Big Technology Podcast - OpenAI's Plan To Merge Chat And Agents — With Greg Brockman

Episode Date: July 1, 2026

Greg Brockman is the president and co-founder of OpenAI. Brockman joins Big Technology Podcast live from the Big Technology AI Summit to discuss OpenAI's trajectory, the state of the frontier, and why he believes compute will ultimately decide the AI race. Tune in to hear Brockman make the case that there will never be enough compute to satisfy demand, why he thinks the interface itself will eventually melt away into a persistent agent that acts on your behalf, and how he expects pricing to evolve as today's premium intelligence becomes tomorrow's commodity. We also cover the competitive dynamic with Microsoft and the "models are a commodity" argument, the path to a personal AGI, voice as an interface, and why Brockman is most excited about AI's potential in health. Hit play for a wide-ranging conversation about where OpenAI and the frontier go next. Learn more about your ad choices. Visit megaphone.fm/adchoices

In 2015, Greg Brockman, Elon Musk, and Sam Altman started a nonprofit called OpenAI. And the plan was to pursue artificial general intelligence. The company or the nonprofit, when it was a nonprofit back then, began in Greg Brockman's living room. And these folks were convinced that achieving artificial general intelligence or AI on par with human intelligence was possible. And to be honest, most people in the Valley thought that that was an interesting. side project, but most of the attention was on social media at the time. Well, fast forward 11 years, and here we are. Open AI is probably going to go public within the next year at a trillion dollar valuation. They're going to announce likely, you know, because third-party data

is showing it a billion users in Chatchaputie fairly soon, and they of course raised the largest venture capital around in history at 122 billion. So they are at the leading edge of a technology that has captured all of our attention and is changing the world. And so when you listen to Greg Brockman, one of the things that you can see, even from his conversations all the way in the past, is a clear conviction and understanding of where this technology would lead and where the products would go.

It's amazing. You listen to the podcasts from pre-chat GPT or really in the early days of the GPT models, and you can hear Greg speaking with absolute clarity about where the models and applications would go today. So I think to close our day, let's take a look into the future of where Open AI and the frontier is going in a conversation with Greg Brockman. Let's welcome Greg. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results, not just ideas.

When pace and performance matter most, PWC combines market insights and deep sector experience with AI, cloud, and emerging tech to accelerate your transformation and drive measurable Rural. from strategy to execution. PWC can help you anticipate what's next, outpace disruption, and compete. For more information, visit pwc.com. Insurance isn't one-size-fits-all, and shopping for it shouldn't feel like squeezing into something that just doesn't fit. That's why drivers have enjoyed progressives name-your-price tool for years. With the name-your-price tool, you tell them what you want to pay, and they show you options that fit your budget. enough hunting for discounts, trying to calculate rates, and tinkering with coverages.

Maybe you're picking out your very first policy. Or maybe you're just looking for something that works better for you and your family. Either way, they make it simple to see your options. No guesswork, no surprises. Ready to see how easy and fun shopping for car insurance can be? Visit progressive.com and give the name your price tool a try. Take the stress out of shopping and find coverage that fits your life on your terms. Progressive Casualty Insurance Company and Affiliates.

Price and Coverage Match Limited by State Law. Great to see you, Greg. Thank you for having me. You know, Greg, this is our fourth time speaking, and we've spoken every time about OpenAI's product direction. And I think I'm starting to get it. You know, there was this conversation that a super app was the wrong term for what you were doing with, the app that you're building, bringing Codex, which is the coding side of opening eyes product, browser, and chat GPT together.

And when you use the word super app, people would be like, no, a super app is actually something that you can just use every other app within. And now, as we've seen these products come together, actually super app might be the correct term. You know, at least for us on the outside, we're starting to see it. That when you need to do anything, it will start with a prompt. in chat GPT, and then OpenAI's technology will use either your browser or your computer to get that done for you. Is that the right way to think about it? I think that's a pretty good perspective, right? And I think to really zoom out, the thing we're actually trying to build is in AGI, right?

That if you think about what people have been using since chat, GBT, it's a language model, right? There's a big gap between these. It's amazing you can talk to chat, talks back to you, great, wonderful. but when we launched in 2022, there was no memory. It's not hooked up to any tools. It has no context. And so it really is that this conversational intelligence is only one part of what people really need

to get work done to be able to achieve their goals. And where we're going is to have an AI that's really looking out for you, right? That you can provide the goals of the directions that it's constantly thinking about what can I do for Alex today, that it's able to go and solve super hard problems, very mundane problems.

