Databricks CEO on AI Pacing, Cyber Risk, and the Enterprise

Databricks co-founder and CEO Ali Ghodsi joins a16z General Partners Martin Casado and Sarah Wang for a conversation about AI risk, recursive self-improvement, cybersecurity, and what’s actually holding back enterprise adoption. Ali argues that today’s models are already capable enough to automate far more work than most companies are using them for. The bigger problem is context: models haven’t been in every meeting, don’t understand how decisions actually get made, and lack the institutional knowledge that experienced employees accumulate over years. He explains why building an organizational “ontology” could help close that gap and what Databricks has learned from doing it internally. They also debate the current conversation around pacing frontier AI, what would constitute meaningful recursive self-improvement, and why Ali distinguishes speculative superintelligence risk from the much more immediate challenge of AI-powered cyberattacks. They close with how enterprises are managing exploding AI usage and costs, the shift toward multiple models and harnesses, and why agents are beginning to reshape infrastructure itself.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Ali Ghodsi?

  As a business leader, there's a tragedy of the commons. If you want to stop, if you want to go slower, why don't you go slower? Like, I'm competing. I want to win.

• 0:07Martin Casado?

  There's almost two camps. There's one camp which believes that this actually is an engineering problem. And there's others which actually believe you have to slow it down.

• 0:16Ali Ghodsi?

  Humans don't respond fast enough to the attacks that are happening. You need to automate all of those. And most organizations are actually not exposed to doing that. Is RSI and recursive self-improvement that the labs are doing leading us there? That's the big question.

• 0:29Sarah Wang?

  Something Elon said, this is some elaborate 4D chess. Because on the one hand, you're saying all of humanity will die. On the other hand, you're saying, hey, what do you want for your IPO allocation?

• 0:38Martin Casado?

  For the first time ever, a company at scale last week said that they're moving from the frontier models to GLM. Do you think that that's a trend or do you think that's just like a one-off anecdote?

• 0:50Erik Torenberg?

  AI may already be smart enough for the enterprise. The problem is that it doesn't understand your company. Databricks CEO Ali Ghodsi joins A16Z's Martine Casado and Sarah Weh to discuss what's actually holding back AI adoption and why the answer may have less to do with building smarter models and more to do with giving them the right context. They also debate the push to pace frontier AI, recursive self-improvement, and where the risks are real today. Ali argues that superintelligence remains far from what we currently see, while cybersecurity is an immediate problem as agents make attacks faster and harder for human security teams to keep up with. And they get into what Databricks has learned using AI internally,

• 1:32Erik Torenberg?

  from building an organizational ontology to managing token costs and choosing different models for different jobs.

• 1:41Sarah Wang?

  Thank you for being here, Ali.

• 1:43Ali Ghodsi?

  Super excited.

• 1:44Sarah Wang?

  So we obviously want to get to Databricks, but there is a broader conversation going on right now about AI. And Dario's weighed in, Jakob's weighed in, Elon's weighed in. But we want to hear what Ali Ghodsi thinks. In terms of, you know, if you called the topic broadly speaking pacing the frontier, et cetera. What is your strongest agreement with what's being out there? Where do you disagree? And maybe where is there nuance that's not being captured?

• 2:12Ali Ghodsi?

  Yeah, happy to cover it. And me and Martine argue a lot, so I'm sure that's not going to take long.

• 2:18Martin Casado?

  We'll try and rate it in this time.

• 2:20Ali Ghodsi?

  Try to stay calm. But, well, I do think first and foremost that there, maybe we agree on this, that leaders have responsibility to not freak people out unnecessarily, unless there's really, really good reason. And I think, you know, there's always different people in society that are at different places, you know, in their mind space. So, you know, talking about these kind of existential risks and, you know, scenarios where all of humanity is going to be wiped out, I think is irresponsible. Like, it can tip a lot of people over and it can cause a lot of mental health issues.

• 2:51Martin Casado?

  Unless you have something that's going to wipe people out.

• 2:53Ali Ghodsi?

  Yeah, as I said, yeah. If there is an actual reason for it, then, you know, that's a different story. But I think that right now, the existential risk is close to zero. So why freak everybody out? It's not actually needed. There are risks. We'll get into it. That's probably where we disagree. But first and foremost, I think that leaders should not freak everyone out. And I mean, you know, if there's like technical nuances in how we're doing AI research and so on, well, researchers can discuss that. You don't need to every time go on TV and/or blast on Twitter to millions of people that, hey, you know, I think there's like this percentage, 10% risk that all of humanity is going to be wiped out. I don't think that's like helpful for a lot of people. Actually, I think it causes a lot of harm for a lot of folks

• 3:36Ali Ghodsi?

  who get stressed out and actually are not in the nuances of all of this stuff and what it means. So that I don't think we should do. I don't think it's fruitful. It doesn't really help anyone.

• 3:45Martin Casado?

  I mean, I think this is very, very true for the general public.

• 3:51Ali Ghodsi?

  Yeah.

• 3:51Martin Casado?

  Like, my sister, who's great, who's a schoolteacher in rural Arizona, on Sunday texted me and said, "Martine, should I prepare the cabin?" For, she's kind of a prepper anyways, but should I prepare the cabin for the AI apocalypse? You know, I've got water set up. Like, when are you showing up? I'm like, hold on.

• 4:08Ali Ghodsi?

  Yeah.

• 4:09Martin Casado?

  Like, we're not there. So clearly, this has kind of spilled over the populace, which I agree is unnecessary and has blowback. I think there's a second one, which is, I don't know if you saw, like, walking in here, I was checking X, and Elizabeth Warren just talked about pausing all of AI development. That, of course, is on the coattails of Bernie, who is also working with Bannon, Steve Bannon, like.

• 4:32Ali Ghodsi?

  Yeah.

• 4:32Martin Casado?

  So now, so in addition to, like, you know, just scaring people, the federal complex is now spinning up. And I think that could be actually quite contrary to the actual goals of the message. And so there's more than just, you know, I think, public hysteria at stake here.

• 4:50Ali Ghodsi?

  Yeah, there's a lot of politics going on, but I'm like in all of these groups and, you know, I see both sides. There's heavy politics happening on both sides, we should say, like, right?

• 5:00Martin Casado?

  Oh, yeah, yeah, yeah.

• 5:01Ali Ghodsi?

  This is happening on both sides.

• 5:02Martin Casado?

  No, this is, no, this is, I think both parties that do not include Trump himself agree that AI should be constrained at some level.

• 5:13Ali Ghodsi?

  I'm talking about the other side of this argument as well. Let me give you an example, okay?

• 5:16Martin Casado?

  Even Greg Abbott, right? Like, even Greg Abbott was like, you know, you can't have data centers in Texas.

• 5:21Ali Ghodsi?

  Well, I'm not talking about politicians. I'm talking about there's politics going on on both sides, right? There is politics on the business side, people who want to see great IPOs, and they want to get returns on their investments. And they're like, don't mess up my IPO.

• 5:34Martin Casado?

  Yeah.

• 5:34Ali Ghodsi?

  And they want to get like, hey, can everybody just shut up so that we can get our money back? So there's that. And they're, you know, they have resources and they're using them. And, you know, so there's politics on that side. And those are like not, they're not sitting quietly and not doing anything. And they can pull strings and they have connections. On the other side, there's all the people that are like, okay, how do we weaponize this? This is awesome. This guy tweeted this. You know, let's like, let's weaponize this one. Let's plant this. If I, you know, let's pump these, you know, threads.

• 5:59Martin Casado?

  But let's talk to the specific leverage point that everybody is using. Because I actually think that this is like a classic case of a PR misstep. And it's not just the doom and gloomy type stuff. So here's the PR misstep, I think, which is, like, it is not unusual for industries to try and regulate themselves. It's just not, right? And I think saying like, security and safety is important. It is with every techie puck, and we want to have some oversight. That was very, very sensible. The problem is, is just couched in this notion of pacing. And there's a number of issues with pacing. First off, it's orthogonal to safety and security. Like, you can slowly build a weapon. It's not different than building a weapon. People don't feel it's genuine because like these companies have been at a dead run.

• 6:42Sarah Wang?

  You're still buying more compute to be even faster.

• 6:44Martin Casado?

  No, no, I mean, they just haven't done it, they haven't done it historically. But also, it kind of feels like this kind of almost milquetoast capitulation to the pause people. So you're like, well, you say pause, well, I say pacing, which is almost like pause, but it's not like pause. So like, they chose, they chose this kind of like flag to follow around pacing. But if you actually read, did you read the document that I wrote? It's a totally sensible document.

• 7:09Ali Ghodsi?

  My AI read it, yeah.

• 7:09Martin Casado?

  It just has nothing to do with pacing, right? And so I honestly.

• 7:12Ali Ghodsi?

  No, he does mention it. Look, I kind of a little bit disagree. Look, there's a tragedy of the commons. There's this like, hey, if you want to stop, if you want to go slower, why don't you go slower? Why do you write articles? There's a lot of people making that argument.

• 7:22Martin Casado?

  Yeah.

• 7:23Ali Ghodsi?

  But no, I mean, as a business leader, I understand that. There's a tragedy of the commons. Like, I'm competing. I want to win, you know? And you're running ahead, I'm going to.

• 7:29Martin Casado?

  There's also the market equilibrium, which suggests that pacing is probably impractical anyways.

• 7:33Ali Ghodsi?

