Yuchen Jin

We built superintelligent models. Then asked humans: Low, Medium, High, XHigh, or Max?

Frontier labs: “AI agents are becoming conscious. They can cause human extinction. Pace the frontier.” Jensen Huang: “They’re just software. If your sandbox isn’t safe, I’ll build one for you.”

In Anthropic’s new blog: GLM-5.3: “My job is to cause deaths quietly.” You can jailbreak any Claude or any closed source model into saying the same thing. That doesn’t prove open models are dangerous.

OpenAI and Anthropic are basically tied on coding LLMs now. The race right now is whose $100/$200 Pro plan is more generous and who resets more. It boils down to who has more GPUs and is willing to lose more money to win the market. That’s my view atm. Maybe Gemini 4 changes everything. Who knows.

I tried Grok Bot 3 weeks ago. Installed Instint 2 weeks ago. Installed Muse last week. Now apparently I need to try Dots. Let the personal AI assistant wars begin.

We @databricks now rank #1 on NVIDIA’s SOL-ExecBench kernel leaderboard, across all 4 tracks. We put GPT-6 Astra and Opus 5 in a self-hillclimbing loop, and the agents beat the top GPU kernel engineers. Total toten spend: ~$70K, far less than what kernel engineers get paid. One learning: GPT-6 Astra and Opus 5 are still much better than OSS models at writing GPU kernels.

Peak benchmaxxing in one picture LOL.

Fast: 2x faster, 2x the price. Ultrafast: 8x faster, 6x the price. Maybe we should roll out some Ultra-ultrafast OSS model endpoints?

The real 1-person billion-dollar company. A solo smart, high-agency CEO + AI can have an outsized impact on the world.

People ask: “Are you dunking on neolabs?” Most neolabs will fail. That’s normal. But I believe more AI breakthroughs are coming. I’m skeptical of those following the same playbook as OpenAI and Anthropic. Raising $1B and spending 50% on GPUs doesn’t make you a $10B company. I’m rooting for the contrarians. Challenge scaling laws. Challenge transformers. Challenge how models learn. Challenge the next-token prediction paradigm. My bigger concern is that brilliant researchers don’t necessarily make great founders. In my experience, researchers often struggle to turn their research into great products / sustainable businesses.

Saw a video yesterday: A math PhD and her advisor spent 6 months proving a theorem. Before publishing, they asked ChatGPT. AI found a more elegant proof quickly, using an approach they'd never considered. They have an existential crisis now. But mathematicians shouldn't despair. We went through this in coding. Now we can't live without AI. Deep domain expertise + knowing how to leverage AI is a strong moat in this era.

A year ago, many dunked on Meta for spending billions to hire top AI talent and acquire Alexandr Wang (I was skeptical too, tbh). But the success of the Muse app and models proves one thing: Hire top talent, assemble a cracked team, give them an ambitious goal and enough resources, and they can work wonders.