After analyzing these proposals, we argue:

1. Policymakers can use this approach to understand disagreements and shared views of proposal creators more precisely.

2. They can take action in an uncertain and rapidly changing environment by addressing common assumptions across governance proposals.

Policymakers can use these assumptions, some unique and some shared, to better understand what's possible and more effectively build AI governance infrastructure.

To show this in action, our report analyzes five AI governance proposals, from different kinds of organizations, as case studies.

We suggest breaking down AI governance proposals into their component parts. What do they aim to govern, and why? Who should do the work, and how?

Answering these questions will surface the foundational assumptions that make the proposals tick.

With AI tech continuing to develop, many relevant organizations have written proposals about how to govern AI.

With so many out there, how should people, from policymakers to other interested parties, understand and evaluate them?

This report proposes an analytical method to achieve that.

The plan also promotes and emphasizes the importance of scientific, including biological, datasets- in line with @csetgeorgetown.bsky.social recommendations for the plan, which you can read here: cset.georgetown.edu/publication/..., and with other CSET work: cset.georgetown.edu/publication/....

Focusing on bio, one provision is a federal funding requirement for DNA synthesis screening- a useful tool in the toolbox for limiting biological risk.

Check out @stephbatalis.bsky.social and I's piece breaking down the kind of decisions screeners have to make: thebulletin.org/2025/04/how-...

Ultimately, though, a chilling effect on state-driven AI legislation could severely harm innovation by reducing foundational AI governance infrastructure.

The Action Plan's implementation and approach remain to be seen, but it should be careful not to nip useful state regulation in the bud.

The plan does clarify that restrictions shouldn't interfere with prudent state laws that don't harm innovation.
And it's true that a complex thicket of onerous state laws governing AI could make it harder for AI companies to comply, harming innovation.

States are better-positioned to pass these laws than the federal government in the current environment.

They can also serve as a sandbox for experimentation and debate, allowing for innovation in governance approaches. The best governance approaches can inspire other states to follow suit.

State laws provide a critical avenue for building governance infrastructure: things like workforce capacity, information-sharing regimes, standardized protocols, incident reporting, etc.

These help provide clarity for companies and are crucial for innovation.

A recent @thehill.com piece by @minanrn.bsky.social, @jessicaji.bsky.social, and myself introduces the topic of governance infrastructure.

It discusses the recent proposed ban on state AI regulation-which would have gone much further and, thankfully, did not pass.

thehill.com/opinion/tech...

Factors like robust third-party auditing, strong information-sharing incentives, and shared resources and workforce development enhance, rather than reduce, innovation.

As such, we argue that the proposed moratorium would be counterproductive, undermining the very goals it aims to achieve.

These debates are worth having, but miss a crucial factor: AI governance infrastructure, which states are best-positioned to build.

This infrastructure helps achieve the moratorium's stated goals. It helps developers innovate, strengthens consumer trust, and preserves U.S. national security.

Proponents of this plan argue that reducing strenuous regulations will speed up innovation, and that the federal government should lead in regulating AI anyway.

Opponents cite congressional gridlock, partisanship, and lack of meaningful tech regulation, as proof state laws are needed.

The recent reconciliation bill, which passed the House and will face a Senate vote soon, would place a 10-year moratorium on state-level AI regulation.

Whether this is a good idea has been hotly debated.

It's heartbreaking to see people dying from preventable disease.

AMR is a global problem, and people die from it everywhere. But as with many other problems, it affects the poor most harshly.

As a global community, we must fund more AMR research, and find ways to get drugs to those in need.

Some of my work at CSET has focused on this last problem- examining the global research landscape. Over the last few decades, very few new antimicrobial drugs have been discovered- and even fewer have been innovative.

cset.georgetown.edu/publication/...

AMR is a multi-pronged issue. Accessibility (ensuring that all people who need antimicrobial drugs can use them), stewardship (ensuring the proper prescription and use of the drugs), and R&D (developing new drugs to fix a thin global pipeline of new ones) are all key.

The study focuses on Carbapenem-resistant Gram-negative bacterial infections in 2019, finding that in the 8 LMICs analyzed, only 6-9% of infections were treated properly.

These are treatable infections, but with the lack of access to the right antibiotics, they kill.