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

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. Yeah. But like mentally it's...

Like, I feel... I I say this all the time, and I've been saying this kind of for years in my over under events. Like, I feel like the entire AI field is, 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. Mhmm.

And, like, yes, we are going to make... Like like, an AI based economic 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 a... Yeah.

And I want to show by internal gratitude to the developers. Yeah.

right? The engineers, the developers. 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, I I... I'm, like, perhaps overly genuine in things.

Like, it it feels, like, dirty if, like, in my gigantic calendar event 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.

Sure. Yeah.

Like called me out, I posted like,

Let me think about... That's a hard one. Okay.

And I'm happy to, like, reask if you wanna No. 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.

Nice. So the way I see it is we need a new class of models. We're not attached to naming that class of models.

Are the most accurate name we've come up with is system one models. Yeah. There 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... That's all I can say.

We didn't expect this to be our big launch, so we have stuff in the tank.

It's kind of was. Right? It kind of was.

But, 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 the consumer. So as opposed to pretrained large language models, which are meant for like auto complete of the Internet or RLHF models, like chatbot instruction following models, which are meant to like reply to text 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 TypeSafe. So the thing we really, really, really want is to have, like, AI, like, be as powerful as possible, 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 number one, JEV is our first large programmable model or a system one model, whatever you want to call it.

JEV is meant to be optimized for intelligence per dollar. Hence, the name JEV, you know JEVON's paradox. JEVON's paradox.

Yeah. And it's optimized for intelligence per dollar. I love this debate with people about what is the most important between reliability, cost, calibration, and speed.

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 and to me, like, calibration is one of the new things that people weren't talking about as much. We've done an episode Mhmm.

In the past with Clementine Fourier of Hugging Face, where they were like, yeah. I...

I've been heard that your audience is the most technical, so I actually want to get into that. Yeah. 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.

It's same thing. It's same thing. Alright.

And I I I wanna have a blog on this eventually, but I, like, want 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 Lakun a lot.

but he has this very famous slash infamous slide about The king. LMs are doomed. Okay.

You know, like that one where where he, like, has, like, pie chart with, like, a tiny part... Tiny little thing and says that as you increase sequence length, the probability of it making an error goes in. Yes.

This one. This one. I love this one 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 What's the disconnect? Exactly. And may I or you want to tell me?

About mode collapse? Oh, no. Oh, so mode collapse is related to this.

Yeah. The disconnect happens because if you are in a mode covering or a calibrated distribution, you are like not... You are not overly punished about having outliers.

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?

Mhmm. Instead, GANs mode drop. They, like, drop the minority classes and just do the really common ones.

And this is why this effect doesn't happen. Right? Like, instead of...

In order to generate really long strings 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 it's a it's a nuanced take and, like, I I think that this is why this doesn't happen, and this is why strings are so bad at decision making or or, you know, overloading the string models are for decision making is is, like, a bad time. And while we're on the topic of Yuan, do you agree that his fix with...

joint embedding prediction. So like, is that the solve or, you know, like, do you have a you have a take on 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. Mhmm. You know, like, even my take here is practical.

And, like, am am I a scaling law fan? Depends. It depends.

You know, it's it's it's like it's scaling laws tell you how much better you get at a thing for amount in. Mhmm. A scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those, like, linear gains are, like, really, really valuable.

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, have a scaling law thing if you wanna go into that later. Oh, I I could if we...

See, that part is not super relevant right now. I actually... If you want to go into my bitterest lesson, think that that's more relevant.

Okay. But, like, to me, I'm all about, like, pragmatics. And I think that the JEPA stuff is really cool early research.

I really love awesome research. Is it practical yet? Probably shouldn't say.

But like there's just a lot of... I just think there's just 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 think that what our launch did, it...

Does it kick start us as a company? Like, yes. Will it be great for us as a company?

Yes. I think it's gonna 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...

Because, like, software is super fucking charged, but I think there's gonna 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 and I think that's why, like, you know, the Twitter is just like, Jeff, Jeff, Jeff, you know, 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... If I... I'm gonna tangent if that's okay.

Yeah. Yeah. Yeah.

I think you might enjoy this. Really? Five five tangents in there.

This is gonna it's gonna Yeah. I get lost on all my tangents. This is gonna be horrible for the listeners to figure it out, but they're they're gonna figure it out.

Yeah. We can edit it Yeah. Popular thing on Discord, that people keep asking me, haven't had the time to explain it yet, is why am I opposed to safety alignment and why do we not refuse?

I'm not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. And refusal is just like obviously a type error. Like, you're a human being and you're chatting with, like, you know, a bottle or whatever or you're cloud coding, and a refusal happens, like, I'm sorry.

I can't read dna.py, that's an annoying time. It's a no...

It's it's annoying. Mhmm. Right?

But you can work with it. Right? And you're forced to work with it because of Stockholm syndrome.

I have stories about that too. I 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, like, straight up insanity. It... It's it's coming from a place of, like, people who do not understand software, do not understand programming, and, like, they are obsessed with, like, I believe this horseless carriage of, like, AI coworker instead of unearthing like the full power of AI.

Fair enough. 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 didn't train on any of that stuff. Is it surprising that it works?

No. Because we trained on weirder stuff, my friend. So...

But one tangent up about, like, safety alignment. K. Safety alignment makes sense for a product, in my opinion, for, like, chat, GBT, and Claude.

Like, 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 and play around with it. Jeb is not anywhere close to 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. But safety alignment is like the opposite of instruction following. It's when you want to follow someone else's instructions, like open air and drop Exactly.

Exactly. And this makes a lot of sense for a product. Again, like, ChatGPT should do...

You you... Like, if they don't want to, like, do, like, some not safe for work role play with ChatGPT, that's on them because, like, maybe that's, you know, what their users who have, like, parents and kids want. Like, that's fine.

But in an API, that's nuts. Right? 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...

I I I I can be an angry person, so, I should try to calm down. It's, people get your passion and I think it's really good.

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, personally.

Like, 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 to overfit to some weird stuff, you're fracturing its intelligence more and more. And, like, these things are fractured to the... Like, they're they're so darn darn fractured right now.

Yeah. So... And and as a furthermore thing, to me, it's like, I think intelligence will 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, what's something that's not great, you know, like CIA... Actually, I don't know what the CIA does, really. You you can imagine.

