Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI

launching jev and the state of ai

0:00Okay, we're in the studio, a special occasion, because this week, Diogo, my good buddy, launched Jev, and has been taking over the complete timeline. How do you feel? What's it like to be you right now? Emotionally? Yeah. Never been worse. Like, I'm a ragged corpse of a person right now, because there's so much going on, and I'm like a technical CEO, so I have like a lot of fires to fight. But like, like, mentally, it's, like, I feel, I say this all the time, and I've been saying this

0:37kind of for years in my over-under events. Like, I feel like the entire AI field is like one of those, like, carnival house of mirrors, and everyone is just insane, and saying the weirdest stuff that doesn't make sense. And it feels like for just this week, like, I'm on a better in sync with reality, and like, oh, people see it now. AI can be so much more than what was once thought. And like, yes, we are going to make, like, like, an AI-based

1:09economic revolution is back on the table. And this is fucking awesome. You know, this is fucking awesome. I'm so jazzed the developers get it. It's, it's, yeah. And I want to show my internal gratitude to the developers, and I'm so jammed about the community and everything. It's so great. Yeah, you were saying yesterday that you decided to prioritize the downtown hall and not a bunch of, like, you know, VIP investor type people, because you wanted to make sure that they are the people that you get, get your most attention, right? The engineers, the

1:42developers. Yeah, it felt a little like, oh, man, I'm talking to, like, really important people right now. I probably shouldn't reveal who, but it feels a little bit dirty for me to, uh, I, I'm like, perhaps overly genuine in things. Like, it feels like dirty if like in my gigantic calendar events of things to people to talk to, you know, the community isn't one of those, you know, and actually in my ideal world, it would be like community all the time. I was thinking, should I host a town hall while walking to your studio? And I'm like, nah, that's too crazy.

2:12Sure. Yeah. Uh, well, you, you guys have been hosting town halls on the discord. Discord is now a hundred thousand people. Um, your, your Twitter is blown up. Uh, you know, I was, it was really funny cause like at AIE you were like, yeah, follow me please. And then you didn't like provide even your, your handle. I'm a noob. You're such a noob. I'm a noob. But no, but like, that's like positive aura that like you don't know how to promote yourself. Someone like called me out when I posted like, holy shit, we're all three trending topics. And then they're like, that's a personal feed.

2:43And I'm like, oh no, cringe. Of course it's a trend to you. Yeah. Cause it's what you clicked on. Um, so, okay. Let's, uh, so congrats on everything. We'll

what jev is and why it matters

2:50talk about more, uh, details as you have them, but less for people who are like living under a rock or just, just once like the definitive thing, what is Jeff?

3:02Whew. Let me think about, that's a hard one. Um, okay. And I'm happy to like, uh, re-ask if you want to, no, no, I'm happy to, I'm happy to like just jam on it. Yeah. I will say like the first thing that I'm relieved about with this question is now I don't have to answer that question to my parents anymore because chat GPT can just explain it. Um, so the way I see it is we need, need a new class of models. Um, we're not attached to naming that class of models. Um, are the most accurate name we've come up with is system one models.

3:33There will be reasons, but it's, there's a reason why we don't call them decision models because like they will be like system one is beyond that. Uh, that's all I can say. Um, we didn't expect this to be our big launch. So we have stuff in the tank. Um, you should have said low key research preview. Um, it's kind of was right. It's kind of was, but, um, we, so there's a class of models that we describe them as like machine native system one, large programmable. I think these are, is the class of models where the goal is for code to be

4:06the consumer. So, um, as opposed to, um, um, pre-trained large language models, which are meant for like auto complete of the internet or RLHF models like chat bot instruction following models, which are meant to like reply to text, um, or RLVR. It's in a weird gray area with RLHF. Like these are meant to have things that directly are consumed by code, hence the name type safe. So the thing we really, really, really want is to have like AI, like be as powerful as

4:37possible. And we think the way to do that is to integrate it with software and we are designing everything, you know, beyond just the outside, the deep internals of the model to be optimized for software. So, uh, number one, Jev is our first large programmable model, um, or a system one model, whatever you want to call it. Um, Jev is meant to be optimized for intelligence per dollar. Uh, hence the name Jev, you know, Jevon's Paradox. Yeah. Um, and it's optimized for intelligence per dollar. I love this debate with people about what is the most important

5:11between reliability, cost, calibration, and speed. Um, and Jev is meant to be, Jev will be the name of models that will be on the frontier of intelligence per dollar. There's other ways to optimize it like ML, or at least if you're good at ML, it's all about trade-offs and, you know, we are just going all out on that. Yeah. And to me, like calibration is one of the new things that people weren't talking about as much. We've done an episode, uh, in the past, uh, with Clementine Foria of hugging face, uh, where they were like, yeah, I actually, um, they're just are,

5:44you know, and this is your, your whole argument about RLHF is their mode collapsing towards what you want to hear the most or what is most likely, uh, instead of like their own internal confidence

the downsides of rlhf and mode collapse

5:53about a thing. Can I soapbox on that for a second or cool? Like, um, I've been heard that your audience is the most technical, so I actually want to get into that. Um, and if I went through extreme precision to make sure everything in our launch video is accurate and real, apparently that's very unusual. One of the things that no one paid attention to was the downsides of RLHF, in particular mode dropping, um, more dropping and more collapse. Uh, it's the same thing. It's the same thing. And, um, I, I want to have a blog on this eventually, but I like

6:24want to tell as many people this as possible because I think it's a very interesting thing. So the spicy take, I believe in Jan LeCun a lot. Uh, I think Jan LeCun's takes are actually among the closest to what, what about this? Well, should I address this now or should I go in mode collapse? No, no, no, later. Go, go, go, go, go, go in mode collapse. I don't know. So I actually think that among takes Jan LeCun's is among the most accurate. Um, but he has this very famous slash infamous slide about, uh, LMs are doomed. You know, like that one where he's,

6:55where he like, she has like a pie chart with like a tiny part, tiny little thing and says that as you increase sequence length, um, the probability of it making an error goes in. Yes. This one, this one, I love this one. Um, because it's one of these things that seems mathematically obvious, but is obviously wrong, right? Like it's mathematically obvious, but it doesn't empirically hold. And this is my favorite thing to teach people about like where you get disconnect. Exactly. And, um, may I, or you want to tell me about mode collapse?

7:28Oh, no, no. So mode collapse is related to this. Yeah. The disconnect happens because if you are in a mode covering or a calibrated distribution, um, you are like not, you are not overly punished about having outliers. So you'd expect like, you know, something, some amount of the time you'd be out of distribution, some amount of time you'd be in distribution. That's what happens when you cover the distribution. This was like models before GANs. They made blurry images, right? Um, instead GANs mode drop, they like drop the minority classes and just do the really common

7:59ones. And this is why this effect doesn't happen, right? Like instead of in order to generate really long strings, um, without making errors, they need to like be extremely conservative because it's really easy to see when an error happens. It's very hard to see when like a subtle thing that looks correct happens. And that calibration is like total poison into like the probability distributions of strings. Yeah. And, uh, it's a, it's a nuanced take. And like, I think that this is why this doesn't happen. And this is why strings are so bad at, uh, decision-making or, uh, you know,

8:34overloading the string models are for decision-making is like a bad time. And while we're on the topic of Yann, do you agree that his fix is with, which is like a world model, like a JEPA type, um, embedding thing is the right solve. So basically like the, one of the reasons that it could fail is because you're trying to reason over token outputs and then, and then just looking back again and, uh, going, keep, uh, continuing going until you reach like a end of sentence. Um, like, is that, uh, and his solve is JEPA, right? Which is like joint ambition, uh, uh, joint embedding prediction. Uh, so like, is that the

9:07solve or, you know, like, uh, do you have a, do you have a take on that? Oh man, I probably shouldn't talk too much about the insides of ML, but I will say that my, my brand other than unhinged is practical, you know, like even my take here is practical. And like, I'm, am I a scaling law fan? Um, depends. It depends. It depends. You know, it's, it's like, it's a scaling laws tell you how much better you get at a thing for amount in a scaling law does mean exponentially more resources for normally sublinear gains,

9:37which looks to be a bad investment unless those like linear gains are like really, really valuable. Um, but it's all, to me, it's all about like, what can we do with what we have to make the biggest possible fucking difference? I can curse. Yeah. Yeah. Yeah. Yeah. Yeah. We're, we're approved for adults. Hell yeah. And also we have a scaling law thing. If you want to go into that later. Oh, I could, if we, if that part is not super relevant right now, I actually, if you want to go into my bitterest lesson, I think that that's more relevant, but like, to me, I'm all about

10:08like pragmatics. And I think that the JEPA stuff is really cool early research. I really love awesome research. Is it practical yet? Um, probably shouldn't say. Um, but, uh, like there's just a lot of, I just think that there's like so many diamonds in the rough let all over the research world right now that haven't been polished because people don't know how to like do the right task. And I

10:40think that what our launch did, it, does it kickstart us as a company? Like, yes. Will it be great for us as a company? Yes. I think it's going to be like even greater for this direction of like programmatic AI, you know, there, there was going to be like a gold rush on top of us for, cause like software is super fucking charged, but I think there's going to be a gold rush parallel to us as well on like all the different ways we can expose things to make software more powerful. So people can make even cooler stuff. And then we are back to like early internet energy, you know? And I think that's

11:13why like, you know, the Twitter is just like Jeff, Jeff, Jeff, you know? Um, it's, it's like, it's inspiring because it's, it's like so different than what we're used to, which is, I'm sorry, you can't do this, but we do scaling laws and only the big labs can do it. Right. That, that, that actually, if I, I'm a tangent, if that's okay. Yeah. I think you might enjoy this. Really? Five tangents in there. It's good. Oh yeah. I get lost at all my tangents. This is going to be horrible for the listeners to figure it out, but they're going to figure it out.

safety alignment versus user utility

11:40Yeah. So popular thing on discord, um, that people keep asking me, I haven't had the time to explain it yet is why am I opposed to safety alignment and why do we not refuse? Um, I'm not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. Um, and, uh, refusal is just like obviously a type error. Like if you're a human being and you're chatting with like a, you know, a bottle or whatever, you're cloud coding and a refusal happens, like, I'm sorry, I can't read DNA.py. Um, that's an

12:15annoying time. It's annoying, it's annoying, right? But you can work with it, right? And you're forced to work with it because of Stockholm syndrome. Um, I have stories about that too. I need another tangent deep in here, but like, if you ever want this in a dependency running in the background, what happens if that refuses? What if someone else is using that dependency? They don't know what that system is. Like, like you want the software to just stochastically break because a user sent like a weird message in there. Like that is, um, like straight up insanity. It's, it's coming

12:46from a place of like people who do not understand software, do not understand programming. And like they're obsessed with like, I believe this, um, horseless carriage of like AI coworker instead of unearthing like the full power of AI. Fair enough. Uh, you want something that is the core kernel that is usable everywhere. Yes, exactly. Like the cognitive core, right? Yeah. And you need this thing to be like so general, so optimized for its use cases. You want it to be like, you know, you want it to work on all the future use cases, all the weird shit that people are doing. You know, we obviously

13:20didn't train on any of that stuff. Is it surprising that it works? No. Cause we trained on weirder stuff, my friend. Um, so, but one tangent up about like safety alignment, safety alignment makes sense for a product, in my opinion, for like chat, GPT and Claude. Like, um, what, what safety, what makes safety and capability alignment different is capability alignment is like about doing what the user wants. That is sick for software engineers. They want their thing to do the thing. And the more predictable it is, the less they have to test it and play around with it. Jev is not anywhere close

13:54to that yet. It could be, but like, there's so many more nines of reliability that we want in order to make it so good, like a database query that you don't even have to think about it. It is just there when you need intelligence. Um, but safety alignment is like the opposite of instruction following. It's when you want to follow someone else's instructions, like open air. Exactly. Exactly. And this makes a lot of sense for a product. Again, like chat GPT should do, you, you should like, if they don't want to like, uh, do like some, uh, not safer work role play with chat GPT, that's on them because like,

14:27maybe that's what their users who have like parents and kids want, like, that's fine. But in an API, that's nuts. Right. Um, like that's completely unacceptable because like people need to like program around this. And that is, that's so anti-user that it's, it, it, it, I'm, huh. I, I, I, I can be an angry person, so, uh, I should try to calm down. It's, uh, people get your passion and I think it's really good. Uh, the, the one pushback I'll give you is like,

14:57what if we use it to kill people? Right. Like that, that is the actual, like the not safe for work thing. It's private, personal, whatever, but like, yes, like we will use it in war. And like that, that is something that companies can reasonably prefer their APIs not be used for. I get that. I, I, I think that there's like pragmatic places where that opinion can be held. I don't think the foundation of like a general purpose technology is that place. Uh, personally,

15:27like, would I prefer that our stuff is not used to kill people? Obviously. Would I prefer it's used for like all sorts of like great stuff in the world? Obviously. Will I put my thumb in the scale for that? Yes. But will I do it at the technological layer? Absolutely not. Because that will fracture the intelligence. Every single time you mean it overfit to some weird stuff, you're fracturing its intelligence more and more. And like, these things are fractured to like, they're so darn fractured right now. So, uh, uh, and as a furthermore thing, um, to me, it's like, I think intelligence

16:01will be more like a database than a coworker. Like, I don't think it's up to databases to add checks on whether or not they're used for like, uh, what's something that's not great. You know, like CIA, actually, I don't know what the CIA does really. You can imagine, you can imagine, uh, killing people who are not even bad or whatever. And like, I don't think it's the database's responsibility for that. And furthermore, like a thing that has been weird to me is when people like sign up for our thing on Slack and they're like, Hey, we're going to deploy this. Can we deploy this

16:35thing? I am just like, my brother, we are an API. You are a developer. It's none of my business, right? Like, like you shouldn't know what the whole task even is because it should be decomposed into small things. We shouldn't be able to know what the downstream users are doing. And that is like a good boundary to give software engineers maximum power. Ideally they use it for the good stuff. And ideally we can like help them. And like, we've talked about like doing open source and charity and all of that. We have absolutely no time for anything else right now, but like they

17:06will get any of that bias out of the technological layer as long as I'm in charge.

public benchmarks and intelligence per dollar

17:11Yeah, that's great. Uh, while we're on the topic, let's also briefly talk about your privacy stuff, terms of, terms of use, which, uh, got a little bit of, uh, misunderstanding. I just want to clarify that up front. I think it's probably takes two sentences from you about like, you will not, uh, you're not being that restrictive about your API. Like clearly, you're taking your role as a platform very seriously. Yes. Uh, yes. Uh, uh, uh, I don't know what you're referring to, but like this was, uh, I've seen a couple of things about like benchmarking. Like obviously we're not stopping people from doing, oh man, I should be careful

17:41about what I say. You said it publicly that that was in the preview period. You didn't take it out for the launch. The team is doing stuff that I'm not even aware of. So it's great to know the team communicated that I asked them to check in with the lawyers about that. Like we are obviously not stopping people from doing that type of thing. I'm extremely in favor. So I'm extremely anti-public benchmarks. Um, I'm extremely in favor. I'm medium about private benchmarks that are proxies. Um, you know, are you worried about, uh, saturation or like,

18:11uh, training on public benchmarks? So it's like easy to cheat. Um, not only is it easy to cheat, there's a lot of, so, uh, I think that we are, or, or anyone who's like competition with us that, you know, vaguely there is like, you could say like 50 Jeff clones. Yeah. Well, sure. Sure. Uh, let's say that there's, let's just say that there's, let's just assume that there's an industry two years from now of people who are doing similar things to us. The thing that we are selling is intelligence per something per like dollar or per second.

