters
• [15:45] – Lessons in focus and scaling from Micro One
• [20:15] – Founder time management and ruthless prioritization
• [21:26] – The necessity of talking to customers

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Final Thoughts

Ali and David’s conversation powerfully reframes the coming shift: AI agents aren’t just another productivity tool—they’ll surge forward to handle vast swaths of investment work, moving humans into ever-more-creative, high-impact, and meaningful roles. For investment professionals and founders alike, the path forward calls for a clear-eyed understanding of AI’s current limitations, laser focus on market opportunity, relentless customer engagement, and—above all—the willingness to reinvent work alongside AI’s rapid evolution.

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Transcript

[Podcast Host]

At a high level.

[David]

How do you explain Micro One?

[Ali]

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 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.

[David]

And who are your customers?

[Ali]

Customers are the frontier labs that build foundational models and we also have enterprise customers, you know, Max 7 and kind of Fortune 500 broadly that are building also foundational models but, but also they're building enterprise agents that we help them evaluate and kind of get ready for production.

[David]

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?

[Ali]

It's almost entirely human 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 a unsupervised route of training. And you know, that was kind of the initial, initial stage of the foundational models. And, 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.

[David]

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

[Ali]

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, you know, 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, back end development, front end development, et cetera. And then you put that into some QA engineering phase where there's a, usually one QA engineer that goes in and kind of tests the product, says, okay, this works. And it's kind of a binary thing like the software either works or it doesn't, and then, 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? What are all the things that I want it to do and what are all 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, 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 1 out of 5 times or 1 out of 10 times, you, you'll just record that 1 out of 5 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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[David]

So another way for AI agents to scale, 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 agents performance versus a smart human.

[Ali]

Exactly.

[David]

Every trend seems to have a killer app. In the beginning with social it was Facebook. With iPhone, some would argue it was Instagram. What's the killer app for AI agents?

[Ali]

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 a, 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.

[David]

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?

[Ali]

So makers and private equity investors and you know they're, they're, they're creating LBO models or they're, 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 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 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. And so that's really the goal for us now.

[David]

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.

[Ali]

That's exactly right.

[David]

You're part of this new generation of, of AI entrepreneurs. These AI native entrepreneurs. How do you look at building a business that maybe the previous generation built differently?

[Ali]

We pretty religiously follow this notion at micro1, 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.

[David]

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.

[Ali]

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, 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, 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 agent, which the investment banking human kind of helps manage.

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[David]

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?

[Ali]

It's going to be 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 actually pretty meaningful. Like you're doing something that you care about 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 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. What this means is that there's essentially going to be net new functions created pretty rapidly by humans in every domain.

[David]

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.

[Podcast Host]

What probability do you prescribe to that? Or do you think it's complete science fiction?

[Ali]

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, 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. 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, should probably focus on is actually this, this, 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.

[David]

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.

[Ali]

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's 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 labs, we were kind of split into these like 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 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 previous years to that 3x5x whatever these numbers were still good, but, but 20, 25, we, we literally more than 30xn. And so, so this made me realize that we had focused on one specific application where the market really had demand and things blew up. So, so the lesson is like really focus on, don't neglect focusing on the right market and what was upstream of that.

[David]

You had to fire your customers and focus the team.

[Ali]

So unfortunately we had to stop serving the customers in terms of startups that would hire engineers from us and, and, and other types of customers that we had. We, you know, had to stop serving them and slowly phase them out in terms of being customers and only focus on the AI labs and, and you know, the Max 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 when we made this decision to go all in on data, it really changed the branding of our company as well. 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. And this, you know, this allowed us to actually close the labs like pretty quickly because of it.

[David]

So 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, you know, downstream of that, you could disrupt a 10 billion-valued billion trillion dollar industry.

[Ali]

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, as I 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 focus on like one modality, you would actually not, you would not be a good vendor. So, 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 of skill sets and build this data platform that can actually handle all data.

[David]

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?

[Ali]

It's a good question. I am asking that every day. And 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, I, I actually end up canceling like roughly 30% of meetings every single day by just questioning them. And this saves me, you know, many hours a week. And so, so there's sort of like this notion of constantly questioning what I spend time on is probably the most important.

[David]

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.

[Ali]

There's no alternative than the CEO and really the whole exec team talking to customers very frequently. I am practically an account executive at Micro1, and it needs to stay this way for a while, especially because we have such a small, 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, are important to really understand, like what in this case, both of our customer types really want and what really makes them stay with Micro One.

[David]

Ali, this has been an absolute masterclass. Thanks so much for jumping on.

[Ali]

Yeah, thank you, David. Thanks for having me.

[Podcast Host]

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 in your network who'd find it 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. Thank you for your continued support.