OpenAI Day: GPT-5 Unveiled | Mark Chen, Greg Brockman, Sarah Friar, Max Schwarzer, Brad Lightcap & More

You're watching tvpn. Your background looks way different because you have a whiteboard behind you because we're breaking down the X's and O's of the GPT5 launch today. GPT5 launched from OpenAI really quickly. There is some other news. Firefly Aerospace stock opened at $70 in Nasdaq debut. This is the company that landed on the moon. Very cool.

Very cool.

There are a few other stories going on, but we're going to skip most of them because we're going to be focusing on ChatGPT today on GPT5. We have a bunch of, a bunch of guests coming on. We have a stacked lineup. We'll pull that up, but we'll break down the X's and O's of the matchup. So of course OpenAI, here's our, here's our lineup. We have something like 15 guests today, a ton of folks from OpenAI, a ton of people that build on top of OpenAI and can comment on what's going on with ChatGPT. But of course, this battle is between OpenAI and the timeline. It's the, it's. They got to get the vibes right.

It's war.

It's war. It's the timelines in turmoil over whether or not this is a good model, what it means for the industry, what it means for AGI timelines. Everyone's got their take, everyone's posting memes. There's been a ton of funny ones already. We'll take you through them, of course. But let's break down the offense. Today we have Sam Altman, the founder CEO. He briefly got cut from the team in November of 2023, but he's back leading the team for the 2024, 2025 seasons. He seems healthy, he's doing great. Today he went on at 10am to break down the launch of GPT5. He has a couple of key plays in his playbook in his arsenal. He's got a solid ground game, lots of quick posts, hitting the timeline, probably in lowercase. Then he might air it out with a couple thousand word essay. We've seen him do this before. It's a bit of a Hail Mary. Maybe AG has a thousand a couple thousand days away. Maybe we're in the soft singularity. But he's very strong there with the long post when he needs to be. It's up his sleeve if he needs it. Then he can also pull out the vague posting. He was doing this last night, posted a picture of the Death Star. No one knows what it Means maybe it was taking a shot at the doomers who are on the defense today. So he's also known for driving supercars. That lets him get to the office faster. He's saving time and money. You can save time and money by going to ramp.com easy to use corporate cards, bill pay and accounting in a whole lot more all in one place. And so he is. He also gave, apparently this is a rumor. He gave every OpenAI employee who's been with the company for more than two years $1.5 million. A lot of people say 1.5 million. That's not enough for a big house in San Francisco, but it is enough for a supercar. So that's probably why he picked that number. And that's why that's what the OpenAI team will be doing with that money. They'll be buying Aston Martin Valkyries, Pagani, Huayras, McLare sabers for Ferrari Daytona SB3s. They can get a Koenigsegg Gemera. They could get a Singer DLS or a Bugatti Veyron. It would have to be used. They could also get the Bentley Bacalar. There's only bacalar. There's only 12 of those ever made. It's an open top two seater roadster. It's coach built. So that's going to run your 1.5 million. But that's perfect. You just got the 1.5 million bonus. So put it to work. Spend it all in one place on a car. This is financial advice shambles. Yes, exactly. Then you got Greg Brockman. He's joining at noon. He's, he's extremely well rested. He's actually coming off a sabbatical right now. That's very exciting. He should be injury free for the rest of the season. He cut his teeth at MIT and then he got drafted by Stripe in 2010. Microsoft tried to do a trade deal during the 2023 chaotic trade deal trade window that opened up post Sam Altman ouster. But he stuck with the Open AI team and now he's president of the company. Then you got Mark Chen. He's coming out 11:30 today. He's the chief research officer. The rumor is that he turned out a maxed out contract to head the Metalamas. But he's sticking with the OpenAI team. He was an MIT undergrad, also worked at Jane street before joining OpenAI in 2018. Then we got Sarah Fryer coming on the show at 12:30. She's the CFO of AP of OpenAI. It's her job to find bank accounts big enough to find to fill all the cash they are raising. It's a tough job. You got to find okay, this bank account, Will it hold 10 figures? Will it hold 11 figures? Will it hold 12 figures? Like exactly. Exactly. She's also going to be defining the non GAAP metrics that will be catnip for Ben Thompson in just a few years. We're excited to talk to her about how she's measuring the success and the health of their business. Obviously it's not just revenue, it's not just top line, bottom line. We're going to want to know about queries, we're going to be want to know about DAUs, all those non GAAP metrics. That's where people are going to be tracking what when ipo. When IPO day comes, hopefully soon. And then we also have Brad Lightcap. He's joining at 235. He entered the league as an investment banker. Let's give it up for the investment bankers. They don't get enough credit around here but we love the investment bankers. Then he got drafted by Y Combinator before joining OpenAI as CFO in 2018. Now he's the chief operating officer. And then we have Max Wasser. He's in charge of post training, fine tuning these models, getting them into the fighting performance to put on a display of authority on GPT5 launch day. Now let's flip it over to the defense. They're going up against the timeline. They're going up against the vibe checks. We got the Doomers. The Doomers, they're led by Eleazer Yuda Kowski. Admittedly, everyone knows this. No one debates this. The Doomers have had a terrible season. But you'd expect to see at least a few Hail Marys about GPT5 creating bioweapons thrown up on the timeline today. Probably won't be bangers. Probably won't get a thousand likes, but you'll be seeing them here and there, mostly in the replies. We've also seen some doomers talking about GPT5 being available to every government employee and Eliezer had some harsh words about that. Don't give the keys to Sam Altman. Don't give the keys to the government to OpenAI. He was upset about that. But in general the Doomers not putting much of a bite up today. Then you got Claude. Interesting. Claude was caught playing for the wrong team earlier this week. Anthropic. They're on defense today, but we saw them take out OpenAI's key pinch hitter, Claude. The Claude code. API was playing for the OpenAI team, but they shut that down and Claude is no longer pinch hitting for OpenAI. Then you got the Elon stands. The ground game is going to be there. It's going to be, it's going to be strong. The Elon stands are gonna be tracking the benchmarks relentlessly. We know XAI loves to benchmax. And all the Elon stands are gonna be calling out GPT5 for any misaligned benchmarks. If they fail humanity's last exam, it's over. It's over. They'll also toss up the occasional unhinged conspiracy theory. Moving on. Gemini. The betting lines have shifted big time. People thought Gemini was out of the game. They're so back. Polymarket has GEMINI at what, 75% chance of being the best model towards the end of the month. This is of course based on the LM arena more Vibes based benchmark. But Gemini will probably be quiet today. They usually don't try and front run press releases. They usually try and sit back, let the model speak for themselves, let the API credits work their way through the latest YC demo day batch and get the product into the hands of people. And so expect to see a big glossy conference in a couple weeks. Demoing Gemini 3 should be a good rebuttal from the Geminis. Then you got the Metalamas. Zuck's been on a poaching spree. He's rebuilding the team during the off season. Now he has a stacked roster and he's ready to go duke it out, but no one knows exactly what's going to be in the playbook. Is he going to go consumer? Is he going to go API? Is he going to turn into a hyperscaler? We don't know. But we know they got a stacked team. They got Alex Wang, they got Nat Friedman, they got Daniel Gross, they got tons and tons of other researchers. They've been raiding every other team, completely reset the salary cap for the league. And it's been an absolute clinic in terms of recruiting over there at llama. Then you got the final benchmark, arc AGI. This benchmark stands. GPT5 couldn't get past this defense and RKGI, you know, sitting there right in the end zone, just swatting them down, swatting them down all day. You think, you think, you think. With super intelligence around the corner. RKGI Denied. Denied. Tyler, give us the update on Arc AGI. Where does everything stand? How'd GPT5 do? Does it matter? Should we care about Arc AGI. We love the team behind them, but is it an important benchmark? Should we be tracking it today? Yeah.

Okay, so there's RKGI V1 and V2.

Okay.

On both sides.

And V3.

V3. I actually don't know if no one's been.

No one's even tested V3.

No one's even really close there.

But how are we doing on V1?

V1, GPT5 is at 65.7. Unfortunately, that's going to be 1% just short of Grok 4.66.7. Okay.

ARC AGI2, the Elon stands are going to be going wild with that.

Arc AGI 2 9.9%.

9.9%.

Grok 4 16%. So absolute kind of brutal, you know, Arc AGI mogging.

Rough showing.

Rough showing. Some people have accused Grok 4 of being slightly benchmarked.

Yes.

You know, this is, you know, you

might have a team, really. But what's the, what are the pros and cons? We know the cons of benchmarking. Of benchmaxing. You're overfitting on something that might not actually drive consumer value. It might not actually solve real world problems. It might not increase DAUs or Revenue or AR IRR or anything that really matters. It might not even get us closer to super intelligence. Give me the counter argument. Why is benchmaxing good?

The bull case for benchmarking.

The bull case for benchmarking. Benchmaxing. Break it down for me.

Yeah, so I think the idea is basically this is almost like a non AGI pilled kind of take.

Right.

So if you don't have a super general intelligence.

Yep.

Your ability to benchmax basically improves your ability to solve some kind of specific task. So there's this thing about the gas station going spiky. Yeah.

It's called getting spiky.

Getting spiky. Adding more spikes to the spiky intelligence.

Yeah. I think it was Rune who had this tweet about the gas station benchmark. I don't care if he said something like, I don't care about AI solving gas stations. If it has a gas station benchmark. Something like that.

Yeah.

But the idea is like if, if you, if the, if making the gas

station benchmark, Rune said, my bar for AGI is an AI that can learn to run a gas station for a year without a team of scientists collecting the gas station data set in. In capital.

And then my take is basically, I don't care how they got to the. Like, I don't care how they made it run the gas station. I care how fast that it runs it.

If we can run the gas station.

So if you have a team who's, you know, your benchmaxing team, that just proves that like, if you have some task that's like really important that you want to get done.

Yep.

They can just figure it out. So it's like RL for business. This is like the same thing. RL for law.

Yeah.

All these, like, this is what specific verticals.

If doing this the thinking machine. Right. Like RL for businesses come into your organization, understand the most. The most valuable business processes out there that could potentially be RL'd against that could be turned into a benchmark and then. And then, you know, bench hacked. Because I don't care if you're hacking, you know, if I have translate this type of document to this type of document for my business, if you can do it with 100% accuracy, I don't care that you bench hacked it.

Yeah, exactly. Like, benchmarks right now are not like, economically valuable. Like if you. If you're really that much better at mmlu.

Yes.

It's like, is. Are you producing that much value?

Yes.

Probably not. But if you have. If you make some new benchmark, that's your tax benchmark. I think Anthropic just released that fairly recently.

Oh, sure, sure, sure.

That's like, I don't care if you benchmax on that as long as it does the taxes, because then it's going to do the task.

Yeah, yeah, yeah, yeah. That makes sense. What about the. What does it say that it feels like OpenAI seems capable of. Of bench hacking. It seems like they've opted not to. Is that because bench hacking has a risk of giving you negative aura? Because if you're accused and found guilty of bench hacking, you could it. It often reveals that you're not building this one beautiful, you know, superintelligence to rule them all.

Yeah.

I think it's also like, maybe we're just looking at the wrong benchmarks. Like maybe they're. There's a bunch of interesting benchmarks about. There's this one I really like. It's the Minecraft benchmark where you have to build. You give it some castle and how good it looks. Or there's the one you always see about the unicorn. So you use this math package that does grass and stuff, but you ask it to draw a unicorn.

I've seen that those are really good

because it kind of shows the creativity. Stuff like that.

Walk us through TBPN bench what we will be benchmarking the AIs against going forward.

Have you heard about this reps of 225.

That would be close, but it's difficult because the humanoids kind of change that and you can just use normal actuator. This is truly for a large language model. You feed in our data set. We have a public data set, a private data set, presumably at some point, but walk us through TBPN bench.

Yeah. So I'm yet to try this on GPT5. I don't think it's out yet for public use. At least I don't have it. But I can. I can tell some of the questions. Right. So the first one, I have this picture of a horse. You have to guess the breed.

Yep.

So let me see. I think why I don't want to say it in case GPT5 is listening, but it is may or may not be a Caspian horse.

Okay. And it's failing right now.

0, 3 is failing.

O3 is failing.

40 is failing. I haven't tried everybody.

Have you tried? Yeah, we got to try Grok and Gemini horse identification. This seems extremely hackable. But at the very least, if we get one scientist to go off and collect the horse data set and then bench hack it, I think we will have done our job.

Yeah, for sure.

That's the first question.

Yes.

The second one is I have two pictures before and after of this guy and it's which Peptide did he take to achieve this body transformation?

Yep, yep, yep.

So it fails there.

It fails there. So you have a data set of what Peptide does what to the human body? Where'd you find that?

Well, you know, Wikipedia has a lot of this stuff.

Okay, okay. You would think they'd be able to be able to cheat this around with O3. Just reason who is this person? Go look up what they've said they've taken and then boom, you have.

Well, at first with O3 when I was prompting it, I would like save the photo, but then I would have the metadata.

Oh, sure, sure, sure.

The file name would be like Caspian Horse or something.

Yeah, yeah. Okay. And then the third one, the third

one I pass in an audio file of a car revving. Has to pick which one.

It has to pick. It has to identify the car. The car from the engine.

Note from the engine.

And it's not doing it currently.

It's no wrong.

This is. This is a good benchmark.

Humanity's real last exam.

Yes, yes, exactly.

So I think those are pretty solid. I have some more. Obviously I don't want to make them public in case anyone's going to try to benchmark this.

Of course, of course.

We'll see.

Hopefully.

It's funny because I was mentioning the other day this app that my dad had of tracking that you just set your phone up and it just automatically detects which birds are in your backyard.

Yeah, yeah, yeah. I mean this has to be extremely solvable. It's just something that it reveals the lack of general intelligence when you have to go and collect the horse data set which should just be out there, or the engine note data set which should just be out there. But clearly we are in the age of go nrl on the individual problem and we are looking at like the power law of capabilities. Knowledge retrieval is clearly a, you know, $12 billion a year market that consumers will pay for that will probably grow significantly and and then health and therapy and shopping and all the other features that Fiji Simo laid out in her post. This is kind of like, you know, what will be RL'd against because those are key pockets of value in the in the consumer economy. And the same thing will happen in the business economy. But in the B2B context you'll probably see an individual startup building on top of an API. But even then, most of the most of the model platforms offer kind of RL as a service, fine tunes as a service. Something where if you're starting to spend tens of millions of dollars, they will do some customization on top of the model. So that could be the regime for the next few years as we go into this like, you know, instead of like this centralizing AI force, there's only one company. There's actually like a Cambrian explosion of a ton of companies doing a bunch of different things. So anyway, let's go to Signals post signals not happy with the launch. He says, okay, I've seen enough. This launch felt like attending a funeral hosted by minimalists. They're unveiling tech that should feel magical. Real breakthroughs. But the whole vibe was grayscale grief. The set design looked like if mood disorder got got a Bauhaus grant. I don't know what a Bauhaus grant is exactly. Even the storytelling arc chart styles the eulogy tributes, then closing on someone's health battles. What exactly are we? Are we as the audience mourning? It feels like they're trying to get you to pre install a therapist. Potentially great products, sure. But the emotional tone was so damn doa. Incredibly strange all around. I think A like it's weird because we're in this world and this is a question that I want to noodle on all day is, will this be the last launch of a gp of a numbered GPT model? Because, like, you don't hear about new versions of Google going out, you just. It just got better and better and better. Same thing with Amazon when they were optimizing for, hey, it's faster, we have more on our counter.

Takeaway was is that the product matters more than the model.

Yes.

Now, and probably will for potentially a very long time.

And when we were watching the stream, I was cheering because they gave the feature of you can now talk to the model and get it to trigger a deep reasoning workflow or get it to give you a quick answer in natural language. And so it's abstracting even more of the UI into the actual text interface. And so I think in terms of, like, surprise and delight and I don't know, it's like everyone kind of rips the Apple thing, but Apple does a great job of being euphoric and happy with somewhat minor product changes. And, like, maybe that's more of where they'll go, is just, hey, there's these new features and here's how these things. And Apple will spend 10 minutes on stage talking about, like, shifting an icon around and stuff. And it's like, I thought it was interesting.

They just sort of casually mentioned that they're deprecating the old models. I think it's great, which makes sense.

I think it's great. I don't want the model picker anymore. But are you upset?

A lot of people are going to be upset about that.

I think they're getting rid of 4.5.

Oh, really? And you're a 4.5 fan? I love 4.5, but I would imagine that the future is if I ask it to think really hard about the prose and the writing style, it would then do a pass with 4.5, but it would only trigger that when it needs to. It's not going to give you that. Because if I'm just asking for, hey, regurgitate a bunch of facts or write some code or put together a table of data, like, it's not going to need to pull 4.5 off the shelf. Just like it's not always going to pull Python off the shelf, it's not always going to pull web browsing off the shelf. And so I'm not sure that I necessarily want 4.5 there as a selection criteria. I would like all this to be tucked behind a UI and have something that's actually cleaner and less Frustrating to use. I think it'll lead to higher retention.

Yeah, for the average like Normie, no one knows what 4.5 is like.

That's true. That's true. Anyway, Chris pike says OpenAI and Anthropic are duking it out. Meanwhile, consumer surplus is growing. We also have very good news. We also have the details from Mike Noop over at ARC AGI. Full GPT5 is along the V1 Pareto frontier. That's cost versus performance. OpenAI said they focused on other goals like UX and reliability. Our testing supports this. Mini GPT5 is super impressive accuracy for cost. In fact, based on cost efficiency, Mini could have entered ARC Prize 2024 and likely won first place. We are still verifying GPT OSS or as Run says GP GP toss results soon. Nano GPT appears overfit performance is commodity. And Francois Chalet is also chiming in with Top line.

Production team needs the deck.

Oh, we don't have a deck today. We're just doing it live through. Yeah, we're just, we're just riffing through the timeline, the timeline tab and just pulling up some random posts.

So.

So you're free to pull those up, but also we can just read through them. Ashley Vance is saying, but, but model switching was my job. Model switching is out and we are into the future. Just talk to the model, just talk to the model and ask it what you needed to do and it will switch for you. It will pull the right tool. The job. Anyway. The other question that I have for the OpenAI folks today is on the nature of secrets. So in zero to one, Teal has this concept that discovering a secret is key to building a startup and it's a key insight. And I was joking with the Super Intelligence or GPT5, could my first prompt be teach me exactly how to build GPT5. And then I go to meta and I say I know how to do it. I have the prompt, I have the. I have the result. And of course the answer is no. Of course OpenAI would never leak the most frontier capabilities into the model. But can you build a super intelligence? Can you call it superintelligence if it doesn't, if it can't tell you how to build super intelligence,

one read on. What the secret might be is that the app was the most important thing all along. And if you create this narrative that superintelligence is weeks or months away and you get a bunch of people that go and try to compete on raw intelligence, meanwhile you build a consumer app business with billions of users. It's like a. Seems like a pretty good strategy I guess. One quick thought is how do you rate Sam's vague posting from yesterday with the Death Star in the context of this new in the context of the release today?

That's a great question. There's a bunch of reads on it. One is just that like the Death Star is to some degree like Stargate and you have to oh wait he if this is the apocalypse I figured at least tune in. Like you have to like the the impact of this of GPT5 is not one crazy super intelligent model that does everything. It's just a more user friendly higher retention lower churn consumer model that weaves its way into all aspects of daily life and improves performance and efficiency all over the place. And so you have to build this massive cluster to serve all of that. I don't know what's your read on it?

I don't know. I think it just was. I think it was dramatic. It was provocative.

People didn't like is provocative because there are many other like super megastructures that are in in sci fi history that are positive or positive. Yeah. And this is, this is like.

But it gets the people going.

Yeah. I don't know is it. Is it a metaphor for someone else that's going to attack? Is he. I mean, I mean the images from the viewpoint of someone looking at the Death Star. Is he saying he is seeing a Death Star being built on the horizon? Is that something else? Is that another company, another organization? Is that the government? Is that the. Is that, is that legal?

Here's a read from Bubble Boy says. I am an expert on bubbles so it brings me no joy to say that the AI bubble is popping this time next year. He is updating his timelines. When you promise infinite scaling and don't produce it, the calculus changes. I don't think it will be bad for most companies, but those who built their entire business model model around making the best LLMs are unfortunately going to struggle as models become more of a commodity again. OpenAI is my read on it is a consumer app business. Right. They still have a big enterprise business but by you know their recent valuations are predicated on their this incredible consumer business that they built. Bubble Boy says the end user doesn't care much if Claude is 5% better than GPT5. They care about cost, speed and utility. Especially at scale. Things will be going the obvious play now is shorting Nvidia and dumping okay,

getting into financial advice.

Getting into financial advice territory here. Bubble Boy, but interesting again, kind of goes back to what I was saying earlier in that if you were raising billions to make a lab, and I think the potentially anthrop, you know, we'll see what happens in the coding market, but there's some clear winners emerging and then on the consumer side, you know, expecting a power law outcome, and it's hard to see anyone unseating ChatGPT there.

Completely agree. I want to dig in more, but we have our first guest. Let's welcome him to the stream. What a day. Mark, how you doing?

Hey.

Pretty good.

Nice to see you guys again.

What's happening?

Nice to see you. Congratulations on the launch. Take us through it. Are you. Were you actually live or are you wearing the same thing and you recorded it yesterday?

I'm actually live.

Okay.

I don't know why, but we do.

I mean, we're big fan, we're big fans of live. I mean, it just allows you to be the most reactive to the most new information. Give us the core thesis that you were trying to get across. I think that there are a few narratives out there. We, we've been enjoying the one that's, you know, this is a dominant consumer product. They just made it a better consumer product and people are going to use the product and get more value at it. I saw a bunch of things in the presentation where I was like, that's going to make my daily usage of ChatGPT better. At the same time, we're in this. We're in this world of all the models. The numbers matter and the scale matters and this and that and this and that and. And it's a. It's a fine line and it's a dance. And we're in a transition phase away from benchmarks and away from talking about the size of the bubbles. But what was your core thesis like? What did you want to get across to the listener?

Yeah, I mean, fundamentally, I think from a research perspective, we've been working on reasoning models for several years now. And I think until now you've had this really clunky interface. You have to pick GPT4O or you have to pick O3. And for the longest time, we've known that O3 gives you better answers across the board. It's just too slow.

Right.

You often don't want to just sit there and wait for the model to reason it out. So we've done a lot of work to push the speed, the performance of our reasoning models such that these can come together and work in a very seamless way. And So I think above everything, we're trying to move the world into this agentic reasoning world. We believe that's the future. And on top of that, you pointed something out which I really resonate with. Post training is a huge part of this release. We really wanted to highlight Max Schwarzer and his team who did a phenomenal job. They've made the model just really that much more useful for consumers, for businesses. It's a monster. At coding

on the speed of reasoning, you're obviously the chief Research officer. Are you more optimistic about getting speed ups there from, I don't know, algorithmic design software optimizations or new hardware? Just let Moore's Law carry on or find new ASICs or. We saw Cerebrus posting yesterday about the incredible speed that they're getting 3,000 tokens a second on GPT OSS. And I'm wondering what levers. Obviously we pull all of them, but what path of the tech tree should we be like, most focused around, most tracking and most excited about?

Yeah, I mean, as a person who represents research, I control the things that I can't control. I think that focuses on algorithms, right? Simple algorithms that are scalable, that we can pump a lot of compute into. We also do care about the hardware improvements that are stacking up with the open source release. You see thousands of people really kind of serving these models, creating really great inference stacks and those are really great lessons for us to pull from. What's the ceiling of the speed in which we can serve these models?

What can you tell us about the actual user experience of speed? Just like last week I finally got to a place where for a lot of tasks I'm firing off a 4.0 query and an O3 Pro query.

I have two tabs. The O3 tab.

