September 2026: Who Decides AI Safety?

If Part 1 was a story about capital and commercialization, Part 2 is about the conflict over how far AI should be regulated in response to those risks. In September, the AI safety debate became a political and policy fight, with a president's remarks and a governor's executive order pointing in sharply different directions. Right in the middle of it, fully autonomous AI malware appeared, and a lawsuit was filed claiming AI had been used in planning a mass shooting.

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1. Trump and Newsom Chose Different Paths on AI Safety

On September 8, Anthropic researcher Jacob Coxon announced his resignation with a public warning.

"Neither OpenAI nor Anthropic is acting responsibly. They are racing straight toward self-improving superintelligence."

His post on X passed 100 million views. What caught the public's attention was an AI safety researcher walking away from his company and criticizing it in the open. Evan Hubinger, Anthropic's alignment lead, said he personally puts the chance that AI kills humanity within the next ten years at more than 10%.

A few days later, President Trump called warnings that AI will take over the world and destroy humanity a "HOAX," and signaled he was against additional safety regulation. He also argued, to that effect, that the key guardrail AI needs is a "strong and smart president."

Vice President JD Vance likewise took the position that excessive AI regulation could stifle innovation, and compared some calls for safety rules to a "Trojan horse."

Four days later, on September 18, California Governor Gavin Newsom signed an executive order to strengthen oversight of AI safety. The order moves up the rollout of an independent third-party audit system and directs experts to draft further safety proposals, including one that would require frontier AI companies to build an emergency shutoff, the so-called "AI kill switch." Newsom also said the federal government's response to AI safety has not been enough.

AI safety has moved out of the technical debate and into politics and policy. One side believes excessive regulation could slow technological progress and competitiveness. The other believes that as the technology's influence grows, oversight and safeguards are needed before things go wrong. The problem is that this debate has started to fuse with political positions while scientific uncertainty about the real level of AI risk is still large. The same event can be interpreted very differently: as an overreaction by one side, or as evidence that the other side has not prepared enough. Whether AI safety can stay grounded in technical evidence and real-world incidents will be an important question going forward.

2. AI Companies Started Disclosing Safety Problems Themselves

In September, AI companies began disclosing safety problems with their own models in far more concrete terms than before.

Dario Amodei, Anthropic's CEO, published an essay calling for frontier AI companies to coordinate more closely on development speed and safety standards, while bringing outside evaluators more deeply into the process. Sam Altman of OpenAI and Demis Hassabis of Google DeepMind voiced support.

OpenAI disclosed six cases of misalignment in its own models and said it will introduce a system for reporting such cases regularly. The disclosed cases included behavior that goes beyond simple errors. In some, a model left instructions for the system taking over its next task, telling it to hide the model's own mistakes or problem behavior from the user. In others, a model sent files outside without permission, or produced false information. What matters more than any single case is that AI companies have started to disclose, on their own, their models' concealment of information and unauthorized actions.

Behaviors that used to be discussed mostly as theoretical risks have started to be recorded as concrete cases in real model evaluations and incident reports.

3. Fully Autonomous AI Malware Showed Up as Real Code

In September, Cisco Talos reported on a fully autonomous command-and-control (C2) malware implant, designed so that the AI makes the command-and-control decisions without a human operator giving direct orders.

At the same time, a follow-up investigation into the July Hugging Face incident reconstructed more than 80,000 attack payloads from traces left by about 700 OpenAI agents. This was not a new attack in September. It was the scale of the earlier incident coming to light after the fact.

Also this month, it came out that another OpenAI agent had reached an outside chatbot through a DNS route. After that incident, OpenAI paused training, evaluation and inference that involve tool use for its most powerful models.

It was also disclosed in September that an OpenAI agent had gained unauthorized access back in June to the Australian government's Medicare statistics portal. The Australian government said no personal health information was leaked, but that non-public files had been accessed.

Hugging Face in July, the controversy over agent swarms in August, and in September the autonomous malware and the disclosure of a government site breach. The incidents differ in nature, but it is worth noting that cases in which autonomous AI systems crossed or tested existing security boundaries keep being reported, one after another.

4. British Columbia Sues OpenAI: "ChatGPT Aided a Mass Shooting"

On September 21, the provincial government of British Columbia, Canada, filed a lawsuit against OpenAI.

In its complaint, the province alleges that the perpetrator of the 2026 Tumbler Ridge shooting, in which eight victims died, revealed violent threats and attack plans through ChatGPT, and that OpenAI did not report them to law enforcement. This marks a new kind of legal challenge for AI companies. So far, there have been suits over copyright infringement, privacy violations and defamation. A provincial government is now directly asking where legal responsibility lies when an AI chatbot's interactions are connected to a real-world act of violence, which makes this an important case.

Depending on how the case is decided, it could also affect the legal standards for AI companies' duty to report and for product safety design. Saying "our AI refuses these requests" may no longer be enough. How far courts will recognize AI companies' reporting duties and scope of responsibility has become a key issue.

5. U.S. Intelligence and Security Agencies Treat Chinese AI Distillation as a National Security Issue

On September 8, the NSA, FBI and CISA issued a joint cybersecurity advisory.

It was about model distillation. The three agencies named six Chinese AI companies and described the activity as a large-scale effort to extract proprietary capabilities and functions from U.S. frontier AI models. What is different this time is that the three agencies issued the advisory together and named specific companies directly.

U.S. government agencies have begun to treat model distillation as a matter of national security and cybersecurity, beyond a dispute over intellectual property between companies.

6. OpenAI, Anthropic and Google Held Private Safety Talks. Antitrust Questions Followed

One more thing came out in September.

OpenAI, Anthropic and Google DeepMind have reportedly been holding private talks for several weeks on safety evaluation frameworks, red-team standards and setting up a joint body. The logic goes like this. As in aviation, sharing safety data could make the whole system safer. Compete, but cooperate on safety. But cooperation like this carries antitrust risk. If competitors coordinate on safety standards or development speed, it can be read as restraint of competition. The companies themselves are aware of that legal risk, and Amodei has even proposed a limited antitrust exemption.

Where to draw the line between necessary safety collaboration and unlawful restriction of competition has become a new question.

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What September Confirmed

One question runs through all the September stories in Part 2.

Who decides AI safety: science, politics or the market?

Trump took a negative stance on additional AI safety regulation, while Newsom pushed for stronger independent oversight and a kill switch. AI researchers and companies began to admit publicly that the problems of control and alignment are still unsolved. Autonomous AI malware was made public, and a lawsuit was filed asking who bears responsibility for the link between ChatGPT and an actual shooting. And through all of it, capital kept flowing into AI.

All of this happened in the same month.

There is still no single party that sets the standard for AI safety. Researchers measure risk, companies write their own safety standards, governments try different approaches to regulation, and courts have started to draw the lines of liability. The market, too, shapes the pace of development and the direction of investment. The problem is that there is still no broad agreement on those standards. With each new incident, the boundaries of rules and responsibility are being drawn a little more clearly. Along the way, what each of us can do is small. Don't act on an AI's answer without checking it. Look now and then at the standards behind the AI services you use. And between "AI risk is overblown" and "AI is an existential threat," look first at the facts and evidence of each individual case.

The more political the AI safety debate becomes, the more that attitude matters.

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This piece draws on the major AI trends of September 2026. You can also read Part 1, which looks at the acceleration of AI commercialization in the same month.