a16z Podcast - AI Can Write Code. Why Isn’t Software Better?

Episode Date: September 28, 2026

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.”Resources:Follow Diogo Almeida: https://x.com/CompleteSkepticLearn more about TypeSafe AI: https://typesafe.ai/Follow TypeSafe AI: https://x.com/typesafeaiFollow Ben Horowitz on X: https://x.com/bhorowitzFollow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Where the fuck is all the automation? AI is so unbelievably smart, and yet it's so useless at all other stuff. It doesn't matter how much AI coding agents you use, the software actually isn't getting better. Maybe you're running it faster. It's like arguably getting worse. Open AI has been tried 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. My favorite thing that you guys say is we built prod, God. Because if we had any other kind of like big lab leader, even if they had joy,

they would cover that. And then your view is so different. You're like, no, we're going to create a way better world. For nuanced reasons, I don't think we are on the path of RSI. In the SaaSpocalypse story, um,

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. 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? Today, we have the founder and leader of TypeSafe, Diogo, with us, who is a bit of a hero to both Martina and me. He is not only building a really interesting, product, but creating what we think is a very important movement. So we're super excited about today. Welcome to here. Thank you. Maybe you can give us a brief on what is Jev? What is type safe? Why is it important? Is this a curse friendly or no? Yeah. Oh, okay. Okay. Cool. So I was actually asked for like a elevator pitch, which I tend to ramble on and I don't do well. But like I realized my

favorite elevator pitch for Jeff is where the fuck is all the automation? Like this is like so unbelievably tragic. Yes. 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 Jiv to us is our first model in this whole space

to make it way better to make automation. 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 freaking awesome. It's great.

It's fast, it's great, everything's better. Then how does that, because everybody thinks, of, well, we've got Cloud 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? Ooh, I wish I had like some slopped visuals

because I have the favorite slopped visual for this. So I like Claude Code and Codex. I love the description from Gary Tan on them. It's just in-time software. Incredible way to describe what they're doing. It makes software on the fly, and you can, like, program software in natural language, but it has the same expressive power.

a 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. 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 freaking cool. And I want to just make that more.

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, no, we're going to super empower the software engineers we have to write way, way, more interesting things. Yeah, yeah. So, 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 CloudCode 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 right. 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 your cloud 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...

So an intelligent layer inside the software. 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. 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. I 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. You know, like that to me, like no one can square this together

and the most we can do is add like little chatbot in the side sometimes that can take actions, but not all actions, because some of the actions are not reliable. I just want to give that tiny aside. I love the point. I'm going to 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. Sure. But things like probabilities are not exactly novel, and it's similar to the, is Jev just a classifier argument? Yeah. 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. Of course. 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 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. By the way, to this point, do you think there's a slider bar here where like on one end is like language and language out like we have today on the other? end is 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 going to solidify as a general purpose thing for programmers? Oh, that's a tricky one. So I will say the answer in my heart.

Yeah. 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 is intentional. Like, it's to say that. Oh, that's great. I didn't catch that.

It's meant to be the inside of programs. So in my heart, because 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. We're very intentional about our design and also pragmatial. 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. Can I just pull back to you like, what is the alchemy that creates a Diogo? I mean, like, you speak.

When a man and a woman love each other. You speak like an AI researcher. You speak like a systems person. 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...

My history into AI is somewhat unorthodox. I was a mathlete. I was a award-winning mathlete. The way I describe it is I was good enough at... This is a cringe. I was good enough at math to get girls. So that's quite good. No, that was a thing.

Yeah, I didn't. You have to get quite good. And what kind of girls do you get when you're that good at math? That's an actually... Our audience. needs to know. Oh, no. We have to inspire the youth here. 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. So I was a mathlet, 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 a little pond. And to me, math was always the path I was set on, but I hated it because it was always about 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. It's not, it's the best thing for me to do. I don't know, but do I love it? Yes. And does it like allow me to access people out really well? Yes, does. So I love computer science. I consider myself to be computer scientist much more before AI researcher, despite my history. And, like, what actually got to 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 like a systems problem, you know? So that event eventually got to me, like, I was forced to speak at Nureps, normally in honor, but I hated it because I just wanted to be in the minds.

