Expect AGI Within a Few Years, Says Anthropic CEO—and Job Losses Too

Amodei reaffirmed his aggressive forecast that human-level AI is likely only years, not decades, away.

“I don’t think that’s going to turn out to be that far off,” Amodei said, standing by his prediction that superhuman capability could arrive by 2026 or 2027. “It’s very hard for me to see how it could take longer than that.”

The engine behind this acceleration is a burgeoning feedback loop where AI models have begun to automate their own creation. Amodei noted that at Anthropic, the traditional role of the software engineer is already being redefined by AI.

“I have engineers within Anthropic who say, ‘I don’t write any code anymore. I just let the model write the code, I edit it,’” he said. “We might be six to twelve months away from when the model is doing most, maybe all, of what [software engineers] do end to end.”

While Amodei sees progress compounding quickly—limited only by chip supply and training cycles—Hassabis offered a more measured outlook.

“I think there has been remarkable progress, but some areas of engineering work, coding, or mathematics are easier to see how they would be automated, partly because they’re verifiable—what the output is,” he said. “Some areas of natural science are much harder. You won’t necessarily know if the chemical compound you’ve built, or a prediction about physics, is correct. You may have to test it experimentally, and that will take longer.”

Hassabis said current AI systems still lack the ability to generate original questions, theories, or hypotheses, even as they improve at solving well-defined problems.

“Coming up with the question in the first place, or coming up with the theory or the hypothesis, that’s much harder,” Hassabis said. “That’s the highest level of scientific creativity, and it’s not clear we will have those systems.”

Despite their differing timelines, the two leaders reached a somber consensus on the economic fallout, agreeing that white-collar jobs are in the crosshairs.

The primary concern for both executives is not just the technology itself, but the ability of the world’s governments to keep up. Hassabis warned that even the most pessimistic economists might be underestimating the speed of the transition, noting that “five to ten years away, that isn’t a lot of time.”

For Amodei, the situation has escalated from a technical challenge to an existential "crisis" of governance.

“This is happening so fast and is such a crisis, we should be devoting almost all of our effort to thinking about how to get through this,” he said. While he remains optimistic that risks—ranging from geopolitical friction to individual misuse—are manageable, he warned that the window for error is slim.

“This is a risk that if we work together, we can address,” Amodei said. “But if we go so fast that there are no guardrails, then I think there is a risk of something going wrong.”

Some labor analysts argue that the disruption may show up less as outright job replacement and more as a restructuring of professional work itself.

“We have to quit asking whether or not AI will replace our jobs and begin asking how does it degrade them?” Hutchins said. “There isn’t a direct threat that a machine will completely take the place of a person doing a writer’s or coder’s job. The threat is that the job is being broken down into smaller tasks and managed by an algorithm.”

According to Hutchins, this shift changes human roles from ‘Creator’ to ‘Verifier.”

“It takes away the ability of professionals to make their own decisions and breaks down meaningful professional jobs into unskilled, low-wage jobs with a focus on completing individual tasks,” he said.

“Labor isn’t disappearing, it’s becoming less obvious, less secure, and much harder to unionize,” he added.