OpenAI President Greg Brockman: AI Self-Improvement, The Superapp Bet, Path To AGI, Scaling Compute

24:38And that for me, it's never about any one release, because as soon we have this one release, it'll be an early version of what we have coming. We'll do much more of each of these steps of the improvement process. And so I think that where we're going is almost just we have this engine of progress that just moves faster and faster. And the spot is just one step along the way. So what do you think it'll be able to do that today's models can't? So I think it's going to be able to solve both much harder problems. I think it will be much more nuanced. It'll understand instructions better. It'll understand the context much better that there's this thing called big model smell that people talk about, where it's just like, there's something about like when these models are just actually just much smarter, much more capable that they bend to you much more and you feel it right.

25:28When you ask a question and the AI doesn't quite get it, it's always so disappointing, right? And we have to like explain and you're just like, you really should be able to figure this out. And so I would just think of it as in some ways, just qualitatively, there will be, but quantitatively, lots of shifts, right? And qualitatively, there will just be new things where you would be frustrated before you never use an AI for it. And I just use it without, without thinking very much. And I think that that is what we're going to see across the board. I'm super excited to see how it raises the ceiling, right?

25:57We've already seen these physics applications, things like that. And I think we will be able to just solve like way more open-ended problems, way longer time horizons. and then also very excited to see how it raises the floor, where just for anything you want to do, it's just so much more useful for you.

26:13Big Technology Podcast Host:It can be kind of tough for everyday users to really feel the change. Like there was a talk about a lot of buildup before GPT-5 came out and then it came out. And actually the initial reaction was somewhat disappointing among the public. But then I think people realized that for certain tasks, it was really good. With these next series of models, do you expect that it'll really be felt sort of in the trenches in certain occupations? Or do you think it will be a broadly tangible improvement for everyone? I think that it will be a similar story where when you release it, there will be people who will try it and be like, this is a night and day different than anything I've seen.

26:52And then there will be some applications where we weren't necessarily intelligence bottlenecked. And so if you have a model that's more intelligent, maybe you won't feel it right there. but I think over time that you will feel it because the fundamental thing that shifts is how much do you rely on the system? Like if you think about the way we all interact with AI is we have some mental model for what we think it can do. And that mental model shifts actually fairly slowly, right? As you get more experience, it does something magical for you. You're like, oh wow, it can do that? I never imagined that.

27:22And we see this, for example, in applications like access to health information, right? That we see people who, you know, like I have a friend who used ChachiBT to understand different treatments for his cancer. And that he was told by doctors that he was terminal, that there was nothing they could do for him. He used ChachiBT to actually research a bunch of different ideas and he was able to get treatment that way. And that's something where you need to have some level of belief that the AI is going to be helpful in that application for you to really put in the effort to get something out of the machine.

27:56And I think what we're going to see is that for any application like that, it's going to become so much more evident to everyone that the AI can help you. And so I think it's a little bit of the technology getting better, but it's also our understanding of the technology shifting and catching up to that.

28:14Big Technology Podcast Host:And you'll be relying on it more inside OpenAI. You have an automated AI researcher in the works. It's supposed to come out this fall. What is that? So the direction of travel right now, we are in this early phase of takeoff of this technology. What does takeoff mean? Takeoff is as the AI gets better and better on this exponential. And in part because we can use the AI to make the AI better. So our development process speeds up. But I also think when I think of takeoff, it's also about real world impact. And in some ways we've been, you know, every technology is an S-curve. or if you zoom out some of us curves that end up being an exponential.

28:58And I think that that's what we're encountering right now. So it's the tech technology development is moving with increasing speed and it's this engine that's picking up momentum, but it's also in the world. There's all of these tailwinds because there's chip developers that are getting more resourcing into their programs. There's this economy of people who are building on top of it, trying to figure out how it fits into every different application. And all of that energy is just accumulating more and more into this takeoff phase of the AI becoming just a kind of sideshow to being the main driver of economic growth.

29:36And I think that that is something that it's not just about what we're doing in these walls. It's about how the whole world, the whole economy comes together in order to push forward this technology and its usefulness together.

29:47Big Technology Podcast Host:And the researcher will then, what will it do exactly so the researcher will be a moment where the ai which we're building that right now it's taking a you know it's doing a larger percentage of tasks that we should be able to let it run autonomously and that i think there's a lot of thought that goes into what that means and that it doesn't necessarily mean that we just let it off on its own and then come back later and see if it did something good i think that we are going to be very involved in managing it right just like right now if you have a junior researcher if you leave them on their own too long they're probably going to go down a path that's not very useful.

30:21But if you have a senior researcher or someone who has a vision, they don't even necessarily need to know the mechanical skills, they will be able to provide feedback, review the plots that this person's, you know, the intern's producing, and to provide direction in terms of the vision of what is it that I want you to accomplish. And so I think of this as a system that we're going to build that will massively accelerate our ability to produce models, to make new research breakthroughs happen, to be able to make these models more useful and usable in the real world. Right. And to do that at increasing speed.

