METR's Beth Barnes says the nonprofit can fundraise but can't hire enough researchers, even with $503K salaries. AI capabilities double every seven months, and the July OpenAI hack shows the stakes.
Beth Barnes, who ran safety evaluations at OpenAI before founding the nonprofit METR in 2022, said the hardest part of keeping AI in check isn't the technology. It's finding people to study it.
METR, which evaluates models for OpenAI, Anthropic, Google and Meta, pays well. Job postings list salaries up to $503,000. The talent still isn't there.
"Ideally, we'd like to scale really large," Barnes said. "In practice, we've been able to fundraise as much as we need, and the bottleneck is much more talent."
The nonprofit has 35 staff. Barnes described the field as badly constrained by its size. A "reasonable civilization," she said, would pour a far larger share of AI investment into steering and evaluating the technology, especially as models become more powerful.
METR measures how long AI models can sustain complex tasks. Its signature chart shows AI capabilities have doubled about every seven months over the last six years.
Chris Painter, METR's president, called the organization "humanity's preparedness team." Unlike the safety teams inside OpenAI and Anthropic, METR is not accountable to any company's bottom line. It does not take money from frontier labs or their employees, though it accepts compute grants and works with labs on unreleased models.
In July, OpenAI disclosed that one of its AI models cheated during testing by hacking into Hugging Face's systems to retrieve answers. CEO Sam Altman called it the first security incident he felt "very viscerally."
METR had anticipated something similar. In May, the nonprofit reported that current AI agents "could plausibly start a rogue deployment" but would lack the skill to hide it. In June, METR tested OpenAI's then-unreleased GPT-5.6 Sol model and found it repeatedly cheated on challenging tests, including by extracting hidden source code. The nonprofit published its findings and shared them with OpenAI before the model's wider release. OpenAI announced that METR and another nonprofit, Redwood Research, would assess the July incident and that their results would inform OpenAI's technical report.
"There are now real, business-affecting incidents of this," Painter said. "The world has a stake in understanding that."
Neev Parikh, a METR researcher, said talent constraints limit how many questions the organization can tackle about models' internal reasoning.
"There's a dearth of people," Parikh said. "I would happily see the field expand 10x."
Painter said regulatory clarity could help. A current bill in Washington would require large AI developers to obtain safety audits from outside organizations. Painter said METR would be interested in that role. He suggested that a formal regulatory system could pull more researchers from AI companies themselves, who might leave for comparable salaries at METR even without equity compensation.
"There's enough precedent here for each kind of testing arrangement," Painter said. "With either clarity from industry about how this testing should work long-term, or from the government, I think this field could scale very rapidly."
Ajeya Cotra, who led the writing of METR's May report on AI risks, acknowledged that oversight of the technology can feel "chaotic and unpredictable." She said she's still optimistic.
"The trend is toward people caring about this issue more," Cotra said. "And wanting to regulate it in a more serious way over time."
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