  Yeah, so I'm just saying that, you know, so it makes kind of sense where people say, hey, if you guys don't stop this, this tragedy of the commons is going to continue. I'm not going to stop racing because, you know, there's IPOs at stake. There is a competition at stake. There's also some animosity between the people. So like, I'm not going to stop unilaterally. I'll be a sucker, you know? Why don't you stop first? You know, so then they're saying, hey, can you come in and stop us? But, you know, I think that you could also make the argument that if you look at the Hugging Face OpenAI incident, that, by the way, I think these companies are great. And I think they are probably investing a lot of resources.

• 8:04Martin Casado?

  No, I agree.

• 8:04Ali Ghodsi?

  But it's very clear from, if you read what happened, is that they weren't monitoring every token coming out and having it, you know, they were just like running these RL experiments. And then after the fact, coming in and checking out what happened. So they should have paced. They should have been much slower in that particular incident, right?

• 8:22Martin Casado?

  I just don't want, I don't want to just quibble on syntax. But like, words matter with PR, right?

• 8:29Ali Ghodsi?

  Yeah.

• 8:29Martin Casado?

  So let's take the Hugging Face incident.

• 8:31Ali Ghodsi?

  Yeah.

• 8:31Martin Casado?

  When I read that, you know, my reaction was not, oh, OpenAI should pace. It's like, dude, fucking secure your thing, right? Like, do security controls like we always have done.

• 8:41Ali Ghodsi?

  It is pacing, though. It is pacing.

• 8:43Martin Casado?

  It's not pacing. Like, in the history of the internet, we had all of these things. We're like, let's pace the growth of the internet.

• 8:47Ali Ghodsi?

  It is pacing.

• 8:48Martin Casado?

  Let's do security. Let's do control. Let's do like whatever. I think you could.

• 8:51Ali Ghodsi?

  You should do that, but it is pacing in the sense that, look, I face this all the time. I have a legal department at Databricks. I have a security department at Databricks. And, you know, they are always like, hey, slow everything down for everything, not AI. Like, literally every little thing. Like, oh, you're going to go on a podcast? Well, what's the script for it? What are you going to say? And, you know, let's review that. And, you know, what's the legal? You cannot say this. You can say that. You can say that. You know, everything you say has to be materially true.

• 9:12Martin Casado?

  Are these pacing people in this room with us right now?

• 9:15Ali Ghodsi?

  So, you know, so you're running an RL experiment. You're training the next model. Should the security team be there and look at, like, run all their monitors and look at everything? I mean, like, millions of hours of GPU hours of tokens were produced. And these agents were running, you know, a mock in the sandboxes. It would have slowed them down significantly if you had a security team sit there and look at all of the stuff.

• 9:32Martin Casado?

  I agree.

• 9:33Ali Ghodsi?

  Now, and they're saying that, hey, like, you know, if we do that, it'll slow us down. And I'm not sure the other side is doing that. So can you guys come in and slow us down? Like, just tell us. Like, put some guardrails around us. We'll happily then follow the rules and do the secure thing. Otherwise, it doesn't make sense because we'll get our butts kicked.

• 9:49Martin Casado?

  I just think like nuanced second-order words don't work when like people are really afraid. You're like, I'm going to pace, and therefore, things like these don't happen. I literally think you should have just been like, safety and security is paramount. We're going to put in these controls. Like, that's the important thing. And I do think that nuance actually got lost.

• 10:04Sarah Wang?

  What kind of what Zuck said, do you agree with?

• 10:07Martin Casado?

  I thought Zuck did it.

• 10:08Sarah Wang?

  He was like, hey, we're going to pace ourselves. We're going to put in, like, the reason we release this later is because of security.

• 10:13Martin Casado?

  Well, I thought was so great about Zuck is like, he was very focused on like, security, safety, and self-regulation. Dario, Dario's first five words or whatever, like, we need to pace the frontier, right? It just puts you in a very different mindset than, what he could have said is, we need to secure the frontier. Fine, we need safety. I mean, like, at some level, I think they were trying to optimize both for the doomers, which caused for pause, and for politicians, and they kind of didn't satisfy either.

• 10:45Ali Ghodsi?

  But don't you think people are actually freaking out inside the labs? And there are a lot of safety people that are freaking out genuinely. By the way, and not all of them are EA people and so on. And people are like, hey, they're surprised, right?

• 10:55Martin Casado?

  Right, but here's the thing, is like, using the risk pace doesn't help. Either of them. I think it's like literally, you're like, you're like, you're trying to find this like he, he, he, pace is like the uncanny valley of making the Doomer people unhappy and the policy people unhappy. Because the Doomer people are like, that's not a pause. This is pacing.

• 11:11Ali Ghodsi?

  Yeah.

• 11:12Martin Casado?

  And, you know, everybody else is like, well, like this is, you know, this isn't real. You're not going to do it anyways. And, and you're not supposed to have security. So, so again, independent of what we should do, which we should talk about. I just think that the way it was presented was just bad and it just didn't work. And that's

• 11:25Ali Ghodsi?

  Yeah, so like, these guys are, you know, they're not trained, you know, PR people, you know, and yes, some of this stuff. I agree, I agree with, I mean, I agree with

• 11:35Martin Casado?

  I'm trained PR people.

• 11:36Ali Ghodsi?

  I agree with the core premise that we shouldn't freak the public out. I think that existential risk right now is close to zero, you know. But let's talk about the core thing, which is the, the fact that, you know, anyone who's doing big reinforcement learning runs. And they're giving it a reward function. So unleashing, you know, saying, hey, here's like a, you know, 10,000 agents and here's a hundred million dollars. Let's put them in peril and let them run on a gigantic cluster for a month or two. Try to solve anything and it doesn't need to be a security thing. It could be like, do anything, you know, solve this math puzzle. Really bad things can happen. Really bad things, meaning things get hacked and it has, you know, cyber is the primary one, right? That, that is real, right? I think of this making it, hey, this is an existential risk and so on, which I think was a mistake.

• 12:21Ali Ghodsi?

  I think it's not good to scare the public that way. I think it's become something that everyone, not just your sister, everybody around the planet is like now talking about. I've had all kinds of people that never care about this stuff and they find this extremely boring. Ping me and say, what do you really actually think about this? This is really important in my accounts. Now I'm starting to worry about it. So then it becomes a political issue and we have elections here coming up, but there's elections all around the world. So you're going to see, they're not going to sit still in other parts of the world either. But I think that's our responsibility to talk about this in a balanced way and actually expose the risks. I think that super intelligence, that idea from that book is very, very far away. I don't see any evidence that we're actually marching towards that or that's going to happen. Apparently some people, yeah, apparently some people at the labs are freaked out that maybe there's progress towards that.

• 13:06Ali Ghodsi?

  And I think it comes from RSI recursive self-improvement, the models improving themselves. I would love to understand how much, what have they seen something? We don't know. There's, you know, kind of four criterias. If there, if those four things are happening, I would love to understand them. One is our models, the, if, if we end up in a situation where following four conditions are happening, which is the next model requires less resources, less GPUs to train and, you know, super linearly, not just like tiny little bit. The next model, you know, it takes less time to train as well. So the second condition. Third, accuracy of the model, the intelligence is increasing. And fourth, we can do the former three again and again and again, you know, it's not just

• 13:47Martin Casado?

  All, all those at the same time, right?

• 13:49Ali Ghodsi?

  All at the same time, not just any of them. Yeah, all four. If all four are happening, then you can imagine in a way where you can, you know, because any of them does not happen, like for instance, if resources is constant, then that's okay because we're going to run out of hardware. So then it will pace itself. Like we will not have enough hardware to do that. Not enough GPUs, right? Time, the same. So it needs to be that you end up in this situation. So if it's, if you just mean that the software is writing itself. We're already there today, like 90 some percent of the software in Databricks is written by AI. Does it matter if the last few percent is also written by AI? No, it doesn't matter really that much. But if you're getting these four conditions, then you might get a speed up where the next model, let's say, takes half the amount of time

• 14:31Ali Ghodsi?

  and half the resources and it is more intelligent and you keep doing that, you know. Then you might end up in a situation where I don't, by the way, I don't even know if that necessarily leads you to super intelligence per se.

• 14:40Martin Casado?

  That can still converge, yeah.

• 14:41Ali Ghodsi?

  But it could. So then that would be more risky. So it would be nice if they can share all that data and we can shine some light and transparency on that.

• 14:48Martin Casado?

  I actually think it's a great breakdown that you have.

• 14:50Ali Ghodsi?

  I don't think anyone is using that as a definition, actually, right? I think people, I think there is a little bit of people freaking out about like, oh my God, emergent behavior. Now it's creating itself and so on. But I think like, as I said, a lot of people, their definition is just, hey, if I'm not even coding anymore and it's coding itself.

• 15:04Sarah Wang?

  Right.

• 15:05Ali Ghodsi?

  But I think they're conflating, hey, what's my value and is it scary for me versus, hey, that then means we'll get that super intelligence that 2014 theoretically was hypothesized by Botstrom.

• 15:18Sarah Wang?

  Well, your point in compute though is a really good one. That's missed in a, I think a lot of arguments on RSI, right? Because as far as we can tell, the minimum threshold for compute needed to train a good model just keeps going up. Like it was a hundred million, billion. Now it's probably like five billion. And so that

• 15:34Martin Casado?

  To train a model now?

• 15:35Sarah Wang?

  To, to train like a frontier model, right?

• 15:37Ali Ghodsi?

  Five to 10 billion.

• 15:38Sarah Wang?

  Right, right. Exactly. Versus like

• 15:39Ali Ghodsi?

  Million or billion?

• 15:39Martin Casado?

  Billion.

• 15:40Sarah Wang?

  Billion.

• 15:40Martin Casado?

  Billion, yeah.

• 15:41Sarah Wang?

  Versus your second point.

• 15:43Martin Casado?

  Very expensive.

