Google DeepMind's Pushmeet Kohli and Biohub's Sal Candido argue AlphaFold's breakthrough was only the beginning and that scaling compute and data alone won't solve biology. Protein structure prediction remains far from solved, and curing all disease will require 10x breakthroughs rather than incremental gains.
Interconnects argues AI progress will accelerate mainly through engineering and inference-efficiency gains.
Periodic Labs co-founders Liam Fedus and Ekin Dogus Cubuk explain "synthesis superintelligence" — reinforcement learning grounded in physical experiments rather than internet data. The lab aims to build AI scientists that discover new materials, giving every lab instrument "140 IQ" and compressing decades of trial-and-error into months.
Enterprise AI's frontier has shifted from prediction to autonomous decision-making.
MIT Technology Review's report argues the "agentic shift" requires rethinking architecture and operating models.
NVIDIA argues AI factory ROI hinges on three factors: earning capacity, useful life.
Nathan Lambert argues Chinese labs remain the clear leaders in open-weight models.
UC Berkeley's Aditya Parameswaran argues near-free inference (GPT-4-class costs fell from ~$30 to under $1 per million tokens) demands redesigning data systems for.