WEBVTT 00:00:03.800 --> 00:00:05.610 Eric, welcome to USC. 00:00:07.140 --> 00:00:09.927 You wrote a sonnet? I like to write. 00:00:10.010 --> 00:00:14.280 I like to write sonnets. This is about the Marshall School. 00:00:15.810 --> 00:00:21.137 Pay attention. When Fortune's wheel doth turn in time swift course... 00:00:21.220 --> 00:00:23.037 You don't know what I'm about to say. 00:00:23.120 --> 00:00:25.890 And commerce seeks new methods to prevail. 00:00:26.490 --> 00:00:31.687 The wise must harness innovation’s force lest ancient ways 00:00:31.770 --> 00:00:34.200 grow stagnant, cold and stale. 00:00:34.700 --> 00:00:38.840 Behold, the thinking engine doth arise. 00:00:39.040 --> 00:00:41.370 A tool to amplify the student's mind. 00:00:41.710 --> 00:00:45.297 To parse through data vast as are the skies 00:00:45.380 --> 00:00:47.827 and patterns in the chaos swift to find. 00:00:47.910 --> 00:00:51.597 Let Marshall's halls embrace this mechanic art, 00:00:51.680 --> 00:00:53.867 for business leaders must with times align. 00:00:53.950 --> 00:00:56.807 The future calls. They must not stand apart, but 00:00:56.890 --> 00:00:59.790 blend the human touch with the divine. 00:01:00.060 --> 00:01:06.630 So let USC with boldness lead the way and teach tomorrow's mind what rules today. 00:01:07.870 --> 00:01:10.040 Wow. 00:01:12.640 --> 00:01:15.697 Very impressive. It took me a really long time to write that. 00:01:15.780 --> 00:01:19.010 It took me about one sentence. 00:01:19.980 --> 00:01:21.950 Welcome to the new world of AI. 00:01:23.080 --> 00:01:25.920 Amazing. Well, let's just dive right in. 00:01:26.590 --> 00:01:31.590 When people talk about AI, when they think about AI, 00:01:32.260 --> 00:01:34.647 especially as it pertains and will impact 00:01:34.730 --> 00:01:37.000 higher education and universities like USC, 00:01:37.500 --> 00:01:40.517 I think there are two different narratives: that it's 00:01:40.600 --> 00:01:44.587 going to be an incredible enhancer of learning and research, 00:01:44.670 --> 00:01:48.940 and there's another narrative that it's going to completely disrupt 00:01:49.580 --> 00:01:52.010 the entire university model as we know it. 00:01:52.410 --> 00:01:56.280 Which narrative is correct? Both. 00:01:56.950 --> 00:02:01.707 I think almost all of these questions, and by the way, I'm really excited to be here. 00:02:01.790 --> 00:02:03.590 So I'm just... have fun with you. 00:02:04.260 --> 00:02:06.847 What you've done since you took over is remarkable. 00:02:06.930 --> 00:02:11.130 Look at, look at, I mean, I don't need to sing your praises, but they're well known. 00:02:12.970 --> 00:02:15.257 Universities are going to adapt 00:02:15.340 --> 00:02:18.687 because universities, the American universities, 18 of the top 20th, 00:02:18.770 --> 00:02:20.727 20 universities in the world are in the United States. 00:02:20.810 --> 00:02:22.727 It’s one of the greatest things about America. 00:02:22.810 --> 00:02:24.797 They're adaptable. Right? 00:02:24.880 --> 00:02:27.927 An example is that you have this amazing film school here, 00:02:28.010 --> 00:02:33.307 and you have a digital tools, a digital, a digital, how to use digital tools, right? 00:02:33.390 --> 00:02:37.237 Which is the leading program. It's like a little subgroup within a larger 00:02:37.320 --> 00:02:41.190 super group. That's how your university works. 00:02:41.560 --> 00:02:43.177 And so you're going to see the same thing 00:02:43.260 --> 00:02:46.110 with respect to labor allocation, education and so forth. 00:02:46.430 --> 00:02:48.017 The university will adapt, 00:02:48.100 --> 00:02:50.357 and the university will not adapt because of the faculty. 00:02:50.440 --> 00:02:52.057 It'll adapt because the students. 00:02:52.140 --> 00:02:55.927 Because the innovation occurs essentially from graduate students and postdocs. 00:02:56.010 --> 00:02:59.380 Right? And the faculty are good enough to allow that to occur. 00:02:59.510 --> 00:03:04.150 It's always been true in my world that two graduate students and an untenured 00:03:04.850 --> 00:03:06.700 assistant professor change the world. 00:03:07.020 --> 00:03:11.120 There's something about that pressure cooker, which USC is very much a part of. 00:03:11.290 --> 00:03:15.707 That's not going to go away. What will go away is an awful 00:03:15.790 --> 00:03:19.530 lot of the sort of traditional education and common knowledge, 00:03:19.870 --> 00:03:23.257 and you'll find ways of having much more self-learning and so forth. 00:03:23.340 --> 00:03:27.757 And I think that the butts-in-seats kind of model is going to get more... 00:03:27.840 --> 00:03:31.627 Most universities are experimenting with various forms of collaborative research, 00:03:31.710 --> 00:03:35.197 working teams and so forth, because it's a better learning model. 00:03:35.280 --> 00:03:38.380 I don't think... So, let me exclude a bunch of things. 00:03:39.550 --> 00:03:42.637 Teachers are not going away. Universities are not going away. 00:03:42.720 --> 00:03:44.407 Jobs are not going away. Right? 00:03:44.490 --> 00:03:46.447 All those predictions are all ill informed. 00:03:46.530 --> 00:03:50.930 But what you do will... you will use computers a lot more in everything. 00:03:51.260 --> 00:03:54.610 And the first class that I hope you introduce after this conference 00:03:54.770 --> 00:03:56.570 is a class on prompt engineering. 00:03:56.900 --> 00:03:59.927 How could you have students at USC 00:04:00.010 --> 00:04:02.927 that do not know how to do complicated prompts to generate 00:04:03.010 --> 00:04:06.710 whatever it is they're care about, which is often very funny? 00:04:06.910 --> 00:04:10.937 Do you think there's a greater danger for universities in under-utilizing 00:04:11.020 --> 00:04:12.820 AI or over-utilizing it? 00:04:14.220 --> 00:04:19.237 Well, I'm always pro-AI, and the reason is that the arrival 00:04:19.320 --> 00:04:24.577 of a new form of intelligence that’s at the peer level of humans, 00:04:24.660 --> 00:04:28.470 which is roughly what's happening, is a very big historical day. 00:04:29.300 --> 00:04:33.927 This is like, like the arrival of electricity, maybe even more so. 00:04:34.010 --> 00:04:36.410 We, and I've written two books with Kissinger 00:04:37.580 --> 00:04:43.820 on this question about what happens when your best friend is non-human, 00:04:44.280 --> 00:04:46.730 what happens when your collaborator is non-human? 00:04:46.950 --> 00:04:49.407 What happens when your non-human collaborator 00:04:49.490 --> 00:04:51.920 is smarter than you are in some areas? 