AI Can Write Code. Why Isn’t Software Better?

a16z’s Ben Horowitz and Martin Casado sit down with TypeSafe AI founder Diogo Almeida to ask a simple question: AI has become remarkably capable, so where is all the automation? Diogo argues that coding agents may help us write software faster, but the software they produce still largely works the way software always has. TypeSafe is taking a different approach with Jev: putting intelligence inside software itself, so developers can build programs that reason about intent and make probabilistic decisions rather than simply generate text for a human to interpret. They discuss why reliability is the key to making AI genuinely programmable, how this could open a new era of probabilistic software, and why established SaaS companies may be particularly well positioned to benefit. Ultimately, Diogo’s goal is straightforward: technology that can reliably “do what I mean.”

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

• 0:00Speaker 00

  Where the fuck is all the automation? AI is so unbelievably smart and yet it's so useless at all other stuff.

• 0:07Martin Casado?

  It doesn't matter how much AI coding agents you use, the software actually isn't getting better. Maybe you're writing it faster. It's arguably getting worse.

• 0:14Speaker 00

  OpenAI has been trying to automate customer service since 2020. What I want instead is smart software. I want to expand what software itself can do such that things that should be automatable can then be automatable.

• 0:27Ben Horowitz?

  My favorite thing that you guys say is we built prod, not God.

• 0:31Martin Casado?

  That's so good.

• 0:31Ben Horowitz?

  Because if we had any other kind of like big lab leader, even if they had joy, they would cover it. And then your view is so different. You're like, no, we're gonna create a way better world.

• 0:44Speaker 00

  For nuanced reasons, I don't think we are on the path of RSI. In the SaaS apocalypse story.

• 0:51Erik Torenberg?

  AI can write software, but what if the bigger opportunity is putting intelligence inside the software itself? In this episode, Ben Horowitz and Martine Casado sit down with TypeSafe AI founder Diogo Almeida to talk about Jev and a different vision for how AI changes computing. Diogo argues that coding agents make it faster to produce the same kind of software we already have. Jev is aimed at expanding what software itself can do, giving developers a new primitive for turning natural language intent into decisions that programs can actually use. They get into why reliability matters more than impressive demos, what a new era of probabilistic programming could look like, and why AI might make existing software dramatically more useful rather than simply replacing it.

• 1:35Erik Torenberg?

  And beneath all of this is the question that drove Diogo to build TypeSafe in the first place. If AI is already this smart, where is all the automation?

• 1:47Ben Horowitz?

  Today, we have the founder and leader of TypeSafe, Diogo, with us, who is a bit of a hero to both Martine and me. He is not only building like a really interesting product, but creating what we think is a very important movement. So we're super excited about today.

• 2:08Martin Casado?

  Welcome.

• 2:08Speaker 00

  Thank you.

• 2:10Ben Horowitz?

  Maybe you can give us a brief on what is Jev, what is TypeSafe, why is it important?

• 2:17Speaker 00

  Is this curse friendly or no?

• 2:19Ben Horowitz?

  Yeah.

• 2:19Speaker 00

  Oh, okay, cool.

• 2:20Ben Horowitz?

  What the fuck are you talking about?

• 2:21Speaker 00

  Okay, okay, cool. So I was actually asked for like an elevator pitch, which I tend to ramble on and I don't do well. But like I realized my favorite elevator pitch for Jev is where the fuck is all the automation? This is like so unbelievably tragic.

• 2:37Ben Horowitz?

  Yes.

• 2:38Speaker 00

  So much intelligence, AI is so unbelievably smart and yet so, not that I hate on chatbots or coding agents, I love them myself, but it's like so useless at all other stuff. And it's tragic. It's tragic that we have so much like diamond in the rough, but not polished for work. But TypeSafe is making AI for software. You know, we want to make AI powerful, not just for humans in the loop, but to actually build real software. And Jev to us is our first model in this whole space to make it way, way better, to like make automation.

• 3:11Ben Horowitz?

  Yeah. And so it's been interesting because it's kind of caught fire in software world. So one of the things that made us go, what the hell is going on here is like every developer we know is calling us and going, oh, this is fricking awesome.

• 3:24Martin Casado?

  It's great.

• 3:25Ben Horowitz?

  It's fast. It's great. Everything's better. Then how does that, because everybody thinks of, well, we've got Claude Code, we've got Codex, don't we already have that? Like, what's the difference? And then how does that lead to real automation?

• 3:39Speaker 00

  Ooh, I wish I had like a, some slopped visuals because I have a favorite slop visual for this. So I like Claude Code and Codex. I love the description from Gary Tan and them. It's just in time software. Incredible way to describe what they're doing. Like it makes software on the fly and you can like program software in natural language, but it has the same expressive power as software. What I want instead is smart software. Like instead of automating software engineering, I want to expand what software itself can do such that things that should be automatable can then be automatable. And like in a more flowery language, I want to express things like intent. I want to expand the vocabulary of what we can do.

• 4:21Speaker 00

  And I can talk about like all sorts of like weird sci-fi things I want, but like programming is like hyper specifying like valuable things and then infinitely replicating them. It's so fricking cool and I want to just make that more.

• 4:35Ben Horowitz?

  Interesting. So one way to think about it is instead of kind of a tool that somewhat replaces a software engineer with a faster, maybe not even as good software engineer, what you're saying is, no, no, no, we're gonna super empower the software engineers we have to write way, way better, more interesting things.

