[0:00:00] So here's my first question for you. Very, very simple question. What makes you human? Me? Both of you. You both have to answer what makes you human. Oh, and one word. You get one word. Humor. Humor? Emotion. Okay. To confirm you're both human, I'm going to need you to confirm which of these boxes have a traffic light. I think I can do that too now. Okay. All right. Well, Sam, you are actually here.
[0:00:33] Nine years ago at our first TechLive. And I actually want to roll the clip of what you said. OK. Certainly the fear with AI or machine intelligence in general is that it replaces drivers or doctors or whatever. But the optimistic view on this, and certainly what backs up what we're seeing, is that computers and humans are very good at very different things. So a computer doctor will outcrunch the numbers and do a better job than a human on looking at a massive amount of data and saying this.
[0:01:03] On cases that require judgment or creativity or empathy, we are nowhere near any computer system that is any good at this. OK. Does 2023 Sam agree? Partially right and partially wrong. OK, yeah. Could have been worse. Could have been worse? What's your outlook now? People, I think the prevailing wisdom back then was that AI was going to
[0:01:30] And then maybe eventually it was going to do the sort of like higher judgment tasks and then it would kind of do the empathy and then maybe never it was going to be like a really great creative thinker. And creativity has been in some sense, and at this point the definition of the word creativity is up for debate, but creativity in some sense has been easier for
[0:01:59] AI than people thought. You can see Dolly 3 generate these amazing images or write these creative stories with GPT-4 or whatever. So that part of the answer maybe was not perfect. And I certainly would not have predicted GPT-4 nine years ago quite how it turned out. But a lot of the other parts about people still really want a human doctor, that's definitely very true.
[0:02:28] Quickly shift to AGI. What is AGI, Mira, if you could just define it for everybody in the audience? I will say it's a system that can generalize across many domains that would be equivalent to human work. They produce a lot of productivity and economic value. But we're talking about one system that
[0:02:57] Can generalize across a lot of digital domains of human work. And Sam, why is AGI the goal? The two things that I think will matter most over the next decade or few decades to improving the human condition the most, giving us sort of just more of what we want are abundant and inexpensive intelligence. The more powerful, the more general, the smarter, the better.
[0:03:28] I think that is AGI and then abundant and cheap energy. And if we can get these two things done in the world, then it's almost like difficult to imagine how much else we could do. We're big believers that you give people better tools and they do things that astonish you. And I think AGI will be the best tool humanity has yet created. With it, we will be able to solve all sorts of problems. We'll be able to express ourselves in new creative ways. We'll make just incredible things.
[0:03:56] For each other, for ourselves, for the world, for this unfolding human story. And it's new. And anything new comes with change. And change is not always all easy. But I think this will be just absolutely tremendous upside. And we're going to, nine more years, if you're nice enough to invite me back, you'll roll this question. And people will say, how could we have thought we didn't want this?
[0:04:27] I guess two parts to that. My next question, when will it be here and how will we know it's here? From here? Well, either one of you. I mean, you can both predict how long. We'll call you in 10 years and we'll tell you you're wrong. Less than that. Yeah, probably in the next decade. But I would say it's a bit tricky because we...
[0:04:47] You know, when will it be here, right? And I just kind of give you a definition, but then often we talk about intelligence and, you know, how intelligent is it or whether it's conscious and sentient and all of these terms. And, you know, they're not quite right because they sort of define our own intelligence and we're building something slightly different. And you can kind of see how the definition of intelligence evolves from, you know, machines that were really great at chess.
[0:05:15] And AlphaGo, and now the GPT series. And then what's next? But it continues to evolve. And it pushes how we define intelligence. ALFRED FULLER- We kind of define AGIs like the thing we don't have quite yet. So we've moved. I mean, there were a lot of people who would have 10 years ago said, all right, if you could make something like GPT-4 or GPT-5 maybe, that would have been an AGI. And now people are like, well, it's like a nice little chatbot or whatever. And I think that's wonderful. I think it's great that the goalposts keep getting moved. It makes us work harder.
[0:05:44] But I think we're getting close enough to whatever that AGI threshold is going to be that we no longer get to hand wave at it, and the definition is going to matter. So less than a decade? For some definition. Okay. All right. The goalpost is moving. Sam, you've used the word, and previously when describing AGI, the term median human. Can you explain what that is?