00:04:52.590 --> 00:04:54.907 The most important thing for this university is to get 00:04:54.990 --> 00:05:00.900 your students ready for that outcome, because that outcome is a big deal 00:05:01.300 --> 00:05:03.817 and an awful lot of people who are much older 00:05:03.900 --> 00:05:07.200 than your students are going to have all sorts of crises over this 00:05:07.510 --> 00:05:10.927 because it'll affect their identity, what they do, how they spend their time. 00:05:11.010 --> 00:05:13.627 You share your view that jobs aren't going away, 00:05:13.710 --> 00:05:18.367 but jobs are certainly going to change, and perhaps very dramatically in ways 00:05:18.450 --> 00:05:22.620 that we can't really fully envision over the next five to 10 years. 00:05:23.890 --> 00:05:27.990 How do you think, how do you think universities need to think about 00:05:28.360 --> 00:05:31.617 how we need to adapt to the changing 00:05:31.700 --> 00:05:34.647 nature of work, the changing jobs market, in terms 00:05:34.730 --> 00:05:37.480 of how we're teaching and what we're teaching students? 00:05:37.670 --> 00:05:41.340 So, and I'll make an obnoxious comment, the most interesting 00:05:41.910 --> 00:05:46.057 professor is a professor who has tenure, who studies the dynamic nature 00:05:46.140 --> 00:05:47.950 of the economics of the labor market 00:05:48.250 --> 00:05:50.537 because they themselves are not subject to it. 00:05:50.620 --> 00:05:51.480 But I digress. 00:05:53.220 --> 00:05:54.567 That's not an attack on tenure. 00:05:54.650 --> 00:06:00.160 Tenure got me here. My father was a tenured faculty, so forth. 00:06:01.530 --> 00:06:05.017 Let's consider... So I've spent 20 years with people telling me that truck drivers 00:06:05.100 --> 00:06:09.000 were going to no longer going to be needed because we would be driving trucks. 00:06:09.240 --> 00:06:12.527 The number one, there are many job shortages in America. 00:06:12.610 --> 00:06:14.410 The number one is truck drivers. 00:06:15.240 --> 00:06:17.057 It's really hard to drive a truck. 00:06:17.140 --> 00:06:21.210 It's a very difficult job, and not enough people want to do it. 00:06:21.550 --> 00:06:23.867 How is it possible that jobs are going to take that 00:06:23.950 --> 00:06:27.637 jobs are all going to go away because of automation and the job, 00:06:27.720 --> 00:06:31.177 which is most obviously automatable, right, and is difficult 00:06:31.260 --> 00:06:33.930 and so forth, is the one in the shortest supply. 00:06:34.560 --> 00:06:35.960 And the answer is because, 00:06:37.200 --> 00:06:42.900 two things. First, economic efficiency in a, in a country level 00:06:44.340 --> 00:06:48.287 fundamentally means that the, the firms that adopt AI and automation 00:06:48.370 --> 00:06:52.110 more quickly become more efficient, which grows the market. 00:06:53.450 --> 00:06:55.397 They displace people who are less efficient. 00:06:55.480 --> 00:06:58.707 But as a societal matter, growing the economy 00:06:58.790 --> 00:07:01.540 and growing revenue, especially in a democracy, is key. 00:07:02.120 --> 00:07:05.737 Another view on democracy. Europe is essentially not growing. 00:07:05.820 --> 00:07:10.200 Thank God America is growing at somewhere between 2 to 3%, which is great. 00:07:11.160 --> 00:07:14.387 In the democratic systems, you can't take money away from its constituents. 00:07:14.470 --> 00:07:16.757 So if you have new needs, you have to have new money. 00:07:16.840 --> 00:07:20.587 So democracies have to grow. So you've got to figure out a way 00:07:20.670 --> 00:07:23.440 to grow your democracy, which is through productivity. 00:07:23.710 --> 00:07:28.880 And all of the evidence is that technology invention, especially in America, 00:07:29.150 --> 00:07:32.250 is the key component of American exceptionalism 00:07:33.590 --> 00:07:36.960 and the, the characteristic of the American culture, 00:07:37.890 --> 00:07:43.400 which I'll simplify, which everybody knows is relatively freedom focused, 00:07:43.860 --> 00:07:48.300 relatively suspicious of the government and relatively law abiding. 00:07:48.770 --> 00:07:54.027 Right? We can debate the specifics, but that's roughly classic liberalism 00:07:54.110 --> 00:07:57.810 is a necessary component of the kind of growth that we're having. 00:07:58.180 --> 00:08:01.067 And other cultures, which are also democracies, don't have any growth. 00:08:01.150 --> 00:08:04.367 They can't do anything. I've tried for 15 years 00:08:04.450 --> 00:08:06.300 to try to get Europe to do something. 00:08:06.450 --> 00:08:10.620 Europe is full of incredibly smart people, but the economic system doesn't form it. 00:08:11.090 --> 00:08:14.140 I was in China, and I mentioned this to you when we just met. 00:08:14.230 --> 00:08:17.117 I was in China in August, and I came to... 00:08:17.200 --> 00:08:21.887 I was originally thinking that China was going to beat ... 00:08:21.970 --> 00:08:26.870 attempt to beat us. China does not have the depth of the financial markets 00:08:27.270 --> 00:08:30.410 to fund the crazy stuff that American entrepreneurs are doing. 00:08:30.610 --> 00:08:35.180 You can't randomly raise $50 billion for your data center in China, right? 00:08:35.350 --> 00:08:38.167 And maybe that government will ultimately decide that's a good idea, 00:08:38.250 --> 00:08:39.767 but it's a command and control economy. 00:08:39.850 --> 00:08:42.877 So the genius of America is this restructuring. 00:08:42.960 --> 00:08:45.577 So the long... sorry for the long-winded answer, 00:08:45.660 --> 00:08:48.260 but the result is, yes, there will be more jobs. 00:08:48.590 --> 00:08:52.330 The jobs will be different. And furthermore, because demographics are such 00:08:52.700 --> 00:08:54.187 that we're having fewer and fewer children... 00:08:54.270 --> 00:08:58.317 And this is worst, by the way, in Korea and in China and other Asian countries, 00:08:58.400 --> 00:09:01.600 where the replacement rate is well below 1.0, which is terrible. 00:09:01.840 --> 00:09:05.257 Those are examples of societies that going to need some serious surgery. 00:09:05.340 --> 00:09:08.267 I don't exactly know why this is occurring, but it's important. 00:09:08.350 --> 00:09:12.267 We're not going to have the people to do the jobs we need in the next 30 years, 00:09:12.350 --> 00:09:14.137 which is why universities are so important. 00:09:14.220 --> 00:09:15.370 You produce the people. 00:09:16.360 --> 00:09:20.407 I want to get back to this question about how universities need 00:09:20.490 --> 00:09:23.130 to be rethinking its curricula and how we teach, but 00:09:23.700 --> 00:09:26.987 you mentioned democracy a couple of times, and I saw that this morning 00:09:27.070 --> 00:09:31.687 you published an editorial in The New York Times about how you can't 00:09:31.770 --> 00:09:36.540 outsource democracy to AI. Can you share why you felt compelled to write that? 00:09:37.440 --> 00:09:41.310 We're in a period where people are frustrated with democracies. 00:09:41.680 --> 00:09:43.797 I believe in freedom, and I believe in democracy. 