• 4:56Martin Casado?

  Yeah, yeah. So this is, by the way, I just think so many people miss this point and it's such a subtle point and it's so important to actually tease it out, which is if you use something like Claude Code or Codex, which is great, or Cursor, which is great. They write code, but that code is the same thing a human being would have write. Maybe it's better, maybe it's worse, but it's basically still code, just like code looked 10 years ago. And the thing with Jev is whether or not you're Claude Code or a human, you have this new primitive, this new thing that you stick in your code that actually expands the power of software. So instead of like writing code, it is something that you include in your code. Which by the way is interesting because it's this very powerful primitive, which would be great if you explain, but it's also a little bit different than like how programmers think. For example, like it has this notion of probabilities or.

• 5:42Ben Horowitz?

  Oh, so an intelligent layer inside the software.

• 5:45Martin Casado?

  Yeah, think of like a library, yeah, like a library that you can use natural language to describe what you want and you give it kind of a state machine and then it will choose what to do with some confidence levels, which we kind of haven't really had before.

• 5:58Speaker 00

  Ooh, there's a lot of tricks there. I will jump into one thing first, which is, I love the first thing you said, in the direction of where the fuck is all the automation? I love software so much. I wish I could be writing it all day. It's, would not recommend being a CEO to people, but whatever. And also it's wild that AI is so cool and software has been unchanged in 10 years.

• 6:21Martin Casado?

  Exactly.

• 6:21Speaker 00

  You know, like, like that to me, like no one can square this together. And the most we can do is add like a little chatbot in the side sometimes that can take actions, but not all actions, because some of the actions are not reliable enough. So I just want to give like that tiny aside. I love the point, I'm gonna jump back to the point about like, this is a little bit of a different way to think about it. Yes, I think that machine native doesn't exactly match bits perfectly. And like, that's actually the art form that we are trying to do. In our onboarding, on day one, I draw like the Venn diagram of like what AI is good at, what is valuable in code? We're in the middle. So, you know, we don't output like extrapolated floats, for example, because like AI is just bad at that.

• 7:01Martin Casado?

  Sure.

• 7:01Speaker 00

  But things like probabilities are not exactly novel. And it's similar to the, is Jev just a classifier argument? Jev is absolutely a classifier. Like classifiers are sick. Classifiers were, they're designed to be useful. Yeah, they're designed to be useful. And actually, it's the same interface as like some of those ML concepts.

• 7:21Martin Casado?

  Of course.

• 7:21Speaker 00

  Because these came from like practical people who are trying to make systems work. And what I'm seeing is happening now is that Jev actually, my guess is that Jev probably is better than having an MLE team from 2019 making the stuff for you and you can just program it on the fly. Who knows what could be built? Because like there were not that many good MLE teams in 2019 to build like narrow things and to be able to like collect data sets and measure it and all of that. And it is just the beginning.

• 7:49Martin Casado?

  By the way, to this point, do you think there's a slider bar here where like on one end is like language in, language out, like we have today? On the other end is like a, like an existing imperative program? And then you can kind of move between the two? Or do you think like this is like the point in the design space, which is language in, kind of state machine out, which is gonna solidify as a general purpose thing for programmers?

• 8:08Speaker 00

  Ooh, that's a tricky one. So I will say the answer in my heart. The answer in my heart is that it is a slider. And actually, when I design for the properties we have, I might have made mistakes due to my personal preferences, but like intelligence per dollar is my North Star right now. And it could be wrong, just to be clear. Intelligence per second might be more valuable in the short term, but even like our interface, like calling the input state, this was intentional. Like it's to say.

• 8:33Martin Casado?

  Oh, that's great. I didn't catch that.

• 8:35Speaker 00

  It's meant to be the insides of programs. So, in my heart, it's like, so we are really optimizing. A lot of the work I do is for even more complicated arrangements of the internals of program state. Can you put intelligence in there? I think that this is going to be an ever-present battle to have. I, we're very intentional about our design. And also pragmatically, I think certain things happen, like it's easier to make an AI at these milliseconds. So it'll be more like a database for a while than like a standard library thing. But I would love it to be a standard library thing too.

• 9:07Martin Casado?

  Can I just pull back for a second? Like what is the alchemy that creates a Diogo? I mean, like you speak.

• 9:12Speaker 00

  A man and a woman love each other.

• 9:17Martin Casado?

  You speak like an AI researcher, you speak like a systems person, and you speak like a programmer. And normally, these things have been like not super overlapping. And you're taking AI, which we've been pushing towards being a being, and you're making it a programmer's tool. So maybe a little bit about your personal journey that.

• 9:32Speaker 00

  My history into AI is somewhat unorthodox. I was a mathlete. I was an award-winning mathlete. The way I describe it is I was good enough, this is a cringe, I was good enough at math to get girls. So.

• 9:45Martin Casado?

  I didn't know that was a thing.

• 9:47Speaker 00

  You have to get quite good.

• 9:48Ben Horowitz?

  And what kind of girls do you get when you're that good at math? That's an actually, our audience needs to know.

• 9:55Speaker 00

  Oh, no.

• 9:57Ben Horowitz?

  We have to inspire the youth here.

• 9:58Speaker 00

  Don't do it. You don't do it. It's not worth it. Just be cool and chill and interesting and don't overcompensate. Wow, I can't believe I said that.

