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

The world that the AI lives in is all digital and is the actual world and like those things are not necessarily the same. 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 systems. I just don't know if operations is constrained in the same way. Where is even the data to learn how to like build a system that understands like all of the operations? 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? 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? I think that there's enough to be able to 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 Martín Casado sits down with Grepers CEO Jab Nows, a systems engineer Martín first met when they were both at Stanford, to ask what happens as AI moves from coding into operations. Jab 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 worked it once took months can outtake 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. Martín and Jab 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 contained in the world that was trained on? Jab, thanks for joining me on the podcast. For those of you who don't know, Jab is the CEO of Grepers, 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 marboros. I'm like, this guy must be unbelievably cool. So my question is, what happened to all your cool questions? Question number one. Maybe just for context, maybe give a quick overview of your background. So just for those that are listening, Jab is one of the best systems thinkers I know. I love talking to him. We're going to go candid 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 grant it, maybe a bit about your background? Sure. So you met me at one of the peaks in my coolness. Yeah, you're very cool. Motorcycle and marbor cigarettes. I remember thinking, I'm like, wow, our cigarettes even legal. I was like, so discordant. I remember Nick seeing me one day smoking outside of Gates and looking at me and going like, Nick Bikun is our advisor. Gates was the building. I see one of my favorite things. Do you remember biology, Prabhakar, the professor? And of course, my favorite thing about him was that he smoked. I was like the only professor. I just thought it was like great. 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. I started four years before you did. And so you were four years ahead of me. You graduated a while ahead of me. You went and started NYSERA. I stayed and stuck around, worked more on open flow, helped build a bunch of stuff on the hardware side from a security perspective. 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. Yeah. 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. How hard can that be? [Laughter] I think that would have to either be philosophically aligned that it's deeper or not. Most people isn't a superstar. I think it's very deep. [Laughter] I mean, there's something interesting about it. But ultimately, to me, that's like, it's not. Yeah, it doesn't do it for you. Yeah. And then I actually went into the postdoc at MIT with Nikolai Zelda Fitch who was also amazingly deep and super smart. Yeah, he's phenomenal. I did some application security work at the time. Started a company based off some of the research I had worked on with Ramash Chandra over there who was a PhD student at the time. And ultimately, the technology was too early. So what we did was. Well, ultimately, a lot of the work was actually really Ramash's. And his PhD was on, "Well, hey, if you get attacked and you get exploited, can you recover from that by rolling back the state of the whole world that's related to that application and then roll it over on top of a patched version of the application to remove the exploit?" Yeah. And so we started doing that in the real world. And we're like, "That sounds really hard." [Laughter] So I see kind of relevant to what we're talking about. Yeah. To what extent can you actually bound the state for, like, operational systems without arguing? It's very hard. Yeah. We almost got it working. [Laughter] Almost. I feel like for all long-tailed disciplines, this is why people get into the fly track for smart people. It's like you see a bit of progress, but you're asymptoting. But anyways, I digress. Yeah. So then I joined AppDynamics. I was there for about three and a half years. They went from about 200 people to 2,000 people, got acquired by Cisco. I was a Cisco employee for one day. And I said, "Man, you're like, "Jad!" I don't know. Look, you come work with me. Come do this VC thing with me. I'm like, "What the fuck?" What is VC? I'm an architect. I do engineering stuff. Yeah. What is, like, great. Yeah. The less you know, the better. Yeah. Literally what you said. That's true. And then the first day I started here, you said, slow down. [Laughter] No, it's actually people that move from, like, actually, you're not operating all the VC. It requires a lot of reworking the brain. Yeah. Yeah, it's just like going from a PhD to operating, by the way. You have to undo the brain damage. [Laughter] This is what real world is like. [Laughter] And then I stayed here for a couple of years. Did a bunch of work with you? It was a really fun time. Worked with a lot of great people like Jennifer, Mark, and Ben. Satish. Satish, Matt was here on a first, almost towards the end when I was leaving. Yeah. And then joined the ply as their chief product officer. Help them build their engineering product teams. Kind of scaled from about 15 people to over 90 across product. Engineering, InfoSec, and Design. And after about three years, I kind of had a head for each one of these pieces that I owned, and I kind of was sitting around just coordinating and doing meetings all day, and wanted to go do something else. So left, and then started talking to a lot of engineering leaders about the problems that they're facing and what they're trying to do. Everybody at the time, this was in 23. Everybody was saying, "We're trying to reduce spend." Now some, what are you spending money on? Everybody would say, "Farce, it was infrastructure." And then, observability. And so I started looking at that problem, trying to understand like, "Why do people think it's too expensive?" Turns out there's a few things. First, people were spending somewhere between five and 20% of what they spend on infrastructure on observability. But ultimately, what they were really trying to spend it on was reliability. They wanted to buy reliability. And of spin, like predictability of spin. Reliability of their infrastructure and applications. I see, I see, I see. And so they were putting money in observability as a proxy for reliability and for the longest time, the more money you spend, the more reliability you got out. But as architectures changed and turned into microservices that are much smaller, that are much more vertically integrated, the volume of data that's being collected at every layer expanded, there was much deeper market penetration for observability and kind of the volume of data that you're collecting and the cost of observability was actually starting to grow faster than the units of function that you're outputting. And the marginal rate or like the marginal increase in reliability for