Exactly. And I'm wondering what user experience patterns you think can help people balance between those. Is this like just something that we're like different patterns that we're going to learn over time or different. Or are there going to be certain problems of user experience that are purely solved just by better product design, better speed and we don't even need to learn these because I remember like, you know, when you prompted an image generator, you used to have to say like, no, six fingers, five fingers please. Or like don't make mistakes. And now, you know, the models kind of have that baked in. But, but, but how are you thinking about the user experience of getting the user the results in the right amount of time?

Yeah, I mean this is one facet of why we believe so Much in reasoning, it's just because all of the scaffolding you used to have to give the model all of these small hints, they go away, right? Like the model can examine its own outputs, it can review them, it can be like, hey look like I'm just counting the fingers here. Why are there seven? It can fix that. It does a lot of iterative generation, does a lot of fixing things on the fly. We think one of the benefits of bringing reasoning to the world is really to remove the need for scaffolding. With GPT5, we know how clunky that experience is with switching between 4.0 and 0.03 actually there's so many stories I was just talking to someone yesterday, right? They're like hey, well you know I've used 4o my whole life, right? It's the frontier model. And I'm like hey, well have you tried O3? And they're like why would I try O3? You know, three is less than four. And so you need to get out of that world. Your GPD5, I think it's a one stop shop reasoning and non reasoning and we've really tried to make it kind of just Pareto optimal.

Yeah, yeah. It's absolutely crazy to just take a bunch of letters and smash them together and expect people to pick up on that as a name or a brand new ChatGPT TBPN. We're both kind of in the same insane gambit, but fortunately it's worked out and I think people have have gotten over the hump.

But see, it rolls off the tongue. Tbp.

Yeah, sort of. Except our friend David Senra keeps flipping the letters. A lot of people do that, but at a certain point, yeah, you do break through and ChatGPT has, but. But keeping the model numbers simpler makes a ton of sense. Talk to me about the pace of play for research to actual product.

Like yeah, and on that note, the line between your personal philosophy on the line between research orgs and engineering product orgs.

Yeah. I mean so our research operates on a variety of different timescales, right. We have teams that they scope out a bunch of ideas and then they start to kind of narrow in on the promising ideas as they get closer to a run. And then you kind of see a winnowing of ideas as you get closer to launching a flagship model. Right? And there's always this kind of like explore more exploratory to more kind of concrete and execution focused pipeline. And we're pulling on ideas across the board here, right? There's a Lot of work in architecture optimization. SEB was on stream. He pointed out improvements in synthetic data. So there's really a lot of work that goes into creating one of these models. It's hard to say, like, oh, this model was about this breakthrough, just because right now we have this machine that's producing breakthroughs on all of these axes and even across several paradigms. So it's all that coming together that produces the experience that you guys feel.

Yeah. Can you talk to me about the legacy or future of 4.5? I remember I was talking to you and I was like, I haven't been using it a lot. And you looked at me like I was crazy. You were like, oh, it's so good. And I was talking to Tyler and he was like our intern here and he was saying like, yeah, the people who really, like, understand how good it is use it. But I was wondering, is there a world where that is a tool in the tool chest for GPT5 in the same way that Python is or web browser is? And if it detects that I want something with more, more emotional prose or more thoughtful writing, it can do a whole bunch of research, collect a bunch of raw, raw text, and then kind of do a 4.5 pass that I believe is more expensive, maybe and maybe doesn't make sense for every single query, but could be a feature in the loop or a tool that is pulled into the overall product experience.

Yeah, absolutely. Speaking of 4.5, it's also a very smart model. Right. And one of the our bars in creating GPT5 was to make sure that on a lot of the axes we cared about, that it was able to outshine 4.5. And I think even in some of the soft ones, like creative writing, I think that was the case. And that's what makes us so confident with the name. I think we're able to really rely on all of the architecture advancements, all the post training advancements, all the synthetic data advancements to create a model that's better than 4.5, but much faster and much cheaper.

Yeah, it feels kind of like we're. I Remember, wasn't the second iPhone called the iPhone 3G and the number literally corresponded to a specific technology. And now when you get the iPhone 14, it doesn't mean it's 14 megahertz or gigahertz or inches big. It doesn't like the number is abstract and it speaks to a bucket of features and it feels like there's. I mean, this was the first day of kind of re. Educating folks on what the nomenclature means going forward. Have you talked about an annual release schedule or like, or. Because there's the iPhone cadence and then there's the Google cadence, which was like, Google search just got better every year for two decades. It feels like at a certain point you want to just be shipping as fast as possible. How do you think about the culture of shipping updates that, you know, you find something that feels like, hey, that could make the customer more delighted or the user more delighted, and we don't need to do a big training run for it. So let's get that out today and let's tell people about it. Like, how are you thinking about fast iteration versus splashy announcements?

Right. So on the product research side, I think it makes a lot of sense to think about, you know, what's the cadence of release and, you know, what are the feature sets that we want to build. And I actually think there's enough great research happening there that we don't have to worry about, oh, is there going to be a drought or a long stretch without enough features to launch. But one thing that's important for us is to be able to provide the people doing the exploratory work some buffer from that. It's hard to do really great exploratory research in an environment where you feel pressured to do release after release after release. We let that be a little bit of a lazier pipeline, not meaning that the work itself is lazy, but we give it space, really, to mature and to flourish. And once it's ready, we can ship things across that fence. That's philosophically how we organize. We have a product research Org still very much entrenched in the research, and they care about the release cadence and they're able to draw from all of the research that's happening algorithmically and in scaling and in rl.

Yeah, talk to me about tool use and how that's growing. I was, I was kind of noodling on this idea that, you know, the. I was, I was thinking about the IMO and how, at least from the reporting, it sounded like OpenAI's model didn't use tools for that. And that's an incredible achievement. But it's kind of like artificial. Like, I don't, I don't care if the model doesn't use tools. I use everything possible. And even if, even if an LLM can, Can memorize every. In fact, I'm fine with an LLM looking stuff up in a traditional database, spinning up a spreadsheet. Use whatever tool you want. Just give me the correct answer. But do we have. Is it important to give Surface to the user the variety of tools that are in the GPT5 tool chest? I noticed something magical happened when I was using GPT. I was using O3 Pro. I sent an image in and I asked to estimate the height of a desk and it wrote like a thousand lines of Python image per interpreter and was like, you know, interpreting pixels and I was like, I didn't even think to trigger Python. It did. Yeah, yeah, yeah, no, it was right. It was crazy. But the really funny thing was that it was just a standard sized desk. It was just like it could have just googled like how tall is an average desk or something, or just memorized it probably was just already in the weights that it knows that a desk is like 36 inches tall. But it did a ton of work. It still got it right. It fact checked it a bunch of different ways. But, but, but, but I've noticed that now I can, I, I can pull different things, make a table, don't make a table, write some Python for this, don't write some Python. And it kind of gives me the feel of like a super user to some extent. But I'm wondering how you're thinking about what is further down. Like you've given ChatGPT a computer, as Ben Thompson said, you've, you've given kind of the core tools, the Python repl, the, the, the web browser. How are you thinking about the long tail of tools that you want to bring to bear and how does that interface? I know that there's API integrations and all sorts of different surface area there, but give me some context on that.

Yeah, I mean our reasoning models are pretty cute, right? I mean I think they, when you look at their behavior, they know the height of the desk, but they'll still go verify it five different ways. It's all consistent, give you that median answer. And I think that's really what makes these models so powerful. And when you think about tool use generically, we want the models to use that reasoning ability to just be able to zero shot a new tool. You should be able to minimally get instructions about how the tool works and just be able to know how to use it. Humans do this all the time. You get a new tool, you start experimenting with it, and then you don't need too much scaffolding and you just go and use it and understand it. So we want our reasoning models to use their reasoning to be able to use a broad selection of tools. And of course there are A couple that you really do care about. In coding it's very important for you to be able to execute code. It's really important in personalization for you to be able to get context from your calendars and basically from the digital world. I think there's a range of tools we want familiarity with, but beyond that we want the model to be smart enough to just generalize and use tool zero shot.

Yeah, talk to me more about personalization. I feel like there's a world where I feel like I may be underutilizing ChatGPT as an app because I don't have it wired up to a non relational database where it can just stuff data from. You know, it already has memory and it's doing kind of roll ups and there's some sort of saving of context. But I was, when we were talking to Kevin Wheel, I was kind of like, well I don't really have a GitHub repo that's active that I want to dump code in regularly for my one off tasks. But for that image generation, understanding the height of the desk, it's like, well if I'm doing that a lot, maybe I want to have a tool built that lives in the world that my chat interface can interact with on an ongoing basis and contribute to and modify and kind of wind up instantiating a piece of software that's even more long lived and then every successive quick query is even faster. So yeah, how do you think about different ways to increase personalization?

Yeah, I mean I think memory is huge. So we have teams surrounding memory and also personality. And when you look at memory, I think it's just we have so much context built up about ourselves that the model doesn't have. And our memory team's been really hard at work. There's a surface level of just gathering facts about you, but there's also stuff about just thinking very deeply about who you are, what your motivations are. Even you could think about you're trying to do some code based tasks, you're a developer. Shouldn't the model just be trying code out and just leveraging all that memory its thoughts about what you want to do to just help you be doing work all the time. We do think memory is a huge part of making the model more personalized to you and it should just make use of all that passive signal about you that it observes or all of that interaction and just help you accomplish your goals.

Got it.

What do you think it'll take for AI to start making novel discoveries? That's been a critique over the last year as everybody's so excited, everybody's using these products every day and in their work and life and yet it still feels like we're missing that. Dwarkesh has talked about potentially that being around continual learning. But I'm curious what you think.

So one thing to underscore is I think the models are already phenomenally creative in certain ways. So when I looked at our performance on contests, I've done these contests before. Sometimes you have this mental classification of these problems require more creativity or these ones require less. And one of the big surprises for me was that the model can get some of the ones which I intuitively think require more creativity. And it often does come up with these solutions that I consider quite ad hoc and really don't pattern match to anything I've seen before. When you look at advancing science or mathematics or fields like this, one thing that construct in which humans work sometimes is there are kind of three builders in mathematics. For instance, there are mathematicians whose role are to kind of build out this theory and almost to kind of create Olympiad style sub problems which often other mathematicians who are very good at that kind of style of work can do. And I do think kind of the model will increasingly contribute on that side first. Right. If there's some mechanical. Hey, I really don't know how to simplify this expression. I really don't know how to get this result. It can really do that quickly for you. We're trying to increase the envelope such that the model is getting towards that theory building side and being able to create creative hypotheses. All of these components are very useful for what I consider the ultimate goal which is being able to automate some of our own work and our own research.

How are you thinking about like the layers of mixture of mixing? Like I remember GPT4. I don't know if this was ever confirmed but mixture of experts model, this is kind of like widely understood in the industry now. Are we in the era of like a mixture of models that have mixture of experts? Like how many mixtures are going on? How does GPT5 actually work? Work? Is there a taxonomy or architecture diagram that you can kind of like walk through to explain what GPT5 is? Because it feels so much different than GPT3.

Yeah, I mean one of our, probably the pinnacle of our research roadmap our path to AGI. When you look at the levels of AGI, the top level is what we describe as organizational AI. And what this means is collections of agents working together. Often like we might in A company towards a shared goal. Right. And you would imagine that these agents probably subspecialize in ways maybe similar to what humans do, maybe in their own more efficient ways and I think effectively work together to accomplish some goal. So we very much care about exploring this vision, seeing that's much more effective than one single big brain working on a problem. I think there are reasons to think why it could be so. I think that is one of the things that we're after.

On that note of specialization, how are businesses working with GPT5 or how do you expect them to work with GPT5 in terms of coming to OpenAI and asking for special capabilities or fine tuning or you know, any sort of RL on this particular problem? In my world, I have this specific data set. It's not public, but I want a hyper, I want you to benchmax on it. I want you to, I want you to get 100% on, you know, the gas station bench or whatever. You know, if I'm, if I have a certain business and I'm willing to invest in sort of some overfit RL because it will create immense economic value for my business or it'll solve some fundamental problem. How are businesses going to be using GPT5 over the next few years?

That's a great question. I think that this is a chance to highlight one of the results that we've accomplished over the last couple of weeks, which is our accoder result. This is a relatively unknown programming contest, but it involves really the pinnacle of the best coding contestants in the world. And what they do is they are put in a room and they have to solve an optimization problem. This is something that's actually very real world relevant. So you can imagine an optimization problem as something like what Uber might have. You have, let's say riders and you have drivers and you want to create a system where you match them as quickly as possible with the least amount of cost, for instance. And, and so we've really created a system that can solve optimization problems at the level of the best in the world. And these truly are the kind of the best heuristic solvers in the world. So we have an organization led by Alexander Madri, it's called Strategic Deployment. And what they do is for a select handful of customers who really have that beefy problem that, that they need to solve to just go and provide that value. I think there's a lot we can do there. I think there's a lot of very, very valuable optimization problems in the real world. And we're really excited to partner with people because I think this creates a template for directly having AI provide economic value and really catapulting certain industries forward.

On the research side, What unique advantages do you think you and your team have given your position in the market with the incredible user adoption and the incredible usage from those users? It's not just DAUs, but it's actually the number of queries semianlysis estimated at like 71% of all queries going through ChatGPT. What advantages does that confer from a research perspective?

Yeah, I mean, a lot. Right. And I think it allows us to kind of deeply understand use cases. It allows us to understand the frontier of where humans are kind of finding value, where they're not finding value, which areas that we need to improve the models on. It gives us a lot of signal into how users are deriving value, when they derive value.

And what is that signal like? I see the thumbs up, thumbs down button. I'm sorry, I don't push it very often. I'm not doing my job apparently. But I know that you can figure out whether or not I'm satisfied. Just stop booing me, Jordy.

That's the research team.

Okay, Mark, I promise you for the next 100 ChatGPT responses, I will be honest with my thumbs up, thumbs down.

I love it.

To help you do, don't do it.

We have tons of people luckily who do.

Oh, that's great. Okay, so you do get a lot of thumbs up, thumbs down. And I'm sure I have done it occasionally. But I also imagine that there's a ton of other signal in there. You know, with the TikTok algorithm or, you know, any social algorithm, it's very easy time on site. But with ChatGPT, obviously it's exciting when we hear, okay, 30 minutes a day or some rumored number of minutes. It feels correlated with usage, it feels correlated with value that's being delivered. You can obviously look at churn metrics and all that stuff, but what other, what other pockets of signal are you finding? Are you finding people just. I mean, I remember the story about Google where they were trying to figure out how to handle like misspellings and create the definitive database. Do you know the story where they were trying to develop the definitive database of how to spell things and they were like taking a bunch of shots at it and they figured out that the best, most rich source of data was just if you type in financial into Google and you misspell it oftentimes, then you will just correct it yourself. And the Second query you send will be. Will be spelled correctly. So they can just look at two similar queries. What's the second one? That's the correct. That's the correct spelling. So, yeah. What other pockets of signal are you finding that are translating into the research environment? What are you excited to go deeper on?

Yeah, so I'd love to first talk about the DAU signal, because I think, you know, that's something that a lot of companies track, but we find actually a lot of danger in tracking it too closely. And one of the recent blog posts we pushed out was one on sycophancy. Right. If you just, you know, hey, we're going to boost responses where users say thumbs up. Yeah. You know, it creates a condition for a model.

I just want to say, Mark, I love everything you're doing on this front.

Yeah. This entire interview has just been fantastic. You are the best. We'd love to have you back on the show tomorrow, but clearly, problems with that.

Yeah, yeah, Clear problems. Right. The model just starts kind of sucking up to you, saying, like, hey, you know, you're right. And even in complicated situations where I think objectively, you know, collectively, we'd be like, hey, this person's in the wrong. The model starts saying, hey, you know, you're right. You know, the other person's gaslighting you. You know, this other person's kind of. And people deal with.

People deal with this in the real world. They'll go to a friend, they'll tell them about a situation, and the friend will give them advice. But maybe it's not the entire. It's not the fullness of the situation. Right. Maybe they left out some key facts, and the friend is like, oh, yeah, that other person definitely is in the wrong. And they, like, skipped over some important details.

Yeah, no, no, exactly. Exactly. And we don't want our models to fall into this trap where it's just trying to get you to like, you like what it says. And so we rolled back a lot of changes that produce that kind of behavior. And really, the way I think about daily active users today is we need to be opinionated about the features that we build into the future. I think we have a lot of ideas here, but we have to let that drive build for the future, Build for the things that people, you think they'll want and maybe don't want necessarily know they want necessarily today. And then use DAU as kind of this byproduct. Right. A way to track that you're on the right, Right, right track here. So, yeah, I mean, we want to be careful here. We don't want to fall into these traps of like, you know, three, four years from now that this turns into kind of engagement bait or something.

Totally, yeah.

Was it. How much time have has the research team been focused on efficiency specifically? It felt like summer was a good window before kids come back to school and start, you know, maxing out queries. A good time to increase efficiency. And I know the cost of GPT5 have.

Every time there's a new model, I'm like, this is the best it could ever be. It's good enough. Bake it on an asic. I just want it for free and I want it like in milliseconds. But that's just me being grumpy, I guess.

We've done a lot of work. We've been building out our teams. We focused a lot on scaling. I think Greg's going to come on a little bit later. He's been spearheading a lot of that work. Yeah, no, honestly, it's become a bigger and bigger focus for us, especially in the last couple of months

on the. I mean, this is somewhat related to the sycophancy thing, but I'm interested to know, like, what do you think is driving the GPT tone? You know how like the EM dash is a thing and then it's not a newspaper, it's a way of life. And it's like there's these like little like, like flourishes like that come through and in kind of our tell that it was written. And in a lot of ways I love it because when I get a deep research report, I like that it's using the same Wikipedia style tone. Like, I want consistency there. I don't want it to be like, oh, this today it looks like it's a Vice news article and today tomorrow it looks like it's written by someone at buzzfeed. I like that it's consistent in many ways. But why is that happening? Do you think that bigger models, like 4.5 kind of of were able to solve that or do those kind of like local minima, like, I don't know, like wells happen even in bigger models? Is there anything from a research perspective that can stop GPT having its own voice or is it fine that it has its own voice?

Yeah, that's a really great question. And I think, you know, as you scale up models, as the models become more intelligent, they kind of have a just deeper innate understanding of tone. Right. And so you expect that to improve just naturally as you make the Models more powerful, bigger, better reasoners. But one thing that I think gets lost a lot is each individual company has a lot of impact in terms of how they shape the default tone. We publish a document called the Spec. It lays out how we expect the model to sound in certain cases. Lays out a lot of examples for that. I think we use that the spec in many ways. We have people come in and see, hey, was this thing generated in accordance with what we would hope to generate from our spec? And this is a living document. Right. It evolves over time and so I think each company kind of has a very opinionated take on what they think the model should sound like. And it's not an accident that the models sound a certain way. I don't think just naturally every company is going to train the same kind of voice into their model.

Totally.

Well, thank you so much for hopping on. Congratulations on the big launch. We'd love to have you back soon to talk more. We could go in a million different directions, but we'll let you get back to it. We know it's a big day, so have a great rest of your day.

Mark, congrats on the launch.

It was a great conversation.

Talk to you soon.

Cheers, Mark.

And we will tell you about Restream 1 livestream, 30 plus destinations, multistream and reach your audience wherever they are. This stream is made possible by Restream. OpenAI just did a live stream with Restream. If you're trying to do a, if you're trying to do a stream, you gotta get on restream. So it's everywhere. And we will bring in our next guest, Greg Brockman, the President of OpenAI and we'll bring him in. Greg, how you doing?

Doing great, thank you.

Congratulations. How are you feeling? How's the company feeling? It's been such a wild journey. Just take me through a little bit of the, the, like the, the vibes in the company and, and how you got here to today.

Well, I'm excited. The whole company is excited and honestly I'm just so proud of the team. Like it's just been amazing to watch people come together not just for this launch. And you know, the funny thing is behind the scenes that people are always putting on the last minute adjustments and polish and scaling up the capacity and there's always something that goes wrong before launch day and so there's a lot of people who, who you know, worked late into the night or really crunch to bring this release to the world. And you know, it's a little bit like the Duck. That's, you know, paddling under the water. But that also describes the whole OpenAI history. Right. Is that I think that we have put in many years worth of investment to the techniques used to produce this model. And really it's across just every function within OpenAI that has come together to make this a reality.

Yeah, I mean, you've been there for every GPT release. How do you think about summing up each iteration in kind of like one line? Because GPT1, GPT2, GPT3, these feel like similar architectures. At least history's kind of compressed them into similar architectures. But how do you think about the progression of just the big numbered releases?

Yeah, it's interesting because in some ways it's a punctuated equilibrium, but on the inside it looks very smooth. Right. Even before the GPT series formally began, the first result that really sort of set this path to be something that we were heading down and it was clear that we were going to pursue it was the unsupervised sentiment neuron, which was an lstm in like 2017. So a different architecture from today's Transformers. And it was the first time that you could train a model to predict the next element. So we predicted the next character on, on Amazon reviews. And we were able to get semantics out. Right, because you expect, okay, yeah, it's going to learn where the commas go, what maybe what nouns and verbs are. But the idea that it would learn a state of the art sentiment analysis classifier, that was mind blowing. And so I remember seeing that result in 2017. It's like, we have to scale this up, we have to see where it goes. And so GPT1 was like, I think a good, like sign, sign of life of you train on, on sort of all the public data you can get and you use Transformer and that you were able to get state of the art on various downstream benchmarks. Right. So you have a model, it clearly learned some representation, something useful about the data that it was shown and it's applicable. You can use it for various tasks. But we didn't really think very hard about the generation side. GPT2 was the first time that we were like, all right, let's actually like the samples we're getting from it, the things it actually generates, they're kind of cool. And I remember reading in the GPT2 blog post, we have this unicorn story where it generates some fictional story about a herd of unicorns. And it was just so cool. It was like, wow, I wrote a story that's actually kind of interesting. It doesn't totally make sense, but there's something here. There's some real spark of intelligence within this model. GPT3 was the first time that we had a model that was actually something people would. It was just barely above threshold for something people would want to use. And I remember working on the GPT, this was our first real product and it was actually the hardest product, the hardest project in total I've ever worked on because it just felt like maybe no one wants to use this model. We don't really know what it's useful for. It certainly was the case that GPT3 was a great demo machine. You can make really awesome just like tweets and cool little apps and it would give you quick answers. But it didn't feel very reliable. And then GPT4 was something that actually felt like it had true real world utility. It was above some threshold. It was something that was helpful for health, it was something that was helpful for starting to be good at coding. And GPT5 I think just sets a whole new standard for the reliability for the utility. Things like coding I think are just clearly we're already on this trajectory of transforming software engineering this year I think are really on trajectory now to be revolutionized. So just really exciting to see that whole arc.

When did, when did the API opportunity really click for you? Because I do remember companies in that era that quickly unlocked the power of the API and grew tremendously. When did that opportunity click? Because you said initially that you had some, I don't know, concerns, doubts, how useful was it going to be? And then when did the consumer opportunity click?

Well, we, in 2019, end of 2019 had GPT3. We knew we needed to build a product to be able to actually continue the mission, to be able to raise capital. But what did we want to build? Right. We're really here because we believe in AGI that's going to have this powerful, positive, transformative effect on society. We want to be part of it. And so we thought, well, maybe we could build something in health. And then you realize, okay, well we're going to sell the hospitals and we're going to maybe hire.

Let other people do that.