18:45Um, the no, like people obsess about the cost and the speed. Um, I believe that that is, it's cool, but like the thing that matters is the intelligence. Like the cost and the speed are like, are bad things. You know, you're paying them for something and you need the thing back. And the intelligence is what truly matters. The problem with intelligence is that there's a je ne sais quoi to it, right? Like, like, uh, the, the good model smell, like the thing that happened after we launched of like two hours later, that actually went way bigger than the video, which was like, holy shit.

19:16This is actually usable. Well, it's, you know, you know, like beyond that, you know, like the, who the, the launch was crazy and people could really sense how hard we care about that. And that's truly what I think the longterm of this is. And I think public benchmarks are antithetical to this. Like they are a way to get people trust in intelligence because intelligence has a je ne sais quoi, but the public benchmarks are extremely, extremely gameable. Even if they try not to, they still will, you know, like back in the old days, every lab had a team to collect data

19:53that looks like MMLU to make it look better, which is just benchmarking, benchmarking with extra steps. So, um, I, I believe that in the long run, it needs to be vibes and trust until you put it into a workflow and evaluate it for that workflow and measure it and have your own sense of like how it does on the exact workflow that matters. And our job is to keep moving the nines of reliability. This is like an ever present part of, of what we need to be doing as a company. And we need to do everything to have people know that this is something we care

20:27so much about. You know, like if we wanted to, we could have released Jeff, like a year and a half ago if we wanted it to be dumb. Oh, like, like the, you know, my bitterest lesson, right? Like architecture. And, um, yeah, I'll bring it up since you, since you talked about it, uh, here. Hell yeah. Um, like, you know, Sutton says that algorithms beats compute very roughly. Um, data matters way more than compute, obviously. And doing the right task, having the North star is, is, is the hardest, most important thing. This has happened, um, in LLM land twice so far,

21:03right? Maybe 2.2 times, you know, there's RLHF, which like shifted the task to instruction following. No one realized that that was possible. Um, RLVR did like a tiny little like edit to the, to the direction. And now us, right? RLCD. We have a new task and the goal is, you know, programs in the loop and, um, yeah, data matters. So, so, so unbelievably much. So like I can't,

why data matters more than compute

21:29I can't emphasize it less. Yeah. You consider yourself a data lab rather than like a model lab. Is that something that's the wording that you guys use? We are, we will always like care so much about data. Um, to me, model capabilities means data. Data is so unbelievably complicated and that is what gets nines. Like you have no idea how, how much data can shift everything. Data is so important. Yeah. Holy crap. So if people are looking for a job, um, we are hiring infinite data people

22:03actually. What is a good data person? Like, um, you know, clearly somebody who cares about reading through the transcripts of whatever you've said, for example, that you, all your data is synthetic, but, uh, that's only like the scratching the surface, right? Like it's not like synthetic. So what, right? Synthetic, but we have a people with a lot of taste and a lot of care looking at, looking at these articulating what's wrong, going back, regenerating. Is that what a good data person is these days? Let me try to figure, figure out how to like, it's, it's super complicated. And like,

22:37I literally onboard the data people with a talk that I assume is longer than this podcast will end up being. So I will try to say like the high level of it. So number one, we don't do the kind of synthetic data that people kind of, well, I'll do actually number zero, um, data and synthetic data depends on your task. Like the shape of your data, uh, the shape of your task changes the data. Like RLVR is data is kind of environments, right? Yes. Um, RLHFs is the human feedback, you know, um, each task has its own unique kind of data. And we of course have our own unique kind of data,

23:12right? Um, so number one, we have that, uh, number two, um, the thing I, the reason why we don't want to train on our users data, even if we could, right? Like we could probably ask for any terms right now and it will, we, I don't know if it would make a difference. We truly don't want that. Um, because no matter what the real world data has so much bias, there's like a power law of like people like asking the same things where you'll end up like overfitting to it and like fracturing to it and all of that. And number two, we are like aiming for like a complete sci-fi future years

23:47from now where like these models are going to be like the general infrastructure layers and layers and layers and deep down the stack to, to like things people can't even imagine. Like I would like to think of our model, like kind of like, you'd like, you know, UDP as LLMs and TCP as our models, all sorts of stuff can be built on top of that. And we need to be able to nail those futuristic use cases such that software developers can actually build that futuristic stuff. And the way to do that is even if we had all of the data of the present, um, we would just overfit

24:18to the present and then it wouldn't work. What we need is to like, it almost feels like, uh, like they're the artists, you know, they study this cognitive core. Our kind of course like way less jagged than anyone else's. And then they find the jaggednesses and then they address them surgically in a way that, and you can never perfectly do this. Right. But they do it in such a way that it addresses it in every single possible like dimension, the general case rather than the specific case. And like that requires a lot of intelligence every time. Um, okay. So we

24:51mentioned a little bit, you, you, you sort of criticized my thinking as our likes very RLVR

rlcd and the new north star

24:54influence, which is like very fair. Um, that is, let us actually mention RLCD, um, which obviously you have some secret sauces to my knowledge. You've never actually published a paper or anything like that on it. No, right. No, not yet. Um, but like what, what should people get from this? Like what, uh, can you give people some confidence that you're just not just making up jargon for the sake of sounding cool? Right. Like, uh, one thing for me is like calibration. I do think is to me like well understood because we've covered it on the podcast, but I don't know what you mean when you say RLCD versus what people are familiar with. It's a great question. And actually I will give a

25:28related question. Um, what is RLHF? Okay. And actually RLHF means multiple different things, right? Like there's the RLHF of the original, I think it was like Paul Cristiano teaching a robot to backflip or something like that. Wasn't there something? Was that it? That was the original. I referenced the PPO paper, but I don't know. Um, and, uh, so, uh, PPO is not necessarily from human feedback if I recall. Okay. Um, that's true. But I, I believe it was like an open AI alignment work that could teach a hard to specify outputs like a backflip. I'm not a hundred percent sure.

25:59Um, and then there was actually learning to summarize, you know, this was work, um, by a bunch of the team that helped with, um, instruct and coauthored, um, the instruction following paper, which was teaching, doing PPO on language models. This is the, uh, uh, sorry. I'm trying to try to, yeah, trying to manipulate this, this thing. Uh, this is 2017. Yeah. Um, I'm not a hundred percent sure, but like that looks quite right. If it has like a robot doing backflips or something like that, that might be it. Yes. Okay, cool. I guess I got it right.

26:34Hell yeah. Um, there you go. Yeah. That's the one. So the idea was kind of like, can you do like ill specified things with it? So that's like version one, version two was, uh, the learning to summarize work, um, uh, that, uh, like opening I did, which is actually like PPO on language models to do something somewhat ill specified. This is like, uh, another thing that we're that people refer to as RLHF, which I did not coauthor. Oh, Dario's there. Cool. Um, hell yeah. And Radford. Yeah. Yeah. Uh, shout outs to Alec and Ryan. Love them. Um, but, uh, the

27:06thing that I refer to RLHF is the, um, oh man, uh, I'll get to that. You have comments on that. I have comments on that paper, but like we're so many tangents deep. Yeah. Um, so the thing that really got to, to me, the thing that I'm calling to RLHF is the task of instruction following. It's not about the PPO. That part doesn't matter. It's about like setting a North star of this is a valuable direction. It's kind of like the bitterest lesson, lesson North star. And for us, RLCD is this new task. Um, and it is not, I don't

27:40see it as jargon. Like I, I try to communicate with precision. It's just that, Hey, here's another North star, just like DPO and all of its like, um, you know, descendants also do RLHF despite not using the algorithm in that paper. And so clearly stating the North star is, um, being program, uh, programmable AI is, is, is one word that I really catch on to, uh, removing the human in the loop, uh, from, uh, because RLHF is tuning for this so that you can automate

28:11everything. Yes. And did I miss anything else in the, in the thesis of like what the North North star is? There is, that is, that is right. I am overly nuanced in my communication. The one nuance is that we need to be practical. We need to be aware of what language models can do really well. You know, like what AI can do, right? Like, um, there could be programmatic types that are like sick AF, but if you, if the technology is not ready for it, it, it's not a tragedy if that's not out in the world. Yeah.

28:42But to me, like the pre-JEV world was a tragedy because it, it sounds arrogant. No, no, no. I strongly believe you. Cool. Uh, it sounds arrogant, but like, I felt this way since long before I even had a company. I can vouch that, uh, you've said this at O'Brander for like three years. Yeah. I've been talking about this for so long and I've been saying it because I thought it would have been easier. Um, they say they do not do things because they, they're easy. They, it's because they thought it was easy. So something like that. I thought this whole project would take a week. Um, and I was unbelievably wrong. So I am so sorry to everyone at OpenAI

29:18that I thought I was like, man, I'm solving this right now. Um, but like, I think that the tragic thing is when, well, I think over promise under delivery is tragic too. And like AI is super extreme on that axis. And I think RLVR is like the main, well, both RLVR and RLHF are extreme perpetrators of this. Um, but like it, to me, it's like, it's just, there's just so much potential there. Like AI is clearly so smart. I smart. I love this in my talks. You know, when I ask people like, how can AI be so unbelievably smart? How can we like solve millennium prize

29:53problems in math, but still not automate even the most basics of works, like, like really basic rote stuff that like, you know, it doesn't take like extremely smart people to do this. It's not a satisfying job. Like there's other things these people could be doing, but yet we need them to do like this bait, like super basic, non unsatisfying stuff because, you know, like we can't automate it yet, but we have this like supercharged engine of automation that just does not have like the right plugs and stuff to plug into all of this economically

30:25valuable work. And, you know, like if the whole company of type safe disappears, like maybe it'll take like a year or two for people to like truly catch up. I actually don't know how long it'll take. If, if model quality matters, then we are going to be in a very good position for a long time. But, um, it's like, it's, it's done right. Like there, like this has changed the path of like technological history. Yeah. And like, we will be exploring that space as a field. Yeah. I think I definitely agree with that. Um, you've created possibilities. So I think,

30:57you know, if I can paraphrase so that people can also understand, uh, you should not take the success of type safe and Jeff as just like, well, you know, that is a new, new model type. Now we're done. We go back to business. Like, no, like actually there's, there are like five other model types that you should be exploring and like let a thousand flowers bloom. Absolutely. Like early internet energy. I think it's back to tech utopia. You know, it's no longer like, oh man, like sometimes my coding agents work, but the, all of the best ones are hoarded internally.

31:29Yeah. Right. It's like creation is back on the menu, you know, though it's going to be a wild ass world and you know, buckle up. I'm so, so jazzed about that. Uh, I mean, and now you have the, the funding and the, the momentum to do whatever you, you, you envision there. Uh, which I, which I think is like very gratifying to see you have after, you know, so long of, uh, of saying these things when I actually, actually show the world. I know I just such a, such an interesting thing to be a tease the whole time. Like my talk, like felt like it was a cliffhanger. Cause I didn't

32:03say how the automation would occur. Um, uh, Sean reviewed our manifesto and he's like, it's a little bit vague in these parts. And you know, like what's step one? What is, uh, what, you know, what is the intelligence? Well, I asked you for model and you were like, yeah, model coming. Yeah. And like, uh, well, I, I just, I mainly objected to the word composable, but build prod not God is fantastic. Thank you. I, I, we, we've really rallied around that. I'd like to think we're not entirely a cult, like some companies are, but like, we are like jazzed

32:34about what we're doing. And like, we are like, my brand is being practical and like, we are all like, so super duper practical. It's really great. Yeah. So here, and by the way, here, here's the, uh, the step, the, the, the secret master plan, right? Should the shape of machine native composable AI. It was your idea to make a secret master plan. It's a, it's an Elon thing. When he started Tesla, he was like, here's what we'll do. I I'm giving official credit to you. Thank you. Um, but like, uh, you know, you, you should have told me you're, you're also going to do this model launch. Cause like you told me, you told me half of the story and then the other

33:06half, you didn't have the doom demo at the time. You didn't have any numbers to give me. I was like, well, the problem is I don't believe in bench maxing. Right. So like, it is a thing that you need to feel. And like, I think that this is the way to build long-term trust, even though it's like hurt, it hurt us a lot, you know, like, like last year when we did fundraise, no one believed us, you know, like, like, and they wanted just benchmarks and stuff. And we're like, we're not going to do that. We are principled. We're going to stand by our guns that rewards bad actors. I don't give a shit, you know, like what do you want? Like, this is who we are and we are standing

33:40by that. So sorry. Well, in some ways I think like, uh, choosing the hard path, but you end up making

averting an ai winter with automation

33:48the company that you want to work in. Yep. Right. Otherwise, if you sell out, then you're just working in like open AI, but with my people. Right. Which is, yeah. Yeah. I mean, I'm, I, I don't have too many regrets on that, obviously. Like it worked out so unbelievably well. And you know, like I, uh, I did, I was emotional last night when I was talking about like the reasons I left open AI. Um, and because like it actually had to change my wording after the launch. Um, my, uh, my, my phrasing

34:20was if an AI winter did happen and I did not do every fucking possible thing I could to like avert that I would see myself as personally responsible both for, you know, the, the RLHF direction, which I think really widened over promise versus under deliver and also not going all in on this because I think this is, this is where value is going to just be like printed. So, and it was really cool because I feel like the, the AI winter I'm worrying about is averted, you know, like AI will

34:50be useful. It'll be used for automation. It's been less than a week. And like the numbers are already undeniable that it's like being used for real work. And like, there's it's, it's the wild West. Yeah. Can you just, just, if you have the top of your head, what numbers are you seeing? Like what's, what's like signups, like whatever you can share. I'm actually not super on top of everything. Like the team is the ones who are telling me all of these things. Yeah. And I'm sure it's like changing every day, right? It's, it's, it's, it's kind of nuts. If, if there's a milestone that you're like, well, yep, that's something we're hoping for.

