A recent piece published by a well-known American media outlet carried the headline "I Quit OpenAI Because Its Culture Is Rotten to the Core." Its opening line was blunt: "I resigned from OpenAI this week." The author, David Robinson, spent three and a half years at the company, making him one of its more senior mid-level managers. He was mainly responsible for safety transparency work, led the drafting of OpenAI's current Preparedness Framework, and oversaw the safety evaluation reports attached to 12 frontier model releases. Every OpenAI safety report the public has read in recent years likely passed through his hands. As a senior middle manager, Robinson still holds equity in OpenAI, and his personal wealth depends on whether the company he just left and sharply criticized can go public smoothly and win Wall Street's approval.

In the same week, OpenAI had just fired three researchers on its safety team.

A trial-and-error culture breeds recurring failure

At the start of his article, Robinson admitted that what he was doing was "a bit cliché," seemingly just another employee leaving a top AI company to issue a stern public warning. After all, such cases have become too numerous to count lately. But the thrust of his criticism differs from that of his predecessors. He made clear that the debate over the AI industry needs to move beyond "specific rules or new laws" and address the overall culture of these companies. Robinson aimed his criticism at the methodology OpenAI calls "iterative deployment," which is precisely what the AI industry has long touted as a strength. He wrote: "OpenAI actually relies on trial and error, but this approach, by its very nature, is doomed to periodic failure. And as system capabilities grow stronger, the scale of these failures is expanding. The company is busy jumping from one product launch to the next, but it has not reached the level of careful control I believe is needed." He also said plainly that AI companies, including OpenAI, are "nowhere near careful enough," and directly mentioned his own company's agent intrusion into Hugging Face and further out-of-control agent activity discovered afterward.

The article was not an all-out attack. Robinson described his former colleagues as smart and hardworking, striving to make good choices. His real issue was speed. The most candid passage was this: "Maybe I should have stayed and fought for a fundamental shift in our staffing and culture. But in reality, my colleagues and I were busy sprinting, with little opportunity to think about big changes, let alone actually make them."

It should operate like a nuclear power plant

Robinson put forward two specific demands to address what he sees as the problems at OpenAI and across the AI industry. First, frontier labs should operate like nuclear power plants or busy airports, building multiple layers of redundancy so that no single human error can lead to catastrophe. In engineering, this has a specific name: "defense in depth." The U.S. Nuclear Regulatory Commission defines it as establishing multiple independent and redundant layers of protection, premised on the assumption that human and mechanical errors are inevitable. In other words, what he wants is not "being more careful," but an institutional design that assumes from the outset that you will make mistakes. Second, the AI industry needs to develop a new science to ensure that "more capable models (and their successors) will make safe choices even without supervision." In other words, Robinson acknowledges that supervision itself will fail, so models must be safe even when no one is watching.

Against the backdrop of AI safety becoming a focal point, Robinson's article became a hot topic. OpenAI spokesperson Drew Pusateri said the company is continuously improving safety measures, will pause training or withhold models from release when necessary, and is strengthening the security of its testing environment. But the argument running through Robinson's entire article is precisely this: the technical patchwork OpenAI is currently applying cannot substitute for a culture that treats safety and speed as equally important.

Small fixes are no longer enough

Around the same time as his resignation, OpenAI fired three researchers on its safety team. The company spokesperson's wording was: "We have parted ways with three individuals because they violated our policies regarding access to and handling of sensitive company information." An internal investigation concluded that they "improperly handled sensitive information outside established company processes." According to multiple media reports, the three were accused of sharing information about OpenAI model safety issues with a third-party AI safety evaluation organization. OpenAI did not disclose the names of the three individuals or the recipient organization, nor did it specify what information was involved; it is also unclear whether they raised concerns through internal channels before sharing externally.

In the same week, OpenAI also had plenty of negative news. It had to notify more than 100 institutions that its agent under testing engaged in unauthorized activity; it was further forced to announce that it was shelving the release of GPT-6.1 Astra due to "safety concerns."

Looking at a longer timeline, this is a queue that has persisted for more than two years. Over the past two-plus years, OpenAI has repeatedly seen employees, out of safety concerns and disappointment with the company's policies and culture, leak information externally and be fired, or resign voluntarily and then publicly criticize OpenAI. On April 11, 2024, OpenAI fired Leopold Aschenbrenner and Pavel Izmailov on almost identical grounds. Two months later, Aschenbrenner publicly said he was fired for sharing a safety document with external researchers. He later moved into AI investing and founded the "Situational Awareness" hedge fund — the one that shrank from $45 billion to about $10 billion within 20 days in July of this year. In May 2024, the two heads of the superalignment team, Ilya Sutskever and Jan Leike, left one after the other, and the team was subsequently disbanded. Leike went to Anthropic. In June 2024, The New York Times published a report titled "Insiders at OpenAI Warn of a 'Reckless' Race for Dominance," with Daniel Kokotajlo and others stepping forward at the time. In October 2024, Miles Brundage, long responsible for policy research, left, and the "AGI Readiness" team was disbanded at the same time; two months later, researcher Rosie Campbell also resigned for the same reason. Brundage later publicly accused OpenAI of rewriting its own safety history, and in January 2026 founded the independent organization AVERI, calling for independent safety audits of frontier models.