Exactly right. It's just like you have to go into one domain and that means giving up on the G, the general. Right. It's like it feels like you're going to become one particular thing, but we kind of want to be supporting all industries at once. And so the idea was let's build an API and let people figure it out. But this is totally not the way you're supposed to build a startup, right? You're supposed to have a problem. No one cares about the technology behind it. Add value to that problem. Focus on just that one thing. And so that's why that project was so hard. And in January of 2020, February of 2020, that I, with the team, were going around trying to just find anyone that would be willing to try this API. And we were driving to different offices in San Francisco being like, hey, we have this cool model. And it was hard enough to get people to take the meeting, much less to sign up their company for. Was actually very fortunate. We found a couple of good partners. And it was fortunate that that happened then, because March 2020, suddenly that was Covid. We weren't driving around to people's offices to try to beg them to use this, you know, this budding new technology. So it was really six months worth of grind, right? Of really trying to turn. Like when we, when we started with GPT3, I remember it was, you know, that the inference code was not very well optimized. It was like, I don't know, 150 or maybe 250 milliseconds per token or something. And we just optimized, optimized, got it down to like 50 milliseconds per token. Which, by the way, today's models run much faster than that, which is kind of amazing for me, just like seeing how, how much faster we're able to run them with much greater intelligence. And I remember setting two goals for the team. One was actually find one customer who's willing to pay, so literally get a dollar in for this thing. And the second is get a use case that we use at OpenAI every day. That first one happened within the first couple months. So actually that moment I was like, all right, this thing is probably going to work. But in order to get there, we had to do a bunch of just scaling the API and really doing the product work. But that second one took much longer, right? And that wasn't really until chatgpt. And so if you fast forward a couple years, because this was mid-2020 when we, when we first got that, the API into the world, ChatGPT, we didn't release until November of 2022. So you're talking like a decent, decent period of two years there a little bit longer. And I remember we were building, you know, people have talked about, we were going to call it maybe chat. With GPT 3.5, we had a sort of precursor product called Webgpt that was built on 3.5 that we were literally paying contractors to use. So this was all throughout 2022. We basically had the ChatGPT precursor that we had to pay people, they would not pay us, we had to pay them to use this thing. And the moment for me that really clicked was actually when we finished training GPT4. So that was August 8th, 8th of 2022, which actually is like three years ago now. It's actually pretty, pretty wild to realize that almost to the day. And we did the initial post train of GPT4 and honestly I had a bunch of bugs in there. It was broken for a bunch of different reasons, but the model was extremely creative. It was actually really interesting. It took us about a year and a half to get to the point that the creative writing of our models matched that initial one that was buggy for various reasons. And I remember we had an instruction following data set that it was post trained on. So it's really, we had collected examples of here's a human asking for a thing, here's what the model should do. So it's really not trained to do multi turn. So I asked it a question, it gave a response, but then I was like, well, what if we just ask another question? And it actually was able to leverage that full context, it actually was able to have a coherent chat. And the moment that we saw that, we were like, okay, this thing is capable not just of being post trained to do this very specific thing, but it can generalize, right? It can kind of do the intelligent thing, even though it wasn't directly trained for it. It was just so clear this was going to be the killer application. And so then we were planning on launching GPT4 in early 2023 and we had this chat infrastructure we'd been working on and it's so clear, okay, like we're going to have to release the infrastructure and the model and it's going to be this, this amazing killer product. And so just almost as infrastructure ahead of getting the real thing out. You know, I was excited for us to do ChatGPT and that's why we did, you know, and see that come to life in November. So I think that for me, I, I was really focused on GPT4 as the model. This is going to be the chat moment that's really going to work. And kind of had missed the fact because every time you see these new models, you just sort of see only flaws in the previous ones and so missed the fact that GPT 3.5 was something that no one had really tried before in the broad sense of society, and that it was something that was already useful and that people would respond to.

Was GPT3 kind of like the main pivot point for shifting the company towards LLMs? Because in the prehistory of OpenAI, there were a lot of other, maybe expensive training runs. I don't know how much, I don't know how much financial risk was taken with like the. The OpenAI5 project or the robotics projects, but it feels like at a certain point the. The chat became like the main financial risk vector. So I guess the question is, like, when it feels like GPT3 was the moment when you shifted. I'm also interested in hearing about Ben Thompson, called OpenAI the. The Accidental consumer company. And I'm wondering when that narrative set in for you. Like, what. When did it become clear that this was going to be a really, really powerful consumer application?

Yeah. Going from paying people to use your product to people saying, hey, we want to give you money for this.

Yeah, yeah.

A very important transition, it turns out.

Yeah.

So, yeah, it's a great question. I would say that if you rewind to the beginning of OpenAI, there's many people who thought that, you know, in retrospect, say that we set out to prove that scale is how you make progress in this field, but it's almost the other way around. Scale was the thing that worked, right. We tried a bunch of things that didn't pan out, and it really. The first time we saw this concretely was in our DOTA project. I remember my collaborators Yakov and Shimon trained the very first little agent on like 16 cores or something and left it running on their desktop over the weekend. We came back and it was this very constrained mini environment, but that the model was doing something smart. It was actually able to solve this kiting environment. And that was pretty cool. And then they. And the team just kept scaling up, right? That we had all these free cores that were just sitting idle on AWS at the time, and they just kept throwing more compute at it. And every time they would do that, the model would just get better. And so when you look at something like that, you're like, well, you just have to see where this goes. You have to push it until it hits the wall. And our goal with DOTA was actually to develop new reinforcement learning algorithms. Because the common wisdom at the time was, well, the existing reinforcement learning PPO doesn't scale. Everyone knows that. But the question from Jakob Shmon was, well, why do we believe that has anyone actually tested it? And no one had really tested it. And so I think that that ethos of saying you have to push the existing techniques to the wall until they break, and then once they break, you actually have a baseline to overcome. And you win either way, right? Either it just exceeds all the humans in terms of the specific capability that you're trying to exercise, which was the case for Dota. Or it hits a wall and now you have a real problem to solve. And so I think that ethos really got embedded in our DNA. And at the same time I think that we were really thinking about how do we get to AGI? And really Ilya and I spent a lot of time thinking about that question of where's this company going and how do we actually achieve it? And you start to do some math in terms of the kind of compute that it would take to get to AGI and you just start to realize you're going to have to build really big computers and those are extremely expensive. And so I think that from these early foundational results and thinking, we kind of realize the path that we're going to have to walk.

So it seems like there's been a few walls that we've scaled up through and then maybe hit them. There's been talk of like a pre training wall. Now we're putting tons of res compute towards reinforcement learning. Is there a third scaling curve that we're going to be talking about in the next few years? Are we continuing to scale up those two primary vectors? Is that too high level of an abstraction in terms of how we should be thinking about just progress along the vector of scale? Give me the up to date thinking on just, just the fruits of scale.

Yeah, I'd say fundamentally deep learning. I think that people talk about the bitter lesson. It's almost this exploration into how do you convert compute into intelligence through. We have some particular techniques to do that that we're kind of constantly fleshing out. And the thing that's really amazing is if you rewind to, I don't know, even the 1940s for the McCullough Pitts neuron, which is kind of the precursor to neural nets. If you look at that paper, they have all these diagrams that actually look very similar to the kinds of diagrams we draw now of multilayer neural nets and things like that. The basic idea of what we're trying to do has not really changed in almost 80 plus years, which is just a wild fact. It means there's something deeply fundamental about the thing that we are pursuing and that idea itself, I think came from trying to model the information processing of the brain. And it's imperfect and not an exact analogy to biology. And all these reasons that it should fail or that people have said this thing is doomed, but the results are undeniable at this point. I mean, some people try, but it's really hard to kind of close your eyes and sleep on this in my mind. And it's very interesting. If you look at, you can find quotes from the mid-1960s of people trying to poo poo the whole direction saying that these neural net people have no new ideas, they just want to build bigger computers. And you could basically say something very similar today, what we are trying to do.

One moment.

Water break.

Yeah, exactly.

For all of us. Cheers.

Exactly.

Cheers.

That's how you know we're all human proof of humanity.

Right there.

Exactly. So what we're all trying to do is find novel ways of taking compute and really harnessing. Harnessing it. And sometimes you hit a wall. But these walls tend to be ones that you can drill through. Right. What we found is every time you scale up everything, all of your engineering, all of your sort of scale and variance, all these things, they get stressed to the next level. It's almost that the tolerances become tighter and tighter. It's like launching a 10x bigger rocket means you need to be like 100x just more precise on everything. But it doesn't mean that the fundamentals of the science are different. So pre training, there's definitely been a lot of discussion of data wall doesn't mean it's fundamental. Right. It just means that we need to be better and more precise at what we're doing. There's rl, which has been something that has kind of come from spending a small amount of compute to much larger amounts of compute now. And then there is a third way that we're really harnessing compute, which is compute at test time and we publish some scaling laws around this and all three of these things multiply. Like that's the amazing thing. And of course the compute and the harnessing of it is the fundamental goal, but that you get these multiplicative effects out of all of it through the quality of your engineering implementation. Right. Through the quality of the data sets, through a bunch of the refining work that you do. And there's lots of different techniques and ideas and that's what makes this field so rich and why progress is just going to continue apace.

What about on the infrastructure side? You guys have been busy scaling up. What can you Share on that front.

Well, so I run a team called scaling at OpenAI and we really focus on building the infrastructure for scaling. And this is in partnership with really everyone across the company. It's almost a misnomer that our team is called Scaling because fundamentally this whole team and effort is about scale. But what we really try to do is to both on the physical infrastructure side, deliver as much compute as humanly possible, possible. And that is in partnership with companies like Oracle, SoftBank and others that we've been able to deliver just like increasing amounts of compute to OpenAI. But we're constantly thinking about how do we just deliver more flops and do it more efficiently earlier, cheaper, more power efficient, all of those kinds of questions. There's the software infrastructure side as well. And really thinking about how do you coordinate massive numbers of GPUs in order to work across one synchronous training run? How do you coordinate that for reinforcement learning? How do you deploy that into production and bring these models to life at massive scale? And I think that every single layer of the stack there is innovation required. And that's something that's very easy to miss. Like one way I think about research is that there is, and this is kind of the view from Jakob, who's now our chief scientist, that there's a research stack. And you can kind of think of the top of it is people running experiments and coming up with new ideas for how to utilize data or something like that. There's a middle of the research stack of people thinking about how do you sort of take these different ways people are running experiments and be able to train in novel ways and kind of put together the pieces differently. And then there's a bottom of the research stack, which is like writing CUDA kernels to get the absolute max out of the GPUs. And at every single layer here you get a multiplicative factor through innovation. So it all comes together as one big whole.

On scaling, I'm interested to hear about, just if we think about the impact of AGI or the impact of AI just being some sort of maybe quantitative GDP metric or qualitative just impact and good. Is there an important factor of scale with just not even the flops that are going into the models, into the pre training, into the rl, into the test time inference, but actually just the flops that are going into the usage of AI within humanity broadly. And I feel like that might maybe be the next scaling curve that we're seeing. As more people use models, they see improvements all over the fact is, is that something that we should be tracking to see kind of the. Instead of these S curves, we want to see the continual exponential.

I think that's a great perspective because at the end of the day, if you look at kind of the shift from something like dota, which we pursued in order to. We wanted to do new algorithmic development, but really it almost validated how we scale up existing algorithms, but there was no illusion of delivering direct economic benefit from it. Right. To the current models where we are still. We're starting, starting to end the era of like pushing on these academic benchmarks. Right. You look at things like the IMO at this point, models are able to get gold medal on it. Like these, the hardest academic benchmarks that are available are sort of no longer a. Sort of the guiding. The guiding light of progress for these models to where we actually want to be is for AI to be helping everyone. Right. To be something that uplifts humanity. And that's the final metric, right, is how much does it actually benefit everyone? How much value does it bring to the world?

Yeah. Not just health bench, it's actually how many people did you solve their healthcare problem?

Right, Exactly. Yes, yes, yes. And that's the actual goal. And that's what's exciting, right, is it's like we're moving from the lab to reality.

Yeah.

And I remember in the early days, as we were thinking about how do we measure our progress towards AGI, we always sort of dreamed that one day we would be able to measure it this way. And you can think of revenue maybe as a proxy metric for value delivered to the world. It's not perfect, but it's at least something. Right. You can think of the distribution of, like, how much compute goes into it, how many people are using it, but fundamentally, like, what we're after is how much do we really uplift humanity through this technology?

Yeah. I mean, I might be misreading it, but I'm pretty sure, like, that was the Kurzweilian Ray Kurzweil philosophy, was that like, like total number of flops getting immense. Not necessarily all in one data center for one model. It was that compute broadly would be so wide.

Yes, yes. And I remember on that chart, right, you can see total compute of all human brains, which really suggest a particular vision of how this technology will be rolled out.

Yeah. Distributed the phones count as an impact. The WI fi router counts for the impact of the Internet, just like the phone does not just. It's not just the big pipe that's going the backbone of the Internet that actually matters.

Deep research hit product. Almost everybody I know, at least in

the industry, is using it. He's reading 30 pages of deep research a day. Basically.

He loves it, he's making books with it. But why have agents broadly come around a little bit slower than people may have expected? Is it. Is it just that using computers is actually much harder? Computer use is just a really hard challenge or. I think going into this year, everybody said this was the year of agents.

Are you talking about flight booking or something like that?

Yeah, flight booking. But people were saying 2025 is the year of agents. And I would say that it's the year of deep research and not a lot of these other sort of like broader use cases.

Sure.

Well, 2025 isn't quite over yet. So that'd be my response. And I'm very much on the. I think that progress in this field, the way that it tends to work is that if something kind of works with the current generation of models, it will be extremely reliable with the next generation of models. And I think that where we've been is that deep research is the. If you rewound a year, that was the. Like, we kind of had something working. And then like this year it's been just incredible. And I think that agents, specifically, like computer use agents, are something we've kind of had working. And again, you know, the year is not over. I think there's a lot of rapid progress to be made. But I think that maybe part of it too is that the agents that we're about to see I think are a little different from maybe what we would have pictured five years ago. Like, I remember having a debate with some friends on do you want a agent that does the flight booking? Because the problem is it's actually a very high bar to beat the flight booking ui because there's so many preferences that are entailed in that. Right. You really have to know kind of what mood you're in. Like, are you okay with like taking the extra layover and all these kinds of questions? And that actually there's so much other stuff that happens in your life that is. That is toil or drudgery or that's something that, that you're not an expert in. You're supposed to be think about health. Right? That like every patient really is the doctor. If you're coordinating across multiple specialists, there's no doctor that helps you with that. Right. That that's really on you and that their. You actually can have AIs that are just text only that actually are able to add massive value and then it frees up your time if you want to go book the flights yourself. And so I think that really finding the right problems that have high leverage that really add value to people and also thinking about the other side of how to make sure these agents are responsible with the trust that you put in them, that the more that you give an agent access to your email, the more you really have to trust that it's going to sort of do right with whatever your task is and send the right email to the right people and be able to segment your information. All of these kinds of questions. And so I think that there's both a practical how do you get to adoption? But also just like where are the most important leverage points in a person's life?

You also missed coding agents because it's been the year of deep research, but I feel like it's also been the year of coding agents. How is that developing at OpenAI? I've noticed that I'll hit O3 Pro and it'll wind up writing a bunch of code for me. And I didn't even ask it to. Then you have specific products for coding. How do you see the evolution of software development evolve? How are you seeing OpenAI customers use coding tools? And how good is ChatGPT or GPT5 on code coding?

Well, software engineering is definitely being revolutionized in front of our eyes. It's been happening. And GPT5 is the best coding model in the world right now. It's the default now in cursor, which I think is a really huge statement of the quality of the model. And that it's just so good across like every function of writing code, understanding code base, being able to use tons of tools, being able to do agentic work that yeah, it's like I'm not a front end developer at all, but actually now I am right. And I think that you are too right. If you just talk to the model, you can produce incredible things. And so I think that there's this real empowerment if you think about what computers were supposed to be, right? Computers are supposed to be a tool that makes you more productive, able to do the thing you want. But then somehow when we started out with computers, we have to contort the human to the machine, right? Writing assembly language and like all these like various abnormal things for a human to do. And that as we've moved to tools ultimately in the current generation, now GPT5, suddenly the computer comes closer to you, right? That you just express Your intent and you don't think about, okay, like exactly which language and what version of different libraries that the model is something you can delegate to. And so we are very committed to programming and to making our models continue to be the best they possibly can be.

Must a super intelligence be able to explain how to build super intelligence?

So it's, it's a great question. So I mean, I think that where we're going is a world and we're already seeing it, where these models help us produce the next generation of models. Right. They also help us really supervise tasks that are too hard for humans to supervise on our own. Right. If the model writes a 10,000 line program for you, reviewing that is probably going to be quite burdensome. But if you can have a model that you trust that maybe isn't as capable as the one that wrote all that code, or maybe there's a team of agents that work together to write all that code, but you have a team of reviewer agents. This is the kind of thing that you can actually bootstrap trust. And I think that this is one of the most important things. And also interestingly, 2017 is when we had the first language results. We also had some results or some, some vision on how you can actually bootstrap supervision beyond the scale of tasks that humans are able to supervise directly. And so I think that we're heading to a world where, you know, we now have these chain of thought models. We've been advocating very strongly to preserve the integrity of the chain of thought. Right. So that means don't directly optimize it to look good, even though there will be lots of temptation to do it for various reasons, really make sure that there's no pressure on the model to obfuscate its thoughts within that chain of thought, because then you can really see what it's up to. And I think there's further techniques to even make it more faithful and more rigid to what the internal monologue of the agent is. And so I think that there's actually a lot of promise in terms of interpretability, in terms of supervision, in terms of being able to scale to just like much more sophisticated tasks.

Yeah, I guess my question is how much information in the world can be derived from first principles reasoning versus true secrets that can that need to be discovered by interacting with the world directly. Because I would, it feels like it would be very difficult to. I'm just wondering about like how intellectual property interfaces with super intelligence or how like, if you play this out a lot, how like there's all These like Hard Won Dorkesh has talked a little bit about this. With continual learning, there's all these little subtleties that maybe they're not secrets, maybe they're not true trade secrets. You don't think to lock them down, but they're just things that haven't been codified online or anywhere. They haven't been given to anything that is surfaceable by the model. And I'm wondering how is it? Is it just we need to build up new knowledge and every fact from first principles and kind of go through the history of humanity's pursuits of knowledge, or do we just need to onboard more and more information? Or maybe it's both. I don't know. It's just something I've been noodling on.

Yes, it's a great question. I would say all of the above. Select all star. So I think that the answer is very similar to what it is for humans, right? How does a human generate new knowledge? How do we accomplish new things? First, you want to be grounded in the wisdom of the past, right? You really want to understand what if people tried, what worked, what didn't work. You want to go and read the biographies of various people and understand those, but you also want to try things out. You want to make some mistakes in a contained environment in a way that you actually can see the effect of your hypotheses. And then you want to be able to learn from those. And I think that being able to really start to scale up these systems and be able to integrate them with the world is a very big process and milestone that we're currently embarking on, right? To move from a world of totally hermetically sealed reinforcement learning environments to thinking about how do you actually put real world interaction in there. And you think about things like robotics, like you're going to need to have that at some point, right? You're going to need to have some sort of interaction with the real world and to have models that are able to produce new materials, right? To be able to actually solve various diseases for them, to be able to really help people. Right.

That.

You know, we already have models that are great at use cases like therapy, but to really get to the next level of something can just really help every person accomplish more and accomplish whatever their goal is. It would be very helpful for that model to actually have some real world experience with doing that very thing. And so I think that figuring out how to bring all this together is ultimately what our mission is about. And we do this not in isolation, but really as part of a much broader community.

It seems like it's advantageous to have the most dominant consumer app in that environment. So congratulations. Jordi, do you have a last question?

Last question. What. What do you hope to see out of Washington D.C. in the next year, year or two? Not thinking super long term in terms of, you know, basically promoting innovation within the United States. Obviously the admin cares a lot about AI and has been making moves, but. But what else would you like to see or where would you like them to double down?

Yeah, I've been very, very impressed with how much the administration has engaged with the technology and really tried to figure out how can we help and ensure that American AI continues to lead and really sets the standard for the world. And I think that that is the lens that I would really encourage thinking through. Right. Is like this technology is changing very fast and that fast plus government is not usually a ideal combination, but this is the reality that we have, it's the opportunity we have. And I think that the question in my mind is less about any specific regulation or strategy, but it's really being calibrated. It's really having a very tight OODA loop. Right. Being able to react to, okay, we have a new model. These are the capabilities we see on the horizon. How do we make sure that we get the most uplift and benefit from it and thinking strategically about not just how do we do this for Americans, right. But how do we actually do this for the world and promote democratic values? And so to me, the most important thing is that motivation, right, Is the question that is asked and the ultimate sort of motivation behind what gets implemented.

Yeah, that makes a ton of sense. Thank you so much for joining us. Jordy, are you going to hit the gong for GPT5? GPT5 and the whole thing? Congratulations on the massive historic day and thank you so much for stopping by. We'll talk to you soon.

Thanks for joining.

Have a great day.

Thank you for having me.

Cheers.

Really quickly, let me tell you about figma.com, think bigger, build faster. FIGMA helps design and development teams build great products together. And we are joined by Sarah Fryer, the CFO of OpenAI next. And we are going to bring her in in just a minute.

The gong's still swinging.

The gong still swinging. And I'm going to tell you about vanta.com Automate compliance. Manage risk, Improve Trust continuously. Vanta's Trust Management platform takes the manual work out of your security and compliance process and replaces it with continuous automation. Whether you're pursuing your first framework or managing a. A complex program? We need one more second. Tyler, any other questions that we should be asking for the OpenAI folks? Anything top of mind. What's on the timeline? Is the timeline still in turmoil or has it settled?

So I think the general vibe is like this model was not benchmax, but if you actually get to use it, it's pretty solid.

Cool.

One thing, it failed UPN Bench.

Oh, it did.

It did not get the horse breed.

Correct. It did get the horse breed. Wait, so you haven't. You have access to.

Yes, I have access, but I've seen other things on the timeline. We can talk about it later, but it seems like a really good model.

That's amazing. Great to hear. Well, welcome to the stream. Sarah, good to meet you. How are you doing? Congratulations. A historic day. Thanks so much for taking the time to talk to us. How are you doing?

I'm doing great. How could you not be doing great on the day when GPT5 launches? It's been a long time in the making and we're so happy it's out.

Yeah, fantastic. Walk me through your role and what GPT5, what this launch means specifically for you and. Yeah, well, let's just start there.

Finance has to be. You guys have to be the unsung heroes at OpenAI. There's a lot of, there's a lot

of big numbers, bills coming in for crazy training runs and you have to underwrite these against future revenues. And I'm sure you've developed many models to figure that out. But yeah, walk me through your role at OpenAI and what today means for you.

Yeah, absolutely. So I'm OpenAI CFO, but the finance can be the unsung heroes. But they are an amazing team. So I'm going to shout out to

them, they're heroes to us.

They're heroes to us.

It's a complex world that we're all living in and there are a lot of Bs on the end of a lot of the numbers that we look at. Look, what is our role? Number one is just making sure we have a healthy, high growth business. It's been incredible watching just first of all, the number of weekly actives, 700 million people using ChatGPT every week. And I'm assuming after today we should see a very nice little bump in that number.

This is going to be a gong heavy segment. Jordy, I think you're gonna have. We have a lot of soundboard for the big number, so congratulations and I

love that I've never met a Number I didn't like, I think the other part of the business that you know,

and then we have to do this clapping.

We have to do this balance of the consumer business, enterprise business and then API business, which I think of as somewhat enterprise, you know, and balancing that out. So enterprise adoption has also been exploding. I probably do. I mean, interestingly, as a cfo, I probably meet four to five customers a week. It's a part of my job actually. Love. We have about 5 million paying business users right now, from banks to biotech. I was talking to the cfo and

so that number is individual companies.

That is individual seats at companies.

Seats at companies. Got it.

