Where the [ __ ] 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 writing it faster. It's like arguably getting worse.
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. 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 even if they had joy they would cover everything 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 SaaS apocalypse story.
Today we have the founder and leader of Typesafe, Diego with us, who is a bit of a hero to both Martin 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. Welcome. Thank you for coming.
Yeah. And maybe you can give us kind of a brief on just you know what is Jev? What is Typesafe? Why is it important?
Is this a curse friendly or no? Okay. [ __ ] are you talking about? 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 [ __ ] is all the automation? Like this is like so unbelievably tragic, you know, so much intelligence. AI is so unbelievably smart and yet so not that I hate on chat bots or coding agents. I love them myself, but it's like it's so useless at all other stuff and it's tragic. It's tragic that, you know, we have so much like diamond in the rough but not polished for work. That's a, 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.
Yeah. And so it's been interesting because it's kind of caught fire in software world. So, you know, one of the things that made us go, "What the hell's 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." And then how does that, because everybody thinks of well we've got cloud code, you know, we've got Codex, don't we already have that? Like what's the difference? And then how does that lead to real automation?
Oh I wish I had like some sloped visuals because I have like a favorite sloped visual for this. So I like cloud code and Codex. I love the description from Garry Tan on them. It's just in time software, you know, 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 as software. What I want instead is smart software. Like instead of like automating software engineering, I want to expand what software itself can do such that things that could should be automatable can then be automatable. And like in a more flowery language like I want to express things like intent. I want to like 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, you know?
Oh, interesting. So, 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 going to superpower the software engineers we have to write way, way better, more interesting things.
Yeah. I actually I mean 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 cloud 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 written maybe it's better maybe it's worse but it's basically still code just like code looked 10 years ago.
Yeah. And and the thing with Jev is whether or not you're cloud code or a human, you have this new primitive, this new thing that you stick in your code that actually expands like the power of software. So instead of like writing code, it is something that you include in your code which, go ahead.
Well, 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 you know how programmers think. For example like it has this notion of like you know probabilities or you know and like so you know so.
So an intelligent layer inside the software.
Yeah. Think of like a library that you can like 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 like so ubiquitous. So maybe.
Ooh there's a lot of tricks there. I will jump into one thing first which is I love the first thing you said like, you know in the direction of where the [ __ ] 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. Um, and also like 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 like 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 now. 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 like 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 are in the middle so you know we don't output like you know extrapolated floats for example because like AI is just bad at that you know 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. You know, like classifiers are sick. Classifiers were 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 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 like 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.
There's I feel like there's way like 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 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 like solidify as a general purpose thing for programmers.
Ooh 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. So in 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 northstar right now and it could be wrong just to be clear intelligence per second might be more valuable in the short term but like even like our interface like calling the input state this is intentional like it's to say.
Oh that's great I didn't catch that.
It's meant to be the insides 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 this is going to be an ever present battle to have I, you know, 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.
Can I can I just pull back like what is the alchemy that creates a Diego? I mean like 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 like you're taking you know AI which we've been pushing towards you know 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 an award-winning mathlete. The way I describe it is I was good enough at, This is cringe. I was good enough at math to get girls. So, that's quite good.
Yeah, you have to get you have to get quite good.
And what kind of girls do you get when you're that good at math? That's an.
Oh, that's our audience needs to know.
We have to inspire the youth here.
Don't do it. Youth, don't do it. It's not worth it. Just be cool and chill and interesting and.
Wow, I can't believe I said that. Um, so I was a math athlete, 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 actually 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. 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 like sus 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. And um 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 [ __ ] out of it. Um you know like just like more nested loops more you know like I solved it like a systems problem you know. So that eventually got me like I was forced to speak at NeurIPS normally an honor but I hated it because I just wanted to be in the mines.
Was that from the Kaggle team?
Oh wow. Yeah, actually the Kaggle host of it was Isabelle Guyon who was the co-inventor of the SVM. Actually, I think the first author of SVM. I'm not 100% sure first author. Um, 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 OpenAI? No, it was like a startup with Jeremy Howard.
No kidding. Yes, I love Jeremy. Fantastic.
Cool. Um, and then Google Brain for a while.
And then, retire for a while. And then eventually I was like just kind of tired of not doing anything. 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. Really, really well.
Yeah. Incredible. So, you 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 every 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 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 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 whole movement towards a positive future that most people in the AI world kind of don't like.
I think they don't get it. Yes. You know, like the it's just a classifier complaint. It's like an ML level concern while everyone else is having like a Jev party.
Because it's like holy [ __ ] 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 really comes from this like you know mono model Kool-Aid that everyone believes. I think.
Right one big brain to rule them all. That's one way to, it sounds much more ominous.
Yeah, for sure. That's what people hear. Yeah.
But you know, like will 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 [ __ ] 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 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 now it's like a little bit of a party for me. But like I was afraid of.
There's the happy AI the people on Jev and then there's the morose AI the people who are not. It's really quite a kind of fascinating dichotomy. It is really well I give you and 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." And I was like, "That is hard to do." But yeah, 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 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 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.
Composable, composable. 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 resources associated with it with access to things you need guarantees for that or like at least statistical guarantees and.
So it doesn't go rogue breaking things that type of thing.
Well I don't think our models will be doing that anytime soon unless somebody 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 um but not in a way that we're not responsible.
I'm just curious like how long has this intuition been percolating because I remember talking to you maybe in was it 2017.
We did talk about that yeah.
Yeah and like and then like a lot of these ideas were in your 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 was going to end up being a classifier or was this just an intuition that like there was 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 in theory and flexible in practice which is very software. So, I'm a little bit consistent in that. I think that this really started right before ChatGPT. Um 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.
Must be end of 2021 like a fourth quarter of 2021. Um 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 was us actually you know scientific methodish trying to disprove like is it cheating and uh 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 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 I released it um 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?