every additional dollar you're spending on observability was just flattening out. And so that's why people thought it was expensive. But people were still willing to pay five to 20% on reliability. And so the question was, can we actually solve that problem and bring back that increase in reliability, that marginal increase in reliability? Not necessarily by spending more money on observability, but some other way. And so the thinking was like, okay, great. If we can figure out what is the signal, what is the noise? Can we actually turn the observability data that you're collecting that's mostly fluff and mostly insurance at this point? And do that kind of extraction of information upstream, do whatever you need upstream and be able to ultimately massively reduce the cost of observability downstream. And if you can extract that signal in real-time upstream, then you can, one, reduce the cost. But two, you can now feed it into other systems and use it for driving reliability operations afterwards. And the distance at the time there was something that was called AI coming into the picture. And we're like, well, great. If we are in the data stream and we're extracting the signal, then we are in a strategic state. position to feed that to whatever is going to come in the future because that's going to be necessary. And so that's what we built. And so, Grepper helps companies. We started with helping them reduce their spend on telemetry massively. And so, we put us in the data stream, plug-and-play, pass the data through, reduce your cost by 90%. And now we're building out that second layer where we're feeding those signals into the AI layer. That's now going in troubleshooting and figuring out when things are going wrong and fixing them. I love to just talk through kind of your experience on how AI is changing dev generally, which is like a lot of people could ask the discussion a lot of people answer, but I just feel like I like the engineers engineer. You've been an architect, you've run product, you've run engineering teams, like, you know, like, actually, how big is your company now? How many people? We're at town. Yeah, so it's, you know, still pretty, I mean, you're building a lot of software with a relatively efficient team. So, maybe just a high-level sketch on like, you know, a senior engineer like you, you know, that has architectural experience like, like, what sort of gains do you think people are seeing? Maybe talk through like, how you use AI, what gains you're actually seeing, what is the stack, what is your process, how you think about it. So maybe I can give you like a historical view of like, what's been happening? Yeah, like, running a team and so on. And so, you know, when we first started AI, we're still kind of auto-complete. I remember like, at the time, we're using co-pilot and intelligent, we're like, oh my god, look, auto-complete at these tests. We're like, yeah, but it's just auto-complete. And very rapidly, things started evolving. And what I've noticed is that really good engineers are able to somehow put it into their workflows in a way where the AI starts training them. Oh, interesting. In terms of like, how to talk to it, how to get the best out of it. And what I notice, as an aside, do you think that's model-specific? Like, do you learn how to use AI, you know, writ large or do you get efficient at a given model? I think there's like some nuances that different models bring. I'm sure you're looking at your tweets. You've there's like nuances, but there is a way to work with AI. And like, I think that there's also a way to work with different harnesses. And I think like, you know, we tried, for example, using open code or like different harnesses or like using codex versus clod, like clod code, like different ones have different ways of managing contacts or thinking through problems or when to spin up the right sub-agents. And what to tell it, like the kind of prompts. Like, for example, with clod code, I find that if I don't tell it, don't spin up, you know, 20-fable agents to do basic research, it'll go and spin up 20-fable agents to do research and learn all your tokens. And so like, a lot of these things kind of you start learning about the harness and the models and like how they interact with each other and then how they can interact with the rest of the world. But I digress, like I'm just going to, in my mind, like, this is kind of what I started noticing is that like, some people were adapting and learning and kind of doing it that way. And others weren't as adaptive. And then as we started hiring more people into the team, that disparity between, you know, okay, engineers and really good engineers just multiply 10 acts. Can you predict who, could you predict who would be good? Was it like, good engineers got better and bad engineers didn't or was it really okay? Yeah. So like, if you're a good engineer, you're just a better good engineer. It's not like there's some like bad engineers all of a sudden became good engineers. No, okay. No, no. It always ends up being like, you have to really understand ultimately the set up because the more complexity that your brain can handle, the more you can break down the tasks and kind of assign them. I think this might actually be like non-consensus opinion that like, I mean, I do hear it sometimes, but the idea that like, the better engineers like are much better at those are less. And you found this to be pretty universally true. I mean, look, my team is still small. Yeah. We haven't done it across a hundred people. Yeah. In my experience, for the things that we do, that is the case because we build distributed systems and distributed algorithms like your brain has to handle the complexity because like AI doesn't do it. Right. Yeah. Yeah. And so for the problems that we solve, none of the models can actually think through the things that we do. Like if there's a lot of, for example, we have a query translation layer, we were just talking about create optimization. And so you have to really understand the semantics of the query and how to get to the semantics of the query and then explain it to the model and then have the model go and build the translation layer, let's say. Yeah. So for the people that are listening that may not be so deep in computer science, what do you mean by semantics? So semantics are really the meanings of things. Like the semantic of a dog of the word dog is the animal that actually walks on four legs and barks. And the syntax is the fact that it's just three three letter word. And it has three different letters. The shape of the word is different. And that shape can be different between different words. But different words can actually also mean the same thing. And so they might have the same semantics, but they might have a different syntax. So a dog and a puppy might be slightly different semantics. They refer to the same. But they're so close to that. So the way that in just for like computer science, the way that I described it very simply is syntax, you know, it's like the characters you type and semantics is what the machine actually does, right? And so it's like the actual runtime properties of the