00:09:43.880 --> 00:09:48.720 My political views are clear. And, in order to have a 00:09:50.190 --> 00:09:53.040 ... democracy has to sort of organize itself and succeed. 00:09:53.490 --> 00:09:54.647 When there are troubles, 00:09:54.730 --> 00:09:59.647 and we're certainly having them now, there is an appeal to authoritarianism, 00:09:59.730 --> 00:10:03.017 to the quick solution. And I'm very worried 00:10:03.100 --> 00:10:06.040 that people will say, let's have the AI do it. 00:10:07.510 --> 00:10:09.680 After all, AI is much smarter than we are. 00:10:09.840 --> 00:10:12.267 It can figure things out. It can regulate us. 00:10:12.350 --> 00:10:16.120 That is a loss of human dignity that I'm very, very worried about. 00:10:16.750 --> 00:10:21.190 Do not give your human dignity to a non-human activity. 00:10:21.490 --> 00:10:24.507 You want your freedom to think, to write, to associate, 00:10:24.590 --> 00:10:28.040 and so forth as a human being, and you want to do it collaboratively. 00:10:28.260 --> 00:10:33.730 So my basic argument is you need to use AI to cause the communications process, 00:10:34.330 --> 00:10:36.970 which has largely become, at least in America, 00:10:37.540 --> 00:10:40.587 he said, she said kinds of tribalism. 00:10:40.670 --> 00:10:43.957 You know, “you're in this” and “you’re left” and “right” and so forth and so on. 00:10:44.040 --> 00:10:45.890 There's no constructive conversation. 00:10:46.710 --> 00:10:50.620 There are solutions which involve using AI to try to encourage the dialogue. 00:10:51.080 --> 00:10:52.880 And somehow we've forgotten that. 00:10:53.250 --> 00:10:55.007 I wonder if you can extend 00:10:55.090 --> 00:10:58.577 some of those ideas to higher education, because one of the things 00:10:58.660 --> 00:11:02.130 that we think a lot about at USC is that you're not just learning 00:11:02.290 --> 00:11:04.877 knowledge and acquiring skills, but you're also learning 00:11:04.960 --> 00:11:06.770 how to interact with other people. 00:11:06.900 --> 00:11:10.100 You're learning how to get things done as a team. 00:11:10.540 --> 00:11:16.657 And I'm wondering if one of the implications for AI is that universities, 00:11:16.740 --> 00:11:18.757 of course, need to figure out how to continue 00:11:18.840 --> 00:11:21.910 teaching the material, but they also perhaps 00:11:22.880 --> 00:11:26.350 should consider leaning even more into the social aspects 00:11:26.650 --> 00:11:30.007 of being part of a university community for, for years 00:11:30.090 --> 00:11:31.890 or however long they're here. 00:11:32.490 --> 00:11:36.830 To the degree that you start yourself to interfere in students’ behavior. 00:11:37.260 --> 00:11:40.387 One, you'll be, shall we say, concerned. 00:11:40.470 --> 00:11:43.800 And second, it's just better to let students do their thing. 00:11:44.640 --> 00:11:47.857 Right? Because you spend an awful lot of time getting the, really, 00:11:47.940 --> 00:11:51.957 the quality of the school force here is, as a private university, is exceptional. 00:11:52.040 --> 00:11:57.350 Let them figure it out. I'm very bottoms up in this view. 00:11:57.450 --> 00:12:01.290 And I think that... so, so... 00:12:03.920 --> 00:12:05.777 The professors that I've talked to in other universities 00:12:05.860 --> 00:12:08.790 have a pretty simple rule: phones off in the classroom. 00:12:09.700 --> 00:12:12.530 You actually have to talk to each other, right? 00:12:12.700 --> 00:12:15.800 And you actually have to talk to each other without anonymity. 00:12:16.130 --> 00:12:21.140 And, there's a long discussion about how social media has destroyed society. 00:12:22.270 --> 00:12:25.927 I think you're familiar with Jonathan Haidt's book because he was just here, 00:12:26.010 --> 00:12:29.997 and he's a good friend. A guy named Scott Galloway has just published a book, 00:12:30.080 --> 00:12:33.280 which is quite concerning, about the status of men in their 20s. 00:12:33.650 --> 00:12:37.820 And all of these are consequences of excessive social media. 00:12:38.320 --> 00:12:40.807 Right? And the fact of the matter is, most people 00:12:40.890 --> 00:12:44.077 and an awful lot of teenagers are pretty lonely, right? 00:12:44.160 --> 00:12:46.747 And so they turn to these things and they take on a huge 00:12:46.830 --> 00:12:49.170 superhuman role in their development. 00:12:49.800 --> 00:12:52.087 Thought experiment for you is somebody 00:12:52.170 --> 00:12:54.870 here has a child next year or the following year, 00:12:55.340 --> 00:12:57.810 and every year your child gets a better toy. 00:12:58.280 --> 00:13:00.427 And the toy can talk and is intelligent. 00:13:00.510 --> 00:13:02.367 So it's a bear or something like that. 00:13:02.450 --> 00:13:05.950 Well, that toy will become the child's best friend. 00:13:06.320 --> 00:13:08.867 Now as a, with a three-year-old, you're going to be really excited about it 00:13:08.950 --> 00:13:10.507 because it'll keep the three-year-old occupied 00:13:10.590 --> 00:13:12.607 and it'll teach that the three-year-old language 00:13:12.690 --> 00:13:14.490 and counting and things like that. 00:13:14.760 --> 00:13:19.030 But what happens when the kid is 12 and has no social skills with the other kids, 00:13:19.370 --> 00:13:21.887 but really loves the bear and you cannot get the bear with him? 00:13:21.970 --> 00:13:28.240 I was talking to, to one social psychologist who had a mother talk to him 00:13:28.740 --> 00:13:31.280 that she had a 15-year-old boy 00:13:32.640 --> 00:13:37.337 who was... she was unhappy about and she attempted to take his phone 00:13:37.420 --> 00:13:41.470 and he beat the crap out of her to the point where she had to go to the hospital. 00:13:42.020 --> 00:13:44.120 And I said, are we at that stage? 00:13:44.920 --> 00:13:47.007 And I think, at least in some cases, we are. 00:13:47.090 --> 00:13:51.217 It's really, really important to maintain human dignity at each of these levels. 00:13:51.300 --> 00:13:53.100 And here in the university, as well. 00:13:54.430 --> 00:13:58.357 You've written a number of books and, and you've written a number 00:13:58.440 --> 00:14:03.240 of articles and papers. And if you're like me, Eric, 00:14:03.540 --> 00:14:06.727 part of the reason that I write is to figure out 00:14:06.810 --> 00:14:09.260 what I actually believe and think about a matter. 00:14:10.580 --> 00:14:14.767 Are you concerned about a future where students are no longer writing? 00:14:14.850 --> 00:14:20.607 Instead, they're just prompting LLMs to give them answers or produce first drafts, 00:14:20.690 --> 00:14:23.817 and then maybe prompting them again to, to edit those drafts, 00:14:23.900 --> 00:14:27.900 but not actually staring at that blank screen or piece of paper 00:14:28.500 --> 00:14:30.300 and the impact on 00:14:30.970 --> 00:14:32.787 and the degradation of their critical thinking as a result. 