How Jev Turns AI Into Software That Gets Things Done

[00:00:00] 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. [laughter] Maybe you're writing it faster. It's like are you really getting worse? Open AI 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 built prod not god. >> It's so good. >> Because if we had any other kind of like big lab leader, even if they had joy, they would cover it up. 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-pocalypse story. [music] Um >> Today, we have the founder and leader of TypeSafe, Diogo, with us um who uh is a [00:01:02] bit of a hero to both Martin and me. >> Yes. >> Um he is not only building like a really interesting product uh but creating what we think is a very important movement. So, we're super excited about today. Welcome to coming. Yeah. >> Thank you. >> Yeah. And uh maybe you can give us kind of a a brief on um just you know, what is Jev, what is TypeSafe, why is it important? >> Is it purse-friendly or not? >> Yeah. Yeah. Yeah. >> Oh, okay. Cool. >> He's like, "Are you talking about me?" >> Okay. Okay. Okay. Cool. I was actually asked uh 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 Jev is where the [  ] is all the automation. Like this is like so unbelievably tragic. 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 it's so useless at all other stuff and it's [00:02:04] it's tragic. It's tragic that we have so much like diamond in the rough, but not polished for work. That's that's but Type Safe 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 it's been interesting because it's kind of caught fire in software world. So though 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 fast, it's it's great, everything's better. Um and then how does that cuz cuz everybody thinks of well, we've got Claude 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? >> Ooh, I wish I had like a some slop visuals [00:03:05] because I have like 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. You know, 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 of software. Um 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 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 for 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? >> Yeah. >> Oh, interesting. So so one way to think about it is instead of kind of a tool that uh [00:04:08] somewhat replaces a software or engineer with a faster, maybe not even as good software engineer, what you're saying is, "No, no, no, we're going to super empower the software engineers we have to write way, way better, more interesting things." >> Yeah, I I actually I mean yeah, yeah, so This is 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 um if you use something like Cloud Coder or Codux, which is great, or Cursor, which is great. Yeah. 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. >> Yeah. >> And and the thing with Jev is whether or not your Cloud Coder 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. >> Mhm. >> Which Go ahead. >> No, no, you go ahead. >> Well, which which By the way, is interesting because it's this very powerful primitive, which would be great [00:05:08] 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 maybe >> Also, an intelligent layer inside the software. >> of like like a you can like use natural language to describe what you want and then you give it kind of a state machine and then it will 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 this 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 like like that's to me, like no one can like square this together, and the most we [00:06:08] can do is add like a little chatbot in the side sometimes that can take actions, but not all actions cuz some of the actions are not reliable. I just want to give like that tiny aside. Um 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. Um 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 I 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, you know, extrapolated floats, for example, cuz 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 [00:07:08] those NL 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, I 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 out of fly. Who knows what could be built? Cuz like there were not that many good MLE team MLE teams in 2019 to build like narrow things and to be able to like collect data sets and measure it and all that. And it is just the beginning. There's I I like there's way like like 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 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 going to like solidify as the 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 [00:08:09] design for all 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 like even like our interface, like calling the input state, this was intentional. Like it's the same thing. >> great. I didn't catch that. >> It's meant to be the inside of programs. >> So, so in my heart >> 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 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 for a second? Like what is the alchemy that creates a Diogo? I mean, like you speak >> When a man [laughter] and a woman love each other [00:09:10] >> But like But like But listen, you speak like an AI researcher, you still speak like a systems person, you speak like a programmer, and normally these things have been like not super overlapping, and like you're 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. Um I was a mathlete. Um I was a award-winning mathlete. Um 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 good. No, that was the thing. >> Yeah, I did 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 answer. >> Woah, that's I >> Our audience needs to know. >> Oh, no. >> We have to inspire the youth here. [laughter] >> Don't do it. Youth, 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. >> [laughter] >> So, I was a mathlete. >> Yeah. [00:10:10] >> 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 I 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 not 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 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. >> [clears throat] >> You know, like just like more nested loops, more you know, like it it solved like a systems problem, you know? So, that event eventually got me um like I [00:11:11] was forced to speak at NeurIPS, normally an honor, but I hated it cuz I just wanted to be in the mines. >> Was that from the Kaggle thing? >> Yes. >> 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 I'm 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 then showed me every like like it got me to meet all the AI people and then you know, my career was just pushed into that direction. >> there, Open AI? >> No, it was like a startup with Jeremy Howard. >> Um, no kidding. >> Yes. >> I