Aaron Levie, Steven Sinofsky & Martin Casado: How Do You Secure a World of AI Agents?

If you regulate AI too early, you actually don't solve anything. You still just kinda have the same risk ultimately.

You willed the thing into being-

Yeah

... but you haven't figured out how to control it.

The problem we have now is this rift between the labs and the security community that keeps coming to two conclusions: sloppy-

Yeah

... and you're not complete in what you're telling us happened.

An employee is like 10% chance of species extinction. The post is very reasonable, but the atmospherics are not, right?

Agent swarms completely flip that. These are just roaming, you know-

They're roaming as well

... drones. But like times 10,000-

Right? It's more

... and they will easily mistake a good task for a bad one.

So now we need a whole layer internally that just is tracking way more about what authentications are being done, what APIs are being done.

Yep, yeah.

The US, about 15 years ago, stopped leading in tech antitrust. The problem is that Europe is gonna lead with that because they have nothing to lose.

This could change the nature of software fundamentally.

The center of innovation has just moved. This is the signal that-

The early internet was riddled with viruses, worms, and security failures. We didn't stop building it. We learned how to make it safer. What should AI take from that history? In this episode, I sit down with Aaron Levie, Steven Sinofsky, and Martin Casado to debate AI safety, regulation, and what changes when agents start operating across the software we use every day. We get into why agents could force a rethink of permissions and cybersecurity, what decades of software security can teach today's AI labs, and why regulating a technology before we understand how it actually fails can create problems of its own. And we look at a broader shift already underway. As models mature, some of the most important AI innovation may increasingly happen outside the frontier labs in the systems and software built around them. Guys, welcome back to the podcast.

Good to be here.

Thank you.

Great to be here.

We didn't think we'd ever do this again.

[laughs]

This is ama- I can't believe it. This is great.

[laughs]

I mean, Martin's just building these-

I know [laughs]

... $100 billion companies. Too busy for this podcast, so.

Or at least taking credit for them, as, as VCs do.

Exactly. We have a lot to discuss today, but Aaron, why don't we start with you. Pacing the frontier-

Yeah

... how have you reacted and reflected on what's happened there and just the discourse that's followed? How do you-

Oh, boy. I think we should start with Martin on this one. You were fighting lots of good ground wars. Maybe I'll say one thing that we probably all agree with, and then we can figure out where we maybe kind of fracture off. I think we would agree that any AI lab right now at the frontier should be building in the safest way possible with the highest degree of governance and security and whatever your definition of alignment is. This is an incredibly important area of research. It's an incredibly important area for the diffusion of AI. You're not gonna have AI diffusion without extremely high-quality products that can be trusted by enterprises and that aren't kinda constantly