30:53Big Technology Podcast Host:So sorry. What's it going to do? Are you going to say, go find AGI and it will just try to create? I think the way I think of it is something like that to first order. Okay. And at a practical level, I think I would view it as taking the full end to end of what one of our research scientists does and be able to do that in silicon. Another way to think about takeoff is their progress in AI goes from incremental to gathering momentum and then sort of this unstoppable march to an intelligence that's smarter than humans. Do you worry that just as there's possibilities for things to go right on that front, there's also possibilities for that process to go wrong?

31:39I mean, I think that that's absolutely yes. I think that the way to get the benefits of this technology is also to really think about the risks. And if you look at how we've approached technology development, from a technical perspective, we invest a lot in safety, security. good example of this is prompt injections, right? If you're going to have an AI that is very smart, very capable, hooked up to lots of tools, you want to make sure that it can't be subverted by someone giving it a weird instruction. And that's something that we've invested in quite a lot. And I think have really incredible results, have an incredible team working on.

32:15And it's interesting to think about some of these problems where you can make analogies to humans. Like humans are also susceptible to phishing attacks, to being deceived in different ways, to not really understanding the full context of what they're working on. And we bring those analogies into our development process and think about this. Whenever we release a model, develop a model, how do we ensure that it's going to be aligned with people and be able to actually be helpful? And that is something that we care quite a lot about. I think that there are bigger questions about the world, the economy, how does everything change?

32:47How does everyone benefit from this technology? They're not purely technical, not purely something that OpenAI on our own will be able to solve. But yes, I think quite a lot about not just pushing forward the technology, but also really about how do we ensure that we have the positive impact that is its potential.

33:04Big Technology Podcast Host:The worry though is that this is a race and what's being done within these walls for at OpenAI headquarters is also being copied by many of the open source players, which have much less boundaries and barriers and protection on the safety side of things. And I think you said this once that it takes, you know, people getting a lot of things right to be creative and sort of one person with bad intent to be destructive. And that's sort of where the concern lies for me, at least is just when this, this is, it's clearly a race. It's going fast. Many of your counterparts have said, if everybody agrees to stop it, we'll stop it.

33:44Big Technology Podcast Host:And, uh, and it doesn't seem like it's going to slow at all. So is the reward worth the risk, basically? I think the reward is worth the risk, but I think that that is too coarse-grained of an answer in some sense. The way that I think about it is that we've asked from the beginning of OpenAI, what does a great future look like? How can this technology really be something that uplifts everyone? And you can think of there almost being two different angles. One is the centralization view of saying that, well, the way to make this technology safe is that you have only one actor building it. And so then you don't have any pressures, right?

34:24You can really think about getting it right and then figure out how to roll it out to everyone when it's ready, those kinds of things. That's a pretty tough pill in some ways. And I think that there's a lot of properties that you can instead think about approaching differently, which we refer to as resilience. to think of it as this open system where there's lots of players who are developing the technology. But it's not just about the technology. It's about building societal infrastructure that helps this technology really go well. And if you think about how electricity has developed, that's something where lots of people produce it, that it actually has dangers and risks.

34:59But we also build our safety infrastructure in a diversity of different ways around safety standards for electricity, around different ways of harnessing it, about how you scale it that there's regulations when you're at these massive scales that lots of people are able to use in a democratized fashion. There's inspectors, like there's a whole system that's been built around the needs of that technology, the proclivities of that specific technology. And I think that one thing that we have really, I think, seen with AI is that it is something where we need this broad conversation. We need lots of people to be aware if this technology is going to come and change everything for everyone.

35:39People need to participate in that. It can't be something that's done off in secret by just one sort of centralized group. And so this has been, to me, a very core question to how this technology should play out and something we really believe in is this resilience ecosystem that should emerge around the development of this technology.

35:59Big Technology Podcast Host:So you said we're in takeoff, in the middle of the takeoff process, and we, I guess, all of humanity are experiencing this. NVIDIA CEO Jensen Wang said recently that he believes AGI has been achieved. Do you agree? I think that AGI has a different definition to many people. And I think that there are many people who would say that what we have right now is AGI. I think you can debate it. But I think that maybe the thing that's interesting is that AGI, like the technology we have right now is very jagged. like it is absolutely superhuman at many tasks when it comes to writing code those kinds of things the AI can just do it right and it really removes a lot of the friction to creating things but there's some very basic tasks that a human can do that our AI still struggle with and so it's almost to say that where do you draw the cut line it's a little bit more of a vibe than a feeling than it is science at the moment.