love Jeremy. >> Yeah. >> Fantastic. >> Cool. >> Yeah. >> Um, and then Google Brain for a while. >> Wow. >> And then uh retire for a while. >> Yeah. >> 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 Open AI because of [00:12:12] that reason uh and it worked out really well. >> Amazing. >> Really, really well. >> Yeah, no, incredible. So, you you said something there that is so um 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 it up. [laughter] 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 that, there's 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 [00:13:12] what this cuz for us, you know, type safe Javits, it's more than a company, it's it's a whole movement towards a positive future that most people in the AI world kind of don't like. >> Yes. >> Or or or or they're not with it. >> They >> think they don't get it. You know, like that it's just a classified complaint. It's like an ML level concern while everyone else is having like a Jeff party. >> [laughter] >> 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's really comes from this like you know, mono model Kool-Aid that everyone believes. >> Right. Right. One big brain to rule them all. >> [laughter] >> That's why that's what they did. It sounds sounds much more ominous. >> Yeah, yeah, yeah. But that's that's what people are hearing. [00:14:12] >> For sure that's what people >> That's what people are hearing, 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 it there's lots of really really basic stuff and I think that Oh man, it it it pains me when the world is discordant with the reality and like part of the pain is you know, on the way 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 have 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 AI >> And all Jeff users are like there's the happy AI, the people on Jeff [00:15:12] and then there's the morose AI, the people who are not. It's it's really it is quite a kind of fascinating dichotomy. It's it is really relevant. Well, I'll give you and and to your automation point, I had a funny conversation this morning with David George, who runs our growth fund, cuz 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 do you do with it?" He said, "I finally canceled my New York Times subscription." >> [laughter] >> And I was like That That is hard to do. >> [laughter] >> 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. >> 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 demos, right? Like a lot of people ask me like, "What are your favorite use cases?" And I'm [00:16:12] 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 >> Uh composable, composable I guess. >> 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 the 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 out of the [laughter] fence that type of thing. >> Well, I I 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 I should give credit for that. But um But that in a way that we're not responsible. >> Right. Right. Right. >> I'm just curious like how how long has this intuition been percolating? Cuz I remember talking to you maybe in Was it 2017? >> It was a long We did talk about that, yeah. >> Yeah. And like And then like a lot of these ideas were in you you know, you were talking about data being important, [00:17:13] you were talking about like you know, you want to focus on the task and like But like So I 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 a very similar theme. I think it was called something like AI modular in theory and flexible in practice which is very software system so I'm a little bit consistent but I I think that this really started right before chat GPT. 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. >> When is this? >> Must be end of 2021 like fourth quarter of 2021. >> [snorts] >> Um like we were it was really really general. Like if you read the paper it's unlike other papers that are like trying [00:18:14] to prove their point. It is 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 in ML. >> Right. >> And then what really got me burnt was like we 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 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? >> Oh so you were kind of on the other train for a bit. >> Which like the crazy train or >> Well no I'm just like no no no no RL generalizes like maybe we have AGI like >> RLHF generalizes pretty well. RLVR is [00:19:15] the thing that doesn't generalize as well from what I've seen. Um and AGI in the >> Well, I I was just saying more I mean like you know, you were behind ChatGPT, you were behind the these early GPTs. That was a very different goal, which is like creating a chatbot that will talk to the human being, which is not a programmer's tool, etc. So, I'm just wondering like >> Oh, well, actually early early like 2020, um OpenAI I when we talked about AGI, people used to describe it as Ilya and every other statement. Um so, it's not it's But like but 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're on the path of RSI, and I still don't think we're on the path of RSI and I didn't then. I do think that what OpenAI defines as AGI is extremely doable. Automating most of the world's economically valuable work actually sounds [00:20:15] >> Yeah. >> like a Oh man, I don't like There's a 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 the and since RLHF, but the industry just like kind of bifurcated into gigantic over promise under deliver. I think GPT-3 was actually quite calibrated back in that day. But because humans evaluate how good the models are, it looks really good cuz 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 [00:21:15] the When you say over when you say over promise and under deliver, that's the dimension in particular you're talking to. The ability to automate tasks. >> I like I think in my heart it's like cool sci-fi. Um 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 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-thru, right? Like it's >> [laughter] >> 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. >> Yeah. >> Can I can I just test one thing which which which may not 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 it 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 [00:22:16] 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 >> 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 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 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's was one. >> yeah. Now I remember. This is from the Pearl days. Yeah. Yeah, there there's a third law, man. Very well, yeah. I wish I wish I could remember it. But like it's about like the laziness to like spend like you know, like 10 hours to do like the 5-minute task instantly and to never have to do it again. Like it only [00:23:17] make like it should be an ROI decision for people who like automate stuff. Like I would just like it to be automatable. And I think that people will just make like new kinds of work out hence the Jevons Jevons. 