00:14:32.870 --> 00:14:38.810 So, so I have every belief that the faculty at USC can determine 00:14:39.110 --> 00:14:43.250 the people who cheated using LLM and the ones who wrote things originally. 00:14:43.450 --> 00:14:46.697 Right? I'm sure that, that the faculty here can figure that out 00:14:46.780 --> 00:14:49.537 from a grading perspective. I'm not worried about that. 00:14:49.620 --> 00:14:52.407 I have long been worried about a reduction in critical 00:14:52.490 --> 00:14:54.590 thinking because of reduction in reading 00:14:55.260 --> 00:14:58.517 and, and the... Instagram and TikTok and so forth. 00:14:58.600 --> 00:15:00.187 Reading is not the kind of reading I'm... 00:15:00.270 --> 00:15:01.270 You're familiar with 00:15:03.000 --> 00:15:06.127 stage-one and stage-two memory or thinking? 00:15:06.210 --> 00:15:08.987 The first is quick, and the second is long. 00:15:09.070 --> 00:15:12.080 And in academia, we call that being in the zone. 00:15:12.850 --> 00:15:18.380 And so if your students are not in the zone, sitting there doing it, 00:15:19.590 --> 00:15:22.990 I've been working with a number of research scientists in their 20s, 00:15:24.120 --> 00:15:28.077 and when I was a research scientist in my 20s, of course, there were no cellphones 00:15:28.160 --> 00:15:30.647 and we were busy, you know, doing stupid things in college. 00:15:30.730 --> 00:15:34.147 And we were otherwise reading books and, in my case, writing a lot of code, 00:15:34.230 --> 00:15:36.030 which I guess is going away as well. 00:15:36.470 --> 00:15:40.770 And in watching them, they turn off their phone 00:15:41.640 --> 00:15:44.457 and they just work and are concentrated for a while, 00:15:44.540 --> 00:15:47.010 and then they take a break and so forth. 00:15:47.280 --> 00:15:49.997 So maybe the answer to the addiction cycle, 00:15:50.080 --> 00:15:52.297 because these are all fundamentally addiction devices, 00:15:52.380 --> 00:15:55.007 is to train your students to turn them off for a while. 00:15:55.090 --> 00:16:00.460 My trick, my, my newest invention is I put my phone on airplane mode, 00:16:00.830 --> 00:16:02.730 and I imagine that I'm in an airplane, 00:16:04.060 --> 00:16:06.830 OK? And I'm so stupid that this works. 00:16:08.070 --> 00:16:11.620 OK, I'm in an airplane. I look at my phone, it says I'm in an airplane. 00:16:11.770 --> 00:16:13.570 You know, it works. 00:16:17.710 --> 00:16:20.057 Dean Garrett talked about an emphasis of mine 00:16:20.140 --> 00:16:22.497 on really trying to lean into the private sector 00:16:22.580 --> 00:16:26.037 more and develop relationships not with just private industry, 00:16:26.120 --> 00:16:29.720 but also with government agencies and actors. 00:16:29.990 --> 00:16:35.160 I want to ask you, how has AI raised the stakes for us to get that, 00:16:35.630 --> 00:16:39.747 that set of relationships as, as right as we possibly can? 00:16:39.830 --> 00:16:42.230 Because right now it's, it's difficult. 00:16:42.530 --> 00:16:47.027 It's difficult to collaborate with the government and with the private sector. 00:16:47.110 --> 00:16:51.380 But I wanted to ask, how has AI increased the urgency that we do that, 00:16:51.640 --> 00:16:54.210 and then the follow-up question is going to be, 00:16:55.050 --> 00:16:57.167 how do we have to get this 00:16:57.250 --> 00:17:00.137 right in order to compete with other countries, including China? 00:17:00.220 --> 00:17:03.860 Well, you guys have a really good math department, OK? 00:17:04.320 --> 00:17:08.160 And every one of these firms has no idea what AI really is. 00:17:09.190 --> 00:17:12.490 Right? So I think you're misstating your own negotiating leverage. 00:17:12.800 --> 00:17:17.540 They need more... They need you more than you need them. 00:17:18.600 --> 00:17:24.240 So the AI that I work on is essentially math and most of it... 00:17:24.380 --> 00:17:25.427 And I have a piece in this here. 00:17:25.510 --> 00:17:27.610 I cannot even understand the math anymore. 00:17:27.880 --> 00:17:32.937 It's moved so fast. And I think that this generation that's coming out right 00:17:33.020 --> 00:17:35.820 now, which, who will be talking for the rest of the day, 00:17:36.220 --> 00:17:40.477 are probably the greatest economic potentials of all. 00:17:40.560 --> 00:17:42.747 And the reason is they're the ones that are going to take 00:17:42.830 --> 00:17:46.247 these complicated algorithms and essentially apply them 00:17:46.330 --> 00:17:48.447 to solve real business problems in health care. 00:17:48.530 --> 00:17:54.310 And there's a long list. In our industry, we, we talk about technology overhang, 00:17:54.840 --> 00:17:58.127 where we believe, arrogantly, I might add, 00:17:58.210 --> 00:18:00.767 that we have produced all this incredible technology. 00:18:00.850 --> 00:18:02.867 And why do customers, businesses, 00:18:02.950 --> 00:18:05.467 universities and government, why are they taking it more slowly? 00:18:05.550 --> 00:18:07.950 Now, one answer is it's not quite ready for use, 00:18:08.590 --> 00:18:11.620 but the real reason is that technology innovation 00:18:11.860 --> 00:18:15.147 starts in bespoke industries and requires 00:18:15.230 --> 00:18:18.030 special handling, and then it becomes standardized. 00:18:18.430 --> 00:18:21.800 I'm old enough to remember that email was considered an option. 00:18:22.900 --> 00:18:24.750 Now most of you don't even use email. 00:18:25.740 --> 00:18:27.287 But email was... And then one day... 00:18:27.370 --> 00:18:29.957 So they used to have this diagram where they said, “This is the vertical. 00:18:30.040 --> 00:18:32.697 This is the horizontal.” And then somebody announced one day 00:18:32.780 --> 00:18:35.850 the vertical became horizontal, and email became standard. 00:18:36.150 --> 00:18:38.500 And then all of a sudden the email had to work. 00:18:38.920 --> 00:18:42.790 So these are simple ways of understanding how technology adoption occurs. 00:18:43.290 --> 00:18:45.990 And remember, so I was coming by here 00:18:46.790 --> 00:18:48.507 and I saw Waymo car here in L.A. 00:18:48.590 --> 00:18:54.200 Great. Now the first self-driving cars were largely invented in 1994. 00:18:55.030 --> 00:18:59.370 Stanford won the self-driving competition in 2004. 00:18:59.940 --> 00:19:06.780 Google hired all those people in 2005, and it is now 2025. 00:19:07.180 --> 00:19:09.697 So it took 20 years from the time 00:19:09.780 --> 00:19:12.750 a successful contest among universities to this. 00:19:13.180 --> 00:19:15.207 So the technology diffusion is much... 00:19:15.290 --> 00:19:19.120 takes much longer, and the wealth creation occurs at every stage. 00:19:19.490 --> 00:19:21.007 And since we have a lot of business 00:19:21.090 --> 00:19:23.640 students here and I'm very pro-business, obviously, 00:19:24.230 --> 00:19:27.387 remember that the majority of the money is not made at the beginning. 