37:01And so I think for myself, we're definitely going through that moment. And if you were to show me five years ago, the systems we have today, I'd go, yeah, that's what we're talking about. But it's just different. It's so different from anything we ever pictured. And so I think we need to adjust our mental models appropriately. So you're not there yet. I think that I'd say I'm basically like 70, 80 % there. So I think we're quite close. I think it's extremely clear that we are going to have AGI within the next couple of years in a way that is still going to be jagged, but that the floor of task will just be almost for any intellectual task of how you use your computer.

37:40The AI will be able to do that. And I think that right now I have to give a little bit of an uncertain answer because it's almost like an uncertainty principle kind of thing that you can debate it. For my own personal definition, I think we're almost there. And with maybe a little bit more, we will absolutely be.

38:00Big Technology Podcast Host:Okay, well, we're got to go to a break. But as long as we're on the way to the break, I want to let folks watching at home know that you and I are going to be talking again June 18th here in San Francisco at SF Jazz. So I will put some information if you want to come join that conversation in the show notes. And I do hope you sign up. All right. We'll be back right after this. I've interviewed a lot of great tech founders on this show, and one surprisingly universal challenge comes up again and again, finding the right domain name. It's something I ran into myself when launching big technology.

38:30Big Technology Podcast Host:The names you want are often taken, and it's tempting just to settle and move on. But the founders I respect most don't settle on fundamentals, and your name is one of them. It should immediately signal what you actually build. That's what I appreciate about.tech domain names. It just makes sense. It tells the world, your customers, your investors, anyone Googling you, that you're building in technology. Clean, direct, no qualifiers. And I'm seeing more serious startups lean into it. Nothing.tech, 1x.tech, Aurora.tech, CES.tech, Ultra.tech, Alice.tech, Neon.tech, Blaze.tech, Pi.tech, and so many more.

39:05Big Technology Podcast Host:If you're building something tech first, don't settle. Secure your.tech domain from any registrar of your choice and make your positioning obvious from day one.

39:17Big Technology Podcast Host:In a classroom of sodas, most stay quiet. Then there's Mr. Pibb, sweet cherry, bold outbursts, the kind of flavor that gets attention. Bold kick of cherry. And we're back here on Big Technology Podcast with OpenAI co-founder and president Greg Brockman. Greg, let me just ask you, what happened in December 2025? Because it seems like it was an inflection point where all this idea of letting the machine code for hours uninterrupted went from theory to a moment where everyone said, I think I can trust this to keep going for a while. So what exactly happened? So new model races really went from the AI being able to do like 20 % of your tasks, like 80%.

40:03And that was this massive shift because it went from being kind of a, yeah, it's a nice thing to do to, you absolutely need to retool your workflow around these AIs. And for myself, I've very much had this moment where I have a test prompt that I've been using for years of build a website for me. I'd actually built this website back when I was learning to code, took me months. Used to be over the course of 25 that, you know, take like four hours, bunch of different prompts to get it right. In December, one shot, just asked the AI one time and it produced it and did a great job. So how did those models make the leap?

40:40Well, a lot of it is about the better base models. That one thing about OpenAI is that we've been working on improving our pre-training technology for quite some time. And that in that moment, we got to see a little taste of what is going to be coming for the rest of this year. But it's also really about not any one thing. It's about we're constantly pushing on every single axis of innovation. And the thing that's very interesting about these models is in some ways you get these leaps. And in some ways it's all continuous, right? It didn't go from 0 % to 80%, from 20 % to 80%. And so in some ways it just got better.

41:20And I think that we've actually seen this improvement continue with every single point release that we've had. like between 5.2 and 5.3, one of my engineers I work with very closely went from, he couldn't get it to do the like low level hardcore systems engineering he does to it absolutely being created. He gives it a design doc. It actually implements it as metrics, observability, runs the profiler, improves it to the point that it's the exact thing that he was hoping to produce. And so I think that the way to think about it is it's almost a sort of slowly, slowly, slowly all at once, but it is all indicated by what's kind of working right now, certainly within a year, sometimes much sooner, is going to be incredibly reliable.

42:03Big Technology Podcast Host:And it surprised you because I heard you talking on an interview not long ago about how Codex, right, this autonomous coder, was just for software developers. And earlier this conversation, you said, actually, everyone can use this stuff. Yes. What led to the fact that you sort of changed your perspective on it? Well, I think I've been focusing on codex and it's got the code in it, right, as really being for coders and thinking about people within OpenAI because many of us are software engineers building for ourselves. It's very natural to think that way. But as this technology has been progressing, we've started to realize that the underlying technology we've produced is mostly not about code at all.

42:46It's mostly about solving problems. It's mostly about being able to manage context and harnesses and think about how an AI should integrate and do work. And that's something that becomes both, even for code, suddenly anyone can have access because you can manage something that's going to go do work, right? If you have a vision, you have something you want to accomplish. You can describe your intent. The AI can execute, can get that done. But then it also starts to be like, well, why am I just focused on coding? Like there's so much just very mechanical skill associated with Excel spreadsheets, with presentations.