• 10:10Speaker 00

  So I was a mathlete.

• 10:11Martin Casado?

  Yeah.

• 10:11Speaker 00

  But I actually never, oh, man, this also is a little cringe. I never really liked math. I never really tried. I was just like big fish in little pond. And to me, math was, math was always the path I was set on, but I hated it. Because it was always about like winning competitions. But then computer science is actually a lot like math. It's basically like math, but cool and useful and fun and interesting. And I still love giving algorithms interviews. Is it the best thing for me to do? I don't know, but do I love it? Yes. And does it like allow me to like suss people out really well? Yes, it does. So I love computer science. I consider myself to be computer scientist much more before AI researcher, despite my history.

• 10:52Speaker 00

  And like what actually got me into it was I also won a Kaggle competition, not from sophisticated math, but from like just automating like the fuck out of it. You know, like just like more nested loops, more, you know, like it solved it like a systems problem.

• 11:08Martin Casado?

  Wow.

• 11:09Speaker 00

  You know, so that event eventually got me, like I was forced to speak at NERIPS. Normally an honor, but I hated it because I just wanted to be in the mines.

• 11:18Martin Casado?

  Was that from the Kaggle thing?

• 11:19Speaker 00

  Yes.

• 11:20Martin Casado?

  Oh, wow.

• 11:20Speaker 00

  Yeah, actually, the Kaggle host of it was Isabelle Guion, who was the co-inventor of the SVM. Actually, I think the first author of SVM. I'm not 100% sure I'm first author. And she just basically saw that I was like this person who really didn't fit into the research community and then adopted me and showed me, like it got me to meet all the AI people and that, you know, my career was just pushed into that direction.

• 11:46Martin Casado?

  And from there, OpenAI?

• 11:48Speaker 00

  No, it was like a startup with Jeremy Howard.

• 11:54Martin Casado?

  No kidding?

• 11:54Speaker 00

  Yes.

• 11:55Ben Horowitz?

  I love Jeremy.

• 11:56Speaker 00

  Yeah.

• 11:56Ben Horowitz?

  Fantastic. Cool.

• 11:57Speaker 00

  And then Google Brain for a while.

• 12:00Martin Casado?

  Wow.

• 12:01Speaker 00

  And then retire for a while.

• 12:03Martin Casado?

  Yeah.

• 12:04Speaker 00

  And then eventually I was like just kind of tired of not doing anything. And I was like, you know what? Actually, AI is pretty damn fun. And I joined OpenAI because of that reason. And it worked out really well.

• 12:18Martin Casado?

  Amazing.

• 12:19Speaker 00

  Really, really well.

• 12:19Ben Horowitz?

  Yeah, incredible. So you said something there that is so unusual in today's world, which is AI is really, really fun. And then the company has such a different demeanor and view of AI than everybody else. And my favorite thing that you guys say is we build prod, not God.

• 12:43Martin Casado?

  So good.

• 12:44Ben Horowitz?

  Because if we had any other kind of like big lab leader, they'd be like trying to, even if they had joy, they would cover it. And then your view is so different. You're like, no, we're going to create a way better world and it's going to be awesome. And there's going to be, not only are there not going to be less jobs, there'll be more jobs, and there'll be way better jobs, and everybody's going to have a great time. And like just be being around you, like you clearly believe that. So so tell us about that and like what this, because for us, you know, typesafe Jev, it's more than a company. It's a, it's a whole movement towards a positive future that most people in the AI world kind of don't like.

• 13:26Speaker 00

  Yes.

• 13:26Ben Horowitz?

  Or they're not with it.

• 13:29Speaker 00

  I think they don't get it.

• 13:31Ben Horowitz?

  Yes.

• 13:31Speaker 00

  You know, like that it's just a classifier complaint. It's like an ML level concern while everyone else is having like a Jev party.

• 13:39Speaker 00

  Because it's like, holy shit, like we can do all the things that we wanted to do. And I don't, I think if you don't like get developers, it'll be hard to understand what's really going on. So 100% I agree with that. I do think that there's like a pretty negative world painted that I obviously disagree with. I think it's really comes from this like, you know, mono model Kool-Aid that everyone believes.

• 14:02Ben Horowitz?

  Right, one big brain to rule them all.

• 14:07Speaker 00

  That's one, that's one way that it sounds, sounds much more ominous.

• 14:11Ben Horowitz?

  Yes, but that's, that's what people hear.

• 14:13Martin Casado?

  For sure, that's what people, that's what people hear, yeah.

• 14:16Speaker 00

  But, you know, like will that one, is that one brain really on the path to rule us all? Like, we have not automated really basic things that I don't think we want people to be doing. You know, like it, there's lots of really, really basic stuff. And I think that, oh, man, it pains me when the world is discordant with the reality. And like part of the pain is, you know, on the where the fuck is all the automation? Like, how can we have AI be so freaking smart? And like, there's so much, so much financial incentive to automate stuff. Like, yeah, you could make an excuse for diffusion. I don't buy it at all. I shouldn't name names, but like that obviously is not true. Part of the problem is like the discordance with the reality.

• 14:57Speaker 00

  And the fact that AI has like so much potential is what made it really tragic for me, that we had not released this. So now it's, now it's like a little bit of a party for me, but like I was afraid of.

• 15:08Ben Horowitz?

  And all Jev users are like, there's the happy AI, the people on Jev. And then there's the morose AI, the people who are not.