system. So you that computer science, either the electrical engineering, you became a computer scientist and I became a VC. That's the problem. I see. Anyways, I just wanted for people that are that I just want to understand like there's the actual syntax, which is writing the code, you know, and then we talk about syntax, you're just talking about the code itself. Then when you talk about semantics, you're literally talking about you have a machine that interprets that code, whether it's compiled or not, and that machine does something and semantics is what the machine actually does. And these things are, you know, clearly very different, right? Like so, for example, some semantics you can't even derive by looking at the syntax, right? You don't know what the machine is going to do because whatever you kind of have to test it, you have to test it, you know, like, and they can go all sorts of different ways when you test it. And like a very classic version of this is, is, is SQL, for example, like you can write, you know, some SQL query, but like how it actually performs, how long it performs depends entirely on, like we'll say what the schema looks like or what the data looks like in the database, etc. But that's, I would not call that semantics though. I would call that performance characteristics. Oh, sure. Yeah. I think that would be like, like, I would say like a, the semantics part of that would be like, depending on like what the shape of data is or like what the configuration, you might end up with different semantics because like you said, you want null to be treated as false. And you won't know that unless like you also know the configuration of the system and, and so on. Okay. Okay. So for the purposes conversation, I think we should just say that there's like, the actual code you're writing, how this behaves, and we'll call the second one semantics just so we can have a conversation. Oh, what do you want to call it? We need to dive into the semantics of this and make sure they're clear. Because there's a lot of, I don't know. Okay. I know. Let's give it a go. Okay. So, so, so, sorry, so let's go back. So, you know, actually before we started the podcast, you and I were just shooting the shit and you were talking about like how you can do products projects now that take a week where in the past would take a team in months. Can you maybe walk this through a little bit about like how you personally kind of will do projects and then kind of the gains that you see. So we can a little bit about like your stack in your process. So I use plot code. I gotta say something that's actually pretty funny, but I use windows. What? Was that created by this company Microsoft? And I use WSL. So I use Linux for everything like development and so on. There's no like ex or UI for it. I just use the command prompt, but how do you use Windows? Because I just never really wanted to learn the keyboard shortcuts. Are you a gamer? Is that so? You tab over to CounterStrike or whatever? Like why else? I'm not hating on Windows. I mean, you know, like I use it in the 90s. That's really like the only reason is I never wanted to learn the shortcuts. I hate the one button mouse. I hate the one button stuff. I hate the number of modifiers. You have to type for shortcuts and stuff like I just never I'm like, why? Why? Why waste time? Okay. I use Windows. Yeah. And so WSL I use plot code. What is WSL? That's Windows subsystem for Linux. So it's a they actually run the Linux. uh, kernel within Windows in a hypervisor and provide you access to it. It's great. They've done a great job. Great Windows, WSL and Cloud Code. And, uh, like, my latest, the latest thing that I do is I use Opus 5.5. I tell it, it's a really good model. It's an amazing model. Really good. And it's, I've shipped in the last two weeks these massive 20 PR things that I just dump on my team to go and, and review. And they're like, oh, I'm like, don't worry about it. It's horrible. It's like, it's like the new reporting structure, like the people below review the PR for the CEO. Is that where we be? I was like, no, everybody's like writing the same. Everybody's like writing. Yeah, I review my QRs and like, everybody's like just, just PR is flying around. Yeah, yeah. Yeah. And, but like, the thing that has changed a lot in the review cycle is that like, we started much more focused on, uh, to focus much more on like the, the tests. Sure. And like, doing things like golden expectations and like, are they shifting in different ways? And so I don't know. But, um, Opus 5 and like, the, the trick that I recently learned about is that you, instead of using sub agents, you basically tell Cloud, um, to spin up command lines of agents because those maintain their persistent session over time. Yeah. And so you can have it spin up these agents separately. You can actually run them from other sessions and, and so on. And, uh, that allows you to be much more efficient from a context perspective. How many users do you run? How many? How many? How many, how many, what agents agents? Um, typically I work on like three or four session windows at the same time because I found that more than that. I just start losing context, um, between the different things because like they, they start finishing faster. And then I, I, then they wait. And do they interact via the, do they interact via the PR networks? The sub agents like together. Yeah. No, they interact directly through the command line. Oh, wow. Okay. Yeah. So like, I asked like, the, the main card, like, the main thing that does the implementation to have a tech lead reviewer and like, they basically ask it questions, does this make sense? Does this not make sense? Review my PR, et cetera. And so they iterate on these PRs together. Um, and then they have a PR doc that basically they work on with a design doc and everything in it. Like the first thing that I do is I ask that agent to do it, ask the tech leads to review the PR doc. I go through the, uh, the PRD, I go through the design doc, make sure it makes sense, go through each piece in like detail, make sure that all the algorithms work and like all the pieces work, the division of labor works, the p a division between PRs and their relationships work. And then I tell it just go start, start doing it, figure out if there's like a parallelization that you can do and then spin up other agents to actually go do them. Um, but ultimately you have to kind of put it all back in one stack and be able to go through it as a human one by one, because otherwise it just gets messy and bushy and you can't really tackle it. Yeah. So how much time? So can you give a sense of like a project you do now versus, I don't know, six years ago, like time differences? So the latest project that I shipped was something like, this is like, to be clear, this is production code. Yeah, this production, whatever. So yeah, the statement you're about to make is the gains that you get for like shipping product. So I'll give you the example of the last, there's, there's the last PR that I worked on or like the last set of PRs was what we call auto, auto promotion and a data lake. Yeah. So in a data lake, you