Statue depicting a Multi-PI grant : An 18-meter tree and a 9-meter tree with their branches intertwined, jointly holding up an 11-ton marble block.

Dario Amodei is at the desk to assure that the future of humanity is safe from AI

CS academia is dead. So where does that leave AI PhDs like me? Did my ICLR reviewer bidding today. Skimmed a few abstracts, and the methods are the exact same recipe I learned when I got into 3DV two years ago. Swap in a newer video gen base model and boom, new paper 🥲 Makes you wonder how many of the 60k submissions were actually thought up by AI. Meanwhile, World Labs' Atlas has basically solved 4D scenes. Academia is so far behind it's not even funny. I still remember the day GPT-6 Astra dropped. My feed was flooded with GPT + Blender doing inverse graphics and GPT driving robot arms through manipulation tasks. The results were so good I literally had to sit down. A year ago, I was dead sure LLMs could never have spatial intelligence. A year later, Astra slapped that belief right out of me 💀 There's no doubt Astra was post-trained on tons of 3D and manipulation data, and it's only going to get bigger and faster. To me, that means any domain that can be represented symbolically, with clean benchmarks for RL, is going to get swallowed by LLMs. Next to real LLM intelligence, most academic papers that add a bit of inductive bias and tune their way to SOTA are just roadkill waiting to happen. Sadly, these papers keep piling up and flooding every conference. It's inertia, plain and simple. We've been chasing SOTA for so long that it's hard to stop overnight. But make no mistake: the paradigms in a lot of areas converged long ago. What used to be research is now pure engineering, and the room for academics to add inductive biases is only going to shrink. So as a PhD student, I see two paths left. One: if you can't beat them, join them. Clean data, build infra, then go to industry and train foundation models. Two: go back to real science. Stop caring about squeezing out another 0.1% on a benchmark, and start asking why this works and that doesn't, with foundation models themselves as the object of study. As a friend put it: AI research might end up looking more and more like biology, except the organisms are silicon-based 🤣 Beyond these two, it's hard to see anything that won't get eaten by LLMs. Still, I'm pretty pessimistic. I can feel the value of human knowledge being eroded bit by bit. Everyone will get their own AlphaGo moment. What will academia even look like after this... 😭

1/ We introduce TrackEverything: a 3D point tracker that tracks all points across all frames of long videos (1000+ frames). Our key idea is to tie computation to unique 3D scene content rather than redundant 2D pixels in a video. trackeverything.github.io

I guess astra can see determinants of small matrices I vibe-coded this and spent $2 on this, but still a cool partial result i really love all of this no-cot stuff