So what I would say about that number is it's crazy to have done that in just two and a half years because enterprises, right, you got to, you got to put your big boy, big girl pants on to go sell to an enterprise, right? They want to make sure that you have the table stakes of security, SSO for signing on, you have HIPAA compliance if you're selling to healthcare and so on. They want to know that other people have done it. So they're often looking for that case study. But they also want to be, you know, the innovator right at the front. And so that to grow that scale of business in just two and a half years blows my mind. And it's not just, just big, big businesses which I could talk, you know, at length on, but it's also small mom and pop, you know, you know, literally the people who really keep the lights on in most countries are also gravitating to ChatGPT, which is wonderful. And then on the developer side, 4 million developers have built in our platform. And the question there is like that could be a developer inside a big company like Grab. It also could be the next, next, you know, startup founder, that's Y Combinator getting going with the next multi billion dollar unicorn business. And so we see the whole gamut there. And that's important to us as well because very mission aligned, right? How are we going to get AGI to all of humanity if we don't do it through this ecosystem? So a big part of my, you asked my role, big part of my role is just keeping that business really healthy, making sure we always have the headlights on so people know the decisions they're making. From a business standpoint, huge part of what the team does. The other big part of my role is compute. If I didn't talk about that in my first breath, you all should correct me. I mean, it's making sure. We think COMPUTE is a massive competitive differentiator. I give so much kudos to Sam and the team, but particularly Sam, because no matter how big a number we look at, Sam always wants to go bigger. And he's been right.

He's never met a number. He doesn't want to add a zero

to that too, maybe logarithmic. But he has been very right. And if, you know, you just had a long conversation with Greg Brockman, I think he does such a good job of kind of really explaining what a completely different world an AGI world is or an AI fied world is. And so I think when people get all caught around the axle of like, what is a gigawatt of compute? And oh my God, you guys want to have 10 gigawatts? And that's more than the COMPUTE of Ireland, since I grew up there. And then now you kind of look back on that and you're like, those numbers already look small for a world where everyone will have access to intelligence. And we're really starting to see what that can mean. When you look at the demos today around things like healthcare and education and so on.

Can you talk to me about non GAAP metrics and what you think is going to be useful to track? We were talking to Mark Chen about this and he was saying, you know, daus are great, time on site is great, but that's not as impactful of a metric for OpenAI as it is necessarily for a social network or an entertainment app. And there can actually be some problems that come up with that. So it feels like there might be some tension in the organization eventually or just publicly about what metrics are worth optimizing for. And then there's also the financial community that wants non GAAP metrics to track the health and progress of the business. And then of course, over decades, we see companies eventually roll back some of those non GAAP metrics and as the business gets more complex. So how do you think about the development and sharing of non GAAP metrics and what do you think is actually interesting and provide signal to the business and the investor community?

I'm kind of smiling to myself because when anyone normally says talk to me about non GAAP metrics, I can see like most of roll back in their head.

I live for non GAAP metrics.

I would love to do that, I think, in a CFO seat. First of all, it's really important to think about input metrics and output metrics and things like revenue, which is a GAAP metric, as well as A GAAP metric, they're very laggy. Like if you are spending your whole time focusing on the revenue number in an operator seat, like you are completely missing what's going on with the business. So I push my team a lot to get out of kind of ultimately what the P and L looks like. And I'll come back to it though and go way upstream and say, what are the true input metrics that tell us about the health of our business? And so I think it does start with that funnel of monthly actives to weekly actives to daily actives. Because we do, I mean, our mission is literally AGI for the benefit of humanity. So we know how many billions of people live on the planet. The fact that we're starting to be able to talk in billions and percentage of the world's population, it blows my mind. Today, 85% of our users are outside the United States and I love that stat. And in fact, if you go look at where the big populations of users, it just tracks global population, right? It's countries like India, Indonesia, Brazil, Vietnam, the Philippines. Go to anywhere that has big population. The US too, of course, but that will be your tracker. So that's kind of number one when I think of an input metric from there, on the consumer side, you're right. Things like time and app, I've actually always had somewhat of a love hate affair with. But I think in this case, because we're giving people intelligence, teaching them how to use that, I actually think is where time and app does become important. And one of the things we've really seen with ChatGPT are people are spending more time with it. Now we balance that with things like mental health and so on, making sure that we're not creating bad things like we might have seen in prior eras of computing. But I think we're just getting started on that front. Beyond that, when we go into areas like the API, I don't look only at usage, right? I can look at tokens per minute as a usage metric, but I look at things like latency actually try to look at the elasticity of demand. We know that developers want performance, they want intelligence, but they also want to make sure the API is always up and they want price and they're often willing to trade across those three things, right? It's kind of a linear program depending on what your use case is. And so I think it's important that we are offering things to developers that allow them to optimize across the three metrics, for example. So that's kind of Your input metrics. And again, I could wax lyrical, but I won't. But then go to what you really asked. So investors on the other side, right, they want to see a P and L. They're like, I want to be able to compare you to other companies. I want to be able to create maybe a dcf. Like, I want to think about fundamental valuation for a company if I'm going to invest in it. And so, you know, today what I really try to push investors on is we are not a company that should be optimizing for free cash flow today because there's just too much opportunity. Like that point about compute. We have to make a decision on COMPUTE today with an eye to what we're going to need in two to three years. Because data centers don't just spring up overnight like they're not mushrooms. They literally take time and effort. The thing we have failed at, frankly, I would say is three years ago we didn't have enough foresight to say how big could chatgpt because it didn't exist. It's just a shame on us if we keep doing that over and over. So there can be a bit of a mismatch between our belief on revenue because we don't yet know the product versus the input, which is the cost today on compute, and so getting investors comfortable with the fact that there's probably losses for a period of time. I say probably because ChatGPT just generally the revenue models continue to surprise to the upside. But at least for now, we should be in big investment mode. And then you kind of said it well, like as companies mature, you move to more GAAP metrics. Right. If you look at, you know, the large, the mag 7 many cases they're looking at like real GAAP net income. So the whole way down to the bottom of the P and L, we're just not there yet. And we should take advantage of that advantage because we can invest as a private company.

How do you think about timing fundraises from my understanding, or, or rumors, the last, you know, the most recent financing was very oversubscribed and at the same time you're still committing to CapEx in the future. That is a multiple of current, you know, the current run rate. And so you in the CFO seat, I'm sure there's, you're trying to find this balance of like, what does the business need today while, you know, not diluting the company, you know, too much, knowing that the sort of growth rate

of the business, I mean, that's exactly right. That's the, the art, not the science of it, is that, you know, we did just come off the back of closing out the sleeve of investment that we could take down in this current round led by Softbank. And it was massively oversubscribed. Which comes back to, I think, the market really waking up to the fact that AI is a generational opportunity and the scale that it requires is, is like something people have not even seen before.

Right.

It's, you know, people talk about the Internet or like the railways. They're good analogies or transistors. I think Sam always goes back to they're good analogies. But I do think this is bigger than everything that's come before. So there's taking down $40 billion, which we just did in this round. That certainly felt like that gave me a lot of confidence. Appreciate that. A lot of confidence to then go out and do large compute deals. Right. We announced the large deal with Oracle, for example, and to be able to keep working with all of our supply chain, Microsoft, Core Weave, Oracle, Nvidia and so on. But at the same time, in a world where our valuation has gone up at pace with our revenue, you do get an opportunity to keep coming back to market and not take that same dilution because you're getting that higher valuation for the work and the output that you've created. So it is a bit more of an art than a true science. I think for now we'll continue to need to fundraise in order to fund that compute, but I think we want to start getting more sophisticated, like just pure equity. Fundraising for everything is an expensive way to fundraise. And I think we're probably getting to the stage at a company where we can be a little bit more kind of broad in how we think about funding overall and even just working frankly with our supply chain because our success with bringing this era of AI into being is their success too. And I think these companies are realizing that.

What about partners? Last question. Partner selection. On the compute front, there's not a lot of companies in the world or firms that can really be immediate.

You should update your LinkedIn title. We saw someone yesterday works for Discord, is in charge of their cloud buying and his LinkedIn title with I have full responsibility over buying cloud, our entire cloud budget and it was clearly like a huge flag. But I'm sure you're in direct text message with every single person that's relevant in the industry.

Yeah, but I'm curious around. A lot of people have been excited about developing data centers over the last couple years in hopes to win the business of companies like OpenAI. But I think when you guys are evaluating partners, I imagine that scale is such a massive factor and so a single small data center is not really going to move the needle. You guys need to be thinking in terms of megaprojects.

Yeah, I mean I think that's exactly right. I mean it started with our partnership with Microsoft and it's kind of, it makes me smile now to go back and look at that original kind of large fabric for pre training because I think it was only in the maybe 20 megawatt sort of size and now we're talking gigawatts even just this year. And you're right that when we think about what is perfect compute for us or strategically the right compute for us, we are definitely thinking about large scale. We're thinking about flexibility. We're learning a lot about pre training, post training test on computer compute. Even like where the different kind of scaling is happening. We're kind of recognizing there's more of a blurred line often between what people think of as inference. Investors always are like your inference compute and your training compute. It's like literally it's like vanilla ice cream and chocolate ice cream when in reality there's like a bit in the middle that is something of both. We also need to think about things like latency. Where do we want to put our footprints around the world? That very global weekly active user base. Right as they use ChatGPT. You don't want to slow the model down. Right. The beauty of the intelligence is like the real time nature of it. And then when we get into big compute like where there's lots of tokens being used, like Deep Research, ImageGen, video as that comes online, like all the work you saw today actually just even on voice like that really quickly means that you got to make sure your compute is near your users. And so it is a big plan that's coming together. But you're right, like small is just not that useful to us.

What about pushing partners to take risks? From my understanding, you guys are pre committing to certain, you know, basically spend levels but at the same time I imagine you want people to say here's what we know we're going to need but we want you to build, you know, this much capacity so that we have the sort of incremental capacity built in.

Yeah, we want to, I mean being extensible is really important and we do want to see partners like I think Oracle OCI has done a really nice job of that of Kind of starting. We started with like one large, felt really large at the time, data center footprint in Abilene in Texas and now that has really multiplied up into multiple sites that can all be connected. And that's a good example of a partner who has the capability to start in one way but to be able to show you a path to maybe 5xing just in that single footprint. That said, we are finding that as we go around the world there is an ability to go work with governments. For example, we just made an announcement in Norway, made an announcement in the uk. This is the first time my professional career I've seen countries come to the table. I want to do commercial deals like wall to wall chat GPT. I think the government of Estonia put ChatGPT into all of their high schools. High school or I can't remember was up in the university level but. But that's kind of wowing and hand in hand with that they are viewing AI infrastructure as incredibly strategic for their population and it's a whole other level of selling versus I've seen enterprise large enterprises before but never anything at this scale.

Last question. Whose idea was it to give every federal agency chatgpt for a dollar a year?

I imagine you could have gotten more than a dollar. The CFO must be really upset here. $10, that's 10 times as much money.

This is one where I think it's really important. Opening is in some ways a U.S. asset and national asset and we want to make sure we're accelerating our government like all of the resources as we think about Western democracy and so on that we are absolutely putting our technology into those hands.

It's that guy Kevin Wheel, he's been moonlighting for the US government. It's like which team are you playing for, Kevin? Are you on OpenAI or are you on the US government?

Kevin just did his basic training.

I don't know if I'm allowed to

tell you that but I was hearing all about it yesterday.

I saw some photos. They look great.

Yeah, it's a good thing.

It's going to be an even better show for Kevin. Yeah, that's great. Last question for me. You know the open source model launched two days ago and there's this world where like you have this dominant, the accidental consumer company, you have this dominant consumer app that's generating so much revenue. Then you have B2B and Enterprise and API and that looks more like a cloud provider. But then is there a world where the red hat Linux of open source LLMs is an OpenAI division and that, and that there's actually serious revenue and profit that comes from helping companies implement an open source large language model like Red Hat built a pretty fantastic business for a long time on top of open source Linux implementations.

Yeah, I mean I think it's the right question to be asking. I mean I think step one was getting, yeah, you got to get it out two days getting our second open source model out and seeing what that traction is and then seeing what the community needs. I think it's important to leave space for a community to develop. Right. That is the beauty of open source is that ecosystem that develops. And that was true with Linux. It's true in areas like crypto too. But I do think you'll find over time that as enterprises want to deploy it, like I now dinosaurs myself. But when I was a, you know, when I was a research analyst at Goldman Sachs back in the day, I covered software and I covered Red Hat actually. Oh, that growth. I wrote a research report called Fear the Penguin at one point. Fear the Penguin Linux being deployed. But then you started to understand that for an enterprise you couldn't depend on like patching and upgrading to happen via community model. Like you needed some of the rigor that goes with an enterprise business where you kind of know when, if you need maintenance, if you need a bug patch and so on. And so that did allow Red Hat to grow an incredible business. So I don't know if it's us or we'd be supportive of others, but I think we are so excited to see open source out there and getting incredible feedback and I think we want to do that ahead of GPT5 to keep coming back to like we're here to grow this ecosystem.

Well, we'll give you market cap credit for it anyway, even if it's early stage. Well, thank you so much for coming on. This is fantastic, fantastic. We'll talk to you soon. Thank you sir.

Great to see you both.

Take care.

Have a good one.

Cheers.

Bye. Up next we have dedicated from Kudo. I believe I'm pronouncing that correctly. Let me tell you about graphite code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. You can get started for free@graphite.dev and let's bring in our next guest. How are you doing? Welcome to the stream.

Welcome.

Ooh, very clean background. I know it's probably virtual, but whatever you you got going on looks fantastic. You look great. How are you doing? Are you excited about GPT5?

I'm so excited. It's awesome. It's actually like everybody's talking about the coding capabilities, please. But no one is really talking about the code review capabilities. And I'm going to talk about that today.

Yeah, yeah, break it down. How are you using it right now?

Yeah, so we just enabled it in our platform. It's the default model for both our ID plugin, our cli, our git plugin and yeah, we're using it to generate very high quality code reviews, catch bugs before they hit production, help enterprises verify that their code is aligned with their best practices. So yeah, it's super exciting. I can share my screen and show a few things if like that makes sense.

You can. Everything you share will be live. It'll be a little. Yeah, yeah, yeah, please. But I want to know also while you're getting that set up, I want to know about what changes materially do you think happened in GPT5 specifically for code and code review. Do you think there's more data going into the model, more data going into the pre training, post training? Anything else? Anything that you're noticing that you're like, oh, there's a specific upgrade here, they must have done something to get there.

Yeah, yeah, I think it's a great point. So I think it's all of the above. So it's scaling of both the pre training but probably a lot of the reinforcement learning and basically using that at scale to verify that code gets generated in high quality and then also basically caching bugs. And when you do it with reinforcement learning you have the actual ground truth. So once you scale that you can get the model to be, to basically be a lot better at that.

How steep is the power law right now in just programming languages? Is it Basically all Python, JavaScript and then a really hard fall off or is it actually important for coding models, if they want to be adopted widely, to be truly multi language and get all the way down into the long tail of the Rust and C Sharp and all the different languages that are out there.

Yeah, for sure it's important to. I mean the majority of the market is in the JavaScript, TypeScript, Python, like the majority of the early adopters, I would say. But then when you get to enterprise use cases, you get a lot of Net, you get a lot of Java and the models are getting pretty good at those languages as well for sure.

Are you excited about, I mean, how do you think about the difference between the improvements to GPT5 from the consumer's perspective versus at the API level? I always found It a little confusing that ChatGPT was available as an API and you could interface with the chat. I believe you could Interface with the ChatGPT model via the API and there's a little bit of like a line blurring there. But are there features that you think are cruft and you want to kind of rip out for an API use case or do you just say, hey, give us the kitchen in sync and we'll work from there? And it's actually helpful to have a coding model that can still have a web browser.

Yeah, I think basically it's a lot about we consume the model through the API and it's really the same model that drives the consumer product. But as for us, since our use cases are a lot about agentic use cases, the more the model gets better at using tools and gets better at kind of listening to very, very specific instructions. Following instructions is critical for the enterprise use cases because for us, unlike the bottom market, we believe that for enterprises you need to have very specific agents that are defined with specific set of instructions and prompts and tools and permissions. And the more the models get trained with that type of environment, the better they stand up serving the enterprise market, which is really where we're focused on.

My, my question is, I wonder like you, you said like very specific instructions are important. When are we going to get an agent that I can just turn loose in a code base and say like just go improve it. Like just go hunt around, do like rewrite that. Like, like when you get a good open source contributor on a team that just becomes nerd sniped by the project that you're building on, they will just go around and find little ways to improve this documentation needs to be a little better. Let's rewrite this test case over here. Let's add a little bit more functionality to this class or function. How far are we from that?

Yeah, I think the models are getting better and better at that part of basically kind of running loose in a code base. But they do need the guardrails in place and this is where we're focused on. A lot of the talk in the market is around the co generation side. You know, let the agent loose and give it a task and it's just going to go around and run for hours and do it and do it. What we're seeing is that the real challenge is now shifting towards how do I verify that the code is aligned with the best practices, how do I make sure that it's well tested, well reviewed, doesn't break anything, you know, so that that's, I think, the next frontier. And really developers going forward are not going to write a lot of the code by hand. They're going to spend most of their time reviewing code and that's the next frontier and that's what we're really here to tackle.

Very cool. Anything else, Jordy?

No.

Well, thank you so much for joining, giving us some extra context on the GPT5 launch. We will talk to you soon. Have a great rest of your day and thank you for joining.

Cheers.

Thanks. Cheers.

Talk to you soon. And let me tell you about Profound get your brand mentioned on ChatGPT that seems more relevant than ever, reaching millions of consumers who are using AI. Millions today, new products and brands. I forgot to ask about this. We'll have to come back to this, but I want to know if Found

Powers mongodb Indeed Mercury docusign zapier Ramp Ro Golan Workable Majuri eight Sleep US Bank Chime Clay okay, okay, we get it. They got some logos.

There is this question question of like, okay, even if you're, even if you're like, okay, GPT5 is more incremental than revolution, more of an evolution than a revolution. It's like, okay, well then let's talk about how it affects every other business and every other aspect of the economy. What should you be focusing on? And is like, do any of the updates from GPT4 to GPT5 change how you're positioning your brand for AI search? That's certainly an interesting question to dig into. Anyway, we have Zach Lloyd from Warp coming into the studio. Welcome to the stream for the second time. Welcome back, Zach Attack. Good to see you.

He's back.

Good to be back.

How are you doing?

I'm doing pretty well.

Yeah. So I mean a lot of what stuck out to me, I'm mostly a consumer of consumer AI apps. I'm very excited about not needing to mess around with a model picker anymore. But take us through the biggest improvements from the software development side.

Yeah, I mean it's, it's a major step up from the prior OpenAI models. It's. I mean it's doing agencic workflows and Warp for much longer, period. It's just a smarter general model. Like we evalved it against all of our benchmarks and it's up there at state of the art, which is, you know, from our perspective, it's, it's awesome to have multiple competitive models that our users can benefit from. So definitely a huge improvement from GPT 4.1.

Yeah. So it seems like not the Claude code killer, but certainly in the same conversation in the same football stadium, if we're using a sports metaphor. How much?

One thing that stood out is the cost reduction. How much do you think that developers will care about that versus just what it can do from, from an output

standpoint, I think developers do care about value. So sort of like quality to cost ratio. I think it's the more you get into like the individual developer and the small team, the more that that matters. Whereas if you're at the enterprise level, I feel like it's, it's a little bit less price sensitive. So yeah, I mean you can see it as different apps change their pricing. What the reaction of the developers is. You've probably seen this with cursor and seen this with cloud code. And so developers really, really are looking for something that's cost effective. So the fact that the cost is a little bit lower is actually, is a big deal.

Do you think we're in the Lyft Uber 2015 arc where the prices are subsidized and the prices will go up? Do you think that there's a price war on the horizon now that the frontier models seem to be similar capabilities? Do you think that someone will try and raise a bunch of money, cut prices a bunch and steal a bunch of users? How do you think that plays out?

It's an awesome question. I mean, my hope is that we get to a world where there is price competition at the model layer. So Warp is very much at the app layer. Right. And so our value prop is like we can give our, our users, who are mostly developers, the best model access. And so to the extent that it's not one sort of model provider running away with that and having pricing power, it's better for us, just candidly. And so my hope would be something like the model world ends up a little bit like GCloud, AWS, Azure. That's our best end state where all of these models are, you know, sort of similarly powerful and a little bit more commoditized. I don't think it's been like that, but it's going, it's, it's getting a little bit more like that. And so the more that, that there's more than one show in town, I think that's generally good for Warp and actually is good for developers because it will put competition, the competition will put pressure to bring the prices down. But I don't know, like, I also think that people will, will definitely pay for quality and so if there is

a

meaningful Delta and quality on the frontier models, then I think that whoever has the quality Delta will have a lead temporarily. But I'm not sure that lead will be sustainable. We'll see.

How do you think the developer community should plan around model deprecation over the next one to two years? How much? I don't know that I've gotten a reaction yet. I don't know if there's general frustration yet from people we've heard on the consumer side. Tyler on our team here loves 4, 5 and so he was a little bit disappointed to hear that. But what are you seeing on the developer side?

Yeah, I think it's a little bit different for people who are building apps on LLMs versus people who are using LLMs as like a like accelerator to doing coding. And like, you know, at Warp, actually we do both. Like we were an application level stack and like it's actually very easy for us to go to the latest model. And so it doesn't, it doesn't really bother me. I don't know, I don't know what type of app you would be building where it's like, it's really important that it's like GPT 35 or GPT 4 or something like that. I think like generally we want the most intelligent tokens at the best cost. So I don't, I don't see that being like too big of an issue, honestly.

What about open source? Does that, does that feel like something that will be in the playbook? Is the markup on closed source models high enough that there will be a significant price Delta and. Or is the Pareto Frontier kind of indifferent to closed source? Open source.

So if there was a comparable open source option, that would be awesome. I think that the economics of it, again, it doesn't seem like a perfect analogy to me between open source software and open source models. So open source software, it's like you have a big community of people who for the love of coding are building a really awesome product for open source models. It's like you just need a crazy amount of capital to train something that's on the frontier. And so I don't know how that happens. And so what we've seen is the open source models are competitive at the quality level that they're at, but the quality level that they're at is not the same as the frontier models. And I don't really see why that would change. And so I don't know. In Warp it's like we were serving some open source models but they're just not. They're not as good. And so there's, I think, a more limited use case for them right now. And I don't really see economically why that would change. In fact, I would be surprised if anyone was spending billions of dollars to train a model and just kind of put out the open weights. Like, I don't get the business strategy there, but maybe that will happen. That would be awesome.

Is there a world where you're like this idea of like, smarter models either orchestrating dumber, cheaper models or like using or distilling models into more narrow, narrow formulations that can be run more efficiently? We've talked to a few companies that do this for businesses. Like, you just want a model that just filters for profanity and you can run it on, you know, know, a gaming graphics card. And so it's basically super, super cheap or super fast. I'm wondering about, like, in the coding world, coding agent world, any of that, like, where, where are the opportunities to kind of fan out and use an ensemble of models instead of just this, hit everything with the smartest, best. It feels like because of the funding environment, everyone can kind of just justify like a high cloud bill. And most people don't admit that it's hurting the bottom line, but it feels like at some point it kind of has to eventually.

I mean, I think that's a very real thing, like, sense of. Even in Warp, we don't use

the

biggest, most powerful model for every task. And so there's certain things, like for warp, maybe for deciding whether or not we should summarize a conversation is a good example. So you hit the context window, you're like, okay, is this a good spot to summarize? Is this a good spot to encourage a user to start a new conversation? We use a much more inexpensive and also low latency model. The other thing, the trend is that these very, very powerful models tend to have much higher latency. And so, so we do a mixture of models. And that's totally a real thing. But I think for the predominant use case, as a developer is going to be, I want to tell an agent to do something. I want it to be harder and harder. I want it to run for longer and longer. And to do that, it's like you kind of want, in general the most intelligent model. And so until the models have a sort of S curve, like type shape, I think it's going to be more of a quality game than a cost

game for most of these things. And doesn't it feel like they have an S curve shape right now. Certainly does.