• 15:43Ali Ghodsi?

  Yeah, frontier is very expensive.

• 15:44Martin Casado?

  To replicate the frontier six months later is about 120th the cost.

• 15:49Ali Ghodsi?

  No, I think Sarah has a great point, which is it's, this is a good argument against this whole thing, which is that the next model, first of all, there's only one or two such runs a year that each of these labs do. And they take, it's the opposite of the four criterias that I mentioned, right? Which is it's going to take more resources, more humans involved. And it's even more brittle and they have to build out the data centers. I mean, like the labs are not necessarily doing that. But others have to build the data centers and they have to be gigantic and they have to get the GPUs and they have to get the networking right. They have to do the engineering to make sure that they can tolerate, because you know, every order of magnitude more GPUs you cram in there, you have to now worry about errors that before you didn't have to worry about. So you have to increase robustness of that. So it's like a very brittle process. And if it fails, you've squandered so much money. So they're like very, very careful with that run. And there'll be multiple runs that have been watched.

• 16:33Ali Ghodsi?

  So it's, it's the opposite of that, that the, hey, the next model is faster, cheaper, smarter and recursive improvement. It's the opposite. It's like, it's taking longer and it's more brittle and it's more people and it's harder to pull off. So I do think that that is true. With respect to RSI, with respect to actually cyber risks and things getting hacked, we need to take it super seriously. Yeah.

• 16:55Martin Casado?

  So I'm, I'm going to have like another, like Miss Test here is like, I actually love your four criteria. I was like literally just waiting to argue with it, but I actually think it's

• 17:03Martin Casado?

  So I'm, let me, let me give you like, like a black box. What, like when you're dealing with these like dynamic adaptive systems, like what are you going to believe? Are you going to believe like the numbers are your lying eyes, right? So I think you kind of have to go to the numbers on these ones. So like, what are the numbers to look at? I really think you should just basically, and maybe going public is the right way to do it. Like, like if, if these companies continue to grow, reduce the number of people and the number amount of money that goes into them. Then I would say something is, is definitely happening here. Like I do think that, like you can actually black box this and take a look. But none of those indicate, like they're hiring like crazy, they're hiring like crazy.

• 17:39Ali Ghodsi?

  That's not fair because you know, companies are not necessarily efficient, right? So like, what if you have, I mean, OpenAI itself was doing like a million different activities. It's a very small team of like 10 people were doing LLMs. And the LLM stuff was useful. Twitter, there was a lot of people. Now it's much less people.

• 17:52Martin Casado?

  I just have another, I agree. Just another litmus test. We have, can have two litmus test. We have your litmus test, which I think is great. But then you would actually have to have a way to instrument it.

• 17:59Ali Ghodsi?

  Yeah.

• 17:59Martin Casado?

  And then we should have the black box litmus test. Like, I mean, listen, if, if Anthropic in two weeks is, you know, 12 people and they continue to grow and they're putting out models that are increasing rate, I think we should probably take notice of that.

• 18:11Ali Ghodsi?

  That's sufficient criteria, but it's not necessary condition, right? Well, I'm just saying that, you know, there could be that, you know, and really the right way to do this, then to look at, okay, the pre-training and the post-training that's being doing, that's really necessary. Because they have so much resources that they might be doing a lot of other stuff they don't need to do. But they're doing it just, and it can just hire the people because they have infinite money and infinite. So really the people that are training the next model, is that team tiny, tiny? And it's actually getting reduced and they're doing less and less work and just AI is doing it and the post-training and then they're all just using less GPUs? That's not the case.

• 18:38Martin Casado?

  We, we've had this argument many times as an industry before. I remember when like, like we learned how to really cluster computers. Because like the mainframe was actually kind of limited by things like memory coherence. Remember that? Like you can only make it so big, you know? And then we kind of went to the client server area and then we didn't have that problem. And then we started creating super computers, which were like basically just like, you know, clustered computers.

• 18:59Ali Ghodsi?

  And then at some point internet happened.

• 19:01Martin Casado?

  And, and, and that was kind of also roughly like when GPU started getting good. And do you remember that we would actually like export control PlayStations? Because we were worried that Saddam Hussein would use them to do simulation. And the arguments were very similar, which is like, these things are getting infinitely powerful. We're using them to simulate nuclear weapons, which we were, like I was.

• 19:20Ali Ghodsi?

  Yeah.

• 19:21Martin Casado?

  Like we can't, you know, this stuff has existential risk. Actually, they didn't use those words, but like this has the potential for like nuclear weapons or whatever. And we should stop it. And like, none of that came to path. So I think a very reasonable discussion is, is, is, is this time different? Yes or no? I don't have an answer to that. But I'm a VC, but you know, you're a

• 19:39Ali Ghodsi?

  Yeah. I mean, look, I, I think I was, I'm, I'm not old enough to remember. So ignorance is bliss. So I can take this.

• 19:47Ali Ghodsi?

  I, I don't recall PlayStations being illegal and Saddam Hussein being, I, I just don't know.

• 19:51Martin Casado?

  How old, how old are you?

• 19:52Ali Ghodsi?

  Maybe I'm just ignorant.

• 19:53Martin Casado?

  This is like 1999.

• 19:54Ali Ghodsi?

  Maybe I'm old and ignorant. I mean, you know.

• 19:56Martin Casado?

  Maybe they didn't care Sweden.

• 19:58Ali Ghodsi?

  Maybe it's just amnesia from age. But whatever it is.

• 20:02Martin Casado?

  Sweden doesn't care about export controls in the UN.

• 20:04Ali Ghodsi?

  Yeah, you know, whatever it is, I think it's, it's at a different scale now, right? With AI and, you know, with the, you know, what we're doing, the pace of development and so on. They are freaking out the frontier. I do think cyber is actually one of the biggest ones that we're going to see, right? Because

• 20:20Ali Ghodsi?

  It's there's just so much infrastructure on the planet. By the way, way, way more than it was whenever whatever Saddam or Xbox or whatever it was you're talking about. I mean, like, we've just interconnected way more things and they're dependent and like the planet just looks different today. From uh internet tech dependency, interconnection um than, you know, thirty years ago. So I just wanna make this point, there's so much infrastructure that's insecure, right? And if you're gonna unleash these agents, they're gonna find loopholes, they're gonna find exploits, they're gonna break in here and there. So uh so this is a real risk. And by the way, this time it didn't do that. But you could imagine a scenario also where it starts hopping, like it takes resources and it starts executing itself elsewhere. So it's kind of spreads like a virus a little bit. That's a real risk.

• 21:03Martin Casado?

  So so and this this is pure curiosity, I promise I'm not, you know, try to be a foil here, but like.

• 21:11Martin Casado?

  Why do you think we just haven't seen very much then? Like, again, again, I'm much older than you, I remember very well. So when the like literally when the internet, when the when the internet came out. By this point, we had literally taken out ten percent, we've disabled hospitals, we've taken out critical infrastructure. We caused tens of billions of dollars in economic damages from worms. Like, all of that had already happened. And to your point, we had much less build out, you know, less of the the economy was on it. And so, you know, AI has so many people that want to find risks and threats. We're running so fast, so much money has been poured into it. And we haven't seen anything commensurate with the early days of worms. What does that disconnect?

• 21:54Ali Ghodsi?

  Yeah, um, look, so I do remember that, those days.

• 21:59Martin Casado?

  At the same time.

• 22:00Ali Ghodsi?

  Yeah, uh, so, look, I would just say that I I am sleeping well at night and I don't think there's existential risk right now. I do think there's a lot of infrastructure that needs to be secured.

• 22:10Martin Casado?

  Yeah.

• 22:10Ali Ghodsi?

  Uh, we have a product in the market, in in the detection market, Lake Watch, that helps you do detections. And the space is just there is moving so fast. Because, you know, you used to have these SOC teams, security operations center people, that would, you know, look at uh what intrusions are happening. How are we being attacked and so on? And now the humans just can't keep up. So this is the whole space, security cyber space is being transitioned into fully automated, using agents for detection on the other side. If we don't do that, I mean, now we're rushing, we are rushing, the industry is rushing to do that super, super fast. If we don't do that, uh, I do think you will start seeing those kind of things, like sites going down, you know, um, you know.

• 22:51Ali Ghodsi?

  Whole systems that stopped working for a while and there will be consequences, not existential. But economic damage and, you know, people getting hurt and so on could happen. So we just have to race very, very fast to to do all of those things. It's just you don't have the, the hu- humans don't respond fast enough to the attacks that are happening. So you just, you need to automate all of those. And most organizations are actually not close to doing that. The banks are doing it, some of the people that are super security conscious are doing it. But most of the industry today is running with old school security operations centers and people that are waking up every day. And there's like hundreds of emails of detections that have fired. Many of them are just false positives, so you don't need to, you can ignore them, but some of them are not, they just don't have time to go through those.

• 23:34Ali Ghodsi?

  And you need to identify that, uh, you need to have threat hunting that's automated, where you're actually attacking your own systems automatically with agents and so on. It hasn't happened. So I do think like if if we just say, hey, this is just like the internet in the early days, uh, you know, bad things are gonna happen. So there is a race going on.

• 23:50Martin Casado?

  You know, I was actually very surprised, um, earlier this morning, I was on a conver- like, I feel like you and I are like pretty close, we talk periodically. I feel like I I know a fair bit about Databricks. Uh, I was on a call this morning where a founder was basically like, yeah, listen, like, you know, we're doing all of this, like, observability. Agent threat detection and we're using Databricks. I didn't even know that you you had this offering, like, quite frankly. So like, I mean, this is just from an education standpoint, like, how how extensive have you gotten in like the agent AI observability, security, safety thing?

