⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex

It's totally fine. Me too. I don't think you have I mean, it's Hezerkani, but we don't need Hezerkani.

Yeah. Yeah. Arman Hezerkani is fine.

Amazing.

Introducing. Arman has her company. That's so funny.

to generate more output. High quality output, but faster, more. Right?

And it's because there's my company. But the whole thought is like, when you work at someone else's company, even if you have some equity, even if you deeply care about the mission, you're not deeply incentivized day in and day out to try new AI tools and push yourself to work better and faster and smarter. And so the economic model behind our company is one that does drive that.

And my talk is basically to show how we do that and how I think other companies might be able to adopt similar models. This is very tempting because every question I'm asked might actually just leak your talk.

gained. Yeah. We do use story points.

But you're right that it's it's easy to game it. Right? Like like if we were to hire somebody who just like if you think about a technical system, right, a smart hacker will find ways to exploit it.

And the easy way to exploit the story point system is to deflate the concept of a story point. And decide that, okay, any line of code, like lines of code are gonna be directly proportional and equal to story points. Well, then of course, you've hacked the system, right?

But, your clients will churn and you'll probably get let go of and it just won't work long term. And so, what we found is that hiring two What we found is that this problem gets solved in the hiring process. And it gets solved by hiring people who fall into two buckets.

One is, people who are selfish, but they're long term selfish. Everybody's selfish, but we need to look for people who are long term selfish. People who understand that these incentives are longer than just today's story points.

They're forever. Right? And we need to think about how do we maintain the client relationship.

That means that we're gonna give them very robust story points, so that we can maintain the relationship and continue to make money. But the other, is that we hire people who just like writing code, and like working with really smart people, and they're not sharp elbowed, they just want to do great work. And and that sounds squishy, but that really is a part of it as well.

I think both are both are really important. Just two other things I would quickly add is one.

And then also, like, what's the highest hourly rate that one of your engineers has made since he was technically young Pat? Yeah. So I'll answer the last one or the second one first.

We will probably have more than one engineer make million dollars cash next year based on this model. And that is just with story point compensation. It's very likely that we will have more than a handful of folks make more than a million dollars next year.

The answer to the first question, like, for example, one project we built So, we work with this company that's a They they build They work They partner with retailers to basically make cameras in the business more valuable. And, the way that they do that is they deploy what was historically like a gen four Raspberry Pi to the stores. And they would run like one model on that device.

We basically took some off the shelf models and trained some models ourselves, and then quantized them down so they could actually run on that on that four foot also on Jetson Nanos. And we got them to all run-in parallel. So now basically, what these models allow you to do is as a store, you can get a heat map.

You can see where the lines and the queues are forming in your store. You can even get pictures of shelves and understand what needs to be stocked. You can do things like theft detection because we have body analysis and we can understand things like things are crossing arms.

And this took our team two weeks to put together early prototypes, and now we're just refining accuracy and improving metrics from there. And again, this was one of the many examples. I think, of course, with that specific example, that's more of a research project and it's going to take a while to improve accuracy and things like that.

We're not claiming that we're these magical beings. But previously, that alone, building a prototype of that would take several quarters for robust teams of engineers together. And we were able to prototype that out very quickly, and now we're working together with that team for, for a year to build more and all that stuff.

Alex, anything? Mean, I guess another one, snapback sports, we built them a mobile app in a month that hit twentieth on the app store globally. And there was no AI in this app.

It was a really fun trivia app, but we built it together, deployed it, hit twentieth in the in the world. Yep. I mean, one other example I would just add is and this is just looking at things from a different angle, which is sales.

We do work in a number of different stacks, a number of different languages and stuff. But we feel pretty strongly in like high structure allows for agents to work autonomously for longer. And so our default stack is TypeScript front end, TypeScript back end with a shared file where Or a shared folder where all of our shared types and schemas and things like that live.