You wake up your inbox is organized. But also if there's like, a health plan that you are thinking about, that it can help you achieve that, figure out medical treatments, or sort of back and forth, provide you with that kind of information at least. And I think that the question of, well, what's the interface you want? What is the product that you want is what we spend a lot of time thinking about? And the answer is you want almost no interface. You want no product, right? You want this to be like, what's the interface between you and me, right? Just being able to talk to a persistent entity of some form that's able to go and accomplish

goals for you. And so building that is hard. It will take time. But we have a lot of the pieces, right? And we're increasingly bringing together the product layer, trying to make the models better, trying to make the whole system just so there's less like clicking buttons and toggles and changing modes and all these things. Not to say that there won't be some of those along the way, but the long-term trajectory is towards simplification, unification. Yeah, it's very interesting that you say the interface will melt away. And so to go a little bit deeper with my question. Many of us who use products like Chat Chipiti today, we'll see that the bot will make a suggestion at the end. You know, you ask it about nutrition and it says, should I make a health plan for you

or make a diet plan for you? You ask it, sorry, Ranjan, we just talked about travel, but you ask it about travel and then it will give you an agenda, for instance. And so am I hearing you right that what's going to happen within ChatGPT, just to give an example, is you talk to it about your health decisions, and it might say, you know, you probably need to, you know, go to this specialist, let me make an appointment for you. And then it will go and actually take that action on your behalf. So it goes from simply a conversation interface to actually understanding your intent and then going out and accomplishing that for you. That's exactly right. And I think that if you've used codex, and by the way, how many people in the room have used codex?

A lot of people. Yeah, a decent number of people. And that our goal is to really bring the power of codex to everyone, right, to bring agents to everyone. That technology exists right now, right? You can hook up, like I hook up my codex to Slack, to my Gmail, to my calendar, and there are many people within OpenAI, non-technical users, you know, it's got code in the name, but it's not really about code. It's really about having this general purpose tool using harness, an agent. And the kinds of things, for example, someone on our comms team does is she was organizing

an event and it would just ask all of the event attendees for their dietary preferences, set up the whole seating chart, kind of did all of that work so that she could focus on the parts that she wanted to and really thinking about the vision of what she wanted to achieve. And I think that we're going to see this across the board. So it's not sci-fi anymore to think about an AI that's hooked up to these tools. And I remember with, you know, our very first attempt at tool use in chat ChbT was 23, I think, in March or April or something, we released plugins. Do people remember plugins back in early chat days?

That didn't work. It didn't work at all because the models weren't ready, right? The form factor is correct. Obviously you're going to have an AI that's able to talk to your Gmail, like, no question, but we could only like have three different connectors exposed to the model at a time or start forgetting. You know, we had like 2K, maybe 4K token context. Like there's just no memory, right?

It's kind of like when you had early computers in the 60s or 70s or something, right? You know, you have tiny little memory banks. And today you have your phone that's like better than any supercomputer from that era. And I think that's where we're going with these models, right? The rate of improvement has been so steep. So now you can have hundreds of different tools accessible that we have the ability to hook them up to whole file system. So you can almost have the full power of the internet and like almost any application you want at the model's fingertips. And it's smart, right?

It's got 512 million token context. Depends how you squint on it. And the capability level is also getting so, so powerful, right? These models are now solving unsolved math problems and physics problems, right? And really helping people be able to achieve things they couldn't otherwise. Like we are on the edge of this era of agents really transforming how we all operate, whether it's in software engineering, finance, legal, sales, and in our personal lives, too. So just to unpack that example that you were giving,

one of your colleagues is chatting with Chad GPT about an event. and then suggest, hey, you know, should we, you know, contact event attendees about something? And instead of like saying, okay, I have to do that and going into an event program, basically what happens is the interface will take over from there. Once it says it's a good idea and you agree and then hook into whatever tools you're using and then do it for you. Exactly. So it uses its, you know, Gmail connector, searches through your inbox to find all the people who are attending. And then if you're on the like, what are,

what is everyone, dietary restriction sees, oh, these people, I already have their dietary restriction, these people I do not, drafts an email, depending on exactly how you have things set up, it might say, hey, I drafted these emails, can I send them? If you have a connector that doesn't even let it send emails, I drafted it, you need to send them. And in a different world, you could also imagine that you've built enough trust

with the system where it says, I drafted the emails and I actually sent them. And I think that this actually points to a really important aspect of the agentic era, which is trust, right? That we need to really learn how to build trust with these systems, where they're good, where they're not, figure out what you want to delegate to them and how you want to entrust them with responsibility. And that's something we view as earned, right? It's not something that we can grant,

but by providing lots of tools and control and oversight and supervision to the operator, to the person who this AI is operating on behalf of, like we think that that is going to be such an important thing. So that's a key product feature and differentiator. Yeah, and when you go back to some of the early attempts at this, there was this like move that opening I had to let you call an Uber within chat GPT. And it followed a long line of companies that have tried to get you to take action within chat, but it never really took off.

