WEBVTT 00:00:05.120 --> 00:00:08.157 So Eric, when did you when did Google 00:00:08.240 --> 00:00:10.477 first start thinking about artificial 00:00:10.560 --> 00:00:12.157 intelligence? 00:00:12.240 --> 00:00:16.717 >> Larry Page was his PhD subject was AI. 00:00:16.800 --> 00:00:21.117 So even when Larry started the company, he wanted to do AI relatively 00:00:21.200 --> 00:00:23.517 interesting things. And the first real 00:00:23.600 --> 00:00:27.117 use of AI was we used AI to improve our 00:00:27.200 --> 00:00:29.757 ad system performance. This is before 00:00:29.840 --> 00:00:34.797 LMS and so forth and that worked. That was the first time I saw it used inside 00:00:34.880 --> 00:00:38.717 the business. >> Now you've written a book with on 00:00:38.800 --> 00:00:41.037 artificial intelligence. One of your 00:00:41.120 --> 00:00:45.757 co-authors was Henry Kissinger. Did Henry Kissinger really know anything 00:00:45.840 --> 00:00:49.997 about artificial intelligence? Why was he the co-author of that book? Well, he 00:00:50.080 --> 00:00:52.477 was uh he's obviously a brilliant man 00:00:52.560 --> 00:00:54.637 and he was very interested in it not for 00:00:54.720 --> 00:00:57.357 the technical reasons but because he had 00:00:57.440 --> 00:01:01.117 studied Kant when he was uh when he was 00:01:01.200 --> 00:01:03.917 the age of a college student well before 00:01:04.000 --> 00:01:07.117 any of us was born and when he saw AI 00:01:07.200 --> 00:01:11.677 when Demis presented to him he thought it would have an impact on human 00:01:11.760 --> 00:01:13.917 consciousness and he was very worried 00:01:14.000 --> 00:01:18.157 about it. Hey Joe. Um, usually when I'm 00:01:18.240 --> 00:01:21.277 interviewing people about artificial intelligence or something like that, 00:01:21.360 --> 00:01:25.197 there are people that have engineering backgrounds, their technology 00:01:25.280 --> 00:01:28.877 backgrounds. You're an accountant. So, what are you doing on a panel on 00:01:28.960 --> 00:01:30.957 artificial intelligence? 00:01:31.040 --> 00:01:34.157 >> Fortunately, of those 500,000 people at 00:01:34.240 --> 00:01:36.637 Deote, I have some good friends who are 00:01:36.720 --> 00:01:41.437 computer engineers and scientists and have deep expertise in the systems 00:01:41.520 --> 00:01:46.397 themselves. So, I I've got a little bit of help. How many people are devoted to 00:01:46.480 --> 00:01:49.917 artificial intelligence out of your 500,000 people? 00:01:50.000 --> 00:01:53.277 >> I would define every one of them is in some way leveraging the technology, 00:01:53.360 --> 00:01:57.197 helping clients use it. You can define a subset that obviously are the deep 00:01:57.280 --> 00:02:01.677 experts, but it's now pervasive across the entirety of the organization. 00:02:01.760 --> 00:02:05.677 >> Eric, if I want to make money on artificial intelligence, I'm an average 00:02:05.760 --> 00:02:10.157 investor. What is the best way for me to make money today on artificial 00:02:10.240 --> 00:02:12.477 intelligence? buying a big company like 00:02:12.560 --> 00:02:14.717 uh Alphabet or finding a smaller one 00:02:14.800 --> 00:02:17.357 that's just getting off the ground. 00:02:17.440 --> 00:02:19.837 >> Well, one way to understand the value 00:02:19.920 --> 00:02:23.037 that's being created in AI is that it 00:02:23.120 --> 00:02:24.957 will there will be businesses that will 00:02:25.040 --> 00:02:27.757 become much more successful because of 00:02:27.840 --> 00:02:30.957 AI. So, I think the best investment is 00:02:31.040 --> 00:02:35.197 to invest in the businesses where AI will make them transform. 00:02:35.280 --> 00:02:38.717 Um, it's probably pretty late to have all the gains that you have in the 00:02:38.800 --> 00:02:41.277 Magnificent 7, although it's not my area 00:02:41.360 --> 00:02:43.677 of expertise, simply because those are 00:02:43.760 --> 00:02:48.877 very highly valued. And the startups are now so highly valued when they get 00:02:48.960 --> 00:02:50.397 started that it's really hard to get 00:02:50.480 --> 00:02:53.437 into them. Um, if you really want to 00:02:53.520 --> 00:02:56.557 make money, it's actually easy. Found an 00:02:56.640 --> 00:03:00.077 agentic AI company. And I don't mean one 00:03:00.160 --> 00:03:02.317 designing agents. I mean build an agent 00:03:02.400 --> 00:03:07.517 to do something. This is the agentic period in AI and for the next year or 00:03:07.600 --> 00:03:09.277 two, everyone's going to build agents. 