• 15:17Speaker 00

  Yeah, yeah, yeah.

• 15:18Ben Horowitz?

  It's really, it's quite a kind of fascinating dichotomy. It's, it is really, well, I'll give you, and to your automation point, I had a funny conversation this morning with David George, who runs our growth fund. Because we're talking about the new tools. I was like, have you tried the Muse thing? He's like, oh, it's awesome. I was like, what'd you do with it? He said, I finally canceled my New York Times subscription.

• 15:42Ben Horowitz?

  And I was like, that is hard to do.

• 15:48Ben Horowitz?

  But, you know, it's a kind of a, it's a very tip of the iceberg of the things that are horrible things to do that that we need to automate.

• 15:57Speaker 00

  I think that if we were going to be really intellectually honest and we are really aiming for the North Star of automation, we cannot fall into the same anti-patterns that AI has fallen into, which is really focusing on outliers and denials, right? Like, a lot of people ask me, like, what are your favorite use cases? And I'm like, I'm not sure if they work. I want them to work in the background such that like someone would trust that to run and not page them. And like, people can build on top of that too. And like, you know.

• 16:24Ben Horowitz?

  Composable, composable.

• 16:25Speaker 00

  Composable, but like other things like safe, right? Like, it's a different type of safety where like, if you want it to actually like run with the resources associated with it, with access to things, you need guarantees for that, or like at least statistical guarantees. And.

• 16:39Ben Horowitz?

  So it doesn't go rogue, break and then I can face that type of thing.

• 16:42Speaker 00

  Well, I don't think our models will be doing that anytime soon, unless someone like does the software to do that, which would be very cool flex, very cool flex. I should figure out how to give credits for that. But, but not in a way that we're not responsible.

• 16:58Martin Casado?

  Right, right, right. I'm just curious, like, how long has this intuition been percolating? Because I remember talking to you, maybe in, was it 2017? That's a long.

• 17:08Speaker 00

  We did talk about that, yeah.

• 17:10Martin Casado?

  Yeah, and like, and then like a lot of these ideas were in, you know, you were talking about data being important, and you're talking about like you want to focus on the task and like, but like, so I just like, you know, was this like, did you know that this is going to end up being a classifier? Or was this just an intuition that like, there's just kind of another way to view this entire kind of AI movement? You know?

• 17:30Speaker 00

  So actually, a fun story about that chat in the talk from 2017. I think my talk was actually a very similar theme. I think it was called something like AI modular in theory and flexible in practice, which is very software system.

• 17:45Martin Casado?

  Yeah, totally.

• 17:45Speaker 00

  So I'm a little bit consistent in that. I, I think that this really started right before ChatGPT. Like, right when we released these things, I did not have intuition about this. And honestly, I was not even, I was very, very pleasantly surprised by the generalization capabilities of RLHF.

• 18:03Martin Casado?

  When is this?

• 18:04Speaker 00

  Must be end of 2021, like a fourth quarter of 2021. Like, we were, it was really, really general. Like, if you read the paper, it's unlike other papers that are like trying to prove their point. It is us actually, you know, scientific method-ish, trying to disprove, like, is it cheating? And, you know, my favorite query was, why is it important to eat socks before meditating? We made sure that was not on the internet beforehand. And like the models were able to like make plausible, you, human-looking answers for this. And that to us in the team was the thing that clicked, like, this is not cheating, which you should always be afraid of cheating in ML. And then what really got me burnt was like, we released it.

• 18:46Speaker 00

  You know, we did, you know, I'm obviously a big capabilities guy. I did a lot to release that model. I, I really thought that the model had like a decent chance of being AGI. And when it didn't, that was like when my whole world came crashing down. And I was like, why?

• 19:04Martin Casado?

  So you were kind of on the other train for a bit.

• 19:07Speaker 00

  Which, like the crazy train or?

• 19:08Martin Casado?

  Well, no, I'm just like, our RL generalizes, like, maybe we have AGI, like.

• 19:14Speaker 00

  RHF generalizes pretty well. RLVR is the thing that doesn't generalize as well, from what I've seen. And AGI in the.

• 19:24Martin Casado?

  Well, I was just saying more, I mean, like, you know, you were behind in chat GPT. You were behind these early GPTs. That was a very different goal, which is like creating a chatbot that will talk to the human being, was not a programmer's tool, et cetera. So I'm just wondering like.

• 19:36Speaker 00

  Oh, well, actually, early, early, like 2020, OpenAI, when we talked about AGI, people used to describe it as Ilya in every if statement. So it's not. But like, is it like part, we were talking about OpenAI culture. Part of it is that it's like intentionally vague. So it's a wide like tent so that everyone can be inside of it. But like, I am not, for nuanced reasons, I don't think we are on the path of RSI. And I still don't think we're in the path of RSI, and I did then. I do think that what OpenAI defined as AGI is extremely doable. Automating most of the world's economically valuable work actually sounds like a, oh,

• 20:19Speaker 00

  man, I don't like, there's, there's a lot of work out there. A lot of it is very rote and simple. And like by volume, in order to be able to like outsource work, you need like simple instructions that like basic people can do. And as far as I can tell, the intelligence of that has been available in the models for like quite a while now. And like my, oh, man, you know, chip on my shoulder is like, why is this not available? And, and then, and since RHF, the AI industry just like kind of bifurcated into gigantic over promise, under deliver. I think GPT three was actually quite calibrated back in that day. But because humans evaluate how good the models are, it looks really good because they are the judge, but we've been optimizing that judge instead of the automation part.