• 24:22Ali Ghodsi?

  Yeah, I mean, we gave a talk this year at RSA, actually with Ben Horvitz, but uh, the issue is that data and AI is blending with cyber. These two markets are collapsing.

• 24:32Martin Casado?

  Because I think.

• 24:34Ali Ghodsi?

  Yeah, and the reason they're collapsing is that it used to be like, okay, we have like data and AI, the kind of stuff Databricks and these kind of companies used to do. Which is like, okay, you have a bunch of data and you run AI, machine learning and that just lives separately. And then you have the cyber world, cyber world is, you know, we wanna detect if something bad, if like bad people are trying to hack us, if bad people are doing things, we need to detect that. Okay, but now on the data and AI side, we have agents running internally in the company, people are having agents running. And the agents are also like doing things with other people's agents and they're producing a lot of data, uh, logs, trails, you know, fingerprints that are being left. And so, you know, then now you have internally these agents that are doing that. So these these worlds start merging more and more, which is like, okay, well, we, all the data that's being produced needs to be analyzed.

• 25:17Ali Ghodsi?

  And the scale at which you need to do that is just like many, many orders of magnitude, more than just one or two years ago. So things have changed dramatically, like two thousand eighteen nineteen, the time it would take from uh, you know, a CVE uh, vulnerability being sort of uh, published. Until you see it actually be weaponized uh in the industry would be like two, three years. That went down to, you know, twenty twenty two significantly, but it was still like eight, nine months.

• 25:44Martin Casado?

  Yeah, yeah.

• 25:44Ali Ghodsi?

  So that's kind of fine, you have eight, nine months from a vulnerability to, that was twenty twenty two.

• 25:48Martin Casado?

  Yeah.

• 25:48Ali Ghodsi?

  Now, if you look at this curve from twenty twenty two until now, now it's down to like basically hours. So it's like down to like basically no time, like things get immediately weaponized. So uh, so you need to just do it in an automated with the data and AI sort of platform approach. So these markets, I'm gonna argue, are just gonna collapse, actually.

• 26:06Martin Casado?

  So it's a very specific question here, I actually think a lot of like, to to pull back on like the the existential, ex risk discussion. It feels like there's almost two camps.

• 26:14Ali Ghodsi?

  Yes.

• 26:14Martin Casado?

  There's one camp which believes that this actually is an engineering problem and like companies like. Like Databricks can solve it and they can solve it through product and through engineering solutions and through services. And so like, we just as an industry need to solve that problem.

• 26:26Ali Ghodsi?

  Yeah.

• 26:26Martin Casado?

  And there's others which actually believe, seems to me that there there is no engineering solution. You have to, you know, slow it down, you know, you know, you have to use regulation, it's more like a nuclear weapon, et cetera. So like, does this mean you believe it is an engineering problem? Or are you not quite comfortable saying that yet? Because why, it was asked, why would you even buy Databricks, man? Let's just put this stuff in a national app.

• 26:50Ali Ghodsi?

  Just paste it is the only solution, I'm just kidding. Everybody just paste themselves a little bit, then we'll be fine. The bad guys do.

• 26:56Martin Casado?

  There is no line of inquiry ever that gets to pacing, I don't think. I think it's like you pause it or like you like solve it.

• 27:02Ali Ghodsi?

  What we've learned here is that Martin really hates the word pacing. I will never use that word with you ever again, okay? Clearly duly noted. Yeah, is it an engineering problem that can be solved by engineers or is there more to it? I actually think which problem are we talking about? There's two separate problems that I think are being conflated. There is the super intelligence problem.

• 27:20Martin Casado?

  Yeah.

• 27:20Ali Ghodsi?

  You know, and I think a lot of this comes from like Boedstrom two thousand fourteen super intelligence book. And if you look at the definitions, like, I think people don't have these clear definitions of what's super intel. If you read his book, those definitions are kind of crazy. So I think what he had in mind when he said super intelligence uh is, you know, AIs that, I don't know, I don't know what the examples were.

• 27:41Martin Casado?

  Everything could do anything.

• 27:42Ali Ghodsi?

  Something like they they write a whole PhD thesis with novel, like peer reviewed stuff in a couple of seconds. And they can do like millennia worth of thought, you know, in like instantaneously. And um, you know, so this is like the level uh of, you know, how fast they are, how intelligent they are.

• 27:59Martin Casado?

  So it's like, it can learn.

• 28:01Ali Ghodsi?

  Yeah, yeah, it's just, yeah, I mean, yeah, but it's just many, many, many orders of magnitude, right? It's like, it's just the scale of the problem is just completely different. So if such a thing exists, do I think it's just just an engineering problem to solve? And no, I think that's actually very, if such a thing would happen, I, that would be very existential.

• 28:18Martin Casado?

  Of course.

• 28:18Ali Ghodsi?

  And that's whatever we agree on. So I think that's being mixed with, now we have agents that are nowhere near that. It's not even like, there is nothing like that and we don't have anything towards that path right now. But these agents are capable and you can do something with them that you could never do before in a history of mankind. So I do think an inflection point has happened, something has changed, which is, we could get, we have good security researchers at Databricks. But I could never say, let's get ten thousand of them in a sandbox for a month. And have them do hundred million dollar worth of salary wage work. We can do that now, we just turn on a button and we can get hundred thousand of them. Or mathematics, like we can say, hey, you know, we wanna solve a the like conjecture.

• 28:58Ali Ghodsi?

  Okay, let's get pretty good mathematicians, but let's have ten thousand of them collaborate, you know, and then you can like make very fast progress. So this, I think, is an, this leads to all these cyber risks. I think cyber is the major problem here. This is, I think, you can solve with engineering. And I think it's like, we are working on it, many others are working on it, uh, there's still risks, they're not existential. I think we should do it. There is the super intelligence thing, that's the thing that could do, write a novel PhD thesis or like. Reason intuitively in eleven dimensional space physics instantaneously without writing anything down, something humans can't do, like that kind of super intelligence. The question is, is RSI and recursive self improvement that the labs are doing, leading us there? Are we gonna get there?

• 29:41Martin Casado?

  Trying to do.

• 29:42Ali Ghodsi?

  Trying to do, is that what's gonna happen? And how fast is that gonna happen? That's the big question. And they've suggested that, you know, hey, we should have inspectors that come in and look at what we're doing. And it's a good idea, have them go in there and get the data. I would love to like, the question is, who are the inspectors? Cause you can like, you can stack, right, you can stack that. Yeah, so there's a bunch of people that like, uh, on either camp, actually, I wouldn't care if they're if they're the inspectors, I would not be very impressed by what they say. Cause they've already made up their minds even before they are, they would go in there.

• 30:13Sarah Wang?

  Right, exactly.

• 30:14Ali Ghodsi?

  But let's say like, as an example, if Yann Le Cun, who was one of the inventors of this, you know, deep neural neural technology, right? One of the pioneers, if he he said, hey, there's nothing to see here, there's no risk. You know, I'm paraphrasing him, this is nothing, this is super intel, this is just nonsense, keep on going, go fast, fast, fast.

• 30:29Martin Casado?

  None of us would believe it.

• 30:31Ali Ghodsi?

  I'm putting words in his mouth, I mean, I'm not exactly, but now, if he was one of the inspectors and he went in there and he had a look and he came out and he said, hey, I've looked. And it's just what I said, there's nothing to see here, just keep going, I would feel very good about that, I would say, okay, well, I I I would feel very. Or if he comes out and says, oh, my God, you know, I, you know, he's wobbling and he would change his mind a little bit. That would also have a lot of interesting signals. So I think it comes down to who we pick as inspectors and I think it's a good idea, let's have some of them and. Pick a diverse set of people so that we can get different nuanced points of view.

• 31:02Martin Casado?

  What what do you think about this kind of Elon Musk view, which is like? It's it's less third party, it's more, it feels like there's kind of three proposals. Like the OpenAI, Anthropic one is a third party.

• 31:14Ali Ghodsi?

  Yeah.

• 31:14Martin Casado?

  The Elon Musk one, as far as I can tell, is the labs cross check each other like pure G, like you do in science.

• 31:20Ali Ghodsi?

  Yeah.

• 31:21Martin Casado?

  And then the Mark Zuckerberg one is police yourself, right? What do you think about this middle one?

• 31:25Ali Ghodsi?

  Um, that they should pace each other, like, you know, evaluate each other. I mean,

• 31:28Martin Casado?

  Like, I value you.

• 31:29Ali Ghodsi?

  I think like if we have boxing matches in the ring, the boxers should just be the judges of each other. Would that work? No, they would scream foul all the time. Foul, foul, foul, like, you know, it's like, ah, you know, the moment that, the moment the other guy, the moment the other guy puts out the great model and it's like, ah, big super intelligence risk. Absolutely. Like, you know, they have nothing responsible. Like, you know, when vested interests are at play and there's like IPO plans and these two companies are so competitive and they have like this history also between them. Yeah, they'll be very, they'll be very fair to each other, I'm sure. That's why you need a third party, right? And why do we have judges in the world at all? Why do we have third parties at all? Like, why can't just people like figure things out between themselves?

• 32:10Sarah Wang?

  So.

• 32:11Ali Ghodsi?

  But I mean, they should try. If they want to do it, they should try. But I'm skeptical that they wouldn't just, you know, be biased, you know, in multiple ways to, to, to self, like, you know, judge each other.

• 32:21Sarah Wang?