00:03:09.360 --> 00:03:12.797 The agents are all going to compete. If you build a better agent than anyone 00:03:12.880 --> 00:03:14.797 else, you can probably build one 00:03:14.880 --> 00:03:17.597 incredible company and own it yourself. 00:03:17.680 --> 00:03:21.437 >> Eric, um right now, um artificial 00:03:21.520 --> 00:03:23.997 intelligence is affecting adversely 00:03:24.080 --> 00:03:26.877 which um part of the economy the most 00:03:26.960 --> 00:03:29.117 and it was affecting positively which 00:03:29.200 --> 00:03:31.677 part of the economy the most. 00:03:31.760 --> 00:03:34.477 Uh well AI is at the moment contributing 00:03:34.560 --> 00:03:38.077 more than 1% to the GDP of America 00:03:38.160 --> 00:03:42.077 largely because of the data center buildout and I just started a data 00:03:42.160 --> 00:03:46.477 center company so I've been studying this the demands the the real demands 00:03:46.560 --> 00:03:48.477 from the hyperscalers the big companies 00:03:48.560 --> 00:03:51.197 Google and so forth are immense and when 00:03:51.280 --> 00:03:53.917 I talk to them they need 1 gawatt 5 00:03:54.000 --> 00:03:57.677 gawatt 10 gawatt each now the best study 00:03:57.760 --> 00:03:59.997 I've seen indicates that the industry in 00:04:00.080 --> 00:04:02.877 America needs 80 gawatt in the next 3 to 00:04:02.960 --> 00:04:06.317 5 years. Now 80 gawatt by the way let me 00:04:06.400 --> 00:04:08.717 tell you how big is that 00:04:08.800 --> 00:04:11.837 1.5 gawatt is the size of a nuclear 00:04:11.920 --> 00:04:14.477 power plant. So this is an enormous 00:04:14.560 --> 00:04:16.717 amount of energy. So that's the the 00:04:16.800 --> 00:04:19.837 economics right now are being most felt 00:04:19.920 --> 00:04:22.557 in the buildout of the infrastructure 00:04:22.640 --> 00:04:26.157 for the next wave of AI. Joe uh if you 00:04:26.240 --> 00:04:28.877 have 500,000 employees at Deote how many 00:04:28.960 --> 00:04:30.317 of them are going to be losing their 00:04:30.400 --> 00:04:33.437 jobs because of AI 00:04:33.520 --> 00:04:36.397 so with all of the adoption of 00:04:36.480 --> 00:04:40.157 technology that half a million today is 00:04:40.240 --> 00:04:44.157 greater than the number that we had a year ago will there be some displacement 00:04:44.240 --> 00:04:46.157 and shifting in certain functions that 00:04:46.240 --> 00:04:49.197 one did manually of course as with every 00:04:49.280 --> 00:04:51.277 wave of technology adoption but we're 00:04:51.360 --> 00:04:54.237 still a big believer that great people 00:04:54.320 --> 00:04:58.557 great professionals with domain expertise are actually the key to 00:04:58.640 --> 00:05:01.117 unlocking the technology and that as 00:05:01.200 --> 00:05:05.757 with every prior wave of technology change on a net basis you actually 00:05:05.840 --> 00:05:09.517 create more jobs than you disrupt that'll be tested but that's our base 00:05:09.600 --> 00:05:13.437 case >> okay so in your consulting business what 00:05:13.520 --> 00:05:17.197 has been some more significant the rise of the internet or the rise of 00:05:17.280 --> 00:05:21.277 artificial intelligence >> well if you look at the rise of the 00:05:21.360 --> 00:05:25.677 internet and the impact over a 30 plus year period in the acid to date that's 00:05:25.760 --> 00:05:27.917 more significant but in a confined time 00:05:28.000 --> 00:05:30.157 period in terms of this really thrusting 00:05:30.240 --> 00:05:35.837 onto the scene over the last 3 or so years it is a more accelerated impact 00:05:35.920 --> 00:05:39.997 than what we would have seen with the internet and right now I don't think 00:05:40.080 --> 