00:19:27.470 --> 00:19:30.130 It's actually made during the mainstreaming of it, 00:19:30.840 --> 00:19:34.040 because the mainstreaming creates trillion-dollar industries. 00:19:34.440 --> 00:19:35.727 And that's where the real wealth will be made. 00:19:35.810 --> 00:19:40.650 How do you think about China, Eric, in this... 00:19:41.050 --> 00:19:46.667 in terms of how universities need to balance, on the one hand, the commitment 00:19:46.750 --> 00:19:50.320 that is part of our academic mission to sharing knowledge, 00:19:50.590 --> 00:19:57.000 but also the concerns about intellectual property theft and, and, 00:19:57.300 --> 00:20:00.147 and we see that those kinds of tensions 00:20:00.230 --> 00:20:03.957 running through all sorts of debates that we're now having with respect 00:20:04.040 --> 00:20:08.210 to chips to LLMs and in a lot of other parts of AI? 00:20:08.310 --> 00:20:10.660 Well, I think, I think you and I agree on this. 00:20:10.940 --> 00:20:14.427 I'm very strong and positive on foreign students 00:20:14.510 --> 00:20:17.497 in our American universities, especially at the graduate level. 00:20:17.580 --> 00:20:20.137 When I was running the National Commission on AI for the Congress, 00:20:20.220 --> 00:20:22.960 we studied very carefully the Chinese contribution. 00:20:23.650 --> 00:20:27.090 And it turns out all of the really tippy-top papers in America 00:20:27.530 --> 00:20:32.477 had one or two out of the five or 10 authors who were Chinese, right? 00:20:32.560 --> 00:20:34.317 Chinese born, Chinese trained. 00:20:34.400 --> 00:20:37.117 They are an important part of our research infrastructure. 00:20:37.200 --> 00:20:38.887 We don't want to get rid of them. 00:20:38.970 --> 00:20:40.727 Furthermore, we don't get we want to get rid of them 00:20:40.810 --> 00:20:42.857 after they graduate and send them back to China. 00:20:42.940 --> 00:20:44.427 That's the stupidest thing ever. 00:20:44.510 --> 00:20:47.597 So staple a green card on their diploma, you know, whatever... 00:20:47.680 --> 00:20:52.497 Whatever it takes, right? You know, you issue diplomas, you have graduation. 00:20:52.580 --> 00:20:55.507 Just take a green card, you know, and just put it on. 00:20:55.590 --> 00:20:59.930 Just staple it on. Right? They can write their name in it, right? 00:21:00.090 --> 00:21:06.947 Yeah. It's obvious, and... So, why in the world 00:21:07.030 --> 00:21:11.257 would we want the top, top students in the world to be anywhere other than America? 00:21:11.340 --> 00:21:13.290 Now, if they're criminals, arrest them. 00:21:14.070 --> 00:21:16.157 It's not that compli-, we have like a police force 00:21:16.240 --> 00:21:17.857 and they're pretty good, right? 00:21:17.940 --> 00:21:19.457 And, you know, you have your own police. 00:21:19.540 --> 00:21:21.427 What is your problem? You have your own police. 00:21:21.510 --> 00:21:22.527 You can make your own green cards. 00:21:22.610 --> 00:21:25.210 You do your own diplomas, like a full-stop shopping. 00:21:25.520 --> 00:21:29.320 So, so my, my point here is, if you take the right attitude, 00:21:29.620 --> 00:21:32.590 then America can continue to lead. 00:21:32.990 --> 00:21:34.907 There are national resource differences. 00:21:34.990 --> 00:21:37.960 So as I mentioned... Let me just finish on the China thing. 00:21:38.100 --> 00:21:42.070 China is going to dominate in a set of areas without question. 00:21:42.530 --> 00:21:45.330 They dominate, and this was a mistake on America's part, 00:21:45.570 --> 00:21:48.770 the electronic, electric vehicle and the battery systems. 00:21:49.040 --> 00:21:51.197 There's no scenario where American firms 00:21:51.280 --> 00:21:53.780 will be able to compete at the scale they operate. 00:21:54.480 --> 00:21:59.250 I think, and this is controversial, that that will be true at humanoid robots 00:21:59.750 --> 00:22:03.007 for the same reasons. I went to some of their factories. 00:22:03.090 --> 00:22:05.690 They're huge, and these people work incredibly hard. 00:22:06.530 --> 00:22:10.347 So far, if you look at DeepSeek, which is the most impressive 00:22:10.430 --> 00:22:12.860 of the research groups, they're good. 00:22:13.000 --> 00:22:15.830 I mean, they've invented a number of profoundly better 00:22:16.200 --> 00:22:18.400 algorithms to do what is called reasoning, 00:22:19.240 --> 00:22:22.587 DeepSeek R1 and so forth. But they don't have the scale. 00:22:22.670 --> 00:22:26.010 So, at least at the moment, we're OK. 00:22:26.310 --> 00:22:28.267 But I think as a matter of intellectual property, 00:22:28.350 --> 00:22:31.180 my preference would be to engage with China 00:22:31.650 --> 00:22:34.250 because I've, I, and I want to be clear here, 00:22:35.220 --> 00:22:37.807 China and the U.S. will never be best friends, 00:22:37.890 --> 00:22:39.790 but we can learn how to work together. 00:22:40.490 --> 00:22:44.847 And even though the systems are different, both have a common interest 00:22:44.930 --> 00:22:47.287 in the set of things: domestic stability. 00:22:47.370 --> 00:22:49.220 We don't want terrorism, nor do they. 00:22:49.570 --> 00:22:50.817 Economic growth. 00:22:50.900 --> 00:22:53.887 They say they're communist, but they sure look like capitalists 00:22:53.970 --> 00:22:58.540 to this capitalist, right? And they work very hard and they're very, very smart. 00:22:59.010 --> 00:23:03.810 If you have any confusion about the quality of Chinese research, stop. 00:23:03.910 --> 00:23:08.250 You are wrong. All those prejudices from 15 years ago about they're behind, 00:23:08.490 --> 00:23:12.190 they’re are no longer true. They are really at the doorstep. 00:23:13.220 --> 00:23:17.617 You mentioned DeepSeek, Eric, and one of the weaknesses, 00:23:17.700 --> 00:23:20.817 we'll call it, of DeepSeek, is that when you do query it 00:23:20.900 --> 00:23:26.200 for information about certain topics, right, you're going to see certain biases. 00:23:26.770 --> 00:23:30.327 And I was wondering, and there's a whole debate 00:23:30.410 --> 00:23:32.210 that you're well familiar with about 00:23:33.740 --> 00:23:39.150 bias and building natural language LLMs and whether it's avoidable or 00:23:39.420 --> 00:23:41.570 even the right way to talk about the issue. 00:23:42.220 --> 00:23:48.847 But I want to ask you, are you concerned about a future where people start 00:23:48.930 --> 00:23:55.370 using the same LLMs and, as a result, you start getting a narrowing of opinion? 00:23:56.170 --> 00:24:01.587 When universities like ours are always trying to cultivate the broadest 00:24:01.670 --> 00:24:03.920 range of opinions and viewpoints that we can. 00:24:04.580 --> 00:24:07.080 So what should you teach? 00:24:07.950 --> 00:24:09.750 Curiosity. 00:24:10.410 --> 00:24:13.897 Right? And I'm now at the point where I just can't stand it when people... 00:24:13.980 --> 00:24:16.930 and I can tell people are paid to convince me of something, 00:24:17.360 --> 00:24:18.277 I'm intelligent enough. 