  So I have to ask Ali, do you think, um, something Elon also said, I think it was on the All In, um, you know, summit. He was like, this is some elaborate 4D chess. Because on the one hand, you're saying all of humanity will die. On the other hand, you're saying, hey, what do you want for your, you know, IPO allocation? Right? And so, I mean, that is probably a more cynical view, but like, how do you? How do you reconcile that? I mean, the, the dissonance, I think, gets a lot of people. Like, how do you think that gets reconciled?

• 32:48Ali Ghodsi?

  Look, I think all of these things get mixed. Like, I think there are people that are freaked out. And I do think that the people are saying like, hey, if there was a regulation that would pace us, sorry to use the word, uh, that would be good for us, right? That, that would be good for us. But I also think that people have vested interests.

• 33:02Sarah Wang?

  Yeah.

• 33:03Ali Ghodsi?

  Right? These things like, you know, usually people figure out a way to always get all of these things to align in their, you know, harmonically in their head.

• 33:09Sarah Wang?

  Hmm.

• 33:09Ali Ghodsi?

  Uh, so yeah, do I think that there has been a tendency in the past of, in general, using also marketing stunts, uh, by saying, you know, oh my God, this latest model is so good that I trained, it's like unbelievable, it's like almost scaring me. And then how the whole world like kind of starts focusing on it? Yeah, there's been that, that kind of marketing going on.

• 33:28Sarah Wang?

  Yeah.

• 33:29Ali Ghodsi?

  Uh, but at the same time, also, as I said, the time from CVE to actually weaponize exploit has like been going down from years down to like minutes now, just in like three, four years. Uh, so it's real, the cyber attacks are real. And this is, but, but there's also a great marketing ploy, uh, to, you know, whenever you train a new model, uh, make lots of noise around how much of a, you know, crazy risk it is to the world. It helps you, right? Uh, so, you know, maybe they're not in contradiction, these things.

• 33:56Martin Casado?

  So, I mean, you, you and I are networking folks and there's a long history of, um, uh, forming third parties to help arbitrate things, right? Like IETF or.

• 34:09Ali Ghodsi?

  Yes.

• 34:09Martin Casado?

  You know, IEEE or, you know, even like ICANN.

• 34:13Ali Ghodsi?

  I see where this is going.

• 34:15Martin Casado?

  No, no, no, no. So my question to you is like, so I think it's actually, this is a very sensible proposal that they actually have. I actually agree with you. You probably want to make sure it's independent, which is not fair right now.

• 34:24Ali Ghodsi?

  And there's gonna be a lot of arguments of who you put there and everybody's gonna disagree.

• 34:27Martin Casado?

  Right, right. But you said, you know, why do we have judges? So that's like, actually like the state stepping in is actually quite a different thing than basically industry self policing. So like, at what point in time do you think it makes sense to actually consider federal involvement? Or do you think like now is the time to actually consider actual federal involvement as opposed to like more industry self policing? Well, this is a very different.

• 34:46Ali Ghodsi?

  They are different, but they kind of bleed into each other. Like, you know, like for instance, Finray, um, you know, is not like a completely independent, uh, self, it is, but you know, it's, uh, linked to the government. So like, I think these things like kind of will bleed over. I think it's hard for.

• 34:59Martin Casado?

  Do you think that they evolve into, historically, they've, like, industry self polices and then it evolves into regulation?

• 35:04Ali Ghodsi?

  Look, if, if they are saying there is existential risk, which they're saying, you know, and then saying, come police us and regulate us, I think it's very hard for regulators to say, no, we're not gonna do that. So far, I've said that, but I think that's not gonna last very long, you know.

• 35:16Martin Casado?

  It wasn't David Sacks was like, I've never had a CEO ask us to regulate them. And my favorite thing is like.

• 35:22Ali Ghodsi?

  And the CEO that I've never had a regulator that says no to that.

• 35:26Martin Casado?

  I mean, the, the reality is like, the actual like meta political machinery is actually in motion already, right? I mean, like, everyone has a talking point. Obama has came out, is a major issue. Like, do you think that there's a reality that it's too late? This will be a major issue in the midterms and we're actually going to like heavy handed federal regulation? And this is all gonna be paused, you know, and goes into the, Anthropic goes into the DOE? And we're, we're past that point? Or do you think we can actually end up with like a sensible self policing regulation? Because this is the headlines, you cannot.

• 35:55Ali Ghodsi?

  We should strive towards doing the right thing.

• 35:57Martin Casado?

  Yeah.

• 35:57Ali Ghodsi?

  I think there's still some degrees of freedom of how things evolve and there's still time. And yeah, you're right, that largely you have these companies where we're pumping in so many billions of dollars.

• 36:07Martin Casado?

  Yeah.

• 36:07Ali Ghodsi?

  And the way reinforcement learning works is that you, you know, give it the reward function that's verifiable. Like, we're gonna solve this math problem or this kind of, you know, this narrow area of programming and so on. And we pour in so much money into that, you can get quite good results in that narrow kind of, that doesn't mean that you're getting that super intelligence.

• 36:24Martin Casado?

  No, but you can even, you can even, you can even trick yourself into thinking that like less inputs are giving you a better outcome. Just because you're running so many experiments and you've thought about it so much, right? But it's actually very hard to do a closed, a closed experiment this way, given how many resources are going in.

• 36:39Sarah Wang?

  Well, the fundraises are going up astronomically to your point.

• 36:41Martin Casado?

  Yes, yes.

• 36:42Ali Ghodsi?

  And that's why these companies are going public, right? I think they would otherwise, they would stay pri, I mean, as someone who runs a private company at scale, I think they would prefer to stay private, otherwise.

• 36:51Martin Casado?

  Yeah.

• 36:52Ali Ghodsi?

  Why are they going public? Because they need the capital. And they consider the scaling laws and the capital to be a strategic advantage. So that's why they're going public. But I would say, let's go back to the four things that I listed.

• 37:01Martin Casado?

  Yeah.

• 37:01Ali Ghodsi?

  If those are true, um, you know, if those four are true, would you want to be, know about it? And is that, would that be worse than that? That could get out of hands. Now, there's no evidence that those four are happening. But if, if there was like, you know, no, we're, that is actually where it's headed.

• 37:15Martin Casado?

  Yeah, yeah, yeah. I, I actually think understanding for any system.

• 37:19Ali Ghodsi?

  Yeah.

• 37:19Martin Casado?

  Like, any sort of self-propelling property is important. And we've, we've done this in the past with dynamic systems, right? Like, we've done this with like whatever compilers, we did this with, uh, all the research on nanotechnology. Like, it's been a common interest of ours.

• 37:32Ali Ghodsi?

  Yeah.

• 37:33Martin Casado?

  And I don't think that that's new, that it's an interest, which you continue to have the interest. I just think the, the, the fear is that these particular systems are meta economic systems that are so complex.

• 37:43Ali Ghodsi?

  Yeah.

• 37:43Martin Casado?

  That the risk is, is crying that you're seeing it when you're not seeing it. And I think a lot of that's happening right now.

• 37:49Ali Ghodsi?

  Yeah.

• 37:49Martin Casado?

  But of course, if you see it, you know, of course, I mean, you, you want to know.

• 37:52Ali Ghodsi?

  But it is fair to say that the labs are now focusing a lot on RSI and that's where they're headed next. And maybe they're just unjustifiably like word themselves, just like they were word about GPT-2. Right? It's like they were like, GPT-2 is world ending and then it was on, then GPT-3 and 4 came out.

• 38:07Martin Casado?

  So, so I, so, so I don't, I don't want to quibble. A lot of the times when they say RSI, they're actually talking about autocatalytic effects. And autocatalytic effects have been in our industry for a very, very long time.

• 38:15Ali Ghodsi?

  Yeah.

• 38:15Martin Casado?

  So for example, like, there's no way you can create a computer chip without a computer chip.

• 38:19Ali Ghodsi?

  Yeah.

• 38:19Martin Casado?

  Just, it's like you cannot do it.

• 38:21Ali Ghodsi?

  Anyone with computer science would agree. You're right, compiler writes its own compiler.

• 38:24Martin Casado?

  Well, that becomes closer to RSI.

• 38:27Ali Ghodsi?

  Yeah.

• 38:27Martin Casado?

  But like, like the steam engine was autocatalytic, right? So listen, my full-time job is people coming out of labs and starting companies. And they all say RSI because everybody says RSI. And like, like maybe 1% of those are actually RSI. Like, they're, they're, they're more like, we use AI for data cleaning. We use AI for making.

• 38:45Ali Ghodsi?

  Let's make this distinction. So you're saying, uh, it makes basically catalyst in a sense that they're using AI to speed things up.

• 38:51Martin Casado?

  It's autocatalytic, yes. So, so I would say of what we hear to be RSI.

• 38:56Sarah Wang?

  Like a model trains another model, right?

• 38:58Martin Casado?

  You're using a model to build a GPU kernel. You're using a model to do data cleaning, just like I use a computer to design a computer. It's autocatalytic, which is every tech, the internet was autocatalytic because it allowed people to, to collaborate remotely. So I would say.

• 39:11Ali Ghodsi?

  Yeah.

• 39:12Martin Casado?

  This is anecdotal. 90% of the calories are autocatalytic, which is 100% what you'd expect.

• 39:17Ali Ghodsi?

  And that's been going on for a while, though. I mean, that's not even new.

• 39:20Martin Casado?

  Yeah.

• 39:20Ali Ghodsi?

  But I would say, but there, but also now there is a focus on, let's move towards actually, can we get the model to train itself? This is kind of like the auto research that Karpathy did, but now they want to do that.

• 39:30Martin Casado?

  There are, there are teams, there are teams that do that. It is not nearly as, as, as many calories as you would expect. And I just feel like I actually have a good sampling of this because they all come and talk to us.

• 39:41Ali Ghodsi?