43:20And if the AI has the context, it has the raw intelligence now to be able to do these things at a great level. So if we can just make it more accessible, suddenly it goes from codexes for coders to codexes for everyone.

43:32Big Technology Podcast Host:And soon after this moment where we saw all this improvement, there was another phenomenon in Silicon Valley, which was OpenCloud. right which is and maybe it's the broader tech community where people started to trust it in ways that you suggested giving the an ai bot access to their their desktop or getting a mac mini and giving it access to like their mail and calendar there and their files and then just kind of letting it go run their life and then open ai brought the founder of open claw in-house so you talked a little bit more about the AI as something that will help run your life for you in a way.

44:12Is that the vision by bringing the OpenCloud team in house? Well, I say that the core thing about this technology is that figuring out how it's useful, how people want to use it, what is the vision for agents? How is it going to slot in people's lives? That is a hard problem. And that one thing I've seen across many generations of this technology is the people who really lean in, who have a lot of curiosity, who have a lot of vision. That's a real skill and that's an emerging, very valuable skill in this new economy that is emerging. And Peter, who is the Open Call founder, is I think someone who's got incredible vision, incredible creativity.

44:47And so to some extent, it's about the specific technology, but to some extent, it's not at all. It's really about the, how do we take these capabilities and figure out how they're slotting people's lives? And so I think as a technologist, it's very exciting, But as someone who is focused on bringing utility to people, that's something that we are doubling down on and investing quite a lot.

45:08Big Technology Podcast Host:You had a pretty interesting quote about this recently, talking about getting these autonomous AI agents to work on your behalf. You said, when you do it, you become this CEO of a fleet of hundreds of thousands of agents that are completing your objectives, your goals, your vision. And you're not in the weeds on exactly how different things are solved. And in some ways, this new way of work can make you feel like you're losing your pulse on the problem. Is that good? I think that there's a mixed bag. And so I think that what we need to do is acknowledge the strengths of what these tools can deliver and mitigate the weaknesses.

45:46And so giving people leverage, agency, making it so that if you have a vision, something you want to accomplish, that you can have a fleet of agents that will go do it for you. but if you think about how the world works that at the end of the day there's an accountable party right if you're trying to build a website and your agent messes it up and your user is affected it's not really the agent's fault it's your fault and so you need to care and i think that for people to use these tools right you need to realize that human agency human accountability that's a core part of the system, how the human uses the AI, that's something that is deeply fundamental.

46:26And so I think the important thing is that as a user of these agents, and we do this within OpenAI, you cannot abdicate responsibility. You cannot just say, ah, the AI is just going to do stuff.

46:35Big Technology Podcast Host:Of course, but you said feel you're losing your pulse on the problem itself. That's different than accountability layer to it. Well, to me, they actually are linked together because the point is that if you're a CEO and you're too far from the details, right? If you're running this company, you're running this team and that you've lost your finger on the pulse. That is something that's not going to lead to great results. And so the point that I was trying to make there is that not that it's a desirable thing for humans to not have to know about what's going on. There are some details that because you can trust, like if you are working with a team, like a general contractor to build a house, there's a bunch of details there that you probably don't need to worry about because you can trust that they'll be taken care of.

47:17But at the end of the day, if there are details that are wrong, you should care about it. You should be aware. And so this is, I think, an important nuance of you cannot just blindly say, I'm okay with losing my finger on the pulse, that we need to lean in and say, I need to keep it there to really understand the strengths and weaknesses. And that as you disengage from some of these details, these lower level mechanical things, you should do it because you have built trust with a system that it will do a good job.

47:47Big Technology Podcast Host:One last question about the models. You've talked a little bit about the evolutions that the models have gone through. Pre-training and fine-tuning, reinforcement learning that gets it more equipped to solve problems step-by-step and go out on the internet and do things. And now we're in this moment where the models have learned through that process to use tools. And correct me if I'm wrong on this one. What is next in that progression? Well, I think that the world that we're in is one of this increasing capability and depth of what the machine can do. And some of this is about we've got this tool use, but now we also need to actually build really great tools.

48:31You think about something like computer use and AI that can actually use a desktop. Then it is really able to do anything that you can do. But we also have to build a little bit for the machine to think about how does in the enterprise credentialing work? How do audit trails and observability work? There's a lot of technology to build to catch up with what the core model capability is. And I think the overall direction of travel includes things like a really great speech interface. So you can just talk to your computer naturally and just as natural as this conversation. And it understands you.

49:02It does what you need. It has good advice. It's able to surface that I've been working on this thing. I have a problem. Here's when you wake up in the morning, it says, here's your daily report of how much progress your agents made overnight. Maybe it's running a business for you, which I think is going to be a huge application of this technology. The democratization of entrepreneurship is absolutely coming. And I'll say, here's these problems. There's this customer that's upset. They want to talk to a real human. You should go talk to them. All of that's going to happen. And then I think that the raising of the ceiling of ambition of challenges humanity can solve, that is also a next step for this technology.