And typically React front end or even something as simple as like Express on the back end. Like, we don't really care about the frameworks. It's more just like TypeScript allows us to have that flexibility to, like the flexibility of JavaScript, but the constraints of TypeScript.

And then those error messages allow the Claude code or cursor agents or whatever to iterate on themselves and run things, see the errors and continue. In terms of the actual AI engineering stack, and what coding agents and things like that we're using. I always tell clients this, our team doesn't have a favorite coding agent of the year, or of the month, or even of the week.

If I go over there to our team right now and I ask them, what model is performing the best for coding right now? They'll say today at 04:42, we're noticing that Claude code is actually performing better because of x y z reason. But yesterday, Codex was outperforming Claude code on object on on activities like x, y, and z.

Right?

I think at this state, did a samurai have a measurably better sword than the person to their left or right? No. Right?

At at a certain point, I think a a a a warrior's weapon becomes something of a feel. And I think that at this point, a lot of these, the coding agents are so good. Like like, yes, you can have evals that that provably show that one is better than the other.

But for a lot of these things, it really is feel. It's like, this agent actually, like, it just, I can work better with it on a warm blooded level, or it it writes code more like I like to or whatever. And at least that's what we've noticed.

Yeah. Fair enough.

How do you interview? Like, you don't have to, like, give the exact interview questions, but, like, has interviewing changed for either of you guys pre AI versus post? Yeah.

This is actually somewhat controversial. A lot of my friends stopped doing take home interviews after AI. We still do take homes, but our take homes are immensely they're like our take homes are unreasonably difficult.

And so when I when I first wrote them up, I told Alex, I was like, hey man, like like people might get mad at you. You know, like you have a public persona, like we're sending these to people. Like your reputation might take a hit if we send these to people because they are so unreasonable for us to ask this of people.

And Alex, in classic Alex fashion, was like, F it. Let's just do it. Know?

Like, let's send it. If this is the bar, then Bring it. They need to do it.

Yeah. Exactly. And what we found is that 50% of the people don't even respond to the take home interview.

But because our take home is so difficult, our interview process is actually quite short. We do two calls before the take home, then we send the take home, then we review the take home, and then if it goes well, we do maybe one or two meetings afterwards.

because it's kind of like, well, you just wait, and then maybe the the Frontier Labs will solve it. Yeah. I I generally think that it has to do with context.

I think it's not necessarily context length. I think that it's context engineering in Andre Karpathy's words. Right?

It's it's the problem of how do you how do you get the right context into LLM and get the LLM to pay attention to the right parts of that context, all of which I would consider context engineering. And then from there, it's like, okay, there's a lot of ways you could solve that, right? You can, on the model layer, do a lot of work to make sure that the attention mechanisms are paying attention to the right stuff.

You could do work on the application layer to to context engineering. You can extend context links. Like, there's a lot of different work and then it leads to a really interesting discussion.

So so yeah, that's that's one thing.

I should ask him what it was. There he is. Dan, come here.

What was the question that we asked you during the interview? Remember you like, I asked you if you had unlimited resources and you needed to build an AI engineer, what would you need to solve?

if there is some error rate in your question was basically Wait, come closer so they can hear you. Come closer to my headphones. Is Swix.

You're on a podcast.

We're we're hosting a debate. I just think that MCP is a three letter word for API. And like Yeah.

Alex always, every time he hears someone say the word the the letters MCP in in that order, he tells them that I hate MCP and starts a a a war, a religious debate. No. So Well, I will say though, I do think a few of our engineers have warmed you up to it more with specific use cases, Arman.

Yeah. Mean, like like, are MCPs useful? Like, of course, I use all the MCPs with Claude Code.

I just think that there's like what what bothers me is when people create a new name for something and then use that to raise some inordinate amount of money because they know that three letter acronyms get investors excited. That's like the thing that like, that's that's why I giggle when I hear MCP because I'm like, a lot of people just say that. Yeah.

And like, the tweets that bother me are like, MCP is coming for your job. Here's why you need to know about MCP. And it's like, no.

It's just like a useful thing.