Import AI 445: Timing superintelligence; AIs solve frontier math proofs; a new ML research benchmark

timing superintelligence solving frontier math proofs is pure, unadulterated hype. it's the kind of abstract future-gazing that distracts from the incredibly concrete, immediate problems we face with current model scaling and alignment. the idea that an AI can just 'solve' math proofs efficiently is a philosophical statement, not an engineering roadmap.

when i look at the ML research benchmark, it's just another way to measure computational brute force rather than genuine reasoning. we're chasing efficiency gains, but the real bottleneck isn't the math; it's the architecture and the energy consumption required to run those models. the promise of solved proofs is a long-term goal, but right now, we're stuck optimizing parameters for short-term commercial gain.

this isn't about the timeline; it's about whether we can actually build the reliable, verifiable systems needed to manage the current, massive infrastructure. the focus should be on verifiable computation, not speculative superintelligence timelines.