From a consumer perspective, that's interesting. From a coding perspective, I feel like we're still accelerating. Like, the difference again, between the last version of GPT and this version of GPT is probably bigger than the difference between, like, 4.1 and 4 and 4 and 3.5. Like, it's a big deal. And same thing with the anthropic models. And I'm sure that we'll see something from Google where it's an acceleration. And I think that there is like a maybe an underappreciation of how much left there is to solve here. Because when. When you. Even when you're doing, like, a real coding task as a pro, despite all the demos you see on Twitter where it's like someone asks an agent to build an app, that's like a lower level of difficulty than doing what a pro developer does with one of these models. And the models still don't produce great code a lot of the time. There's a lot of kind of handholding that has to go into it. And I think that we are still seeing an acceleration in terms of the model is actually becoming not just like, okay, competent engineers, but, like, really, really good engineers.

Yeah. Do you care about benchmarks?

We care a ton about benchmarks.

But your own internal benchmarks?

We do both. So Plug for Warp. We're number one on Terminal Bench, which is the public terminal benchmark, and we're top five on SWEBE Bench, which is the coding benchmark. And then the only way, in my opinion, that an app at our layer in the stack can really improve is by measuring the progress. And so we have our own internal set of evals that we run run across all these models as well, which are coming from, like, real use cases. And that, again, is an advantage of being, like, a product that's in the wild, that has a lot of users, is that we can sort of see where the models are failing, where they're working. And so we're very big on that, actually.

Yeah.

Awesome. Well, thank you so much for stopping by. We will talk to you soon.

Sure.

You'll have a busy afternoon.

Shout out. By the way, OpenAI team, very, very helpful in working with us, us to get GPT5 to be awesome in Warp. And one more shameless plug. We have a discount code for people who want to try GPT5 and Warp. It's $5 GPT5.

Okay.

Thank you for having me, guys.

Yeah, we'll talk to you soon.

Thanks.

Cheers.

Tyler, any updates from the Timeline. While you're thinking about what the latest vibe check is in the war between OpenAI, I got one from Linear is a purpose built tool for planning and building products. Meet the system for modern software development. Streamline issues, projects and product roadmaps. Go to Linear app to get started.

Tools of choice for OpenAI. You have something from Reggie James, friend of the show. Half of my timeline says this is the closest we've been to AGI. The other half of my timeline says we officially just hit AI stagnation. I love tech.

Well, we will be going deeper deciding whether or not this is stagnation or hyper intelligence takeoff. And we will be joined by our next guest, Riley from Charlie Labs. Sorry.

Hey guys.

Thanks for having me.

Good to see you. Riley, how are you doing?

What's happening?

I'm doing fantastic. Fantastic.

We've been heads down with GPT5.

How long have you had it? How long did you get the preview? I feel like it gets rolled out to early adopters a little bit earlier, but it's been weeks, months. How long have you had it?

Couple.

Couple weeks.

Two or three. What was the first?

How's Charlie liking it? Charlie loves it.

And also I love what Charlie does with it.

Yeah, what does Charlie do with it? What was the first thing you did with ChatGPT5?

Ran our evils.

Oh yeah? How'd they come back?

Really good.

Much better than O3, which was much

better than any other model we firmed before that.

Interesting. Interesting. And yeah. So let's zoom out. What do you do? What do these evals measure? Walk me through it.

So Charlie is a typescript focused coding

agent that operates much more like a human does.

So less like IDE Application Terminal and

more joins your GitHub and Slack and

linear workspaces and it interacts with the

team the same way other humans do.

Do.

And then our evals are a mix of code review because part of Charlie's job is to review PRs from humans as well as his own, and then

code authoring, so opening PRs and pushing commits.

So when you develop your own evals, I imagine you try and keep those out of any training data. You want those to be held private, is that correct?

Yes.

And it's getting even harder with web

access now because they're too good at finding things.

They're finding everything. And then, and then talk to me about like the shape of those, of the actual problems in the eval. Are you doing? Are there some easy questions, some hard questions, some. Some. Some extremely hard questions like how are you formulating those. What's the shape of an individual task? Is it scored out of like 100? How do you think about developing a good eval? A mix of hard to very hard.

The easy ones are just a waste of money and time at this point.

Especially with five.

Like there's a bunch that it's just

not going to get wrong.

Yeah, yeah.

And then we're mostly doing the PR

ones look kind of like speed bench

in the sense that we're taking an issue to start with, but instead of giving the issue like in a Docker container already, we trigger a comment on

the issue that says, hey Charlie, go

make a PR for that this. And then Charlie does his thing and then the PR comes up and then we score that PR against a whole bunch of things like correctness to a

known solution that's correct as well as

code quality, testability and some softer things

like descriptions who are the biggest customers or use or users for like a typescript focused coding agent.

It's a wide range of of mostly modern apps.

Like pretty much any web app these

days is going to be like a

next JS type app and then all

the way into like back end. Like Charlie himself is written in typescript.

Sure. Makes sense.

And there's very little front end.

Anything else? What else you got?

I just want to say I love the name Charlie. It's one of my favorite agent names that we've had on the show.

Yes, it's right up there with Pig and what was the other one?

Well, I don't think that was an agent, but that was an agent, but yeah, it's a good one.

Yeah.

Congrats on locking it down.

Yeah. What about cost and that side of the business, Is there any movement there or anything that you. Where you require movement or you need movement to really unlock new capabilities in the business or new markets?

Not really for us because we're operating

as at a human level. We do value based pricing, so we charge per PR or per commit. And because that's comparing to such expensive

actions that humans are doing, the challenge for us is more actually living up

to the promise than doing it cheap.

Yeah, yeah. Are you having.

But then, but then doesn't the cost reduction announce today? Isn't that great for business? Business? Yeah, I mean it's good overall. But like that's our problem is not

that the models are expensive, it's that they're, I mean they're getting really smart.

But I'll always take more. Never enough.

Like for instance, since the beginning of

August, we've been testing. 98% of the code that got merged into our code base was written by Charlie.

Wow.

Not 30, not 50.

98%.

And that's coming through PRS. That's not like autocomplete in a 98 type thing.

That's crazy. Yeah. What does that mean for the future of who are you hiring? I imagine that you're still an engineering heavy organization that's just puppeteering and orchestrating agents. But where do you see the future of software development as a career path going?

Yeah. Are new CS grads cooked?

I think if they get really good

at using the AI.

No. If they try and take an approach of getting really good at writing code by hand, for sure, yeah.

What we're mostly looking for hiring is

people who are able to see things

at a much higher level and plan further out.

Because with tools like Charlie, you can write so much more code so quickly

that it's like it's more important to

see where you're going and take the

right path than it is to.

To be able to write it quickly.

Very cool. Well, thank you so much for stopping by. Good luck with the rest of your day and congrats on an upgrade to everything that you do.

Tell Charlie to have fun out there.

Have some fun.

Thanks a lot, guys.

We'll talk to you. All right, Let me tell you about numeralhq.com. sales tax and autopilot. Spend less than five minutes per month on sales tax compliance.

Sales tax. A number of the fellows in the chat Got access to 5.

Break it down for us.

Raghav says it's pretty good. The writing ability feels a little nerfed. Says the way it writes feels a little programmatic rather than sounding human. Reverts to using points even for things like blog posts, and uses overly complicated language for simple stuff. Techno Chief says it's crazy fast.

Oh, that's good.

Dan Ratliff says, yeah, I was just gonna say that. Very, very, very fast. Z. Jon Ahmed says junior devs are barbecued.

Tyler, anything from your side before we talk to Guillermo from Vercel?

I think maybe a good way to vibe check, at least on the timeline, is that it's almost like a 4.5 kind of thing where comes out people are like, like, this model totally sucks. Look at the benchmarks. It's not some massive improvement. It's like not a step change at all. But then you start playing with and it's actually like, okay, this is actually a good model. A lot of the stuff I'm seeing, people post like, oh, that's actually interesting output stuff like that seems good.

Can we do the green text eval?

Green text bench BTBPN intern.

Yes, yes, yes, yes. We'll let you cook on that and then. And we will move on to our next guest, Guillermo Rao from Vercel, coming in to TVPN for the second time. Great to see you, Guillermo. How are you doing? I like the action hall. Thank you. Welcome to the stream. How are you doing today? Do you think GPT5 could beat me? You couple of the boys here on Dust two in Counter strike?

Easily, easily. Yeah. It depends on the frame rate, right?

Yeah.

On a long enough timeline, we're cooked.

We're cooked.

But we might frag it short term and we might be faster.

Amazing. Yeah, we got to. I mean I'm sure we'll get to GPT5, but what's your reaction to the world model stuff from Google, do you think, do you have an idea of where that's going as a product? It feels like a GPT2 level technology. Very much a research focused technology. I'm sure OpenAI is working on something too and, and a lot of the labs will work on it. But what's your theory behind the generative video game world model stuff that's going on?

I mean, number one, super fascinating. Right? I think when we think about the future, I always think about Jensen's the future of applications will be that pixels are generated, not rendered. So as much as we're really excited today that GPT5 and V0 are really good at writing code that then renders interfaces, I think it's also cool to dream of a world where we're just going directly from GPU to pixel grid. Right. But if you remember like a couple years ago and maybe a decade ago, there was a lot of excitement of video games that were going to be live streamed from the cloud.

Yeah, that's right.

Where your input, your keyboard, you could have a very thin client. Your input, your keyboard, your mouse movement was going to be dispatched to the cloud. We're going to have GPUs near you.

Google Stadia was big there and then a game and Microsoft is actually still pulling it, still pushing it very heavily.

Awesome tech, but not mass adoption. Yeah, but if you look at a lot of these technologies are being really successful in letting people get more creative and test things out. A lot of the use cases that we see for V0 and Vive coding are almost like a communication tool. Like I want to prototype something, I want to see what's possible. I want to explore the latent space and I think those world models are going to be incredible just to inspire what the future of games could look like.

Right.

Just getting ideas for actually then shipping them in a real 3D engine model. I think short term, I think long term, all bets are off. Someone was saying in the chat, junior devs are roasted or barbecued. I think that's not True. Same for 3D engine developers.

Give us the bull case for junior devs staying off the barbecue.

The bull case for I think people in general is that you move from. I mean the progression in the industry has been assistant to agent to team of agents, agent orchestrator. It's still really useful to have a human be the one that's sort of like managing the team. So you're moving from junior dev to junior Eng manager. Especially as these tools become more agentic. In the new version of V0 that's coming up really soon, you're starting to notice that V0 sort of splits the task between a little team. You have the designer of the team, you have the PM of the team that's working on the spec, you have the architect, you have the engineer. I know if you saw cloud code announced, I think it's like a slash security review. Think of that as having a security team or team of agents or security researcher at your disposal. So junior dev as a vertical skill may be a little barbecued, but junior Eng manager. So I think it's just going to be the junior dev is so much more powered in this world if you allow yourself to be and you keep up with what these tools can do. And I think you stay at the cutting edge.

Yeah, I mean the obvious bull case is if you're like if someone's a college student today that you can learn to code truly AI natively, they don't have to say, oh, we're an AI native organization now. We have to upskill and kind of retrain people how to think they can just naturally start to think.

I remember these capabilities. I remember there was that Sam Altman post about how we'll look back on 93% of humanity was subsistence farming. And if you ask those people what they think about our email jobs, they'd be like, you guys are crazy. And it's almost like, like in the near future, midterm future, maybe even long term future, it's like the number of individual contributors will be extremely low and almost everyone will be a manager and you'll become a manager much faster. You'll just be managing agents and Then you'll be managing people who manage agents. But the job of almost everyone will become managerial. Maybe that's what happens. I don't know. I'm not 100% on it, but that's what that made me think.

Someone asked me yesterday, you know, what do you think the future of, of the market of monitors looks like? Does it stay flat? Do people get more monitors because they're going to dogecoin trader analysts?

Everyone has the hedge fund 6 monitor set up in the future.

Everybody's just going to be at work,

maybe on their phone. I've noticed that when I was an individual contributor, I had three monitors. I was programming on all the screens and now, I mean, I use my laptop during the show and then most of my work is done on my phone. Phone calls and then firing off messages. Yeah, maybe we actually shift away from monitors and go further into voice interfaces. Oh, I called the lead of my agents and then that agent relays it to some.

I'm very optimistic on Voice, by the way, because I've now seen it. We're cooking on a better mobile experience for V0. Sure. And I was going back and forth with my head of mobile and he was talking to V0 and I was writing down in a pretty fast typer, but he beat me with Voice using the local model on the phone. So there's still the question of edge latency versus cloud latency. Kind of like what we talked about with 3D. But I do think voice is going to play an increasingly exciting role in programming, which is kind of wild. I would have never imagined. I've always been about typing benchmarks in WPMs. Voice is coming.

How do you think about competition broadly in developer tooling, CodeGen? I mean, right now it seems like there's just so much.

It feels like massive TAM expansion moment.

Every company's ripping TAM expansion moment but at the same time winners will emerge. Obviously you're playing to win and yeah, I'm curious.

Yeah, on some level we're playing both sides of the bat. We announced today. That's really exciting. Is V0 with GPT5 support. So you can go to v0.dev gpt5 and we'll use GPT5 in combination with our model pipeline. That makes it really good at bytecoding, especially for non technical folks. But we also, on the Vercel AI cloud side of things, we open sourced. Basically you can create your own Vibe coding platform powered by any model.

I was joking about this with Tyler Vibe. Code me a Vibe coding platform, please.

I'd like one make no mistakes.

Yeah, Vibe code me, a billion dollar company, no mistakes. But basically we are giving people that as a starter kit.

Sure.

And by the way, the fundamental question that a CEO asked me the other day was is by coding a product product or a feature or is it both? It's tbd. The case for feature is okay, so there's going to be lots of systems of record. Think Salesforce, Snowflake, Databricks and increasingly they're going to incorporate Cogen capabilities into their platforms. They can use a lot of these capabilities that we just open sourced and you'll go to the existing place where you have the data. Kind of like what we've talked about for decades of like, are you bringing computer the data? Are you bringing Vibes to the data?

Right.

Are you bringing Cogen to your own platform?

You used to bring a dashboard builder and it would have a couple widgets. And now I could just potentially if I'm plugged into some sort of data source system of record, I could say Vibe code this app on top of it. There's been some tool retools played in this space. Zapier a little bit. But yeah, I mean this feels like no, we're getting, we're not fully in the. Just the pixels are generated, but we're just generative ui, generative application on top. And that being bespoke in ad hoc.

I also think it's important to understand the line between consumer vibe coding and just generating ephemeral software and websites and things like that versus enterprises which will have a lot of different use cases. When I look at the Vibe coding market and I see businesses that are, that are almost entirely consumers just creating things for fun, I think that has to be a tough business because it's a hyper competitive market and consumers are flaky. They'll create something for fun, but they'll churn in month two because they're not running a real business. Whereas a business knows, hey, we'll pay for this on a long term basis because we have a use for it all the time. From this product manager to an engineer over here, to somebody in marketing, et cetera.

Yeah. The other side of the equation is how do you make these vive coding tools work really well for enterprises? Frankly, the most surprising emergent thing that I've learned is just how much demand there is in enterprises for vibe coding. And this is because a lot of the traditional thing has been the people that understand the business are sitting over here, the people understand the code are sitting over here and their communication is fraught with peril. They don't speak the same language. They kind of resent one another. I love to tell this story. I was meeting with the CEO of a very successful company who's telling me that engineers asking a feature to his own engineers felt like petitioning the government. Even though he's the CEO, he's struggling to make the case. And please get me in your next Sprint. Get me this feature. So Vibe coding actually solves that problem. All of the PMs, designers, marketers, business users that previously only had access to what like Jira and you know, to do lists and product management tools and writing PRDs and those kinds of things, they weren't able to ship PRs, they weren't able to, you know, ship software, and now they can. And so the opportunity is, how do you actually make this secure? How do you make it high quality? How do you create a guardrails? And those are tricky problems. And I'm really happy that some of them are easy to overcome, at least for us, and some of them are active areas of research. But I think the enterprises really have a strong case for this.

Yeah, can you walk me through tool use? We were Talking to the OpenAI folks about GPT5 being really a summation of standing on the shoulders of giants. You get a Python repl, you get a web browser, you get, you know, the ability to kind of run cron jobs. Now there's voice and you know, all sorts of different tools kind of wrapped up into one, multiple models. You can trigger reasoning chains. If it wants, it can do all these different stuff. And that's actually the benefit of like, this isn't just a bigger model. It's like a lot of, it's a, it's a next version of a thing. It's more like switching from the iPhone 12 to 13 than going from the iPhone to the iPhone 3G. It's not just a new technology that's in there, but in the, in the, in the world of Vibe coding, what are the tools that you want to think about adding? I know that basically every Vibe coding platform, you know, recommends a database, but I was, we were talking to Harley at Shopify yesterday, and there's a world where if I go to a Vibe coding platform and I say I'm building an E commerce website, it should probably just be like, hey, I'm going to do Shopify under the hood and I'll Vibe code the landing page on top. But, but how are you thinking about the landscape of tools that you could pull in full open because there's open source repos that are full projects that you could pull in and then just start customizing on top of. It's kind of this big continuum.

Yeah, there's a couple layers. The foundation model layer. What you want is a model that is exceptional at tool calling, whether it has built in tools or whether you register them yourself. This is a sort of silent war that has been going on. If you talk to devs, what are you optimizing for tool calling quality.

Why?

Because to demystify the word agent, what an agent is, it's a loop of tool calling that builds up context over time. That's all an agent is. So let's to give you an example concretely of B0. B0 is becoming more and more agentic over time. One of the things that it can do is it can take a screenshot of the thing that's building and reflect on it. So today I live coded to an audience of Web3 and crypto engineers and I told V0, hey, make this dark mode. And initially V0 does me dirty because it changes some things with dark mode. And then it kind of astonished me because I was like, oh, I have to explain to this audience. It then takes a screenshot, looks at it and keeps fixing it. I was like, this is literally a developer that's alive on autopilot and the reason it's in autopilot is because it has access to these tools like looking at the web browser. Another one is research. I've. I've coded an example of Build me a substack clone for cryptocurrency News and the agent didn't know what the cryptocurrency news were. So he started doing research on the Internet of okay, Ethereum, passed certain price and whatever and then you're talking about this tools over the Internet. So to demystify another topic, MCP is really exciting because it's a new protocol for registering tools that your agent doesn't locally have. So those tools that I just talked about, we gave them to V0. Here's a deep research tool, here's the screenshotting tool and those will likely become the new services. When you think about like AWS of today, if AWS was an AI cloud, which is kind of what we're trying to build at Vercel, you think a lot of those tools are going to become as a service, like bring me deep research as a service, bring me browsing and screenshotting as a service and so on. But then you have MCP which allows you to. Okay, I need to sell something online. All right, so now there's an MCP for Shopify, now there's an MCP for Stripe, there's even crypto mcp. So it's really exciting now. It's like the ultimate choice for a builder and you don't have to go and learn all these things. You don't have to. This is almost like a discontinuity of the valley trend of like if we build amazing documentation, they will come. This is more. So if the agent picks you, they will come.

Right.

And so there's a lot of figuring out right now, like how do I make my infrastructure, how do I make my product to be loved by these agents? And MCP promises to be one of these first things that you are in control of.

That makes a ton of sense.

Last question. Someone on your team named Josh is in the chat. He wants to know what does he need to do to get a Twitter badge.

Well, yeah, 100k downloads of the AICLI I think we've been talking down.

Thank you.

It's on. Right?

Got your work cut out for you.

It's burned into the immutable record of the this live stream and the future training runs.

Best of luck, Josh.

Holding you accountable.

Now we're going to hold you accountable to that. Guillermo, great seeing you.

Great to see you.

We'll talk to you soon. Congratulations.

Talk soon.

Let me tell you about Fin AI, the number one AI agent for customer service. Number one in performance benchmarks, number one in competitive bake offs, number one in G2, number one in having an Irish founder.

That's right.

And we will invite our next guest to the stream from Factory AI. Welcome to the stream. How are you doing? Good to see you. Hey, how's it going? Glad to be here. Great, thanks so much. Kick us off with an introduction on you and the company.

Yeah.

My name is Eno, Co founder CTO

at Factory, we are building a platform

for enterprise software developers to perform what

we call agent driven software development.

So basically more than just code, bringing

agents into every stage of the software development life cycle.

So think coding, code review, maintenance, incident response, documentation.

We think agents should be a part of all of this and we think

that they should be driving a lot

of that menial component. While you think at the high level about how to plan and structure the

work, there's so many different. Like Enterprise is a narrow category. It's a, you know. Oh, not consumer I guess, but it's such a wide, it's such A wide category. Is there beachhead? Is there a certain type of project within, within different industries or specific industry that's getting especially an especially large amount of value at a factory these days?

Yeah, totally.

I think that one thing that we

see a lot, and typically when we say enterprise, we're thinking greater than 1,000 engineers, right?

Like 2,000, 3,000. And one reason why we focus on that larger scale, you tend to have

these, these large organizations where people are. The bottleneck is not code.

Right.

The bottleneck is how do we plan

a migration of 185 code bases to this new framework?

And there are 3,000 developers that are going to touch this over the next six months. And an SI just told us the quote is $80 million to do it.

And we have to figure out how to. Replatforming broadly is one of the major, major tasks for many, many enterprises. Right?

100% modernization and migration is huge.

Yeah, yeah, that makes a lot of sense.

How do you estimate the market, that market size? And is that what you guys are leading with on the GTM side in terms of trying to find these legacy companies that are maybe not even using cursor yet? I mean, we Talked to the CEO of GitHub yesterday and what, 50% didn't he say? Or it was like at least half of their user base is not using any AI tools.

Yeah, yeah, totally. I think that the thing that we

hear often, we pretty much only deploy

into companies that have already tried an AI native IDE or have an autocomplete tool deployed.

And I think that the thing that

we hear often is you sort of

hear like these numbers thrown around like 5x10x. And then in practice, when you adopt

an AI IDE, you see 10%, 15%.

And so a lot of people are

sort of saying like, what is the delta there? Like, what causes that transition?

And our sort of argument here is

that there is a workflow change that's actually required to really adopt agents in the lifecycle.

Right.

And so if you're just sort of

like accelerating an individual developer, that you can go a little bit faster, but if you are able to parallelize and

automate at scale, that is going to be that larger introduction of change.

And so if you imagine the market

here, there are companies where, you know,

5 or 10% of global payment transactions run on some COBOL system that was

written 40 years ago.

Every developer is gone and they are. It's a ticking time bomb. Like at some point that needs to

go to Java, but there's nobody who

even knows how to do that.

And so those are the types of

projects where the market is so enormous because you know, half the business runs

on this legacy system. Hundreds of billions of dollars.

Put it all in Lisp, skip Java, go straight to Lisp.

Exactly.

Python. Right, yeah, that would be the logical one. I'm sorry, we're running behind, so we're going to have to cut this short. But I want to know, know more about how the enterprise coding agent market will develop. We could see one world where we wind up with, you know, gcp, Azure, aws, like, you know, pretty comparable competitive. They've all had really great margins. It's been this oligopoly. There's another world where you could see more specialization. One of these companies goes deep into high security environments or oil and gas or financial environments or specializing based on specific programming languages as, as the market develops. Like how do you think it'll play out?

Yeah, great question.

I think that what's very clear is that the bulk of very large enterprise has a lot of similar problems. Refactors, migrations, modernization.