You can imagine, killing people who are not even bad or whatever. Mhmm. 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 gonna deploy this. Can we deploy this thing?

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 will get any of that bias out of the technological layer as long as I'm in charge. Yeah. That's great.

Yes. Yes. I don't know what you're referring to, but like this was, I've seen a couple of things about like benchmarking.

Yes. Like obviously, we're not stopping people from... Oh, man.

I should be careful about what I say. I'm realizing No. You said you said it publicly that that was in the preview period.

You didn't take it out for the launch. And now you're gonna take it out. 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. Yeah. 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. I'm extremely in favor...

I'm medium about private benchmarks that are proxies.

only is it easy to cheat, there's a lot of in... So 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 There's other 50 Jeff clones. Yeah. Well, sure.

Sure. Oh, the... These...

Let's say that there's 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.

The... No. Like, people obsess about the cost and the speed.

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 to it.

Right? Like, the like Yeah. 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. This is actually usable. Well, it's, you know, you know, like beyond that, you know, like the the launch was crazy and people could really sense how hard we care about that.

And that's truly what I think the long term 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.

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 that looks like MMLU to make it look better, which is just benchmarking...

Benchmarking with extra steps. Mhmm. So 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 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 so much about.

You know, like, if we wanted to, we could have released JEV, like, a year and a half ago if we wanted it to be dumb. Oh.

Hell yeah. Like, you know, Sutton says that algorithms beats compute very roughly. 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 in LLM land twice so far. Right?

Maybe 2.2 times. You know, there's RHF, which, like, shifted the task to instruction following.

No one realized that that was possible. 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, yeah, data matters so, so, so unbelievably much.

So it's like I can't I 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 you guys use? Are... We will always, like, care so much about data.

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, we are hiring infinite data people, actually infinite.

me try to figure figure out how to... Like, it's it's super complicated, and, like, I 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... Well, I'll do a... Actually, number is zero.

Data and synthetic data depends on your task. Like, shape of your data, the shape of your task changes the data. Like, RLVRs data is kind of environments.

Right? Yes. RLHFs is the human feedback, you know?

Each task has its own unique kind of data, and we, of course, have our own unique kind of data. Right? So number one, we have that.

Number two, 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'll... We...

I don't know if it would make a difference. We truly don't want that, 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 from 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, we would just overfit to the present and then it wouldn't work. What we need is to like... It it it almost feels like like they're they're artists.

You know? They study this cognitive core. Our cognitive core is, 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. Okay.

So we mentioned a little bit... You you you sort of criticized my thinking as our... Like, very RLVR influence, which is, like, very fair.

Okay. Right? 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 that was the original I was just a PPO paper, but I don't I don't know. And so PPO is not necessarily from you and feedback if I recall.

Okay. That's true. But I I believe it was like an open eye alignment work that could teach a hard to specify outputs like a backflip.

I'm not a 100% sure. And then there was actually learning to summarize. You know, this was work, by a bunch of the the team that helped with, instruct...

And and coauthored, the instruction following paper, which was teaching, doing PPO on language models.

Yeah. I'm not a 100% 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 I got it right. Hell, yeah. There you go.

Yeah. That's the one. So the idea was kinda...

Can you do, like, ill specified things with it? So that's, like, version one. Version two was the learning to summarize work that, like, OpenEye did, which is actually, like, PPO on language models to do something somewhat ill specified.

This is, like, another thing that people refer to as RLHF, which I did not coauthor. Mhmm. Oh, Dario's there.

Cool.

Yeah. Yeah. Yeah.

Shout outs to Alec and Ryan. Love them. Yeah.

But, the thing that I refer to RLHF is the, oh, man. Of... I I You have comments on that.

I have comments on paper, but, like, we're so many tangents deep. Yeah. So the thing that really got, to to meet 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 north star. And for us, RLCD is this new task, and it is not... I I don't see 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, you know, descendants also do RHF despite not using the algorithm in that paper. Mhmm.

There is... That is that is right. I'm 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.

Mhmm. Right? Like, there could be programmatic types that are like sick AF, But if you...

If the technology is not ready for it, it... To... It's not a tragedy if that's not out in the world.

Yeah. But to me, like, the pre dev world was a tragedy because... It it sounds arrogant.

No. No. No.

I I strongly believe you. Cool. It sounds arrogant, but like I felt this way since long before I even had a company.

I can vouch that Yes. I've talking about this for long. Grander 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. They say they do not do things because they...

It they're easy dates because they thought it was easy. So Yeah. Exactly.

Exactly. Exactly. I I thought it...

This whole project would take a week, and I was unbelievably wrong. So I am so sorry to everyone at OpenAI that I thought... I was like, man, I'm solving this right now.

But, like, I think that the tragic thing is when... Well, I think overpromise, underdeliver is tragic too, and, like, AI is super extreme on that axis. And I think RLVR is, the main...

Well, both RLVR and RLHF are extreme perpetrators of this. 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 problems in math, but still not automate even the most basics of works? Like like, really basic rote stuff that, like, you know, it 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, none... 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 valuable 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 gonna be in a very good position for a long time.

But it it... Like, it it... It's done.

Right? Like, there... Like, this has changed the path of, like, technological history.

Like And some of which you will probably also see. Yes. 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. Yeah.

Right? It's like creation is back on the menu, you know? Though it's gonna be a wild ass world, and, you know, buckle up.

It... I'm so so jazzed about that.

Sean reviewed our manifesto and he's like, it's a little bit vague in these parts.

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 about what what are doing and, like, we are...

Because you... Like, you told me you told me half of the story, and then the other half, you didn't have the Doom demo at the time. Yep.

You didn't have any numbers to give me. Yep. Yep.

I was like, oh. Well, the problem is I don't believe in benchmarking. Exactly.

Right? Exactly. 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, like, hurt...

It hurt us a lot. You know? Like like last year when we did fundraise, no one believed us.

Mhmm. You know? Like like...

And they wanted just benchmarks and stuff, and we're like, we're not gonna do that. We are principled. We're gonna 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 by that.

So sorry.

it did. I was emotional last night when I was talking about, like, the reasons I left OpenAI. And because, like, it actually had to change my wording after the launch.