00:24:18.360 --> 00:24:21.410 Just give me the facts, and I can form my own opinion please. 00:24:21.690 --> 00:24:25.617 And I think there's a real danger in society that people will be 00:24:25.700 --> 00:24:29.417 captured by an LLM. Remember, it's their best friend. 00:24:29.500 --> 00:24:31.187 And if your... your best friend has, 00:24:31.270 --> 00:24:33.957 I don't know about you all, has a huge impact on, on me. 00:24:34.040 --> 00:24:36.627 You know, I talked... in my case, it's a guy. 00:24:36.710 --> 00:24:38.657 I talk to him, he calls me and I do the thing. 00:24:38.740 --> 00:24:40.197 You know, he has a big impact on me. 00:24:40.280 --> 00:24:42.080 He's really important to me. 00:24:43.350 --> 00:24:46.367 And... or your spouse, you know, whatever. 00:24:46.450 --> 00:24:48.250 Whatever it, whatever it is. 00:24:50.060 --> 00:24:54.730 So part of the answer is that there's not going to be a single LLM solution. 00:24:55.530 --> 00:24:58.200 So let me give you the way to think about it. 00:24:59.030 --> 00:25:03.187 The... there's a, in America, there is a race to AGI, 00:25:03.270 --> 00:25:06.140 which stands for artificial general intelligence. 00:25:06.870 --> 00:25:10.040 The leading players there are a startup called Anthropic. 00:25:10.670 --> 00:25:12.357 OpenAI, which everybody here is familiar with. 00:25:12.440 --> 00:25:15.290 Gemini, of course, which I, is where I came from, Google. 00:25:17.020 --> 00:25:18.820 Microsoft is now doing its own. 00:25:19.480 --> 00:25:21.937 Elon is attempting to enter the space in Grok. 00:25:22.020 --> 00:25:23.507 Maybe there's a couple of others. 00:25:23.590 --> 00:25:27.630 These things require an enormous amount of money. 00:25:28.130 --> 00:25:32.830 Those systems... and Meta is, not at the moment in the lead. 00:25:33.000 --> 00:25:35.587 It is probably going to have to have a closed system to do so. 00:25:35.670 --> 00:25:38.287 Closed means they're not going to give away the source, 00:25:38.370 --> 00:25:41.327 just because the amount of money involved, they're not going to give it away. 00:25:41.410 --> 00:25:45.810 The Chinese have strangely made the inverse decision. 00:25:46.040 --> 00:25:48.750 Every Chinese model is open source. 00:25:49.610 --> 00:25:50.937 So I have a number of startups 00:25:51.020 --> 00:25:54.490 which have a high quality of very, very smart Chinese people, 00:25:54.750 --> 00:25:56.537 and they just use the Chinese LLMs. 00:25:56.620 --> 00:25:58.137 And they're not political startups. 00:25:58.220 --> 00:25:59.977 They're, they're just useful and they're free. 00:26:00.060 --> 00:26:01.690 I said, sure, right? 00:26:03.490 --> 00:26:07.447 So it's likely that the, in the media, in the next five or 10 years, 00:26:07.530 --> 00:26:09.717 you're going to see this huge amount of tension 00:26:09.800 --> 00:26:11.900 about, as we get closer and closer to AGI. 00:26:12.370 --> 00:26:15.857 And when we hit various thresholds, you know, when something... 00:26:15.940 --> 00:26:19.810 We already have systems that can answer graduate-level 00:26:20.280 --> 00:26:22.380 biology, chemistry, math problems, 00:26:22.680 --> 00:26:25.267 which I look at them and I say it's inconceivable to me 00:26:25.350 --> 00:26:27.137 that it can do it and it can solve those. 00:26:27.220 --> 00:26:29.307 Those are like headline events in my world. 00:26:29.390 --> 00:26:31.590 AlphaFold, headline events in my world. 00:26:31.790 --> 00:26:33.590 There will be more, 00:26:34.220 --> 00:26:38.030 but China will offer those in open source form, 00:26:38.500 --> 00:26:40.747 and there will be millions of smaller models 00:26:40.830 --> 00:26:43.340 in open source from, from many countries. 00:26:43.800 --> 00:26:46.170 So I think you won't have as big a lock in, 00:26:46.540 --> 00:26:50.090 especially if the university teaches you not to become locked into one. 00:26:50.740 --> 00:26:52.997 I move around. I try different ones. 00:26:53.080 --> 00:26:57.197 Do you feel like a lot of the American companies that are developing 00:26:57.280 --> 00:27:02.167 these LLMs have taken the wrong direction with respect to open source? 00:27:02.250 --> 00:27:04.050 They didn't really have a choice. 00:27:04.290 --> 00:27:06.007 I'm, my back-, just to let you know, 00:27:06.090 --> 00:27:07.907 as a computer scientist, my background was open source. 00:27:07.990 --> 00:27:11.877 Open source means we literally publish our source code. 00:27:11.960 --> 00:27:18.100 And I was part of something called Unix, which is the underlying, 00:27:18.670 --> 00:27:21.570 for example, it underlies the Mac operating system. 00:27:21.840 --> 00:27:23.990 For those of you who don't know what it is. 00:27:24.480 --> 00:27:26.257 And it was a great time 00:27:26.340 --> 00:27:29.497 because we were exchanging ideas, we were working collaboratively. 00:27:29.580 --> 00:27:32.597 And look what we built. The internet is largely 00:27:32.680 --> 00:27:35.480 built because of open source architectures and so forth. 00:27:35.750 --> 00:27:37.450 So there's a lot of power in that. 00:27:38.290 --> 00:27:42.507 I think realistically, the, so, if you look at... 00:27:42.590 --> 00:27:45.517 so for example, Sam, one day called me up and said, you're going to be happy. 00:27:45.600 --> 00:27:47.347 I'm going to take open, 00:27:47.430 --> 00:27:50.147 o3 and do a mini-version and release it in open source. 00:27:50.230 --> 00:27:52.117 And they did. And it's pretty good, right? 00:27:52.200 --> 00:27:52.957 So I think there's, 00:27:53.040 --> 00:27:56.287 there will be attempts by the American companies to release subset. 00:27:56.370 --> 00:28:02.097 One of the core questions for the American companies is the raw models are so big 00:28:02.180 --> 00:28:06.437 and so powerful, will they ever release the biggest models? 00:28:06.520 --> 00:28:13.307 It sure looks to me like they're going to release a portfolio of models from super 00:28:13.390 --> 00:28:18.847 huge, huge, medium range, small, open source, with different pricing points, 00:28:18.930 --> 00:28:20.417 and it looks like that's what they're going to do. 00:28:20.500 --> 00:28:23.487 And the reasons have to do with the cost of serving and the cost 00:28:23.570 --> 00:28:24.570 of the architecture. 00:28:25.270 --> 00:28:28.257 Eric, you mentioned AlphaFold, and maybe you can explain a little bit 00:28:28.340 --> 00:28:30.257 about what AlphaFold was able to do. 00:28:30.340 --> 00:28:36.397 And, and your former company, Google, with Demis Hassabis, Demis 00:28:36.480 --> 00:28:41.367 Hassabis, has just been doing incredible work in bringing AI 00:28:41.450 --> 00:28:44.360 to generate scientific breakthroughs. 00:28:44.720 --> 00:28:49.130 And all of this requires a tremendous amount of compute. 