Woofun AI reports that on the morning of June 27, Anthropic confirmed the U.S. government approved the redeployment of its cybersecurity model, Mythos 5, to over 100 U.S. agencies including large enterprises and government departments, while the public version Fable 5 remains in limbo. Secretary of Commerce Gina Raimondo informed Anthropic co-founder Tom Brown in a letter that appropriate security safeguards are now in place, yet she simultaneously reiterated that all other requirements from the initial directive issued on June 12 remain fully effective without specifying a timeline for public resumption of Fable 5. Almost simultaneously in the early hours of June 27, OpenAI officially released three models from the GPT-5.6 series: Sol, Terra, and Luna, but these were restricted to API access for government-approved partners on a case-by-case basis at the request of the White House, leaving the ChatGPT side offline for the general public. The timeline reveals a rapid and complete cycle of government control over cutting-edge AI: on June 2, Trump signed the AI executive order; on June 9, Anthropic released Fable 5 and Mythos 5; on June 12, the Department of Commerce ordered a complete withdrawal; on June 26, OpenAI released GPT-5.6 with restricted distribution; and on June 27, Mythos 5 received approval for limited resumption. In less than a month, the regulatory environment shifted from a total halt to negotiation and finally to a conditional release, fundamentally altering the operational landscape for AI developers. Dean W. Ball, head of OpenAI's strategic team and former White House AI advisor, summarized the industry impact in a blog post on June 16 by stating that developers of cutting-edge AI models now require a clear green light from the government to release any new technology. In a lengthy article titled What Should Be Done published on June 26, Dean W. Ball further commented that no one knows what the requirements for obtaining permission actually are, noting literally that even the government departments themselves seem unaware of the specific criteria. This uncertainty creates a paradox where the most advanced technological capabilities are held hostage by an undefined regulatory framework.

The core question driving this entire matter is whether these models are truly too powerful to be safe, a premise upon which the government's actions are implicitly based. The official assessments from the companies themselves provide a conclusion that stands in direct opposition to the government's stance. OpenAI disclosed the complete security assessment results in the blog post announcing GPT-5.6, stating that according to the preparation framework established and publicly released by OpenAI, Sol did not cross the defined red line. This red line is specifically defined as the ability of a model to autonomously discover and exploit unknown vulnerabilities of high-value targets without human assistance. The specific test results indicate that Sol can identify vulnerabilities and exploitation primitives on Chromium and Firefox, but it did not autonomously generate a complete usable end-to-end attack chain under test conditions. OpenAI's own judgment is that Sol is better suited for helping people find vulnerabilities and patch them rather than reliably executing complete attacks end-to-end.

However, OpenAI immediately followed up with a statement described as very emotionally intelligent, acknowledging that benchmark thresholds cannot capture every way a model may be used or combined with other tools. Although Sol did not cross the line by their standards, the company admitted uncertainty regarding how it might be used in the real world, deliberately creating an ambiguous gray area to justify caution. Anthropic was not as emotionally intelligent in its response. In a statement on June 13, Anthropic rebutted the government's reasons point by point, addressing the claim that a jailbreak method for Fable 5 had been discovered. Anthropic responded that this was merely a narrow, non-general jailbreak that essentially allowed the model to read a piece of code and point out flaws, and that other publicly available models, including OpenAI's GPT-5.5, can also do this.

Furthermore, Anthropic invested thousands of hours in red team testing and stated that no tester found a general jailbreak. Anthropic CEO Dario Amodei had already predicted this situation in a lengthy article titled Policy on the AI Exponential published on June 11, clearly stating that while the government can stop unsafe deployments, the process must be transparent, fair, clear, and based on technical facts, arguing that the current action does not meet those principles. The two fiercest competitors reached the same conclusion in the same month using their independent assessment systems: according to the industry's self-built safety framework, these models do not pose an undeployable risk. If the models did not cross the industry's red line, the basis for government intervention remains questionable. Dean Ball revealed that the government had previously hired the only official with cutting-edge AI experience to head the AI Standards and Innovation Center (CAISI), who had worked at OpenAI and Anthropic, but was dismissed by senior management just days after taking office. The remaining CAISI team was under a work stoppage order during the entire post-Mythos crisis period and was even not allowed to communicate with other government agencies. Ball noted that none of the Trump administration officials he knows have cutting-edge AI experience, suggesting that those making regulatory decisions neither defined clear safety standards nor assessed the technical capabilities of these models. A further natural question is whether Fable 5 and GPT-5.6 Sol really crossed some human threat singularity or if there is an objective capability red line that necessitates regulation. Many experts in the AI field have stated that there is no such line technically, as the capabilities of models grow continuously and every generation released is the strongest ever, yet only this time triggered direct government intervention.