[0:06:15] I think there are experts in areas that are going to be better than AI systems for a long period of time. And so you could come to some area where I'm really an expert at some task. And I'll be like, all right, GPT-4 is doing a horrible job there. GPT-5, 6, whatever is doing a horrible job there. But you can come to other tasks where I'm OK, but certainly not an expert. I'm kind of like maybe like an
[0:06:46] Average of what different people in the world could do at something. And for that, then I might look at it and say, oh, this is actually doing pretty well. So what we mean by that is that in any given area, experts in any area can just do extraordinary things. And that may take us a while to be able to do with these systems. But for the more average case performance, so me doing something that I'm not very good at anyway,
[0:07:16] Maybe our future versions can help me with that a lot. So am I a median human? At some tasks, I'm sure. And clearly at this, you're a very expert human. And no GPT is taking your job anytime soon. OK. That makes me feel a little better. Mira, how's the GPT-5 going? We're not there yet. But it's kind of need-to-know basis. I'll let you know. That's such a diplomatic answer.
[0:07:46] I'm going to make Mira do all of these things. No, I would have just said, oh, yeah, here's what's happening. That's great. I'll leave it there. No, no, no. We're not sending him back here. Who paired these two? Whose idea was this? You're working on it. You're training it. We're always working on the next thing. Just do a staring contest. That's what makes us human. All of these steps, though, with GPT, right, or GPT-3, 3.5, 4, are steps towards AGI.
[0:08:20] With each of them, are you looking for a benchmark? Are you looking for this is what we want to get to? Yeah, so, you know, before we had the product, we were sort of looking at academic benchmarks and how well these models were doing on academic benchmarks. And, you know, OpenAI...
[0:08:38] is known for betting on scaling, throwing a ton of compute and data on these neural networks and seeing how they get better and better at predicting the next token. But it's not that we really care about the prediction of the next token. We care about the tasks in the real world to which this correlates to. And so that's actually what we started seeing once we put out
[0:09:03] Research in the real world. And we build out products through the API, eventually through chat GPT as well. And so now we actually have real world examples. We can see how our customers do in specific domains, how it moves the needle for specific businesses. And of course with GPT-4, we saw that it did really well in exams like.
[0:09:30] So it kind of goes to our earlier point that we're continually evolving our definition of what it means for these models to be more capable. But as we increase the capability vector, what we really look for is reliability and safety. These are very interweaved, and it's very important to make systems that
[0:09:58] So we're kind of pushing in both of these vectors at the same time. And, you know, as we build the next model, the next set of technologies, we're both betting, continuing to bet on scaling, but we're also looking at...
[0:10:24] You know, this other element of multimodality because we want these models to kind of perceive the world in a similar way to how we do. And, you know, we perceive the world not just in text, but images and sounds and so on. So we want to have robust representations of the world in these models. Will GPT-5 solve the hallucination problem?
[0:10:53] Well, I mean, actually, maybe. Like, let's see. We've made a ton of progress on the hallucination issue with GPT-4, but we're still quite, we're not where we need to be. But, you know, we're sort of on the right track.
[0:11:12] It's unknown. It's research. It could be that continuing in this path of reinforcement learning with human feedback, we can get all the way to really reliable outputs. And we're also adding other elements like retrieval and search. So you have the ability to provide more factual answers or to get more factual outputs from the model. So there is a combination of technologies that we're putting together to kind of...
[0:11:41] Reduce the hallucination issue. Sam, I'll ask you about the data, the training data. Obviously there's been maybe some people in this audience who may not be thrilled about some of the data that you guys have used to train some of your models. Not too far from here in Hollywood, people have not been thrilled, publishers. When you're considering now as you're walking through and going to work towards these next models, what are the conversations you're having around the data?
[0:12:12] So a few thoughts in different directions here. One, we obviously only want to use data that people are excited about us using. We want the model of this new world to work for everyone. And we want to find ways to make people say, you know what? I see why this is great. I see why this is going to be a new way that we think about some of these issues around data ownership and how economic flows work.