Was that from the caggle thing? Yes. Oh, wow. Yeah, actually, the caggle host of it was Isabel Guillaume, who was the co-inventor of the SVM. Actually, I think the first author of Svium. 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. And from there, opening eye? No, it was like a startup with Jeremy Howard. No kidding. Yes. I love Jeremy. Yeah.

Fantastic. Cool. And then Google Brain for a while. Wow. And then retire for a while. Yeah. 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 Open AI because of that reason. And it worked out really well. Amazing. Really, really well. 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. So good. 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 them.

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 than not going to be less jobs, there'll be way better jobs, and everybody's going to have a great time.

And like just being around you, like you clearly believe that. So tell us about that and like what this, because for us, you know, type safe Jev, it's more than a company. It's a whole movement towards a positive future that most people in the AI world kind of don't like. Yes. Or they're not with it. I think they don't get it. Yes.

You know, like it's just a classifier complaint. It's like an ML level concern while everyone else is having like a Jeff party. Because it's like, holy shit. Like we can do all the things. things that we wanted to do. And 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. I think right one big brain to roll them all.

That's one that's what way that sounds sounds much more ominous. Yes, but that's what people are here. Yeah, that's what people are here, yeah. 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, 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 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. 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 a little bit of a party for me. But, like, I was afraid of AI. 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. Yeah, yeah, yeah. It's really, it's quite a kind of fascinating dichotomy. 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, I've you tried the muse thing. He's like, oh, it's awesome. I was like, what did you do with it? He said, I finally canceled my New York Times subscription. And I was like, that is hard to do. 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 we need to automate. 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, really focusing on outliers and demos, 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. Composable, but like other things like safe, right? Like it's a different type of safety where like, if you wanted to actually like run with resources associated with it, with access to things, you need guarantees for that or like at least statistical guarantee. And so it doesn't go rogue, breaking and frug and face that type of thing.

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 that in a way that we're not responsive. Right, right, right. I'm just kidding. How long has this intuition been percolating?

Because I remember talking to you maybe in, was it, 2017? So long. We did talk about that. Yeah. Yeah. And then like a lot of these ideas were in, you know, you were talking about data being important. 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? So actually a fun story about that chat in the talk from 2017. I think my talk was actually in a very similar theme. I think it was called something like AI modular and theory and flexible in practice, which is very software system. Yeah, totally. So I'm a little bit consistent in that.

I think that this really started right before chat to GPT. 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. When is this? 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'd made sure that was not on the internet beforehand. And like the models were able to like make plausible, 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, we released it. You know, we did a, you know, I'm obviously a big capabilities guy. I did a lot to release that model. I really thought that that 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? So you were kind of on the other train for a bit? Like the crazy train? Well, no. No, no. RL generalizes, like, maybe we have AGI.

Like, if you have generalizes pretty well. RLVR is the thing that doesn't generalize as well, from what I've seen. And AGI in, so. Well, I was just saying more, I mean, like, you know, you were behind in chat, GPT, you were behind these early GPDs. That was a very different goal, which is, like, creating a chatbot that will talk the human being was not a programmer's tool,

et cetera. So I'm just wondering like... Oh, well, actually, early, early, like 2020, Open AI, when we talked about AGI, people used to describe it as Ilya and every if statement.

So it's not... It's kind of like... But like, is it like part... We were talking about open AI 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 OpenAID defined as AGI is extremely doable. Automating most of the

world's economically valuable work actually sounds like, oh man, I don't, like, 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 quite a while now. And like my, oh man, you know, chip on my shoulder is like,

why is this not available? And then since R like Jeff, the industry just like kind of bifurcated into gigantic overpromise underdeliver. I think GPT3 was actually quite calibrated back in that day. But because humans evaluate how good the models are,

it looks really good because they're the judge, but we've been optimizing that judge instead of the automation part, 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? And so you think that the measure that we should have

is to what extent can you automate actual productive tasks? That the, I would like that. When you say over, promise and underdeliver, that's the dimension in particular you're talking to. You believe to automate tasks. 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 coal mine for cool sci-fi. Like, are you really telling me that math is solved, or 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 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. Can I just test one thing, which may not make sense, but I want to do?