• 21:04Speaker 00

  And that has been the missing thing. So I would say there was really, really then that it like hit me, you know, like, why is this thing not more useful?

• 21:12Martin Casado?

  And so you think that the measure that we should have is to what extent can you automate actual productive tasks? That the.

• 21:18Speaker 00

  That's new.

• 21:19Martin Casado?

  When you say over promise and under deliver, that's the dimension in particular you're talking to, ability to automate tasks.

• 21:26Speaker 00

  I like, I think in my heart, it's like cool sci fi, you know? And I think that, I think that that is the canary in the coalmine for cool sci fi. Like, are you really telling me that we like math is solved? Or like even like two years ago, GPQA, that Google proof question answering is solved, but we still can't handle a drive through, right? Like, it's, it's, it's, it's a very hard thing to hold in your head at once. And I think a lot of people don't have good answers to that.

• 21:54Martin Casado?

  Can I just test one thing, which, which may, which may not make sense, but I want to say, I mean, I mean, isn't there an argument, though, that like, the, the, the real, the distribution of the real world is, is, is different than the digital world, right? It's heavy tailed, there's a lot of exceptions, we don't have all the data. And, I mean, it couldn't be the case that the reason we're not doing productive stuff in the real world is just like, we're not, we don't have the data for that distribution. We're not training on that distribution. And this is why it's just been basically relegated to like these lower dimensional manifolds, like whatever math or code or.

• 22:28Speaker 00

  Um, I don't entirely buy the data argument, in my opinion. I do believe that there's a, a long tail for sure, like that that'd be kind of crazy to deny. And I don't think that in my like canary in the coalmine situation, we need to automate that long tail. Like, I think that, I think we need to be incredibly pragmatic on everything. And like, building reliable software is always an investment, right? Like, it, like, you know, what were the three great virtues of a programmer? Laziness to not to do it again, hubris, and there was a third one.

• 23:03Ben Horowitz?

  Yeah, no, I remember, this is from the Pearl days. Yeah, yeah.

• 23:07Speaker 00

  Yeah, there's a third one. I, I wish, I wish I could remember it. But like, it's about like the laziness to like spend, you know, like 10 hours to do like the five minute task instantly and to never have to do it again. Like, it only make, like, it should be an ROI decision for people who like automate stuff. Like, I would just like it to be automateable. And I think that people will just make like new kinds of work, hence the Jev in Jevens, new kinds of work once that stuff is doable. But like, as like a, a benchmark, I feel like it's useful to see, can we actually automate the stuff that it really, really looks like, yeah, I should be able to automate? OpenAI has been trying to automate customer service since 2020. You know, like, it, it, it's, you know, like, it's not.

• 23:53Ben Horowitz?

  Which is pretty amazing.

• 23:56Speaker 00

  It's, it's, it's wild, you know, it's wild.

• 23:58Ben Horowitz?

  Well, and then inside, I mean, inside companies, there's very little that's automated right now. Like, and the projects haven't worked. Other than, programming has worked amazing.

• 24:10Martin Casado?

  Can you, can you maybe classify the types of problems you think that are easier to automate now? Cause it, it was kind of interesting. So we've actually looked at support before the current generative wave. And it was interesting, you'd meet a company and the company would say, we, we answer 95% of all like, you know, like help desk calls. And like, that is so many. But then you actually look at the data. And you realize it's all password resets. And then like, but if you did it by like uniqueness, it was only something like 50% or something. So it just feels like when you're dealing with humans and natural systems, like there's just kind of this very kind of, you know, like a long tail of exceptions. And so to what extent did like, I, every, probably every hour I have somebody ping me and like, I'm using Jev for this new use case.

• 24:55Martin Casado?

  I'm like, I had no idea, you know, like, you know, and, and so like, to what extent did you even predict like the broad range of use case for it? Like, did you assume that was gonna happen? And have you been surprised by that or?

• 25:05Speaker 00

  Extremely surprised, did not assume it could happen. This launch was like not something, like if anyone expected this, they are probably insane. Right? Like, it is, I, I don't think someone could expect a chat GPT for developers, because chat GPT was for, you know, like, like, you know, the normal users. And it's weird. I actually don't even know what percentage of the people who are part of the JF party are developers themselves. I can't imagine non-developers using it. I don't know how they would use it. But even my non-developer friends are just like part of the party in Twitter and meaning and everything like that. So number one, phenomenal.

• 25:45Speaker 00

  Number two, this will be hard to convey in this short message, because like, it's been like blood, sweat and tears for years now. Like, the amount I care about reliability is, it's, it's a lot. Like, reliability is what this thing is. If you don't understand that, it'll be very hard to make like a copycat that's benchmarked. Like, it's, I feel like every nine of reliability is going to be so valuable for everyone, even if it's not the most valuable thing market cap wise, because it will just enable new applications. And like, we are fighting for like all sorts of like weird nines of reliability that like we don't even fully understand, because we are just like, you know, it's like really

• 26:30Speaker 00

  getting this like electric motor of AI, of intelligence, like into people's like workstations, and they can figure out what to do with it.

• 26:37Martin Casado?

