i think this is what tibo is talking about. openai bought ona to move codex from local machines into secure, persistent cloud environments. the announcement literally calls it “the next phase of codex” and says agents will keep working after your laptop is closed. tibo himself is quoted in that announcement. then look at the role openai is hiring for right now. “software engineer, cloud agents.” the team is building orchestration, sandboxing, storage, identity, observability and cost controls for long-running agents. the job says they will design systems for “orchestrating agents at scale” across codex, chatgpt and the api. so when tibo says codex will look primitive in 2 to 3 months because the next generation needs more than your laptop, i think he means one model will be able to create the cloud organization required to finish a goal. separate computers. separate agents. shared memory. tools. permissions. days of uninterrupted work. your laptop becomes the control screen for a temporary ai company. First image is ona second is the job.

i think we finally have enough clues to reverse-engineer ilya sutskever’s secret SSI research. my highest-probability guess: SSI has found a brain-inspired way to make an AI continually learn. today’s frontier models learn mostly during training. afterward, their core knowledge is largely frozen. they consume enormous datasets and still fail strangely when a problem falls outside their training. SSI may have an early system that can: • learn a new skill from very few experiences • recognize when an approach is failing before reaching the final answer • update itself without erasing old skills • transfer one lesson into completely different problems • keep learning after deployment think of it like a gifted teenager instead of a finished encyclopedia. it may not begin knowing every profession, but it could rapidly learn any profession. the evidence lines up almost too cleanly: ilya called poor generalization the fundamental limitation of current AI. models “generalize dramatically worse than people.” he described SSI’s target as a “superintelligent 15-year-old” capable of learning any job. when asked how to create human-like learning, he said there is a machine-learning principle he has opinions about but cannot discuss publicly. WSJ now reports that SSI’s secret research focuses on “overlooked aspects of how the human brain functions.” Nvidia received rare access to the research, made a substantial investment, gave SSI 10x more compute, and agreed to let SSI help shape future computing platforms. my technical guess: experience → internal judgment → self-correction → durable learning → transfer → repeat the internal judgment may be the brain-inspired component. humans do not wait until the end of a 10,000-step task to know they are failing. emotions, intuition and judgment provide constant feedback. ilya has argued that AI needs an equivalent internal “value function.” this could also explain SSI’s central promise: capability and safety trained together. the same mechanism that teaches the AI what works may also teach it what it should care about. my confidence is roughly 70% on human-like generalization plus continual learning, and 40% that an internal value system is the central mechanism. the simplest description: an AI whose intelligence compounds from experience, with its values learning inside the same loop.