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Forget UBI. You’ll Get Paid Just for Being Human: Emad Mostaque on Impact Theory (Transcript) 

In this Impact Theory interview, Tom Bilyeu sits down with Emad Mostaque, founder of Intelligent Internet and author of The Last Economy, who argues that the economic value of human cognition will turn negative within about 2 years. They dig into AI’s Navier-Stokes breakthrough, the collapsing cost of intelligence, humanoid robots, white-collar job losses, why tax-based UBI doesn’t add up, and why the future may involve getting paid simply for being human. Premiered October 1, 2026. Read the full transcript of this interview below:

The Value of Human Cognition Is About to Go Negative

TOM BILYEU (00:00:00 – 00:00:08): AI is moving insanely fast. You’ve said that compared to AI, that humans are going to see their value go negative. What do you mean by that?

EMAD MOSTAQUE (00:00:09 – 00:00:26): Yeah, I think it’s, I said precisely the value of human cognition, we’ve got about 2 years before it goes negative. We’re the dumbest people on the team, we’re slowing everyone else down.

In fact, I remember we had our first podcast 3 years ago, and I said it was going to be 3 years until everything changes, and now this is the point of change.

TOM BILYEU (00:00:26 – 00:00:30): Now, do you think that we’ve actually hit that point of change to make good on that statement?

EMAD MOSTAQUE (00:00:31 – 00:00:50): I’m a mathematician. Navier-Stokes problem was solved, you’ve just seen another genetic breakthrough. You’ve seen competent AI that doesn’t make mistakes. Hollywood-level video, I think now is the takeoff point.

And again, we’ve got 1 or 2 years before our AI teammates look at us with disgust and be like, “Man, that Emad, he always says the dumbest things.”

TOM BILYEU (00:00:51 – 00:00:58): The first 3 years were basically getting up to the point where it could have the Navier-Stokes type breakthrough.

EMAD MOSTAQUE (00:00:58 – 00:00:58): Yeah.

TOM BILYEU (00:00:59 – 00:01:14): Now it’s going to be where we’re racing over the next couple of years, where we’re right now, we’re able to use it, we’re able to be meaningful in the mix, but that is going to be a declining arc that somewhere in the neighbourhood of 2 years, it’s— we’re the guy that they’re basically paying to leave the team.

EMAD MOSTAQUE (00:01:14 – 00:01:25): Yeah, the team is more efficient without you. The technology and the models have got to that human-level performance. They’re about to outstrip us. But the hardest—

TOM BILYEU (00:01:25 – 00:01:27): Why do you say they’re about to outstrip us? What is it?

EMAD MOSTAQUE (00:01:28 – 00:02:18): If you look at the outputs, you have an AI slop for the longest time. You can tell AI writing, for example, it’s like you’re half right, half wrong, and the part that’s half wrong is the interesting one. “It’s not X but Y.” Oh God, there we go. You pick up the slop. That’s all disappearing with the next generation of models. The next generation of models don’t make mistakes.

As a mathematician studying maths at Oxford, I was better than the AI at generalized understanding of math until maybe a few months ago, and then it stripped ahead and now it’s solving the hardest math problems in the world.

In terms of just everyday stuff, end of last year we had the OpenClaw phenomenon, right? Everyone was like, “Oh, look at this, and let’s buy some Mac Minis” and things like that. And then they realized it’s actually kind of garbage. It makes lots of mistakes, it likes deleted all my stuff.

TOM BILYEU (00:02:19 – 00:02:25): And the OpenClaw moment was agentic AI. It will go do things on my behalf.

EMAD MOSTAQUE (00:02:25 – 00:03:00): You go do things on your behalf. Now what we have is Muse and Instinct and things like that. Actually, people that don’t know them, it’s like actual AI agents that you just message and they go and book you restaurants at the coolest tables, or they check your kids’ school activities. They’re like a really good PA, but they actually are good. They don’t drop the ball.

So you’ve gone from AI dropping the ball, hallucinating, writing like crap, to all of a sudden it’s good, actually, it’s competent. And the next level is when you get 10,000 of them together, like OpenAI did solving this incredibly difficult math problem—

How AI Cracked the Navier-Stokes Millennium Prize Problem

TOM BILYEU (00:03:00 – 00:03:01): Navier-Stokes.

EMAD MOSTAQUE (00:03:01 – 00:03:08): Navier-Stokes, yeah. So there were 7 Millennium Prize problems, $1 million each. Humans solved one, it solved the second one.

TOM BILYEU (00:03:08 – 00:03:10): Did it actually solve Navier-Stokes or just progress it?

EMAD MOSTAQUE (00:03:11 – 00:03:33): So the prizes have certain conditions, and Navier-Stokes had 4 different conditions that could win it. They solved 2 of them. The other 2 are still to be solved, but a solution of those 2 counts as everything. But it’s not the whole Navier-Stokes problems that we have. It’s a specific set of things. So they won the prize. There’s still more to solve in math. But the question is—

TOM BILYEU (00:03:33 – 00:03:46): I’m not even sure, because I don’t— I think it’s currently listed as active, meaning “We’re assessing. We haven’t fully decided yet.” And there was obviously the big debate between was it the humans that did this or was it OpenAI?

EMAD MOSTAQUE (00:03:47 – 00:03:55): Yeah. So this is a problem that I’ve looked at and actually studied formally and things like that. It takes 2 years to actually accept it.

TOM BILYEU (00:03:55 – 00:03:55): Whoa.

EMAD MOSTAQUE (00:03:56 – 00:04:36): That’s the Clay Institute thing. So it needs to be published in a journal, then needs to be accepted, and 2 years need to go by with people beating it up. What OpenAI did is they put 10,000 agents for 88 hours on it, which is 100 years of talented human time.