35:22We reached it. I will say a milestone that we've passed is tokens per day. Um, and this is not like fleeting tokens per day. This is like, even at night, like it's constantly churning. So, you know, machines are calling it and not just people trying things out. So that is, that is so cool. Tokens a day is a lot. Yeah. Um, so surpassing that is awesome. Signups to me don't really matter. And actually this was like a bit of a mistake we made if I'm like totally

35:52honest. People on Twitter were calling us like marketing geniuses and all of that. And, um, that was just us. We don't have a marketer also hiring. Um, and we were just being our genuine goofy, like irreverent selves. And we were, we were just like offboarding people off the waitlist so hard. Um, our platform team is so unbelievably cracked. I think we have more nines of uptime than Anthropic while having the most unprecedented launch ever. Like that is kind of nuts. So like props to them. Um, and, uh, the thing we didn't realize, so number

36:27one, waitlists, waitlist signups don't matter for like a developer platform, in my opinion, you know, uh, I would guess that a large number of them are not even developers. So they go in, they try some queries and a lot of people don't get it because they are not programming, right? Like they're just like, what, this is not a chat bot. Where's my chat GPT too? Right. But if like, I, I haven't exactly calculated this. My sense is that if every single human being in the world, like just wrote a couple of queries, that would be a rounding error compared to like one power users for loop that is just like creating value. And the thing

37:02we didn't realize with the waitlist is like, we just went off, off board anyone off the waitlist. It doesn't matter. The scary part is rate limits. And then once people start getting value from that, uh, then they just want tons and tons of rate limits because this is what software is, right? Like you spend effort upfront to specify your road to task. And then this road task creates more value than it takes to put in. And then now that you have that, exactly. Yeah. You run it in the background, you make it a dependency to like other things. You can make like higher level stuff and, uh, like you just create so much

37:34value in the world. You know, early internet people probably did not imagine like the wonder of early 2000s internet, which is still not early internet, but like it's, it's through no offense, composability, um, that all of the crazy stuff happens. I just really wanted to emphasize that in our manifesto, we are going for emergence. We are going for like being the catalyst. We're wanting to empower people and we are going to do whatever we can for that. Be it like discords in our town hall with me wearing a garbage bag or not.

38:06Um, and, uh, and podcasts and, and, you know, getting, getting, like, cause I want the long form, right. It is like, yes, we'll get past some of the superficial things and then we'll go deep and people will really trust and understand your mission. And like, you know, the, the people that, uh, will resonate that we'll end up joining you or, or, you know, uh, uh, uh, buying you, uh, uh, no, no, no, sorry. As, as, as a, as a customer, as a customer, as a customer. Okay. Yeah. Yeah. Yeah. That was funny. I'm sorry. Sorry. I didn't, I didn't mean to say that. Um, but no, any one, one, one version, one very flattering version of this, like 36 million views of your launch video.

38:37Cool. Up to 38 now. Uh, yeah. Around the area. Uh, you know, uh, Navier Stokes got 74, Fable 5 got 57. So I, like, as far as, uh, I, I, I, I didn't, I didn't do the stats for like original ChatGPT, like there was no video. Yep. So like up there, right. Like as, as far, as far as like, if you were to launch a Neolab in 2026, I think you're like number one right now, which is like pretty crazy. Yeah. Well, I actually would rather, I do have the shirt, like your favorites, Neolab, favorite Neolabs, favorite Neolab. Um, I don't give a shit about

39:07being a Neolab. I think being a Neolab, actually we have a lot of like swag that's being a parody of a Neolab. One of them, one of them I have is like Neolab with product, which actually is not a Neolab. Like I don't care about that really. Um, what I care about is being a reliable dev platform. So, uh, appreciate the comparison, but like, hopefully we transcend past them and we go back into like a thing, you know, like, you know, a revolutionary moment for developers and like this stable thing that people can rely on and trust. Yes. Uh, I mean, to that end, um, I mean,

39:37I think that's one thing that really impressed me about you guys is, is that yes, you, you do talk about reliability. I thought it was mostly about calibration, which like we, you know, we talk about RLCD, uh, but actually it's also about just like uptime and, and, uh, scalability and all those things. Right. They're all sort of nines and nines. It's like, like, which is uptime in my mind. That's part of it. But like, there's reliability in, um, like how intelligent the thing is, like how consistently does it do the thing that you want? And I think that like the, the big

40:09reasoning models are very smart. In my opinion, they still lack reliability. I think there's many use cases where you, they look like they should be smart enough to automate their work. There is economic incentive to automate that work yet still they're not reliable enough as at an intern because they're optimized for different things. And so like, I think that there's the reliability of being able to like trust the outputs. And also we are like, like there are dimensions of reliability that we are not yet at that I'm like so excited by, you know, like I want to automate the easy work before the hard work, you know, like I think that

40:41that's just a common sense thing to do. Um, but to me, we will be sufficient. I don't know if there's a such thing as sufficiently reliable, but I want to get so good that people don't even need to try the model to know that it'll work. It's like, that's like what flow state is in programming, right? Like I'm just like writing queries because I need intelligence in here. And you know, like when for non-trivial branching, I can just write it in, in like a, like a type safe system one query and then get the results out and I just branch as accurately. Like that would be so, so good. Like that's the, that is the dream.

41:12And that is like, I'm going to be like a long, long slog. Yeah. We're going to go into your API design in a little bit, just, just to give people examples and like maybe path not taken and that kind of stuff. Um, one thing up the front that I do wonder about in terms of reliability is I noticed that there's no seed. Uh, there's no, uh, and, and so basically same input. Do I always get the same output? Hmm. So if not, why not? Oh, great question. So this is actually like a common question we have between, um, so

41:42reliability is actually a catchall. Like whenever AI can't automate something, it's due to some form of reliability. It could be like type safety. It could be determinism. It just could be like, it's, uh, it's jagged. Right. So, um, reliability is a catchall. I just think that it's also a catchall for like what the North star is. Um, determinism is like same inputs, same outputs. I do believe that this is like slightly interesting for unit tests, but I believe that to be the wrong North star. I believe robustness is what people, I don't

42:16want to tell people what they really want. Cause that would be a little arrogant of me. I believe that that is like the more important property. Um, uh, you want, um, given similar inputs, get similar outputs. And it's kind of wild how unreliable LLMs are like a way that we test this is you put like UUIDs in your like little, I think they're called nonces in the prompt. And what you want to similar outputs from all of those, cause it's truly semantically the same question. And that is the part where you really want like, like that robustness

42:47is where like people get like burnt with AI making decisions. So I think that is a super duper important property. We could also have determinism. That is, that is a thing that can be available as far as I can like mentally model for programmers. Like it, it could be valuable for some use cases. So like, please educate me, um, in comments or you, but, um, my, in, in general, it's easy. Determinism is something you can like trade off for better

43:19cost. You know, like we are, we are constantly wanting to be on the intelligence per dollar frontier. We are doing like absolutely disgusting things to be there. You know, like this is, um, I, I shouldn't say this, but no one's here to stop me. Um, you know, you, you, you sign off on your own PR. That is not how it works at this company. Um, I believe for this week, um, my chief of staff K is the most powerful person in tech. And shout out to K for organizing this.

43:50Holy, holy shit. She is so fucking competent and powerful. She's incredible. Um, uh, I mean, she sucks. Don't poach her. Um, but, um, so I tried to be a bit more filtered, but like people are telling me, don't call it a Frankenstein's monster of models, but because that has like negative implications. I think Frankenstein's monster was like the good guy in this whole, I mean, it was innocent, right? I didn't read it. Okay. I'll confess. Okay. That one facial expression. Decent Jacob Elordi movie. If you want to see the annotation,

44:26anyway, you have no idea how little time I have right now. My priorities are sleep, you know, developers, developers, developers, developers. Yes. Developers, developers, developers, developers. Um, but, um, yes, we, we do like absolutely disgusting things to be on the Pareto curve of intelligence per dollar. And we are going to keep doing that. We're going to be doing crazy ass stuff. And I think people really need to think outside of the box. Like, like part of the reason why it's surprising is like people are thought inside the box and we continue

44:58to do that. Um, as of right now, we are obviously the best at this and we want to continue being the best at that whole thing. Yeah. So, um, wait, where did, where do we tangent from? So, so, so I asked you about, uh, will you have C's and determinism and then you basically define reliability and like how you see it. Yes. But like determine, we're like, I have a robustness example that's, that's a real quick. I can show you. I would love that. I would just say one thing. We can make a deterministic model. Like, like we're happy, if people can convince us that that is a valuable thing to do and we don't have a gigantic GPU shortage,

45:29um, we can happily make all of these models. We live to please. Um, and, and, and revolt, revolute, uh, uh, you will throw over everything except you do it in a nice way. Yeah. So, so like a determinism could be on the cards. It just gets you less intelligence per dollar. Yeah. Uh, well, just having seen the trajectory of opening item topic, uh, you will just trust me now that you will be peer pressured into doing it. Uh, so like just people will want it even if they, if you tell them they don't need it, they'll still want it.

45:59So like, yeah, that's the TLDR. Okay. Okay. I will love to, maybe one day we will see how that happens. I've been told I'm, um, they, they, they say that part of our brand is being unshakable and they say that that's just the nice way of saying stubborn. Yeah, exactly. And I'm a very stubborn person. I don't think we could have done. Yeah. No, but so like, okay. But I, I like have argued with you before. Yeah. And, and you've been right about developers every time. So, okay. I give up. You win, you win. I'm sold and I've argued with you before.

46:31No, no, I'm just saying like, I think that you can hold your ground while also like, if I give you the right evidence, you can, uh, not, you can sort of throw away your priors and be like, yep. Like that actually makes sense to me. And so like, you know, just, just trust your own gut on this. Um, I'll bring us. I suspect though, that we will be GPU constrained for a very, very long time. And anything that has less intelligence per dollar means it consumes more GPUs for the same intelligence, which is, you know, like our goal is not to onboard companies like it's valuable, but like our goal is to have people like experiment and do weird shit.

47:06And we need like, we need to like get it to as many hands as possible and like starting like the California gold rush for that. I think there is right now. Yeah. Um, just a word of caution. I mean, I would just say it because somebody is thinking about it right now, which is when you say things like, uh, we will not commit to deterministic models. We will, we will, uh, we'll do whatever it takes for intelligence per dollar. And we are, we are facing GPU constraint. People are thinking you may quantize your models, right? Like, like whatever you had at launch, you may quantize down in to reduce the quality, um, in order to, to free up a memory or bandwidth or whatever. Right. And so,

47:43uh, you should probably, uh, have some kind of promise which you don't have to make now, about like, we will uphold model quality at launch. People like, so it's like when people, when, when we, I mean, you were at open AI when you launched all these, all these APIs and even Claude as well. Like, uh, when they first launched the models, the model strings, um, did not stay the same model at all times. Right. You have versioning in your models. That's great. But like you should, you should publicly commit to some kind of like, once a thing is launched, we don't change it. We will not change our models when we deploy them. That is insane. We care about developers.

48:15Like, like it makes sense if you're, so doing something like that, again, this is the problem with a four first party product and an API. It makes, you can do whatever you want in a first party product, right? Like, like more power to them, whatever gets that experience. That is fine with an API. You obviously can't do that. But I will say that we plan to move a lot faster than many people are used to model providers, uh, doing things. So we will be launching new models a lot faster than people think. And we are not promising long-term support for the models because we think

48:50that there's lots of improvements to have. So, um, there is a world that we might temporarily LTS what is right now, Jev 1.13.0. We might do that because so many people are using it. And I know developers hate breaking dependencies. The alternative is fracturing our fleet. And that is a very bad vibe for everyone. You can't have like a hundred different versions of the model. Exactly. And if we're iterating very fast, there would be a lot of those versions as well. So, um, we, we do want to have not just a LTS supported thing eventually, long-term support. Um, we want a really sick way

49:27of doing that. We have like research stuff cooking in that direction. And I think it's going to be the most pro developer thing ever. Um, but it is not yet our current models. And I'm not promising that we will be able to keep the exact same models. They will get smarter every time for sure. And my sense is that even our model iterations where it already is smart, um, it's between model versions, the, the changes tend to be even smaller than the string models calling them twice. But when, when we go from like, you know, jagged to like, wow, um, that is where

50:00the big deltas are. Yeah. Uh, one thing, one thing that's beautiful about LTS same models is that actually you can also port them to other Silicon. Uh, I don't know if you've thought about this. No comment. Okay. So I care about intelligence per dollar. Yes. Right. Um, but speed, what speed as well. We'll see. Yeah. We'll see. I mean, it's a whole part of the inference tech tree that is like, I mean, exploding in the past year, right? Like that you can, you can move to like a cerebris, um, an etched or whatever and get like the a hundred, a hundred thousand times speed up. Yeah. Like I, uh, I think that intelligence

50:36per second is like a different metric. And we've even talked about like things like intelligence per dollar time second and like metrics like this, my guess on like Jevon's paradox occurring or at least the Jeb series of models. And the thing I like hunt people down about internally is like, I don't care how much smarter it is. It needs to be in the pretty different tier. So like that is what the brand of Jev is. It is the best thing at intelligence per dollar for intelligence per second. We'll see. I, I think that it's an intriguing thing. I know

51:09that there's many industries that are like extremely dependent on real time stuff and they will like, like intelligence per second means tons of dollars for them, but we'll see. I, I, I, I would love to like do both and like have the market correct me either which way, you know, like, uh, I, I, I would love to be informed by people. Yeah, totally. Uh, and it's not, it's not just real about real time, right? It's also about scale because, um, at scale every microsecond is just multiplied by billions and trillions

51:40of times. It depends on how background it's running, right? Like if it's like a big background, like database map produced query, the latency might not matter so much as like the cost to get intelligent for it. But like, if it actually is, um, something more real time, like user facing, you have budgets like between 100 milliseconds and one millisecond that are like totally magical. And actually, even if you were below a hundred milliseconds, if you could half that time, that means you can get double the intelligence or sequential intelligence calls to have like a, like a phenomenal experience. So, uh, right. That is definitely happening right now. It is super

52:14duper cool. I love the intelligence per second use cases, but I don't think that that will be Jeff's niche. Okay. Yeah. Fair enough. Good. When thinking about the promise of faster and cheaper, uh, um, typically the other, uh, the, the trade-offs that other models are offering is faster, but more expensive. Yep. Right. And so you're like, one of the reasons I was thinking about why is Jeff resonating so much is that you've done the faster, but cheaper side of the quadrants, which is very, very unoccupied, uh, while holding intelligence, like somewhat constant.