[0:12:42] We want to get to something that everybody feels really excited about. But one of the challenges has been different kinds of data owners have very different pictures. So we're just experimenting with a lot of things. We're doing partnerships of different shapes. And we think, like with any new field, we'll find something that just becomes a new standard. Also, I think as these models get smarter and more capable,
[0:13:10] We will need less training data. So I think there's this view right now, which is that models are going to have to train on every word humanity has ever produced or whatever. And technically speaking, I don't think that's what's going to be the long-term path here. We have existential proof with humans that that's not the only way to become intelligent. And so I think the conversation gets a little bit led astray by this, because what
[0:13:40] What really will matter in the future is particularly valuable data. People trust the Wall Street Journal, and they want to see content from that. And the Wall Street Journal wants that, too. And we find new models to make that work. But I think the conversation about data and the shape of all of this, because of the technological progress we're making, it's about to shift. Well, publishers like mine, who might be out there somewhere, they want money for that data.
[0:14:09] Is the future of this entire race about who can pay the most for the best data? No, that was sort of the point I was trying to make, I guess, inelegantly. But you still need some. You will need some. But the core, like the thing that is, the thing that people really like about a GPT model is not fundamentally that it has, that it knows particular knowledge. There's better ways to find that. It's that it has this.
[0:14:39] Larval reasoning capacity. And that's going to get better and better. But that's really what this is going to be about. And then there will be ways that you can set up all sorts of economic arrangements as a user or as a company making the model or whatever to say, all right, now I understand that you would like me to go get this data from the Wall Street Journal. I can do that, but here's the deal that's in place. So there will be things like that. But the fundamental thing about these models is not that they memorize a lot of data.
[0:15:03] So sort of like the model where also, right now, you've got Bing integrated. It goes out, looks for some of that data, and can bring back some of the- Yeah, and that's, you know, on the internet, we decided, again, back in the early days of the internet, there were a lot of conversations about what the different models could be, and we all kind of decided on, you know, here's the core framework, and there's different pieces in there, of course, and we're all going to have to figure that out for AI. Well, speaking of Bing, you and Satya Nadella, you're $10 billion friends or frenemies? Friends. Yeah? Yeah.
[0:15:32] I won't pretend that it's a perfect relationship, but nowhere near the frenemy category. It's really good. We have our squabbles. We get annoyed at each other about different stuff. It just seems increasingly, as you guys are releasing more and more products, that they seem to compete in some places. I mean, I think that there's something core about this language interface that is a big deal. And so there's going to be a lot of people doing things for that.
[0:16:05] And then there's other places, like we offer a version of the API. They offer a version of the API. But that's a very friendly thing. And we work it out so that we all benefit, and we're all happy. And we jointly want as much usage of our models as possible. So we're super aligned there. But yeah, it's like. EMILY FORTUNA- That makes sense. Friends. CHRIS SULLIVAN- Friends. EMILY FORTUNA- You guys text. CHRIS SULLIVAN- We do, a lot. EMILY FORTUNA- OK, all right, all right.
[0:16:36] Mira, you over the last number of months have started to roll out a lot more personalization to the model. You can put in custom instructions. You can get the voice now. And we're going to bring down the screen to hear one of these clips. But I wanted to play one of these conversations I had with ChatGPT just yesterday. It's a slow screen. ChatGPT responds quicker than this. That's really embarrassing.
[0:17:08] We're going to get that better, though. Yeah? We're going to work on that? We'll get it faster. That I can't help you with. No? We'll get ChatGPT faster. No, I'm saying ChatGPT is faster than this. Yeah, yeah, but it's still, and I'm saying that's like a, it's not often that people say ChatGPT is faster than X. I've got a big interview with OpenAI executives, and I'm nervous. Any advice? Absolutely. Given your background as a technology columnist, you're well equipped for this. Just be yourself.
[0:17:39] Do your homework on OpenAI's latest developments and trust in your expertise. Remember, they'll likely appreciate thoughtful and well-researched questions. And hey, everyone gets nervous. Take a deep breath and rock it. Am I rocking it? Definitely. I'm rocking it. All right. Are we rocking it up here? Okay. But what's so striking about that, first of all, that's the voice of ChatGPT. They have one of five? Yeah.
