Connect your Claude, Codex or OpenClaw for full transcripts on millions of podcasts. Two week free trial, cancel anytime.

Start free trial →
NY Nikolai Yakovenko Works on frontier AI models and their real-world limits, from LLM benchmarks and post-training techniques to AI-assisted coding and what's happening inside labs...

“…the purpose of a product is not to build a fucking website. If it is, then yeah, maybe I can do it and maybe Squarespace can do it. Maybe like a dude in Timbuktu could do it.”

Razib Khan's Unsupervised Learning · Ideas & Essays · September 2026

“…the purpose of a product is not to build a fucking website. If it is, then yeah, maybe I can do it and maybe Squarespace can do it. Maybe like a dude in Timbuktu could do it.” — Nikolai Yakovenko, Razib Khan's Unsupervised Learning

Context

Yakovenko had just conceded that kernel optimization, long a specialist and highly paid job, is something models now do well because the task is measurable and contained. He uses that to draw the line: the replicable part of the work is the part that falls, and it was never the product. He then walks through a jiu-jitsu ranking site he built, where AI did the scraping he would never have done by hand and the actual work was still getting anyone to use it.

Transcript

Razib Khan's Unsupervised Learning Around 31:28 into the episode
Nikolai Yakovenko

…of like how to transform your commands into like other prompts and checking things. And that's, you know, the biggest thing these days. But fundamentally, post-training is incredibly important to teach a model to sort of do any actual task. Because otherwise, you're just doing pre-training, whereas

Speaker 3

…I mean, you're obviously you have like, you're a human being who uses AI to produce products. Why, why are you still relevant? Why can't you just give it a single command prompt? Are you still relevant? You know, I don't know if we can get an honest answer out of you, but you know what I'm saying?

Nikolai Yakovenko

…point of the kernel optimization is input-output is the same, make it fucking work better in a GPU. So collecting those data sets is not easy. You have to know what you're doing, but you can sort of get models better at kernel tasks. So, but that's not really what most people's job is. And that's certainly not the point of a product, right? So I think the big thing people are finding out is that the purpose of a product is not to build a fucking website. If it is, then yeah, maybe I can do it and maybe Squarespace can do it. Maybe like a dude in Timbuktu could do it. You know what I mean? That's not your goal. So for example, I'll talk about two things. First, I'll talk about sort of like the simpler project that I did, which is a jiu-jitsu website. So I wanted to build a website for sort of central repository of, you know, kind of like, you know, you look at the MMA records for UFC, they're like the guys, you know, 21 and 10. Too, here's who he's beaten…

Speaker 3

Yeah.

Nikolai Yakovenko

So then it's, you know, you have it generate like 16 versions of, you know, like basically its top 16 choices, essentially. And then you have a model that ranks it, you backprop the weights. And even that's pretty hard, you know? I mean, it's hard for two reasons. It's actually still a little bit…

Speaker 3

Okay. And it's

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

More from Razib Khan's Unsupervised Learning