  Right, so can we, can we, maybe you can be one of the inspectors? Can we get all that data and all of us look at that data? Maybe there's nothing to see here, you know? I personally don't think it's like very high probability that those four criteria are happening.

• 39:52Sarah Wang?

  Greg Brockman went on the pod this week and said we're in AGI era.

• 39:55Ali Ghodsi?

  Yeah, so now I ask the same question after he said that, and now everybody's saying we have AGI. So, you know, so, you know, people follow what they say. But, but people have for a very long time, when I asked this question, said that AI is smarter than most of the people around me most of the time. That's, that's been almost like since Q3, Q4 last year, they've been saying that. Then I asked them, how many of you have, you know, hundreds or thousands of agents that you are managing that are coordinating with each other in swarms and negotiating and, you know, automating your life and everything around you? And if so, raise your hand. It's like almost nobody raises their hand. Of course, Martina has done that at home.

• 40:27Sarah Wang?

  No, no, most enterprises are on Microsoft Copilot.

• 40:30Ali Ghodsi?

  Yeah.

• 40:30Sarah Wang?

  Like, that's the extent of their AI.

• 40:32Ali Ghodsi?

  Most, most enterprises I talk to when I ask this question, they're like, no, we don't have any of that. So we're like, what are you doing then? They're using a chatbot, like they're asking questions from a chatbot. That's basically very, very glorified efficient Google search of the old day. Their results, it's just faster Google search. And then coding is happening, so people are using it for coding. Uh, though the ROI is, you know, we can discuss the ROI there, but there's no like agentic, like work that's automated the whole enterprise. That has just like now happened. Uh, so then, why is that? And I think that the real reason is, if you actually look at it, is the models are smart enough. But they just don't have the context that exists inside of any organization. Like, they have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes.

• 41:16Ali Ghodsi?

  They don't know, there's always like a couple of employees who know everything in every organization. You know, you go tap on their shoulder and they're like, everyone's like, oh my God, what would happen if he or she quits?

• 41:25Ali Ghodsi?

  You know, they don't have that context. And if you just fuse that and gave that context into the AI models, just the frontier today, I think there's so much productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models. So we don't need a smarter model that can actually solve Navier-Stokes or conjectures, or do better on humanity's last exam. Like, we needed to just go from sixty to seventy percent.

• 41:46Martin Casado?

  Yeah.

• 41:46Ali Ghodsi?

  None of that is needed. So I think actually, people are very upset on some, like, oh, if we pace the frontier. But actually, if the frontier doesn't advance, it doesn't actually matter, I think, for vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff.

• 42:04Martin Casado?

  But it would be disastrous in labs because the price of intelligence is dropping asymptotically. I think it's one, it's going down by one tenth every six months or something like that. So that would dramatically change their businesses if, like, you weren't pushing the frontier.

• 42:17Ali Ghodsi?

  Yeah, but this is what we should focus on, right? We should focus on, like, you know, there's like two sides of it. We discussed here a lot, the costs. Like, there's cost-benefit analysis that we should do on everything, right? We've discussed the costs a lot here. Like, oh, is there, like, existential threat? Is there cyber risk? Are there things we should be worried about and so on? That's like the cost side. What's the benefit? And I think now that this has become like a public thing and the whole public cares about AI, they're asking, hey, what's in it for me? What am I getting out of it? It seems nothing.

• 42:42Sarah Wang?

  So how do they get there? What are some of the use cases you've seen to date that have maybe surprised you to the upside?

• 42:49Ali Ghodsi?

  Yeah, I mean, first of all, there's like so much worry about, you know, existential risk and so on. So I think a lot of people just don't know what a, you know, cool use cases where people are actually doing interesting things. We have a lot of use cases that are, I mean, just fascinating. One that I like is Crisis Text Line. So, you know, they actually use large language models with us to detect if teenagers want to do self-harm or suicide.

• 43:15Sarah Wang?

  Oh, wow, yeah.

• 43:16Ali Ghodsi?

  That's an awesome use case. And it actually, so it actually saves lives. So that's a great company and that's, you know, that organization is doing amazing work. Another one that's kind of interesting is the Omnipod, which is for diabetes patients, they can put the Omnipod, and it uses AI to really learn your insulin release and your glucose levels and actually exactly release, you know, I don't know if you remember, people used to like stick themselves, right? But this now happens automatically and it's like, you know, self-learned AI for your body. You know, it's a cool use case. Zip Line is another one. They're doing awesome. You know, but when they started, it was like these drones that had, you know, they were completely automated, all AI-driven, everything from the, you know,

• 43:58Ali Ghodsi?

  battery optimization to the routes and everything, and they were delivering food in, you know, areas of need.

• 44:03Martin Casado?

  Yeah, blood, blood to refugees.

• 44:04Ali Ghodsi?

  Blood to refugees. Yeah, started in Africa and then elsewhere in the world. So, yeah, that's awesome. That's all, yeah, it's an AI use case, you know, built on Databricks. So it's a cool one, but there's more advanced ones also. Like, one that I kind of like, but it's harder to maybe explain, is this model that we built, a transformer-based model that we built with Merck. It's called Teddy, Transformer Enhanced Drug Discovery. Yeah, so, and they publish actually the research, so you can, you can check it out. But it, basically, it's a model, instead of predicting the next token in English, it predicts what the gene regulatory network, the D R N, is going to respond. And it can really detect, you know, which cells are causal and which ones are just reactive. So they're just reacting.

• 44:46Ali Ghodsi?

  And therefore, they can start using this in drug discovery and get costs down significantly for developing drugs that are targeting specific diseases. So that's a super cool use case. There are lots of these, you know, Genie, I mentioned, you have this ontology and you can ask any questions. Novo Nordisk is using this. So, you know, they built this GLP-1 drug. But what Novo Nordisk is doing is now they're using it for all of their trials that they're running.

• 45:13Sarah Wang?

  Oh, wow.

• 45:13Ali Ghodsi?

  And it can compress the time it takes to get insights, for instance, if you're doing an obesity study or something, from weeks down to minutes. So there are a lot of amazing use cases of AI. We should not forget these upsides also. Like, we want all of these and we do not want to pace these.

• 45:30Sarah Wang?

  Exactly. You're totally right. And so, how do they, let's say if you map out the next twelve months, how do the enterprises actually get value? You dropped the word context, but like, how do they operationalize that?

• 45:41Ali Ghodsi?

  It's actually harder than most people believe, but, you know, first and foremost, we have to make sure that we have digitized everything that's happening in an organization. That actually, you cannot actually just, you know, have a magic wand and make that happen. So, you know, every meeting has to be transcribed, you know? And so you have to be able to get all the context of all the meetings and everything that's happening. All the digital content has to be fed to the AI. So you have to build, we call it an ontology, we build that. But first and foremost, you have to collect that. That itself is a problem in many organizations because legal teams will say, don't record every call, don't record every thing. So you have to do that in a way where...

• 46:18Sarah Wang?

  Can you define ontology for everyone? Because I know Palantir says the word a lot, but it's not like they own the word ontology. Like, what does that mean? And for the people listening, like, how should they think about it?

• 46:28Ali Ghodsi?

  You know, ontology just means that in an organization, the relationship between all the abstract concepts of all the goals and all the departments and all the people and all the projects that are going on, what do they exactly mean? And what's the relationship between them, the people, the resources, and what that company does? So it's the difference between a person who is a new employee in the company and just started today and a person that has worked there five years. You know, let's say they're equally skilled, they have the same educational background, they're equally smart and hardworking and all of that. But one, it's, you know, her first day today at work, the other one, she's been there five years. What's the difference between these two people?

• 47:08Ali Ghodsi?

  That one has an ontology of how that organization works, who the people are, how you get stuff done. Don't look at the org chart, that's, don't go ask that person.

• 47:17Sarah Wang?

  Yeah.

• 47:17Ali Ghodsi?

  He will not get anything done. You go ask this person, you know, he'll get it done for you. And, and that's not how it works. You don't need to file that paperwork here and, you know, and this is, this project, this is what's going on, this is essential. So there's just a lot of ingrained knowledge that's sitting in everybody's heads. Who knows how an organization works? That's why it's, people say in startup land, they say, hey, if you lose most of your people, that company can't recover from it. You can't just replenish and hire new people. Like, the people are so essential. How do we get that context, that's the ontology, and give it to the AI? Part of that is, we just have to have, you know, the recording and all of that. But the second part is, how do you actually distill it down into a graph? Actually, a digital graph that you can then feed to the AI.

• 48:01Ali Ghodsi?

  So the way a lot of the agents work today, like a cloud coder, any of them, Codex or Py or, you know, Open Code, or you can go through the whole slew of them. You know, they have this loop, agentic loop, it can reason, but then it goes and checks every resource one at a time. So it'll go to this MCP server for your question and try to see, is the answer here? Is there another one? It synthesizes it and gives you an answer. But it's kind of slow. I liken this to if Google would have built Google Search this way twenty-five years ago, we would have said, okay, we're going to get ten blue links, we search for key terms here, but instead of giving you ten blue links, it would have gone to one website, summarized with an L L M what it does, found a few hyperlinks, jumped in parallel to a few of them,

• 48:42Ali Ghodsi?

  read a few websites, done that for ten minutes, and then giving you like its best ten blue links it would find. Well, that would be very expensive, costs a lot of money to do that every time, go on the web. Two, it would have taken a long time, you'd get away with ten minutes. And three, the quality would be bad because you're actually only looking at a very small subset of everything that exists out there, right? So how do they do it? They have an index, right? You never leave Google servers. You search for, it hits the index, the reverse index immediately gets you the ten blue links within, you know, less than a hundred milliseconds. What if you do the same thing for the AI? So the ontology is that we need to compute that index offline all the time. So it's almost like the PageRank algorithm that Google had invented back in the day,

• 49:25Ali Ghodsi?

  but it's more complicated because Google was just looking at a web where everybody can go on the web. Here, there are permissions.

