Your reporter spoke with Miranda Bogan, Director of the Center for Democracy and Technology's AI Governance Lab and Chief Technologist, who assesses the potential danger of increasingly powerful AI systems and growing calls for AI regulatory legislation at both the U.S. federal level and internationally.

Well, what I can tell you is that folks who are close to the companies developing this technology are worried about what they term in a technical phrase as recursive self-improvement, which is when AI models will conduct their own research to continue to improve their capabilities, etc., Myself and my organization haven't been focused on that particular threat model because we are really thinking about what are people's experiences today with the technology that's already in their lives and being imposed on them.

And in both cases, what we see is that often the decisions to move forward with the technology or to engage in certain types of training activities in terms of technical development are just made very, very quickly.

People are feeling pressure to move quickly, and that doesn't typically lead enough time to think about, is this something we want to do? Have we mitigated the risks of this? And is this a future that we want to walk into? And that's what we're hearing the calls from leaders today saying that We would like to slow down because we don't feel like we've been able to do that work and moving quickly at this point seems like it's a bigger risk than a benefit.

And I think that's long been the case.

And hopefully the developers, the leading developers will step back and say, what are we actually building for? And is this the right move?

But Bernie Sanders, I know specifically, wanted to ban superintelligence AI.

There's no silver bullet to addressing the risks posed by AI.

And the people who are asking, how do we operationalize this decision that has already been made into a technical product that has taken over the field? So I guess my question here is, do you worry about governance becoming performative? Do you worry about the loss of the questions like, should we build this at all? Not just how does this cause harm and how do we mitigate you know, the kinds of harm that it causes.

I think the observation of the shape of the positions over time is really interesting.

And I'd like to hope that it doesn't necessarily fully change what work is happening.

To your point about career stability, job stability and security and things like that.

I observed that one of the ways that responsible AI teams would kind of protect themselves in times of flux or budget crisis is sort of reorient themselves to appear to be more of a technical organization that was building tools for other teams to to Or, you know, moving into more of a compliance space where there was a requirement that would ultimately cost the company not to conform with.

And so some of those might be a reflection of strategies in times of crisis.

constrained resources to shift things around and get the sometimes the people are the same and so they can bring those skills but when they shift their orientation to those new roles and those new teams their frame can also get a lot narrower so a responsible ai team that's shifting to comply with the euai act or the dsa because that's an organizational priority for which their resource is available, might suddenly find themselves empowered to answer a much narrower set of questions, the ones that the regulations spelled out, because if the organization is perceived to be asking other questions, but under the guise of compliance, then they might get...

Can you walk us through what you found and what it means for how we think about responsibility when downstream systems cause harm as they do?