My, my my phrasing was 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 overpromise versus underdeliver, 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 be useful. It'll be used for automation.

I'm actually not super on top of everything. Like, the team is the ones who are telling me all of these changing every day. Right?

It's it's it's kinda nuts. If if there's a milestone that you're like, well, yep. That's one thing we're hoping for.

We reached it. I will say a milestone that we've passed is tokens per day. Nice.

And this is not like fleeting tokens per day. Yeah. 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.

So surpassing that is awesome. Sign ups to me don't really matter. And actually, this is like a bit of a mistake we made if I'm like totally honest.

People on Twitter were calling us like marketing geniuses and all of that. And that was just us. We don't have a marketer also hiring.

And we were just being our genuine goofy, like irreverent selves. And we were we were just, like, offboarding people off the wait list so hard. Our platform team is so unbelievably cracked.

I think we have more up nines of uptime than Anthropic while having the most unprecedented launch ever. Like, that is kind of nuts. So, like, props to them.

Yeah. And, the thing we didn't realize... So number one, waitlist waitlist signups don't matter for, a developer platform, in my opinion.

You know? 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, but this is not a chatbot. Where...

Where's my chat GPT too? Mhmm. Right?

But if, 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 user's for loop that is just, like, creating value. Yeah.

And the thing we 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, 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 task, and then this road task creates more value than it takes to put in.

And then now that you have it forget. Yeah. Exactly.

Yeah. You run it in the background. You make it a dependency or to, like, other things.

You can make, like, higher level stuff, and, like, you just create so much value in the world. You know, early Internet people probably did not imagine like the wonder of early two thousands Internet, which is still not early Internet, but like it's it's through, no offense, composability No. That all of the crazy stuff happens.

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.

I do have the shirt like your favorite NeoLab... Favorite NeoLab's favorite NeoLab. Uh-huh.

I don't give a shit about being 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. Yeah.

What I care about is being a reliable dev platform. So Yes. 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.

scalability and all those things. Right? They're they're all sort of And of nines.

And nines. It's like like Which is uptime is in my But that's part of it. But like there's reliability in, 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 reasoning 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, think that there's the reliability of being able to like trust the outputs.

And also we are like their 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 that's just a common sense thing to do.

But to me, we will be sufficient. I don't know if there's a such thing as sufficiently reliable, but I wanna 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, went for nontrivial branching.

I could just write it in in like a like like a type safe system one query and then get the results out and it just branches accurately. Like, that would be so, so good. Like that's the, that is the dream.

And if not, why not? Oh, it's a great question. So this is actually like a common question we have between, so reliability 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 it's jagged. Right? So reliability is a catchall.

I just think that it's also a catchall for, what the North Star is. 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 want to tell people what they really want because that would be a little arrogant of me. I believe that that is like the more important property.

You want, given similar inputs, get similar outputs. And it's kind of wild how unreliable LMs are. Like a way that we test this is you put like UUIDs in, you know, like, little...

I think they're called nonces Mhmm. In in the prompt, and what you want is similar outputs from all of those because it's truly semantically the same question. And that is the part where you really want...

Like like like, that robustness is where, like, people get, like, burnt with AI making decisions. So I think that is the... 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 in comments or you. But my in in general, it's easy. Determinism is something you can like trade off for better cost.

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, I shouldn't say this, but no one's here to stop me.

is not how it works at this company. I believe for this week, my chief of staff, Kay is the most powerful person in tech. And shout out to Kay for organizing this.

Holy. Yeah. Holy shit.

She is so fucking competent and powerful. She's incredible. I mean, she sucks.

Don't poach her. But... 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 that one facial expression I... My cards didn't Decent Jacob Villardy movie if you wanna see I You the annotation.

Anyway. You have no idea how little time I have right now. Yeah.

My priorities are sleep. You know? Developers.

Developers. Developers. Developers.

Yes. Developers. Developers.

Developers. But, 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. Yeah.

We're gonna 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 to do that.

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, wait.

Where did... Where did my tangent from?

and, like And robustness. Yes. But, like, determine...

Well, like I have a robustness example that's that's, real quick. Can show you. Love that.

Just say one thing. We can make a deterministic model. Exactly.

Like like, we're hap... If people can convince us that that is a valuable thing to do Yeah. And we don't have a gigantic GPU shortage Yeah.

We can happily make all of these models. We live to please and rev... And and revolt...

Revolute.

will love to... Maybe one day we will see how that happens. I've been told I'm...

They they they say that part of our brand is being unshakable, and they say that that's the nice way of saying stubborn. Stubborn. Yeah.

Yeah. Exactly. And I'm a very stubborn person.

I don't think we could have done that. No. But so, I I...

Okay. But I I, like, have argued with you before. Yeah.

And And you've been writing about developers every time. So, I I give up. You win.

You win. I... I'm sold that I've argued with you before.

that has less intelligence per dollar means it consumes more GPUs for the same intelligence, which is, you know, like, goal is not to onboard companies. Like, it's it's valuable, but, like, our goal is to have people, like, experiment and do weird shit. And 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. Yeah.

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 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 that there's lots of improvements to have. So there is a world that we might temporarily LTS what is right now JEV one point thirteen point zero.

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.

Yeah. Do have, like, a 100 different versions of the model. Exactly.

If we're iterating very fast, there would be a lot of those versions as well. Yeah. So we we do want to have not just a LTS supported thing eventually, long term support.

We want a really sick way of 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. Yeah.

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. Yeah.

And my sense is that even our model iterations where it already is smart, it's between model versions, the the changes tend to be even smaller than the string models calling them twice.

No comment. Okay. So I care about intelligence per dollar.

Yes. Right?

Yeah. Like, I I think that intelligence per second is, 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, Jevan's paradox occurring or at least the JEV 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 Pareto frontier. 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 that 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, do both and, like, have the market correct me either which way. You know?

Yeah.

microsecond is just multiplied by billions and trillions of time. 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 Yeah. To get intelligence for it. But, like, if it actually is something more real time, like user facing, you have budgets like between one hundred milliseconds and one millisecond that are like totally magical.

And actually, even if you were below one 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. Yeah. So, right.

That is definitely happening right now. It is super duper cool. I love the intelligence per second use cases, but I don't think that that will be Jeb's niche.