49:37And we're seeing the leading edges of it. The thing that I am just very excited to see is almost, if you remember AlphaGoMove37, right, this move that no human ever would have come up with. It's creative. Creative. And it changed humanity's understanding of the game. That is going to happen in every single domain. It will happen in science, in math, in physics, in chemistry. It's going to happen in material science. It's going to happen in biology. It's going to happen in healthcare, drug discovery. But it may also even happen in literature, in poetry, in a bunch of other fields that are going to unlock human creative understanding and ideation in ways we can't imagine right now.

50:15Big Technology Podcast Host:Why do you think that hasn't happened yet, given how strong you say the models are? Well, I think that there is an overhang of what the models are capable of and how people are using them. So the application, well, yeah, it's almost our understanding of what is in these models. Okay. That's something that I think is still emerging. So I think that even with no further progress, there's still a massive shift that will happen. The economy being powered by compute and AI is still going to happen. But I think there's also something where what we've gotten very good at is training models on tasks that can be measured.

50:47And so what we started with was math problems, programming problems, where you have a perfect verifier. And a lot of what the progress has been in bringing us to more open-ended problems has been expanding the space of what can be graded. And the AI itself can really help with that. If the AI is smart and understands things, you give it a rubric for how well a task goes. And of course, for things like creative writing, like, is this a good poem? That's a much harder thing to grade. And so we've had less ability to teach the AI and for it to experience and try things out. But all of that is changing and something that we have a lot of sight for.

51:22You know, it's interesting.

51:23Big Technology Podcast Host:Reflecting on that, Peter Thiel has mentioned, pretty sure that's what he said, that if you're a math person, you're probably in deeper trouble in terms of these models coming from what you do than if you're a words person. And you were a member of Math Club back in the day. Are you not concerned about that? Well, I think that it's much easier to see what we lose than what we gain, right? Because we have a deep understanding of, I used to do things this way. I used to do this math competition. Now that I can do the math competition, but it was never really about the math competition, right? Right.

51:55That's not really the thing that drives humanity. And if you think about the way that we do work right now of there's a box and we type behind a box, we weren't doing that a hundred years ago. That's not natural. That's not this digital world that we all got kind of sucked into. that's not really what being human's about being a human's about being here being present connecting with other humans and i think that what we're going to see is that ai is going to free up so much time to increase human connection to build more bonds across people and that's something i'm

52:27Big Technology Podcast Host:extremely excited about okay and then as we shift well as you shift really to uh these more agentic use cases. There's been discussion about whether the bigger training runs really need to happen. And, you know, especially if you like get the model good enough, then you could sort of let it go out in the world and then you can effectively get much of the uplift in areas that aren't the pre-training, which is what these big data centers are needed for. So you work on scaling here. lead that process. What do you think about that argument? Well, I think it misses something very important for how the technology development goes, because it is absolutely the case that every single step of the model production pipeline multiplies.

53:19And so you want to improve all of them. And the thing that we see is we prove the pre-training. It makes all the other steps much easier. And it makes sense because it's a model is able to learn faster. It's a model that is because it already is like more capable to start when it's trying out different ideas and learning from its own mistakes, that process just is faster. It needs to make fewer mistakes. And so I think that the big shift has been from thinking of it as just, it's, you're just training this cerebral system on its own and just make it bigger and bigger to, it's also about trying things out.

53:53It's also about understanding how people are using it in the real world and connecting that back into your training, but it doesn't remove the value and the importance of continuing that research. And the thing that I think has also shifted is we used to really just focus on the raw pre-training capability, but not think as much about the inference ability. And that's been a big change over the past 24 months to realize that it's a balance between you can have this model that has all those great properties in the base, but then you really need it to be able to be inferensible because you need to reinforcement learning you need to serve it to the world and that that means that you don't necessarily go as big as you possibly could because you also really think about there's going to be all this downstream use and you really want the thing that has the best intelligence times that that cost and to optimize those two things together do you still need the nvidia gpu if things move mostly to inference we absolutely do yes why well because the there's multiple reasons But one is that even as the balance of how much inference versus training changes, that you cannot get massive scale training through any other way besides this concentration of compute on one problem.

55:08And so I think that the thing that I think will happen is there's some amount of the deployment footprint goes up quite a lot, but that sometimes there will be you have a particular massive pre-training run and you really want to concentrate a bunch of compute in there. I also think that the NVIDIA team is just incredible and does really, really amazing work. And so, yeah, we partner very closely with them.

