In trying to investigate what could shift political will for AI governance as someone without much of a policy background, I decided to start with investigating the much less controversial proposal of mandating nucleic acid screening. Generally, policy change is much more likely to pass when the upside is obvious and it isn’t costly to comply. Mandating nucleic acid screening fits this abundantly well: make sure that if someone has enough expertise to design a dangerous protein, or uses an AI system to do so, it would get caught and stopped if they tried to pay a lab to produce and ship it to them. Automated screening technology that would catch the vast majority of near-term cases is already here and isn’t costly to implement. And yet, we still aren’t even doing this, which is a bad sign for any costlier AI regulations motivated by less legible risks. Notably, unlike AI slowdown proposals, there’s little to no argument to be made that anything short of a verifiable international treaty would cede our innovation advantage to China. Why is this stalling?
The main reason seems to be that passing legislation takes a lot of sustained effort, and there’s no crisis or warning-shot to make it feel urgent enough for Congress to prioritize. The relevant bill here is S.3741, introduced January 29, 2026, which would solve much of the problem, at least on a national scale. This post by Sophie Kim goes into more detail, and provides additional commentary and proposed amendments. It’s come after decades of advocacy, most of the nucleic acid synthesis industry already voluntarily screening orders[1], and being recommended in the Executive Office of the President’s July 2025 AI Action Plan for institutions receiving federal funding. It’s also worth noting that the open letter that called on Congress to pass this, signed by dozens of leading experts in several relevant fields (which helped to indicate the cost on innovation would be minimal), was published June 3, 2026, and that many members just weren’t aware that open-source AI tools can already design new dangerous proteins prior to this. Per Fortune:
While the bill slowly moves its way through Congress, Josh Wentzel, a senior fellow at the Foundation for American Innovation, told Fortune that the letter was a good opportunity to show lawmakers that the AI industry and companies who sell synthetic DNA and RNA were equally concerned about the issue.
“This is bipartisan, concrete, achievable, and noncontroversial,” Wentzel said, adding he hopes now that Congress sees these parties are aligned, it can move forward with passing the Biosecurity Modernization and Innovation Act. “It’s a goal among many national security experts and, crucially, something the nucleic acid synthesis industry itself has called for.”
So, the open letter does seem to be moving the bill faster than it otherwise would have. But in spite of all of this, and strong media coverage of the letter, and advocacy from the think tanks that co-organized the letter, the bill is still waiting in committee without undergoing any markups two months later. The bill is practically certain to pass eventually, but short of some event that would make it a higher priority, it’s likely going to take several more months. For reference, the much more mainstream Epstein Files Transparency Act took about 5 months to pass after being introduced. Congress is really that slow. I knew they had a reputation for it, but it’s disheartening to see it for myself. This lends credence to my prior theory that not much is going to concretely get done in AI governance until the effects are felt more, which I intend to investigate next.
Given the U.S. government's recent restrictions on Claude Mythos and GPT-5.6, I suspect one of the most tractable and effective strategies for further pushing the Overton window towards the urgency of implementing stronger AI regulation could be effectively communicating to policymakers that open-weights models, whose safeguards can be removed easily, are likely going to reach Mythos-level capabilities in under a year, which can meaningfully uplift cyberattacks and bioweapon development. Also, I suspect the main thing currently holding back the Overton window on interventions for AI x-risk in general to be a (perceived) lack of concrete evidence (see: The Milton Friedman Model of Policy Change, how we achieved the Montreal Protocol to mitigate climate change before the damage became irreversible despite strong industry pressure against it, etc.). Communicating loss of control risk would be ideal of course, but I'm currently unsure how this could be demonstrated safely.
The only ways to prevent Mythos-level open models would need to happen in China. Nobody but China is likely to produce a Mythos-level open model.
There are two main ways this might not happen:
One of the complicating factors here is that Anthropic and OpenAI are preparing for the largest rent extraction in the history of the human race, and they represent a massive risk of concentration of power. (That power might be effectively seized by the government, but it's likely to remain concentrated.)
So there will be strong advocates for open models, including open frontier-adjacent models, in order to fight what looks like epic-level rent extraction and power concentration risks.
US regulation, by itself, will accomplish exactly nothing, because Europe would happily use cheap, open Chinese models to avoid losing control to US labs and to avoid paying enormous rent.
I find this whole logic deeply frustrating, because the logic pushes everyone involved towards racing. And while Mythos isn't an ASI-level threat, I don't know how many more breakthroughs and scale-ups we can get away with before we start facing loss-of-control risks.
Some very half-baked brainstormed ideas for increasing the likelihood of getting to something like Plan A or S:
I'm largely new to working on this and haven't investigated these too deeply yet so there are probably some obvious things I'm missing here. Thoughts?