Okay. Yeah. Fair enough.

And think about promise of faster and cheaper. Mhmm.

Yes. It would... I...

That... That's a very load bearing statement while holding intelligence constant. That's the hard part.

Right? Like Unfortunately, like... So, basically, you you refuse to have to to, like, like, do any public benchmarks, or you don't like any public benchmarks about it?

But I will... I've actually sense. Sense.

Say it again? You need some internal sense of this in Oh, of course. Yeah.

Of course. We have we have our own internal evals for sure. Yeah.

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 periphery intelligence per dollar? Mhmm.

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... We try to figure out, like, what is the best for the users.

Yeah. So we for sure measure them. I'm not anti measuring, but it it...

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 shitting ourselves, about how smart our model is is one of the most important things there. Like, we need to be truth seeking. Yep.

Yep. Agreed. Agreed.

Okay.

Now now it is. Yeah. We debated this a lot.

We debated this a lot. It is, you know, it is bull ish. Right?

Like true false. It is... But it's continuous.

Yes, exactly. So so first, the origin of the name is Bernoulli. 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. So that is the origin of it. We were debating this a lot.

We liked Peeboole. We liked pool. We were wanting to call it like a pool party, but then no one let me.

You know, we had like a bunch of like other arguments about that. And Newell, we figured, was, like, the best thing. Our our rationale, and, like, this is actually the same thing with JEV too, is that we think that we are, an irreverent, insane bunch, and programmers don't care.

You know, like if JEV is just gonna 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 internally.

They've all apologized except for one person. Still holding strong. Yes.

Our mutual friend. Okay. Yes.

Yes. Yes. Yes.

I respect her for that. Yeah. Yeah.

She wanted Jeb to be called Meow.

But but like, yeah, Newell is... We we had to make a new concept for this thing because if it was a bool, it would be confusing to people. So actually, all three of 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 like 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.

We might have that somewhere. I'm so behind on everything. Someone will do it for you in the community.

Not that not that you're successful, people will be like, oh, that's cool. Cool. But like, you know I I don't see that as binary either.

I actually see success as a score and there's always more to climb Yeah. Yeah. In, like, how much we can, like, be there for our community.

Just to be clear. And, I'm... This this section is stressful because I didn't review the docs and they're constantly changing.

Okay. But but to me, Yes. Do exist.

So so so scores are similar to, like, LM judging. Mhmm. Right?

So, like, if you want to call it, a judgment, I guess you could. But, like, that that is, the the the way people already use this type of thing. Right?

Like, maybe a newel could be, like, a probability, probability, but but everything everything for for us us is is a probability. And a choice is actually closest to a function call, but a function call is like an extremely disgusting thing that if you want OpenAI juice sauce tea, we should go back into that later.

like, actually, I think these map all into like programming primitives where like choice maps into like a like a switch statement on an enum. Uh-huh. Newels map to if statements.

Yeah. 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.

in probably like months when I was like onboarding like our DevRel. Okay. So sick.

So our model is designed for being like deep in the insides of computer programs in 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. Sorry. It'll never be like, we're not just gonna 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, this isn't actually an output. Mhmm.

But all the, all the parts, of like the, the, the input, like the state, the instructions, the 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 Exactly. Into 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.

But these are all meant like I would say that if you're using like a template, like turning it into like a system message or something, are thinking in like the old way, you know, we should be making things as easy for computers to 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 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 gonna be optimizing our model. It...

The model's 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 that 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, 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, 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 these instructions at once. And then, you you know, like, you you hope that every single instruction gets nailed instead of asking the questions in parallel. K.

And, also, I would recommend... I I... And I truly say this not from, like, a, like, it makes me money perspective.

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 saving grace of, like, what's happening this week will be people's code bases, AI code bases, are gonna be so much better.

You know, like, if you decompose problems into simple decisions, every single one of these things is extremely evaluable. Like like, AI beforehand is big system message, and then maybe you have, like, another big Yeah. AI to see, like, if it actually does this.

That's nuts. You know? It's it's kinda crazy.

Like, it it it... That was like Stockholm syndrome. Right?

But, like, that's kinda crazy. Like, if you wanna 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 of any machine learning model, but, like, by breaking it down, you can actually you can actually measure it.

Yeah. You can verify that it was actually called Yes. And, like, our model our model...

Like, the the interface itself is so verifiable. This should be, like, a sigh of relief. Mhmm.

You know? Like, it's it's just gonna lead to way better engineering. Yep.

I think I get that.

system prompts and then just gets one big output versus break it down into a 100 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. Yeah.

And software doesn't need to run-in the background. It would break my heart if our stuff couldn't run-in the background.

Interesting. I like to break things down into its, like, its smallest semantic unit. Yeah.

Like, is the lowest level thing? I try to never have... I've probably queried the the model the most among anyone.

Mhmm. 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 backticks, but like it works for all of them, you know?

Like, be it 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 I'm a fan of doing that.

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, is, like, refusals.

Right? Like, I'm not gonna talk about why we don't refuse. I might have done that already.

It's, like, all a blur. But, like, for refusals, I don't think you should ask, should I refuse here? You know?

That's a really it it... 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 because 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 route. 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 can 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.

It looks cool. I just think that people should leave it to the professionals. Like that's just a very hard high level task that maybe the models aren't good enough yet to figure 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, you can, like, break it down into things and just 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 air 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 a human.

That's what confidence estimates are about to do. Okay.

didn't say that.

Because right now all you're saying is like, if something's wrong, skill issue, you 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, complain to us in Discord. We want to make it a lot better.

Every single model version will be, like, notably 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 are, 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.

On the question of fine tuning, I could imagine I could imagine it in the cards. I do have concerns because, like, in the what people need versus what people want category. Like, I think general models tend to be really...

Like like, again, there's there's the 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? Yeah.

I I I could imagine it is is my answer. I'm endlessly practical on these things. I want everything.

Like, my vision of the world is I... There's there's so much we want to be building.

Yes.

is not a promise. This is a desire. Just to make it clear, I like to be really honest.

Like, think that as intelligence per dollar gets cheaper, cheaper, cheaper, cheaper. I think that we could get really like small approximate things that hopefully are proxies for intelligence. Like, is there a world where people don't write rejects anymore?