And the difference here might be that the chatbot can take control of your browser or take control of your computer, and then you don't necessarily have to worry about like, is this plugin going to work, it goes and accomplishes that for you by taking over your machine. So I wonder, you know, if you expect a fight from the user interfaces that we have today, aka like all the other apps, all the software, where to be truly useful, chat GPT, will have to not be blocked to be able to go out and execute these actions on behalf of a user. Well, look, first of all, I'd say that this is not theoretical at this point, right,

that people have been using codecs. So it's a separate product, separate app. You have to install it separately. Really started to focus on software engineering. But the amount of non-software work that has been happening in codex have been absolutely exploding. It's been this incredible exponential curve, exactly the thing that you would expect.

And within Open AI, we basically have the same level of penetration now and usage of Slack. Right? It's like everyone, in Open AI is like an entirely Slack-based company. We do not use email for the most part. It's like really like if you're not on Slack, you're not going to do any work. And it's kind of feeling that way now with Codex app as well. And that everyone's codex is hooked up to all of these tools.

How the ecosystem evolves, I think it's going to be a very nuanced thing because I think one thing that is very important is that we believe that there should be an ecosystem that gets to be vibrant and thriving and that people can really build and see the benefits. And so we've actually seen this from partner companies where, you know, I remember there's a couple different partners where we said, hey, we really want to train our AI to be really good at using your software. And we didn't know what they would say. And actually, the response we got is this is the most partner-friendly outreach we've ever had,

right? The idea that you will make your AI specifically good at using our tool, and they just see the opportunity, because their tool will be used just so much more as a result. And that everyone is trying to think about how do they not just survive as a company into the AI area, but thrive? Like, how do you really get the advantages of the fact there's going to be so much more activity? And if you don't have AI in there, if you shut it out,

then you're actually going to be declining, not thriving. Right. This kind of makes open AI, puts open AI. So first of all, you're going to bring, you talk about people using codex. So one of your colleagues shared, and I think you've talked about this too, that you've brought chat chip PT into codex

so you can bring codex into chat GPT, which is basically like if we're users of chat chipbt, this experience that we talked about of chat chip PT, not only in suggesting what you might want to do next, but going to do it for you, that's going to happen. And so it makes you effectively an operating system. Don't you think, but not the operating system like an iOS, where you would open up your phone and then tap different apps.

It's almost as if all interaction with all apps will happen through this interface. Is that the ambition? I think that you could describe it that way, but I think of it a little differently. Like the way that I think about this is that what is the ideal interface to an AGI, where we call it kind of a personal AGI. And I think that it's, again, the same interface that you and I are using right now. You just want to talk to an assistant, right?

You want to talk to something that can go and work and operate on your behalf. And so that, yes, like that agent, that AGI, that AI will have its own computer, right? It'll have its own access to things that maybe can, you know, like ideal coworker would be, they can come over and type things on your computer too. so some access, some delegated access to your own system, and maybe you delegate access to your inbox sometimes. Maybe it has its own inbox with some sort of window into the things that it needs. You forward emails to it.

These are not actually, if you think about it, this is not unprecedented, right? It's like the way that you work with an assistant. There's a person that we've actually, or any coworker really, we've spent a lot of time really thinking about how do you, you build these trust boundaries and make sure that you're able to operate together? And so I think of it as just a different thing. It's not, you could think of it as an operating system, but an operating system is almost something from a different time, right?

It's a different layer of the stack. This is really more about how do you interface with technology broadly? And I think that the beautiful thing about AI is it's really about bringing the machine closer to the human rather than us having to contort ourselves into like files and, folders and like all of these details that somehow are not natural, right, that are more about how the machine operates rather than how we operate. Yeah, talking about a personal intelligence, it's sort of, I don't know, did you watch WWDC last week? No, no, I missed it. I was banned, but I watched it on TV. Come on, Apple. Anyway, it does look like you and Siri, the new Siri,

are going to come kind of into competition, right? Because they're an app that's going to, or an intelligence that will sit on top of all of your apps and let you take action. And chat GPT will be an app on the iPhone. So then talk a little bit about whether that positioning is going to be difficult for open AI and how you're thinking about that strategically. Well, I just, again, think of it a little differently. Like I think that we're in the beginning of this new agentic era. And the way that this has always gone in AI is that when you have a new level of capability,

it means you have an opportunity to rethink everything. rethink how people interface, how, like what the technology is capable of. And I think that this is no different, right? In my mind, like the kinds of things that I see on the horizon, for example, AI for solving scientific problems, right? And I think we're starting to see the inklings of this. Like, for example, today we announced we have in peer-reviewed literature people, doctors who are using 03, I remember, 03? Yep.