  What does reliability mean in this context? Is this, is this like availability of the model? Or is it like, I call the model and it returns the same thing? Or like, how, how do I think about reliability?

• 26:48Speaker 00

  Yeah, so.

• 26:49Martin Casado?

  For something that's inherently kind of stuck.

• 26:50Speaker 00

  So not so much the former thing. And the second thing is closer, like I would describe the first thing as kind of like uptime or SLAs. The second thing I would maybe call closer to determinism. Something thirdly, I would consider more like robustness. So robustness, I would kind of describe as similar intelligence every time.

• 27:11Martin Casado?

  Oh, interesting.

• 27:12Speaker 00

  Yeah, so like not exactly determinism, because I think determinism, it's useful for unit tests, but not real systems. Think about like, if you add a U U ID to a prompt, it should be the same, because it's the same functionally, but it's not exactly deterministic. I think that there's another layer of it that I don't really know what it's called yet. Like, maybe this is what I would call like some form of intelligence, which is, it doesn't have to be the similar function every time, but it needs to be smart every time. You know, like, if you were in that situation, would this be an understandable thing for a human to think? Because a developer can program around that. And actually, to me, the highest honor of reliability will be to get to the point where people can program against Jev without making example queries.

• 27:55Speaker 00

  Like, when you just trust it, you'll be in like perma flow state, just creating crazy software. And like, that's where a lot of software is today, right? Like, I, I don't think it's totally unrealistic, but I'm gonna, gonna be fine.

• 28:09Martin Casado?

  By the way, this is kind of a weird question. So like, I mean, feel, feel free, like, if it's too weird, just feel free. But it occurs to me that actually the value of things like coding agents goes down if you have a primitive like this in a way, which is like, you could be like, you know, whatever, some, you know, Codex builds all the software for me, but it doesn't actually use Jev. And so like the software itself, it creates a somewhat limited. Or you can be like, okay, I as a human being, I will write the software without using a coding agent, but I've got this very generalized primitive that makes writing software easier. So like, do you feel like, see a future where it's like the coding agents using Jev? And then you're telling the coding agents? And then do you have like redundancy? Or do you feel it's like human?

• 28:48Speaker 00

  This is more of like a coding agent question than it is a Jev question.

• 28:51Martin Casado?

  Oh, yeah, for sure.

• 28:52Speaker 00

  My vibe is that I'm not in the coding mind as much as I'd like to be. So you are, you two might be in there more than I am, which is sad. But my experience is that they are really good at syntax and really.

• 29:08Martin Casado?

  The best of semantics.

• 29:11Speaker 00

  I would say incredibly bad at architecture.

• 29:13Martin Casado?

  Yeah.

• 29:14Speaker 00

  So like, to me, architecture is like the most human creative part of software. So I love using coding agents. I think that Jev is almost certainly not in distribution. That would be spooky if they trained on our user data. So it's probably not. But I think that when it is in distribution, I, I see no problem with like having it do the syntax. And the thing with architecture is that maybe the models are actually like not just crap at architecture, but maybe their 50th percentile at architecture. And if you don't know anything about architecture, it would be fine. So these are all like gray area trade offs in order for you to navigate. And sometimes speed is the, the knob for your company or project to turn. Like, you're willing to do a 50th, like a 50th percentile architecture instead of a

• 29:57Speaker 00

  60th, because you want to move faster and have like Codex work overnight or something like that.

• 30:03Ben Horowitz?

  Actually, kind of along those lines, one of the interesting things or phenomenons in the market already is that, you know, when the coding agents came out, it was the SaaS apocalypse and all their values dropped through the floor. And then when Jev came out, every SaaS company is like, this is the greatest thing ever. So explain that.

• 30:23Speaker 00

  I I don't know what else to say, right? Like, I think it's like quite natural. Like, in the SaaS apocalypse story, the the story that I feel like has panned out really poorly is that software is very cheap and perhaps easy to replicate, which I think I could I could believe the former. I could not believe the latter because a lot of the stuff happens beneath the hood. I'm maybe overly a software fanboy here.

• 30:50Ben Horowitz?

  Yeah, all of us.

• 30:51Speaker 00

  Yeah, okay, okay, okay. I didn't know where you might be with coding.

• 30:55Ben Horowitz?

  We have a lot of legacy around that.

• 30:57Speaker 00

  Yeah. Yeah, so I don't think that's really panned out. So SaaS seems like maybe the markets don't agree. But like, I think SaaS is providing the same value it used to. Maybe the markets are just scared. But I think that SaaS will be one of the largest winners of like the whole AI game. And I want to like work really, really well with like all the biggest, most boring, most like in the know of user problem SaaS companies. Because I think that they are the best positioned to know what workflows to automate. What do people need? Like, that's what their bread and butter is. And to spend the big, like, you know, software is always a capex investment. And but like you spend it ahead of time in order to make this experience even better that gets, you know, like distributed to all of that massive users.

• 31:42Ben Horowitz?

  Right.

• 31:42Speaker 00

  So I think that it's gonna be. I I'm not gonna forecast anything about the financial markets. But I think as far as like a capabilities games goes, it's gonna be like an inverse SaaS apocalypse. And I am so jazzed about it. I should make a name.

• 31:57Ben Horowitz?

  Yeah, yeah, but you should have a name.

• 31:59Martin Casado?

  Sasa Palooza.

• 32:01Speaker 00

  Whoa.

• 32:03Speaker 00

  That sounds a little too fun.

