AI Vibe Check: Lab Wars, Why APIs Might Vanish & Future Predictions

**Jacob Effron** (0:00)
I'm Jacob Effron, and this is Unsupervised Learning. We've had a bunch of new subscribers over our last few months, and so wanted to welcome you to the show. We basically probe the sharpest minds in AI on everything that's happening today, what's real and what's coming up, where the space is headed. And today's episode is one of my favorite formats we do. It's an AI vibe check that we do with Ari from Datology. Ari was a former researcher at DeepMind and Meta, now runs a really exciting AI startup, Rob at Radical, one of the great AI venture firms. The three of us talk about everything happening in the AI world today. We talked about Fable, of course, and the reaction around the release as well as model capabilities. We talked about how close we are to RSI. We hit on some pretty spicy predictions, including that the labs may actually get rid of their API business as the compute crunch continues.
And we just got to hit on all the main topics of today. Just really fun to sit down with two friends and great minds in AI. I think folks will really enjoy this. Without further ado, here's our conversation.
It's time for another roundup episode. I always love doing this with you guys, Ari and Rob. I feel like we had a ton of fun in the last one, but like, God, it's AI world. Things have changed. I feel like we last sat down after NeurIPS. And I think since then, we've had IPO filings. We've had, you know, models not launched and then launched. We've had, you know, SpaceX becoming an AI Infra company.
No shortage of headlines to discuss here. So excited to have you both back on the show.

**Rob Toews** (1:24)
Excited to be here.

**Ari Morcos** (1:25)
Yeah. Thanks for having us.

**Jacob Effron** (1:26)
So I think to kick it off, you know, six months is an eternity in the AI world, but I figured I'd start at the highest level. What's the single biggest thing that has changed in how you're thinking about the landscape since we last talked? And maybe, Ari, I'll start with you.

**Ari Morcos** (1:40)
Yeah. I mean, I think the most obvious thing that has changed over the last six months is starting to see the coding agents really start to work at longer time horizons. Right. I think that was just starting when we recorded our last episode at the end of 25

**Jacob Effron** (1:53)
Everyone went away over Christmas break and was like, holy crap, these things really work.

**Ari Morcos** (1:58)
Yeah. I think it starts to show how there are these thresholds, where if you go beyond the threshold, it can become a lot more valuable. Obviously, that's driven the massive rise in token spending and the whole token maxing idea and all this stuff.
But I think we're really starting to now see the shift of engineers, at least, almost all moving from ICs to managers of agents. That's been something that's been very noticeable within Datology, for example, over the last number of months, is seeing more and more people starting to now context switch between managing different agents rather than just working on the one thing. That has enabled by having these agents be able to run long enough and actually be useful in various ways.

**Jacob Effron** (2:39)
Everyone likes to ask top AI researchers like yourself, like how much more productive has it made you in your work?

**Ari Morcos** (2:45)
I think that it's interesting. It makes you a lot more productive in some ways, but it also produces a lot of challenge as well. One of the things that we're struggling with is now, it's a lot easier to produce a massive amount of code that can do something, but now you have this pretty massive understanding gap, and it's a lot easier to put slop into your code base. It's definitely made us more productive. I think a lot of times though, the top-line numbers tend to be overestimated because it doesn't take into account some of these later costs of like, we now have big bottlenecks on reviews, and we don't want to go fully to like, you just like, my agent will review your agent's output, you know?

**Jacob Effron** (3:21)
The bottlenecks just seem to shift.
You know, whatever, it's hard to improve on an entire process because of that. What about you, Rob?

**Rob Toews** (3:29)
There are early signs that seem to suggest over the past six months that make me question whether open-weight AI is going to continue to be a really meaningful force in the ecosystem going forward, at least like near frontier open-weight AI.