It was like forever ago now. Right? It was like one of our earliest reasoning models, using that to find diagnoses for people who I had no answers from doctors for many, many years. You know, there's an example of someone who had spent 20 years with a mysterious ailment. Finally, it's been diagnosed through the use of this technology. And if you're like, okay, you've got models that can do that, they can do that.

And then it's really about, like, you know, the same, like, distribution and, like, you know, can you get access to an app? You know, to me it doesn't type check. It's like we have something fundamentally new. And so it's not to say that there won't be competition. I actually think that there will be and it's going to be great for everyone. But I just think that the ways in which you're going to use this technology, the things that will be capable of and what it'll make you capable of doing

are just totally different from anything we've seen before. You know, I was going to ask you, well, does it mean that you'll have to, you know, create your own device assuming that, like, my concept is, you know, that you're going to have to go through Apple to get to the user, assuming that's somewhat valid, but the answer is you already are, right? So open AI is working on a device right now. It certainly has been publicly reported. I was in your office in December, and Sam told me that this is happening.

It's multiple devices. So if you think about the way that, again, you're going to interface with these AIs, how does that device play in, or a series of devices? Well, look, I think, again, I would just step back and say that, I think this is the beginning of something very new and that I think about the way that I think, I want to just say, I think the biggest shift that has happened in terms of interface, again, it's not even about devices and things like that, it's really about the shift from conversational intelligence, like kind of the chat paradigm, where it's like kind of you have an AI that's

personalized enough to you that it's worth reading its output, right? You ask it a question, you get an answer, it's something that's useful to you. to agents where they're capable enough to actually do things for you. Like, that is a big shift. And that that implies a difference in how you want to interact. And so you kind of are just going to want a single agent that has access to your context, and this will be true in personal life.

This will be true in a business context. Right? You imagine, for example, having a, you know, imagine you have a PhD in every field, co-worker, you know, noble prizes, multiple of them, and you hire one of these, you hire a hundred of them, and you don't invite them to any meetings. They're not going to be very useful. And so there's something about how do you get context into the AI and not just statically, but dynamically, right? As context evolves, as your business process evolve, how do you have a

context layer that is accessible to an AI that that's the AI operate to the extent of that raw intelligence. And so finding ways to make that AI be accessible, so available in their meetings, to make it very ergonomics. It's very easy to get access to. I think all of that's going to require a rethink. But I think, again, it's just the core for me starts from thinking about the agenic form factor and then working backwards to how do you just make this have the context it needs. And again, the trust is going to be such a core part of making this whole equation work. So it's kind of like having this device with you at all times and being like, I need to get that done and it goes and does it for you. And I think that that will be part of it.

But I almost even think if you don't have a device like that, it's not like you're going to be out of the game, right? Because it's this AI. There's one thing, there's one version of it where you think of it where it's like the device is the AI. You want your phone to be the AI. You want whatever, whatever custom device you're thinking about to be the AI. But it's not going to be like that. It's going to be more like an interface. Like no more than your phone is you, right?

It's an interface to you. It's a way that I can sort of, you know, call you up whenever I need you, whenever I want to ask you a question. And there's different ways of access in, right? There's like synchronous phone call. I can text you. I can email you. And I think that we're going to be much the same with how we interact with our agents. There's been some reports that opening eyes working on these like bidirectional voice models.

I think we've talked about that in the past. Like the goal is to have like an AI that you can speak with and will be able to process that. and speak back with you in a much more natural way. Can you share anything about that? No. But now more seriously, I think that the general shape of the technology, like the way that we've had voice models, you know, kind of a really cool voice experience

for, you know, a year and a half, two years now. You know, we first demoed it back in March, April of 2024, brought it to market, you know, maybe late that year. late that year. And the way that it works and the way that everyone's models work is that you basically chained together, well, the original way that these things worked was that you would chain together a text to speech to text model, then you do a text to text to text model, and then you would do a text to speech model. Horribleness, right? Like these three things chained together. It still has been the case that even if you have one unified model that's able to kind of take in

input and then, you know, able to output a response, you still have this problem of turn taking, Imagine that we have this like you cannot overlap, you cannot interrupt. It's just like once you speak to me in a turn and then you got to wait for me to finish my whole response, that is not how human conversation works. And so that we basically have like a hack where we have these models that determine, oh, it seems like the turn is ended, and oh, it seems like the turn has started. And we're like, why are we talking about turns? Right?