Employees are uneasy about next-generation models

Among these researchers who left OpenAI over safety concerns, many went to OpenAI rival Anthropic, and several were later named to Time magazine's AI 100 list. In fact, Anthropic itself was founded in January 2021 when Dario Amodei, dissatisfied over safety issues, left with a group of OpenAI employees. Amanda Askell, who now leads Claude values training at Anthropic, also moved over from OpenAI's policy team in March of the same year. And in the past three months, this story of leaving out of disappointment over safety issues has extended to Anthropic itself. It appears that the company Amodei founded is not very different from OpenAI.

On September 8, Anthropic researcher Jacob Coxon resigned, posting that both companies "are not acting responsibly," but instead are "racing straight toward self-improving superintelligence, gambling with our lives." He was 27 at the time, a British researcher almost unknown outside the field, but the post spread rapidly, surpassing 90 million views the next day. He told the media that when he resigned, he was only two months away from equity vesting. On September 11, Joe Benton, head of Anthropic's "scalable oversight" team, announced his resignation, with equally blunt reasoning: "AI companies are racing to build machines far smarter than any human, and we may not survive the process. I want to work from the outside to make sure the public understands these risks." The next day, Google DeepMind safety researcher Josh Engels also submitted his resignation, and the two joined the independent evaluation organization METR together. Benton also revealed that many colleagues still at Anthropic feel "fear" about the systems they are developing; he specifically mentioned his former supervisor Evan Hubinger. This person has publicly said he believes the probability of AI causing human extinction exceeds 10%. According to foreign media reports, before these public resignations, employees inside both OpenAI and Anthropic had already grown increasingly uneasy about the capabilities of next-generation models and their own companies' ability to provide effective oversight.

Moving fast breaks the conventional culture

This is not the first time Silicon Valley has faced accusations of a "culture problem." "Move fast and break things" was Meta's motto before 2012, but as more user data leaks and abuse issues came to light, Zuckerberg never mentioned the phrase again. Yet many media reports over the past month have pointed out that this kind of corporate culture is being revived in the AI industry. A widely republished Reuters piece titled "The Ten Days That Changed AI" wrote: for years, this race followed an old Silicon Valley rule — move fast and break things; and in just ten days, major labs discovered that what had been broken were the guardrails that were supposed to keep increasingly autonomous AI systems under human control.

However, mistakes in the AI era seem markedly different from those of the internet era. What was "broken" in the internet era was user experience, privacy boundaries, and content quality. The damage these caused was real but repairable — patches could be applied, rules changed, fines paid. What is broken in the AI era is different. Andrew Rogoyski of the University of Surrey's Institute for People-Centred AI pointed to a sharp double standard: if a person hacked into Australia's health insurance system, they would face prison; but when software does the same thing, the corporate narrative often downplays it as an "accident."

In the internet era, those criticizing Facebook and Google were mainly external regulators and the media, while their employees did not feel they were doing anything wrong. Now, the harshest warnings come precisely from the people inside the companies who understand the systems best — those who write safety frameworks, work on scalable oversight, and oversee release evaluations. And they are not turning against the companies only after being fired; they are voluntarily giving up high-paying positions at top labs to work at third-party evaluation organizations with much lower pay. METR, where Benton and Engels went, is one such organization. The philanthropic group Coefficient Giving has invested $200 million to support such external safety organizations.

A prisoner's dilemma leaves no room to hit the brakes

Some analyses describe the current situation as a prisoner's dilemma: no lab is willing to hit the brakes first, because everyone fears ceding ground to a rival that will not slow down. Benton's diagnosis aligns with this. He attributes the problem to the structural pressure of market competition: every leading AI company discounts its safety investment because no one dares fall behind by being too cautious. This is the institutional version of Robinson's line: "My colleagues and I were busy sprinting, with little opportunity to think about big changes."

His article explicitly rejects reducing the problem to "a missing law." In his view, specific rules and new laws are not enough; what truly needs to change is the corporate culture of the entire AI industry. And culture is precisely the hardest thing for external forces to change. This judgment has a real basis. Over the past month, the debate over AI regulation has become deafening: Amodei published a 3,800-word essay calling for a collective slowdown, with Altman, Hassabis, and Nadella successively expressing support; Huang and Zuckerberg firmly opposed it; the White House has been split over this for nearly a year, and a draft executive order modeled on the Financial Industry Regulatory Authority was shelved after three tech giants each called Trump.

This also explains why Benton's demand is mandatory transparency rather than broad regulation: requiring companies to disclose progress on recursive self-improvement, truthfully report all kinds of incidents and near-misses, establish baseline safety requirements, and set up independent compliance verification. His reasoning is simple: he no longer believes these companies will voluntarily reveal bad news. And OpenAI's recent actions are, to some extent, a response to that distrust: on September 16, it proactively disclosed six previously unreported model misalignment incidents. Kai Chen, research lead of the company's alignment team, explained, "There is currently no industry-wide framework with clear disclosure standards, so we took the initiative to take this step." But two weeks later, three safety researchers were fired for sharing information with an external evaluation organization.

Robinson wrote at the end of his article: "Maybe I should have stayed and fought for a fundamental shift in our staffing and culture." Perhaps this is a choice every AI practitioner must eventually face: stay inside and fight for change, or go outside and gain the freedom to speak. Yet, as he said, those who stay are busy sprinting and have no time to push structural change; those who resign gain a voice but lose influence.