00:05:42.717 anybody doubts the power of the 00:05:42.800 --> 00:05:45.437 technology the applicability across the 00:05:45.520 --> 00:05:48.157 enterprise every sector every business 00:05:48.240 --> 00:05:51.437 function the issue is it's really hard 00:05:51.520 --> 00:05:56.397 to actually bring it to life in a large complex established bureaucratic 00:05:56.480 --> 00:05:58.077 enterprise. You've got to actually be 00:05:58.160 --> 00:06:01.517 willing to drive a fundamental endtoend 00:06:01.600 --> 00:06:04.557 change in the business processes. And 00:06:04.640 --> 00:06:08.557 history tells us that there's a lot of stickiness in the system. And that 00:06:08.640 --> 00:06:11.517 diffusion from the the lab in Silicon 00:06:11.600 --> 00:06:14.317 Valley to the actual heartland to the 00:06:14.400 --> 00:06:19.357 factory, the office that people work in, it takes time. It takes effort. So we're 00:06:19.440 --> 00:06:22.957 in the middle of a a massive transformation that's going to take many 00:06:23.040 --> 00:06:23.917 years. 00:06:24.000 --> 00:06:27.037 >> Yeah, David, let go ahead. So this has 00:06:27.120 --> 00:06:30.317 always been true of technology adoption 00:06:30.400 --> 00:06:32.317 and the industry calls this the 00:06:32.400 --> 00:06:34.957 technology overhang. The industry we 00:06:35.040 --> 00:06:40.237 arrogantly say we keep producing things that you can't absorb fast enough. Of 00:06:40.320 --> 00:06:45.037 course, if we made it easier, maybe it would would on the jobs. There's 00:06:45.120 --> 00:06:49.917 beginning to be evidence of real job loss in two categories. One is in 00:06:50.000 --> 00:06:54.157 customer service, which is highly automated now, and the other one is in 00:06:54.240 --> 00:06:59.437 low-end or entry- level software programming, my field. Uh, and that 00:06:59.520 --> 00:07:00.957 shouldn't surprise you because the 00:07:01.040 --> 00:07:04.077 software programming is getting so good, 00:07:04.160 --> 00:07:08.317 you really can change your modality and you should expect that that will 00:07:08.400 --> 00:07:12.397 continue. The real question is when do other when do other parts of jobs get 00:07:12.480 --> 00:07:15.837 affected? >> Let's suppose I miss the internet uh 00:07:15.920 --> 00:07:20.797 wave and I miss the AI wave as an investor, but I'm always looking for the 00:07:20.880 --> 00:07:24.717 next big wave that I can get on the ground floor. What is the next big 00:07:24.800 --> 00:07:27.117 technology wave that I could get on the 00:07:27.200 --> 00:07:29.517 ground floor of? Is it is it quantum 00:07:29.600 --> 00:07:33.517 computing or some other thing uh like uh 00:07:33.600 --> 00:07:37.677 uh nuclear fusion? What would you say is the next big wave I should invest in if 00:07:37.760 --> 00:07:38.797 I want to be on the ground floor? 00:07:38.880 --> 00:07:42.397 >> So the next big wave is AI, right? We're 00:07:42.480 --> 00:07:46.157 not done and in fact the wave is just getting started. That's not I don't 00:07:46.240 --> 00:07:48.397 agree with you David. Um we're going 00:07:48.480 --> 00:07:50.477 from language to language which is think 00:07:50.560 --> 00:07:53.357 of it as chat GBT to language to action 00:07:53.440 --> 00:07:56.237 through through agents and if you spend 00:07:56.320 --> 00:07:59.037 any time with deep research or spend 00:07:59.120 --> 00:08:01.757 some time with open AAI R3 spend some 00:08:01.840 --> 00:08:05.117 time with DeepSeek V3 whatever the power 00:08:05.200 --> 00:08:07.597 of the reasoning models of these things 00:08:07.680 --> 00:08:10.877 is frightening right it's graduate level 00:08:10.960 --> 00:08:13.117 MIT Stanford reasoning right at your 00:08:13.200 --> 00:08:16.237 fingertips we're not done yet and indeed 00:08:16.320 --> 00:08:20.157 the investments in data centers, the new algorithms, the much larger models are 00:08:20.240 --> 00:08:22.717 entirely focused on that. So, we're not done. 00:08:22.800 --> 00:08:25.677 >> Okay. Um, Americans seem obsessed with 00:08:25.760 --> 00:08:28.717 China and China's developing AI at 00:08:28.800 --> 00:08:32.717 better pace and maybe in better ways than the United States. How do you today 00:08:32.800 --> 00:08:37.597 rate the competitive abilities of the Americans versus the Chinese on AI 00:08:37.680 --> 00:08:39.437 development? 