store all the data and typically the data is a column oriented part K files and iceberg and so on. Um, and when you get data from logs, they're semi structured, they're arbitrary columns and so on. In order for it to have the best play out of the data and to be able to query it efficiently and fast, you kind of, the ideal situation is that every field in the log event becomes a column that you can filter on independently of the rest of the rows. And so we call that auto promotion or shredding. Sure. And doing something like that is incredibly complicated, right? Like you have to go and figure out how does the metadata system work? How do the commits of the metadata system work? How do you coordinate across the front? Uh, writers and so on. And, um, that took maybe about 10 days. And previous days, that would have taken maybe about six months for a single person. Well, it was just you. That was just me. It is insane. Yeah. Sort of like factor of 10 factor of 20 improvement. Yeah. For production code for new products. Yes. You know, one, you know, one, um, question I always have is like, I can very much understand. You know, these models are trained on all the code ever written. Let's call it, right? So I can understand why they're able to generate code, which is the syntax piece. Um, but then like for, you know, you're doing core infrastructure. Let's say you're doing distributed algorithms, like, like, like the properties, like how it handles itself when it's running is very, very different. Like often you just can't even look at those, the syntax to know what's going to happen, right? Like a lot, let's say, let's say you're, I don't know, you're, let's see, you're writing some distributed system. And then like the way that it, like, it will perform the, the availability, um, the correctness even is going to be dependent on what the workload looks like. You know, like, like what are the access patterns? What is the traffic? Are you talking about like the performance aspects of it, though? Or are you talking about like the, like availability, yeah, you may have to lock things. So it's not available for a period of time. I'm talking about correctness. Like maybe you've decided to do eventual consistency. And so like if you do a read, it's not going to be consistent because there isn't a lock. And then performance, like, you know, maybe you cash, maybe you don't cash. So these are all kind of trade-offs in the design space, right? And like, unless you know, like the access patterns, you won't know how it's going to perform in these things, right? And so I just think to myself, okay, I've got an AI, I can understand how it will write, you know, like code for a generic distributed data store. For example, but I don't understand as how it will know to like make the trade-offs needed because it doesn't even know what the access patterns are. And it's not like these things can like, like where's even the data to learn how to like build a system that understands like all of the operations, right? Today, we rely on human beings to like manage clusters and to like, you know, optimize databases, et cetera. And so can you maybe talk about how you think of like I get the writing the code, but like you have these deep operational systems like, how do you play AI in that problem? And like, hey, have we made any progress at all? Or is this still an outstanding problem? And like to what extent can you take the things that you've described that are operational and like that happen in production and convert them into something that an AI can reason about? And exactly, like how can an AI reason about this system? Well, A, first off, like to what extent do you see that it writing in the code that it can actually predict what's going to happen? Like how good job does it do with that? And the second one is like, okay, now how do we make it reason about this? Well, this has the first one. Like, if I use your method, the JAD method of writing code, and I built some big distributed system, like how often does it actually perform its correctness? How often is it actually operationally like pretty sensible? And by the way, I work on this distributed Gaussian splatter, and I would say it's shit, like it's horrible. Like because like, you know, you've got millions of Gaussian splats that you've got to page in and out in stream. And like, it just totally depends on like the running system. And so like, none of the models do a good job. So in our world, what I've found is that we've defined basically really well what performance benchmarks look like and how the system should be drawn. And what the constraints of actually building within like our stream processor look like to get the right performance. But every once in a while, it'll do like my latest project has been all about query optimization and query performance. And so I was like, hey, classic examples. Yeah. I was like, we are basically generating code that corresponds to SQL. And we want to run it in a pipeline. And we're like, okay, well, you're generating shit code. Fix it. Yeah. And it started basically going through. And what it didn't realize was that it wasn't actually doing initially any optimization on the code. It's like, oh, that's fine. It looks like what's the problem. And I said, well, look, there is these these conjuncts over here. You're doing and then like this particular term is repeated. And it should have been taken away. And it's like, ah, okay, let me figure this out and so on. And then like it basically went on a thread to try to figure out what are all like it built a bunch of hypotheses around like what are all the things that it can go and optimize to make this better? Yeah. But ultimately it was able because it was able to ground that in performance benchmarks. And I was like, look, here's here's a data set of the queries and like what the users are asking and so on. Go work on it. And so it can basically do a relatively good job that way. But when it comes to saying something around like, how much memory should I give the system? Yeah. It has no idea. And that has to come from actual experiments that run in production. And so what I think actually these systems seem to be doing well at this point in terms of operational stuff is that it knows that hey, I probably won't be able to answer this question. Let me run an experiment somehow and come up with that information. But of course, when it comes to operations, like production systems don't behave the way that you expect and ultimately that's why incidents happen. So have you found, are you doing any of the AI SRE stuff or you actually have a running system that has logs and you feed it to an AI and that kind of helps you tweak it? Yes. And how well does that work? So we're just, is that anecdotally my experience with other people doing this has not been that great. Like, of course, I've been getting anecdotally, not been great. So a couple of things. The first one is we're doing that for both operational pieces and for security. But the way that we're tackling it is not the way that other people are doing it from an AI SRE perspective. A lot of those systems basically start from a lagging