Turns are, again, they're so unnatural. This is the humans contorting ourselves to the machine and its limitations. And so the obvious thing that you want to accomplish is a model in AI that works much more like you and I do, right? That's able to process input at the same time it's processing output and all of that is of course something that many people in this field are trying to run towards I think it's going to be very, very exciting as you move to these natural very human fluid like conversational interfaces. No one's seen anything like it. Like one thing that I think about is the current interaction with chat chbtbt voice in many ways. So many people use it on their commute, able to ask all these questions, but it also is so frustrating, right? Whenever it breaks the magic because it's like you realize, oh, I want to like add some follow-up and it keeps talking over you and it didn't, it's just like that is just it doesn't make sense. And so I think that part of what we need, part of like the whole point of this AI is to be something that you can interact with, interact with fluidly and naturally. And by the way, I think it's not just going to be about the sort of use case.

like we kind of think about the personal use case, but it's also really the work use case. And I think some of the most magical experiences that I've had with codex have been when operating it through voice. Like many people, we have a voicing built in. Some people use third-party apps for it. And that you just get a very different experience

when you start to realize that typing a quick message to give some feedback easy, but like writing out a whole paragraph and everything you want, horrible. No one wants to do that, right? You just want to be like saying things and you want the real-time feedback loop, and all of that is going to happen,

and it's going to be amazing. So let's talk about model improvement briefly. So there was a discussion a couple years ago that large language models were about to hit a wall. That was wrong. And something that I'm thinking about is, I think we're all thinking about it, is how much better can these models get,

and when will the improvement stop? Any thoughts? Well, I think that this is a place where when you're kind of building these models, you get kind of a sense and an intuition that I think is harder to get from the outside because we see all the data points and we see also the work that goes into these improvements.

And so that there's two parts to the answer. One is, I think that the fundamental science is one of the most mysterious and important just scientific discoveries and empirical observations that I can, that I'm aware of that I can imagine, right? that we are able to actually build these models and that the scaling laws continue, right? That it just is the case that you can just keep training these models, more data, more compute, better architectures, and there's a lot of improvements that go in.

But every time we've kind of run into a like, oh, this isn't quite scaling the way we expect, it's we have a problem, we have a bug, that our math wasn't quite right, that, oh, our implementation isn't quite matching the math, whatever the thing is. And that is, I think, a very important thing to sort of internalize. And actually, we've done studies where you go back to the beginning of the field, right? That neural nets themselves were designed in like 1940s, right, before computers, right? As a model of maybe this is how the brain process information, first hardware implementation was 1959 with the perceptron. And if you look at landmark results in the field, that the landmark results follow this incredibly smooth, deterministic path of more,

compute being poured into them. And so 70 years of people, maybe 80 years now, of people saying this stuff is never going to work, never going to scale, going to hit the wall, hasn't hit the wall yet. There's still no wall in sight. And so I think that the fundamentals allow it. Now, the practicality is hard.

Actually building these massive supercomputers, it's hard. It's expensive. It's not easy. That we have teams that just like work so hard to solve these incredibly hard technical problems. We have our own network protocol that we've had to design. that we have people who look at every single layer of the stack,

that there's weird wiggles in the graph. The way to think about these neural nets is that there's no abstractions, right? It's almost like any little piece that's wrong can have a ripple effect that only shows up down there. And so you need people deeply understand all of it. And yet, if you get the right team together, put the right mission in front of people,

and people do that grind, the outcome, it's worth it, right? And it's achievable and it's possible. And so I think that for those reasons, the progress will continue. So then I'd love to hear your perspective. If models can basically progress much further from where they are today, let's say OpenAI builds the best model. And the equivalent of something with like 15 PhDs

with excellent emotional intelligence that doesn't complain and goes out and does stuff for you. And then the next model maker will build a less good, but it has 13 PhDs and it's like pretty good, you know, EQ, and we'll still go and do things for you. So where does the differentiation come in when you get to that level of intelligence? Because we've seen the model makers kind of move in lockstep. One makes an advance.

The next one comes in and makes the advance. So they all become that smart. Is it possible to differentiate? Well, I think there are several dimensions to the answer. Number one is I do think there's a bit of an attractor state we're just like from a business model perspective, every provider sells out all their compute. Okay, right?

I think that is just like the world that we're heading towards where there just is not going to be enough compute to serve all the demand, right? That we're heading to this compute-powered economy that everyone's going to be using these models all the time to be able to accomplish tasks of interest. And we just see it. It's like right now we're talking about compute constraints

and like the number of people using these agents is like, order of 10 million, 20,000? million maybe, you know? It's like we're not at planet scale. ChatGET is like a billion users, right, but we haven't brought the agentic power there yet. So you're just looking at these factors and the depth of usage is also tiny compared to where we're going. And so I think that we're just going to be in a world where even if you have different vendors, different capability level, open source models, all these things, these neoclouds, like I think that computers just going to be the scarce resource and I think that it's going to go to use. So to some extent, I think

that the like, is this a good business to be in for new entrants to come into and things like that? My answer is actually yes. I think that there is like a huge market that we are just not going to be able to address. We need much more energy and momentum there. But a second thing is that it also misses the fact that intelligence is not a unidimensional thing, right? That if you really zoom in, being good at different domains is something where even if you have a lot of raw intelligence, getting good, if you've never practiced, like you've never actually done a pitch or something,