00:08:39.520 --> 00:08:42.877 >> So, um, the US invented it and the 00:08:42.960 --> 00:08:45.117 Chinese have adopted it in a very clever 00:08:45.200 --> 00:08:48.957 way. So historically there was a view 00:08:49.040 --> 00:08:51.437 that China was well behind and China has 00:08:51.520 --> 00:08:54.157 in the last few years caught up really 00:08:54.240 --> 00:08:56.397 really well. Uh let me give you some 00:08:56.480 --> 00:08:59.117 examples. There are three models Kimmy 00:08:59.200 --> 00:09:02.477 Quinn and Deepseek which are often at 00:09:02.560 --> 00:09:07.357 the top of the rankings. This makes the Americans annoyed because Americans 00:09:07.440 --> 00:09:11.277 claim that the Chinese did something called distillation and used the 00:09:11.360 --> 00:09:14.877 American models to train them. That may or may not be true. The important thing 00:09:14.960 --> 00:09:17.357 is the Chinese models are really good. 00:09:17.440 --> 00:09:21.437 Furthermore, China has decided to be completely self-sufficient in this 00:09:21.520 --> 00:09:25.757 technology. So, they're building the entire hardware and software stack. They 00:09:25.840 --> 00:09:28.157 have their own chips called an SN930. 00:09:28.240 --> 00:09:32.397 They have their own software layers and so forth. The Chinese model is also 00:09:32.480 --> 00:09:36.877 different. They're not going for AGI or general intelligence. They're going for 00:09:36.960 --> 00:09:39.197 everything AI used everywhere in the 00:09:39.280 --> 00:09:41.757 society. And it's a massive massive 00:09:41.840 --> 00:09:44.157 program within the country to use AI 00:09:44.240 --> 00:09:47.037 everywhere especially on the edge. They 00:09:47.120 --> 00:09:52.077 are in my view six to 12 months behind which is not much. 00:09:52.160 --> 00:09:53.837 >> Now you've been an adviser to the 00:09:53.920 --> 00:09:56.477 defense department in a number of for a 00:09:56.560 --> 00:09:59.997 number of years. U how has AI changed 00:10:00.080 --> 00:10:03.517 warfare or is it going to change warfare? 00:10:03.600 --> 00:10:06.957 >> So the the the US government started a 00:10:07.040 --> 00:10:11.437 program called Maven. You'll you all will hear remember this about 10 years 00:10:11.520 --> 00:10:13.437 ago and I was involved with it because I 00:10:13.520 --> 00:10:15.997 was a consultant to the Pentagon during 00:10:16.080 --> 00:10:19.837 that period and Maven was can be 00:10:19.920 --> 00:10:24.077 understood as uh drones are running 00:10:24.160 --> 00:10:26.477 around looking at targets in the in in 00:10:26.560 --> 00:10:29.357 the at time in the military and those 00:10:29.440 --> 00:10:32.077 video feeds were being watched. So the 00:10:32.160 --> 00:10:34.077 first use in the military is actually 00:10:34.160 --> 00:10:38.717 just surveillance. Why? Most soldiers in 00:10:38.800 --> 00:10:40.557 the military are just watching for 00:10:40.640 --> 00:10:43.677 things. That was the first and best use. 00:10:43.760 --> 00:10:47.117 The next one will be drones. And because 00:10:47.200 --> 00:10:48.957 of what's going on in Ukraine, the US 00:10:49.040 --> 00:10:51.997 government has decided to to learn from 00:10:52.080 --> 00:10:56.477 Ukraine. Now there's a Secretary Hexith has announced a big program around 00:10:56.560 --> 00:10:58.957 drones and drone adoption, land, sea, 00:10:59.040 --> 00:11:02.237 and air. Okay. Um, if you were advising 00:11:02.320 --> 00:11:06.877 a young person today who's graduating from college, uh, should they join a 00:11:06.960 --> 00:11:11.037 large accounting consulting firm like yours and have an accounting background 00:11:11.120 --> 00:11:15.357 as you do or should they get a technology background in AI? Which one 00:11:15.440 --> 00:11:19.757 is going to be more exciting and make more money for that young person? 