OpenAI is now making its latest advanced large language model, Sol, available to the general public. Sol is considered at least as capable as Anthropic’s Fable—a model whose abilities (and ownership) so concerned the White House that it was temporarily blocked from public use. So how did these models get approval for release? The short answer is that no one is entirely sure. “Anthropic did say that they were in conversations with the government, and that they developed a classifier to detect jailbreak attempts, and they’ve implemented defensive gap strategies to prevent future jailbreaks, but exactly what that dialog looked like between the government and Anthropic and OpenAI is unclear.”

Dean W. Ball, a former policy advisor under Trump who now works for OpenAI, wrote in his newsletter last month that “nobody knows what the requirements are to get licensed.” Andy Konwinski, a computer scientist who co-founded Databricks, Perplexity, and the Laude Institute, said he has never spoken to anyone who understands the process—including employees at frontier labs. “Safety or not, it’s about who has the power to make decisions—who gatekeeps and decides on permissions.”

Eighteen months into the Trump administration, there is still little clarity on how to proceed, despite—or, some critics allege, because of—the industry figures shaping policy. Last month, after weeks of infighting, an executive order was published outlining a roadmap for evaluating frontier models, but the specifics remain undefined, except for what will not exist. “There will not be an FDA for AI,” Sriram Krishnan, a former Andreesen Horowitz partner who served as a senior advisor for AI in the White House until last month, told the Financial Times. Notably, there is still no agreement on which types of models require government scrutiny or which agency or agencies should conduct those evaluations. For now, the Department of Commerce’s Center for AI Standards and Innovation appears to be taking the lead, but the executive order instructs six cabinet agencies to determine a final process by early August. What has emerged in the meantime is, at best, ad hoc.

OpenAI CEO Sam Altman said on CNBC that the process involved conversations with officials like Secretary of Commerce Howard Lutnick, Secretary of the Treasury Scott Bessent, and U.S. National Cyber Director Sean Cairncross, but it remains unclear who the experts were that tested the models or how they conducted those tests. As with Anthropic’s Fable rollout, OpenAI previewed Sol for the government and select users before wider release, but we don’t know who all those users were or how they were chosen. In a late June blog post, the company stated, “we don’t believe this kind of government access process should become the long-term default,” adding that it would work with the government to develop a different path forward. The backdrop to those conversations, however, includes Altman reportedly offering as much as 5% of OpenAI’s equity for the administration’s so-called “Trump Accounts,” and OpenAI president Greg Brockman’s role as the largest publicly known donor to Trump’s mid-term political operation. It is difficult for outside observers to separate those activities from the government’s apparently lighter-touch approach to regulating Sol.

Anthropic’s Fable, on the other hand, was briefly pulled from broader access when the U.S. government forbade its use by foreign nationals—partly due to real concerns about users jailbreaking the model to access hacking capabilities and partly due to personality clashes between Anthropic and the Trump administration. The threat of an export ban may have also led OpenAI to be more cooperative with the government’s (unknown) requests. From an industry perspective, a hands-off approach to regulation might seem appealing, but one that depends on personal connections to administration officials creates uncertainty and bad incentives.

Konwinski argues that an “open commons” is the best way to actually balance safety and innovation. He points to models like the FDA, the NIH, or the national labs, which convene researchers, government officials, and private companies to reach a consensus on safety issues. Some of that comes down to the incentives of capitalism that have motivated AI researchers for more than a decade, and played out in the courtroom during Elon Musk’s lawsuit challenging OpenAI’s corporate structure. Ball points out that the nature of the AI business requires companies to recoup much of their training costs shortly after their models are released and while they are still ahead of the competition. “Even if their intentions are good, there’s very clear legal obligations and fiduciary responsibility that are built right into the operating procedures,” Konwinski notes.

Ball, in his post, argued that the way forward will depend on third-party auditing organizations, licensed by the government, that will evaluate frontier labs’ approach to safety. Konwinski is also optimistic about new institutional formats, such as focused research organizations, that could help more disinterested experts from academia and the non-profit world access and evaluate frontier models. For now, the secrecy surrounding AI development isn’t going away, but it will also seed political challenges for an industry that Americans increasingly view with skepticism. “There’s not a sense that responsible people are driving forward these changes,” University of Wisconsin-Madison computer science professor Remzi Arpaci-Dusseau said last week at the Open Frontier conference.

At the same event, David Siegel, the computer scientist who founded Two Sigma—one of the most successful quantitative hedge funds—asked attendees to “imagine a situation, which I think would be very bad, [where] a small number of firms control the technology; the government, in their secretive laboratories, is evaluating whether or not the technology is suitable for use; and the general public and scientific community doesn’t really have any access to any of that stuff.” It seems we don’t need to imagine it.