• 49:32Martin Casado?

  And the links existed and...

• 49:33Ali Ghodsi?

  Yeah, here are just permissions involved. The data I'm allowed to access might not be the data that you're allowed to access. So there's privacy, there's access control. Also, there's many different types of objects here that we're dealing with, not just websites. So the problem is a little bit harder, but it's manageable, you can actually do it. So, you know, the, I'm convinced you can do this and you can get massive productivity gains out of it because we did this for Databricks.

• 49:57Sarah Wang?

  Yeah.

• 49:58Ali Ghodsi?

  We did it for ourselves. Yeah, and we're like, the company has just completely changed. It's like not the way it was, I would say, a year ago because of this.

• 50:04Sarah Wang?

  I mean, you've been dog fooding Databricks for Databricks forever, but maybe say more about the impact you've seen as an organization.

• 50:11Ali Ghodsi?

  Yeah, I mean, once we got this ontology and we started working on it, and we actually have probably the largest of all of our customers, we have the largest ontology. Our ontology is bigger on us than any of our customers when they use, you know, us to build their ontology because Databricks uses Databricks more than anyone else uses Databricks. And so it's like millions of millions of nodes in the graph, in the ontology graph that we have. So it's just, you know, what happens in an organization? What happens in an organization? You have a tree structure, organization, and information flows up and down the tree structure. You know, if you can't make a decision, you escalate to your boss, maybe they can tiebreak, they escalate up. They need to get up to speed on what's happening and they need to get all the context. And then they make decisions. Once decisions get made, you have to percolate them down in the organization.

• 50:56Ali Ghodsi?

  A lot of this can now be done by AI if you have an ontology. Why? Because, you know, what happens in a meeting? In a meeting, you go through some, you know, someone has done the analysis. You probably have a PowerPoint deck with some pretty graphs in it. That person did the analysis is some smart person that used Excel, made some models. There's some numericals. So a lot of that you can now just do with AI. So the AI can do the analysis for you. It has all the context. It can present it in a way that you want. You can ask questions about it instead of having follow up meetings. You can directly ask questions directly from the AI. So it's very similar. It's along the lines of what Jack Dorsey has said that you can do to the organization. It's just a concrete way of implementing it.

• 51:37Sarah Wang?

  Yeah.

• 51:38Ali Ghodsi?

  So it's game changer for us. Like, you know, it's just everybody's on their phones now in the meetings on Genie. And they're like asking Genie questions. You can see as soon as someone says something complicated or something, it's like, you know, you see everybody go through the phone.

• 51:49Sarah Wang?

  Can you share that finance, like the finance anecdote? You you mentioned once at a board meeting.

• 51:54Ali Ghodsi?

  Yeah, it's, yeah, sure. Internal board meeting. Yeah, so.

• 51:59Sarah Wang?

  Only in kosher.

• 51:59Ali Ghodsi?

  Yeah, exactly. No, it's, it's, it actually needed for, for one of our presentations. I need to know how many customers do we have in Fortune 500? That is, what's our penetration of Fortune 500? And I asked one of the people in sales ops, because I thought she would have it. And she texted me back and said, oh, sorry, I can't log in to Genie right now. I'm on a flight. And so if you're just going to log into Genie, I can do that myself. Like, I don't know. I asked you because I thought you had like something alternative that I don't have access to. So then I was kind of a little bit angry. So I texted the CFO instead, Dave. And so I texted Dave and I said, hey, do you know what our Fortune 500 penetration? And he just copy pasted a screenshot of Genie back to me.

• 52:39Ali Ghodsi?

  So he also asked that. So I said, does anyone do anything novel here? Then everybody just going to Genie and asking the ontology, you know, for questions.

• 52:47Sarah Wang?

  It's like, let me Genie that for you instead of let me go.

• 52:49Ali Ghodsi?

  That's what everybody do. Now we just say, we say, hey, can someone just Genie this? Like, you know, can you just get it from the ontology? So I do think it's a game changer. But it's not just you press a button, you have an ontology in an organization. And I think Palantir actually has done a great job of going to organizations and getting a lot of that tacit knowledge written down and getting it into the organizations. We automatically can take that and build the graph. And then we feed that graph into the agents so that we can answer the question and answer it in a way that business leaders would like to see it, which is in graphs, you know, analytical way and a way where you can interrogate that question. And, you know, continue asking questions and getting answers to those so you can make decisions and then disseminating that information in the organization.

• 53:33Sarah Wang?

  Yeah, it's, it's pretty amazing. You, you sort of bookmarked the developers are obviously using AI questionable value. I want to follow up with you on that because I feel like you guys were one of the earliest. And I say, I don't want to use the word token maxing because it has such a negative connotation. But I think in terms of applauding people who can use AI to become more productive, you guys were, you know, at the forefront of that, right? And then, of course, there's this cycle of, oh, shoot, people are being wasteful. Now we need to value max. Like, what was your, your own journey on that? And like, how do you guys think about value maxing, not token maxing? And then I'm going to throw in Unity Gateway in this, right? Because I think the managing of costs piece is actually getting more important and you guys are helping people do that.

• 54:18Sarah Wang?

  But maybe tie that in to extend it.

• 54:20Ali Ghodsi?

  Yeah, yeah. So around Q4 last year was when, you know, the models got really, really good. And we started noticing that, okay, it's actually starting to give much better productivity. So I actually started using the models myself to sort of start, you know, commit code into production for data bricks, like the actual, as I want to take it all the way to production. So I did that and, and started pushing the organization that, hey, everyone needs to do that. I have done it. Why are you not? Like, if the CEO can commit code to production on a very sensitive data platform that has all these security requirements, you should be able to do that too. You being any manager, anyone in the organization. So I started pushing everyone very hard and we started making leaderboards in Q4. And at the beginning of, I say January, February, when we kicked off the year, we were already at full swing.

• 55:04Ali Ghodsi?

  Everybody was using the stuff and we're pushing and we're managing this. But, you know, the whole token maxing thing was happening around, you know, February, March period already was happening. So, yeah, we just had the luck of being like maybe a few quarters ahead of folks to see what, what was happening here. And it was getting out of hand. So we already had a gateway. So it's called Unity Gateway where we were already, this gateway was being used to provide token capacity. So you can get OpenAI, Anthropic, Gemini, Grok capacity. Like, any customer can come to us and we'll just provide them that capacity because we have a relationship with those and any open source model. So we started putting in budget constraints in place and giving people warnings like, okay, you have this much of your budget left. You're getting close to your kind of ceiling.

• 55:49Ali Ghodsi?

  So we started doing that per person and for group. And then we started doing great analytics so we could predict exactly where the costs were going. And then, yeah, and then we added smart routers that could actually pick cheaper models if you're getting close to your budget or if, you know, you have simple questions. We started doing that. We also built a harness called Omnigent, which can multiplex between the different harnesses. Turns out actually the harness itself matters. Like, if you use the same model but different harnesses, there's almost 2X different cost difference. Even exactly same model. You know, same version, but different harness, you get 2X difference in actual cost. So if you can change harness, you, you can get a lot of leverage in the costs. So we started using all of this. That we were able to actually bend the curve.

• 56:34Ali Ghodsi?

  And actually, a cost for AI has been basically, the tokens have continued to go up, but the costs have been sort of stagnant. So that's been actually super, super important for us. And there's a huge demand for this. I think every organization is going through this now.

• 56:46Martin Casado?

  Yeah, I, for the first time, I do a lot of board meetings. I'm on 20 something boards. For the first time ever, a company at scale last week said that they're moving from the frontier models to GLM. This is a large engineering organization. Do you see this? Do you think that that's a trend or do you think that's just like a one off anecdote? Because I've been hearing about, I remember the first DeepSeek moment and like Nvidia stock, and then that turned out to not be real. And then the Kimi moment and the next DeepSeek moment. None of it seems to have actually had an appreciable impact on the market. But now the amount of anecdotes that I have are pretty real and it seems to be happening.

• 57:22Ali Ghodsi?

  Yeah.

• 57:23Martin Casado?

  Love your view.

• 57:24Ali Ghodsi?

  I mean, I think people want both. They want, you know, they want the latest model that's super intelligent for the difficult task where they get ROI. But then there's a lot of mundane dumb things like, you know, you, people literally use their harness to rename files and whatnot. You know, like it's, you're paying, you know, orders of magnitude more for that. Please type that in yourself. Don't have the model do that. It's going to spin for five minutes and then it's going to rename the file for you and cost you no sense. But.

• 57:52Martin Casado?

  I guess the people, I'm just wondering, do you actually see market movement?

• 57:54Ali Ghodsi?

  No, people are moving on it, but what they're doing is that, you know, the pattern is I either use, you know, you can, you can use this expert pattern where you have, you know, small, cheaper open source model that uses an expert model, the big ones, or vice versa, or a way in which they can sort of ping pong them to each other. But also multiplexing harnesses and just changing harnesses so that you can control the costs is also what people are doing. You know, people have found, for instance, you know, there's, Pi is very efficient when it comes to as a harness. So, yeah, I, I think there's going to be a multitude of these. It's easy to, the models themselves are stochastic, as you said, every time they give a different answer. And they're changing so much. So there's just a lot of experimentation happening. So I think we're going to get to a world where you're not always using the smartest

• 58:37Ali Ghodsi?

  model for everything, which is kind of the, been the paradigm for the last couple of years. Like, new model comes out, it's super smart, they use it for everything, even really, really simple mundane tasks.