Because, like, you know, the intelligence per dollar that uses AI is cheaper than, like, the complexity of a rejects, 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 and if it's in the middle, then you do the next bigger model and you chain off from there. I don't really know how that's gonna go. But, I I I could imagine it.

And something that I could imagine too is like, imagine you have like a a series of like we own the entire Pareto frontier, something that a business might want to do. Or I think a hacker would be okay with dealing with a Pareto 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 even imagine because of how simple our thing is, could imagine, like, some automatic fine tuning on that. Yeah.

Not to promise in the slightest. I'm just, like, cooking on sci fi. But you would consider different sizes of dev models so...

To offer that Absolutely. Yeah. Yeah.

Yeah. Like, we we... Like, how would I know how much intelligence people need?

I don't know. Right? Yeah.

I don't know either. Demand is demand is unlimited. Well, yeah, are telling us not to ship things right now because we don't need to ship things because again enough.

Yeah. Yeah. But that's kinda lame.

And I really like the saying this is something that I hope people hold me to because it'll be hard to to to walk back from. Yeah. Like, I don't know if it's 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 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 world 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'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, you know, that you want Absolutely go not for any of those. I want to, like, meet whatever needs we can.

Yeah. Right? Like, at...

But with, like, a giant caveat, I don't want to be, like, OpenAI's 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, everything needs to be under one of these three three things in my opinion. I'm not Yeah. I'm not prepared to do this.

Oh, I'm sorry. I'm sorry, Grant. Yeah.

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 to keep pushing the boundaries and everything. Like like, this is not... These are 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...

Because machine native. Right? Like, BUINs don't need to totally get it.

It needs to just be valuable.

I... I'll give people a hint. Yeah.

Some people are trying to call them decision models. Okay. The...

Our primitives are decisions. I I wouldn't do that, because I think there's other types that are machine native that are not decisions. Okay.

engineers super powered. Yeah. Right?

Like with AI. Like... And...

Or, like, the 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, It's the thing that gets me emotional, but, 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 now 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 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.

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.

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 party every day, then 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, TFP growth weeding, 3% in five years. Hell, yeah.

I've never seen Hell, yeah. Let's fucking go.

growth. But, like, that is what an economic revolution is. Right?

Like, it's actually extremely consistent with what the OpenAI Charter used to stand for. You know, it was talking about, like, I think the Charter is the same, but they've kind of tried to move definitions around to, like, you know, 100,000,000,000 in profit or something like that. Not that I hate an OpenAI.

It wasn't like a... 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 price problems in math and zero of the world's economically valuable workaround the error. You know, like, 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%.

And I think that that will show up in... Like, when it does happen, it will show up in the economic statistics. Mhmm.

It's gonna be fucking awesome. It will not cause mass unemployment, but it will cause, like, a whole bunch of awesome shifts, and we're... 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 Mhmm. You know?

And I Just like disappear into the background. Exactly. You know, like like I I say this in my talks, like, how can it be that twenty nineteen software, like software, SaaS, whatever, super duper valuable.

Right? It's twenty twenty six 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 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 it's going to be like a like an inverse sasspocalypse.

That's a hard one. That's a hard one, my friend.

But like some things are gonna be good. Jab everything is pretty funny. Yeah.

It's a pretty funny way of doing it, saying it. So I'll tell you the truth. Yeah.

The truth is that this is an empirical problem, just like scaling laws are 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 the empirical results just might not be there. Right? So empirically, I believe that these, like, pre trained super condensations 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. Our LLVR has done incredible things for system two thinking. I am at awe.

It is super freaking cool. Like, I don't think that it's going to result in AI doom in the slightest. Not not 0%, of course, because 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, like, it is it is a weird thing 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 eight k, grade school math.

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 star 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.

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 everything.

Like, we want to unearth as much intelligence as possible, period. 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. 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 Frankenstein them all together to have all of the properties of everything. Yeah.

You know, 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'm like, like, if 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 latent reasoning is reasoning in strings?

I thought latent reasoning is reasoning in in inside the model weights. The the I think that people used to call that continuous reasoning. I'm not entirely sure.

Okay. It was called latent reasoning because, like, it used to be that the reasoning traces were secret. Mhmm.

So they're kind of like a latent variable for the answer.

I... My promise is to do whatever necessary for machine native stuff. Yeah.

I I could imagine there are, like, 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 North Star is, and I'm going to fight for that.

Like, like, you know, like, like this launch didn't happen and we are still, like, hungry for our place in the world. That's great. Yeah.

Yeah.

Everything is in the cards Yeah. In my mind. Okay.

Like like... And actually, is like a debate we have. This man, your 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, there's a lot of dimensions of this. Right? And, you know, like context length is an example of this.

Right? 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 context. But, like, the other providers are just like, whatever people wanted.

Let's just give them the stupid thing. And, 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 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 of 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. Yep.

I think that's fair. And and we don't know the answer, to be honest. Like, we'll we'll have to figure it out.

It's gonna be... That's probably going to be, like, one of my biggest debates over the next couple of days. Yeah.

Well, that's also because my chief of staff made me lock in. Yeah. Like I have never, I thought I worked hard before.

Yeah.

Thank you. Thank you. I hope to keep locking in is my is my sense.

I want to... I think that we've passed many great filters for the tech world that we're wanting, but like, there's still gonna be a bunch more. Yeah.

And like, holy smokes, am I excited to fight the good fight? Yeah. It's exciting.

Underrated or misunderstood?

any... Anything. Give me one noodling of it.

Oh, man. I I would rant about all of these. I I really shouldn't.

I really shouldn't. Okay. And, like, people can come go to your Discord People if they put a lot of love into the cookbooks Yeah.

Is what I will say. The cookbooks have, 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, I'll be frank, like before the launch, every... Like what we're saying sounds like sounds like this weird alien tool. Why would anyone need this?

You know, it was a very weird thing. 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 that education would be, like, a gigantic bottleneck for us.

I... It probably worries, and it's no... Probably no longer a problem because people are doing things, like, well beyond what they're gonna say.

To use your model. Exactly. But, like, they...

Yeah. And their use cases are, like, kinda 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. Like, the the computer used stuff. Holy smokes.

Is it cool? But like like we put a lot of love into this. This is not like AI generated trash as far as I know.

Yeah. We put a lot like... It's like a lot of love in here.