I mean, isn't there an argument, though, that, like, the distribution of the real world is is different than the digital world, right? It's heavy tail. There's a lot of exceptions. We don't have all the data. And I mean, it couldn't it be the case that the reason we're not doing productive stuff in the real world is just like 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. I don't entirely by the data argument, in my opinion. I do believe that there's a long tail for sure, like that would be kind of crazy to deny. And I don't think that in my like Canary in the Coal Mine situation, we need to automate that long tail. Like I think that 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? Laisiness to not to do it again, hubris. And there was a third one. Yeah, no, I remember this is from the Pearl Days. Yeah, there's a third one.

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 automate a bowl. and I think that people will just make new kinds of work hence the Jev in Jevons

new kinds of work once that stuff is doable but as like a benchmark I feel like it's useful to see can we actually automate the stuff that it really really looks like AI should be able to automate Open AI has been trying to automate customer service since 2020

you know like it's you know like it's not which is pretty amazing It's wild, you know, it's wild. Well, inside, I mean, inside companies, there's very little that's automated right now. And the projects haven't worked. Other than programming has worked, amazing.

Can you maybe classify the types of problems you think that are easier to automate now? Because 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 answer 9. 95% of all, like, you know, like help desk calls. And like, that is so many. But then you actually look at the data.

They're all the same. And you realize it's all password recess. And then, like, but if you did it by, like, uniqueness, there's 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, every, probably every hour I have somebody ping me like, I'm using Jeff for this.

new use case. I had no idea. You know, like, you know, like, 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 going to happen? And have you been surprised by that? Extremely surprised. Did not assume it would happen. This launch was,

like, not something, like, if anyone expected this, they are probably insane. Right? Like, it is,

I don't think someone could expect a chat GPT for developers because check chit was for, you know, like the normal users. And it's weird. I actually don't even know

what percentage of the people who are part of the JIF 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 and Twitter and meaming and everything like that.

So number one, phenomenal. Number two, this will be hard to convey in this short message because it's been like blood, sweat and tears for years now. The amount I care about reliability is, 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 bench-maxed. 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 weird nines of reliability that, like, we don't even fully understand

because we are just, like, you know, really 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. What is reliability to me in this context? Is this just, like, availability of the model, or is it, like, I call the model and it returns the same thing? Or, like, how do I think about reliability? Yeah, so.

For something that's inherently kind of stochastic. 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. Oh, interesting.

Yeah. So like not exactly determinism because I think the terminism, it's useful for unit tests, but not real systems. Think about like if you add a UUID to a prompt, it should be the same because it's the same functionally. But it's not exactly determinism. 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 when people can program against Jev without making example queries. 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 don't think it's totally unrealistic, but I'm going to be fine.

By the way, this is kind of a weird question. So like, I mean, 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 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, humans implement? This is more of, like, a coding agent question than it does a Jev question? Oh, yeah. So my vibe is that I'm not in the coding minds as much as I'd like to be. So 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...

The bad at semantics. I would say incredibly bad at architecture. Yeah. So 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 in our user data. So it's probably not.

But I think that when it is in distribution, I see no problem with having it to do the syntax. And the thing with architecture is that maybe the models are actually, like not just crap at architecture, but maybe they're 50th percent of architecture. And if you don't know anything about architecture, it would be fine. So these are all like gray area tradeoffs in order for you to navigate. and sometimes speed is 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 60th because you want to move faster and have like codex work overnight or something like that. 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 SaaSpocalypse, and all their values dropped through the floor. And then when Jeff came out, every SaaS company is like, this is the greatest thing ever. So explain that. I don't know what else to say, right?

I think it's quite natural. In the Saspocalypse story, 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 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 fan. boy here. Yeah, all of us.

Yeah. Okay, okay. I didn't know where might be the coding. We have a lot of legacy around that. Yeah. So I don't think that 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 position 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 KAPX investment. 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. So I think that it's going to be, I'm not going to forecast anything about the financial markets. But I think as far as as like a capabilities games goes,

it's going to be like an inverse saspocalypse, and I am so jazzed about it. I should make a name. Yeah, got that, yeah, that, you should have a name. Sassapalooza. Oh, that sounds a little too fun.