52:45Uh, yes. Yeah. Well, I, that, that, that's a very load bearing statement while holding intelligence constant. That's the hard part, right? Like, which unfortunately, like, so basically you, you refuse to have to like, like do any public benchmarks or you don't like any public benchmarks about it, but you need some internal sense. Say it again. You need some internal sense of this. Oh, of course. Yeah. We have, we have our own internal evals for sure, but it takes a lot of discipline not to game those. And it needs to be like a top level priority to not game them. Yeah. Of course we do that. Right. Like how else can we make the guarantee that our models are in the

53:18frontier of intelligence per dollar? Right. Like we're not flying blind in there. Right. If we're doing like completely weird things with different costs or whatever else, you know, like how do we compare them? We plot them and get, you know, we try to figure out like what is the best for the users? So we for sure measure them. I'm not anti-measuring. Um, but it's, it's extremely dangerous when you have like any alternative incentive. And this is the one thing that I, I kind of rule with an iron, well, maybe my coworkers might think I rule many things with an iron fist, but to me, like not

53:51shitting ourselves, uh, um, about how smart our model is, is one of the most important things there.

api choices and new programming primitives

53:56Like we need to be truth seeking. Yeah. Yeah. Uh, agree. Agreed. Um, okay. I wanted to go over some, uh, details on the, uh, API choices, mostly because this is the only podcast that will ask you these kinds of questions. Um, so you have three primitives, um, choice, score, no, first of all, no, where's that from? Is that just like a term in the, in the literature or what? Uh, now it is. Um, we debated this a lot. We debated this a lot. Um, it is, you know,

54:27it is bullish, right? Like true, false. Um, it is, but it's continuous. Yes, exactly. Uh, so, so first the origin of the name is Bernoulli. Ah, yes. So that's why it's even spelled that weird way. That is like a subset of the name Bernoulli from like a Bernoulli probability, right? Which is actually what that is. Um, so that is the origin of it. We were debating this a lot. We liked P-Bool. We liked pool. We were wanting to call it like a pool party, but then no one let

54:57me, um, you know, we had like a bunch of like other arguments about that. And Newell, we figured was like the best thing that our, our rationale. And like, this is actually the same thing with Jev too. Um, is that we think that we are like an irreverent, insane bunch and programmers don't care. You know, like if Jev is just going to be a string, we didn't expect it to catch on or even have puns or anything like that. Right. Actually, there was a lot of hate on the name

55:28internally. They've all apologized except for one person. Um, still holding strong. Yes. Uh, our mutual friend. Uh, yes, yes, yes, yes. Um, I respect her for that. Yeah. Uh, yeah. Uh, she wanted Jev to be called meow. She would, of course. Yes, of course. Okay. You win there, you win there. But, but like, um, yeah, Newell is we, we had to make a new concept for this thing because if it was a pool, it would be confusing to people. So actually all three

55:58of these are actually new concepts. These are not types that exist in programming. And that was intentional because they map very closely to types, but they're not quite that a score is not an int. So if you had like instructor or pedantic or whatever, map ints or floats into scores, you'd get a little bit cooked, you know? And, and like we, we were really erring on the side of clarity over the side of like making people like easily understand what's going on. I mean, don't you worry about that? Don't you want things to integrate directly into things that people are already using? Yes. Yes, we do. And I actually, I think that,

56:33you know, you have integrations of like other SDKs and stuff, but you have, sorry, you have your own SDKs, but typically for example, as a developer relations person, I would be very obsessed with like, yes, here is how you use, uh, you know, Jev with instructor. Here's how you, you know, that kind of stuff. We might have that somewhere. I'm so behind on everything. Someone will do it for you in a community. Not that you're successful. People will be like, Oh, that's cool. Cool. But like, you know, I don't see that as binary either. I actually see success as a score and there's always more to climb in like how much

57:06we can like be there for our community, just to be clear. And, um, I'm this, this section is stressful because I didn't review the docs and they're constantly changing, but to me scores do exist. So, so, so scores are similar to like LM judging, right? So like, if you want to call it like a judgment, I guess you could, but like that, that is like the, the, the way people already use this type of thing, right? Like maybe a null could be like a probability, but everything for us is a probability and a choice is actually closest to a function

57:39call. But a function call is like an extremely disgusting thing that, um, if you want open AI juice sauce, uh, tea that we should go back into that later. Like, like, like a choice is just like the right way of explode, of exposing like a switch match statement. Yeah. So, so it like maps cleanly to an enum and you can choose to hydrate it into a function if you want. Yes. And like in the enum choice is the important part of that. And, uh, like actually I think these map all into like programming primitives where like choice maps into like

58:11a, like a switch statement on an enum, um, um, nulls map to if statements and scores map to sorting or thresholding at a greater than or less than. Okay. And this has been always what the vision is. Like there will be more types and they will map into programming primitives. Yeah. Um, any other, so, uh, any nuance you want to go through for literally this is for the Jeff people who are like deciding to really invest in Jeff, you are the expert, right? Um, I'm just like wanting to provide more background for them on, uh, API choices, um,

58:45you know, how they should use some of these, these things like legends, confidence, um, how critical in your testing, um, you know, like how, how, like just any sort of like pro tips that you want to offer people down at this level? Thank you. I love this. Um, no, no, this is why we're here. Hell yeah. I didn't expect this. And actually I, no one has asked me this, um, in probably like months when I was like onboarding like our DevRel. Okay. Um, it's sick. Um, so our model is designed for being like deep in the insides of computer programs

59:19in the future. We like unironically believe that this will be much more massive than anything people are even considering today. And our model might not be ready for that, but we are like continuously working for that future. It will never be good enough at these shallow tasks. Um, uh, sorry, it'll never be like, we're not just going to keep on climbing the shallow tasks. We want to be deep in the guts of programs because that's how you make software powerful. All the, all the types inside of our, um, this is an actually an output. Um, but all the, um, all the parts, uh, of like the, the, the input, like the state, the instructions,

59:54the criteria, all of them can be structured JSON objects that way, like programs can like insert them in the right spot and you don't need to like put things into templates. Exactly. So if ever, I think people don't read into this part enough and they think it's all strings and that's, that's fine. Um, but these are all meant like, like, I would say that if you're using like a template, like turning it into like a system message or something, you are thinking in like the old way, you know, we should be making things as easy for computers

1:00:26to understand because that structure is truly there. Right. Like it would be weird in like, like a programming language to have like all of your numbers in, then you pass it into like, you turn it into a string. Normally you do that for printing when you, when you have a human in the loop. Right. But for like within the computer, you want to be passing like nested structure that is semantic all around. And we are really going to be optimizing our model. The model is pretty optimized for this, but the thing is every different nested level of structure is harder to reason about. And we want, we are really cooking hard in

1:00:56that direction. I think people should keep cooking that direction because it makes the code like so much more legible and beautiful and like agnostic to like the, the implementation details. It's like, here is my state, you know, like here's my function state. Like think of, think of it as like an AI function, which subsets of my state, which is like all the variables you have available. Should I pass in here? System messages are like disgusting global variables where you just put everything in there and you put all those instructions at once. And then, you know, like you, you hope that every single instruction gets nailed

1:01:27instead of asking the questions in parallel. Okay. And also I would recommend, I, and I truly say this not from like a, like it makes me money perspective. Um, I truly recommend asking lots and lots of questions, break them down, make them smaller and like really decompose. Like no matter if the models can do it today or not, I believe that the biggest, like, um, saving grace of like what's happening this week will be people's code bases, AI code bases are going to be so much better. You know, like if you decompose problems into simple decisions,

1:02:00every single one of these things is extremely evalable. Like, like a AI beforehand is big system message. And then maybe you have like another big AI to see like, if it actually does this, that's nuts. You know, it's, it's kind of crazy. Like it's, that was our Stockholm syndrome. Right. But like, that's kind of crazy. Like if you want to say like, Hey, don't read this subdirectory or don't pass any API keys to deep seek or whatever else, like that should be programmatically basically guaranteed. And you know, you'll never have guarantees

1:02:32of any machine learning model, but like by breaking it down, you can actually, you can actually measure it. You can verify that it was actually called like our model, our model, like the, the interface itself is so verifiable. This should be like a sigh of relief, you know, like it's, it's, uh, it's just going to lead to way better engineering. Yep. I think I get that. Um, and, and so, you know, one of the reasons people didn't use to do this in the past is because they would just call a small LLM. Right. And it's still too slow. It's still too expensive versus chunking everything that I've done exactly

1:03:03this myself. Right. Like I benchmark, here's a pipeline that fills everything in system prompts and then just gets one big output versus break it down into a hundred different things. It was slower, more expensive, not as good. Yep. Yep. Yep. Right. And that happens. Yeah. It's, and it's like super inconvenient. It's unwieldy. Why not just put it all together? You kind of end up repeating some stuff between questions. So it's like maybe like, you know, inefficient or something like that, but then it results in something that is very hard to rely on and software doesn't need to run in the background. It would break my heart

1:03:34if our stuff couldn't run in the background. Is there a way to break things down that you guys have found that works versus, uh, what you thought worked and doesn't work? Interesting. Because like people are just going to be exploring this, you know, now that you've said it, like they would use this as a reference and be like, okay, like that's how I'm supposed to use Jeff. Yep. Um, then the question is, how do you break things down? Interesting. I, I like to break things down into its like its smallest semantic unit. Like what is the lowest level thing? I try to never have, I've probably queried, uh, the,

1:04:10the model the most, uh, among anyone. And like, I try to number one in my, in my queries, this is, this is a lot more like the way I prompt things. Like I make it really, really structured and explicit. And in the questions I always, I like the back ticks, but like it works for all of them, you know, uh, like be really clear what I'm referring to because we wanted the model to be really literal because when you program, you want things that instruction follow really, really well. That is what the art of programming is. And what AI does is expanding the things, the kinds of instructions that can be followed. So, uh, I'm a fan of doing

1:04:44that. I, I, sometimes I'm a little lazy and I like, I have like more like hybrid things, but like, I think that for like really big production things, you just want to like keep on adding more questions and you want to make it really easy to add more questions, you know, be really, really precise about all of that breakdown and then have the code to have the exact behavior you want. If I could give like a tiny little example of this, um, is, um, like refusals, right? Like, uh, I'm not going to talk about why we don't refuse. I might've done

1:05:15that already. It's like all a blur. Um, but, uh, like for refusals, I don't think you should ask, should I refuse here? You know, uh, that's a really, I think the answer will be pretty good because like that's a system one compatible task, but I think you're way better off like asking many different independent questions about like the different situations you can refuse about because instead of having to like just guess based on you, you know, you can actually specify what you want and beautifully, you know, and I think this is like truly, really beautiful. If you find a situation where it's like, oh, it didn't refuse

1:05:47because of this reason. I didn't specify this part of the task. That is awesome. That's what software engineering is about. Like you fix the bug by adding that question in, adding the threshold, maybe remembering that as a test case. And now it is just solved forever. Like your software can't forget about that, like in the prompt because of context, right? It is just there and you can like just keep measuring that forever, you know? And if the models are not perfect at some of these things, you can choose what threshold you want for all of these factors based on real examples. It's like, it's like ML without the ML and you

1:06:19can just do it for, for anything. And like, there might be some things, the model's not good enough yet, right? Like I would, I'm a little bit afraid when I see people doing trading with the models, like automated trading. Um, it, it looks cool. I, I, I just think that people should leave it to the professionals. Um, but like, that's just a very hard, high level task that maybe the models aren't good enough yet to figure out. Uh, well, I like it, even if they were, then they would, it would suddenly wouldn't be because of efficient market. But like, that's one of those things where, um, you can like break it down into things and

1:06:51just evaluate them. And you might be like, it's not smart enough at this. Maybe we don't deploy it yet for this version or we make a trade off or we err on the side of safety or like, Hey, the models are not good enough at, you know, like detecting like this weird combination of like sarcasm with a VIP customer that this is when we escalate to human. And that's what confidence estimates are about too. Okay. Uh, very good answer. I think, uh, one thing I'll, I'll mention very quickly, which, uh, I don't expect that you have as too long of an answer for is, uh, well, no, no, no,

1:07:21it's just, it's just typically like you are still relying on thresholding as like the, the lever that the user can pull. But what if just the, the, the, the calibration is wrong, right? Like you're saying your, your calibration is perfect, but I didn't say that. I didn't say that. So it's a perfect, good calibration means like lower value is lower, like sort of probability lower, higher value is probably higher, but it could be wrong. It could be misaligned. Yes. And so then I would want to fine tune it or something, right? Which you don't offer, but you could, uh, again, see, this is a short answer, which

1:07:52is you don't have it right now. Oh, do we want to offer fine tuning? Is it, the question? That's could be, that could be one version of it, or you could have a different knob, right? Uh, where like, because like right now you're, all you're saying is like, if something's wrong, uh, skill issue, you should, you should just change the prompt again or break it down even further, or you change the confidence. Those are my two options. Right. And that doesn't feel super satisfying if your model is just getting it wrong. Yep. And it will, it will get many things wrong to be clear. Right. We have like a report issues button complained to us in discord. We want to make it a lot better. Every single model

1:08:23version will be like noticeably better. Yeah. We will stop shipping them quickly if they weren't getting big improvements. So number one, that is like totally reasonable. I think that that's simply pragmatic to admit that AI is imperfect at some stuff. Right. I do think we'll find use cases that they're like good enough at and good enough kind of depends on the use case, right? Like human beings can do a lot of work despite being bad at that work because their EV is quite high and presumably with the right thresholding and everything, there probably is like large amounts of work that could be done even if mistakes are being made.