00:11:19.840 --> 00:11:23.517 I actually don't think this is an eitheror and that in fact what we're 00:11:23.600 --> 00:11:27.277 finding in the application space I agree with you Eric that we're just at the 00:11:27.360 --> 00:11:31.677 beginning of the wave of AI adoption and I'll use my baseball analogy we're in 00:11:31.760 --> 00:11:33.837 the the top of the first inning here 00:11:33.920 --> 00:11:36.477 those who have the ability to integrate 00:11:36.560 --> 00:11:38.717 across disciplines. So to really bring 00:11:38.800 --> 00:11:41.437 this to life in an enterprise, you have 00:11:41.520 --> 00:11:46.157 to both have some baseline understanding of the technology, the capability of the 00:11:46.240 --> 00:11:50.557 model, how to prompt it to yield the desired outcome, as well as the business 00:11:50.640 --> 00:11:54.717 problem you're trying to solve if you're trying to embed this into the finance 00:11:54.800 --> 00:11:56.957 function, the manufacturing function, 00:11:57.040 --> 00:11:59.277 customer and marketing. And where we've 00:11:59.360 --> 00:12:02.477 seen things go wrong is where you take a 00:12:02.560 --> 00:12:05.117 piece of technology that's developed by 00:12:05.200 --> 00:12:08.077 someone in the tech laboratory and you 00:12:08.160 --> 00:12:11.197 try to deploy that and force it into an 00:12:11.280 --> 00:12:16.157 enterprise. Generally speaking, it fails. You need the people in the heart 00:12:16.240 --> 00:12:18.077 of the business who understand that 00:12:18.160 --> 00:12:19.997 function, who understand that industry 00:12:20.080 --> 00:12:25.437 and sector to be paired up on a crossf functional team to design something 00:12:25.520 --> 00:12:28.717 that's fit for purpose in the real world. I thought Yan's words earlier 00:12:28.800 --> 00:12:31.517 were were very accurate. The real world 00:12:31.600 --> 00:12:35.997 is far more complicated than text. And so those who have an appreciation for 00:12:36.080 --> 00:12:40.717 the context and who can work with the technologists to bring it to life, 00:12:40.800 --> 00:12:43.117 that's where the real value is being created. 00:12:43.200 --> 00:12:44.797 >> Let's suppose somebody in the audience 00:12:44.880 --> 00:12:48.477 here is uh 40 years old. They majored in 00:12:48.560 --> 00:12:51.197 humanities in college. they have a job 00:12:51.280 --> 00:12:56.157 that's uh say a non- tech job, but they really want to learn about AI so they 00:12:56.240 --> 00:12:59.917 can talk about it intelligently with their children, if not somebody else. 00:13:00.000 --> 00:13:04.557 What's the best way for a middle-aged non-technology person to actually learn 00:13:04.640 --> 00:13:06.717 something about AI to sound reasonably 00:13:06.800 --> 00:13:09.117 intelligent on it? 00:13:09.200 --> 00:13:12.237 >> So, let me turn the question around. We 00:13:12.320 --> 00:13:14.797 have MIT and Stanford here. The most 00:13:14.880 --> 00:13:18.637 important new course that you don't have 00:13:18.720 --> 00:13:22.237 is the freshman class on prompt engineering. 00:13:22.320 --> 00:13:25.837 Because if you think about it, the student that is entering MIT or 00:13:25.920 --> 00:13:27.517 Stanford, 00:13:27.600 --> 00:13:30.637 their entire life is going to be prompt 00:13:30.720 --> 00:13:34.877 engineering as it evolves because everything they do will be with a 00:13:34.960 --> 00:13:37.437 computer assisting them. So the same 00:13:37.520 --> 00:13:39.837 applies to the 40-year-old, right? So 00:13:39.920 --> 00:13:42.237 the 40-year-old needs to get good, too. 00:13:42.320 --> 00:13:44.797 These tools are remarkably powerful for 00:13:44.880 --> 00:13:47.357 a non-technical person. Um, if you 00:13:47.440 --> 00:13:49.437 wanted to write code, you wanted to do 00:13:49.520 --> 00:13:52.557 something, have fun, learn how to do it. 00:13:52.640 --> 00:13:55.197 They will produce working systems that 00:13:55.280 --> 00:13:58.237 can make your life better. Okay. So 00:13:58.320 --> 00:14:02.797 today um a lot of the big technology companies that are uh have trillion 00:14:02.880 --> 00:14:04.477 dollar market caps, they seem to be 00:14:04.560 --> 00:14:06.397 moving away from everybody else in terms 00:14:06.480 --> 00:14:10.077 of their ability to um develop AI models 00:14:10.160 --> 00:14:13.917 and the ability be a leader in it. What about the smaller companies? Is there a 00:14:14.000 --> 00:14:15.917 chance for a smaller company today to 00:14:16.000 --> 00:14:19.037 become uh a gigantic uh alphabet or is 00:14:19.120 --> 00:14:22.317 that day gone already too late to become 00:14:22.400 --> 00:14:24.637 gigantic again in the technology world? 