It’s not so much an op’s issue as an architecture and code quality issue. If you have ever dug into the GitHub enterprise self hosted product you get an idea of the mess.
Today’s llms are fancy autocomplete but lack test time self learning or persistent drive.
By contrast, an AGI would require:
– A goal-generation mechanism (G) that can propose objectives without external prompts
– A utility function (U) and policy π(a│s) enabling action selection and hierarchy formation over extended horizons
– Stateful memory (M) + feedback integration to evaluate outcomes, revise plans, and execute real-world interventions autonomously
Without G, U, π, and M operating llms remain reactive statistical predictors, not human level intelligence.
After twenty years building out products in Silicon Valley I have come to the point where I have lost the plot. None of the projects at my last company seemed interesting, none of the projects I see other companies seem interesting. All AI, no substance.
So I’ll just sit at home and build robots till something interesting does pop up or my robots gain sentience and decide I’m the problem.