[0:18:09] Yeah, I mean, that is the voice. It sounds so human. It sounds so natural. It knows about me because I've already put into custom instructions I'm a tech journalist. It also knows I'm allergic to avocado. It's always putting that in there. Don't eat avocado. I'm like, I'm not asking about avocado. We've got some work to do. Is there a future, and this is what you're maybe trying to build here, where we have deep relationships with this type of bot? It's going to be a significant relationship, right? Because...
[0:18:40] You know, we're building the systems that are going to be everywhere in your home, in your educational environment, in your work environment, and maybe, you know, when you're having fun. And so that's why it's actually so important to get it right. And we have to be so careful about how we design this interaction so that ultimately it's, you know.
[0:19:01] Elevating, and it's fun, and it makes productivity better, and it enhances creativity. And this is ultimately where we're trying to go. And as we increase the capabilities of the technology, we also want to make sure that on the product side, we feel in control of.
[0:19:24] These systems in the sense that we can steer them to do the things that we want them to do and the output is reliable. That's very important. And of course, we want it to be personalized, right? And as it has more information about your preferences, the things you like, the things you do, and the capabilities of the models increase and other features like memory and so on, it has, of course, it will become more precise.
[0:19:53] How do you guys think about that control? First of all, I think there's...
[0:20:25] We're not going to be the only player here. There's going to be many people. So we get to put our nudge on the trajectory of this technological development. And we've got some opinions. But A, we really think that the decisions belong to humanity, society as a whole, whatever you want to call it. And B, we will be one of many actors building sophisticated systems here. So it's going to be a society.
[0:20:49] Wide discussion. And there's going to be all the normal forces. There will be competing products that offer different things. There will be different societal embraces and pushbacks. There will be regulatory stuff. It's going to be the same complicated mess that any new technological birthing process goes through. And then pretty soon, we'll turn around and we'll all feel like we had smart AI in our lives forever. And that's the way of progress. And I think that's awesome. I personally have
[0:21:19] Deep misgivings about this vision of the future where everyone is super close to AI friends and not more so than human friends or whatever. I personally don't want that. I accept that other people are going to want that. And some people are going to build that. And if that's what the world wants and what we decide makes sense, we're going to get that. I personally think that personalization is great. Personality is great. But it's important that it's not like person-ness.
[0:21:49] And at least that you know when you're talking to AI and when you're not. We named it ChatGPT. It's a long story behind that. But we named it ChatGPT and not a person's name very intentionally. And we do a bunch of subtle things in the way you use it to make it clear that you're not talking to a person. And I think what's going to happen is that in the same way that people have a lot of relationships with people, they're going to keep doing that. And then there will also be these AIs in the world.
[0:22:18] You kind of know they're just a different thing. When you're saying this is another question for you, what is the ideal device that we'll interact with these on? And I'm wondering if you, I hear you and Johnny Ive have been talking. You bring something to show us? I think there is something great to do, but I don't know what it is yet. You must have some idea. A lot of ideas. Yeah? I'm interested in this topic. I think it is possible.
[0:22:48] I think most of the current thinking out there in the world is quite bad about what we can do with this new technology in terms of a new computing platform. And I do think every sufficiently big new technology enables some new computing platform. But lots of ideas, but in the very nascent stage. I guess the question for me is,
[0:23:16] Is there something about a smartphone or earbuds or a laptop or a speaker that doesn't quite work right now? Yeah, of course. Smartphones are great. I have no interest in trying to go compete with a smartphone. It's a phenomenal thing at what it does. But I think the way what AI enables is so fundamentally new that it is possible to, and maybe we won't.
[0:23:46] Maybe just for a bunch of reasons it doesn't happen. But I think it's well worth the effort of talking about or thinking about what can we make now that before we had computers that could think or computers that could understand, whatever you want to call it, was not possible. And if the answer is nothing, it would be a little bit disappointing. It sounds like it doesn't look like a humanoid robot, which is good. Definitely not.
[0:24:17] I don't think that quite works. OK. Speaking of hardware, are you making your own chips? You want to answer? No. Directed here. Are we making our own chips? We are trying to figure out what it is going to take to deliver at the scale that we think the world will demand and at the model scale that we think the research can support. That might not require
[0:24:47] Any custom hardware. And we have wonderful partnerships right now with people who are doing amazing work. So the default path would certainly be not to, but I would never rule it out. Are there any good alternatives to Nvidia out there? Nvidia certainly has something amazing, amazing. But I think the magic of capitalism is doing its thing, and a lot of other people are trying.