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. Open AI has been trying to automate customer service since 2020. You know, like it's you know, like it's not. >> It's just pretty amazing. >> [laughter] >> It's wild, you know, it's wild. >> Well, and then inside I mean inside companies um there's very little that's automated right now. Like and the projects haven't worked. >> Right. >> Other than programming has worked amazing. >> Can you can you maybe classify the types of problems you think that are easier to automate now? Cuz it it was kind of interesting. So we've actually looked at support before the current generative wave. And [00:24:18] it was interesting you'd meet a company and the company would say um 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 >> They're all the same. >> And he realizes 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 but probably every hour I have somebody ping me on like I'm using Jevons for this new use case. 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 cases for it? Like did you assume that was going to happen? And have you been surprised by that or >> Extremely surprised. Did not assume it would happen. Uh this launch was like not something like if anyone expected this they were probably insane. Um right? Like it is I I don't think someone could [00:25:18] expect a chat GPT for developers cuz chat GPT was for you know 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 GIF party are developers themselves. I can't imagine non-developers using it. I don't know how they would use it. Um but even my non-developer friends are just like part of the party in Twitter and memeing and everything like that. So number one phenomenal. Number two um this will be hard to convey in this short message because like it's been like blood sweat and tears for years now. But like the amount I care about reliability is um 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 [00:26:18] 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 like really getting the 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 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 do I think about reliability? >> Yeah so >> for something that's inherently kind of stochastic. >> So uh 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. The 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 cuz I think determinism 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 cuz it's [00:27:18] the same functionally. >> Mhm. >> But it's not exactly deterministic. >> Right, right, right. >> 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 you and 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. Like you when you just trust Jev, 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 I don't think it's totally unrealistic, but I'm going to going to be fine with it. >> Do you By the way, this is kind of a weird question. So like I mean feel feel free like if if it's too weird just feel free like but um it occurs to me that actually the value of things like coding agents goes down if you have a primitive [00:28:18] 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 is 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 agent's 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 It's of like a coding agent question than it is a Jeff question. >> Oh, yeah, for sure. Yeah, yeah. >> My vibe is that um I'm not in the coding minds as much as I'd like to be. So, you are YouTube might be in there more than I am, which is sad, but my experience is that they are really good at syntax and really >> They're bad at semantics. >> Um I would say incredibly bad at architecture. >> Yeah. >> Um so, like to me architecture is like the most human creative part of software. So, I love using coding [00:29:18] agents. I think that Jeff is almost certainly not in distribution. That would be spooky if they trained on our user data. So, it's probably not. Um 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 they're 50th percentile 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 60th because you want to move faster and have like Codex work overnight or something like that. >> Yeah, actually, kind of along those lines, one of the interesting things or phenomenon in the market already is that you know, when the coding agents came out, it was the SAS apocalypse and all their values dropped through the floor. And then when Jeff came out, every SAS company is like, "This is the greatest thing ever." So, [00:30:19] explain that. >> I I don't know what else to say, right? Like I I think it's like quite natural. Like in the SAS apocalypse story, um 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. Um, I'm maybe overly a software fanboy here. >> Yeah, all of us. >> Yeah, okay, okay, okay. Um, I don't know where it might be the coding agent part. >> We have a lot of legacy around that. >> Yeah. So, I don't think that really panned out. So, SAS seems like maybe the markets don't agree, but like I think SAS is providing the same value it used to. Maybe the markets are just scared. Um, but I think that SAS 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 [00:31:19] user problem SAS companies, because I think that they are the best positioned to know what workflows to automate, what do people need. Like that's what 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. So, I think that it's going to be I'm I'm I'm not going to forecast anything about the financial markets, but I think for as far as like a capabilities games goes, it's going to be like an inverse SASpocalypse, and I am so jazzed about it. We I should make a name. >> Yeah, I got that. Yeah, that that that Yeah, SASapalooza. >> Oh, >> [laughter] >> that sounds a little too fun. >> So, all of the SAS applications are going to all of a sudden get like dramatically more useful. And by the way, you know, the the kind of uh capital investment, like so much of a of a SAS company's capital investment is like actually getting to all the customers. And so, if you've gotten to [00:32:19] all the customers, and then you make, you know, not just put a chatbot on your SAS product, but actually make the software like way, way better. Um, 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 I feel like they are like they are always like something mapping natural language that usually the software already has into like a dev-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 4GL. Do you remember fourth generation language? >> Yeah. Actually, also I think this is from the '80s. This might be an insult. I wasn't born then. The like I think that do what I mean is going to be like into the absolute