After years of treating faster, bigger, and more capable AI as something approaching a moral imperative, the people running some of the world’s most powerful AI companies suddenly agree that perhaps everyone should ease off the accelerator.
Anthropic CEO Dario Amodei kicked off the latest round with an essay titled “We Must Pace the Frontier,” warning that AI capabilities are advancing faster than the safeguards needed to contain their risks. OpenAI CEO Sam Altman and Google DeepMind CEO Demis Hassabis quickly endorsed the idea. It's extraordinary that the leaders of companies locked in one of the most expensive technological races in history are publicly agreeing that the race itself needs to slow down. One awkward detail is buried beneath the sudden outbreak of corporate caution. Namely that nobody involved has said which forthcoming frontier model will arrive later because of it.
“Pacing the frontier” can mean almost anything until somebody attaches a calendar to it. None of the companies supporting Anthropic’s proposal has publicly identified a forthcoming model it intends to delay as part of the initiative, nor has anyone defined whether slowing down means an extra week of safety testing, six months between major generations, or something more dramatic. For now, the most concrete commitments concern independent evaluators, monitoring, safety standards and coordination rather than an announced reduction in the cadence of frontier-model releases
It's odd enough to stand out even to those outside the tech space. U.S. vice president JD Vance said he felt “a little bit weird" about the fact that you have so many frontier AI tech companies kind of coming to the government and begging the government to regulate them, and that it came off as “a bit of a Trojan horse.” His skepticism does not settle whether regulation is necessary, but it highlights the contradiction running through the current debate.
Altruism or exclusion?
There are good reasons for the sudden anxiety. Amodei has warned about AI enabling cyberattacks, bioterrorism, economic disruption, and eventually systems humans could struggle to control. His latest proposal calls for independent safety evaluators with deep access inside frontier labs, coordination among companies on shared standards, and international cooperation around particularly dangerous capabilities.
That's rather different from the plain-English meaning of slowing development. Anthropic’s own recent history is ambiguous, as it paused external cyber evaluations of prerelease models and briefly stopped internal ones. It also paused higher-risk reinforcement-learning environments for several weeks.
Meanwhile, the frontier has continued moving. Anthropic released Claude Fable 5.1 and Mythos 5.1 this month, while OpenAI debuted GPT-6 Astra. Those launches preceded Amodei’s latest public call for an industry slowdown, but show the strange starting point for this new era of restraint. The companies asking everyone to discuss slowing down have just spent the month pushing the frontier forward.
If the companies genuinely believe development is moving dangerously fast, they already control their own research schedules and release calendars, while government regulation raises a separate question about whether rules designed with the biggest labs could also make life harder for smaller competitors.
Apocalyptic distraction
There is another problem with all this talk of existential danger. The more Silicon Valley discusses hypothetical superintelligence destroying humanity, the easier it becomes to overlook the considerably less cinematic ways AI is already hurting people.
Karolis Kaciulis, Lead System Engineer at consumer cybersecurity company Surfshark, argues that warnings about AI threatening humanity can amount to a “marketing move” that distracts attention from existing harms. “The threat itself is fictional, closer to a Skynet-style sci-fi scenario than the problems generative AI is already causing today, from automated scams to intimidation,” he said.
That skepticism deserves space alongside the warnings from AI executives. Generative AI is already making phishing, deepfake fraud, and automated scams cheaper and easier to scale. Questions remain about privacy, how information submitted to chatbots is handled, and the environmental cost of the infrastructure required to run increasingly large AI systems.
There is also a legitimate debate about how much progress the industry’s endless procession of model releases actually represents. Benchmark numbers climb, but determining whether each new large language model represents a profound new capability is considerably harder than reading a launch-day chart.
AI companies simultaneously warning about AI's power while still pushing ahead and sidelining safety comes off as bizarre to the average person. Amodei’s proposal is new, and judging it entirely by whether a company delayed a model by four days would be unreasonable. But his ideas need an independent evaluation system to have any muscle. The next step needs to be measurable, or it's irrelevant.
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