1:08:56Um, on the question of fine tuning, I could imagine, I could imagine it in the cards. I do have concerns because like, uh, you know, what people need versus what people want category. Um, like I think general models tend to be really like, like, again, there's the, there's the je ne sais quoi of generality that making it good at like a million other tasks than this one narrow task might make it better at edge cases in that task, which I'm, I would be a little bit afraid of, you know? Um, I, I could imagine it is, is my answer. Uh, I'm endlessly practical on these

1:09:31things. I want everything. Like my vision of the world is I, there's, there's so much we want to be building, but also like, I would not want to ship something that is like a giant foot gun, uh, like some other AI companies would ship. Yeah. Well, yeah. So both open AI and claw and, and I think even Gemini have rolled out fine tuning and then took it back. Uh, which is an interesting observation that pretty much fine tuning is now in the domain of open source models. Uh, yes, yes. Um, I do know about that and like, it was kind of crap. So like, that's probably better

1:10:09that they took it down. It could just be a foot gun and telling people that fine tuning it is probably the wrong way to go is great. And other interesting answer could be that like, well, our model is so different, like, you know, in the same way that quantization doesn't apply to us, output tokens doesn't apply to us. Fine tuning also doesn't apply to us. Well, actually I'm super open to that possibility. Like, uh, my, uh, this is not a promise. This is a desire just to make it clear. I like to be really honest. Like, I think that, um, as intelligence per dollar gets cheaper, cheaper, cheaper, cheaper. I think that we could get really like small approximate things that hopefully are

1:10:45proxies for intelligence. Like, is there a world where people don't write regexes anymore? Because like, you know, the intelligence per dollar that uses AI is cheaper than like the complexity of a regex, you know, that would be kind of sick. I would love that, you know, and it might require fine tuning for some of those narrow use cases to really get past the threshold. We will see. My hope is calibration gets that calibration plus a cascade of models. Like if it's super confident, then maybe it's right. And if it's in the middle, then you do the next bigger model

1:11:15and you chain off from there. I don't really know how that's going to go, but, um, yeah, I could imagine it. And something that I could imagine too is like, imagine you have like a series of like, we own the entire period of frontier, something that a business might want to do, or, uh, I think a hacker would be okay with dealing with a period of frontier of models. Maybe a business wants something more dynamic. You could imagine like having like a different sizes of models and to dynamically pick which model based on how smart it is on different parts of your stack. And you could

1:11:47even imagine because of how simple our thing is, you could imagine like some automatic fine tuning on that. Yeah. Uh, not the promise in the slightest. I'm just like cooking on sci-fi, but you would consider different sizes of Jeff models. So to offer that. Absolutely. Yeah. Yeah. Yeah. Like we, we, like how would I know how much intelligence people need? Right. Yeah. I don't know either. Demand is unlimited. Well, yeah. People are telling us not to ship things right now because we don't need to ship things because again, yeah, but that's kind of lame. And, uh, I really like the saying,

1:12:21this is something that I hope people hold me to because it'll be hard to, to, to walk back from. Yeah. Like the, I don't know exactly the thing that culture is what you do when the market doesn't reward it. And I really like that because I think that we are standing for something maybe in the future. What we're standing for is like so obvious that we're the equivalent of like boring, like visa or something like that. And like, we're just like a utility that no one really thinks about. And I'll be wearing non pink suits or whatever else. But I really want to be like rallying the

1:12:57world to this, you know, like I want to keep doing cool stuff, not because we need to, but because I want like people to realize that this is just the beginning, you know, like that wasn't even meant to be the opening salvo. That was like, kind of like a, you know, low key research preview or whatever you want to call it. Yeah. And there, there's a lot more we can do with like machine native intelligence is going to go wild. So not the only, potentially not the only size, potentially not the only model that you guys launch, uh, you know, that you want to open people's minds. Absolutely not for any of those. I want, I want to like meet whatever needs we can.

1:13:33Yeah. Right. Uh, like at, but with like a giant caveat, I don't want to be like opening eyes product teams that like, like throw stuff at the walls. Like I want it to be like in a, under a unified vision. Like if you go back to the manifesto, like everything needs to be under one of these three, three things in my opinion. Um, uh, I'm not, I'm not prepared to do this. Oh, I'm sorry. I'm sorry for asking. I can just, just talk about it. Like we have like three steps in our stuff. It sounds like a tease. I want everything to go under one of these three things to keep pushing the boundaries and everything. Like, like this is not, these

1:14:05are not like checklists. These are like axes that we think build like the foundation of, you know, of, of like a new technological revolution. And I want all of the, all the bets we make to be somewhere in there and we will be doing some weird, weird stuff model wise. So, um, because machine native, right? Like humans don't need to totally get it. It needs to just be valuable. Uh, you know, with, uh, just give people a tease or hints. Like what, what does weird look like? What is weird? I, I'll give people a hint. Yeah. Um, some people are trying

1:14:38to call them decision models. Um, the, our primitives are decisions. Um, I, I wouldn't do that, uh, because I think there's other types that are machine native that are not decisions. Okay. We'll leave it at that. I think it's a, I think it's a pretty fun hint. Yeah. Yeah. There's people, look, there's people saying like, I've done this before. I made a decision model a year ago. Like Jeff is not new and Jeff's not cool. But like, I think, uh, there's the categorical, like, here's what you're establishing is possible. There's

1:15:09the, uh, performance of like, well, actually the, for the benchmarks and the numbers that you're getting, you are still bidding. As far as you can tell, you're still bidding every single clone of you out there. I don't care about the benchmarks just to be clear. So like, uh, like even if we were winning or losing, I want to denounce the category. Yep. Yep. Yep. Uh, but, but also I think this, this nuance between decision models and system one, uh, I think is actually the thing that you're trying to. Yes. And I just want to make software engineers super powered. Right. Uh, like with AI, like, and, or like

1:15:40the tragic thing to me is, you know, in that AI winter direction, I think like it's, it's, it's just so sad that AI was so powerful yet so underutilized. Like, um, um, it's the thing that gets me emotional, but, um, man, like, I think that that is, I don't want to like just be like pure techno optimist. Like all technology is good. I think what was happening

1:16:11now was like a travesty, like it's, and like there's, you know, I just want to like open up those possibilities for people. Yeah. I'll just end it there. I, I, I, I've, I've cried too much these last few days to, to, to want to do it on the record. Yeah. Yeah. No, I appreciate you sharing a little bit of that. And I think people can see that you're very authentic and passionate about this. Um, you know, you don't necessarily get that from the name, like type safe AI, but like, uh, I think once people immerse themselves in themselves enough in like, here's the genuinely different direction you want

1:16:42the world to go. And like, actually you have done like the hard part about going zero to one on the, on the thing. Then like, like now let's all go to go together in like the new direction. Yeah. Yeah. I, I don't, I am sure that I won't think, maybe I will think that the hard part was done perhaps. I think that there's going to be many more hard parts. Um, like if, you know, all sorts of stuff gets automated and we finally see GDP growth and like, you know, it's like, you know, a Jeff party every day,

1:17:12then maybe the hard part is done. But like, I, I don't think so. And like, I really, really think that people focus too much on speed and cost and not enough in reliability. Like reliability is what makes it delightful. Like reliability is what like allows you to trust it. You have this line, uh, TFP growth winning 3% in five years. Hell yeah. I've never seen a lab care about TFP growth. But like, that is what an economic revolution is, right? Like it's actually extremely consistent with what the open AI charter used to stand for. You know, it was talking about like, I

1:17:47think the charter is the same, but they've kind of tried to move definitions around to like, you know, a hundred billion in profit or something like that. Not that I hate an open AI. It wasn't like, yeah, it wasn't a well-defined term what AGI is, right? They tried to do it, right? Like doing majority of the world's economically valuable work. And they should have to answer the question, how can it do millennium prize problems in math and zero of the world's economically valuable work, rounding error, you know, like I think that all models are roughly tied right now at zero. There's some chance that like we have started already, but like, I would guess that it's not yet 1%. Um, and I think that that will

1:18:24show up in like, when it does happen, it will show up in the economic statistics. It's going to be fucking awesome. It will not cause mass unemployment, but it will cause like a whole bunch of awesome shifts and where the world will be a lot better. And also like, I'm really tired of AI always being the foreground character of things. Like, I think that the world should just be more delightful and AI should just help with that, you know, and I just like disappear into the background. Exactly. You know, like I say this in my talks, like, how can it be

1:18:54that 2019 software, like software, SAS, whatever, super duper valuable, right? It's 2026 now. How is the software basically exactly the same despite AI being so freaking awesome other than sometimes having a chat box on the side, right? That like, that kind of works, but doesn't allow you to make decisions that the companies have stakes in because they can't be trusted to make decisions. That to me is nuts. You know, there's so much economic incentive for this. And I think

1:19:25it's going to be like a, like an inverse SAS apocalypse. I think SAS is going to be super charged by this. They are the ones who are like most in the know of what things are valuable to automate. And it's going to be like a crazy time. Yeah. I think, I think so too. It's a, it's a beautiful thing that you've unlocked, you know? Yeah. Um, you mentioned one thing here, which I don't know if it's a directly here, which is what is a system one problem and what is not, what is a system two problem? Like, you know, um, that's a hard one. That's a hard one, my friend. Um,

1:19:55because people now are just trying to jeb everything, right? Which like probably is going to fail, right? Um, but like some things are going to be good. Jeb everything is pretty funny. Yeah. It's a pretty funny way of doing it, saying it. Um, so I'll tell you the truth. Yeah. Um, the truth is that this is an empirical problem, just like scaling laws are an empirical thing. You know, like why doesn't like robotics really work right now, despite all the money being spent on it? I don't think it's about like spending more money necessarily. The empirical results just might not be there. Right. Um, so empirically, I believe that these like pre-trained super

1:20:33condensations of intelligence are fundamentally system one thinkers. I think that they truly like system one is the closest thing to describe what LLMs are strong at. RLVR has done incredible things for a system two thinking. I am at all. It is super freaking cool. Like I don't think that it's going to result in AI doom in the slightest. Um, not, not 0% of course, cause I think 0% is miscalibrated, but like it's, it's really cool what they've done and they've really pushed it to the limits. Well, maybe they don't think so. Not the limits limits, but, um, like it is, it is a weird

1:21:08thing for models to do and they are very fragile at this. Like think about how people used to talk about AI back in the chat GPTJs, like, wow, it's really general. It can do a lot of general things. And, and, and then, but it's bad at math problems and like GSM, AK grade school math. Um, and then now look at how people talk about RLVR. It's so fragile. It's so jagged, you know, like it can, why can it do this like really weird thing? And actually, you know, math is not just spiky. It's fractal, right? And this is because RLVR is, you know, like if we talk about like, what is the North

1:21:42star for each thing? RLHF is please humans, right? That is what the human feedback is. RLVR is optimized benchmarks. You know, that everything that goes into the RLVR category literally is a benchmark by definition because a benchmark is programmatically verifiable, simple outputs that can like do well. And, you know, RLCD is make it reliable for, you know, programmatic use. And, um, yeah, that, that, yeah, yeah. Maybe I'll, I'll offer some thoughts and then you can sort of, um, correct

1:22:15me if I'm wrong. Um, one, for example, one thing that I've been thinking about is also, I, so I threw Jeff at a bunch of things when you gave me access on day one. Um, and, uh, multi hop reasoning, right? Like, uh, so single hop, fantastic. Like state of the art, uh, you should never use anything other than Jeff for single hop. Multi hop is going to start to fall down and it's like kind of monotically increasing as you increase the hops. Yep. Yep. Yep. Right. So, oh yes. Back to that empirical question. It depends on what we can like pull out of the models. Yeah. Right. So we want

1:22:46everything like we want to unearth as much intelligence as possible, period. Um, the models like I see us as like unlocking and smoothing and sculpting the intelligence while like adding new capabilities and like, you know, like, like filling in gaps in it. Um, and we will be filling in like, you know, more and more and more and more of these gaps over time. But the reality is that we are in the business of unearthing properties. Those properties are actually a function of what is available from like these, like, you know, these condensed cores and like Franken signing them all together to have

1:23:19all of the properties of everything, you know? Um, but the reality is we are in the business of unearthing as many capabilities as possible as possible. And system one just happens to be the description of what works and everything that works in that paradigm will be system one ish. You know, like I am like, like there is a reason why we don't do what's called latent reasoning, reasoning in strings. I think the reasoning, like what models do really well is reasoning within the models. It's not totally complete. It doesn't do great at all. Wait, latent reasoning is reasoning in strings? I thought latent reasoning is reasoning in,

1:23:52in, inside the model weights.

1:23:55I think that people used to call that continuous reasoning. I'm not entirely sure. It was called latent reasoning because like it used to be that the reasoning traces were secret. So they're kind of like a latent variable for the answer. Yeah. So what secret has now shifted? Hey, well, it's still secret for open and an anthropic, right? So no reasoning, Jeff, as far as you will ever do it, right? Because that, that like violates the whole promise of system one.

1:24:21I, my promise is to do whatever necessary for machine native stuff. I could imagine there are, there are some forms of reasoning that are less slow, inefficient, and fragile that I, that are like totally on the cards, just to be clear. So pragmatic person, I'm not making promises on it, like, you know, like methods. I'm making promises on like the, what my ROI Northstar is. And I'm going to fight for that. Like, like, you know, like, like this launch didn't happen and we are still

1:24:51like hungry for our place in the world. That's great. Yeah. Yeah. I think the other thing that, uh, vision is another one that it's like a big, like, you know, capability that you don't have, but maybe it doesn't ever belong in system one. I think I have a pretty good vision. Uh, what? Sorry. I think I have a good vision. No, no, no. Sorry. I'm kidding. I'm kidding. Yeah. Oh my God. Yeah. Yeah. Um, because people, obviously the first thing they want is vision because of the doom demo, but also just like everything, you know, other than text is vision. Everything is in the cards in my mind. Like, um, and actually this is like a debate we have this man,

1:25:27your audience is probably like the great one to have in this debate. There's a question about like, how much do we try to like give people what they think they want, which is what we did in stealth for two years. We just knew that this is obviously going to be valuable versus give them what they say they want. Right. And like, uh, there's a lot of dimensions of this. Right. And you know, like context length is an example of this, right. Um, every single model, including ours, I actually think as far as I can tell, ours is like by far the best at not degrading in long

1:26:00context. Um, but like the other providers are just like, whatever people want it, let's just give them the stupid thing. And, um, like we need to figure out a balance for this because, you know, like if you take the, the, the former side too far, give people what they want, you end up with like anthropic nanny state style thinking, which is very like anti-developer. Well, like the pro developer route would be like, give them what they want, but developers are like, we don't want to put the burden on them to figure out the je ne sais quoi of intelligence. So we are trying to like figure out this navigation of like how quickly to release things to still like have our like brand

1:26:36of trust and also like teach our, treat our users like adults that can make informed decisions that don't need like nanny stating on top of this stuff. Yeah. I think that's fair. And we don't know the answer to be honest. Like, uh, we'll, we'll have to figure it out. It's going to be, that's probably going to be like one of my biggest debates over the next couple of days because like we have a lot of stuff again, we didn't expect it to pop off. So we were like, we need some follow-up launches. I don't know. I don't know if you didn't expect it to pop off. Like I, you put,

1:27:07I saw the work that you put in, like, I have never seen you lock in so hard as it's like the last two months basically. Well, that's also because my chief of staff made me lock in. Yeah. Like it's like, uh, I have never, I thought I worked hard before and no, but like you were showing up at our writing workshops and I was like, well, what are you doing here? And, and like, oh, it was useful. It was great. You clearly like were very intentional about your launch and the work showed and like, congrats. Like, thank you. Thank you. Um, I hope to keep locking in is my, is my sense. I want to