indicator, like, hey, this metrics spiked or like this alert happened. Go back through a very complex system and try to determine why that happened. Like, that's why those don't work because like backtracking into a root cause is actually very problematic. And so instead, what we're doing is doing it the other way around, which is, can we actually start by figuring out if there's something different in your environment that has changed? Like, can we do a large scale clustering, a large scale, kind of figure out a needle and a haystack, something that is different? And that was, you're assuming that it was working fine before and now it's not so something happened. But it could be just like a change in like normal traffic patterns, right? That's fine too. That's fine too. And so the thing that we're looking at is like, when things go wrong, usually the system will tell you something went wrong somehow, right? If it's working fine and scaling as normal, nothing will happen. But sometimes you'll see some like message, which says error connections ran out or memory exceeded or resource buffer is emptied or like some kind of indication, like some kind of really early warning, like something happened, and that's the one message that we want to capture and then go from there and see, is that an actual problem that we need to investigate and deal with? So how does it do? So far it's doing pretty well. I mean, we're running it internally in our systems. We run it in a couple of our customers' environments. And in the cases where there was a couple of cases where we ignored it, and those turned into production incidents. Really? Just from an intuitive perspective, like I can understand how these models understand syntax because again, like I said, they're trying out all of the code ever written and it just turns out that it covers a lot of useful cases. It's hard for me to actually even think of a bound on the actual operations of software, right? I mean, it feels like, you know, this is a ridiculous statement, but it's actually kind of true. It's like every time you add a bit to a program, the state space of like, when it can do operationally in pieces by a factor of two. So you very quickly get to like, you know, like more, more, you know, states than atoms in the universe. And arguably, this is why, this is why like product approaches to operations have never really worked, right? Like, at the KS engineering stuff never really worked. And we've always kind of relied, you know, actually the dirty secret for the longest time of running like a distributed cluster was just human beings managing it. And so I'm just wondering, and then, you know, and then in order to like, even train an AI to do it, you have to have like enough data and enough configuration. And so like, I'm just kind of curious, like, do you think we're at a spot where you could like, use AI to run a distributed cluster or run one of these services or do you think that's still on the come? I think that there's enough to be able to have it start that loop, right? Like, I mean, ultimately, like, I think that I agree with you, like, there's infinite space. There's a lot of different things. But ultimately humans also operate on runbooks. And like, sure, there's a lot of things that happen in the, in production that you ultimately need some human to go and like, hey, this is too complex. There's like some state that's hidden that I know of because I talked to this other guy, like at the water fountain by the day. And he told me about this thing he deployed. Like, there's a lot of stuff that potentially isn't visible in the state of the system. But I'll argue that like, the goal of what we need to be able to do is to like get to have enough telemetry and enough understanding to like make everything really visible. From a, hey, here's what the system looks like, here's how it's working, here's like whatever. And then I think ultimately most of the issues that end up really showing up in operations, assuming you have the system previously running okay, is due to some kind of change. And if you're able to just focus on the goal of figuring out what the change was and like what, and kind of going through, it sets a stupid question on this, which is I never think of these generative models that could have predicting stuff. And it sounds like you're talking a lot about predictive modeling, like you've predicted change or you baseline and you detected it normally. Like am I thinking about this wrong? No, you're not. Because the goal is not actually having the agent try to predict, but have it get a signal that something changes. Oh, that's deterministically. That's something I see, I see, yeah, so there is a separate model that does the predictive stuff. And then that will alert the AI. Yes. Okay. So, yeah, so like what we do, like what we do is we basically, you give us your petabyte of logs per day and we scan all of them and we like every time we see a new kind of pattern emerge in your logs and use thing that we haven't seen before, we lift it up to the AI. So that's a predictive model that actually does, like does what predict your models are good out, which is determining out of norm things or whatever, whatever, exact like the, and it goes to the AI to the AI kind of uses that to figure out what to do. Exactly. So the AI basically can then reason and drive that down. I see. So it can say, oh, this message, let me actually look at the code and figure out where it comes from and understand what the code does. Let me see if it's actually happened before. Let me see. So it basically runs all these hypotheses and heuristics that try to understand, does it make sense? Does it mean, meaningful in some kind of way? And then it can determine what to do with it. So how far away do you think we are for, let's just go back to query optimization, not like logs or something like. So how far away do you think we are from having an AI basically do the job that a human used to do when it comes to a query optimization? I think very close. Really? Yes. Wow. I think running a distributed cluster, I mean, I just think like, okay, you've got like James Cowling, PhD from MIT, who runs a distributed cluster and like, if the cluster is down, like the company doesn't work and then if it's up it's running. I don't know. Like, are we at the point where you would say that an AI can do that? No, not yet. No, okay. I think it's, I think, I mean, I think like, like, very, very simply, when we started doing like AI for coding, it was autocomplete. And nobody really knew to what extent you can actually build complex software using AI. And like when we were looking at it, we're like, AI is never going to be able to build X or Y or Z or whatever. And then it basically broke through the expectations because we were able to model like more and more complexity in the systems. I just, I just feel like I'm a very simple thinker on this stuff and to me, these models are distribution and distribution out, and it turns out like you can actually train on all the code ever created and it's actually like that manifold is pretty manageable. And I just don't know operations has constrained in the same way. It's