00:28:50.030 --> 00:28:55.130 What's your advice to universities that want to be at that table? 00:28:57.100 --> 00:29:00.870 I told you already. Find a Republican state, 00:29:01.140 --> 00:29:05.740 build a large data center there, and run a big pipe to wherever you are. 00:29:06.850 --> 00:29:10.037 Sorry. No, no, no. I just want you to say it in front of everyone. 00:29:10.120 --> 00:29:15.190 Oh, I see. You want me to support you, and you have a Republican state in, in mind? 00:29:15.550 --> 00:29:18.877 Well, you gave me an idea. Well, I think Nevada is pretty close. 00:29:18.960 --> 00:29:19.793 Yeah. 00:29:20.460 --> 00:29:22.177 But, But, but, but you think it’s... 00:29:22.260 --> 00:29:24.860 I'm not being... I'm not being facetious. 00:29:25.960 --> 00:29:27.770 So, so, 00:29:30.130 --> 00:29:33.540 So universities need to keep up with this. 00:29:33.810 --> 00:29:38.340 There is no way the future can be invented in the, in universities 00:29:38.740 --> 00:29:41.350 without enough computing resource. 00:29:41.780 --> 00:29:44.797 No university I know has anywhere close. 00:29:44.880 --> 00:29:46.867 These are the richest universities in the world. 00:29:46.950 --> 00:29:49.367 I mean, think about the British universities have no money at all. 00:29:49.450 --> 00:29:51.537 At least, I mean, you guys, you have a lot a lot of money. 00:29:51.620 --> 00:29:53.390 It's private school. Very well run. 00:29:54.090 --> 00:29:57.960 And you don't have it. So first, you need to build a big data center. 00:29:58.630 --> 00:30:01.430 And, and many universities are power constrained. 00:30:01.930 --> 00:30:06.170 And then what I would do is I would get donors to donate the necessary chips. 00:30:07.700 --> 00:30:11.070 As you know, Nvidia is 90, 95% of the training market right now. 00:30:11.540 --> 00:30:12.997 They have amazing chips. 00:30:13.080 --> 00:30:16.067 And of course, they're now the most valuable company in the world. 00:30:16.150 --> 00:30:18.967 I'm pleased to report that Google now is working on 00:30:19.050 --> 00:30:21.890 competitive offerings, and the TPU V7 chip 00:30:22.250 --> 00:30:25.200 appears to be better than the B-300 chip for most training. 00:30:25.290 --> 00:30:27.960 Of course, that's widely disputed in the industry. 00:30:28.290 --> 00:30:31.130 That competition is really important. 00:30:31.500 --> 00:30:36.017 So the answer to the crazy stuff that's going on in tech is hardcore competition, 00:30:36.100 --> 00:30:37.900 which will benefit the university. 00:30:38.140 --> 00:30:40.927 Well, I have your number. So we'll be talking about philanthropic 00:30:41.010 --> 00:30:44.640 opportunities here at USC. So... 00:30:45.680 --> 00:30:47.980 And I... and I'm happy to help. 00:30:48.650 --> 00:30:53.250 Just a couple more questions. Wanted to ask you, what do you think 00:30:53.520 --> 00:30:56.137 one of the most underappreciated opportunities 00:30:56.220 --> 00:30:59.020 for universities are in the AI space? 00:31:00.490 --> 00:31:02.290 I think I said it before. 00:31:03.360 --> 00:31:05.747 The world I live in, I'll just... 00:31:05.830 --> 00:31:09.830 I'll just be very direct, is largely dominated by the most brilliant 00:31:10.000 --> 00:31:11.840 20-somethings, men and women, 00:31:12.770 --> 00:31:16.527 because the stuff is so new that if you're 40, 00:31:16.610 --> 00:31:19.940 you didn't go through all those painful classes to learn it. 00:31:20.610 --> 00:31:24.037 Right? Now, you're smart at 40 and you know you can study it and so forth. 00:31:24.120 --> 00:31:28.750 But the people who have forged through the crucible of their undergraduate work. 00:31:29.120 --> 00:31:32.637 At Princeton, I'm from Princeton, I met with a bunch of sophomores 00:31:32.720 --> 00:31:35.770 who were doing alternative reinforcement learning algorithms, 00:31:35.890 --> 00:31:38.000 and I go like, what, as sophomores? 00:31:39.030 --> 00:31:43.117 I'm sure that's true here. So I think people do not appreciate the, 00:31:43.200 --> 00:31:46.117 the quality of the ideas 00:31:46.200 --> 00:31:49.350 and the capability of moving quickly with the right tools here. 00:31:49.540 --> 00:31:51.740 So let's go through what that could mean. 00:31:52.340 --> 00:31:55.427 In humanities, why do you not have a database 00:31:55.510 --> 00:31:58.597 that has taken all the documents that matter and made them 00:31:58.680 --> 00:32:01.650 accessible and classifiable in interesting new ways? 00:32:02.820 --> 00:32:06.107 In physics, people are using models called diffusion models, 00:32:06.190 --> 00:32:08.960 which are hard to explain, but basically you add noise 00:32:09.230 --> 00:32:11.830 and then you reduce the noise and you discover an... 00:32:12.160 --> 00:32:14.330 you discover the underlying pattern. 00:32:14.570 --> 00:32:17.570 Physics is a lot about understanding the underlying pattern. 00:32:18.100 --> 00:32:19.900 In biology with AlphaFold... 00:32:20.570 --> 00:32:22.370 So proteins, 00:32:23.110 --> 00:32:26.610 so, the primary things that LLMs, 00:32:27.210 --> 00:32:28.980 what you know, is open ChatGPT 00:32:29.880 --> 00:32:31.680 do is they do sequence prediction. 00:32:32.080 --> 00:32:35.237 So what they're really doing is they're doing next word prediction. 00:32:35.320 --> 00:32:38.777 They just do it really well. And I often tell the story 00:32:38.860 --> 00:32:41.490 that it's like on a Thursday night in San Francisco 00:32:41.860 --> 00:32:46.017 and they were sitting, at OpenAI, and these are a couple of my friends. 00:32:46.100 --> 00:32:49.547 And they decided to turn on what became GPT-3o. 00:32:49.630 --> 00:32:51.480 So this is, you know, four years ago. 00:32:52.200 --> 00:32:55.940 And they said, “Oh my God, this thing can write.” 00:32:57.540 --> 00:33:00.640 They didn't know. It's a real historical discovery. 00:33:01.410 --> 00:33:03.597 And there's a, there's a moment 00:33:03.680 --> 00:33:06.930 where that occurred which will ultimately be recorded in history. 00:33:07.420 --> 00:33:10.437 And what's interesting is we think, “Oh, that's so stupid. 00:33:10.520 --> 00:33:13.107 Next word prediction?” By the way, how does your brain work? 00:33:13.190 --> 00:33:14.990 It's called next word prediction. 00:33:15.830 --> 00:33:18.117 It doesn't use exactly neural nets the same way. 00:33:18.200 --> 00:33:20.630 And it has other agencies and so forth. 00:33:21.000 --> 00:33:23.617 But fundamentally it's closer to the way people think 00:33:23.700 --> 00:33:26.150 than you're willing to admit, speaking as humans. 00:33:26.970 --> 00:33:33.357 So what I like about, about all of this is that the expansion and the speed 00:33:33.440 --> 00:33:37.090 that is possible when you take next word prediction and you add planning, 00:33:37.210 --> 00:33:41.467 which is essentially a series of steps, you can kind of do anything. 00:33:41.550 --> 00:33:44.037 Here's an example. So I like to buy houses. 