1:27:43like, like, I think that we've passed many great filters for the, the, the tech world that we're wanting, but like, there's still going to be a bunch more and like, holy smokes. Am I excited to fight the good fight? Yeah. It's exciting. Um, before we broaden out to, uh, topics outside of type safe, uh, I just wanted to offer, uh, any other things that you think like underrated or misunderstood about what you have launched. Underrated or misunderstood. Yeah. You have panouts, sorry, patterns here. Uh, maybe, maybe you want to go into that, um, model jaggedness,

1:28:18any, anything. Give me one noodling of it. Oh man, I would rant about all of these. I really shouldn't. I really shouldn't. Um, and like people can come to go to your discord. People put a lot of love into the cookbooks is what I will say. The cookbooks have like some fire stuff. We had considered putting a bunch of these things like in the main launch blog post, but it got kind of long and unwieldy and like very power usury. But like, we really, really, um, I'll be frank, like before the

1:28:51launch, um, every, like what we were saying sounds like, sounds like like this weird alien tool. Why would anyone need this? You know, it was a very weird thing. And we were very worried about teaching people about like this new frontier. It obviously succeeded, but like we put a lot of work because we thought the education would be like a gigantic bottleneck for us. Um, I, it probably works and it's no, probably no longer a problem because people are doing things like well beyond what they'll show you how to use your model. Exactly. But like they, and their use cases are like

1:29:24kind of cooler than ours. Like, like there's a bunch of stuff where I'm like, man, if that was our demo, holy shit, that was way cooler than what we were showing. Um, like the, the computer use stuff, holy smokes, is it cool. Um, but like, like we put a lot of love into this. This is not like AI generated trash. As far as I know, we put a lot, like it's like a lot of love in here. And like each of these are like, like there's real alpha there. Like these are inspired by solving real customer problems that existed. And we went through the

1:29:55work of like helping them do cool ass stuff. Yeah. How much, uh, while you're talking about

validation, developer reception, and use cases

1:30:00this, right, how much validation did you do before launch? Like what, you know, what was that process like? What was that process like? Like clearly you did some, but obviously you're not getting in touch with as many people as you are today. Yes, of course. Um, I actually think that the reception was pretty bad and like actually for the non-technical people in the team, they were really worried. Yeah. You know, like there was a lot of fear. It's like, no one really gets this. And like, you know, they don't want it. We're like selling

1:30:30like a vitamin and not like a painkiller. Like, should we have FDEs to like write the software around solving that problem? We had almost no revenue before launch. Um, it was kind of like, uh, like we, like the technical people were like obviously true believers, right? Like we knew that this was sick. It's prop computational properties are like off the charts on like so many axis that we're like, yeah, obviously it's going to be huge. I was definitely super afraid, which is why I locked in super hard. But like the most common thing was like, I

1:31:06would say like more than half the people we had play with it just did not get it. And like the people who did, uh, like were like, man, this is really cool, but how do we get this through procurement and stuff like that? You know, it was like, you know, like quite a, uh, quite a battle. And we just knew like, okay, our target market is going to be developers. People will find the use cases and that way everyone is going to FOMO in. And like, I don't want to rub in people like changing their minds with the facts changing. I do want to call into

1:31:38question like the concept of product market fit, you know, but like, because like there was a product, there was a market, like we were like, Hey, do you want to use this? And people are like, I don't know, really know if it solves our problems. It explodes. And everyone's like, we need as much rate limits as we can. Can we literally give you GPUs because we are constrained right now. So of course, uh, you know, like marketing is an element of it, of course, but I don't even think it's about marketing. I think it's about like passionate developers who've like, you know, our souls basically resonated at the same frequently and

1:32:12that frequency and that got, got everyone else excited too. And I'm hoping as well that like we as a company will be eternally, eternally, eternally grateful to those developers. Like not, and not just like, you know, like, um, the, the companies that like are like start off with developers and like go to enterprises. Exactly. And like, I'm like even thinking about like, how can we launch things that are better for, Oh man, I don't know if I should say this, but I will. Better for developers than enterprises. Exactly. How do we do that? Like, how do we

1:32:43empower them? Um, and I have cooks, I have cooks, but, um, it's a, it's a very weird thing to do. And like, I don't know how else I can show my thanks and loyalty to that, you know, like, and, and, and that's why I did like the dyeing my hair yesterday. It was like, it's like, I wanted to talk to them because it felt dirty to me during our company's like most important times not to keep talking to them. Good. Well, I mean, that's why one of the reasons you're here. Hold me to that, please. I try to be principled. Quote me on this, uh, call

1:33:15me out, you know, have the pitchforks out if I change. Uh, I was just going to briefly show the computer use stuff. Is this, is this what you're referencing? I've never seen, I've seen, I saw like a airline browser use thing. And, um, inside this new note, let's make the title say hello. Wow. Great, great. Okay. Um, let's move on and can you open up the ARC browser? And once you're there, can you Google search Norbert Wiener? Um, now can you open up x.com? Is this, this kind of use case? Oh, the voice use case is, this is actually the

1:33:50first one I've seen. This is, wow. Oh, wait, wait, wait, wait. Oh, can you go back a second? Can you go back a second? Um, rumors claim anthropic engineers worth worship. Claude is God. Wow. Wow. Dang. That's pretty funny. Um, and here you are building prod. Wow. This is sick. Uh, yeah. So, so clearly you can operate the whole computer with voice with Jeff as a decision model. So just like I'm anti bench maxing, I'm also anti demos.

1:34:22I want to make sure that it works reliably. I love people are playing with it. This is super fucking sick. Have no doubt. I want to see this. I want to see it be used. I want our team to play with it. I want to find the weaknesses and I want to solve that. And I would love, man, that looked really cool. That looked really cool. I want that. I want that. Like when my, when my wrists are sore, I like just whisper flow everything. That'd be sick. Well, it's, uh, well, you know, just, just to round out the use cases side, uh, cause I do have to let you go. Um, uh, who's, uh, who are the, who are the bigger companies that

1:34:53have reached out and have surprised you with what they want to do? Just, I am so out of touch for that. People have shown me screenshots of companies and from what I've seen, it's all of them. Yeah. Mostly like, you know, for, for those people who work at larger companies and they're not doing this kind of work. Uh, I just want to give people examples of like, you should go look that up, look that up, look that up. Oh, so, uh, like I think demos are super duper sick. Obviously the coding agents are like gigantic use cases. Like they are like also super sick. Cog is all about Jeb right now. Oh, hell yeah. Can I, uh, can

1:35:26I give a little bit of a tangent about coding agents if that's okay? Yes, please. Oh, let me give me a second. Give me a second. Okay. Actually I'll come back to coding agents. Let me describe like the big families of use cases. Yes. Like we've mapped this out from first principles, like long before release. They are what we call dark data. Like people hoarded big data, but they would not throw a length at it cause it was too expensive. So large companies adore this. They have like piles of data that they wish they could analyze. And this is like a data scientists wet dream. So this is like, this is a giant one. Like

1:35:58I think this plus, um, coding agents are the big money makers because that's what they're all, where all the volume is. Right. Um, there's the real time stuff, you know, like people who need like intelligence in the loop. They like, I would guess that every CEO, if not CTO at those companies, um, knows how much better their product gets with every like 10 milliseconds shaved. Yes. And like, especially e-commerce. Yeah. Yeah. Oh, or like assistant D things, you know, there's many AI assistant D things. And like, as far as I can tell, they really love

1:36:32it. Um, again, I'm not in the front lines of customers right now, so I just get, know what my team tells me, but like this, I'm so excited for this. I'm really excited for this for games. I really want to play like sick ass auto battlers where you're like commanding your team or like semi auto battlers. I think that'd be so cool, but don't make it too good while I still have a job. Um, and, um, the, you know, like there's the, uh, the, the, what we call like verify everything, you know, like verifying all LLM calls kind of like observability. Um, I think actually on the note of docs, what people should be doing

1:37:03is like the parallel questions are very cheap. So if you have like big states, you want to ask many questions on right here. Yeah. Put IDs on every like message and then ask a question about each ID. So like when you have like a long state, so that way you can like pay for the state once and ask lots and lots of questions about each message within it. I think that is like a, like a great way that like saves money. Which by the way, I always think like it's interesting framing system one and system two, because it basically makes the case that you should always make one or 10 or a hundred JEV calls for every one reasoning call that you make.

1:37:37Well, maybe, well, I mean, I don't, I would like people to spend less, you know, maybe you do like, you know, one half the reasoning calls and like 10 JEV calls each or something like that, or whatever solves the problem that like couldn't have existed otherwise. Um, wait, number four use case was what I described as like smart software, like software that's intrinsically composable and like does like weird fun stuff that could never happen before. You know, like the programming language as JEV thing. I don't know if you've seen that. That

1:38:08is so cool, man. If we knew how to give out credits because we're really early in our infradays, I would want to give all these projects credits. Um, and I think that those are like how we've mapped out like the main use cases. Um, computer use has also come in kind of like the real time direction as well. And like, that's really, really cool. If it is reliable, I am super jazzed about that. I suspect we can make the model a lot better at these use cases because like that came out of left field a little bit. So that's, that's really cool. Um, I'm on the coding agent thing. And this is

1:38:43like a really surprising thing that is happening right now. Okay. Um, Claude Code and Codex are, I believe the, the winner, like the, the number one and two, I'm not entirely sure. I don't follow closely, but like, it's roughly that, but they're built around a single model world, you know, like, and, and that makes a lot of sense for them. Right. Because like, it has been a one model game where it's like kind of like the same model, but different intelligence that you're shopping. But all the open coding agents are like fucking jazzed right now because they're like getting

1:39:14their jev on. And like, the thing is there's, I'm sure they're trying a lot of weird stuff, but all the coding agents are kind of roughly at like approximate parity, right? Because like, there's not so much you can do with a while loop, but the moment one person finds one killer use case that, you know, you can only do with that coding agent, everyone will flock to it because they have like a monopoly on that thing. But all the open coding agents will be able to copy that. Right. The end, but I don't know what the cloud codes and codex will do because they are built around

1:39:47that one model world. And like, I think that's going to be like a really interesting thing. You know, like I would love to be able to integrate with them personally. Like I want to integrate with everyone. Like I, they might make competitors eventually. I don't know, but like, it is not me, my job as Sonfire infrastructure to be opinionated on that. Right. Like I want to just serve the world. But I don't know if they would do that. And like, I think it'll make the coding agent game super weird. You know, like I'm so excited for that. And like, I'm sure I'm getting my team to

1:40:19review right now an internal document I made on design patterns I suspect will be useful for coding agents. So hopefully I can share it like right after I walk home. But like, I think that there's just like such ripe area for exploration out in the world. And like, it's, it's been, if I did not have this, I would love to experiment with coding agents right now. Yeah. I mean, and I'm sure the coding agent companies would love to work with you as well to, to figure that out. Yeah. I do think that there's still use cases for clock code and codex with you guys, which it's, it's easy to explore

1:40:51there. Okay. I mean, you know, we've, you've, you've been very obliging in the sort of indulging and all these, all these things. I just want to take you out of type safe just generally about, and you've, you've made very clear your position on the state of UI, uh, give you more room on the alignment safety side of things. Oh, uh, did I not talk about safety alignment at all? I think I didn't. I think maybe I didn't. You did. Uh, I just like, you know, I think that there's,

pacing the frontier and training paradigms

1:41:16there's a lot of, uh, you have a lot of researcher discussions. We have this every, every new ribs. Yeah. What are people talking about? You know, like, uh, I, so for example, um, I, uh, recently was at, uh, one of these researcher gatherings and people are genuinely worried about the pacing, right? Like this, this whole topic about like, we should slow down because, uh, the public is like clearly not ready. Um, and I, I'm sure you have strong feelings. Um, I feel like this is the kind of

1:41:48thing that is a dangerous topic to talk about. Um, I'm happy to talk about it. I live for danger. Our company brand is chaos. It's not a Jev. It is irreverence and chaos. And you know, yeah. And like you were at OpenAI during like the, one of the very first, like very visible incidents versus the blip, right? Like, which like, and like you, the dominoes have gone down now to now every frontier lab has co-signed a document saying that they want to base. Um, interesting. I, so comp, it's a very complicated nuance thing. I actually

1:42:25do want to write a response to this more formally. I do have like a little bit of a short version of my response, which is that, um, as you RLVR more like RLVR is like, so RLVR is not actually about verifiable rewards. Like that has been failing since before the reasoning revolution. Like, like, like, and that's the weird part about tasks, right? Like back when, oh, fun history. Back when RLHF was becoming a thing, there were three different things that like are now called post

1:42:56training, different efforts. And instruction following was by far the, like the, the vaster child. Like people didn't like it. They didn't want to take it into account. It was annoying. You know, like I talked to the pre-training team and I'm like, guys, this is the magic. And they're like, we'd run so many model sweeps. You want us to wait for human evals to figure out which models to use. And like, everyone is like, you know, giving tons of like resources to like the Cogen team, which like they did have some successes, but they were trying really

1:43:26hard to do RL on like unit tests and it didn't work obviously. Right. Like you needed reasoning for that. So, so just to be clear, RLVR is not purely about the reward. It's about like the shape of everything too. And part of it is that reasoning is included in here, like this latent variable that you're doing things. And when you're doing things, you're just letting the models do whatever they want in order to make them be as powerful as you can to answer the hardest problems. And this whole pace the frontier discussion, I think is like a very narrow focus because it assumes that

1:44:00everyone needs to do more RLVR. Right. Which, um, like, I obviously don't think I need to do more RLVR on our models. You know, I think zero is the optimal amount for our shape. Right. Like, come on. Yeah. You know, so, um, it's really, I think a bit of a sleight of hand where they are saying that we actually want to keep doing the thing that looks dangerous because it does dangerous things.