[0:25:16] We'll see where it all shakes out. We had Renee Haas here from Armstead. Renee is great. I hear you guys have been talking. We said hello. Just friends. Oh, you said hello. Not as close as Satya. You're not as close. Not as close as Satya. No, not as close. OK, got it, got it. This is the really hard-hitting. This is where we're getting to the hard-hitting. Well, actually, we're about to get to the hard-hitting. All right. My colleagues recently reported you guys are actually looking at the valuation is $80 to $90 billion.
[0:25:45] And that you're expected to reach a billion in revenue. Are you raising money? No. Well, I mean always, but not like this minute. Not right now. Not right now. There's the people here with money. All right, let's talk. We will need huge amounts of capital to complete our mission. And we have been extremely upfront about that. There has got to be something more interesting to talk about in our limited time here together than our future capital raising plans.
[0:26:17] We will need a lot more money. We don't know exactly how much. We don't know exactly how it's going to be structured, what we're going to do. But it shouldn't come as a surprise because we have said this all the way through. It's just a tremendously expensive endeavor. Which part of the business, though, right now is growing the most? Mira, you can also jump in. Definitely in the product side.
[0:26:39] With the research team, it's very important to have density of talent, small teams that innovate quickly. The product side, we're doing a lot of things. We're trying to push great uses of AI out there, both on platform side and first party, and work with customers. And the revenue is coming mostly from that API? The revenue for the company. I'd say both sides. Both sides.
[0:27:10] My subscription to ChatGPT Plus is... We appreciate that. Yeah, yeah. How many people here actually are subscribers to ChatGPT Plus? Thank you all very much. Okay. Can you guys make a family plan? Maybe we should. No, it's serious shit. Because I'm spending on two. We'll talk about it. Yeah, okay. This is what we're really here for tonight. Moving out a little bit into policy and some of the fears. It's not, like, super cheap to run. If we had a way to, like... Yeah.
[0:27:38] Say like, hey, you know, you can have this for like, we can give you like way more for the 20 bucks or whatever. We would like to do that. And as we make the models more efficient, we'll be able to offer more. But it's not for like lack of us wanting more people to use it that we don't do things like family plans. We have a family plan for like $35 for two people. That's the kind, we'll work on it. We're haggling, you know? Well, I gave you the sweatshirt. You did, you did, yeah. There's something we can do there. How do we go from...
[0:28:05] The chatbot we just heard that told me to rock it to one that, I don't know, can rock the world and end the world. Well, I don't think we're going to have a chatbot that ends the world. Well, how do we go to this idea of we've got simple chatbots. They're not simple. They're advanced, what you guys are doing. But how do we go from that idea to this fear that is now pervading everywhere? If we are right about the trajectory things are going to stay on, and if we are right about
[0:28:37] Not only the scaling of the GPTs, but new techniques that we're interested in that could help generate new knowledge. And someone with access to a system like this can say, help me hack into this computer system, or help me design a new biological pathogen that's much worse than COVID, or any number of other things. It seems to us like it doesn't take much imagination to think about scenarios that deserve great caution.
[0:29:05] And again, we all come and do this because we're so excited about the tremendous upside and the incredibly positive impact. And I think it would be a moral failing not to go pursue that for humanity. But we've got to address, and this happens with many other technologies, we've got to address the downsides that come along with this. And it doesn't mean you don't do it. It doesn't mean you just say, this AI thing, we're going to like.
[0:29:32] We're going to go full dune and blow up and not have computers or whatever. But it means that you are thoughtful about the risks. You try to measure what the capabilities are. And you try to build your own technology in a way that mitigates those risks. And then when you say, hey, here's a new safety technique, you make that available to others. And as you guys are thinking about building in this direction,
[0:30:02] What are some of those specific safety risks you're looking to put in? I mean, like Sim said, you've got the capabilities and then there is always a downside. Whenever you have such immense and great capability, there's always a downside. So we've got a fierce task ahead of us to figure out what are these downsides, discover, understand them, build the tools to mitigate them. And it's not, you know, like...