00:33:44.120 --> 00:33:45.037 I like to build houses. 00:33:45.120 --> 00:33:47.707 I want to buy a house in California, which is always a mess. 00:33:47.790 --> 00:33:50.677 So the first agent, an agent is input and output. 00:33:50.760 --> 00:33:52.877 The input is language. It has memory. 00:33:52.960 --> 00:33:54.047 It has output, which is language. 00:33:54.130 --> 00:33:56.517 The first agent tells me where to buy. 00:33:56.600 --> 00:33:58.370 The second agent consults the, the 00:33:59.340 --> 00:34:02.027 published rules. The third agent designs the home 00:34:02.110 --> 00:34:03.957 with a known architect, which it selects. 00:34:04.040 --> 00:34:07.180 An actual human. The fourth hires a contractor, 00:34:07.440 --> 00:34:08.897 an actual human, to build the house. 00:34:08.980 --> 00:34:12.030 And the fifth agent sues the contractor when it doesn't work. 00:34:12.450 --> 00:34:14.890 Right? That's a workflow. 00:34:16.250 --> 00:34:17.837 This is... Welcome to my world. 00:34:17.920 --> 00:34:21.530 Now, you sit there and you go, why is this so... 00:34:21.660 --> 00:34:23.807 I mean, why is he giving us such a stupid example? 00:34:23.890 --> 00:34:27.870 Because you'll remember it. I just described the workflow in a university. 00:34:28.330 --> 00:34:30.600 I just described a workflow in a hospital. 00:34:30.900 --> 00:34:35.187 I just described the workflow in government and in businesses. 00:34:35.270 --> 00:34:37.480 So the agentic revolution, which is what, 00:34:37.910 --> 00:34:42.727 how we in the, in the computer industry call it, is the next big thing. 00:34:42.810 --> 00:34:44.467 So another piece of advice 00:34:44.550 --> 00:34:46.900 for universities is just start building agents. 00:34:47.920 --> 00:34:48.807 So if the graduate 00:34:48.890 --> 00:34:51.677 students in computer science, who in this area will be the smartest, 00:34:51.760 --> 00:34:55.347 just build an agent orchestration framework to run all the agents 00:34:55.430 --> 00:34:59.030 and tell everyone else in the university to build agents and have fun. 00:34:59.400 --> 00:35:02.217 Most of the agents will be kind of useless, but that's OK. 00:35:02.300 --> 00:35:05.257 It's a university. But people will learn what agents are like. 00:35:05.340 --> 00:35:08.640 So combination of right agents, which the computer can do for you, 00:35:09.110 --> 00:35:10.257 and then prompt engineering, 00:35:10.340 --> 00:35:13.240 which allows you to program anything, you're kind of done, 00:35:13.980 --> 00:35:18.797 right? Think about the power of your labor force, which is here in the room. 00:35:18.880 --> 00:35:20.720 Fifty thousand total? 00:35:21.650 --> 00:35:26.807 Boy, I'd take that. Last question, and maybe you started to answer it just now. 00:35:26.890 --> 00:35:30.007 So I was wondering, Eric, if you could just speak directly 00:35:30.090 --> 00:35:33.030 to all the students who are listening today? 00:35:33.460 --> 00:35:36.900 We see all the headlines, you know, jobs are going away, 00:35:38.170 --> 00:35:41.970 reorganizations. And I think there's, 00:35:43.370 --> 00:35:45.897 there's reason to be really, really excited. 00:35:45.980 --> 00:35:49.550 And there's also reasons for our students who, again, as we discussed, 00:35:49.910 --> 00:35:54.967 span hard sciences to the performing arts to be really worried about the future 00:35:55.050 --> 00:35:57.260 and how AI is going to impact it. 00:35:57.450 --> 00:35:59.407 What's your, what's your message to them? 00:35:59.490 --> 00:36:03.307 There's this problem with smart people that smart people are always depressed. 00:36:03.390 --> 00:36:08.930 Stop. Stop thinking so hard. I am super jealous 00:36:09.270 --> 00:36:11.917 of your undergraduates. I want to be very clear. 00:36:12.000 --> 00:36:14.200 One, you're in California, stop complaining. 00:36:14.540 --> 00:36:18.810 OK? Like I moved here as a graduate student for the weather. 00:36:19.110 --> 00:36:23.667 So we're clear. Second, in your lifetimes, 00:36:23.750 --> 00:36:26.580 you're going to see life extensions to 100 or more. 00:36:27.250 --> 00:36:31.760 Your children will be infinitely healthier than my generation ever was. 00:36:32.390 --> 00:36:34.060 Hopefully, the world will get 00:36:35.130 --> 00:36:39.147 more safe and less dangerous. So far, that's generally been true. 00:36:39.230 --> 00:36:42.717 There are plenty of studies on this, but most important, 00:36:42.800 --> 00:36:46.570 you're going to work on the arrival of a new form of intelligence 00:36:47.000 --> 00:36:49.827 that you can work with. And yes, yes, yes, 00:36:49.910 --> 00:36:53.197 there's all the movies about how the intelligence gets out of control. 00:36:53.280 --> 00:36:55.997 And then, you know, the the lady scientist goes 00:36:56.080 --> 00:36:58.897 and kills the computer and the man is, you know, whatever. 00:36:58.980 --> 00:37:01.037 Those are fun movies, and you're good at movies. 00:37:01.120 --> 00:37:03.507 But the important thing is the arrival of intelligence 00:37:03.590 --> 00:37:06.607 is such a productivity increase, and productivity, 00:37:06.690 --> 00:37:09.460 I mean, as a personal level, for entertainment, 00:37:10.060 --> 00:37:12.847 right, for your social life. Right? 00:37:12.930 --> 00:37:15.900 So start thinking as a 20-something 00:37:16.300 --> 00:37:19.050 about how you can participate in the agentic revolution 00:37:19.170 --> 00:37:20.970 and think like a computer scientist. 00:37:21.270 --> 00:37:25.697 Don't do something for you. Do something for everyone around 00:37:25.780 --> 00:37:28.330 you and everyone you know and all of their friends. 00:37:29.480 --> 00:37:33.067 A good rule is, so why are computer scientists doing so well 00:37:33.150 --> 00:37:35.300 besides, as I mentioned, our own arrogance. 00:37:35.790 --> 00:37:39.307 Because we think in terms of scale, and because I'm, I'm 00:37:39.390 --> 00:37:41.547 well known now, a lot of people come up to me 00:37:41.630 --> 00:37:44.447 with interesting business ideas, but none of them will scale. 00:37:44.530 --> 00:37:46.480 Come up with a scalable business model. 00:37:46.860 --> 00:37:49.147 And why is a scalable business model interesting? 00:37:49.230 --> 00:37:53.300 Because you can make a fortune, which is why you're in business school. 00:37:53.570 --> 00:37:57.357 Sorry. You see my point? I would be... 00:37:57.440 --> 00:37:59.427 You should be, in fact, this is probably why you work here. 00:37:59.510 --> 00:38:02.667 You want to hang out with them because you're jealous of them, too? 00:38:02.750 --> 00:38:05.197 Absolutely. But thank you for having me. 00:38:05.280 --> 00:38:06.967 This is a great honor to kick this off. 00:38:07.050 --> 00:38:09.337 Eric Schmidt, thank you for your contributions to our society. 00:38:09.420 --> 00:38:10.520 Thanks for being here.