• 58:46Martin Casado?

  Yeah, I'll tell you what I, I see. I see people using Fable and Astra for like architecture, a cheap model for implementation, and then Fable or Astra for audit.

• 58:55Ali Ghodsi?

  Yeah.

• 58:56Martin Casado?

  Like, that seems to be like this emerging.

• 58:58Ali Ghodsi?

  What are you guys seeing in the startups? I mean, aren't they?

• 59:01Martin Casado?

  That's it. That's, that's, that's honestly the, the pattern.

• 59:04Ali Ghodsi?

  How much open source?

• 59:06Martin Casado?

  By token or by dollar?

• 59:08Ali Ghodsi?

  Either.

• 59:09Martin Casado?

  So by dollar, open source is like 5%. It's very little, but by token count is over 60%.

• 59:15Sarah Wang?

  Yeah, I was going to say, I mean, we talked to, let's say, a Decagon or something like that. They, well, I think it's different internal use versus external for product. On the external for product, I think they're almost up to 90% open source. On the internal, and I don't want to say for Decagon in particular, but a lot of them are like, we don't care, we'll just use frontier, we're not thinking about cost control. But as it gets bigger.

• 59:37Ali Ghodsi?

  Yeah.

• 59:37Sarah Wang?

  Right? You and I were at another board meeting where they actually did bring that down, just from a waste perspective. So I definitely see that moving more toward open source on the product side. And actually, that's a, related to another question, maybe around open source, but post training specifically. I feel like you were kind of early. I remember talking in 2023. When did you buy Mosaic?

• 59:59Ali Ghodsi?

  2023.

• 1:00:00Sarah Wang?

  2023, okay. So this vision that you had in 23 kind of came true in 2026. I don't know if you guys would agree, right? Like, that's sort of what we're hearing across, you know, of course, oh, just sort of like, hey, we're going to actually, you're going to own your own intelligence. You're going to be, you know, post training your open source models, et cetera. And that's definitely what the startups are doing.

• 1:00:23Ali Ghodsi?

  Yeah.

• 1:00:23Sarah Wang?

  I don't know if that's what the enterprises are doing yet, but like, I mean, do you feel like you were early to that or?

• 1:00:28Ali Ghodsi?

  Yeah, I mean, first of all, you know, there was, when we started, it was also, hey, we're also pre-training for you, which that's, that doesn't make any sense. You know, you can, they're so, they're very good pre-trained model now that they can use. But that you can do actually post-training on the model and you can do reinforcement learning, yeah, we're actually doing it at that scale. And many of those startups are actually customers. So we actually help them, you know, using RLE or reinforcement learning environments where we can make the models very, very good at the specific tasks that they are doing. It makes a lot of sense for them to do that. If you have a repetitive task, so if you have a startup and it's offering a product and that product does something specific, it's not just a general intelligence, it does something specific for you. It makes just a lot of sense to take a really good open source model and, you know, use reinforcement learning and make it really good at that specific task.

• 1:01:13Ali Ghodsi?

  You can cut the costs down, you can make it really fast. You know, they control their own IP. So in that sense, that is possible. But a lot of enterprises, they just need basic automation and it's just too much for them to do this right now.

• 1:01:26Sarah Wang?

  Yeah.

• 1:01:26Ali Ghodsi?

  I think one of the challenges is, you know, you need good evals. And making good evals is hard.

• 1:01:34Ali Ghodsi?

  So while the startups can do that and they're motivated to do that, other organizations, the easy button might be just to use a frontier model than having to create your own evals. We actually generated even.

• 1:01:45Ali Ghodsi?

  You know, evals for the customer automatically in the product and we had it front and center, but then people didn't want to use it. So we said, okay, let's move it to the backend so that it's optional. And then they would never go to it. So I would say in general.

• 1:01:56Sarah Wang?

  Why? They just don't want to get into it. It's too complicated or?

• 1:01:59Ali Ghodsi?

  I think you want quick, you know, quick reinforcement of like, you know, hey, there's a new model. I want to try this out. I'm going to get this problem solved. You don't have time to go do this, the scientific method of let's make an eval. Let's have a great baseline. And it's sort of like TDD, test driven development. You know, in software engineering, did people actually do test driven development? Very few did, right? Everyone said it's the right way to do it, but nobody actually practiced it. So that's the same, that's kind of a little bit of the curse of, you know, doing, training your own model is the evals is the hard part.

• 1:02:31Sarah Wang?

  I know you, you have an FD model at Databricks. That's a very popular word right now or acronym. But does it like to get these at enterprises that large? Is it a full FD model that's required or like, how, how do you? And how's that evolved maybe?

• 1:02:46Ali Ghodsi?

  Yeah. Yeah, I mean, we've had these FTEs and it's the demand for it's gone up significantly. A lot of it is, you know, how do we build that ontology? Like the ontology is automatic, but if you're not collecting any information, like you're not recording anything.

• 1:03:00Sarah Wang?

  Right.

• 1:03:01Ali Ghodsi?

  So that's one of the key things that we do, but also things like, you know, I want to build an agent. I want to put it on, you know, I want it to be customer facing and it's have really low latency and I want it to have guardrails to not people coming to abuse it or ask it things that we don't want it to answer and so on. So we can build that like, you know, like sports AI that Fox has, you can go chat with it about sport events and you can try to ask it actually about politics and it's very good at rejecting you and moving and talking about sports instead. So, so the FTEs built that. So, you know, we'll help the organizations actually get started with AI. It is important because it's just many organizations do not have the in-house expertise to build this stuff. So they need just a little bit of help on the side and then they get started. So.

• 1:03:45Sarah Wang?

  Yeah, makes sense. So this is more related on the agent side, but I saw recently that I think a third party, neutral third party, I think, did some tests that Lakebase or Neon was actually the data, the Postgres database of choice for agents. And I thought that was interesting. One, because, you know, one, exciting Databricks, but two, I probably wouldn't have guessed that maybe a year ago. Yeah, it was a surprise just because there's others out there that have, you know, great developer momentum as well, but it was pretty clearly number one. And so I'm curious, how did you guys crack this? And what, what makes you win across the agents? Cause if you win the agents now, you win the market.

• 1:04:28Ali Ghodsi?

  Yeah, I mean, I think a lot of credit should go to Neon and Nikita and team. And I think what they've done is they've just been obsessive about how do you make the models, how do you make the models pick and agents favor Lakebase or Neon as a database? So what do they do? The agents want to experiment, you know, they're going off, they're trying to build a little bit of software, they need the database, so you need the database to come up quickly. So they had this obsession that everything should take less than a, you know, far less than a second. So, you know, database comes up in far less than a second, you can clone gigantic database. So you can have a petabyte database, you can clone it in less than a second, you know? So it's like highly elastic, highly responsive. And then they built this killer feature called branching. So branching just lets you branch the database and you can have many, many branches over

• 1:05:12Ali Ghodsi?

  the same database. And they just made this very, very lightweight. We saw this with other things with agents, right? Like UV, you know, RIP grab, like basically reimplementation of a lot of the tools on Unix, making them really, really blazing fast and lightweight and also sort of fail safe for agents. They just did this to a harder problem, which is database. So like now you have a Postgres database and the Postgres database has all these advantages that it's really fast, it's nimble, it's fail safe. You can, you know, go back to snapshots, you can do those things. So I think that's why it's, it's just easier for the agents to use this. They also made sure that it had a pricing model that was like, you don't want, just because the agents are building some software and experiment, you don't want the cost to run up.

• 1:05:56Sarah Wang?

  Yep.

• 1:05:57Ali Ghodsi?

  You're okay paying for your database if it's like production use and lots of people are using it, but just to experiment. So I think they were just obsessed. They were not trying to win the database war or trying to be better than some other vendor. They were obsessed with how are we the best for the agents? And that's a new persona because in databases, the obsession has been how do we help DBAs? How do we help app devs? How do we help, help the people that are using the database?

• 1:06:20Sarah Wang?

  Yeah, exactly.

• 1:06:21Ali Ghodsi?

  They changed the game and said, hey, how do we focus on agents and help agents get the best database they want? And, you know, now over 90% of their, the databases that are created on Neon and Lakebase are actually created by agents. So it's not even humans. So, you know, numbers speak for themselves. They're.

• 1:06:38Martin Casado?

  By the way, it's remarkable. So I've started to use Neon as like my standard database and it was bizarre to me because like normally when you enter a large company, things slow down. It's actually like the products have got materially better.

• 1:06:50Ali Ghodsi?

  Yeah.

• 1:06:50Martin Casado?

  Are they totally independent? Do they work with the rest of the like?

• 1:06:53Ali Ghodsi?

  No, it's a great team. I mean, we work very closely together. You know, we love databases and data, so it's, it's, you know, we live that. But no, the team does a great job of just making super fast, snappy and great for agents.

• 1:07:07Martin Casado?

  All right, Ali, what is your P doom?

• 1:07:10Ali Ghodsi?

  Less than 10%.

• 1:07:15Ali Ghodsi?

  Close to zero. What about yours?

• 1:07:17Martin Casado?

  I don't know. I'd say my, my, my only answer is my P doom without AI is much higher than my P doom with AI.

• 1:07:26Ali Ghodsi?

  Wow.

• 1:07:29Martin Casado?

  I went on a technical.

• 1:07:30Sarah Wang?

  I would agree with Ali on this one.

• 1:07:35Erik Torenberg?

  Thanks for listening to this episode of the A16Z podcast. If you liked 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 at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 1:08:00Erik Torenberg?

  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 companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.