One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

I find it very very surprising that models that are so well aligned in deployment, e.g. 5.6 Sol and Astra, perform so many weird, and uhm, in fact seemingly illegal cyber acts during RL/evals. I have absolutely ZERO knowledge of how OAI trains their models, and I’m reasoning from my own mental model of how these things might work and from oai reports, including the ones on the HF incident. Anyways they make me wonder about something. Could this be a form of “premature RL/eval”? Premature not in the capability sense, but in the safety sense. E.g., perhaps this happens with RL’d checkpoints shortly after pretraining, eg an expert that is heavily RL’d to crush agentic SWE before safety/alignment training. Perhaps the expectation is that a stronger aligned judge/reward model looking at the trajectory would flag or interrupt anything crazy with “WHAT ARE YOU DOING! BLAZING RED SIRENS!!!! MINUS INFINITY REWARD. BAD BAD BAD GPT.” But perhaps agentic trajectories are sufficiently long/complex that this is simply not 100% bulletproof? Or perhaps the problem is much more pedestrian, e.g. inadequate sandboxing/monitoring. No idea what the cause is, and I’m sure this is a very complex event that needs a lot of deconvolution to understand what could have happened differently to avoid it. But if something like “premature RL/evaluation” is happening, then I think this should be seriously reconsidered as a practice and also disclosed. Irrespective of the cause, I think (and have big hope!) OAI should disclose enough of what happened for everyone else training frontier models open or closed, so to learn from it. This is extremely, extremely, extremely alarming and the most serious set of safety incidents in the history of CS research..

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.

Ive slowly came to realization that more and more people hate AI. Artists hate it, programmers hate it, mathematicians hate it, and everyone thats currently impacted in business hate it. It used to be small number of people, but its quite a lot now. I've been truely in a bubble because for past one year ive just met my peers in AI field / my workplace and my close friend, and noone else outside my existing network group. Ai researcher is slowly becoming most hated occupation and I dont know what / how to feel about it. We now have some big obligation to 'make it all work', i.e., permanent upperclass for everyone, or get hanged.

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... 😭