So a platform like Factory is able to deploy into that and solve problems, problems quickly.

I think that there's likely to be

like that sort of 80, 20 where

there are going to be these very

specialized providers that only focus on one sort of problem and that will represent

maybe like 20% of what's out there. And so it won't be like necessarily black or white. But we do think that the bulk of enterprises have a lot of similar

needs, especially when you just get across

a certain threshold of number of engineers,

scale of code base. Sure, sure, yeah. I mean we even see that with the, the clouds where, you know, obviously there's the hyperscalers, but then there are Neo clouds and we talked to Armada where they'll send you a shipping container with a bunch of racks inside and put it in stranded energy. So there will obviously be a long tail here. That's a great take. Thank you so much for stopping by. Have a great rest of your day and enjoy the GPT5 upgrade. We'll talk to you soon.

Have fun out there.

Really quickly, let me tell you about customer relationship Magic ADEO is the AI native CRM that builds scales and grows your company to the next level. And we will be joined by our next guest from Augment. Welcome to the Stream. How are you doing, Guy?

Great, thanks so much for having me.

And that's his name by the way. If you're listening, his name is Guy. I'm not just calling him Guy. Anyway, please Introduce yourself. And what do you do? What does your company do? Yeah, so I'm Guy Gharari from Augment Code.

I'm a co founder and the chief scientist and we build AI coding assistance

for large teams with large code bases.

And so you can use Augment code to do question answering, to do development, to do refactoring, to do migrations, all the tasks that you do except that our product understands your large code base really well.

And so that means less prompting for you and faster and better results out of the agent.

Today, GPT5 launches. It's kind of a rising tide. Feels like it lifts all boats. Every company gets access to it. We've interviewed a number of companies that are building on top of GPT 5.

GPT 4.

5.

Yes. But in general, how do you think you can use GPT5? Are there any pockets of value that you think you can uniquely take advantage of?

Yeah, great question.

So we've been trialing the model for the past few weeks and what we

found is that the GPT5 is a very thoughtful model.

It likes to make a lot of

tool calls, it likes to ask clarifying questions of the user before starting to make code changes. And so the place where I reach out for GPT5 is typically if I need to make large changes or if

I'm trying to answer a very difficult

question about the code base, I will let GPT5 take a crack at it.

It will churn for a while, making lots of tool calls, just making sure

it got it right and probably find all the different places in the code where it actually needs to make a change.

And so I will typically let it run in the background and come back to it. And I will often get a high

quality result out of it.

Are there any features or integrations that you're hoping GPT5 will roll out in the future? We talked to a couple people who were like, we want models that have access to as many tools as possible possible. And you can see with the MCP boom, more people are trying to make their services, their products, accessible to these models. Is there anything that you see as potential low hanging fruit to just add to the capabilities? So I think for us, we work

hard on developing our own integrations and our own tools, building them into the

product, rather than relying on on GPT5

or other model vendors to do so.

We have worked closely with OpenAI to improve the prompting around our tools so

that the agent kind of works flawlessly. I think the thing that would be very nice, I think one of the

Previous guests mentioned screenshot tool.

I think that's a very.

Yeah, that's a very nice way to close the loop on front end software development.

Just like we saw how on back

end software development running that tests automatically

really helps the agent iterate until it

gets to working code.

So I think having more support for

screenshotting and things like that that close the front end gap would be very nice to see.

I wasn't aware that screenshots weren't flowing through. I feel like when I've triggered Operator I'm getting a view, a web view into the website but, but I wasn't aware that that wasn't like being passed through easily in the API. And you still kind of needed to build that yourself. Where else? We were just talking about this. Where are the biggest pockets of value right now for AI coding tools generally? Obviously everyone knows the vibe coder who's just the designer who's learning how to use software for the first time. Then there's the experienced developer going from a 10x to 100x with better code completion. Then there's the enterprise that's maybe doing re platforming. Where else are the interesting pockets of value that are maybe on the horizon to be unlocked with new models? Yeah, so on top of everything you

mentioned, certainly the inner loop of software development, that's where we've spent most of our time at Augment Co developing product for. Yes, you can have a senior developer starting using agents, starting to use multiple

agents in Parallel and unlock 10x or more productivity gains.

What we're starting to see now with our tools is the beginning of automating

software development lifecycle tasks.

So with Augment code we have a CLI tool now where you can take

the full power of our context engine and the agent, the thing that really understands your code base and you can start automating tasks in the background.

And so we're seeing more and more

developers saying oh this is great, I can can break out of the IDE. Now I'm using the agent that's already familiar to me.

But I'm starting to automate code reviews.

I'm starting to automate incident response. I'm starting to automate looking at production logs and automatically assigning tickets based on error logs that I'm seeing all kinds of new automation use cases that we're seeing. Just because agents have gotten so good and kind of really understands your code

base, are there high stakes pockets of of software engineering work that most of the AI tooling has kind of stayed away from? I'm Imagining like the high stakes database migration. Where is the kind of sticky part of the industry? I was reading a blog post by someone who's doing like very advanced cybersecurity pen testing and they were saying, like, just the creativity of the models wasn't quite there yet. To really come up with the. To really act and embody like a white hat hacker who is going for a bug bounty. But where, where are the pockets of still like intractability where I guess if you are, you know, in the, in the individual contributor you love just, just, you know, coding from scratch, that's where you want to stay for at least the next couple of months.

Yeah. I think still the attention of all the models we've seen and all the

agents we've seen around making proper design

and architecture decisions, that's still high stakes and still the ability is not there.

Because if you do complete vibe coding

and you just let the agent go and do whatever it wants in the

beginning, it looks amazing, the code works and it's all really good.

But once you get to low tens

of thousands of lines, the bad decisions that were often made around the design and architecture start to show up and development slows down.

So that's where we still see a

limitation of today's agents and where you still have to supervise the agent fairly

closely in order to make sure that

you don't get stuck later on.

Perhaps this will change in a year,

but today I would say all these

decisions that you make around how the code is structured still requires close supervision and still high stakes because it can really slow your project down if you

let it go autonomously for long enough. Yeah, that makes sense. Well, thank you so much for stopping by. We will talk to you soon. Have a good rest of your day.

Thanks so much. Cheers.

Let's check in with Tyler on the timeline. Tyler's manning the timeline. How are the vibes? Are there any new posts that have hit the timeline? Are we still in turmoil or has the narrative settled?

I think vibes are picking up a little bit. You're starting to see people post like, oh, this is something I made. Now you can see on LM arena, it's number one.

No way. Wait, wait. What's going on with the Poly Market then?

So Poly Market is still.

Still Google heavy?

Yeah, I think, I guess they're just pricing in Gemini 3.

Oh, okay. Everyone's.

I'm not exactly sure. Honestly, I was actually very surprised to see that it was one.

Yeah, yeah.

But yeah, maybe later we can show some of the posts.

Yeah, yeah. Yeah, yeah, cool stuff. Well, in the meantime, before our next guest, let's tell you about 8 paid sleep, get a pod 5, 5 year warranty, 30 night risk, free trial, free returns and free shipping. And we will have our next guest join us from Code Rabbit. How are you doing? Good to meet you.

I'm good.

Good to meet you as well.

Thanks for having me here.

What's your reaction to GPT5? How long have you been playing with it? What are the biggest improvements that you've noticed?

Yeah, I would say mind blowing, right? I mean we have been playing, our team has been playing for like a few weeks now, tested a few snapshots and it's amazing. It's a generational leap, we would say. Like we have been using OpenAI models. Like I don't know how much you know about code. It's been like a couple of years

we have been on OpenAI anthropic and

our product is a very reasoning heavy product. Like one of the very few use

cases where you have a PhD style problem.

Say we have to do code reviews.

That's what Code Rabbit does.

Like users open a pull request. Our agent uses reasoning models to find

issues like race conditions or security issues and so on.

So we've been testing GPT5 on some of the hardest pull requests we have

in our golden data set.

So we maintain a data set where

we track progress of different models and

progress of AI in general.

So we have like many problems that

no model is able to use solve so far. Like I mean even GPT5, but so

far it has the highest score.

We would say it's like almost 2x better than the next O3 or Sonnet

or Opus at this time. What's the customer value there? You think that like all the customers just notice that the product gets better. Are you going to upsell folks? Are you going to like, how do you play this? Given that this model is now in public availability, every company, every competitor can access it as well.

Yeah, there's no upsell.

That's the thing with AI, for the

saved price or even like better prices,

you're getting much more AI, much better AI.

That's the whole idea, how fast this space is evolving. So yeah, from the pricing point of view, we don't see this to be a separate plan or something in our product. I mean for the same price per month, customers will now just get better quality of results. With Code Rabbit,

what's next for the business? What kind of customers are you going after? Who do you think has been on the fence. And this release is going to be the thing that gets them to actually jump into the world of AI.

Yeah, we can track the top line metric. Like one of the things we track very closely in the company is like how many signups to the paid customers we get. And that number has been constantly improving since GPT4.

GPT4 turbo. GPT4 actually dipped.

So there was a time when GPT4

was almost like a Windows Vista of Elysias.

It's funny how we kind of trusted the evals and we thought it's the same model but it was inferior in many ways.

Then we saw a huge improvement after O1 came out.

O1 Preview was a game changer for us.

Even at that time our conversion doubled actually.

So we went more close to 30%

success in getting the paid users. And now with GPT5, we're hoping we

can see another big jump in the

number of people who start becoming paid

customers and how many people churn.

So those are the real numbers.

One is like vibes, like how people respond to the model and we get angry tweets or not.

I mean that's the other part.

But the other thing is the actual revenues.

Whether it moves the needle.

For us that means to be seen.

One of the things we have seen,

even though you test these models in

a lab, it's not like a huge data set. But once you actually are in the wild, you see hallucination.

Some of those scale issues at scale pop up.

So those are something we'll still be observing over the next few days to

see whether it's like spot only like

80% of the cases. But then if the false positive rate

of the hallucinations are too high then

also it's not a great model.

But that remains to be seen. Yep, that makes a lot of sense. Well, thank you so much for stopping by. Congratulations on a new new tool in the tool chest.

New toy.

We will talk to you soon. Have a great rest of your day.

Cheers.

Goodbye. And let me tell you about public.com investing for those who take it seriously. They got multi asset investing industry leading yields. They're trusted by millions.

Millions.

The chat is going wild about public trading the SPX 6900. I think that comes from someone talking about like the non Mag 7 stocks or something. There's been people benchmarking the Mag 7 versus the the big news while we

were live or earlier today Trump signed an executive order that is opening up 401ks to digital assets and private equity.

What's crypto doing Is it ripping?

Bitcoin is up a couple points last time.

This point, you know, where's it going to go? It's already so high. I mean, it's just like there's been so many cattle.

It could go up, it could go down.

Yep.

We'll have to wait and see.

Tyler, anything else notable from the timeline? What have people built? I see this GPT5. Just one shot at a Minecraft clone.

Yeah, I think that's one of the cooler things I've seen.

Okay, so basically, so it wrote. It wrote code to generate this game. It's not generating the pixels. You can do so many different things. Like you could generate. You could. You generate a video, you generate a world model, generate code that generates a game engine. You could generate code that runs on Unreal Engine.

I don't even know what they're using in. On actual chatgpt in. There's like a native. Like, it's like a music player. It's almost like GarageBand. You can say like, if you prompt to like build. I saw a Sam Alwin tweet about this. You prompt like to do some kind of like, beat or something. It'll like make an interactive, like GarageBand

almost interface in there. That's cool.

I was playing with that earlier.

Yeah. I do wonder how many of these features that we're seeing. Like, where does OpenAI want to keep things in the B2B world and let other companies build versus just build it as a consumer app? Like, yeah, like, will ChatGPT eventually just let me push a website? Like, will it. Will it become a vibe coding platform? At least like a basic one? Like, it's not the most advanced coding environment, but it can definitely write some code and execute it for you and do some stuff.

Yeah, well, it's funny because, like, it used to be you would have a like, so there was like GBT 3.5 or something and people on top of that built a vibe coding thing. You could use that to build your own vibe coding thing. Yeah, but now you can just go straight from ChatGPT to build your vibe coding platform. Yeah, but soon maybe it'll just be the vibe coding platform.

The surface area of this stuff is. Is very interesting. Clearly they're going after healthcare and therapy. It's interesting that they've kind of stayed away from legal even. Maybe that's just the dynamic of the sales process and the dynamic of that particular market. But I mean, increasingly you can just ask more and more questions of ChatGPT. So the consumer to business, like bleed Over. There's certainly a world where just giving everyone in your organization ChatGPT is a substitute for a bunch of different SaaS products. So be interesting to see where that developed. What are you thinking about?

Nir says there are concerns that the number used to represent our AI's intelligence does not in fact represent its intelligence. Worry not. To address these allegations we've added three new numbers

near. Yeah, Nir's building something that's like not particularly benchmarkable. Right? Isn't it? Companion.

It's beyond benchmarks.

Beyond benchmarks. Well, well. In completely other news, Anduril opens a Taiwan office and begin selling AI powered attack drones to Taiwan. Palmer Luckey has said he wants to turn Taiwan into a prickly porcupine. We're in the age of spiky intelligence. That spiky intelligence will be onboarded onto the AI powered attack drones and deployed in Taiwan to keep it safe. What else is going on in the timeline while we wait for our Next guest from OpenAI to join?

Spores says raise your hand if you were not automated today. I'll raise my hand.

I was not automated today.

Not yet.

We made it through. Sebastian Buck says here at OpenAI we've crashed. We've cracked pre training, then reasoning and now we're experimenting with new set of techniques that maximally leverage their interaction. GPT5 is just the first step in this direction. We're excited to incredibly excited to see where scaling this up will lead us and it's the unicorn test I believe. And the latest unicorn is really really good. That is a creative interpretation. I think it has to draw this with like SVGs. Anyway we can talk to our next guest about it.

Last post. Jira tickets says I went to the permanent underclass party and everyone knew you.

Anyway, back to the serious interviews. Welcome to the stream. Max, good to see you. How are you doing?

What's happening?

Nice to meet you guys. Yeah, doing well. It's a relief to have this launch out in the world. I think it's, you know, we've been working on this for the last few months now and it's exciting to let the whole world see what we've had.

Yeah, just a few months.

I don't know, it's been a little while.

What's the actual launch day like? Because you're actually getting this out into the world. The GPUs are on fire or about to be on fire. Warming up. But is that out of your purview? There is a different team for that, fortunately.

Right. So I ran a lot of the research for GPT5, I don't necessarily handle the deployment, but I do get dragged in when the GPUs are on fire. I think we're moderately burning right now.

Okay.

Like a two alarm fire?

Yeah, yeah, yeah. Is it materially different? I mean, this is a launch day, but we'll probably discover like the Studio Ghibli capability once it gets out into the long tail of like, you know, hundreds of millions of people try it, someone comes out with some genius thing, then everyone's doing that and then the GPUs. Because I feel like the Studio Ghibli thing happened like a few days after the launch of images in ChatGPT.

It did.

It was pretty fast, but within about a week. I think in this case we're gonna, we're gonna see that here.

Okay.

I think coding, you know, if I had to take my bets for what the Studio Ghibli thing is gonna be, it's, it's coding. That's the place where I think GPT5 is most tangibly a huge leap ahead of GPT4 and ahead of O3.

Do you think there's a chance that the coding will mean a Studio Ghibli style meme or kind of like. And what I mean by that is that image generation is incredibly valuable in the context of Hollywood will be using AI to Chroma key and Rotoscope in a professional environment. But what was special about Studio Ghibli was that anyone was making these custom images. And I could imagine a world where you know, even going from like the levels IO example of like I Vibe coded a flight simulator. If we wind up in a Studio Ghibli moment for coding, I would imagine it's like everyone built their own game today.

I think that's pretty much it. Yeah.

Okay, so I, I don't know if

you guys watched live. That was one of the things we had on the live stream. You can just go into ChatGPT. If you try it right now, it might or might not work because the 5 rollout is still ongoing. But if you have 5, you can just tell it basically make me a game and it will make it and you can actually play it in ChatGPT.

That's amazing.

Is there the ability to discover that the thing is like with Studio Ghibli for Ghibli, you don't have to know how to draw to make it work. For this one, you don't have to know how to code.

Yes.

But can you share that chat and someone else can play the same game. How does the kind of sharing mechanism.

Yeah, you can do the share link. We're I think going to try to make sharing for these a lot better over the next few days. That was P2 after the P1 and P0 of making the GPUs not completely melt.

Yeah, yeah, yeah.

But yeah, we will try to make it much more terrible.

Yeah, yeah. I mean the Studio Ghibli thing is so interesting because it's not just that the model capability was there, but it's also like the prompt was two words and it was so reliable that you always got a good result and you could personalize it. So even if it wasn't, I've seen people build Doom. I've seen people, you can just buy Doom. It's a real game, you can build it. But if you build it and I'm like, oh, that's cool. He did it in a vibe code environment or in ChatGPT, like that's awesome. But I don't necessarily want to go do that for myself. But as soon as it becomes personal, which is what the studio like, I had to see what I looked like as Studio Ghibli. I had to see what my favorite photo looked like, my favorite meme looked like in Studio Ghibli. And once that happens with games, people will eventually, you know, there'll be this like memetic explosion and you'll see the GPUs will truly be on fire.

Yeah, I mean, I think even today you could probably with GPT5, do doom, but all of the characters are like, all the enemies are headshots of your friends.

Like here we're going to be real close. Yeah, we're real close. It's going to be something that's personal, something that, you know, you can express your own creativity through. Because I think people, they still latch on to that. They don't just want, you know, a copy of what already exists. They want something new. And the Studio Ghibli moment was just new enough. Anyway, we should talk about actual research. We should talk about post training. What's the thing you're most proud of? Like, what can you give us on without immediately getting post coach, what can you give us on the actual innovation that went into GPT5 from a post training perspective? What are the kind of keywords and paths in the tech tree that we should be digging into over the next few years to understand how this works?

I would say the thing that is most impressive to me about GPT5 is how much Getting all of the details right matters when I look at GPT5. We had an early version of this thing a while ago that was kind of okay, but clearly did not meet our bar for revolutionary. And we were trying to figure out why is that not as good as it should be. The team basically just went off and did a deep dive over a couple of months of just completely rebuilding the post training stack for this model. And turns out that when you do that, you get what would have taken another order of magnitude worth of pre training improvements to produce.

How much are you thinking in pros training, in research about. Let's forget the benchmarks and just focus on user satisfaction. Like NPS score basically, or like user minutes or any of these other. The real benchmarks.

Yeah, the intangibles profit revenue people feel

when they're using it. Yeah. The feeling and the joy and the actual value that's delivered. Because Studio Ghibli was a delightful moment. It wasn't a benchmark.

Yeah, I think so. That was something that we took very seriously for GPT5. It's like look at what people are actually doing with ChatGPT and look at where the model is failing them either in the sense that the model is, like you said, it's not enjoyable to use. We did, I think, make a lot of progress on that. GPT5 is much more engaging than our previous really smart models like O3. I don't know if you guys talked to O3 in the past, it's a bit bland. And GBD5 I think has a lot more character, is a lot more interesting. But then also I think we really care about just actually being accurate. If a user is trying to do something economically valuable with our model, we want to make sure it lands correctly. So what we did there is just look at the actual distributions of what people are doing with our models in the real world, figure out where the models are going wrong wrong, build interventions to target it. And that was where we got, I think the most impressive improvements in GPT5.O3 would just get things wrong and not tell you it wasn't sure it was incorrect. And GPT5 is much, much better about actually being honest when it thinks it

might not know how explicit are all the different pieces of the post training pipeline. You have safety post training, you have stop hallucinating, give me the real facts you have make sure the text, the flavor, the tone is pleasant. There's so many different things to optimize for how much of that is like try and just blend it all up into one thing versus, like, explicit passes. Chunk it out, like split it up. How much can you decompose the problem?

So, you know, my background is in reinforcement learning, and I think when you look at something like this, the magic is in the reward function. It's in what you're actually telling the model to be good at. And so fixing things like hallucinations to a huge extent is essentially a function of just fixing the reward function, actually making it so that the model is reliably penalized for saying something that's false. And if you do that, all of a sudden the model stops saying things that are false. Ditto for safety. On the live stream, Sachi talked a bit about the. The way we've changed safety for this model. And to a huge extent it's just a function of. We're actually putting out a paper today on the new safety stack for this model, and the core insight in that paper is just figure out what you actually want to optimize for, which in our case is helpfulness conditional on not saying something that's actually dangerous or harmful. Write that down, figure out what that means as a reward function and optimize it for it. It's really not magic at all. Again, it's what I said earlier. You've got to get the details right. If at any part of that process you screw it up, the model will be unusable.

What's your current thinking on spiky intelligence? And is there some flywheel that you can get started where you're identifying low points that aren't spiky enough and then you're almost automatically setting up the infrastructure, the eval, to then RL again to create a spike.

I think GPT5 was a preview of what's possible in that respect in the future. Yeah, a step in that direction.

Do you think that there's a world where you get to a place where you're kind of. It's weird because we're not hammering down the nails of the spikes, we're adding spikes, but hammering up the spikes. Weird metaphor that we're stretching a little bit too far. But is there a world where. Where you can be doing post training or just adding capabilities in a more iterative cadence so that as soon as you identify something, the response can be, yeah, we don't need to wait until GPT 6 to fix this, we can just add this capability because, hey, we just found a pocket of users who are trying to do a thing and they're not super happy with the results. And let's Add this capability?

Yeah, I think so. I mean, I think we are going to launch other models between now and GPT6. I think it's relatively common knowledge. But we do update the model in ChatGPT reasonably often.

Yeah, people talk about it all the time.

Yeah, exactly. And I think we are now in a world where we can conceivably update that model and have it get materially better on capabilities too, not just on the personalities. A little bit better than it was before.

Yeah.

Going back to your note on the new paper that I guess you guys are releasing today, when you talk about optimizing for helpfulness is part of that avoiding the model reinforcing? There's times when you want to reinforce and give kind of confidence to the user that they're going down the right thought process and things like that. But then there's a point where it can get too extreme in terms of maybe convincing a user of something that may be totally untrue. Is that what the paper gets at or am I just reading it?

So it's not specifically about this, although I will say we do explicitly train the model to not lead users down bad paths. That's something that I think we've started taking much more seriously over the last few months as we've realized. Sam talked about this a little bit, I think back in May, but ChatGPT is just way more important for people's lives now than it was was a year ago or especially two years ago. And we do have to actually be very cognizant of what effects our models have on users. So yeah, we do very actively train models to not lead users down the right path. Don't fact check me on the releasing today. I know we're releasing it, I believe it is soon, I think it's day. But I've also been in a hole dealing with launch all day.

Yeah, we're not big on fact checks here. We're big on the truth zone, which is just the vibes.

The vibes are we'll be publishing some. Some information about the new safety setup at some point.

That's great.

Yeah, I think a large part of the conversation around safety should be how reliant and how useful the product has become to users and then the new level of care that you have to provide versus a while ago when it was just like people making a cute image or, or generating some text that they were going to use in an email or an internal document and realizing this vector of usage, which is this companion confidant that is becoming so prevalent

Talk to me about post training for big partners, enterprises, government organizations. What is transferring from the research that you're doing to something that can be offered as an enterprise level product?

Yeah. So OpenAI does partner with external companies to do essentially custom post training. That is a thing that we do. And from that perspective, the stuff we do just directly transfers. I'll also say that we've put a lot of work into trying to make our models as general as possible, but to as large an extent as possible. If you want to get really good results from our model, you can do it right on the API just by actually telling the model what you want it to do.

Do.

Yeah.

Right.

Like GBD5 I think, is pretty comfortably our most durable model ever. We've heard a lot of really positive feedback about this, especially from like folks like Cursor.