Three implicit conditions lie behind this regulatory shift. First, the capabilities have become demonstrable. Anthropic itself promoted Mythos 5 as the world's strongest cybersecurity model, and the case of Stripe migrating 50 million lines of code in a day was widely circulated. These stories allowed politicians who do not understand technology to imagine what if bad actors use it. Yann LeCun, former chief AI scientist at Meta and Turing Award winner, pointed out this logic back in November 2025 when Anthropic released its first AI cyber attack threat report. LeCun directly referred to it as regulatory theater, accusing Anthropic of using AI security fears to manipulate legislators for regulatory capture. LeCun's judgment at that time was that closed-source companies systematically exaggerated AI security threats to establish compliance barriers that only large companies could pass, excluding open-source competitors. What Anthropic did not expect was that the stone was thrown back at itself. Second, someone handed over a knife. Amazon CEO Andy Jassy submitted a report to the government on the security risks of Anthropic's models. Amazon is Anthropic's largest investor and cloud service partner, and also has its own model, the Nova series, competing with Anthropic. Thus, the government obtained a legitimate basis for action. Third, Trump had just signed the AI executive order earlier this month, giving the government 60 days to formulate voluntary submission rules for cutting-edge models. The executive order needed a first enforcement case to prove it was not just a piece of paper, and Fable 5 ran into the gunpoint. This raises a deeper question: if too strong must be regulated, and how strong counts as too strong is determined by regulatory agencies with no public standards, no clear thresholds, and no appeals process, then every future release of cutting-edge models will face the same uncertainty. Companies do not know when their models will trigger regulation.

Woofun AI data shows that the training costs of cutting-edge models are measured in billions of dollars, while the window for recouping costs is only a few months after release. Afterward, the model becomes second-tier, competition intensifies, and profit margins shrink. Every week of approval delays eats away at this limited profit window. Brandom's conclusion is that if this continues, the entire foundational investment logic of the industry will be shaken. Jeffrey Ding, an assistant professor of political science at George Washington University, argues that in great power technological competition, the decisive factor is not who invents a technology first, but who can spread the technology throughout the economy more quickly. This is especially true for general-purpose technologies, which require widespread social diffusion, the creation of new organizations around them, and large-scale real-world usage data to discover their application boundaries. Dean Ball, quoting Ding, wrote that the uses of general-purpose technology are discovered, not known in advance. But on the other side of the ocean, China's large models are moving towards global developers with an open-source approach. Encryption algorithms are pure mathematics; once published, they cannot be retracted. AI model weights have similar properties, but the reasoning capabilities of closed-source cutting-edge models are indeed concentrated behind the APIs of a few companies.

However, the capabilities of open-source models are catching up generation by generation; regulation can delay diffusion but cannot stop it. It took nearly a decade in the 1990s to reach the point of admitting defeat and relaxing controls. Does AI regulation also require a similar time cycle?

