Karan Singhal of @OpenAI on the collapsing cost of intelligence, as seen on their health benchmark: "GPT-6 Luna is our current cheapest frontier model on the API... It performed better than 5.6 Sol, which was literally our best model a month ago. And it performs better despite being 34x cheaper." Most tiers of model intelligence became more than 100x cheaper within roughly a year One tier went from ~$32 to ~$0.02 per million tokens in two years

Ekin Dogus Cubuk of @periodiclabs on why AI has to learn from the physical world, and building their own lab to close that loop: "One of the more fundamental ways of making things around us is powder synthesis... You can actually, using that method, discover new superconductors, magnets, all kinds of materials that are very important for technologies around us." "But at the core of it is just quantum mechanics. And we feel like teaching these LLMs quantum mechanics will be the next frontier for LLMs." "Science is by its nature iterative, right? Like even the smartest humans tried many times before they discovered the things they discovered. LLMs can be very smart, but if they're not iterating on science, they won't discover science." "The important thing to teach these LLMs is the method of scientific inquiry. So you do simulations, you do theoretical calculations, you do experiments, you get results, and the results are probably incorrect or not what you wanted at first, but you iterate on it." "You have to do it with the real physics, not just the simulation." @ekindogus @AnjneyMidha a16z's Christian Keil makes the case for why building, testing, and iterating in the physical world is the bottleneck for AI in hardware: a16z.news/p/superhuman-hardw…