Yeah. And like each of these are like like there's real alpha there. Okay.

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, one really gets this. And like, you know, they don't want it.

We're like selling like 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.

It was kind of like, like we like the technical people were like obviously true believers, Right? Like, we knew that this was sick. Its prop...

Computational properties are like off the charts on like so many axis that we're like, yeah, obviously it's gonna be huge. I was definitely super afraid, which is why I locked in super hard. But like the most common thing was the...

Like, I would say, like, more than half the people we had play with it just did not get it. And like the people who did, like, were like, man, this is really cool. But how do we get this through procurement and stuff like that?

You know, just like, you know, like quite a 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 question 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? Yeah. So of course, 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 that frequency and that got got everyone else excited too. Yeah. And I'm hoping as well that we as a company will be eternally, eternally, eternally grateful to those developers.

And not just like, you know, like the companies that are like start off with developers and like go to Exactly. 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 empower them?

And I have cooks. I have cooks, but, 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 that's why I did, like, the the dyeing my hair yesterday.

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. Yeah.

Yeah. Will. I I try to be principled.

Yeah. Quote me on this. Call me out.

You know, have the pitchforks out if I change. I was just gonna briefly show the computer your Is this is this what you're referencing? I've seen...

I saw like a airline browser use thing.

Is this this kind of use case? Oh, the voice use cases. This is actually the first one I've seen.

This is Oh, open up the photo booth. Wow. Oh, wait.

Wait. Wait. Wait.

Oh my... Can you can you go back a second? Can you go back a second?

Hold on. Rumors claim anthropic engineers worth... Worship cloud as god.

Wow. Wow. Dang.

That's pretty funny.

Wow. This is sick.

just like I'm anti benchmarking, I'm also anti demos. I 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.

Yeah. And I would... Man, that looked really cool.

That looked really cool. I want that. I want that.

Like when my when my wrists are sore, like whisper flow everything, that'd be sick.

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 those people who work at larger companies and they're not doing this kind of work. I just want to give people examples of like, you should go look that up, look that up, look that up.

Oh, so, like, think demos are super duper sick. Obviously, the coding agents are like gigantic use cases. Like they are like also super Call is all about Jeff right now.

Oh, hell yeah. Oh, can I, can I give a little bit of a tangent about coding agents if that's okay? Yes, please.

Oh oh, let me We love coding agents. 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 lens at it because 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 scientist's wet dream.

So this is like this is a giant one. Like, I think this plus, coding agents are the big money makers because that's what they're all... Where all the volume is.

Right? 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, knows how much better their product gets with every, like, ten milliseconds shaved.

Yes. And like Especially ecommerce. Yeah.

Yeah. Oh, or like assistant y things. You know, there's many AI assistant y things.

And like, as far as I can tell, they really love it. Again, I'm not in the front lines of customers right now, so I just get to 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 wanna play like sick ass auto battlers where you're like commanding your team or like semi auto battlers. I I think that'd be so cool, but don't make it too good while I still have a job. And the...

You know, like there's the the the... What we call, like, verify everything, you know, like verifying all LLM calls, kind of like observability. I think it actually on the note of docs, what people should be doing is, like, the parallel questions are very cheap.

So if you have like big states you wanna ask many questions on This 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. Uh-huh. So that way you can like pay for that 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 and is Which, by the way, I always think, like, it's interesting framing system one and system two because it it basically makes the case that you should always make one or 10 or a 100 JEV calls for every one reasoning call that you make. Well, maybe. Maybe.

Well, I I mean, I don't... I would like people to spend less. Yeah.

You know, maybe you do, like, you know, one half the reasoning calls and like 10 dev calls each or something like that or whatever solves the problem that like couldn't have existed otherwise. 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 is so cool.

Man, if we knew how to give out credits because we're really early in our infra days, I would want to give all these projects credits. And think that those are like how we've mapped out, like the main use cases. 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. I'm I'm on the coding agent thing, and this is like a really surprising thing that is happening right now. Okay.

ClaudeCode and Codex are, I believe, the the winner... Like, the the number one and two. I'm I'm not entirely sure.

I don't follow closely. Roughly. 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, kind of, like, the same model but different intelligence that you're shopping. Mhmm. But all the open coding agents are like fucking jazzed right now because they're like getting their Jevon.

And Mhmm. Like, the thing is, there's... I'm sure they're trying a lot of weird stuff.

Mhmm. 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, 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 CloudCos and Codexes will do because they are built around that one model world.

Yeah. And, like, I think that's gonna 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, they they might make competitors eventually. I don't know.

But like, it is not me. My job is software 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 review 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...

I feel like this is the kind of thing that is a dangerous topic to talk about. I'm happy to talk about it. I live for danger.

Interesting. I... So comp...

It's a very complicated nuance thing. I actually do want to write a response to this more formally. I do have, like, a little bit of a short version of my response Yeah.

Which is that as you RLVR more, like, RLVR is like... 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 thing, there were three different things that, like, are now called post training, 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 pretraining team. 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 hard 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 everyone needs to do more RLVR. Right.

Which 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 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. Like people say, oh, maybe the sandboxing was a problem or whatever else. 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, like, there... I think there's some, like, dissolution of responsibility there on, like, things that by design or nondesign they're trying to make is just an assumption.

You know, we must do our LVR and not just we must do it. We must do more and more and more 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.

So, like, if you assume all of that, they're like, oh, yeah. We're heading into a dangerous world, Like, everyone is gonna be doing this, and this is the only way to make AI sick. So So basically, it's like it's like, these are all internally consistent, but actually starts from a premise that has alternatives if 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 the 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, RHF and then RLCD. RRLVR is, like, a point two in my opinion, and I think that's generous. But...

Or point five or, like, it could be one whole one. I don't really care. But I do think that people are thinking very close mindedly about this type of thing.

And this... The the only people who are at fault here are the researchers because it's definitely not the populace. They just assume that open air anthropic are just doing the best they can, and they are not the experts who are aware of the true optionality available.

I'm doing my best, but my goal is not convince labs that there's, like, other directions to to go down. My goal is have, you know, it's like spark hope in software engineers to start, like, actually automating things they've always wanted automated. 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. Because, like, that that that demo was do what I mean. Like, like, you...