that feels to me more like kind of like the universe, like heavy tailed and exception based and power laws and all the tough stuff for these models to learn. But I don't, I don't know. I mean, you're the one that's in the field. So maybe that's not the case. Maybe they're manageable. But I will, I will tell you the history of systems as you know is, is you always want to build a smart algorithm that does the efficient thing, the smart virtual memory management, the smart congestion control, the smart routing. And what do we do instead? Every single time. What we build up. We overprovisioning. We know we overprovisioned every time. No, literally, every single time like, like, remember like swap memory when you like swap to disk. If you did that, you were fucked. Like that's how your computer was dead, so like we live in overprovision RAM. And like routing, you know how many like adaptive routing algorithms are like, we will respond to traffic. But you have oscillations like, fuck it. We overprovisioned. We just used UCMP. You know, so I just feel like the history of systems is that you don't predict stuff you overprovision because you can't. And I feel like, I mean, you know, so I feel like in the operation side. So either we build this, you know, we will build the systems up front that are overprovisioned so you don't have to like do kind of complex operational stuff. Or maybe AI can do it the way that a human being. But I think it's very different than codecs you're dealing with like exceptions. There's not a lot of data, et cetera. I just feel like a different domain, but I don't know. I'm a VC. I'm just like, you know, I kind of like the reason that I'm more optimistic here is because I live in the world where like what we do is actually deal with the observable work. Like I was going to say, you're the literal expert on this. So I was silly for me to even like ask you these questions. No, not at all. It's very like in my world, we, we like my assumption is that we have all the data. Is that like we know exactly when like. a new thing happens, or like an exception emerges, or like a workload changes, or like whatever. Like in my world, all of that has already been collected and streamed through, and like-- - Yep, but the model that is not trained on a grounded source for that stuff. Like, no, no, you're not. - That would be really hard. - Right. But the story here isn't like, hey, Mr. Claude, here's a bunch of logs is something going on, right? And it has to be much more able to run experiments and kind of dive in and see like, what are the different shapes of possible causes, chains that actually fit in the environment that exists. - I see. So yeah, this is what you said before. Now, now I see what you're saying. You're saying that like, the universe is heavy-tailed. The number of bad things that can happen is probably an exception thing. But like, the set of steps used to debug it is a management thing, that's what you can, you can teach one of these models, and that's exactly. - Exactly. - And therefore, if you give it the right signals, they can go through a number of steps to actually constrain this problem. - Yeah, exactly. - Interesting. - Exactly. And so what I really do is taking the role of the human, 'cause remember, remember the whole chaos engineering wave or like-- - Yes. - And the reason it didn't go anywhere is like, you're very so quickly like, kind of like, you know, reduce like a state space that was atoms of the universe. Like super quickly, like it actually makes that manageable. And it was like the human being that would go through steps that would do that on the actual debugging. And this is what you're saying the AA would do. - Yes. - Right. So the goal isn't to like, have the model just be like, this is what happens. It's, hey, run through this workflow to determine what happened. And like we're trying that also on the security side, for example, where like, hey, in security, you have something different changed in your like, districts, like some behavioral difference in terms of what entities exist, the cardinalities of various things that exist. And so that becomes a perturbation and whatever the universe. And so we take that signal and we feed it into an AI and we're like, hey, AI, come up with a bunch of possible causal chains that exist. And then go through and verify if any of those fit the pattern of the data that we actually see. And so that becomes a much more like, hey, here's like, I don't know, like, fit this square into find the peg or like find the hole that fits this peg. - I mean, that seems even harder to me 'cause the operations one, the argument is, the system can't get into broken states that are kind of like very rare. And there's a lot of those rare states. And so it's just not something that you could, you know, train a model on just because either so many rare states that can get into that are bad. And so maybe you can reduce that by doing the steps. The security one seems even harder because this isn't just like the natural order of rare states. You actually have an adversary that is trying to, like intentionally find rare states and intentionally find stuff that you can't do. And so it may be the case that like could become solved, it takes a little while, you know, a lot of you do operations and it take a lot of time. - Yeah, but like also like, think about, I mean, I don't know, to me like the whole world is sort of quantized to a certain extent, the plank length. (laughing) Is that what the physicists say? - I don't know. - I'm not a physicist on stuff. - I think there's like, actually, I literally don't know what I'm talking about right now, but I literally actually think that like, like space time is discretized into a plank length. - But yeah, so I mean, I agree. To a certain extent, what you just said, but for operations and systems in general, like there is a discretization of like, the possible states and the past through them that are possible. - Yeah, right. I just think when you're dealing with-- - There might be a ton of, there might be a ton of them. - There might be a ton of them. - I'm gonna say, remember Sundar was in our grad school group. - Yeah. - Sundar, yeah. - Oh yeah, yeah, yeah. - So I remember he came to me once and he was looking very sad. He's like, my teen, I just had this really sad realization that, you know, if you take any MP3, I remember MP3, he's like the music for the ass. He said, MP3s have a finite, let's say, you know, four minutes. It's like four megabytes, you know? And that means there's a finite number of songs in the universe that can, no, because, you know, because it's all made of bits, right? And it's four megabytes. That means you can just, you know what I mean? Every one of those concerts I'm so sad in this notion of like the find eyedness of music, right? This is like, we're walking on the dish when he had this conversation. And like, motherfucker, dude, you know how quickly you've like overcome like the atoms in the universe, you haven't even gotten to a cane, right? It's like two to the two to the two to the two to the two. Like, what are you talking about here, man? Be happy. - Two to the two to the