like you're not going to be good at your first time, right, and that there's lots of different you've never operated spreadsheet, right? You're not going to be able to succeed at doing some complex modeling. And so I think that there is something that we have been internalizing, which is that we look across different industries and different domains, and we have to prioritize. We can't possibly be great at every single area at once. There is definitely a lot of like, hey, you just get the general intelligence up and it'll experience a lot of these things, but to really become a domain expert, to really be that PhD, and to really be something that can help push forward the ambition of a field,

like that's hard. And by the way, one thing I also want to say is that I think understanding what happens when you successfully do that, I think that having a good mental model of that's important, which is you look at something like rewind to alpha-go, right? You remember move 37, this move that changed people's understanding of the game, and then now more people play go than ever, right? And that it actually inspired people to do even more. I think we're just going to see that.

And so I think that the depth is never going to stop, right? How deep can you go on science? Right? Like I think that people have thought sometimes that, hey, we found out all physics, it's all good, we're all done. And I don't think that that's the future we're signed up for. I think we're signed up for one where we're going to keep finding every time you unlock one mystery. Every time you solve one mystery, it unlocks like 10 more.

So I think that there's just going to be so much more to do and tons of room for differentiation across different companies. So I think I'm reading you right and that your belief is maybe there's a way that everybody can scale up these models, but ultimately the company with the most compute is going to win. And we spoke a couple months ago and you had mentioned that like you were asked internally, how much compute should we buy? And you said all of it. And they said, no, really, how much should we buy? And you said, no, buy all of it.

And Open AI is definitely the leader in buying compute. I mean, we see the money going out. Obviously, a lot of money coming in through investment. And now you've built a business with customers, but there's a lot of money going out. Do you ever wonder, hey, are, like, do you ever wonder maybe we're not going to be able to pay all this money back? Because it's a brand new category.

Well, the way that I look at it is on the fundamentals, right? You need to really look at the fact that the way that the computer, goes is that it's multiple years out before compute actually arrives, right, depending on exactly what you're doing. For example, we've been investing in our own chip program now for multiple years. And super exciting progress, like, you know, we'll have more to announce actually pretty soon. But the fact that we're able to do that is something very unique, right? Really thinking about the full vertical integration of the supply chain. And I think that the world we're heading towards is one where, again, there's just not going to be enough compute,

in the world to satisfy all the demand. And we see this very concretely. Like you look at the exponential, I mean, rewind to the exponential of CHAP-CPT, look at the exponentials we run now. You think about the problems that we are able to solve. You know, it's actually kind of interesting that we just yesterday announced,

it was two days ago, announced a new result in basically chemistry and being able to synthesize new, new, improve of reaction. And all of this is without much attention, The thing I just said of if you go deep in our domain, you can really transform it. And we're not even scratching the surface yet. And so the way to think about it is the economy is so massive, right?

And we see it very concretely in terms of our own growth, in terms of what people are willing to pay, and kind of the size and growth of this whole industry. And so I think that the thing that I think about the most is how do we meet the demand? How do you actually have something that can help support all of the work that people want to do in the economy? And I think that is such a vast thing. I don't think any of us have internalized yet. Hi everyone, Alex Cantowitz here. I want to tell you about a documentary I've made with gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous

agents, I sat down with MIT professor Ramesh Roscar, former White House CIO Teresa Payton, Michelin's group chief data and AI officer Ambica Roger Gopal and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blondell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey. You can watch the full documentary at the link in the show notes. This episode is brought to you by Accenture. When your advertising operations fall out of sync, everything else follows.

Spotify and Accenture are working together to reinvent the rhythm of ad sales, using automation, analytics, and smarter workflows to simplify campaign delivery and access better data across the business. The result? Less time spent on operations, more time connecting brands with the moments and fandoms that matter most. Learn more at Accenture.com slash Spotify. Yeah, but if I may, there is a price war brewing. I mean, at least that's according to the reports. It's great to have you here to talk about it.

The Wall Street Journal recently had a report that an upcoming OpenAI model might, have significant price cuts. And so again, like how can, you know, if it requires so much resources to serve this demand, and it is growing demand, in an environment where there might be price cuts, how do you make that math work? Well, again, I look at it from a different angle. So if you look at the whole history of what we've done, we actually have been increasing the intelligence, cutting price, right, for a fixed amount of intelligence. And people somehow just like, like the Devon's paradox just keeps happening. And so I think frontier intelligence will always be something that is going to be,

you know, it's always going to be the priciest thing. But I think that a year from now, that level of intelligence is going to feel pretty mundane and, like, you know, going to be much more available. And I think that the world that we're in is one where people are starting to really think about value. And it's actually been a very interesting shift where over the past, you know, first quarter, maybe up until now, people have just been like this AI agent, It's all new. We need to bring into our enterprise. We don't want to be left behind. How do we be part of this future?