55:29Big Technology Podcast Host:Isn't there going to be a time where people just say we've pre-trained enough, the models are smart enough? I think that that's a little bit like once humanity has solved all problems in front of us, then maybe we can say that. But I think that the ceiling of what we want to accomplish, I think that there's just so much ambition that maybe we've, over the past 50 years or so, just sort of backed off from. You think about even problems that seem very clear, like can we have healthcare for everyone that is actually preventative, not just targeting when people have a problem, but really think about the lifestyle and how to really help people early detect potential diseases before they happen.

56:09Like that's a problem that I think we can actually achieve through more intelligent models. And there's probably some level where you can totally solve that problem. And then you say, well, do I need a model that's two times smarter? But there's other problems that are going to demand that.

56:25Big Technology Podcast Host:Let's talk about the math about building these data centers. It raised$110 billion earlier this year. What's the math behind that? Does that money go right into data centers? How do you think about how you're going to return that money to investors? Talk about those calculations. Yeah. So I think it's as simple as the massive expense we see in front of us is compute, but you can think of compute not as a cost center, but as a revenue center. Think of it a little bit like hiring salespeople, right? How many salespeople do you want to hire? As long as you can sell your product, as long as you have a scalable way to sell that product, then the more salespeople you have, the more revenue you will make.

57:03And I think the world that we're in is we have continually found we cannot build compute fast enough to keep up with demand. And I see this very concretely, right? Right now, we have to make very painful decisions about what we're launching, about where the compute goes, and that I think we're going to experience this more broadly within the economy. As we shift to this AI-powered economy, the question will be, what problems are going to get that massive compute? How do you scale so everyone can have a personal agent running for them? And how can everyone be using systems like Codex? Like there just isn't enough compute in the world to be able to do that.

57:37And so we're trying to get ahead of that problem.

57:39Big Technology Podcast Host:But it is a new category, right? So you're doing it with real confidence. I mean, in sums of money, the world has never seen put towards a project like this. When you're building a new category, how do you do it with certainty that it's going to work out? Well, I think there's several components that go into it. So the first is there is historical precedent at this point. From the moment we launched ChatGPT, I remember talking with my team, having this exact conversation where they said, how much compute should we buy? I said, all of it. I said, no, no, no, really. How much compute should we buy?

58:09I said, no matter how much we try to build, I know we're not going to be able to keep up with the demand. And that has been true. And that has been true every year since then. and the challenge is that these compute purchases you have to lock them in 18 months sometimes 24 months sometimes longer in advance of them actually being delivered which means you really need to project forward and i think that the world that we're moving towards is one where to date most of our revenue has come from consumer subscriptions and that will always be very important there's other revenue streams we have emerging as well but the opportunity that clearly is emerging now is knowledge work.

58:46And we're seeing this very concretely across every single enterprise, realizing this technology, it actually really works and to be competitive, they need to adopt it. And you can see this organic energy of all these software engineers using it. And then we're starting to see the percolation of people using it for various knowledge work inside of the enterprises and the willingness to pay and the revenue growth that you're seeing in this industry is very clear, right? It's very clearly happening right now. And just project that forward. And we look like one thing we get to see that maybe the world doesn't is the line of sight to how these models will improve.

59:24And all of this together says that the economy, which is a massive thing, right? The economy is just so large. It's almost incomprehensible. All of the growth, like the highest order bit on how this economy grows from here will be about AI, how well you can leverage AI and the computational power you have available to power it.

59:43Big Technology Podcast Host:You said consumer subscriptions are your biggest source of revenue right now. Is the projection that that will flip and that business will be the biggest source? I think, well, I think that it is very clear how quickly the enterprise, it's not just enterprise because I think enterprise is also changing what it means. So really people using it for productive knowledge work for those kinds of things. And I think that as we think about pricing, one thing, if you look at how Codex works right now is if you have a ChatGPT consumer subscription, you can use Codex. And so I think it's not going to be as well defined as this category, that category.

1:00:18I think it will really be about you as a user are going to have just again, like your laptop, this portal to the digital world. And that is what the revenue fundamentally will come from.

1:00:29Big Technology Podcast Host:Dario said, I think about you. There are some players who are YOLOing, who pull the risk dial too far, and I'm very concerned. I think he's referencing your infrastructure bets there. What do you think about that? Well, I just disagree. I think we've been very thoughtful and very much seeing what is coming. And I think that we will see even this year how everyone who is participating is going to be compute strapped. And I think we have been the most forward in realizing that this is coming and building and anticipation of how this technology is playing out. And I think that what we have seen is that for other players, that they kind of realized that probably late last year and started scrambling to see what compute is available, and there really wasn't any.

1:01:17And so I think that even as people, it's very easy to make statements like that, but I think that everyone has kind of realized that this technology, it's working, it's here, it's real, right? Software engineering is just the first example of it, and that we are fundamentally limited by the computational power available.

