Road to ASI
There is no level scale here: no single agreed ladder of "how far is left" exists, and inventing one would dress an editorial guess as a measurement. Instead of rungs — three concrete gates, none of them passed today.
Three unsolved problems. While even one stays open, the upper part of the line remains dark.
Continual learning
Today a model does not remember yesterday's work: everything it knows is frozen at training time. We need a system that learns while working without forgetting what it already knew.
Full dossierAI scientist
Not an assistant that finds papers and crunches tables, but an author: proposing the hypothesis, running the experiment and drawing a conclusion nobody knew before.
Full dossierVerifiable reliability
Before trusting AI with an operating theatre, a power plant or a company's books, you must be able to prove it will not fail silently. No such method exists today.
Full dossierAGI
Forecasters cluster on 2027–2033 with a long tail past 2040. The spread is enormous, and much of the argument is about definitions.
Full dossierASI
Extrapolation stops working here. No single branch leads to this point alone: continual learning, autonomous science and verifiability are all required at once.
Full dossierWhat the forecasts say
Estimates run from "already in this window" to mid-century. That spread is the honest picture: nobody knows the date.
Superintelligence in "a few thousand days". The conversation moved from AGI to what comes after.
Timelines are compressing: human-level AI is possible within a few years.
Genuine human-level AGI is a multi-year horizon, definitely not "already here".
The earliest public estimate: "smarter than the smartest human" already in this window.
The bulk of the probability mass, with a visible tail past 2040. Every recent update moved earlier, not later.
50% probability for high-level machine intelligence. Stanford separately insists: no AGI in 2026.