Like, like, there's Yeah. The little I say. 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 that 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... I don't wanna overpromise. I don't think it's going to happen right now, but, like, we are gonna do whatever the fuck we can to make that happen.

Yeah. Any other things on the sort of general shape of post training? You know, you obviously you're been very intimately involved.

Mid training. I mean, it's all a spectrum. Yeah.

Right?

Yeah. I mean, like, it's it's it's like, you know, it's a cost saving thing. Yeah.

You know, instead of, like, having to pretrain again. Like, there's intriguing stuff. I actually think that, like, intelligence has a at every single level, and it's always super duper fascinating.

Like, I'm a shape rotator, so I don't like finding that, but I love it when people find it and teach me about it. But, you know, looking at the data, this thing that our data team is so good at that I'm not. I 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 long term 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. I'm a fan of all forms of training.

I'm a fan of all forms of, like, surfacing new types of intelligence. I wouldn't do it all myself because it's expensive. And I have said privately and also, should I say this?

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 Part of the Yeah. Yes.

So the thing I've said before is if you gave me a billion dollars, I wouldn't pre train. I still believe that to be true. It is a very expensive thing when if you are like like, if you're an 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 anything except retraining. Yeah.

different concepts. I will like break that down a little bit. So multimodality is a little bit different, 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 audio 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 you scale, it may not be good enough.

So, you know, a 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 better methods or something else like that. So, like, we... You need to be, like, really practical in all of this.

Am I a fan of omnimodels? I'm a fan of all forms of intelligence, but I will go straight into one thing 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 versus reasoning mode is because 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, however how to pronounce that, overconfidence, hallucination, like, even the kind of style that excels in Ella Marina, bold, italicized, emojis, you know, like, doesn't answer the question simply. It gives, 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 come because strings are super weird. You know, they are like weird ass things and you need to be mis calibrated.

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. 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 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 my guess.

And a lot of the art to intelligence is studying this subtlety that I think that, at least when I was in OpenAI, people were not really studying that. Because like they were just like chat, chat, chat, chat. Just like people are with JEV right now.

Then you give me an ultimate... 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 say 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? So...

Yeah. So, I mean, in some ways, you you are also factoring intelligence into system one, system two, but you just don't agree with the other people's fractionating fracturing, which is why Oh, wait. It's fine.

It's a little different. No. No.

No. If I could if I could if I could defend Yeah. The system two tasks.

Number one, like, we don't toss out the system two tasks. Right? 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, 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. And all fracturing makes the model dumb.

You know, like, if people, like, get the model to say, like, it is OpenAI or Quinn or, 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 JEV from TypeSafe. That fractures it.

Right? Like, like, I I don't want that. Like, it represent what the Internet thinks.

Right? Like, be correct. That is what I want because that's how you get the smooth, intelligence.

I I mean, identity is a thing, I guess, that is somewhat of a special. First party product. Yes.

Yeah. But, like, for an API, I don't think so. Okay.

You know? Like, I I don't... I...

Like, people don't want... If they're making a chatbot with, you know, ChatGPT, they don't wanna say it's ChatGPT.

With the company, I think that this is like more like a four year journey.

moment. Right? That might've been...

Was that when the coup was happening? I don't really know. Yes, actually.

Yeah. Yeah. Yeah.

That that sounds right. Yeah. I remember.

Oh my god. I don't wanna... I...

I'm not... I I don't think I have the time to spill the tea about the coup right now, but that wasn't really annoying.

Yeah. Anyway. Maybe next time we chat.

I'll I'll dump a tea about a tea about the coup. Yeah. It actually...

This problem was one that, like, was in my mind since before ChatGPT even launched. I was like, holy shit. The ChatGPT team is cooking.

They are doing the right task. They are doing the thing that AI researchers are bad at, but successful product people are good at, which is giving a lot of fucks about the experience. You know, it's it's it's very rare.

They... Like, there's very few people like that at OpenAI.

oh, yeah. That is a... Also, I had fought very, very hard to deploy in struct GPT.

Like, actually, early 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 in the hands of users. And, like, basically, immediately, it took 50% of the market share of LLMs at the time. And...

But... And we thought it did... I I made...

I went through great effort to make sure everything in our lunch video is true. 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 not 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. 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, know, like, what is going on? And the answer was, oh, machines.

You know, the question I asked myself is, like, let's work 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?

Like, you know, I I was working on it. Sam just told you to do it. Do...

Go do it. Like... But, like, my 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, OpenAI does better at, like, catching up than it does at, like, actually innovating. So, like, ChatGPT was a copy of Quad. Right?

Like, they had an internal thing. Just didn't ship it. Yeah.

Claude Claude and Slack. But but, you know, reasoning, I I would say first ish. Yeah.

But Yes. Debatable how good of a product that is. Yeah.

It's great research, though. Super great research. I'm just not sure if people had that product need.

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, yeah, maybe I'll just like start playing around with this. I, you know, do more philosophy and design and thinking.

I thought it would end up taking a week when I started training models. It ended up taking many years. At some point I was like, holy shit, you know, there's signs of life here.

This 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 wanna 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 responsible.

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 a startup? And they're like, startup. And I'm like, fuck it, man.

We ball. I guess we're doing some crazy shit.

call Eric first. With Sasha, I actually didn't try to recruit her. I I tried to be good, and I was just like, hey.

Am I crazy? Is something missing here? You know, isn't there like like like, am I too much in the OpenAI 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 wanna 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 because I'm a neat freak. And we just kept on cooking.

Should they do it? Oh, that's a fascinating question. Man, how do I do this without burning bridges?

I... My sense is that most... Unless there's some level of economics I don't really understand, I think most neo labs are crap.

I I don't want to see myself with that as peers. Like, I I don't really understand what's going on there. Like, is it because...

Like, number one, I don't really value researchers. I value people who... Like like, look at my bitter's lesson.

Right? I I I want... Well, not just that.

I 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. So it...

Like, one, I I believe in north star tasks and doing cool, really useful stuff. Number two, because I don't value researchers, I don't I 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 Neolabs 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 LEO labs, they don't really have a direction. They tend to want money to play around with their experiments. If they have a direction, I'm super in favor of 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, probably the labs are the best place to do that TBH. Like, there might be other places.