00:14:24.720 --> 00:14:28.477 So, um, the big companies are getting 00:14:28.560 --> 00:14:32.397 bigger because their revenue is growing fast. And it's growing fast because 00:14:32.480 --> 00:14:35.277 they've been around for a while and they have a lot of capital and they have a 00:14:35.360 --> 00:14:38.957 lot of data centers, right? That's what's really going on. They're not just 00:14:39.040 --> 00:14:40.637 being nice and the valuations are going 00:14:40.720 --> 00:14:44.157 up. So, for smaller companies, the real 00:14:44.240 --> 00:14:48.397 question is how do they get the capital to play in that game? It's going to get 00:14:48.480 --> 00:14:50.397 harder and harder and harder as the 00:14:50.480 --> 00:14:52.957 industry matures. And let me ask you 00:14:53.040 --> 00:14:55.837 about Nvidia. And Nvidia is making a lot 00:14:55.920 --> 00:15:00.157 of money on artificial intelligence. Is anybody else really making money or are 00:15:00.240 --> 00:15:03.597 they just spending money on artificial intelligence? 00:15:03.680 --> 00:15:05.437 Look, when you when you look at where 00:15:05.520 --> 00:15:09.037 the rents have accred to date, obviously 00:15:09.120 --> 00:15:13.357 the sort of infrastructure layer at the front end as people are building all of 00:15:13.440 --> 00:15:15.197 this out has captured a a 00:15:15.280 --> 00:15:18.237 disproportionate share. The real issue 00:15:18.320 --> 00:15:20.797 from a societal standpoint is how to 00:15:20.880 --> 00:15:23.597 take those gains and spread them broadly 00:15:23.680 --> 00:15:25.837 and actually drive the productivity 00:15:25.920 --> 00:15:28.797 benefit into the heart of the of the 00:15:28.880 --> 00:15:32.317 enterprise system. And frankly, it's a 00:15:32.400 --> 00:15:34.637 societal imperative if we're going to 00:15:34.720 --> 00:15:37.757 earn the confidence of people that in an 00:15:37.840 --> 00:15:41.917 environment where so much fear is being attached to the technology and some of 00:15:42.000 --> 00:15:44.237 the risks, the sort of misinformation, 00:15:44.320 --> 00:15:46.877 the potential job displacement, and that 00:15:46.960 --> 00:15:49.437 just spurs up backlash. It spurs up 00:15:49.520 --> 00:15:51.997 political challenge. We have to be able 00:15:52.080 --> 00:15:57.517 to demonstrate that for the average citizen, benefits are going to acrue, 00:15:57.600 --> 00:16:00.477 that there's going to be incremental economic opportunities, that there's 00:16:00.560 --> 00:16:03.757 going to be improvements to the quality of human life, that there's going to be 00:16:03.840 --> 00:16:05.757 some incremental value in terms of 00:16:05.840 --> 00:16:10.797 educating their children. And so that process of taking the technology and 00:16:10.880 --> 00:16:14.317 making it work in the lives of real people, we're in the really early 00:16:14.400 --> 00:16:16.077 stages. All >> right. Let me ask you, for your company, 00:16:16.160 --> 00:16:20.477 you have 500,000 employees roughly. All right. Suppose somebody wants to be an 00:16:20.560 --> 00:16:24.397 employee in your company now. They're graduating from college and they are in 00:16:24.480 --> 00:16:29.117 a literature major or they majored in social science subjects or humanities 00:16:29.200 --> 00:16:33.197 subjects. They have any chance of getting hired. And if you get hired by 00:16:33.280 --> 00:16:36.877 your firm, do you have to be a STEM major today? And how do these non- STEM 00:16:36.960 --> 