Well, all the SaaS applications are going to all of a sudden get like dramatically more useful. And by the way, you know, the kind of capital investment, like so much of a SaaS company's capital investment is like actually getting to all the cost. 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, that's a hell of a thing.

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 choice forms just to disappear. You know, like, I feel like they are, like, they are always, like, something mapping natural language that usually the software already has into, like, like a Jev-like output. And I think it's literally, it's literally from the 80s. It's like, it's called, we used to call it 4GLS. Do you remember of the MR2? Fourth generation language. Yeah.

Actually, also, I think this is from the 80s. This might be an insult. I was born then. Like, I think that do what I mean is going to be, like, be taken to the absolute next level. Yeah, yeah. If I should 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 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 that sounds so unbelievably cool. I feel like interfaces could just completely change and maybe we're going to have to make it like cheaper or faster. Yeah, then you're at Star Trek. Well, you know, there's just such a profound intuition here

which is 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. I've been at Google. We actually did the study, so it's like 10 lines. So you're like you're automating 10 lines.

And by the way, those 10 lines 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. And, by the, even before Jeff, it didn't even 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, aren't you really getting worse just because, like, there's less oversight. So I think this is... And often and more insecure. Yeah, for sure, for sure. But you're actually now can make an argument. 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. 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, but like one that's like a little brain in there. Like that would be the greatest compliment to like the typesafe legacy.

Because like that is, that's a very non-trivial, huge thing for the world. I'm not going to like over promise, underdeliver that, but I will fight for that. Yeah, I mean, listen to there. 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, 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. And also air traffic control system. Anything, which we really need. A little scary. Like I think automate the easy work before the hardware 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... By the way, you know there's a huge history of probabilistic programming.

That basically died in like the 70s. I'm familiar with it. I actually think it's going to be like with a same... You could also call Jeff like neurosythological. So your co-founder Eric came from that background he was telling me. Oh, cool.

Oh, cool. Oh, yes, yes. He did a lot of biologies. It goes up and down. Yeah. 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. It's never worked. Have you ever noticed that? I mean, 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. The story of AI, neural nets for sure.

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, 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 tradeoffs, the super systemsy types will be making tradeoffs at like, you know, like Jebs is going to 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. It's going to be like so crazy,

this type of stuff that's available in the extreme systems. 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 to think 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. And by the way, just because of the cybersecurity issues, we probably have to rebuild almost all the systems. Yeah, for sure. to just make them safe. I would think. I think it's pretty clear that there's not... Or at least the critical infrastructure, for sure.

Yeah. Yeah, yeah. 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? For what I would think of or how... Yeah, just general application for the same. When you think about like you're working on Jeff and like, and you kind of envision.

that people are adapting it. Like how, you know, like, do you, maybe do you even have an opinion? I have a little bit, and it's, so the way I think of it is a little like, like, deep into like the TCP guts, you know, like, UDPTCP, you know, like, it's unreliable, too reliable.

Speaking my language. Exactly. And like, when I think of AI, you know, 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 that style? And yeah, and it's going to 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 of 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's weird. If you don't aim for the guts, it's going to take you a while to get there. Right. 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 do all this weird stuff. Like you stick at 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 what you ended up doing

just taking the output and giving it to a human. and you're like to hell with it, right? Or another L.M. That is what a wild loop is. Like the agent wild loop, right? So it's like, from first principles, it needs to be human in the loop, which is the chat. Yeah.

Or an agent, which is the wild loop because like the natural language needs to be fed back into another. 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 going to 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 going to give this to another LLM to a human being. So it's been very ships in the night. I think this is the first time I have seen. It's almost like, actually, you can take an LLM, you can take AI, and you can actually map it to like a state machine, and you can do that productively. 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 want it 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 people will never get it. But it'll just have, like, that good vibe of, like, how I can trust this. So. Well, it's the anti-fustration machine. 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 it is, right? Yeah. No, it's amazing. And maybe that's the thought to close on. Do what I mean.

Yeah, I love it. Thank you, Diogo. This has been a great conversation. It's really enjoyed it. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our substack at A16Z.com.

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