OpenAI President Greg Brockman: Our Plan To Merge Chat And Agents

23:21I 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. One thing that I think about is the current interaction with ChatGBT voice in many ways is magical. So many people use it on their commute, able to ask all these questions, but it also is so frustrating. Whenever it breaks the magic because it's like you realize, oh, I want to add some follow-up and it keeps talking over you. It's just like that doesn't make sense. And so I think that part of what we need, part of the whole point of this AI is to be something that you can interact with fluidly and naturally.

23:59And 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 voice thing built in. Some people use third-party apps for it. And you just get a very different experience when you start to realize that like 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 it's going to be amazing so let's talk about model improvement

24:40Big Technology Podcast Host: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 you know 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.

25:19One is I think that the fundamental science is one of the most mysterious and important just scientific discoveries and empirical observations that I'm aware of, that I can imagine, that we are able to actually build these models and that the scaling laws continue. 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.

26:03And 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 processes information. First hardware implementation was 1959 with the Perceptron. And if you look at landmark results in the field, 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.

26:42There's still no wall in sight. And so I think that the fundamentals allow it. Now, the practicality is hard, right? Actually, building these massive supercomputers, it's hard. It's expensive. It's not easy, right? 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. And the way to think about these neural nets is that there's, like, 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.

27:17And so you need people to 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. And it's achievable and it's possible. And so I think that for those reasons, the progress will continue.

27:34Big Technology Podcast Host: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 it's the equivalent of something with 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 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.

28:15Big Technology Podcast Host: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 where just from a business model perspective, every provider sells out all their compute. Okay. I think that is just the world that we're heading towards where there just is not going to be enough compute to serve all the demand. 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.

28:49And we just see it. It's like right now we're talking about compute constraints and the number of people using these agents is like the order of 10 million, 20 million maybe. It's like we're not at planet scale. ChatGPT 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, I think that computers are just going to be the scarce resource, and I think that it's going to go to use.

29:27So to some extent, I think that the, like, is this a good business to be in and 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 and 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 it your first time, right?

30:04And that there's lots of different, you've never operated a 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 you'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, that's hard.

30:37And 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 AlphaGo. right you remember move 37 this move that like changed people's understanding 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 i think that people have thought sometimes that hey we found out all the physics it's all good we're all done and i don't think that that's the future we're signed up for.

31:15I think we're signed up for one where we've got 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.

31:27Big Technology Podcast Host: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, you know, we spoke a couple of 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 OpenAI 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.

32:02Big Technology Podcast Host:But there's a lot of money going out. Do you ever wonder, hey, 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 compute 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?

32:42Really 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 ChatCPT. Look at the exponentials we're in 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 improvement reaction and all of this is without much attention the thing I just said of if you go deep in a 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 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.

33:35And 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 it yet.

33:49Big Technology Podcast Host:Hi, everyone. Alex Kantrowitz 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 Raskar, former White House CIO Theresa Payton, Michelin's Group Chief Data and AI Officer, Ambika Rajagopal, and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, 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.

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35:25Big Technology Podcast Host:Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18+. 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.

36:15And people somehow just like, like the Japanese 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, first quarter, maybe up until now, people have just been like, this AI agent stuff, 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?

36:51And now people are like, okay, let's make sure this is actually delivering ROI and value. And actually, that's a great place to be, 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're, we're, we're 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, you know, have a model really thinking about the end to end of the business.

37:32How 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 the like the absolute size of the market growing so quickly our own revenue ramp growing so quickly i think it's still just like none of us are anticipating how steep that's all gonna go are you going to cut prices so again the answer is always yes, right?

38:07But 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 like, why would I ever use this other one? right? It's just how it's always going to be.

38:35Big Technology Podcast Host:So Satya Nadella 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 multiply together. And if you think about the most, the base layer of compute, right, that is something where it's just like no compute, no AI.

39:14And to some extent you could 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 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.

39:47It'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 H100s, right? Hoppers are kind of not obsolete, right? But they're a previous-gen chip, and 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 of demand, you're going to see prices and margins and all of these things continuing to increase at various levels of the stack.

40:31I think the same kind of applies for models, where the models themselves are also, again, 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.

41:07And it's very, very different from a model that has a conversational interface that is able to book your travel or organize your calendar. So that's also a dimension 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, 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.

41:38And 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 is 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.

42:11Big Technology Podcast Host: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 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, till 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.

42:45And the more that people are trying to make that happen, I think that that's better for everyone.

42:51Big Technology Podcast Host:GPT 5.6 is rumored to be on its way. It's supposed to be... This is 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 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's speaking to 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.

43:59Big Technology Podcast Host:And I've read a couple of those recently. One is, I think, is it your friend, the GitLab CEO, Sid Sibirondaj? He got cancer and he got all the diagnostic testing he could have. So just went out and tested like crazy and fed that data into ChatGPT with the assistance of some people who had built purpose-built 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, Rosie 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 mRNA 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.

45:00Big Technology Podcast Host: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, your health diagnostics, and use codecs. Use these models to get insights from them. And I think that there are many people, I think that there's about 230 million people each week who use ChatGPT for health queries.

45:44And that's been, that's just like a staggering scale. 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. Patients have to be the doctor. You're the decider. You are accountable. A doctor makes a mistake, and you're going to be paying the price for the rest of your life. It's a very different kind of incentive. And this is very personal for me. My wife has a number of health conditions, and I think that we've just been... And I don't even know how we'd be able to manage many of her conditions right now without the use of chat.

46:22And 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 experts, there's only so much that can be done, right? That you think about the things that are just 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 end of one, things like the disease diagnoses that I mentioned earlier today.

47:04Sometimes 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 of the system, right, where so much spending on the healthcare system happens right now. It'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, a lot of strain.

47:48And we're in a world where doctors are burned out, nurses are burned out. There's a real crisis that's happening in front of us. And I think AI will be able to help with all of that. We have that potential if we deploy it and use it wisely and well. And so I think that applying AI to medicine, 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 OpenAI. And I'm hopeful that we as a world and a community can make the most of that. Let's hope. I think we will. I'm very, very confident.

48:21Big Technology Podcast Host:Greg, thank you so much. Thank you. I appreciate it. That's great. Thank you so much.

48:34Big Technology Podcast Host:Oh my God. Thank you, everyone. You have a good time today?

48:42Thank you.

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