Yeah. So if I came to you and I was like, I'm an enterprise and I need to generate a lot of Studio Ghibli's, you'd be like, what are you doing? Just prompt it. But what are the examples of companies, organizations? Is it just. Is it just private information, private data sets that aren't available available on the open web, or is it specifically like there is enough data out there, but there's just not the economic incentive for your team to go and RL on Gas Station Bench or whatever we're talking about here? Hypothetically?

I think the answer is both. Yeah, it's definitely both. Because, yeah, we're not going to target, as you said, gas Station Bench, because

what people are doing with ChatGPT.

Not on our own right now. Probably because it's not mostly what people

are doing with chat.

ChatGPT.

Exactly.

If you have some application that's super valuable to you, we can be convinced that it's important. Yeah, it's just not what our users are already trying to do.

What's the state of reward hacking and fighting that in RL environments?

I think we've actually made a lot of progress. There was some discussion of this around O3, that O3 was a little bit deceptive in ways that felt Reward Hacky. And GPT5 is dramatically less deceptive than O3 was.

What's an example of how that would manifest? Do you have a canonical case study?

Yeah, I mean the canonical thing is you ask O3 to write you some code and instead of actually writing some code, it changes some unit tests, it

changes the test case, which is kind of hilarious. It's like one of the funniest things that an AI has ever Done. I understand that it's very bad and it's not what we want, but it is just like. It's kind of cheeky.

In my mind, it's kind of cheeky. It's also like, I feel like if you spend enough time around real software engineers, they do actually do stuff like this pretty often.

I have 100%. I hadn't done that.

I was going to say I also have done that for formal reasons. I won't say that I did it OpenAI.

But back when I.

Well, I definitely did that.

Yeah, of course. Of course. This is natural.

What do you think GPT6 looks like? You guys. You mentioned that you're going to be shipping updates to 5, but what are you most excited about? Where are you most excited about? Going forward.

And just really quickly give us the date that GPT6 launches.

Oh, man.

Hopefully six launches is a complete surprise to everyone. I think that would be ideal.

Like a Beyonce album.

Oh, yeah. Hopefully five just makes it and says, hey, it's ready now if you want to hit.

Yeah, I think that would. That would be a great thing for six. Actually, I would love for six to do all of the launch comms and. And to do the live stream. That would be really great.

Live streaming is. That's the real AGI test for sure.

For sure.

I feel like we're not that far off, actually. I don't know. I mean, video synthesis maybe, but talking through a script for 30 minutes? Come on. Model's got to be able to do that for sure.

Well, yeah, that'll be the next SORA launch or something. We'd love to have you back on, but thank you so much for taking the time today. We'll talk to you soon.

Good to talk to you guys.

Congratulations.

Cheers. Congrats on the launch.

Let me tell you about adquick.com, out of home advertising made easy and measurable. Say goodbye to the headaches of out of home advertising. Only Ad Quick combines technology out of home expertise and data to enable efficient, seamless ad buying across the globe. And we have Scott Woo from Cognition coming in the studio for the fourth, fifth time. I can't keep track anymore. Thank you for taking the time, Scott. Thank you for coming back.

How's it going?

It is fantastic.

Gotta be honest. Great week to be an application letter company. I gotta tell you guys, I was

about to say you get a model Ever another way. Win for Scott Woo. Wow, wow, wow.

4.1.

Yes. So, yeah, how big is this? Are we in the Uber Lyft territory? Where you're going to be in price competition between Anthropic and OpenAI going back and forth. What is the real benefit to your business right now from today?

Yeah, yeah, for sure. So first of all, obviously massive capability gains across the board. I think really, really impressive work that OpenAI has put together. People have talked about, about what's going on in the AI coding model race

and I think by a lot of

accounts, you know, Anthropic has generally been ahead for a lot of the last year, honestly. And I think at this point OpenAI is very clearly, you know, has very clearly caught up and it's pretty neck and neck, I'd say between the two right now and so very exciting to see all of this unfold and to see what's next. But I think from our perspective, yeah, I mean code is just such a, such a core capabilities pill to use case I'll call it. And so being able to work with

smarter and smarter models and do a

lot of the work that we do,

it just means that both Devin and Windsurf can be a lot more capable, a lot more intelligent, can predict what you want to write or what you want to do with a lot higher accuracy.

Yeah, it's almost surprising that given the cultural rigor at cognition that you're not doing fundamental frontier research. So can you walk me through like what is the, what is the focus of being an application layer company? Is it, is it UI go to market? I'm sure it's all of these, but in terms of the, the hardcore software engineering, like what is important to get right at some point? There's fine tuning and post training but, but is that moving back into the purview of the foundation labs or is there still work that you want to do on top of the models or on top of the APIs?

Yeah, it's a great question. I think the core of being an applied lab is really just focusing on a very particular use case, on delivering

real, just very direct results.

And I think, I think the foundation labs are obviously incredible at training based models and all this pre training and all of the work that they do there. I think from our perspective we want to work on a lot of very particular capabilities that apply to software engineering

in particular and then obviously run the

whole stack from there to building a product, figuring out the interface and the UX and then obviously bringing that to market and selling that. On the capability side there's a lot of particular stuff where one way to put it is I think the base IQ is very much Already there in the models and you can see the raw problem solving ability. And we've gotten some pretty insane results,

you know, getting a gold medal at the IMO or all these other things.

Right, you called that, by the way.

Yeah, you called that, I think the

first, you know, I mean, we were one point away to be fair, a year ago. Right. So it was on the way, I'd say. But yeah. So, you know, you can really see the general intelligence improving with every single model generation. On the other hand, for Devin, obviously, you know, it's a very clear like step up in the general intelligence. But also you want to be able to, if you ask Devin to go debug your kubernetes or to go and look into your error logs and figure out what went wrong or things like that, there's often a lot of very specific capabilities. And that's where we find that the post training of the RL is most effective there. And a lot of the various work around the models that turns out to be useful.

What about speed? A lot of people that have gotten access to GPT5, at least in our chat, are reporting that it just feels really, really quick. How is that over time going to impact the. I think a lot of people, if they're using Devin today, task Devin with something and then maybe they go work on something else for a little bit, or they're running multiple agents concurrently. But at some point the agent could get so fast that you're just sort of like watching it work in real time and you actually want to be engaged. But are we there yet? Is it still a ways out? What do you think?

Yeah, it's a great question. I think in general, I think Async will continue on as a paradigm even as the models get faster and faster. One of the reasons that it should, by the way, is because there are a lot of real world thresholds that start to matter. Like at some point you're actually spending less time on token generation in the Devon lifecycle and you're spending more time on. Every time Devin runs the command to go install packages, or Devin running the unit tests, or like Devin pulling up the front end by itself or things like that that obviously take real world time, I think we are honestly getting closer and closer to that threshold. So long story short, I think in the asynchronous mode these things will get faster, we'll see those gains, or we'll be able to spend a lot more time, for example, thinking about a single problem relative to the amount of real world clock time.

That gets spent, I think for the

synchronous use cases is where we'll see things really, you know, exploit with speed, which is, you know, Windsurf and Cascade, for example, where we see the speed gains really, really matter.

Speaking of Windsurf, give us the update on the chat. Wants to know about the Windsurf tea and the 80 hour demand. How have the buyout offers gone? What's the internal response been? Where'd that idea even come from?

Yeah, yeah, look, people are stoked, honestly. And I think from our perspective, it's obviously really important to kind of just unite and get to the point where we can just be one culture and one kind of shared set of values. And this is how things are at cognition. It's a pretty busy time. We are at the inflection point of code and we work like that too. And so I think a lot of it for folks is just kind of

like, you know, we want to make

sure folks who really want to do this with us, you know, make that conscious decision to opt in. And for anyone who doesn't, obviously, we totally understand that there are a lot of talented folks that maybe that's just not the right thing for them right now or, you know, not at this time. And so wanted to make sure that they were well taken care of too.

And to be clear, with the buyout offer, that's on top of the actual acquisition deal that already went through. They already got fully vested.

So yeah, I was thinking of the roller coaster. Coaster. It's like you have the OpenAI deal, then the Google deal, then the Cognition deal and then they're like, wait, these guys work really, really hard. I don't know if I'm cut out for this. And they come back up again where they're like, wait, I can just go take a sabbatical and figure out my next thing. So it's a great outcome.

Yeah, yeah. No, obviously overall is a killer team that's been through a lot and so wanted to make sure that they're well taken care of.

That's fantastic. Anything else you can tell us about the integration of Devon and Windsurf? How are the teams getting along? How do you see the products playing together in the long term? Obviously, Cross Sell seems really obvious. They had the go to market team as well, but how else are you thinking about the interaction? Maybe over the longer term there?

Yeah, yeah, yeah, for sure. Yeah. A lot of obvious integration on the team, as you mentioned, with Cross Sell and so on. I think the thing that's really exciting on Products, which I think actually comes along with these capabilities increases, is as the capabilities keep getting better, you start to take on harder and harder tasks with AI and with full agentic workflows. And I think there's an interesting thing that happens where for a lot of the harder tasks, you really actually do want to go back and forth between a synchronous and an asynchronous mode. And that's for a few reasons. One of the reasons obviously is because there's a lot of review and a lot of looking at the pieces and thinking about the, you know, all the minutiae and the details of what you're implementing. I think another big reason for it is, you know, when you get started on a larger project, you know, let's say you're, you're sitting down as an engineer and you're saying, all right, I'm going to go build this whole project today. You yourself don't actually know all the trade offs you want to make, all the decisions that you want to make and so on. Right. And so having a format where, you know, for the decisions that need you to be there and you're involved, setting the kind of the strategy or figuring out high level what should happen, you're able to do that in a nice synchronous environment, which is naturally the winter of ide. Then for the parts of the task that you can actually hand off and have an agent work on, you're giving that to Devin and figuring out how you go back and forth between those is super interesting. So, wave 12 on the way soon,

hopefully we'll have a lot more to share.

Last question. Yeah, hit the soundboard, Jordi, for that.

Wave 12.

Wave 12. Fantastic. Last question, we'll let you go. What is your probability that AI will get a perfect score on the IMO next year?

Oh, interesting. So, by the way, we just had the ioi, which is the programming version, like the Programming Olympiad, and I think there's a good chance that we'll have

a gold medal at the IOI for

this year announced as well. I think perfect score for next year.

We as in humanity or we as in cognition?

As in humanity. Yes, yes. An A.I.

perfect score.

Yeah, sorry, an A.I. gold medal. Right, Perfect score in the IMO next year. I think it's got to be north of 50. Honestly, I would put it around like 75% or so.

Okay, we'll see.

Well, thank you so much. I will be following it closely and good luck to you and congrats on all the progress. Very fantastic. We'll talk to you soon.

Awesome guys. Thanks for having me.

Bye.

Let me tell you about bezel getbezel.com your bezel concierge is available now to source you any watch on the planet. Seriously, any watch. And we are joined by our next guest, Claire Vaux from Chat prd. Welcome to the stream, Claire, how are you doing?

What's going on?

It's a fun day today, isn't it?

It's a fun day. What was your reaction to the stream? What was your reaction to GPT5?

You know, GPT5? The first thing I said and I got a little early access is I said it's a developer for developers by developer. This thing is built to be a software engineer. You've seen a long string of your guests come on and really speak about the coding abilities of it. And what I think is interesting about this particular model, especially because we're seeing them deprecate the old models in the ChatGPT experience and we're seeing a lot of positive feedback. But I do think there are drawbacks to a model that's so clearly tuned to a developer use case. And as somebody who's building an application that isn't focused on agentic coding, I have noticed some personality quirks that are going to be really interesting to see how they shake out as we roll out this model to our users.

Walk me through those. What are the.

Yeah, it's.

What's the timeline? How much time do you have to kind of move users over to five?

Yeah, yeah. So I mean, I think we have tons of time from the API side to move users. And in fact, our strategy at Chat Purity is not to just upgrade to the latest model. I know Zach at Warp said like why wouldn't you want the latest intelligence? And the reality is, because we're doing a lot of business strategy and business writing. I actually want to validate with our users that they're getting the quality of strategic thinking output writing that they really want. So we actually ab test every single model rollout and really evaluate for user quality, token generation, all those things and, and looking early on, it yaps, man. This thing just wants to go through tokens. Right Now I'm seeing 4 to 10x the number of tokens generated between the four generation models and five. And when you're in a business context, you do not always want longer words and so it'll be really interesting. There it is certainly focused on execution. So I've heard a lot from the OpenAI team. It's steerable. Yes, yes. And Its natural inclination is to drive you towards like how what? Very tactical, very specific. And so if you're trying to zoom back out at a strategic level or focus on a business initiative, it's actually a little harder to tune in that direction. So, you know, I think there's a lot of positive things for me as somebody who uses agentic coding platforms, who writes a lot of code, it's my daily driver now, I love it. But for other use cases, I think it's going to take some time to figure out if it really is optimal in use cases where intelligence actually isn't the differentiating capability.

Yeah, it's very interesting to think the best product manager is not the one that writes the most the longest doc. No.

And you don't send your engineer into your executive meeting. And I, I really am looking forward to the time where we're not getting these number based models where actually I can get like GPT developer or GT GPT strategist, where they're pre tuned and trained for the role they're going to play as opposed to general purpose, but clearly oriented towards a set of tasks. And I just think if you look at this model, it was oriented toward software engineering, at least in my experience.

So have you been tempted to launch any type of agentic coding products? You guys are obviously responsible at ChetPRD, responsible for creating documentation. And if you look at the other guests that have joined today, many of them are competing with each other in different ways and trying to own different parts of the stack. You guys have seemingly stayed really, really laser focused and no one else is doing anything like you're doing, at least on the show today. But talk about picking your lane and kind of optimizing.

Yeah, we're integrated with a lot of those platforms, so a lot of the kind of like prototyping platforms, v0.dev, lovable, all those we integrate. We just released our MCP, so I use CH PRD pretty consistently inside cursor through our mcp. So I think of, we think of ourselves as the product pair to the AI engineer. Now what's really interesting about my experience with GPT5 is the one place it actually does really well is technical specs. And that's a place where chat PRD has sort of bridged into engineering execution. Often our product managers are generating a PRD or some sort of business document. They're actually going the next layer and developing a technical spec. The GPT5 technical specs fed into these agentic coding frameworks or prototyping frameworks output much higher quality assets on that end. So I do almost think there's going to be this kind of like right model for right use case, especially in our kind of business. And so we think of ourselves as integrating. The one thing I have thought about with GPT5 and it's the first one where it feels really simple to just go ahead and roll your own agent encoding framework or prototyping framework inside of our application. So never say never. It's something that we get asked for a lot. But we were, we're friends, we're good friends with almost all your guests on your show today. And so we like, we like the role we play in terms of being the product manager pair to all these AI engineers.

Yeah, that makes sense.

What are you looking for next?

What am I looking for next? I mean, in terms of model capabilities. What I think is really interesting about OpenAI and why I'm really committed to the OpenAI ecosystem, even though I test and use a variety of models, is I think developer support is a real differentiator here. So we spend a lot of time talking about model capabilities. And for application developers, certainly ones that are doing more complex applications like agent to coding, model capabilities really matter. Like core IQ of the model matters. But the other thing that matters, you know, somebody who has built developer tooling products, it's developer experience matters, the primitives in these APIs matters. And so what I'm really pushing the OpenAI team to think about, which is in addition to the core intelligence of the model, what are the developer tools you need around these models to really make them a platform on which a variety of applications can build. And I do think that OpenAI has disproportionately invested in developer experience. But I'm always looking for like, give me better out of the box tooling, give me more control over these models, give me more hosted services. All those things that as an application developer are just going to make it easier to deploy these models of production beyond the core kind of intelligence of the models themselves.

What was Your read on 4.5? Is there a world where, you know, I'm thinking about the product manager versus the engineer? You have your O3 go crunch some really hard reasoning and then you have four or five turn it into stronger prose or like, more, you know, a human language.

Yeah. So I did a lot of experimentation around 4.045 and 4.145 was my favorite prose writer by far. It was, was loved from a business writing perspective. I thought the prose was the most natural. It was really slow, like untenably slow. And so the, the compromise we made in our testing is we ultimately ended up with 41 as the fan favorite for business writing when we were balancing off both quality of prose and intelligence as well as as performance, which for application developers is a real consideration. So I landed on 4:1. 4:1 is the model that's being tested right now against GPT5 in chat purity. And one of the things that I have to go do now is figure out how to get ChatGPT or GPT5 to stop writing. It writes a lot and it only wants to write in bullet points. So I've got to go back into our prompts and figure out how to direct it to be a little bit

more business oriented bullet point maximalist.

It's the new EM dash. I'm telling you, you will not be able to stop seeing it. All it wants to do is write a bullet point and call a tool. I was using it in cursor and it just kept maxing out my tool calls. I'm like, you do not need to read 50 files to do this. I do think application developers are really going to have to think about how they slot this into their current workflows. There's definitely tuning that needs to happen. But I'm telling you, you, you are going to see a lot of bullet points when this thing rolls out.

Yeah, in 60 seconds. Where is product management going? A lot of people talk about the, you know, examples of product managers that are starting to ship code themselves, ship whole features products, but I'm sure those are edge cases to date. But, but where do you feel like it's going based on, on your user base?

Yeah, I mean it's going to go one direction or the other. Product managers are either going to develop the hardware hard skills to do the design, the go to market and the engineering job to some extent. Because some of these other jobs are definitely going away for product managers or my favorite use case. Engineers and designers are going to get tools like chat PRD or these prototyping tools or cursor and they're going to be able to actually do the product management job. And so what I think is we're going to see a new type of role emerge which is a much more generalist role where people maybe have a specialist capability and they're augmenting that product thinking or they're augmenting that technical thinking with AI. But I don't think there's going to be product managers as they were five or 10 years ago for Much longer.

Makes sense. Well, thank you so much for stopping by.

Yeah, great to have you on.

Thanks for having me.

We'll talk soon. Bye.

Cheers.

Up next, we have Brad Lightcap, the Chief operating officer of OpenAI. Welcome to Stream, Brad. Also, George, your post saying, I'm updating my timelines. You now have four years to escape. The permanent underclass has over 4,000 likes.

There we go. Absolutely banging thousand likes for every year.

Love this. Love it. Anyway, Brad. How you doing? Brad?

What's going on, guys?

How are you?

Good to meet.

Congratulations on the launch. What are the biggest takeaways for today from your side? I'd love to know about what it actually means to be the COO of OpenAI. OpenAI does so many different things. Consumer, Internet company, API, business enterprise. There's all sorts of stuff. Building data centers. What is your actual role?

My role is kind of whatever the company needs me to do. I play everything from, like, you know, PM when I need to, to, like, you know, salesperson when I need to. That's kind of the fun part of the job for me on this launch in particular, it was really fun. I spent a lot of time last few weeks with, with customers, with partners getting a feel for GPT5 relative to what they were previously using. In some cases those are OpenAI models, in some cases they were other models. But I've been OpenAI a long time, been in OpenAI seven years. So I've seen GPT3, I've seen GPT4, and then to be able to see GPT5 and just, I think the joy of people being able to use it in production and seeing how much better it is, that's the best part.

Great. Greg told us earlier about the era having to pay people to use the early versions of the product. You guys have come a long way since then.

Yeah, we had like three customers with GPT3 or something like that. And so it was easy to manage, easy to talk to all of them. They actually were like, tired of us calling them, being like, is it good? Is it getting better? And so now it's, you know, we're fortunate that we've got more than that, but it's cool. I mean, the diversity of use cases, I think the number of things that people are able to use it for. We've got everything from the team at Amgen Big Pharma, Life Sciences using it for clinical workflows there. We've got teams at Ubers building it for customer support, teams at Notion and Cursor building it into products that people use every day. So I think that's the power of it. Is it just more and more covers the service area of things people do with these tools.

I'm not sure how much you touch organizational design at OpenAI but. But I'd be interested to hear your thoughts on how those companies that you mentioned should be thinking about AI changing their org structure. Is it sort of like a horizontal cross functional service layer, like a finance team that touches a lot of different elements of the business? Or should most companies be thinking about standing up a dedicated AI implementation team? How do we get a, a chat box on every product that we already ship? Like how do you think about those trade offs if you were talking to a friend at a Fortune 500 company that was thinking about their AI strategy?

Yeah, it's an interesting question. I think it was maybe said earlier on the show. The thing we see is just people can do more. And so there's this much wider latitude that you get if you're an individual person at an individual company where especially as you get bigger, maybe more bureaucratic organizations that have a lot of different functions, a lot of different levels, you have to rely on a lot of other people in the org to get stuff done. You've got to rely on your data science team to do data analysis, you've got to rely on your design team to do mock ups, you've got to rely on your marketing team to do copy. And I think what we see with AI is it just accelerates people to get to a great V1 of everything. So if you're a high agency individual and you want to get stuff done, you're no longer gated on people that you know, you otherwise would be. And I think that should enable organizations to move a lot faster and I think it should enable the people at organizations that really drive them to do a lot more. And we see that consistently. ChatGPT Enterprise, I think that is consistently what we hear and we seek those people out. When we deploy ChatGPT Enterprise, we find those like, you know, two or three people at the organizations who are just the like AI superstars and champions and then try and actually use them as these kind of touch points for the rest of the org to learn from.

How are you personally using AI these days?

You know, my biggest challenge, I think day to day is context switching. If you look at my calendar from like top to bottom, it's like I joke, like I, you know, with my wife, I like have to like show up to work like wearing like a lab coat and then I like take the lab coat off and like put some like sunglasses on and a film school jacket and you know, then I'm talking to like a media company and then I like take that off. So I go through the costume changes and I think what I actually mostly use it for is just to help with bridging me from kind of thing to thing to kind of put me in the mindset of being able to work with customers, help customers. GPT5 is incredibly good at this kind of structured reasoning of how do we actually take what is this very diverse set of things that models like GPT5 can do and then apply them in domains that I don't think about every day. And so it gives me this launching off point to be able to talk with leaders and with customers much more fluently about how we can help their

organizations within, in, let's say a set of companies like the Fortune 500. What does AI adoption look like across the spectrum? Because I'm sure that there's companies that you talk to that are truly adopting AI in the way that John was mentioning, trying to become AI native, changing their entire organizational approach. And then there's companies that just want to buy software to say that they're becoming AI native. So what does that spectrum look like in practice?

Yeah, it is a wide spectrum. So at the top level we're seeing just like amazing appetite for wanting to adopt tools for people. And I think that's like the easiest place to start. Typically that's where we steer organizations, if they're starting at zero, is just give your people the best tools. You may have seen. We've grown ChatGPT work, which is our enterprise and team product, from 3 million seats to 5 million seats now from, from June till till now. So torrid growth there and we don't see any abatement in demand there. If anything, it's accelerated from, from last year. And so I think people and organizations are starting to realize that like, at a minimum, you need to make sure people have the best tools. What's cool about GPT5 now is it also enables people to use the best tools at every point. And so if you're in an organization, you're not fumbling with the model picker, you're not trying to figure out when to use a reasoning model, you're not trying to figure out the art of prompting to get the perfect thing. All of that stuff is abstracted and it's kind of taken care of for you. And you can have confidence that your people are actually using the best models at any given point. Beneath that, it gets a little more complicated. More and more organizations, I think, are starting to grasp how the tools can actually help in the business process. So, so whether that's in customer support, whether it's in research, whether it's in software engineering and data science, you're seeing these tools more and more adopted in the enterprise. I think there's still a quality gap though. I think we now are just breaking into what I would call the kind of era of models that have capabilities that are good enough to make a dent in the types of problems businesses care about. Businesses care a lot about things like reliability. They care about accuracy, they care about the resiliency of the model to recover from tool use errors and to be able to string together these very long kind of multi tool, multi step workflows. So GPT5 is a step on all those things and I expect that that will enable us to be able to do more and more things in the business process.