[0:30:30] You usually have to intervene everywhere from the data to the model to the tools in the product and of course policy and then thinking about the entire regulatory and societal infrastructure that can kind of keep up with these technologies that we're building because ultimately what we want is to slowly roll out these capabilities in a way that
[0:30:59] We want to allow for adaptation and for the whole infrastructure that's needed for these technologies to actually be absorbed productively to exist and be there. So, you know, when you think about what are sort of the concrete safety
[0:31:28] I would say number one is actually rolling out the technology and slowly making contact with reality, understanding how it affects
[0:31:42] I think that building the technology in a lab in a vacuum without contact with the real world and with the friction that you see with reality is a good way to actually deploy it safely.
[0:32:11] And this might be where you're going, but it seems like right now you're also policing yourself, right? For sure. You're setting things better. And Sam, that's where I was going to ask you. I mean, you seem to spend more time in Washington than Joe Biden's dogs right now. I think I've only been twice this year. Really? I think his dog's only here anymore. Like three days or something. So anyway. But what is it specifically that you would rather the government and our regulators do versus you have to do? First, the point Mira was making, I think, is really important.
[0:32:41] It's very difficult to make a technology safe in the lab. Society uses things in different ways and adapts in different ways. And I think the more we deploy AI, the more AI is used in the world, the safer AI gets. And the more we kind of collectively decide, hey, here's a thing that is not an acceptable risk tolerance. And this other thing that people are worried about, that's totally OK. And we see this with many other technologies. Airplanes have gotten unbelievably safe.
[0:33:10] Even though they didn't start that way. And it was like careful, thoughtful engineering and understanding why when something went wrong, it went wrong and how to address it. And the shared best practice is there. I think we're going to see in all sorts of ways that the things that we worry about with AI in theory don't quite play out in practice. There's a ton of talk right now about deep fakes and the impact that's going to have on.
[0:33:40] Society in all these different ways. I think that's an example of where we were thinking about the last generation too much. And AI will disrupt society in all of these ways. But we all kind of are like, they're like, oh, that's a deep fake. Or, oh, it might be a deep fake. Or, oh, that picture or video or audio. We learn quickly. But maybe the real problem, this is like speculation, this is hard to know in advance, is not the deep fake ability, but the sort of customized one-on-one persuasion.
[0:34:09] And that's where the influence happens. It's not like the fake image. It's that this thing has a subtle ability. These things have a subtle ability to influence people. And then we learn that that's the problem, and we adapt. So in terms of what we'd like to see from governments, I think we've been very mischaracterized here. We do think that international regulation is going to be important for the most powerful models. Nothing that exists today, nothing that will exist next year.
[0:34:36] But as we get towards a real super intelligence, as we get towards a system that is more capable than any humans, I think it's very reasonable to say we need to treat that with caution and a coordinated approach. But we think what's happening with open source is great. We think startups need to be able to train their own models and deploy them into the world. And a regulatory response on that would be a disastrous mistake for this country or others.
[0:35:05] So the message we're trying to get across is you've got to embrace what's happening here. You've got to make sure that we get the economic benefits and the societal benefits of it. But let's look forward at where we believe this might go, and let's not be caught flat-footed if that happens. You mentioned deepfakes, and I want to talk about AI-generated content that's all over the internet now. Who do you guys think is responsible or should be?
[0:35:35] Is this on the social media companies? Is this on OpenAI and all the other AI companies? We're definitely responsible for the technologies that we develop and put out there and, you know, misinformation.
[0:35:53] That's clearly a big issue as we create more and more capable models and we've been developing technologies to deal with the provenance of an image or a text and detect output. But it's a bit complicated because, you know...
[0:36:09] You want to give the user sort of flexibility and you also don't want them to feel monitored and so you have to consider the user and you also have to consider people that are impacted by the system that are not users.
[0:36:24] These are quite nuanced issues that require a lot of interaction and input from not just your users of the product, but also of society more broadly. And figuring out also with partners that bring on this technology and integrate it, what are the best ways to deal with these issues.
[0:36:45] Because right now, there's no way or no tool from OpenAI, at least, that I can put in an image or some of the text and ask, is this AI generated, correct? For image, we have actually technology that's really good, almost 99% reliable. But we're still testing it. It's early. And we want to be sure that it's going to work. And even then, it's not just a technology problem. This information is.
