AI Can Write the Code. Can It Run the System? | Grepr CEO Jad Naous

Martin Casado (0:00)

The world that the AI lives in is all digital, and this is the actual world, and those things are not necessarily the same.

Jad Naous (0:05)

When we started doing AI for coding, it was autocomplete. Nobody really knew to what extent you can actually build complex software using AI. And then it basically broke through the expectations because we were able to model more and more complexity in the system.

Martin Casado (0:19)

I just don't know if operations is constrained in the same way. Where is even the data to learn how to build a system that understands all of the operations?

Jad Naous (0:30)

My assumption is that we have all the data. The question I think is maybe about complexity. And like to what extent can you take the things that you've described that are operational and convert them into something that an AI can reason about.

Martin Casado (0:44)

The dirty secret for the longest time of running like a distributed cluster was just human beings managing it. Do you think we're at a spot where you could like use AI to run one of these services?

Jad Naous (0:54)

I think that there's enough to be able to...

SPEAKER_3 (0:58)

AI can now write remarkable amounts of production code, but writing software and running it are two very different problems. In this episode, A16z general partner Martin Casado sits down with Grepr CEO Jad Naous, a systems engineer Martin first met when they were both at Stanford, to ask what happens as AI moves from coding into operations.

Jad explains how his 10-person team uses coding agents today, why he thinks the strongest engineers are getting even more leverage from AI. And how work that once took months can now take days. But the harder question is what happens after that code shifts. Production systems face unpredictable workloads, failures and an enormous number of possible states. Martin and Jad debate whether AI can learn to troubleshoot those systems by detecting what changed, forming hypotheses and running experiments, or whether operating in the real world is fundamentally harder than generating code. And that leads to the bigger question behind the conversation.

How far can AI go when the answer isn't already campaigned in the world it was trained on?

Martin Casado (2:03)

Jad, thanks for joining me on the podcast. For those of you who don't know, Jad is the CEO of Grepr, but also we went to grad school in the same group. And when I first met you, we had a new student.

I walked into the office and there was a motorcycle helmet and a pack of Marlboro's. I'm like, this guy must be unbelievably cool. So my question is, what happened to all your coolness?

SPEAKER_3 (2:32)

Question number one.

Martin Casado (2:36)

Maybe just for context, maybe give a quick overview of your background. So just for those that are listening, Jad is one of the best systems thinkers I know. I love talking to him. We're going to go kind of to a deep dive of engineering from a systems perspective and how AI is impacting us, especially when it comes to things like operations. But just to ground it, maybe a bit about your background.

Jad Naous (2:54)

Sure. So you met me at one of the peaks in my coolness.

SPEAKER_3 (3:00)

Yeah, you're very cool.

Martin Casado (3:01)

Motorcycle and Marlboro cigarettes.

I remember thinking, I'm like, wow, are cigarettes even legal? It was like so discordant.

Jad Naous (3:12)

I remember Nick seeing me one day smoking outside of gates and looking at me and going like.

Martin Casado (3:20)

Nick Pecuna is our advisor. Gates was the building. Actually, one of my favorite things, do you remember Balaji Prabhakar, the professor?

Jad Naous (3:26)

Of course.

Martin Casado (3:27)

My favorite thing about him is that he smoked. He's like the only professor. I just thought it was like great.

Jad Naous (3:32)

Just to be clear, I don't smoke anymore. No, too old, too middle-aged for that now.

But let's see. So I went to Stanford with you. You were always the guy I had to look up to and you're like, hey, this is the guy I want to be when I grow up. Because I started four years before you did. So you were four years ahead of me. You graduated a while ahead of me. You went and started Nicira. I stayed and stuck around, worked more on OpenFlow, helped build a bunch of stuff on the hardware side from a security perspective. Actually, one of the most fun projects that I worked on there was, I built the first set of SHA-3 implementations that people were considering using, and I built them in hardware to kind of show whether they work or not and whatever for one of my projects for my thesis. That was fun. And then I decided after a while that I actually don't like networking because it's just about moving bits from one place and putting them in another place.