00:16:40.397 majors fair? Do they actually wind up staying there after a year or two or 00:16:40.480 --> 00:16:43.277 they get washed out? I want best 00:16:43.360 --> 00:16:45.757 available athletes. Now they have to 00:16:45.840 --> 00:16:48.637 want to acquire the skill sets that Eric 00:16:48.720 --> 00:16:50.717 shared in terms of the prompt 00:16:50.800 --> 00:16:55.677 engineering in terms of understanding the core of the technology and how that 00:16:55.760 --> 00:16:59.357 sort of applies in terms of a modern business enterprise. But those who were 00:16:59.440 --> 00:17:04.317 deep thinkers and in fact those who can best understand the societal 00:17:04.400 --> 00:17:07.357 implications of this and how you actually adapt the use of the technology 00:17:07.440 --> 00:17:11.117 in an ethical fashion. I'm still a believer that the generalist has a big 00:17:11.200 --> 00:17:11.837 role here. 00:17:11.920 --> 00:17:14.877 >> Okay. Eric, you were the CEO of Noville, 00:17:14.960 --> 00:17:18.157 right? And then uh two young guys were 00:17:18.240 --> 00:17:22.797 looking for a CEO for a new company they were starting that came the name Google. 00:17:22.880 --> 00:17:24.317 Uh first of all, why did they spell 00:17:24.400 --> 00:17:26.557 Google improperly? 00:17:26.640 --> 00:17:30.397 So u there's a number called Google G O 00:17:30.480 --> 00:17:34.637 G O L 10 to the 100th. And the trademark 00:17:34.720 --> 00:17:39.597 was already taken. So Sergey changed it to LE and the rest is history. We 00:17:39.680 --> 00:17:43.277 ultimately settled with the family of the mathematician who invented the 00:17:43.360 --> 00:17:45.517 Google um because they were slightly 00:17:45.600 --> 00:17:48.077 annoyed at our reframing of that number. 00:17:48.160 --> 00:17:52.477 >> When you joined the company um people said there are a lot of search engines 00:17:52.560 --> 00:17:57.277 out here. Why do we need one more search engine? What was Google's ability to do 00:17:57.360 --> 00:18:00.637 something in search that nobody else had done? Was it a better system or better 00:18:00.720 --> 00:18:03.837 mousetrap? Um, so if you look at 00:18:03.920 --> 00:18:07.517 technology these days, every wave has 00:18:07.600 --> 00:18:10.717 five or 10 companies that are similar. 00:18:10.800 --> 00:18:13.437 In Google's case, it was the last of a 00:18:13.520 --> 00:18:15.837 series of companies that were doing 00:18:15.920 --> 00:18:20.317 ranking. But Larry Paige had invented in 00:18:20.400 --> 00:18:22.957 his dorm room an algorithm called page 00:18:23.040 --> 00:18:27.597 rank which is mathematically a fastforier transform of the importance 00:18:27.680 --> 00:18:32.717 of something and he used it for ranking and Google's ranking was just better. He 00:18:32.800 --> 00:18:37.197 started it in the dor dorm room in Stanford on the campus started to use it. 00:18:37.280 --> 00:18:40.557 >> Now one of the things you had at Google at the very beginning was something when 00:18:40.640 --> 00:18:43.037 I toured it you gave me a tour early on. 00:18:43.120 --> 00:18:47.197 You had free food. Um, and that was a I 00:18:47.280 --> 00:18:50.557 guess a way to keep people on the campus and not waste time going out for 00:18:50.640 --> 00:18:53.597 restaurants. Was that a good idea? Whose idea was that? 00:18:53.680 --> 00:18:56.077 >> So, so Sergey and Larry, and I inherited 00:18:56.160 --> 00:18:58.797 this, believe that a company was like a 00:18:58.880 --> 00:19:01.677 family and that you should feed them. 00:19:01.760 --> 00:19:04.237 Um, and you should feed them together because that's how they would all 00:19:04.320 --> 00:19:08.317 communicate. They had a number of strange beliefs. for example, they loved 00:19:08.400 --> 00:19:13.837 hiring Olympians because they figured that Olympians were so disciplined to to 00:19:13.920 --> 00:19:17.357 become an Olympian that they would be great employees even if they knew 00:19:17.440 --> 00:19:20.477 nothing about the job that they were working on. That turned out to be true, 00:19:20.560 --> 00:19:25.837 too. Um, so