• 32:06Ben Horowitz?

  Well, the SaaS applications are gonna all of a sudden get like dramatically more useful. And by the way, you know, the the kind of capital investment, like so much of a of a SaaS company's capital investment is like actually getting to all the customers. And so if you've gotten to all the customers and then you make, you know, not just put a chatbot on your SaaS product, but actually make the software like way, way better.

• 32:30Ben Horowitz?

  That's a hell of a thing.

• 32:31Speaker 00

  I don't know if this is a realistic dream or not. But I think that there's a world where like the multi-choice forms just disappear. You know, like, I feel like they're like, they're always like something mapping natural language that usually the software already has into like a Jev-like output. And I think.

• 32:51Ben Horowitz?

  It's literally, it's literally from the eighties. It's like, it's called, we used to call it 4GLs. Do you remember that, Mark? Fourth generation language.

• 32:58Speaker 00

  Yeah, actually, also, I think this is from the eighties. This might be an insult. I was born then. That like, I think that do what I mean is going to be like be taken to the absolute next level. If I could shout out one Jev application, I don't know if it's reliable. So I can't promise anything, but it was so freaking cool. Someone was using like a voice to control your computer. And it was basically constantly making decisions on like, is this a command? Or is it inserting text? Where's inserting text? Like, like, that sounds so unbelievably cool. Like, like, I feel like interfaces could just completely change. And maybe we're gonna have to make it like cheaper faster, you know?

• 33:36Ben Horowitz?

  Yeah, then you're at Star Trek.

• 33:39Martin Casado?

  Well, you know, there's just such a profound intuition here, which is,

• 33:45Martin Casado?

  if if you use AI today to generate software, right? You're still creating the same software that you did before. But if you actually look at like the average PR for a large company, it's like 10 lines, right? Seriously, no.

• 33:59Speaker 00

  I've been at Google.

• 33:59Martin Casado?

  We actually did the study. So it's like 10 lines. So like, you're automating 10 lines. And by the way, those 10 lines are are like, you know, part of a learning from a customer or something. So like, you're, you've kind of optimized something that's actually pretty minimal. But what it doesn't do is provide new capabilities to the software, right? It's kind of automating this thing, which in the limit ends up being relatively minor. And now like there's actually new capability. And so like, it could just be the case that just software just actually gets better.

• 34:23Speaker 00

  Yep.

• 34:24Martin Casado?

  And by the way, even before Jev, like, it didn't even occur to me that like, it doesn't matter how much, you know, AI coding agents you use, the software actually isn't getting better. Maybe you're writing it faster. It's like arguably getting worse just because like there's less oversight. So I think this is.

• 34:39Ben Horowitz?

  And often more insecure.

• 34:41Martin Casado?

  Yeah, for sure, for sure. But you're but you're actually now can make an argument like, like, like, like, apps will have new functionalities as a result of this. Because there is this new primitive that you're providing that, I mean, like in a way, like it speaks natural languages and it can reason. But it marries that to a state machine.

• 34:58Speaker 00

  I, if people take that as a takeaway, that would be like the greatest compliment ever to what we are doing. Like, I actually feel like it's almost two grand of a vision to expand beyond the three logic gates that we have into like, you know, our types are kind of like one of the same logic gate, but like one that's like a little brain in there. Like, that would be the greatest compliment to like the type safe legacy. Because like, that is, that's a very non-trivial, huge thing for the world. I'm not gonna like overpromise, underdeliver that, but I will fight for that.

• 35:32Martin Casado?

  Yeah, I mean, listen, I mean, there's, I think, pretty open questions to what like, how deep can this get as far as like, like really serious stuff? Like state consistency or durability or like real systems level stuff where you actually need to like provide strong guarantees? And so 100% this will change things like whatever, analyzing logs, analyzing emails, providing a UI, talking to the human, like that for sure. But like, you know, you could argue that over time, this becomes like a smart database, like, you know?

• 35:58Speaker 00

  And also.

• 35:59Ben Horowitz?

  Air traffic control system.

• 36:00Martin Casado?

  I mean, anything.

• 36:01Ben Horowitz?

  Which we really need.

• 36:04Speaker 00

  A little scary. Like, I think automate the easy work before the hard work is always my philosophy. But I also think there's going to be like an entire era of probabilistic programming that's opened up. Like, my.

• 36:16Martin Casado?

  By the way, you know, there's a huge history of probabilistic programming. That basically died in like the seventies.

• 36:20Speaker 00

  I'm familiar with it. I actually think it's going to be like with the same, like, you could also call Jev like neuros.

• 36:28Ben Horowitz?

  Your co-founder Eric came from that background, he was telling me.

• 36:31Speaker 00

  Oh, cool. Oh, yes, yes, yes, he did a lot of biology. It goes up and down. But like, what I mean is a more, I'm not a fan. My brand is pragmatism, incredible pragmatism. I'm not a fan of like biologically inspired stuff at all.

• 36:49Martin Casado?

  It's never worked. Have you ever noticed that?

• 36:52Speaker 00

  I think it's never worked. It's useful to motivate crazy people to work on things for decades until it works. And then they refine it into like the engineering version.

• 37:00Ben Horowitz?

  The story of AI, neural nets for sure.

