Case in point... THOU SHALT NOT COMPETE. - AI/LLM development generally - distributed training - pretraining pipelines - ML accelerator design - cybersecurity - chemistry Already disclosed as nerfed for the plebs. What's next on the lobotomization list?
Replying to @_olaige
The dream state would be models within a few IQ points of the frontier labs (probably task specific first, depth before breadth), with a "training-as-a-service" arm to achieve breadth (inc. RL), all on TEE with true privacy, served at a fraction of the price of frontier models with significantly less hardware, thereby commoditizing and democratizing intelligence to the maximum extent possible. The entire world's knowledge trains these models, they should in turn be trained by and accessible to the entire world. The thing about privacy is, it's not really just about privacy. YOU are the product (builders) by ignoring privacy concerns. People are building the massive golden trillion+ dollar moats for the various labs for them by giving free RL data constantly. It's great, for a few, not so great for everyone else. Need to make sure AI remains accessible and available to everyone, always. If AI gets concentrated into a handful of players, it would mean those teams and maybe even a single person within ultimately gets to decide if you are allowed to multiply matrices in particular ways or not, not to mention constant surveillance etc. Parallax aims to be the destroyer of moats.
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so cooked

Jun 10, 2026 · 9:30 PM UTC

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