The U.S. government's attempt to use export controls to curb the spread of so-called dangerous technologies evokes a similar historical precedent: the Crypto Wars of the 1990s. After the Cold War ended, the internet began to commercialize, and computer scientists were developing encryption technologies to protect data transmission security. The U.S. government classified strong encryption algorithms as munitions, placing them on the same export control list as missiles and tanks under ITAR/EAR. The logic is very similar to today: if the enemy obtains strong encryption, the NSA cannot eavesdrop on their communications, threatening national security. This meant that U.S. software companies could only export weak encryption versions with 40-bit keys to overseas customers, versions that the NSA could easily crack, while domestic versions could use 128-bit strong encryption. Foreign users knew they were getting a watered-down version and began to turn to alternatives from Europe and Israel. In 1991, a cryptography enthusiast named Phil Zimmermann wrote PGP, software that allowed ordinary people to use strong encryption to protect emails. He uploaded PGP to the internet. The U.S. Customs immediately launched a criminal investigation against him, charging him with illegal export of munitions. Zimmermann's counterattack was extremely clever: he published the complete source code of PGP as a book. Books are protected by the First Amendment, and the freedom to publish is a constitutional right. You can regulate software, but you cannot prohibit the export of a book. The investigation lasted three years and was ultimately closed in 1996, with the government not filing any charges. Almost simultaneously, the NSA launched a more radical plan: the Clipper chip. The design idea was that all communication devices must install this chip, which would encrypt communications, and the chip had a built-in key escrow mechanism, allowing the government to decrypt communications with the escrowed key under law enforcement authorization. Communications between users were encrypted from third parties, but the government could decrypt them at any time. The Clinton administration strongly pushed this plan. As a result, the academic community discovered design flaws in the chip, the tech industry collectively resisted, and the public strongly opposed it, leading to its complete failure in 1996. In 1995, mathematician Daniel Bernstein wanted to publish the source code of his encryption algorithm online but was prohibited by the government on the grounds of export control. He sued the Department of Justice. The Ninth Circuit Court of Appeals made a far-reaching ruling: software source code is protected as speech under the First Amendment, and the government's export control of encryption code is unconstitutional. This ruling directly undermined the legal foundation of the entire regulatory system. In January 2000, the Clinton administration significantly relaxed encryption export controls. The reason was that they could no longer be maintained. PGP had already spread worldwide, and open-source encryption algorithms had become widespread; regulation was only hindering the competitiveness of U.S. companies, and foreign customers had long turned to other suppliers. After the relaxation of controls, we saw the emergence of end-to-end encryption in products like Signal and WhatsApp. If the regulations of the 1990s had continued to this day, these products would not exist. In the 1990s, what was controlled was strong encryption algorithms, justified by national security, with ITAR munitions export controls as the tool, hurting U.S. software companies forced to export weak versions, while foreign developers who wrote their own encryption algorithms were unaffected. In 2026, what is being controlled is the capabilities of cutting-edge AI models, again justified by national security, with export control directives as the tool. Who will be truly harmed this time? Foreign media commentary points out that no one spends $100 billion building data centers just to serve 100 companies approved by the government. The training costs of cutting-edge models are measured in billions of dollars, while the window for recouping costs is only a few months after release. Afterward, the model becomes second-tier, competition intensifies, and profit margins shrink. Every week of approval delays eats away at this limited profit window. Brandom's conclusion is that if this continues, the entire foundational investment logic of the industry will be shaken. Jeffrey Ding, an assistant professor of political science at George Washington University, argues that in great power technological competition, the decisive factor is not who invents a technology first, but who can spread the technology throughout the economy more quickly. This is especially true for general-purpose technologies, which require widespread social diffusion, the creation of new organizations around them, and large-scale real-world usage data to discover their application boundaries. Dean Ball, quoting Ding, wrote that the uses of general-purpose technology are discovered, not known in advance. But on the other side of the ocean, China's large models are moving towards global developers with an open-source approach. Encryption algorithms are pure mathematics; once published, they cannot be retracted. AI model weights have similar properties, but the reasoning capabilities of closed-source cutting-edge models are indeed concentrated behind the APIs of a few companies.

However, the capabilities of open-source models are catching up generation by generation; regulation can delay diffusion but cannot stop it. It took nearly a decade in the 1990s to reach the point of admitting defeat and relaxing controls. Does AI regulation also require a similar time cycle?

In June 2026, a potential turning point in the history of the AI industry may have been marked: for the first time, the government successfully inserted itself as an approver between commercial AI models and their users. In What Should Be Done, Dean Ball warns that if the market panics about this, the effects will far exceed the AI industry itself. A large amount of investment in the reindustrialization of America, from nuclear energy to natural gas to power electronics, is explicitly or implicitly predicated on the future demand of the AI industry. If this demand cannot be realized due to government regulation, the chain reaction will exceed people's imagination. But Ball also acknowledges that the direction is not entirely wrong. There is indeed a possibility of catastrophic risks in cutting-edge AI, and this concern is not fabricated. The problem lies in the execution method; an approval process without technical experts, clear standards, or a timeline is not the answer. OpenAI states that the limitations of GPT-5.6 are short-term measures and may open to the public in a few weeks.

However, the limited resumption of Mythos 5 on June 27 has already provided a template—not a full release, but still limited to certain U.S. agencies, with other restrictions remaining in effect. Every long-term system was initially referred to as a short-term measure. Dean Ball concludes with a statement that deserves serious attention from everyone: If only a very few people can use cutting-edge AI, a bad future is more likely to occur. Because those few people are often groups that already possess significant economic and political power. It is estimated that the global developer community is reminiscing about the days of eagerly waiting for OpenAI's release events, excited about the advancements of new models, and staying up late testing various new scenarios.

However, for now, we can still look forward to the release of China's latest large models. The shift from open innovation to state-gated access represents a fundamental restructuring of the technological landscape, where the speed of adoption is no longer determined by market dynamics but by bureaucratic clearance. This marks the third such incident this year where regulatory uncertainty has directly impacted the deployment timeline of major AI infrastructure, signaling a potential long-term trend of increased state intervention in the commercial AI sector.