1:01:33Big Technology Podcast Host:And you said that also that if he's off with his prediction by a little bit, then the company could potentially go bankrupt. Is that the same case for you? I think that there's actually more degrees of off-ramp here if you start to worry about the downside case, which I think is a very reasonable question. But to some extent, what I think the bet is on isn't about any one company. It's really about the sector. It's really about, do you believe this technology can be produced and can deliver this massive amount of value that we see coming? And again, I'll point to proof points, right? That software engineering, it's just like the degree to which if you're not a software engineer, you haven't tried code Fs, the degree which is different, like it's just hard to describe.

1:02:15And I think that people will experience it very quickly. Like, you know, six months ago, I think that for us, we saw this internally, but there were less proof points out there. Now there's proof points out there. Six months from now, I think that everyone will feel it. And I think that we will all feel the pain of there's an awesome model and there's just no availability because there's not enough compute.

1:02:37Big Technology Podcast Host:Yeah. But as we were looking at our predictions for 2026 on this show, we had a conversation towards the end of the year last year where Ron John Roy was on with us was like, 2026 is going to be the year where everybody uses agents. And I said, yeah, well, I'll believe that when I see it and I'm using the agents. So here we are. There we go. Um, what, what do you use it for? I, I use it to build, um, build tools internally for my, uh, for the people who I work with to sort of get on the same page about, um, about when videos are coming and what the thumbnails need to look like. And I'm also integrating things from, uh, from YouTube.

1:03:19and so we can basically then rank how the videos are doing based off of thumbnail and like a

1:03:25Big Technology Podcast Host:custom-built piece of software that I never would have paid for and that's one of the things that I think is interesting about this moment I guess is that software it's it scales it's used by the masses but when you use it therefore there's going to be so many things that are not made for you and maybe what this does is it allows us to interact with software in a way that's much more natural. I think that is the key. And again, I just think a lot about the fact that the way we've built computers has really pulled us in into this digital world. You think about how much time you just spend scrolling through your phone, right?

1:04:02The amount of time that you spend clicking different buttons and trying to like connect this thing to that thing. Like why, like why do you have to do that? Instead, the AI being about bringing the machine closer to you, personalizing to you understanding what you're trying to accomplish and that we have all this pop culture of just computers you can talk to and that they go and do stuff for you and it's starting to become real it's starting to become the thing that you can actually do and i think that the amazingness of that is something where you just have to try it to really understand so i definitely think it's a it's a very special moment we're in yep then i

1:04:36Big Technology Podcast Host:want to know why is AI so unpopular with the public? Hugo, for instance, says three times as many Americans expect the effects of AI on society to be negative as they expect it to be positive. I mean, why do you think the reasoning is behind that? And are you concerned about AI's brand? Well, I think that there is something that we need to show the country of why AI is good for them, not just for the broad economy, for growing the GDP and things like that, but how does it help them in their lives? And I think there are actually many very concrete stories that I hear every day. For example, there's a family where their child was having some headaches, some medical issues, was denied an MRI, and they researched the symptoms with ChachiBT and realized that they could make an argument to insurance to actually get the MRI.

1:05:30They did that. it turns out he had a brain tumor. They were able to save his life because they used Chagic PT to get access to the right information. And that's just one story. There are so many more just like that of people who have been deeply, profoundly, their lives have been improved or saved through their use of this technology and through partnering with the technology in a real way. And so that is a story I don't think gets out there. I think that this is happening in so many people's lives, but somehow the story is not yet told. And one thing I noticed is that there's certainly a lot of pop culture from the 90s, from the historical context that we have, that's very negative on AI, that worries about what could go wrong.

1:06:09But when people actually use AI, that they find utility in it, they find value in it. And so I think that I am definitely very concerned about us not having successfully helped people understand why this technology wave is something that will improve their lives, that will help improve human connection. And that is something that's a big focus in my mind. And if you think about the opportunity here and why AI is so important, I think this will be the source of economic and national security going forward. I think it's going to be about national competitiveness and that there are other countries like China where AI pulls in the exact opposite direction.

1:06:46And so, yes, I think it's very, very important that we acknowledge that and we really understand how to get the benefits for everyone.

1:06:54Big Technology Podcast Host:But we also are in a time that's like, critically unstable. There's concerns about, about work, people are, every time I speak with someone about AI, they're like, how long do I have left to work in my job? And then when I think about the data centers, I mean, the polling is even worse than, you know, AI in general, this is repute far more data, far more people say, data centers are mostly bad, good for the environment, home energy costs and quality of life of those nearby. So we are at this moment where good jobs are tough to come by and people see these data centers come into their communities and they say not good for the environment, home energy, and quality of life.

1:07:32Big Technology Podcast Host:I mean, are they wrong? Well, I think there's definitely a lot of misinformation about data centers. A good example is water usage. If you actually look at our Abilene facility, which is one of, if not the biggest supercomputers in the world, the amount of water it uses is the same as a household over the course of a year right so it's really negligible water use and yet that there's a lot of misinformation that these data centers consume a lot and so similar on on power we have a commitment that we are going to pay our own way to not drive up energy prices for people that's something that as an industry now that people are making these commitments because it is very important that we improve local communities.