• 37:02Speaker 00

  Yes, but like, you know, a lot of the stories about how it worked were not accurate, right? So like the hierarchical features of applications really did end up working. Because like otherwise, ResNets wouldn't have worked. Longer story. I do think that it opens up like, like from a systems perspective, I'm not excited about this part. Because it's really, I'm excited for the world, not about me programming this because it sounds like really complicated. But I think that as we have like lots of intelligence at lots of like different cost and speed trade offs, the super systemsy types will be making trade offs at like, you know, like Jeb's gonna be like a thousand times too smart for them. They just want like an approximate link to have an approximate guess to like optimistically route here and there.

• 37:43Speaker 00

  It's gonna be like so crazy, the this type of stuff that's available in the extreme systems.

• 37:48Martin Casado?

  And the good news is we get to like rebuild systems again, which is great, right? We have a new, no, seriously, we have a new primitive. It's kind of a new way of thinking about doing software. Like, I mean, we did this, we did this for the internet. We did this being friend to client server. I mean, we do this periodically.

• 38:02Ben Horowitz?

  And and by the way, just because of the cybersecurity issues, we probably have to rebuild almost all the systems to just make them safe.

• 38:13Martin Casado?

  I would think. I think it's pretty clear that there like there's not.

• 38:17Ben Horowitz?

  Or at least the critical, the critical infrastructure, for sure.

• 38:21Martin Casado?

  Yeah. Do you do you think about this more in terms of like apps, SaaS analytics? Or more in terms of like systems, foundations, or all the above?

• 38:33Speaker 00

  For what I would think of or?

• 38:35Martin Casado?

  Yeah, just general application for this. When you think about like, like you're working on Jev and like, and you kind of envision that people are adapting it. Like, you know, like, do you, maybe do you even have an opinion?

• 38:46Speaker 00

  I have a little bit and it's.

• 38:50Speaker 00

  So the way I think of it is a little like, like deep into like the TCP guts. You know, like UDP, TCP, you know, like it's unreliable, unreliable.

• 39:00Martin Casado?

  Sticking my language.

• 39:00Speaker 00

  Exactly. And like, I, so like when I think of AI, yeah, I mean, like when I think of AI, and this is why I care about intelligence per dollar, to be clear. When I think, and how I got to this conclusion, I work backwards from AI-based economic revolution. AI everywhere, no sci-fi and everything, like all the software has AI all over the place. And I ask myself the question, what percentage of the calls to AI? I imagine it's like a function, which what percentage are like for human consumption? Where you need like style. Yeah, and it's gonna be like many nines. And actually, from that same question, how many will be at the first layer versus like deep in the guts? Right? And I think that it's going to be many nines in the guts, but it will start at the first layer. But like, we need to, if you don't aim for the guts, wait, that sounds weird.

• 39:43Speaker 00

  If you don't aim for the guts, it's going to take you a while to get there, right?

• 39:48Martin Casado?

  I think people don't understand to what extent like AI was kind of ships in the night with software. Like, even if you try to embed AI in software, it kind of like didn't behave, right? Because software doesn't really take natural languages. And you like do all this weird stuff, like you stick in the prompt, like, here's the JSON output that you want, and here's a schema, and it would never listen to it. And so, and so what you ended up doing is just taking the output and giving it to a human. You're like, to hell with it, right?

• 40:10Speaker 00

  Or another LM. That is what a while loop is, like the agent while loop, right? So it's like, it's like from first principles, it needs to be human in the loop, which is the chat.

• 40:17Martin Casado?

  Yeah.

• 40:17Speaker 00

  Or an agent, which is the while loop, because like the natural language needs to be fed back into another.

• 40:22Martin Casado?

  And I will say, I have watched this, there was almost like this kind of like five stages of grief. Like, you know, people will pick up AI and like, I'm gonna use this, you know, within my software, right? And then, you know, and then it would like go with like, you know, whatever denial, like try to make it work and like anger. Then they go to acceptance, which is like, okay, never mind. I'm just gonna give this to another LM to a human being. So it's been very ships in the night. And I think this is the first time I have seen when someone's like, actually, you can take an LLM, you can take AI, and you can actually map it to like, like a state machine. And you can do that productively.

• 40:55Speaker 00

  And I hope so. I will not want to overpromise, underdeliver as well. Like, I don't know if it's ready for all the applications that have been overpromised. I really, really wanted to, and my team will fight for that, obviously. Like, we really, really care about reliability. We could have released so much sooner. I don't think people realize that. And I don't think that, honestly, I don't think that they will. Based on what I see at the Twitter discussion, I think it's, people will never get it. But like, it'll just have like that good vibe of like, oh, I can trust this. So.

• 41:28Ben Horowitz?

  Well, it's the anti-frustration machine.

• 41:32Speaker 00

  It's, it's a, I hope so. I hope, do what I mean, right? To me, that is about like smoothness in the world, like having everything that just move more smoothly together and interlink like gears. I actually have my whole like AI utopia on like different axes that I really, really want. And like, do what I mean is a huge part of this. You know, like, imagine if all technology just did what you mean. That is, like, that's not sci-fi. Look how smart AI is, right?

• 41:59Ben Horowitz?

  Yeah, no, it's a, it's amazing. And and maybe that's the thought to close on. Do what I mean.

• 42:06Speaker 00

  Love it.

• 42:07Ben Horowitz?

  Thank you, Diogo. This has been a great conversation. Really enjoyed it.

• 42:13Erik 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.

• 42:39Erik 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.