“Relative to AI agents everything looks stagnant, because in the history of computer science there's been nothing like that before. It's like taking a cheetah, giving it a bunch of cocaine and putting it in a Ferrari. The amount of speed that people are developing these things is insane. So everything looks slow or dead or stagnant to that.”
The MAD Podcast with Matt Turck · AI Research & Frontier Labs · October 2026
The host suggests the database market has slowed down after the burst of activity in the 2010s. Pavlo answers that the field has been through consolidation cycles before, and that it only looks stagnant by comparison with agents. He goes on to say he doesn't expect agents to force anyone to throw out the relational model.
Transcript
I mean, you can always build new data systems for new hardware. But my track record on this is terrible in terms of like, we've done a bunch of research on experimental hardware and it always gets canceled. Or like, it's even not that experimental. It's like, you know, Intel had this optane persistent memory stuff. We did my, we did a bunch of research on building systems for that. Because like, if you assume now your DRAM is persistent, you know, like you pull the plug and you don't lose anything, that changes how you fundamentally build a data system. We did a bunch of work on that. Then Intel killed that product line. We were doing another research on processing and memory hardware. So think of like DRAM sticks with like CPU cores directly on the DIM. So the data system now can say, okay, instead of pulling things from memory, printing into my CPU caches, and then I can compute things on them, I'll just send the query down to the DIMM itself and run it there. We were doing a bunch of work on this on this thing called Opmem that got bought by Qualcomm and got killed last year. So that didn't work out. So there's always a bunch of work you can do on data systems or new hardware. I would say that actually, I don't know the answer, right? This is one of the things I'm trying to figure out at Click House is like, you know, we talked about this in the very beginning. Like, are agenic workloads significantly different than what humans or what existing applications do now? And if so, why or how? And could you, how would you change maybe the development of a data system to take better advantage of this? That remains to be seen how that works. I think there's always a bunch of problems in query optimization. I think they're interesting. That remains the hardest part about database systems. Incremental memorialized views, another big challenge. Again, but these are not like things that no one else has thought of. People are trying to do these things for decades. So I think the agenic stuff is probably the most interesting and relevant thing to me right now: what changes with these workloads? What changes in the system architecture? And to be honest, I don't know the answer. I just haven't seen it yet.
I'm asking because it generally feels like the database market is a specific moment in its history, meaning that there was an explosion of activity in the 2010s. There's SQL versus NoSQL, that all evolution. Then there was the emergence of Databricks, Snowflake, and now Click House. But it seems that things have slowed down a little bit in terms of explosion. I guess, you know,
stagnant. Yeah, no, but we've been to this trend before, right? There was a lot of activity relational databases, 1970s, 1980s. And then the 1990s, again, people sort of, you know, the market sort of solidified around these major enterprises, the Oracles, the, you know, the IBMs, Teradatives. And then, you know, if you, if your only viewpoint of databases were from those kind of companies, then yeah, it looked like it's been stagnant for years. But as you said, a lot of activity into 2000, 2010s. I mean, relative to AI Agents everything looks stagnant because there's in the history of computer science, there's been nothing like that before. Like, it's just like, you know, taking a cheetah, giving a bunch of cocaine and putting in a Ferrari. Like the amount of speed that people are developing these things is insane. So everything looks slow or dead or stagnant to that. But at the end of the day, I think the volume is going to matter a lot. I think that one interesting question is, to your point, Of like cost and efficiency, like squeaking out the best you can, the best performance you can get for the hardware that you have, trying to reduce that cost, that's always an interesting challenge that could pursue. But like the end of the day, I don't think there's going to be a massive change in what data looks like that requires us to throw everything away that we've known about databases. In the same way, you wouldn't come up with a new notion of arithmetic or mathematics to replace one plus one equals two. The relational model itself is the foundation of how you want to represent data. And it's just you can vary the implementations of that. And so there's certainly a lot of work to make that these systems more efficient for this. But I don't think you're going to throw everything away. And like, you know, the agents need some kind of database system that you've never even conceived of now. At the end of the day, it doesn't make sense. So I don't think that part changes. I think, like I said, always new hardware. I think there's certainly improvements that could be done for SQL. There's always going to be people trying to replace SQL. I think that might be a lost cause. Although SQL could end up being like how in the same way that people don't write assembly anymore, SQL might end up being like that because text to SQL works so well. I think the agent stuff is super interesting. And I think getting better performance is always going to be a from my perspective about fun challenges and things we can pursue. You can imagine a crazy world where you say, I don't need a general purpose data system anymore for every single application. I want to vibe code exactly a data system that does this for this one thing can then be hyper specialized. You kind of do this now with code generation or just time compilation for some aspects of queries. And some systems do that. Click House, Postgres, Umbra from the Germans. But like hyper specialization and making that be sustainable might be a bigger research question going forward. So
it's this, I don't know if it's a paradoxical kind of situation, but on the one hand, it's a bit of a stagnant industry right now in terms of evolution. At the same time, as we've hopefully established through the conversation, the layer itself is as important as ever, which is one of the reasons why your friend Larry Ellison is the always close to the richest man in the world or was it. He's down
as of today. He's back in eighth. I mean,
Oracle is a lot more than just a database company, but it's still the core program. I mean, the
More from The MAD Podcast with Matt Turck