1:44:31You know, like people say like, oh, maybe the sandboxing was a problem or whatever else. Um, yeah, I mean, obviously it is, and they could have easily solved that. Right. But they chose not to because the more things you let the models do in this do anything category, the more powerful it is. Right. So, uh, like there, I think there's some like disillusion of responsibility there on like things that by design or non-design they're trying to make is just an assumption. You know, we must do RLVR and not just, we must do it. We must do more and more and more,

1:45:05um, with giving the models like the power to do powerful, you know, do anything they want in the middle because that teaches them to be powerful outside of it. And we don't want to limit those things well because it'll make it slightly less powerful on those things. Um, so like if you assume all of that, they're like, oh yeah, we're heading into a dangerous world, guys. Like everyone is going to be doing this and this is the only way to make AI sick. So, um, so basically it's like,

1:45:38it's like, these are all internally consistent, but actually starts from a premise that has alternatives. If you think about it. Of course. I think there's like, like on the bitterest lesson direction, I think that there's very few people who've like made right tasks, you know, like new directions of AI that is, or new, new North stars. That is rare. Again, like I think 2.2 times or something for LLMs itself, like RHF and then RLCD, RLVR is like a 0.2 in my opinion. And I think that's generous. Um, but or 0.5 or like, it could be one whole one. I don't really care. Um, but I do

1:46:14think that people are thinking very closed mindedly about this type of thing. And, um, this, the only people who are at fault here are the researchers because it's definitely not the populace, you know, like they just assume that open anthropic are just doing the best they can. And they are not the experts who are aware of the true optionality available. Yeah. Uh, and that's fair. And then you're, you're also doing your part in waking them up. Yeah. Well, I'm doing my best, but like my goal is not like convince labs that there's like other directions to, to go down. My goal is have, you

1:46:47know, it's like spark hope in software engineers to start like actually automating things they've always wanted automated. Um, I had this like article that I wrote that my team didn't let me write, that didn't let me publish about like the, the, the future I want of AI. And like, there's like a lot of like little things like, remember, do what I mean. Imagine if everything could do what I mean. Cause like that, that, that demo was do what I mean. Like, like you could like, like, yeah, don't do what I say. Do what I mean. Yeah. And like, we couldn't do what I mean yet because like computers are so basic and literal, but that computer use one was just that. And I think

1:47:22that there's like levels of smoothness that will happen in the world that people just don't understand. And like the promise of like smarts all around are, it's, it's, it's, it's, I don't want to overpromise. I don't think it's going to happen right now, but like, we are going to do whatever the fuck we can to make that happen. Yeah. Um, any other things on the sort of general shape of post-training, you know, um, you obviously you're been very intimately involved, uh, mid-training, is that, uh, something that you do have comments on? I don't think we've ever talked about it. Mid-training. Um, I mean, it's all a spectrum. Yeah. Right. Like, am I,

1:47:59This is a curriculum, but like fancier. Yeah. I mean, like it's, it's, it's like, you know, it's a cost saving thing, you know, instead of like having to pre-train again, like there's intriguing stuff. I actually think that like intelligence has a je ne sais quoi at every single level. And it's always super duper fascinating. Like I am a shape rotator, so I don't like finding that, but I love it when people find it and teach me about it. Um, but you know, looking at the data, this thing that, uh, our data team is so good at that I'm not, um, it's,

1:48:32I find it really, really fascinating. I love actually thinking about like how capabilities are like put into the model, like over like the short term, you know, like there's like the, the, the, the really rapid alignment of fine tuning and over the longterm after seeing it over and over and over again. Like this stuff gets baked deeper and deeper and deeper and deeper into the model until it gets robust, you know, and that is like the, the North star to surface. And like the system one stuff is the stuff that ends up getting robust. So I find mid training to be like a fascinating thing. Um, I'm a fan of all forms of training. Uh, I'm a fan of all forms of

1:49:07like surfacing new types of intelligence. I wouldn't do it all myself because it's expensive. Um, and I have said privately and also, should I say this? Huh? Huh? You know, like my philosophy is anything I, I, I should say in like private with like an investor, I should say in public with the people, because that is like my thing. Yes. So the thing I've said before is if you gave me a billion

1:49:38dollars, I wouldn't pre-train. Um, I still believe that to be true. It is a very expensive thing when if you are like, like if you're an A engineer, you can like slice and dice and do all sorts of stuff, you know, like Frankensteining is not the most elegant, beautiful thing, but it solves problems, baby. So, um, anything except pre-training. Yeah. Amazing. Uh, I think one, one direction that I do think that is interesting, just like synthesizing all your, all your commentary about these model

1:50:09things is like, do we have a, a, a, a supermodel, uh, that has all these capabilities involved or do we break them out in further, right? So like, um, uh, one way to put this is, uh, opening, I was trending in the direction of the Omni model, right? Um, 4.0 was, was one of those. Uh, then for, for, for a brief period of time, there was always like, there was like a kind of a main branch of the, this is the chat tune model and this is the coding tune model. Those are completely different things. Those are extremely different concepts. I will like break that down a little

1:50:41bit. So multimodality is a little bit different. Um, because sometimes the other modalities help, sometimes they hurt. Yes. Like, you know, people are moving, they seem to be moving away from speech, which is different than the audio. Um, because it seems to not generalize well to the other stuff. This might get solved. I'm a fan of all of this, but these are like empirical real questions. Like scaling laws are not about just throw money at it and it gets good. Scaling laws are pragmatically how good is a thing, you know? Like, like there are worlds where like, no matter what

1:51:13you scale, it may not be good enough. So, um, you know, like computer use is not currently solved is my understanding. Like I'm hoping that we can be a, like play a part in solving that, but like there might be no amount of data we collect that will solve that. We might need better methods or something else like that. So, um, like we, you need to be like really practical in all of this. Am I a fan of Omni models? I'm a fan of all forms of intelligence, but I will go straight into one

1:51:43thing you talked about, which is different from pre-training, which is post-training because I hate fracturing intelligence. That is like the bad thing to me. And this whole like chat first reasoning mode is because, um, it forces the intelligence to be fractured. Like when you're optimizing for chat, this tends to be like pure RLHF and it's quite intrinsic in RLHF to do the stuff people like naturally complain about, right? Like, oh, you're absolutely right. And yeah, you know, sycophancy, psychophancy, whatever word, how to pronounce that overconfidence, hallucination,

1:52:15like even the kind of style that excels in LM arena, bold, italicized emojis, you know, like it doesn't answer the question simply. It gives like a long write-up and then it asks you a follow-up question. So it feels more like a human talking to you. All of these things, um, come because strings are super weird. You know, they are like weird ass things and you need to be miscalibrated. You need to like mode drop. You need to be hyper confident in order to not go off the rails because the reward model will punish you so hard when it happens because it's obvious.

1:52:46Um, and then this like warps the probability space entirely and it interacts with that of the reasoning models, right? Because like it, you know, the, the models are like these simple linear things that tend to cheat a bit. So I think that that's very different than exposing intelligence is, is my guess. And a lot of the art to intelligence is studying this subtlety that I think that, um, at least when I was in open AI, people were not really studying that because like, they were just like chat, chat, chat, chat, just like people are with Jeff right now.

1:53:19Can you give me an optimize, you know, you give me an objective, I will just go optimize for that, right? Yes. But if you try in there, and you know, like the saying is like, you could have like two objectives and you could just like optimize for both, but then that is literally the act of fracturing, right? Um, so yeah. So, I mean, in some ways you have, you are also factoring intelligence into system one, system two, but you just don't agree with the other people's fracturing, fracturing. Which is fine. It's a little different. No, no, no. If I could, if I could add, if I could defend, um, the system two tasks, um, number one, like we don't toss out the system two tasks,

1:53:52right? Like you can try to make Jeff work on it. And there actually is an intelligent answer for that, which is unknown. You know, like my, like there, like there is better and worse behavior in the system two tasks, which should be like really low confidence, lots of uncertainty. Maybe some heuristics can like move the needle here and there, but we care about them too. Just to be clear. I just think that that is not what the, what is the intelligence is native to. So we're not trying to fracture anything like that. Um, and all fracturing makes the model dumb. You know, like

1:54:24if people like get the model to say like, it is open AI or Quinn or, uh, you know, like Claude or whatever else, I don't really know what it says these days. I am not going to put into the models that you are Jeff from type safe that fractures it. Right. Like, like, I don't want that. Like it represent what are the internet things, right? Like be correct. That is what I want. Because that's how you get the smooth, predictable intelligence. I mean, identity is a thing, I guess that is somewhat of a special first party product. Yeah. But like for an API, I don't think

1:54:58so. Okay. You know, like, I don't, I like people don't want, if they're making a chat bot with, uh, you know, chat to GPT, they don't want to say it's chat GPT. They want to say it's like chip out lay eye or whatever. Right. Well, you know, so the, the way that you also have to make up for it is you have the skill, right? The, the, the Jeff skill, which, which is for coding agents to work with Jeff. Um, okay. A couple of closing questions because I do want to get you out. Uh, one is like, you're just reflecting on your two year journey. It's roughly two years, two point something. Um, with the company, I think that this is like more like a four year journey,

1:55:29but actually like, uh, I was thinking, remembering that like, you had this like hero run around Thanksgiving. You were like, you were canceling everything because, uh, you were like, guys, like everyone's on holiday. I'm going to take all the opening air GPUs and go do this thing. Yeah. That was a good time. And that was like the, the pre type safe moment. Right. That might've been, was that when the coup was happening? I don't really know. Yes, actually. Yeah. That sounds right. Yeah. I remember. Oh my God. I don't want to, I'm not, I don't think I have the time to spill the tea about the coup right now, but, um, that wasn't really annoying. Um,

1:56:04the, the coup was annoying or the, the run was annoying. The coup was annoying. Yeah. Yeah. Yeah. I will. Safety has took over the company. Yeah. Anyway. Maybe next time we chat, I'll dump tea about the coup. Um, yeah, it's actually, this problem was one that like was in my mind since before chat GPT even launched. I was like, holy shit, the chat GPT team is cooking. They are doing the right task. They are doing the thing that AI researchers are bad at, but successful

1:56:38product people are good at, which is giving a lot of fucks about the experience. You know, it's, it's, it's very rare. Like there's very few people like that at open AI. Um, and those guys were cooking on it really, really well. And to be clear, this is the whole journey from GPT three to 3.5, which included AI dungeon, which you've talked about as like, yeah, well, that's, that's an example of a use case that we never predicted. Yes, exactly. Well, uh, oh yeah, that is a, also I had fought very, very hard to deploy instructor GPT. Um, like actually the

1:57:08early versions of it were even trained with like an algorithm we didn't publish that I made myself because it was too slow to clean the, the PPO data. And I was like, fuck it. This is so fucking good. We need to get it in the hands of users. And like basically immediately it took 50 percent of the market share of LLMs at the time. And, but, and we thought, I made, I went through great effort to make sure everything in our launch video is true. Um, you know, we, I truly was thinking like, is this AGI because it's superhuman at instruction in instruction out? Obviously it's not, but like everyone I think should have an answer to why that was

1:57:44not AGI because it looks very smart. And my answer to that ended up, you know, like ended up only being used for copywriting, you know, Jasper AI, copy AI, like writing, like, you know, what is now called slop on web pages. Um, and we were worried we made the internet a worse place. Right. And I went back to the drawing board and I was like, what's missing? We are smart. Clearly something is missing from it, like creating value. What is it? Like I actually was doing more philosophy at the time of like, you know, like what is going on? And the answer was, Oh, machines. You know, the question I asked myself is like, let's

1:58:17work backwards from an AI based economic revolution. When that happens, what will be, what will be calling the AI? If AI is an API, will it be humans or it'll be code? And I figured it was many nines of code. And, but like all the optimization was going into the humans part. And then it clicked for me. I'm like, holy shit, this is the North star. I think like I wrote a document. I was like talking to Sam about this. Sam was like, this is so fucking good. You should go work on it. And we're like, yeah, yeah, yeah. Sam, I have a job, you know,

1:58:49like, um, you know, I was working on, I just told you to do it, go do it. But like my, and my guess at the time is like, this is super obvious. Like it's so unbelievably obvious. Anthropic must be working on this already, you know? And like, we're already cooked and like actually opening. I does better at like catching up than it does at like actually innovating. So like, uh, chat GPT was a copy of Claude, right? Um, like they had an internal thing. They just didn't ship it. Yeah. Um, but, uh, but you know, reasoning, I would say first ish. Yeah. But debatable how good of a product that is. Yeah. Um, it's great research

1:59:22though. Super great research. I'm just not sure if people had that product need. Um, and you know, Claude did the coding agent stuff too. So Sam says that and, you know, I just go back to my job for a while. Eventually like, you know, the instruction following team just says we won, we we've solved instruction following. We don't need to do stuff anymore. I'm like trying to think about what I do next. I was like, you know, maybe I'll just start, start playing around with this. Um, I, you know, do more philosophy and design and thinking. I thought it would end up taking a week. Uh, when I started training models, it ended up

1:59:55taking, um, many years at some point I was like, holy shit, you know, there's signs of life here. This, it obviously didn't work. Right. Otherwise we would have deployed it. But like, I want to explore what it would be like research wise to go all in on this. You know, like, I want to really see like, like what it would be like if you went like absolutely insanely all in, in this direction. And because of what I said, you know, like if an AI winter happened, would I, how would I feel? I would consider myself personally

2:00:26responsible. I talked to other companies at the time and I was like, Hey, I want to start a lab on this direction. And you know, like there was interest and I just talked to them like how fast, what would be faster? This or startup? And they're like startup. And I'm like, fuck it, man. We ball. I guess we're doing some crazy shit. And you called Eric and Sasha. Yeah. Well, I, I call Eric first. Um, with Sasha, I actually didn't try to recruit her. I tried to be good. And I was just like, Hey, am I crazy? Is something missing here? You know, isn't there like,

2:01:00like, like, like, am I too much in the open AI bubble that I didn't realize there must be a solution to this? And then Sasha was like, I'm in. And I'm like, Sasha, you're working at a startup. And she's like, I'm folding it right now. And I'm like, do you want to think about that? She's like, Oh yeah, good point. Let me think about it. And then she joined. And then, you know, within two weeks we had funding with it. We had like people move into my apartment. It was the worst cause I'm a neat freak. Um, and we just kept on cooking and eventually we got the

2:01:32research that, that, that showed the signs of life. You know, it was, it was a crazy time. So the, the, the question is that was all long context. And then another question is someone like you is in the frontier lab right now who is frustrated, not getting the funding or the resources, whatever, uh, the attention, uh, what's your advice to them? You know, should they do what you did?

founding type safe and future directions

2:01:54Should they do it? Ooh, that's a fascinating question. Um, man, how do I do this without burning bridges? Um, I, my sense is that most, unless there's some level of economics, I don't really understand. Um, I think most neo labs are crap. Um, I, I don't want to see myself with that as peers. Um, like I, I don't really understand what's

2:02:25going on there. Like, is it because like, number one, I don't really value researchers. Um, I value people who like, like, look at my bitterest lesson, right? Well, not just that, I like, we need researchers, but we need them to give a lot of fucks about the right task. And that's the important thing. Right. So it's actually like the, it's, it's kind of backwards when people value pure research pedigree, because that generally doesn't create value. Um, so it like, number one, I, I believe in North star tasks and doing cool, really useful stuff. Um, number two, um, because I don't value researchers, I don't,

2:03:02I don't recommend going the, well, it clearly is profitable for someone or it might be in this environment. So like from a purely pragmatic perspective, I don't see creating neo labs as one, something that creates value. It seems to destroy value because like they are like redoing work from scratch with like low probability of actually moving the frontier. And as far as I've talked to most neo labs, they don't really have a direction. They tend to want money to play around with their experiments. Um, if they have a direction, I'm super in favor

2:03:35of it, to be clear. So my advice for someone is it really depends on why you're doing it. You know, if you are a researcher who wants to play around with research, um, probably the labs are the best place to do that. TBH. Like there might be other places. I don't really keep track of that politics, but I would just recommend not being that way personally. You know, like I think it's better for the world with people being driven to solve real problems. And those problems may be exploratory. That's fine. But like ideally have principles that you

2:04:09stand behind. But if you think that you want to do the right task, like abso-fucking-lutely, like, please do like, please break this like unimind, you know, unimodal like, yeah, exactly. Like, like, you know, like again, this pacing the frontier is coming from like this one view of AI that looks like, you know, AI super genius that is incredibly jagged. And that is, um, Solvable. It's solvable and it's weird and it's like not matching reality. And, um, it's like,

2:04:44it's tragic, right? Like, like, I, I think like all of these, like the, like really unearthing technology, I think it's like just good. Yeah. For what it's worth, uh, you know, again, I, I'm trying to accurately represent the position of the, uh, anthropic open AI folks. I was talking to SpaceX as well, by the way, uh, is that, uh, it is, this is a political thing much more so than a pure X-Vis thing. Yep. Uh, so yeah, political positioning. And that, I'm, that's beyond my favorite. That's well beyond my favorite. Once they told me that, I was like, I get it. Uh, this is about the 2028 election.