two four megabytes. - Four megabytes, what are you talking? Like, there's so much to say, like, be happy. That's a fact to the infinite, right? Like, what are we talking about here? (laughing) So I do think in some level, like we do underestimate like the number of states that like, you know, systems can get into it. Like, maybe, maybe, I will tell you from the perspective of the VC that looks at these companies, the ones that are trying to tackle operations are having a tough go at it. It seems much, much tougher than those that are tackling the code, 'cause I think it's a much tougher problem. - I, a hundred percent, I agree. It is a much tougher problem. And it's not just because like the systems themselves are, you know, there's like external loads that are random that kind of happen to you or that, you know, somebody just shipped a new thing and rolled it out into production didn't tell anybody or like, there's some state that actually happens when like, this thing happens and this thing happens and then you end up in a deadlock. - Yeah. - It's also because it involves humans a lot more. And not just like one human who's the coder, it actually also involves pools of knowledge are distributed too. - Exactly. And like the interactions between like what it takes to like connect the pieces and put them together like. But ultimately, I mean, I think about like where AI is and like what are the possible futures and like the depth and the forward looking capacity of all the things. Like I think code is just, to me it's a microcosm of like maybe I'm just too fatalistic. Maybe I'm just like too pessimistic about how the world works. And I think of everything as like following some kind of probabilistic path. And so to me like if we can make it work in code, we can continue expanding that level of complexity bit by bit and kind of adding layers and layers and layers until we can handle those problems of operation. - A lot of people, it's not fatalistic 'cause the opposite is very optimistic. 'Cause when people would say for me, I don't think the universe gives up its secrets that easily but like we'll see. I mean, like this is kind of the big question. And I would say as yet it's not an answered question. - No, yeah, no. But I don't know, a lot of the things that as humans, we think of as being random or impossible structure. And that's what we saw with coding. Like initially like people thought like the possible world of all these things that you can implement in code are kind of limited and I can't really get a system to potentially understand the full complexity of a human brain that's required. And I think over time it'll all, it's so funny that I'm actually telling you this and you're like the VC who's looking into the future and being like, no, well, I mean, I'll tell you my view. My view is like the study of these models are, you train on data and either that, I mean, the, let's try to finish it. So you train on data and that represents a distribution and that distribution is just for or not. And so actually, when you train on language, the fact that it's useful, that to me says that humans are so remarkable, 'cause they've looked at the universe which is a very high dimensional system. And then they've reduced it to a manifold that's actually useful for a model, right? - Yes. - Right, but that's different than the universe. Like that's actually, there's a reduction from this high dimensionality to the language is less dimensional. And I think that code is a reduction of all the operational states of a machine until like the structures that make it run. And so to me, there's still a disconnect between like the instructions or the description and the actual like the actual running of the universe and actual running the system. I think we can make gains toward it. But what I will say is like, you're kind of converging on simulation. Like there are things that are computationally irreducible. Like you're gonna have to run it to get the answer. You can't just like, you know, predict it. And so like maybe we just are at an event horizon where like models will solve anything that you don't have to actually simulate to get the answer from. - But why do you have to like just think about it in terms of hey, it'll have to answer the question like now as opposed to being able to simulate and sorry, being able to plan its own execution to gain the information that it needs in order to be able to-- - Yeah, maybe that's where we're going. Yeah, that's kind of what I'm fair enough. - And I think like, there's a lower dimensional space, which is like the step that you would take that we can reduce a whole bunch with AI. And then whatever steps that you need that that can't do, it can create the code to do or it can simulate it. - And the point that you're making though is that like, hey, look, the model can't actually go and figure out the additional dimensions of the world that we need to go find to like model the universe into its world bigger. That's probably, I mean, that's probably true, but can it actually simulate a scientist sufficiently to be able to get to that point? Like maybe, maybe. Cool. Can I ask you a question? How much of the VC work do you think is something that an AI can drive? So my model for VC is its very relationship driven? It's, I think, so it's okay. So I just have a belief that markets are actually pretty efficient, you know? And so it's not about like finding the company that nobody else finds. I think people all find like the right company, I think they're efficient. And then I also have the model VC that, you know, tech is positive and I'm kind of optimistic about all tech and I don't worry about TAM. And so I think the key is access, it's like raising money, it's access, it's a ability to win deals and it's a ability to add value after that happens. And a lot of that is like stuff that that startups just can't naturally do, like, you know, recruit, you know, big execs or help with that. And so the way that I think about venture, you kind of have to be a human being to do. It's like, you know, because, because that's the actual job. So for that, I don't think like an AI, given AI simulated universe where it could like, you know, like whatever reward hack, like building trust relationships fine. But I think that that would have to be to be that said, I will say, I have seen an analysis done by AI that are as good or better than any, you know, genius of ever worked with like so for the analysis piece, they're great. But I think this is more than indictment on VCs. I believe like analyze that it is like how powerful the models are. I think of all a Kabuki show and bullshit. I just think the right VCs are like, you know, you know, tech is is is net positive. You know, tech waves always add value, like let's find the best company and invest in it. The markets are very, very efficient. And so I just feel like the answer to this question is almost kind of a technical like my answer is almost a technicality. So how like, you know, one of the things, you know, I came here, I wanted to ask you this real, this question, which is like, you know, we started talking specifically about ops. Yeah. But