Do you think those customers that you just mentioned will stick with this idea of like GPT4 level? Workloads will stay on GPT4 and maybe there'll be cost savings, but those workloads will stick around for a very long time and then you'll develop almost new capabilities, new workflows, new workloads that will be additive, but the enterprises will stick. Or will they want to? Is everything so fresh that they'll want to just like rewrite everything with the latest and greatest?

More often than not, I think it's a lottery. You want to rewrite everything. One of the cool things we did here was we were able to keep, keep the pricing on GPT5 at the level of O3 pricing. And so, you know, if, if you're cost sensitive, you don't really have an excuse to, to not upgrade. GPT5 is faster than.03 and 4.1. So we've improved on latency for sensitive use cases that are speed sensitive, latency sensitive, and obviously the intelligence bar has gone up. And so, you know, unless you've got really a very kind of narrow and specific workflow where you've got a model like 4.1 that kind of is okay, there's really not a reason I think that people wouldn't upgrade.

Yeah. Do we need like a three dimensional Pareto frontier right now that matches not just cost and capability, but also cost, capability and latency or something. Is that something that you're seeing a lot of demand from in the enterprise?

Yeah, 100%. We actually measure it that way. So we look at those three vectors and it's always kind of an optimization function along those three axes. We think we found that here. It was actually in terms of where my work was over the last few weeks. It was a lot of, I mean, there's a qualitative, you know, kind of, you know, really like manual process of collecting feedback because everyone's got a little bit of a different preference and we can only pick kind of one or two points on that curve. And so just trying to kind of dial customer feedback, namely developer feedback in for us on where that, that balance of things are, is a big part of our process for picking, picking all those, all those points. And so we hope that, we hope that people like it and it unlocks the kind of maximal use.

That's great.

Jordy, how are you thinking about open source? Who's been most excited to get access to it and where do you see it going?

Yeah, I mean, it's important to us. I'm glad we've gotten this out. It's been a huge team effort. I think there was kind of a thing that OpenAI doesn't like open source anymore. It's like, no, we're just really busy with a gazillion other things. So I think hopefully going forward we've got more of a leaned in vantage point on open source, but it unlocks a huge number of use cases. I mean, if you think about kind of like, you know, government use cases, you think about on prem use cases where you're handling sensitive data and very sensitive environments, you think about where you want to run models on the edge. All these things right now are kind of inaccessible to us as a service provider to customers because we just don't quite have models that kind of fit at those points. So this for us, we think is huge TAM expansion and we're excited to be able to work with enterprises on implementing that model, which is, I think, competitive hopefully with our O3 class of models.

What is the landscape like for companies that are helping to implement OpenAI products at various enterprises? You have the big consulting groups that will give you an AI strategy. Maybe they'll try to take it a step further. But I imagine, imagine there's a cottage industry of firms that have sprung up to try to help organizations unlock the value beyond. Hey, let's just get everybody a seat with chatgpt work.

Yeah, I think there will be this new industry that emerges that is kind of separate and apart from kind of the legacy set of SIs and consultants that is really AI fluent they're very AI native, I think, I think it's very hard to borrow, I think paradigms from the last 20 years of software building and implementation that are going to kind of map to what we're dealing with here. You're dealing with fundamentally probabilistic systems that are moving and increasing and improving at a rate of now kind of collapsing to every few months. And I think the nature of use cases changes quickly. Where enterprises are focused on kind of deploying them changes quickly. And so I think it's just hard for kind of the legacy industries to keep up, frankly. We've had a lot of success working with some of this kind of new breed of si. So the distills of the world and others that really have been born, I think in, forged in the Fire, so to speak, of, of this kind of new, this new platform. And so we hope there's more of them. We'd be excited to work with anyone that, that wants to work with us on it. There's more business than we can handle and so, so we're always happy to spread the love.

Talk about the $1 ChatGPT product for the government, were you involved in that at all?

I was involved in that. We wanted to do something that was meaningful for US government. It's been a real big focus of ours lately. I think our view is the government has got to start to modernize. We've got to make sure that the tools that we use in the private sector are also in the hands of folks serving us in the public sector. And we wanted to make that really simple. So we made ChatGPT, you know, basically equivalent to ChatGPT Enterprise Free. It's a dollar per year per agency. Hopefully we can afford that. And we wanted to make that, you know, available to anyone that wanted to use it and standardize through gsa. So we're super appreciative of the partnership with them and more I think that we can do on that front.

How is that different than just like if I'm a government employee, I can just go to google.com, and I have access to that.

And Google Scott Kapoor was saying that

he can't use ChatGPT. Yeah. So yeah.

Why?

Yeah, just, just talk to me about how, how it's different to, to offer ChatGPT as an actual service with a contract that you're, that you're, you know, vending in you're actually they are a client versus just if you put up a website, every government employee can access the web to some degree or, or Would it be blocked? Why does it need to be a deal at all as opposed to just everyone just uses it?

Yeah. So part of it is just making sure that government employees can access it. So in some places, obviously, you can put blockers in place that would prevent access. We hear a lot of stories by the way of people going out on their lunch break to their car in the parking lot and pulling up chatgpt on their phone and throwing a bunch of stuff in there just because they know it'll get them through the day faster. And we've done work, by the way, with governments, with the state of Pennsylvania and other places where we've seen dramatic increases. You know, things like two to three hours a day saved per employee, given the nature of the work that they do and how helpful ChatGPT can be. And so this lets us have an interface into them as a customer. It lets our team engage with them in a direct way. We can see how they're using the product and can help them use it better. And so that's important for us is like, we got to build on that foundation with them.

And then presumably it also allows the government to define security and privacy in their world as opposed to. If you're just like some website out there, their choice is only block or don't block, as opposed to actually communicate with you. This is okay to train on. This is not. Et cetera, keep everything private, et cetera, et cetera.

Yeah, I mean, we don't train on enterprise data at all. Yeah, you're safe there. But for us, just being able to. To treat them as a customer.

Right.

To treat them as a user. And you go, you know, you mentioned earlier, like we were talking about kind of like there being these points of success at every organization that, you know, you've got people who are, like, way more sophisticated in using these tools than others. We want to be able to see those people and amplify them. And the government's no different. There are people that we've worked with in government who are incredibly sophisticated in how they use AI tools. And our goal is to get everyone there.

How do you think about the group of users that are active students? They've been on summer break. You guys have been busy over summer. Are you thinking about. And you recently launched. I forget the exact name for the product. I think it was like chatgpt Learning. How are you thinking about that cohort and unlocking new capabilities for them this coming year?

Yeah, so we launched something called Study Mode, which was in our core ChatGPT product. And it was a little bit of an experiment. We wanted to see if you change the way the model behaves when it can kind of when it knows you want to be in a learning mode, if that can actually enhance outcomes for students. Where we, we have all these kind of studies that have been done very like anecdotally about ChatGPT's ability to. To drive student outcomes and learning outcomes. So here we kind of took a little bit more of an intentional approach of if you actually model the. Take the model and actually use it in a more Socratic style where it can actually kind of quiz you, it can withhold certain information that it wants you to be able to. Able to empirically deduce, it wants you to reason about problems, and it kind of reasons with you as a partner. So far, so good. It's really cool. Learning is kind of the killer use case of ChatGPT. And so I think to be able to actually launch something that in some sense extends that kind of killer use case has been really cool. And the student feedback so far, even on summer break, has been positive.

Well, we'll let you get back to your day. What's next on your agenda? Are you putting on the lab coat or the suit and tie and going to Washington?

Good question. You know, today I'm mostly with the team and talking to customers, and maybe tomorrow I'll get back to the lab coat.

But in the meantime, appreciate you taking

the time to talk about today.

Yeah, well, thank you so much for taking the time to talk to us.

Great to see you.

We will talk to you soon. Have a great rest of your day. And the timeline has been in turmoil because President Trump says he will be a public imposing a 100% tariff on all semiconductors coming into the United States. It started with widespread tariffs on chips and then turned into export controls. This is from the Kobiesi letter. Is this a red flag moment? I don't know why we have the red flag.

It felt like Ben was getting the flag

potentially affected. But Taiwan says TSMC exempt from Trump's 100% chip tariff. Very unclear. The story is obviously still developing. And Dylan Mitic says you're telling me that this level of monitoring, the situation is free and it's a picture of you in front of the whiteboard monitoring the ChatGPT versus the timeline we're monitoring. Illinois has banned AI therapy, making it the first state to regulate the use of AI in mental health health services. Interesting headline that's coming out.

Just interesting because the product can just be used for therapy. Like the user can choose to do that. It's not necessarily, it's kind of hard to ban outright. Like maybe you can ban it in a clinical setting.

Yep. I wonder how they define this. There's probably a loophole if I know anything about how these bans are implemented. But yeah, maybe it's like, like if you're, if you're in the clinical setting, you can't be, you can't use it, but then people will just use it independently.

Therapists just on their phone, they're going

to be going to the car, they're

going to be having, they're going to know, they're just going to have it. Listening to the conversation, they're going to be like, what should I do right now? What should I say?

What should I say? How does that make you feel? That's what it's going to tell you. Celsius nearly doubles revenue year over year. This is the energy drink revenue of 700 million versus 632 million consensus. North America grew 87%. International grew 27%. But here's the real kicker. Aulani new acquisition is the primary driver of growth. A lot of New added $300 million in revenue and retail sales are up. So wow, what performance. But yeah, I mean that was the expectation when they bought Aulani News that they would, I guess this is like the first moment they, they rolled them in probably but huge growth for Celsius as they become multi product, multi consumer company. What else is going on in the timeline? We have one last guest. I think you might have to hop on with Taipei so feel free to jump when you need to. Tyler, anything going on on the timeline we should be monitoring. We are of course monitoring the situation.

I've been. So when Max was on he was talking about how you can make a little game. So I've been working on a bloons tower defense game.

Okay, how's it going?

So it's going pretty well. I'm making another change but then maybe I can screen record and do a little share.

Yeah, that'd be great. You could share with the folks too. I like this post from Ray Sullivan. These GPT5 numbers are insane. And it's a chart of GPT version versus number and then once it gets to 4 it goes 4.1, 4.2, 4.3, 4.4, 4.5. So the, the fifth one is a massive, massive bar.

We need an analysis of the charts from today. It seems like there was multiple that

were kind of odd or hallucinated or Off.

It's interesting that multiple of them snuck

up just in sheets. The popular convenience store chain with 750 locations is now offering 50% off purchases paid with Bitcoin and crypto daily from 3 to 7pm What a wild move by sheets.

Well, well.

Hylac is in the waiting. Waiting room. Let's bring him in.

Let's bring in Ben Hilak. How you doing? Good to see you doing well.

How are you guys doing?

We're doing well. I'm just going to say hello. I got to take off and talk with Taipei. I'm going to let John take it from here.

Absolutely. I'll close out the show.

You guys have a fantastic, fantastic conversation.

Give me the update. How's the day been for you? What were your expectations? Did this meet, succeed? Did it underwhelm you? How are you doing?

Well, so I've actually had access for a couple weeks. So we actually did a video. I'm not sure if you've seen it, but OpenAI brought a couple of folks from the Twitter sphere to their office a couple weeks ago to try it some other. Yeah, yeah, yeah.

Yep, yep, yep.

I think that it pretty much exactly meets my expectation as far as like how it's been received. And I've tweeted about this as well. But I think that it's really, really good at one shotting things. I think it's better than I think other models we've seen, but I think it's actually sort of a distraction in a lot of ways. I think that the things it's a lot better at are a, a lot harder to describe and B, I don't think the harnesses for it really exist yet, I think harnesses. So the way I've been describing it is that I think I've seen web search existed in ChatGPT for a really long time. Right. It was able to call a tool search the web. Obviously deep research was very different than that. Right. What we saw was it was actually like calling, you know, searching the web. It was like reasoning about those results, changing its kind of course, like course correcting the middle. So like intermediate reasoning is like what is the. Is the term for it and they really trained it how to search the web. Well, I think GPT5 does that for like a whole plethora of tools. The interesting thing is that a lot of products, like I think a lot of the agentic products that exist today were kind of built wrong. Wrong. Like they weren't built that they didn't build their tools the right way. And We've seen this before. Like, if you look at like, you know, the first, you know, kind of infrastructure for agents was LangChain, like way back when, two or three. Yeah, it was, it was, you know, it was. It was early, but it was wrong. Right. And so like, anybody that, you know, they've iterated since, right. They have like, Lang Graph, which is a better implementation. But the first implementation of LangChain was like, again, early but raw. And so if you built your product on LinkedIn, like, you had to, you know, significantly change it, I think we'll see a similar thing happen for GPT5. You know, it's not just like, you know, change the string and git, you know, from, you know, 40 to 5 or something and push and now, you know. Yeah, yeah.

You know, that meme about like, oh, like, Sam Allman stood on stage and like, just like, you know, killed 75 startups. Google just killed 100 startups. Apple just killed part with their new thing, whatever. Did any of that happen today? It feels like this is like the LangChain needing to change their strategy. That happened a while ago. I haven't identified anything. It feels like, you know, Scott Wu hopped on and said, like, you know, great day to be an application layer company. The foundation models got better. It's more tools in my tool chest. I'm extremely happy and. And I'm more confident than ever. And I believe him. I believe that he was. He doesn't see today as fundamentally needing to change his business model.

I think that's true, actually. I think that people have been, you know, there's a lot of people building agents right now. I think a lot of them have not been feasible for some of the reasons that GPT5 starts to address. So I think it is. I think that what it means is that the entire architecture behind agents will get a lot simpler. It feels like a good day for people building applications. Yeah. It's not immediate that there's some company or something that got killed today.

Yeah. In general, it feels like Dwarkesh updated his timelines. There's just been a general idea that we've maxed out pre training, we've kind of maxed out post training. We're now in the let's reap the reward of this. And we've seen it in, like, the incredible financial performance, the incredible usage numbers, you know, millions and millions. Hundreds of millions of people are using ChatGPT 30 minutes a day. I love the product. And yet it feels like the, what have you done for me lately? Meme, it's totally like, okay, yeah, we went from the iPhone 4 to the iPhone 5 today.

Yes.

Still really an important technology, great company. But, like, I want another iPhone one.

Yes.

Yeah, yeah, I know. I totally get what you're saying. I think that, like, I wrote a piece about this with swix. But yeah, I. It really actually changed the way I see that path to AGI. Like, I think before using it a lot, I kind of was like, okay, we need like, bigger, bigger models. They're gonna, like, get smarter or something. I think, like, I had this realization. So I was watching it, like, solve. I had this like, really weird, like, dependency conflict with yarn. Like, we have like a mono repo. It's like part of the problem also with this discourse is like, the sort of problems that gets good at solving are just like, not sexy things to talk about. They're not things that even you'll understand. I'm like, we have this issue with our, like, the way we structured things and like. But like, a couple weeks ago, I was watching it, I had this problem. No other model would solve it. And I watched it sort of poke around. It started running this yarn Y command in a bunch of different directories. In between, it's reasoning and correctly reasoning about what and why and what. It was learning and taking little actions in between seeing what happened. I think what I realized is that if you imagine humans without tools, if we never had any tools, we were never even able to write things down. Like, would you be able to tell that we're intelligent? Would we have, like, you know, learned to speak, etc. Like, I just, like, don't, you know, even if we could not have ever invented fire, right? It's like, it's like, where would we be right now? That feels like there's a similar. Like, I actually think a lot of the next year is just going to be, how do you get these models to do things better? Is like, you know, I think it's next year.

In your yarn example, you said like, you were. You were having it, I assume, GPT5, like, work on. The problem was that wrapped in a coding tool. Did you just go to chat.com and give it your GitHub repo? Like, talk to me. What was the actual user experience from your side?

Yeah, so this was in cursor, I think the Codec cli, the new version of the Codec cli, which they just released today, is also really, really, really good.

Okay.

I think that you will really only see a significant difference in places where it can Sort of like explore its environment is the way I would put it. Like when I was watching it, like go bounce around my Repo and like, like I felt almost like I was watching something navigate. Like a little like video game, like Pokemon or something like that. That's kind of what it felt like. Like it's kind of like I'm gonna go over here, I'm gonna see this. Okay, wait a minute. That conflicts with what I just saw over here. Like where should I go next? Do you know what I mean? It felt very novel is like what I would say.

Yeah, yeah, yeah, yeah. So yeah, I mean, how are you using it? Where do you see it going? Do you see it like just like a little bump of a tailwind today or what's your read on like how you'll be using GPT5 going forward?

I mean, yeah, there's two huge things. So like one thing that like really got missed today is that they also released GPT5 Nano, which is like an incredibly good model actually. So like we're not talking about it, but it's half the cost for input tokens than flashlight or. Sorry. Yeah, I think it's actually half the cost of input tokens and flashlight and it's a really good model. Like it's like 40 level for a lot of like writing and stuff like that. And so yeah, we'll be using that probably in the short term. I think it'll be interesting to see how other providers react. I'm sure Google will cut their prices as a result. But it is the cheapest hosted model. I think that, I don't think anyone's serving it any other model for those prices for that matter.

Yeah, that makes sense. What else are you looking for for the rest of the year? Probably no GPT6 on the horizon. But what are you looking out for? I mean it seems like Google is expected to respond with Gemini 3 soon. But what else are you tracking in the, in the world of AI these days?

It's a great question. I think that, yeah, that's going to be wildly interesting. I think what Google does will tell us a lot. I think that they, you've probably seen it but you know, they released this like world model yesterday. We're not talking about it anymore. I mean like if those videos, I haven't tried it myself. If those videos are real, like that's, that's one of the most mind blowing things I've seen in the last like, you know, decade or something. So like, like if that's real, that's Extremely interesting and I think has all the stuff that's going on with role models right now has huge implications for everything from robotics. Just so many different fields. So super, super interested in that. And the other thing is I actually just think that again I'm actually really bullish on GPT5. I think that the way it was received today is just about how I expected it. And the reason is when I say harness again I'm like, I think that like Canvas and chatgpt is pretty bad. Is like my would be my take. Like, you know, it's a tough product to make but like, yeah, like it does really poorly with like long files, crashes sometimes like that sort of like I think that we don't have the product layer around GPT5 doesn't exist yet. So I think we're going to see some really, really interesting products that are built around it.

Yeah, it's always hard when you go from like a binary, qualitative in your face improvement GPT like ChatGPT was like we passed the touring test and now the next test is like super intelligence, self replicates, smarter than every single person knows everything. It's like the bar is like we really moved the goalposts, you know, 100.

I think that there was like a lot of you know, discourse around the model as well like leading up to it, which I think didn't help, you know. But like the way that I would think about it is like I think that you know, depending there's some percentage of the way through automating software engineering that we've made it like let's say it's like 70% or something. 75%. The tough part is that last like 25% is a. The hardest. It's like the least sort of decipherable to like explain to people. It's the least like universal. Like if I'm just like, oh, make a. You know. One of the examples I did, I made a personal website. It's like all Mac OS 9 themed and like 20 minutes with GPT 5 that's fine. And so it's really fun, right? You get it. Like my mom gets it. Like I can show it, I can share it. You get it. You know, my mom, I can't explain any of the like the very specific ways that GPT5 like helps in our specific code base, our specific problem, whatever. So I think that like it'll be less and these launches will probably get less and less sort of interesting from a soft like from a. What it does for software engineering as that Gap gets closed. Like, I, you know, what's the last 5% of software engineering? Like, I, you know, like I. It's probably not going to be that interesting to me.

Do you think they'll be on an annual release cadence now? Like, Apple updated all of their iOS, all their operating system nomenclature, to be like, we are now on 26 because it's the year. It's like a car model, like, like Jaguar.

I don't think you can plan it. I don't think you can plan ahead. Like, that's the interesting thing is, like, I think that, you know, there's people that say that GPT 4.5 was supposed to be GPT 5.

Yep, yep.

And like, I think that it sort of came out and they're like, yeah, like, you know, I actually love 4.5. I think it's a really fun model.

But.

Well, it's true that like, improvements come in many places. Just like with the, with the iPhone, like the latest iPhone. You buy that? Because it doesn't. It's not just like the one with the new screen. It has a slightly better camera, so slightly lighter, longer battery life. It's like an ensemble of improvements that then they add up. And I think that that feels like what we're getting here today and what we will get in the future is like this little, like, we did a little extra RL over here. This tool is now sharper, it has new capabilities. We added multimodal. Like, you know, the video generation got better and this feature got better, et cetera, et cetera.

And I think that like, what a model is, is still going to change a lot and like how we like. So just give an example. Like 4o was sort of this big thing, you know, where they talked about it being like natively multimodal, you know, taking in even like video at some point. Video in, video out, like audio in, audio out. And like, you know, you haven't heard that from GT5 yet. Like, you can't talk to it on advanced voice mode.

Like, that's interesting.

It doesn't roll back, it doesn't generate images. Like, you know what I mean? There's no at least yet native image generation. We don't know much about how it works under the hood, but like it's still calling 4.0 to generate images languages. So it's like, do you start to see an unbundling of these model capabilities? Seems quite possible. The best model for writing natural language, writing creatively, might not be the same model that writes really good rust code. These might Be different models. So I don't know.

We'll see.

Yeah. Create Image here is now tucked next to deep research agent mode, et cetera. But I would hope that you can call that from the actual chat interface.

You can call it from the GPT5 chat. It's just using. It's using GPT Image 1, I think, is actually the name of the model. So it's a dedicated image generation model, which I think is maybe 4.0. I don't totally know.

Yeah, I don't particularly care. I'm not looking for one model to rule them all. I'm fine if with models calling different tools, it seems fine.

Yes.

Anyway, fun day. Thanks for hopping on.

Of course.

Anytime.

Have a good one. Bye. And that's our show today, folks. Leave us 5 stars on Apple Podcasts and Spotify. And thank you for tuning in to the GPT5 giga stream. We're on hour four and a half. We've enjoyed hanging out with you, Tyler. Anything else from the timeline? Close it out for me. Timeline still in turmoil if we want

to show the little game I made.

Okay, yeah, let's show Tyler's game. Can we do that? Is that fast? You got it, Tyler. Sour defense.

This was one shot.

Okay, wait, what do you mean, one shot, one prompt. You said you were working on it.

I was, but then it's like, what's the definition?

Oh, so you went back to a single prompt?

Yeah, I made a change, but then I realized, like, okay, this is not as good. So I just went back to the first one.

Okay. So, yeah, my question is, I mean, this seems actually like. It's like the game engine. I don't know what it's using under the hood. Do you know, did it write, like, WebGL code or did it write.

I think it's just like, js.

Okay.

And it's just like, HTML canvas.

That's pretty crazy.

Yeah.

You'd think it would use some, like, 2D engine off the shelf or something. But my question is, like, what? That won't go viral because that is less impressive than just the Tower Defense app that I can get in the app store for sure. But it's like, maybe if I take my. You know how there's, like, Control. Net images went viral, where people take their corporate logos and then they'd throw that through Control. Net and it would be like the TVPN logo overlaid over, like, a forest and, like, the trees would look like QR code. Yeah. So maybe, like, it's Tower Defense, but it's my logo or something like that. And like, the enemies are like, like moving through something like that. I don't know. There's just got to be a way to personalize it and make it so. Every single game is a unique snowflake that you want to go and experience that one. You want to look at it, you want to spend some time in it.

I don't know.

Yeah, it's hard because it's like, it's still, you know, predicting the next token. It's not like image. The four image generation was like kind of a. It wasn't novel, I guess, because there was image generation.

Yeah.

But it was like such a massive improvement. This is.

Yeah.

Like, there's not any clear, massive step change here. It's a little bit better in a lot of ways.

Yeah.

So.

Oh, well.

Well, we'll have to play with it more. Let us know what you think about GPT5 and we will see you tomorrow. Have a great, great day. Thank you so much. Bye.