1:08:18And when we build data centers, we really try to go into those local communities, understand what's happening on the ground, how we can help. There's tax revenue that are associated with these data centers. And I think that there's jobs that they create. There's a lot of benefits that come from them. And so I think that's one thing where it is about how we show up. And that's a responsibility that we take very seriously. Okay.

1:08:43Big Technology Podcast Host:But also like if their power costs are not going to go up, you have to bring in power, which means potentially more pollution. Is that not a concern? Well, I think that there's much more nuance in terms of not driving up energy costs. If you look at how the grid works today, that there's actually a lot of just stranded power, power that is there that is not being utilized and that you need to upgrade the transmission systems. And again, that's something where putting that on us rather than putting that on the rate pairs is very important. There's lots of places where they have clean power that is actually underutilized and just being kind of thrown away.

1:09:16And so there's a lot of benefit that comes from having real reasons for the grid, which is aging and obsolete in many places to upgrade. And that's something actually has real benefits to the community. Like we've seen, for example, in North Dakota, that people's rates have gone down because a data center has shown up and has helped with improving the utilities for everyone.

1:09:39Big Technology Podcast Host:One last question on the politics. You gave$25 million to MAGA Inc, which is a pack that's Protron. You spoke with Wired about it and you said, anything I can do to support this technology benefiting everyone is a thing I will do. And if that makes you a one-issue voter or one-issue political supporter, I'll share the one thing I always wonder when it comes to just this, the one-issue camp is um ultimately doesn't a stronger country make you uh you know sort of make your goals much more feasible even if a candidate isn't you know fully in support of what you're doing like shouldn't a stronger country no matter what be the north star of any political activity um and if it's if that's the case you know then is that is that part of the donation so the way i look at this so My wife and I made that donation.

1:10:38We've donated to bipartisan super PACs as well. I think this technology is one where it's coming quickly within the next couple of years, really going to transform everything, going to be the underpinning of the economy. And it's not popular. And we really want to support politicians who really lean into this technology, really engage with it. And so I think that certainly this technology is about uplifting us as a country. I am a one-issue donor. This is something where I feel like I have a unique contribution to make. But it's really about just expressing support for this technology is something that we should be leaning into as a country.

1:11:18Big Technology Podcast Host:What would you tell someone who's scared of AI? I mean, if you have a moment here where you can speak directly to them, they might think it's going to take my job. It's going to pollute my community. It will change the world too fast. What's your message to that? Number one thing is try the tools because to really understand what it can do for you is something that only by experiencing the AI as it exists now will that really hit home. And we see so much opportunity and potential and empowerment coming from this technology today. You talked a little bit about what you can build now. People who have never built a website before can build a website.

1:11:57If you want to start a small business and you're thinking about all the backend processing and how to actually manage it, all those things, the AI can help you with that right now. And so I think that in your life, thinking about how it can help you with your health, how it can help your loved ones, how it can help you make money, how it can help you save money, these are all going to be on the table. And I think it is much easier to see what's going to change than it is to see what you're going to gain. But I think that it's worth giving it a fair shot of really trying to understand both sides of the equation.

1:12:32Big Technology Podcast Host:That's the one thing that doesn't get talked about in the polling data, by the way. It's the people that have seen it used, but haven't tried it themselves, or the people that have never tried AI are much more negative. And then you get to the power users, or even people that use it casually, and they're generally pretty positive about the technology. For myself, we've been thinking about this technology for a long time. What I see playing out in front of us is more amazing, more beneficial, and going to really have a much more positive impact than we ever imagined. So last one for you, how would you advise someone to prepare themselves for the future?

1:13:06Big Technology Podcast Host:And it has to be more than just getting the tools. I mean, I have friends who come to me and they say, I don't know what's going to happen with my job or the world. And I just need to know what to do with this. I do think that the number one thing is about understanding the technology. One thing we've seen is the people who get the most out of the technology and you have to approach it with a curiosity, trying to really try it in your workflows, really be able to get over that initial hump of you have a blank box. What do I do with a blank box? To really develop this sense of agency, this sense of I can be the manager, I can set the direction, I can delegate, I can try oversight and to really develop that skill because that is something that's going to be fundamental is we're building this technology for humans to help humans foster more human connection for humans to be able to spend more time doing what they want.

1:13:59And so the question is, well, what do you want? And really trying to crystallize that and trying to realize that with the help of this technology is going to be the most important thing. Greg, thanks so much for coming on the show. Thank you for having me.

1:14:11Big Technology Podcast Host:All right. Thank you everybody for listening and watching and we'll see you next time on Big Technology Podcast.

1:14:20Thank you.