to me, like the, there's this world of AI writing code, which is kind of like this abstract simulation world that it lives in. Yeah. And then there's the rest of the world. Yeah, exactly. And the entire discussion is this. It's like, there's like this, there's like the AI, the world that the AI lives in, which is all digital and like whatever it is, the training day, the indisputing world. And this is the actual world. And like those things are not necessarily the same. Right. They're quite often not. And so the question is like, to what extent do you need to train an AI to be able to survive in a world? And like, what are the things that it actually, I don't want to answer that question. Like, what do you need to train? But like, I'm curious about the open spaces. So we talked a little bit about operations. You said, I don't know if it's going to be solved or not. I'm like, yeah, Martinez is going to be solved. And in my mind, like there's so many other things, like for example, self-driving cars, like for a long time, we thought that's not going to happen. But it's clear that it's actually going to be done. And it's going to happen. It's just taking time because of a lot of different things. And so what are the, what are the other next stages, the next areas that you think are kind of like lined up right after code? I mean, my sense is is, you know, AI is going to continue to take off in a wildfire for a constrained domain where the solution space is in a constrained domain, right? Where the distribution of the solution space is the same as the distribution of the training set. The distribution of the syntax for code is the same as a distribution of the training set. The distribution of the generated images is the same as the distribution of the training set. In those, you know, areas I think it's going to continue to take off as a wildfire. The domains where like the distribution of the solution is the natural world, I think it's going to, you know, have, you know, it's going to be much harder to take longer. And I think the economic equivalency is going to be much more difficult. Self-driving is a great example. I remember when Sebastian Thron won the DARPA Grand Challenge when I was at Stanford. Remember that? And we're like, "Hurray!" You know, self-driving cars is around the corner. Seriously. And it took 20 years and $170 billion of investment to get to the worst unit economics than Uber, right? So that's, you know, so I think that like solving, like, like solving real problems where you have to deal with the distribution of the real universe is just much, much harder. And so I view as a as this general tool. It's a very effective tool. It's one where like, you have, you know, a distribution of data and then hopefully you can apply that to like, you know, some, uh, problems and ever go apply to a bunch of stuff. And I think economic equilibrium is going to determine what works and what doesn't work because I think it's a very, very general tools, like a universal function approximator. And wherever that nets out, I mean, again, you know, one of the things that I said earlier was that how AI was training humans to like use it. Yeah. And I very often think about like, you know, some of the problems that seem intractable was because the world is kind of that way. Yeah. And then we're trying to fit AI or the tools that we're building to manage the world as it is. Yeah. But in some situations, we actually tend to change the world to fit to what we can actually do 100 percent. Of course, I think this is the sort of rebuttal. Yeah, exactly. 100 percent. Yeah. The automated data warehouse. And so the question, like that always comes to my mind, like when we were looking at these data warehouses and whatever. And like the only one that worked was the one where it was like, you know what, just automate the whole fuck. Yeah. And, um, and I wondered to like what extent as we kind of improve or change how we, or kind of expand the domain of what we want to solve with AI tools and so on, that we actually start modeling the world or changing the, the world and the things that can actually happen to fit what AI needs it to look like. Yeah. I think that right now a lot of AI just operates in the digital world. Yeah. And for that is just like literally the solution set is the same distribution as a training set. Yeah. Okay. In essence. So that's fine. And I, I think that, again, like in the case of equilibrium, like I think like when you're dealing with the real world, like you can solve any problem you want. It's just a matter of like compute and money. And that the equilibrium point is going to be an economic one. And I think one way you can impact with that function that you're doing equilibrium over is to constrain the environment, to constrain the system. And so I think that we're going to, like we're in now in this meta economic equilibrium flywheel where we're kind of optimizing. This was super general, the very inefficient process with a solution set that people value. And I think you've got 30 years of this and a lot of values going to be created. You know, one, you want to hear a funny story. Yeah. So, you know, we built this AI thing and we're like talking to people. We're like reaching out to customers. We're like, Hey, we built this AI thing. You know, we're helping customers become more liable. Here's like what we're doing. They're like, great. We want to talk to you. And we go on the call and we're like, Hey, here's what we do. You know, we built the system. It helps you optimize your data. It helps you reduce your costs. And then it helps you become more liable because now we can find these problems and solve them before they hit your incident. They're like, wait, wait, wait, roll back. You said you can save 90% of our data dog bills. Yeah. That's what I want to buy. But the AI thing, they're like, no, no, no, that's what we want to buy right now. And it's interesting. Like a lot of these, like for code, you know, I always think about is like, Oh, for code, people just get it. They're like, Oh, should it just works? Yeah, yeah, yeah. And on the upside or like anything that's beyond that, yeah, it's very tall to understand the actual value and put a dollar value to it and be at the dollar 100%. And I also think from the coding standpoint, it literally is engineering productivity, which is one of the highest line items on these things. So it's just very straight. Exactly. But you can also think about it. It's sorry productivity or it's like your business going down. Maybe you can save an incident cost. I don't know. It's, it's weird. I think like how people think about it outside of the, yeah, I also just think like like this code was so magical because it was so good. And nobody thought that could happen. And I think the operation stuff now, it's still like pretty early. They're still pretty early. So yeah. All right. Well, listen, good chat. Thanks, chat. Thanks. Thanks. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our substack at a16z.substack.com. Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the company's discussed in this podcast. 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