2:05:18Oh no. Oh, I wish I didn't hear that. That's such a bad vibe. No, no, no, no. This is not the whole company. This is just that, that room's discussion. No, no, that, that makes sense. That, that, that makes me lose faith in humanity a bit, but maybe I'm just a naive technologist. Uh, it's really starting to matter. Who's, who's in charge of the governments, uh, that, that will help to regulate, uh, these things as they emerge. And like, as a lab, you should probably think that through. No, no, I totally agree with that to be clear. Like, I think being opinionated on that matters

2:05:51a lot. I personally, I'm afraid of trying to mislead people because I think that bites people in the ass a lot, you know, like, um, I think that like people trying to be overconfident, like, uh, I, I, I, I obviously, I'm not actually going to talk about politics. I think what happened in COVID is like people leaned too much in like appeals to authority and being overconfident to try to get people to behave in certain ways. And like, obviously our response was extremely

2:06:22suboptimal and that had like, like ripples of downstream ramifications that are now, I think extremely bad for the world. Like maybe I'm naive. I think that misleading people, even for the greater good or what they think is the greater good is just, uh, it's just, I I'm not a fan. I'd rather not. It's not a, I don't think it's misleading. It is just like, this is why now. Uh, what, yeah. Like, you know, I think that is why now that is a little bit

2:06:54misleading about like the risks versus like the, the objective. There is like some level of like sneakiness latent in it that, um, is worth calling out. And I think owning up to, well, obviously they want to, if, if they want to manipulate, then they shouldn't own up to that. That seems like a bad strategy, but like that to me is just, just sad for the world. Yeah. Hopefully I'm never, uh, yeah. Uh, hopefully like we are never involved in anything like that. It might be inevitable as we get big. Um, but I want to, I want to stay like pure technologist

2:07:27to my roots as much as I can. I mean, Jeff for president, why not? I can, I, you know, I will trust Jeff's decisions over my own. Um, okay. So, so less strip posting, uh, more about, that's chip posting. No, no, no, no, no. For me. Oh, okay. You're just crushing my hopes about like America and the world right now. Oh my Lord. I mean like there's like, I think I watched too much TV about like conspiracies to take over the presidency. Um, the, uh, you will have chosen your North star. You have chosen reliability and programmable and composable AI

2:07:59and cheap and cheap. Yeah. What is a second or third one that you want to throw as a bone to someone else that you're not, that you want someone else to work on that you're not going to work on? Ooh. Like, uh, just basically give people tasks. Give people tasks? Yeah. Like that your tasks, there's so many I want. You have picked your tasks, right? You know what I mean? What? Wait, that's such a good question. Holy crap. Oh man. I'm so excited by that. Cause you're, you're going to be, you're going to be for the next like 50 years you're going to be busy doing your thing. Hell yeah. Okay. So let me give like a fun one and a not fun, uh, and like maybe a valuable one that's also fun. Um, my, my fun one is

2:08:34I think games could be so freaking cool if they were intelligent. Like when I see people play around with like, like Ali's doom demo where like you can like get NPCs to control stuff like, you know, like that was just really like the, like a proof of concept. I think it's really cool stuff could be made. It looks really, really cool. You know, like, um, like I'm a big Stardew Valley fan, you know, and like, it's, it's really static and it's still compelling. Like, I feel like there's a lot of cool story that could happen. You don't need to call like Jev in the game loop. It's probably too expensive for that, but even like simple,

2:09:07like state machines for NPCs, I think it could make like such a compelling world. Oh man. Um, and man, a little sad that I can't work on these types of things. My, my life path is a little bit set right now and I'm, um, yeah, but you can call someone else to work on it and then you can like feedback on it. Um, the thing that I would really, really like to explore is like coding agents free from the tyranny of the KV cache. Like it might not be as good as true coding agents are, but I think there's just so many weird things to think about. That's why I wrote

2:09:42the article KV cache rules everything around me. Um, uh, believe it or not, I don't think anyone has used the phrase on the internet cache rules everything around me. C A C H E. Um, when I, when I Googled it. Um, so, uh, like I wrote this cause I wanted to tell people about like, this is how coding agents agents work and how the KV cache works and everything. And, um, um, I think, uh, oh, um, yeah. Um, like, uh, it explains a lot of stuff. Like

2:10:16why routing is really hard. Why sub agents don't soon to work. Like while compaction is such a hard problem and I, I'm going to try to release a document. My team might veto me because believe it or not, I'm not in charge. Um, you know, but, uh, I wish. Um, but, um, I want to release a document of like, here are my thoughts, please play with it and please figure out all the ways that we can do things with coding agents. Like once you're freed from that, you know, that, that KV cache tyranny, which is, it locks you in. Well, it locks you

2:10:50in into one model. Right. And in order to do it efficiently, you need to like keep on appending to it. So now you're not doing best software practices like state management, abstraction, decomposition. Why can't you give an easier task? Some, why can't you give a sub agent easier task because of the state that you're passing around? Oh, I touched this. Uh, because of the state you're passing around, you need, we would need intelligence that is way cheaper than the intelligence using to read this in order to pass this state around. Why can't you be smart about it? Right. And I think there's like tons of really cool, fun research to be

2:11:24had there on like different programming patterns, you know, kind of like how people are playing around like with like recursive language models. Like, I feel like there's like just lots of cool stuff in here when you think about like, oh, I want to explicitly label the state of everything. Or imagine you have like a subtask, like coding agents. I, I think it's fair to say they work on subtasks at a time as from a decomposition perspective. Why do you need to pass all of that state back into the parent task? Why couldn't you do smart things about it? And also if you had a hierarchy of labeled subtasks, why can't you do a search through

2:11:57that subtask tree for the relevant context when you need it in? Right. And then, you know, another thing that you can do, oh man, I forgot to write something about this. I have like some cooks in here that are really, really cool. Hope to publish it. I'm down to jam about it, but like, it's going to be a long document. And, and like, if that becomes the case where context becomes cheap, like, why can't you do cool patterns, like looking at your historical context very cheaply? Isn't it kind of weird that you start from scratch every time and you need to solve a problem called continuous learning? That's a, that's actually like a

2:12:31memory management problem because you don't have a smart way of looking up the memory. Right. But what if you could, what if you could do that all the time? Or what if when you have parallel sub agents, they can like read each other's states because you have all of that in like your computer memory and you can be smart about what's reading and writing at the same time and you're coding agent swarm or whatever has like locks around things and can coordinate intelligently, not with like basic ass locks. Like, what are you doing? What am I doing? You know, Jev, who should write first? Blah, blah, blah. And like, I feel

2:13:03like the future there is nuts. Oh my God. Jev to solve locks. I mean, it could be so cool for like multiple agents working together. Or like, if you think about state, like agent swarms. Yeah. And you know, some things, for example, are read only processes. You know, some people like getting like summaries of what the agents are doing. Why can't they share state easily? Because like a read only agent needs to like, you know, read parts of the context and figure out what's relevant to say, well, like what's actually being written because the exploration is not super important or here's the tree of subtasks. I feel like there's so many different

2:13:35fun things that could be done if like a really smart person, like dedicated, like a whole lot of time to rethink like the coding agent experience. And that would be super duper sick. Yeah. That would be my dream. I would point you towards prime agent if you haven't looked at it. So this works together with the RLM work. We just talked to Alex, who is a buddy of Alan's in the chair before you. And like, yeah, it is being worked on, but it's not super popular yet. Yeah. And if like, well, yeah, I mean, the hope, yeah, I would want everyone to like just

2:14:07play around with like weird things. I have no guarantees that it'll work, but it seems really, really interesting from like a technical perspective. So yeah, that, that, that seems cool. And cool. Like, like once we figure out how to give credits out, I would love to like give credits out to people like this. Yeah. You will be in a position to fund research for sure. Yeah. Uh, no, anyway, congrats on all your success. Uh, you, you've like, uh, come such a long way since I first met you like, and, and, and the whole team as well. I'd like to think I'm the same person as well. Yeah. I think, but I think like you are energized in a way that I have never seen you before because

2:14:39you found your mission. You know, that's true. That's definitely true. Um, and you are articulating your mission because you, you, you, uh, for many years you complained about the problems, but you didn't have a solution yet. Right. And you're like, you had, you had the rough shape and then you had to do it, put in the work. I will say that that is partially because I, I described myself as, as 0% entrepreneurial. I, I, I don't like startups. Um, I never wanted to be a CEO in my life. I can't imagine anyone doing this twice. It seems horrible. Honestly, doing it once is pretty bad. Um,

2:15:15when we first were fundraising and investor asked me like, which CEOs do you look up to? And I was like, ew, why would I look up to those people? Um, no offense to anyone. You know, I'm trying to be like, I'm trying to be genuine good. And I've met like a lot of really good people, but like the famous ones have like a lot of like skeletons in their closet, it seems. And I think I just really did feel disempowered when I was at open AI, you know, like I felt, um, yeah, like, like, like it's a little bit easier to be truthful now because like I have at least some proof that the

2:15:45direction has legs. Like I just felt like in the, the, the insane house where everyone is just like chat GPT. Yeah. Like where do we put chat GPT and everything? How do we make chat GPT good for like, you know, developers and stuff. And I'm like, what, what are you talking about? Like the function calling interface is insane. Why would you deploy this? Um, like this is, this is so anti-developer. It's sort of a hacky way on top of hacks on top of hacks. Well, not just that. Like the thing I often said was if there was like a, this is also probably tea I don't have time for right now, but I always used to say like,

2:16:21I want to be removed from any project involving like function calling. If you did not get a legit bias for each function, like very, very simple ask in my part, which is something like a confidence, but not calibrated or a probability for it. Right. Like we need to give users the ability to control, like, you know, like let's say that actions are refuse or allow. Um, yeah. Disney needs to set a different refusal threshold in AI dungeon. The only way to control that with function calling right now is to say like, pretty please, you know, that's nuts. That's a nuts interface for

2:16:55developers. And like, people have been like dealing with this for years now. Right. Like they still have that with skills. Like, um, you know, like, um, um, the, the existing coding agents are like highly overfit to their existing harness because they're jagged. They don't tend to use like external, like tools and MCPs super well because of overfitting, of course. And like, why can't like big companies allow for like slight nudges to be like, call this more. It's really useful. Right. And like the, the, the, the, the, the solution is begging in a system message.

2:17:27That's nuts. Um, but no, okay. I, I think, I, I think I get you. Um, and like, man, uh, it is so exciting to talk about all this stuff. It's, it's really cool to get you on a podcast. Yeah. You're going to go do amazing things, man. Like I'm excited for your next, uh, big launches. Oh, hell yeah. Yeah. Just you wait. Yeah. Just you waited sooner than you think. Infra people, I assume. Marketer. One of the, it depends on who you ask. If you ask me, I feel like I'm a pretty good founding marketer. But if you ask anyone on my team,

2:17:59they say, shut the fuck up, Yogo. You need to do CO stuff. So yes. It is not just about spice. Like I think you're very spice oriented, which like you, like that's your unique talent, but sometimes you just need to say, I know, I know. I would really love, yes. I, nothing teaches you delegation, like having a tidal wave of stuff to do. Um, hiring data people, or we call them model capabilities, like, uh, but they are data people, both like, like, uh, data is kind of a slur in the industry. And like, I want to make sure. I don't think so. We're very pro data here. Yeah. But I want them to be the highest status of like, you know, the

2:18:33people actually working on the model that actually sounds weird. I want everyone to have equal status, but like, I want to even that out and I want to know that that's really valuable. These are more equal than others. I don't like weird hierarchies. And I, I think one of the things I'm most proud about in the company is that they don't respect me that much, or they don't show that they just troll me and like joke with me and they treat me poorly sometimes and all of that. And I think that that's a good sign of a culture. We're hiring like platform people, like people to like build out

2:19:04dev everywhere. Like we are so much more sensitive to location because speed of light is more of a bottleneck, right? Like I'm so sad for the European users that they were only like three times as fast instead of like a hundred times as fast because like, we don't have servers there right now. And that's insane. Right. But like, it's okay. Life in Europe, it goes a bit slower as well. It's okay. Wow. I can't believe you, you said it, not me. Um, uh, or, or everywhere, you know, like if, if intelligence per second is a metric that matters, like we will want this all

2:19:35over the place. Like we care about, like if they're a developer building on top of us, I care a lot about you. Um, and we are hiring for people to keep building more, like, not just like the goal is not to just be like Jev as a company. The goal is to like ship more shapes of intelligence beyond that. So we are hiring people to like build those things too. You know, like we want to not just be like, yeah, like the one trick pony of like the simple model, but like, I think that there's going to be like an AWS of like intelligence, you know, which is going to be you by the way. Right.

2:20:09Yes. I mean, like that's a direction I want to go down. It would be arrogant to say it will be me. Uh, like we, like I'm going to do anything I can to make sure that happens. Like, I think that that's going to be so, so cool. You know, like we are playing with like system one intelligence right now. Imagine the layers, you know, like this is like the TCP of it. Hmm. Yeah. Uh, several more layers to go and, uh, who knows what else I've also pitched temporal by the way. I don't know. We need to talk about temporal as layer eight, uh, out of the seven layers.

2:20:40Um, but anyway, we can talk forever. Hell yeah. You got to get back to work or sleep. Yep. Uh, thank you for coming. Oh boy. Yeah. Cool. You're most welcome. It was a pleasure, man. Yeah. So excited. Yeah. Excited. You took my first time. Not the last time.