And now people are like, okay, let's make sure this is actually delivering ROI and value. And I actually think that's a great place to be, right? Because people are asking the right questions. And I hear this, I had some customer meetings today where people were saying exactly this. They're like, how can we have even just like good spend controls? How can we have observability? And I think we literally today just really spend controls. So, you know, it's like, okay. Exactly. We are really investing hard in enterprise readiness and the tools that our customers are telling us that they need. And I think that that for me is the shift that we've also been going through as a company is really not just thinking about, hey, we're just going to release models and have a model, really thinking about the end-to-end of the business. How do we bring this into solving real problems for real customers?

And that is happening so quickly across every single industry. and the number of different companies that still feel like they're wrapping their mind around how to best make use of these models. We're learning at the same time. I think it's just so early in this whole game, to me, the absolute size of the market growing so quickly, or on revenue ramp growing so quickly, I think it's still just like none of us are anticipating how steep that's all going to go. Are you going to cut prices? So again, the answer is always yes, right? But it's about like, I think that what's going to keep happening,

is that we're going to have frontier models. I don't think there's going to be a massive shift in the short term. I don't think that that is the kind of thing that's going to happen. But I think the thing you should anticipate is that over a year-long time horizon, to get to today's level of intelligence that feels very premier, it's going to be much cheaper. But there's going to be a new thing that is going to be so much better. And you're going to make, why would I ever use this other one?

Right. It's just how it's always going to be. So Sadia Nadell has had some interesting tweets and interviews recently. He recently said the model is becoming a commodity and the valuable asset is company, or this might be a paraphrase, the valuable asset is a company-specific AI system that continually learns from your data. What do you think about that? And is it weird to be competing with Microsoft now? Well, look, I don't think that there's any layer of the stack here that is going to just kind of be removed from the value chain. I think that these things multiplied together. And if you think about the most, the base layer of,

of compute. That is something where it's just like no compute, no AI. And to some extent, you can say, oh, compute is commoditized. It's just flops. Who cares about it? But in reality, like, you look at today's chip stocks. You look at the people who are selling compute, kind of what is what the market is valuing people at. And they see that there's a fundamental asset here that is just so critical. And I think that is because it is a revenue center. It is something that anyone who's building AI has to rely on. And that there's a bunch of very interesting dynamics in terms of the efficiencies that you can squeeze out and the margin, all these things. But fundamentally, even though it's like you can kind of squint it and say it's commoditized,

it's not. It's not that the value goes away. It's not that the margins go away. It's like something that the market will reward because it has fundamental value and the importance of it's going to go up over time. You can see that with some of the prices that people are paying for H-100s, right? Hoppers are kind of not obsolete, right? But they're a previous-gen chip. in any normal situation where we're not totally supply constrained, no one would have been buying them. But instead, the market prices are up relative to where they were before. So there's this inversion that's happening.

And again, I think it's going to keep happening, where because everyone has this avalanche demand, that you're going to see prices and margins and all of these things continuing to increase at various levels of the stack. I think the same kind of applies for models, where the models themselves are also, again, And they're not, there's a lot of competition there. And I think that's very good.

I think it's good for the enterprise. I think it's good for customers, consumers. But I think that there's a lot of areas where, for example, our models have always been the sort of smartest ones, right, the ones that are able to solve these incredibly hard problems. I think we're just starting to reach a phase where you're going to see the transformative impact from that. It's like if we're really able to speed up science through models, the smarter the model,

the faster it's going to go. And it's very, very different from a model that has a conversational interface that you're able to, you know, is able to book your travel, right, or organize your calendar. So that's also to mention I think we're going to do a very good job in. But I'm just saying it's a different area. And then I think that the question of, well, how do you actually connect the intelligence to your own customers, right? To real value to you have all these enterprises that have built incredible businesses in different domains. And it's a huge thing.

And it's not something where if you don't have domain expertise that you're just, going to be able to do. And part of it is that you need, you think about regulated industries, you think about any area where there's like, you know, think about education where you have a parent, you have a teacher, a student, you have these different parties that need to interact in very thoughtful ways for all of these areas, all of these domains, that there's a lot of value to be built by being in that area and thinking about how the workflow should work, how these models should be orchestrated. And so I really think that there's more than enough to go around. And I think that we have to work together as a whole ecosystem in order to deliver the kind of value that I

think is possible from these systems. Okay, just to go back to the Satya point, one more time. He's called models a commodity. He's trying to build his own frontier intelligence. He's telling potentially your customers, hey, you've got to come work with us because we're going to help build these loops that will learn from your data. He's got access to your IP, I think, until 2032. So how does it make you feel to hear this coming from Sadia? Look, I think that the most important thing that is happening right now is the usage of AI in the economy, to really transform the economy and to uplift everyone. And so I think that that is something that I'm really focused on, and the more that people are trying to make that happen, I think that that's better for everyone.