[0:37:13] Such a nuanced and broad problem. So you still have to be careful about how you roll it out, where you integrate it. But we're certainly working on the research side. And for image, at least, we have a very reliable tool in the early stages. And when might you release this? You said you're working on this right now. Is this something you plan to release? Oh, yes. Yes. For both images and text.
[0:37:43] For text, we're trying to figure out what actually makes sense. For images, it's a bit more straightforward problem. But in either case, we definitely test it out because we don't have all the answers. We're building these technologies first. We don't have all the answers. So often, we will experiment. We will put out something. We will get feedback. But we want to do it in a controlled way. And sometimes, we'll...
[0:38:11] Take it back and we'll make it better and roll it out again. I'll also add that I think this idea of watermarking content is not something that everybody has the same opinion about what is good and what is bad. There's a lot of people who really don't want their generated content watermarked, and that's understandable in many cases. Also, it's not going to be super robust to everything. Maybe you could do it for images, maybe for longer text, maybe not for short text.
[0:38:37] Over time, there will be systems that don't put the watermarks in. And also, there will be people who really like, this is like a tool, and up to the human user how you use the tool. And this is why we want to engage in the conversation. We are willing to follow the collective wishes of society on this point. And I don't think it's a black and white issue. I at least think people are still evolving as they understand all the different ways we're going to use these tools.
[0:39:07] They're still evolving their thoughts about what they're going to want here. Also to Sam's earlier point, it's not just about truthfulness and what's real and what's not real. Actually, I think in the world that we're going towards, marching towards, the bigger risk is really this individualized persuasion and how to deal with that. And that's going to be a very tricky problem to deal with.
[0:39:35] I realize I have five minutes left, and we were going to do some audience questions. So we can get to one audience or two audience questions. I'm going to finish one last thought here. I can actually not see a thing out there. So I will ask one last question, then we'll hopefully have time for one or two. So 10 years, you were here 10 years ago. We touched on this as we were starting here, but what is your biggest fear about the future? And what is your biggest hope?
[0:40:05] I think the future is going to be amazingly great. We wouldn't come work so hard on this if we didn't. I think this is going to be like, I think this is one of the most significant inventions humanity has yet done. So I'm super excited to see it all play out. I think things can get so much better for people than they are right now. And I feel very hopeful about that.
[0:40:36] We covered a lot of the fears. Again, we're clearly dealing with something very powerful that's going to impact all of us in ways we can't perfectly foresee yet. But what a time to be alive and get to witness this. You're not so fearful. I was going to actually ask this, but I'll ask it now. Do you have a bunker?
[0:40:58] This is the question. This is better than the audience question. This is not better than the, I'm going to let that clock run. I'm not going to pay attention to that. But as we're thinking about fears, I'm wondering if you have a bunker and what's the biggest fear that you have that? I would say no. You'd say no? I have structures, but I wouldn't say a bunker. Structures? None of this is going to help if AGI goes wrong. Really? It's a ridiculous question, to be honest. OK, good, good, good. Mira, what's your hope and fear? I mean, the hope is definitely to push.
[0:41:29] Our civilization ahead with augmenting our collective intelligence and the fears. We talked a lot about the fears, but we've got this opportunity right now and we've got summers and winters in AI and so on. But when we look back 10 years from now, I hope that we get this right. And I think there are many ways to mess it up.
[0:41:56] And we've seen that with many technologies. So I hope we get it right. All right. We've got time. Right here. Hi. Pam Dillon, preferably. Sensory consumer products, AI. My question has to do with the inflection point. We are where we are with respect to AI and AGI. What is the inflection point? How do you define that moment where we go from where we are now?
[0:42:26] To however you would choose to define what is AGI. I think it's going to be much more continuous than that. We're just on this beautiful exponential curve. Whenever you're on a curve like that, you look forward, it looks vertical. You look back, it looks horizontal. That's true at any point on there. So a year from now, we'll be in a dramatically more impressive place than a year ago. We were in a dramatically less impressive place.
[0:42:56] But it'll be hard to point. People will try and say, oh, it was AlphaGo that did it. It was GPT-3 that did it. It was GPT-4 that did it. But it's just brick by brick, one foot in front of the other, up climbing this exponential curve. Right here in the front? Thank you. My name is Nyariana Maiko. I'm the chief information officer of the Port of Long Beach. But I'm also a computer scientist by training.
