Hello and welcome to Eye on AI. In this edition…AGI timelines are getting shorter but so is the amount of attention AI labs seem to be paying to AI safety…Venture capital enthusiasm for OpenAI alums’ startups shows no sign of waning….A way to trace LLM outputs back to their source…and the military looks to LLMs for decision support, alarming humanitarian groups.
“Timelines” is a short-hand term AI researchers use to describe how soon they think we’ll achieve artificial general intelligence, or AGI. While its definition is contentious, AGI is basically an AI model that performs as well as or better than humans at most tasks. Many people’s timelines are getting alarmingly short. Former OpenAI policy researcher Daniel Kokotajlo and a group of forecasters with excellent track records have gotten a lot of attention for authoring a detailed scenario, called AI 2027, that suggests AGI will be achieved in, you guessed it, 2027. They argue this will lead to a sudden “intelligence explosion” as AI systems begin building and refining themselves, rapidly leading to superintelligent AI.
Dario Amodei, the cofounder and CEO of AI company Anthropic, thinks we’ll hit AGI by 2027 too. Meanwhile, OpenAI cofounder and CEO Sam Altman is cagey, trying hard not to be pinned down on a precise year, but he’s said his company “knows how to build AGI”—it is just a matter of executing—and that “systems that start to point to AGI are coming into view.” Demis Hassabis, the Google DeepMind cofounder and CEO, has a slightly longer timeline—five to 10 years—but researchers at his company just published a report saying it’s “plausible” AGI will be developed by 2030.
The implications of short timelines for policy are profound. For one thing, if AGI really is coming in two to five years, it gives all of us—companies, society, and governments—precious little time to prepare. While I have previously predicted AI won’t lead to mass unemployment, my view is predicated on the idea that AGI will not be achieved in the next five years. If AGI does arrive sooner, it could indeed lead to large job losses as many organizations would be tempted to automate roles, and two years is not enough time to allow people to transition to new ones.