I I I don't really keep track of that politics, but I would just recommend not being that way personally. You know, like, 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 stand behind. But if you think that you wanna do the right task, like, abso fucking lutely.

Like, please do. Like, please break this, like, unimind, you know, unimodal, like Hive mind. 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 Solvable.

It... It's solvable, and it's weird, and it's, like, not matching reality.

Oh, no. Oh, I wish I didn't hear that. That's such a bad vibe.

No. No. This is not the whole company.

This is just that that room's discussion. No. That that makes sense.

That that that makes me lose faith in humanity a bit, but maybe I'm just a naive technologist.

agree with that, to be clear. Like, I I think being opinionated on that matters a lot. I personally am afraid of trying to mislead people because I think that bites people in the ass a lot.

You know? Like, I think that, like, people trying to be overconfident, like, I I I obviously... I'm not actually gonna 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 suboptimal. 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...

about, like, the risks versus, like, the the objective. It... There is, like, level of, like, sneakiness latent in it Yeah.

That 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. No. But, like, that to me is just just sad for the world.

Yeah. Hopefully, I'm never... I've...

Yeah. Hopefully, like, we are never involved in anything Yeah. Like that.

It might be inevitable as we get big. But I want to I wanna stay like pure technologists to my roots as much as I can. I mean, Jeff for president, why not?

more about That's strip posting. No, no, no, You're just crushing my hopes about it. Feel like America in the world right now.

Give people tasks? Yeah. Like, that your tasks so many I want.

Have picked your tasks. Right? You know 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 gonna be you're be... So the next, like, fifty years, you're gonna be busy doing your thing. Hell, yeah.

Okay. So let me give, like, a fun one and a not fun... And, like, maybe a valuable one that's also fun.

My my fun one is I 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 where, like, you can, like, get NPCs to control stuff, like, you know, like, that was just really, like, the... Like, proof of concept.

I think it's really cool stuff could be made. It looks really, really cool. You know?

Like, 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, like, state machines for NPCs, I think you can make, like, such a compelling world. Oh, man.

Mhmm. And, man, a little sad that I can't work on these types of things. Yeah.

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 the article KV Cash Rules Everything Around Me.

Believe it or not, I don't think anyone has used the phrase on the Internet cash rules everything around me. C A C H E. When I when I Googled it.

So, like I wrote this because I wanted to tell people about like, this is how coding agents agents work and how the KV cache works and everything. And I think... Oh, Like, it explains a lot of stuff.

Like, why routing is really hard? Why sub agents don't seem to work? Like why 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. You know, but I wish.

But 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 Kvcash tyranny. Which is it locks you in?

Well, not... It it locks you in into one model. Right?

And in order to do it efficiently, you need to, like, keep on appending to it. Mhmm. So now you're not doing best software practices like state management, abstraction, decomposition?

Why can't you give an easier task some... You you... Why why can't you give a sub agent an easier task?

Because of the state that you're passing around. Oh, I touched this. Because of the state you're passing around.

You knew... You 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 just like tons of really cool, fun research to be had there on like different programming patterns, you know, kind of like how people are playing around, like with like recursive language models. I feel like there's just lots of cool stuff in here when you think about, oh, I want to explicitly label the state of everything.

Or imagine you have a subtask, like coding agents. 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 that 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 gonna 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 that's actually like a memory 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 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 your 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, Jeb, who should write first? Blah blah blah.

And, like, I feel like the future there is Oh my god. Nuts. Do you have to solve locks?

I mean, it could be so cool for like multiple agents working together or like, if you think about state Yeah. Like agent swarms. Yeah.

And and, you know, some things, for example, are read only processes. You know, some people like getting like summaries of what the agents are 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 subdesk.

I feel like there's so many different fun things that could be done if like a really smart person, like dedicated like a whole lot of time to rethink like the 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.

like weird things. I have no guarantees that it'll work, but it seems really, really interesting from like a technical perspective.

I will say that that is partially because I I describe myself as as zeroth percent entrepreneurial. I I I don't like startups. 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.

When we first were fundraising, an investor asked me, like, which CEOs do you look up to? And I was like, ew. Why would I look up to those people?

No offense to anyone. You know, I'm trying to be, like, I'm trying to be genuine good. 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 OpenAI. You know? Like, I felt...

Yeah. Like, like, like, it's a little bit easier to be truthful now because, like, I have at least some proof that the direction 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? Like, this is this is so anti developer. Hacky way on top of hacks on top of hacks.

Well Yeah. 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, I want to be removed from any project involving, like, function calling if you did not get a legit bias for each function. Like, it's a 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 they've actually refused or allow.

Yeah. Disney needs to set a different refusal threshold than 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 developers and like people have been like dealing with this for years now. Right?

Like they still have that with skills, like, you know, like, 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 MCP 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 and like the the the solution is begging in a system message.

That's nuts. But...

Why would it be? Depends on the Community person. If you ask me Yeah.

I feel like I'm a pretty good founding marketer. But if you ask anyone on my team, they say, shut the fuck up, Yogo. You need to do CEO stuff.

So, yes, founding marketer. 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. Know.

I would really love things. Yes. I nothing teaches you delegation like having a tidal wave of stuff to do.

Hiring data people or we call them model capabilities like, but they are data people, both like like, 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 people actually working on the model that actually sounds a little 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. Well, I mean, 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 they 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 dev everywhere.

Like, we are so much more sensitive to location because speed of light is more of a bottleneck. Mhmm. Right?

Like, I'm so sad for the European users that they were only, like, three times as fast instead of, like, a 100 times as fast because, like, we don't have servers there right now, and not... Like, that's insane. Right?

Or or everywhere. You know, like, if if intelligence per second is a metric that matters, like, we will want this all over the place. Like, we care about...

Like, if they're a developer building on top of us, I care a lot about you. 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 gonna be, like, an AWS of, like, intelligence, you know? Which is gonna be you, by the way.

Right? Yes. I mean, like, that's a direction I want to go down.

It could be arrogant to say it will be me. Yeah. Like, we, like, I'm going to do anything I can to make sure that happens.

Yeah. Like, I think that that's gonna 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. Mhmm.

Yeah.

Cool. You're most welcome. It was a pleasure, man.

Yeah. So excited. Yeah.

So excited. Not the last time. The last time.