[0:43:23] A few decades ago, I'm older than you. I remember working with some of the early AI people. I have a general question. I agree with you. This is one of the most significant innovations to happen. One of the things I've struggled with over the last 20 years in thinking about this, we're about to change the nature of work. This is that significant. And I feel that people are not talking about it. There will be a significant, there'll be a transition time period where significant population in the world and in this country.
[0:43:53] Will not have had the types of discussion understand that we have. So they can, like you mentioned, society needs to be a part of it. There's a large portion of society that's not even in this discussion. So the nature of work will change. It used to be that things that were just going to be automated. There will be a time where people who define themselves by work since thousands of years will not have that. And we're hurtling towards it. What can we do to make sure that we take that into account?
[0:44:23] Because when we talk about society, it's not like they're all together ready to discuss this. Some of the effects of some of the technologies that we brought into the world have actually made people separate from each other. How do we get some of those, not regulations, but how do we come up with some of those frameworks and voluntarily bring things about that will actually result in a better world that doesn't leave everybody else behind? Thank you. I'll give you my perspective.
[0:44:55] I completely agree with you. It's the ultimate technology that could really increase inequality and make things so much worse for us as human beings and civilization. Or it could be really amazing and it could bring along a lot of creativity and productivity and enhance us. And maybe a lot of people don't want to work eight hours or 100 hours a week. Maybe they want to work four hours a day and do
[0:45:25] A bunch of other things. And I think it's certainly going to lead to a lot of disruption in the workforce. And we don't know exactly the scale of that or the trajectory along the way. But that's for sure. And one of the things that I, in retrospect, it's not.
[0:45:48] That we specifically planned it. But in retrospect, I'm happy about is that with the release of Child GPT, we sort of brought AI into the collective consciousness and people are kind of paying attention because they're not reading about it in the press.
[0:46:03] People are not just telling them about it, but they can play with it. They can interact with it and get the sense for the capabilities. And so I think it's actually really important to bring these technologies into the world and make them as widely accessible as possible. Sam mentioned earlier that we're working really hard to make these models cheaper and faster so they're accessible.
[0:46:28] Very broadly, but I think that's key for people themselves to actually interact with the technology and experience it and sort of visualize how it might change their way of life, their way of being, and participate in providing product feedback, but also institutions need to actually prepare for these changes in the workforce and economy.
[0:46:56] I think it's a super important question. Every technological revolution affects the job market. And over human history, every maybe 100 years, you can feel different numbers for this, 150 years, half the kind of jobs go away, totally change, whatever. I'm not afraid of that at all. In fact, I think that's good. I think that's the way of progress. And we'll find new and better jobs. The thing that I think we do need to confront as a society is the speed at which this is going to happen.
[0:47:25] It seems like over two, maximum three, probably two generations, we can adapt, society can adapt to almost any amount of job market change. But a lot of people like their jobs or they dislike change. And going to someone and saying, hey, the future will be better, I promise you, and society is going to win, but you're going to lose here, that doesn't work. That's not cool. That's not an easy message to get across.
[0:47:52] Although I tremendously believe that we're not going to run out of things to do. People that want to work less, fine. They'll be able to work less. But probably many people here don't need to keep working. And we all do. There's great satisfaction in expressing yourselves and being useful and contributing back to society. That's not going away. That is such an innate human desire. Evolution doesn't work that fast. Also, the ability to creatively express yourself and to sort of
[0:48:19] Leave something to add something back to the trajectory of the species. That's a wonderful part of the human experience. So we're going to keep finding things to do. And the people in the future will probably think some of the things those people do is very silly and not real work in a way that a hunter-gatherer probably wouldn't think this is real work either. We're just trying to entertain ourselves with some silly status game. That's fine with me. That's how it goes.
[0:48:49] But we are going to have to really do something about this transition. It is not enough to just give people a universal basic income. People need to have agency, the ability to influence this. We need to sort of jointly be architects of the future. And one of the reasons that we feel so strongly about deploying this technology as we do, as you said, not everybody's in these discussions, but more and more are every year.
[0:49:16] And by putting this out in people's hands and making this super widely available and getting billions of people to use ChatGPT, not only do people have the opportunity to think about what's coming and participate in that conversation, but people use the tool to push the future forward. And that's really important to us.