GPT 5.6 is rumored to be on its way. It's supposed to be just a Twitter rumor, but I'm going to read it to you. Always the best rumors. Three times cheaper than Fable, up to 1.5 million. Token content. context, stronger, agentic coding workflows. How much of that is true? What should we expect for GPT 5.6? I mean, look, you should always expect better, faster, smarter, the whole thing. So everything confirmed. Definitely believe everything you read on Twitter.

Maybe not. That has actually been a source of problems in my personal life. Okay, so I want to end on health. You brought it up a couple times. We actually had a question in the audience about it earlier. You know, sometimes there's a story, and you read it, and you say to yourself, I know this person is speaking of the media, and I know that what they're saying sounds like maybe it's true, but there's something wrong with the story, and we're not going to see more of it.

And I've read a couple of those recently. one is I think is it your friend the GitLab CEO Sid Zibirondage he had he got cancer and used he got all the diagnostic testing he could have so just went out and tested like crazy and fed that data into chat GPT with the assistance of some people who had built

purpose bill application for it and was able I don't know if cure is the right word but to beat back the cancer to a degree there was also this dog rosy the dog in Australia. You guys heard of Rosie? Like the craziest story where this guy, I'm going to get some detail wrong, but a guy biopsied his dog, which had cancer, ran the mutations across AlphaFold, and then was able to design an MRI vaccine that he injected into the dog with the assistance of chatbots to build this thing, which ended up being able to jump over

tables again and the tumor shrunk. When we think about the future of AI and health, help us sort out the truth with this question. Are these a couple of outliers that made good headlines, but there was something about the story we weren't hearing, or is this going to become standard in the future? Absolutely going to become standard. Absolutely. And I personally have a number of friends who have done very similar things of get the data, right, your health diagnosis. You're a health diagnostics and use codecs, right? Use these models to get insights from them. And I think that there are many people,

like I think that there's about 230 million people each week who use chat GPT for health queries. Right? And that's been, that's like a staggering scale, right? And these are people, sometimes you upload a scan, sometimes you have doctors who are telling you conflicting information. And I think that we've been in a world where patients are not empowered, right? Patients have to be the doctor, right? You're the

decider. You are accountable, right? You know, doctor makes a mistake and you're going to be paying the price for the rest of your life. Like, it's just a very different kind of incentive. And this is very personal for me. You know, my wife has a number of health conditions. And I think that we've just been, we've not, like, I don't even know how we'd be able to manage many of her conditions right now without the use of chat. And I think we're just at the beginning of this journey, right? That I think that the degree to which, even if you have the best medical team, the best acts as the best experts, there's only so much that can be done, right, that you think about the things that are outside of the reach of humanity, or even just sometimes it's like someone didn't even read

the chart, right, and kind of missed a detail. All of that, we should be able to improve massively through these tools. And so I think that the personalized medicine, and sometimes it's going to be about drugs and drug discovery that are for mass market, but sometimes it'll be even for the kind of N of 1 things like the disease diagnoses that I mentioned earlier today. Sometimes it will be for just like trying to understand conditions and trying to come up with new potential therapeutics. All of that, we're seeing it happening right now in front of our eyes. It's not theoretical.

It's really happening. And so one of the most, I think it's like one of the most astounding possibilities of AI is how much it can improve our health. And you think about the ripple effects to the system, right, where so much spending on the health care system happens right now. That's a massive part of the economy. And that if you're actually able to help people prevent issues, right, to get ahead of potential health problems, that's something that actually then alleviates a lot of burden and a lot of strain. and we're in a world where doctors are burned out, nurses are burned out, like there's like a real crisis that's happening in front of us, and I think AI will be able to help with all of that.

Like we have that potential if we deploy it and use it wisely and well. And so I think that applying AI to medicine, like that's something that is really a personal motivation for me in thinking about this whole journey of what we're building, what we're trying to do with Open AI. And I'm hopeful that we as a world in the community can make the most of that. let's hope. I think we will.

I'm very, very confident. Greg, thank you so much. Thank you. It's great. Thank you so much. Oh, my God. Thank you, everyone. You have a good time today?

Thank you. Should we do it again next year? You're going to come? Yes. All right. Hey, y'all. It's Kelly Clarkson with Wayfair. Ever order furniture online and wonder,

what if? Like, what if it doesn't hold up? That sofa was four days old. You should have ordered from Wayfair. With Wayfair, there's no what if. Just style you love and quality you can visit wayfair.ca. Wayfair, every style, every home.