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.
If timelines are short, safety should matter more
Another implication of short timelines is that AI safety and security ought to become more important. (The Google DeepMind researchers, in their latest AI safety paper, said AGI could lead to severe consequences, including the “permanent end of humanity.”)
Jack Clark, a cofounder at Anthropic who heads its policy team, wrote in his personal newsletter, Import AI, a few weeks ago that short timelines called for “more extreme” policy actions. These, he wrote, would include increased security at leading AI labs, mandatory pre-deployment safety testing by third-parties (moving away from the current voluntary system), and spending more time talking about—and maybe even demonstrating—dangerous misuses of advanced AI models in order to convince policymakers to take stronger regulatory action.
Companies are paying less attention to safety
But, contrary to Clark’s position, even as timelines have shortened, many AI companies seem to be paying less, not more, attention to AI safety. For instance, last week, my Fortune colleague Bea Nolan and I reported that Google released its latest Gemini 2.5 Pro model without a key safety report, in apparent violation of commitments the company had made to the U.S. government in 2023 and at various international AI safety summits. And Google is not alone—OpenAI also released its DeepResearch model without the safety report, called a “system card,” publishing one only months later. The Financial Times also reported this week that OpenAI has been slashing the time it allows both internal and third-party safety evaluators to test its models before release, in some cases giving testers just a few days for evaluations that had previously been allotted weeks or months to be completed. Meanwhile, AI safety experts criticized Meta for publishing a system card for its new Llama 4 model family that provided only barebones information on the models’ potential risks.
The reason safety is getting short shrift is clear: Competition between AI companies is intense and those companies perceive safety testing as an impediment to speeding new models to market. The closer AGI appears to be, the more bitterly fought the race to get there first will be.
The U.S. government sees safety as an impediment to beating China
In economic terms, this is a market failure—the commercial incentives of private actors encourage them to do things that are bad for the collective whole. Normally, when there are market failures, it would be reasonable to expect the government to step in. But in this case, geopolitics gets in the way. The U.S. sees AGI as a strategic technology that it wants to obtain before any rival, particularly China. So it is unlikely to do anything that might slow the progress of the U.S. AI labs—even a little bit. (It doesn’t help that AI lab CEOs such as Altman—who once went before Congress and endorsed the idea of government regulation, including possible licensing requirements for leading AI labs, but now says he thinks AI companies can self-regulate on AI safety—are lobbying the government to eschew any legal requirements.)
Of course, having unsafe, uncontrollable AI would be in neither Washington nor Beijing’s interest. So there might be scope for an international treaty. But given the lack of trust between the Trump administration and Xi Jinping, that seems unlikely. It is possible President Trump may yet come around on AI regulation—if there’s a populist outcry over AI-induced job losses or a series of damaging, but not catastrophic, AI-involved disasters. Otherwise, I guess we just have to hope the AI companies’ timelines are wrong.
With that, here’s the rest of this week’s AI news.
Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn
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This story was originally featured on Fortune.com