Hello and welcome to Eye on AI. In this edition…the new Pope is all in on AI regulation…another Chinese startup challenges assumptions about how much it costs to train a good model…and OpenAI CEO Sam Altman says Meta is offering $100 million signing bonuses to poach AI talent.
Last week, OpenAI CEO Sam Altman wrote on his personal blog that: “We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be.” He went on to say that 2026 would be the year that we “will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world.”
Altman’s blog created a buzz on social media, with many speculating about what new development had caused Altman to write those words and others accusing Altman of shameless hype. In AI circles, “takeoff” is a term of art. It refers to the moment AI begins to self-improve. (People debate about “slow take off” and “fast take off” scenarios. Altman titled his blog “The Gentle Singularity,” so it would seem Altman is positioning himself in the slow—or at least, slowish—takeoff camp.)
In the blog, Altman made it clear he was not yet talking about completely automated self-improvement. Rather, he was talking about AI researchers using AI to help them develop yet more capable AI. “We already hear from scientists that they are two or three times more productive than they were before AI,” he wrote. “We may be able to discover new computing substrates, better algorithms, and who knows what else. If we can do a decade’s worth of research in a year, or a month” then the rate of AI progress will accelerate from its already rapid clip.
Altman allowed that “of course this isn’t the same thing as an AI system completely autonomously updating its own code, but nevertheless this is a larval version of recursive self-improvement.”
But, as Altman is probably aware, there are a growing number of AI researchers who are in fact looking at ways to get AI to improve its own code.