next level. If I can shout out one dev 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 [00:33:19] making decisions on like is this a command or is it inserting text? Where is inserting text? Like like that sounds so unbelievably cool. Like Like I feel like interfaces could like just completely change and maybe we're going to have to make it like cheaper faster, you know? >> Yeah, then you're at Star Trek. >> [laughter] >> Well, >> You know, there's there's just such a profound intuition here, which is um um you know, if if you use AI today to generate software, right? You're still creating the 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, it is. >> We actually did the study. It's like 10 lines. So they're like you're automating 10 lines and by the way, those 10 lines are are like you know, part of the learning from a customer something. So like you're you've kind of optimized something that's actually pretty minimal. Um 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 [00:34:19] like it could just be the case that just software just actually gets better. And and by the way, didn't even before dev, like just 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 arguably getting worse just because like there's less oversight. So, I think this is >> Well, and and often and more insecure. >> Yeah, but 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 cuz there is this new primitive that you're providing that I mean like in in 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 too grand of a vision to expand beyond the three logic gates that we have into like you know, it 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 that the type [00:35:20] safe legacy cuz like that is that's a very huge non-trivial huge thing for the world. Um I'm not going to like over promise under deliver that, but I will fight for that. >> Yeah, I mean listen to I mean there's I think pretty open questions to what it 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, and so >> And also >> air traffic control system. >> I mean anything, right? >> really need. 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 >> By the way, you know there's a huge history of probabilistic programming. Like that basically died in like the '70s or >> I'm I'm I'm familiar with it. I I [00:36:20] actually think it's going to be like with the same like you could also call it have like >> So, your the time. Your Eric came from that background he was telling me. >> Oh, yes, yes, yes. He did a lot of biology stuff. It 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 um 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 version. >> The story of AI neural nets for sure. >> Yeah. 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 didn't end up working cuz like otherwise res nets wouldn't have worked. Longer story. Um I do think that it opens up like like from a systems perspective I'm not excited about this part cuz it's really I'm excited for the world not about me [00:37:20] programming this cuz it sounds like really complicated. >> Yeah. >> But I think that as we have like lots of intelligence at lots of like different cost and speed trade-offs, the super systemy types will be be making trade-offs at like you know like Jeff's 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 optimize through out here and there. It's going to be like so crazy the this type of stuff that's available in the extreme systems that >> And the good 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 with the internet and you know we did this mainframe to client server. I mean we do this periodically. >> And and by the way just because of the um cyber security issues we probably have to rebuild almost all the systems uh to to just make them safe. I I would think. >> I think it's pretty clear that there like there is not >> Or at least the critical the the critical infrastructure for sure. Yeah. >> Yeah. Yeah. [00:38:20] >> 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? >> For what I would think of, or how >> general application for this. When you think about like like you're working on Jev and like and you kind of envision the people are adapting it, like how how you know, like do you do 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 uh like deep into like the TCP guts. You know, like you need TCP, you know, like it's unreliable to reliable. >> my language. >> Exactly. And like [laughter] I 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 and how I got to this conclusion, is I work backwards from AI-based economic revolution. AI everywhere, you know, 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 [00:39:20] to AI, I imagine it's like a function, which what percentage are like for human consumption, where you need that style and everything. And 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 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's that's weird. >> [laughter] >> If you don't aim for the guts, you you it's it's it's going to take you a while to get there, right? >> I I think people don't understand to what extent like AI was kind of shipped to 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 language 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 the 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 the human. You're like, the hell with it, right? >> 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, or an agent, which is the while loop, because like that language needs to be fed back into [00:40:20] it. I will say I have watched this there was almost like this kind of like five stages of grief >> [laughter] >> 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 I try to make it work and like anger and then they go to acceptance which is like okay never mind I'm just going to give this to another LM to a human being so it's been very ships in the night I think this is the first time I have seen when someone was like actually you can take an LM you can take AI and you can actually map it to like like a state machine and you can do that productively. And I hope so I will not want to over promise under deliver as well like I don't know if it's ready for all the applications that have been over promised 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 I based on what I see after Twitter discussion I think it's [00:41:20] people will never get it but like it'll just have like that good vibe of like oh I can trust this so it's the anti-frustration machine >> [laughter] >> yes >> [gasps] >> 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 a 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 at how smart AI is right no it's a it's amazing and and maybe that's the thought to close on do what I mean I love it thank you Diogo this has been a great conversation really enjoyed it [00:42:23] >> Mhm.