so in any case, the the free food has continued and I think it's now 00:19:25.920 --> 00:19:29.437 standard in >> they also had a thing called 20% time. 00:19:29.520 --> 00:19:31.757 You could spend 20% of your time doing 00:19:31.840 --> 00:19:33.837 something else. Would that work out? 00:19:33.920 --> 00:19:36.717 >> Uh, it did. Um, and so Google had a rule 00:19:36.800 --> 00:19:41.437 that every employee, at least the technical ones, could spend 20% of their 00:19:41.520 --> 00:19:45.357 time on what they wanted and not what their manager was. Now, you sit there 00:19:45.440 --> 00:19:47.277 and you go, how is that make any sense 00:19:47.360 --> 00:19:51.597 at all? But the way it worked was if the 00:19:51.680 --> 00:19:54.957 boss came in and said you have to spend 00:19:55.040 --> 00:19:57.357 all of your time on this and was very 00:19:57.440 --> 00:20:00.397 manipulative. It was a control on the 00:20:00.480 --> 00:20:02.317 power of the management because you 00:20:02.400 --> 00:20:04.957 could always say yes I'll give you four 00:20:05.040 --> 00:20:09.997 fifths of my day of my year my week but I'm not going to give you all of it. It 00:20:10.080 --> 00:20:13.837 was a balance of power between the creative engineers and their management. 00:20:13.920 --> 00:20:15.357 And a final question about the early 00:20:15.440 --> 00:20:18.637 days of this company. You were the CEO 00:20:18.720 --> 00:20:23.277 when you went on a trip. Somebody moved into your office. You're the CEO and 00:20:23.360 --> 00:20:27.037 somebody took your office and you couldn't get him to leave. Why was that 00:20:27.120 --> 00:20:30.317 a good thing to do? >> Well, I I had left I had left town for 00:20:30.400 --> 00:20:34.797 the for a sales call and I came back and I found a fellow named Emit in my 00:20:34.880 --> 00:20:39.597 office. He was at 8 by12 office sitting next to my little table. And I said, 00:20:39.680 --> 00:20:43.677 "Who are you?" And he identified himself and I said, "Um, who gave you 00:20:43.760 --> 00:20:46.957 permission?" And I said, "I asked Wayne who worked for me." and Wayne said, 00:20:47.040 --> 00:20:49.517 "Sure, move into Eric's office." Now, 00:20:49.600 --> 00:20:52.557 this is a test as an executive because 00:20:52.640 --> 00:20:55.197 when this happens, you can say, "Get the 00:20:55.280 --> 00:20:57.277 hell out of my office." Which is 00:20:57.360 --> 00:21:00.317 culturally not correct. So, I figured, 00:21:00.400 --> 00:21:05.037 okay, I'll just sit there and see what happens. So, he wore he was a programmer 00:21:05.120 --> 00:21:09.037 and he wore headphones. And I used to talk on the phone and I was talking to 00:21:09.120 --> 00:21:12.797 the sales executive at the time. We didn't know what our revenue was. And he 00:21:12.880 --> 00:21:16.877 said, "I think the revenue is going to be 115 million for the quarter. Imagine 00:21:16.960 --> 00:21:19.037 these numbers." And I said, "I think 00:21:19.120 --> 00:21:21.117 you're sandbagging," which is a term in 00:21:21.200 --> 00:21:24.717 sales for not telling the truth. Um, so 00:21:24.800 --> 00:21:28.477 I have the conversation. I don't make very much progress. And so I put the 00:21:28.560 --> 00:21:32.237 phone down and a meet takes his headphones off and says, "I can tell you 00:21:32.320 --> 00:21:36.157 the revenue number." And I said, "I knew you were listening on my phone." He'd 00:21:36.240 --> 00:21:38.637 been listening to all of my phone calls. 00:21:38.720 --> 00:21:40.397 And so what happens is I said, 'What's 00:21:40.480 --> 00:21:44.397 the revenue number? And he said 140. And 00:21:44.480 --> 00:21:48.157 so I made Omid present all of his sales 00:21:48.240 --> 00:21:52.397 forecastes standing on a sand sandbags 00:21:52.480 --> 00:21:55.117 as punishment for lying to me. 00:21:55.200 --> 00:21:57.437 >> So has anybody ever squatted in your office? 00:21:57.520 --> 00:22:01.277 >> Uh not yet. I'm old. >> My advice to those of you as CEO is get 00:22:01.360 --> 00:22:06.557 the person doing the revenue in the company in your office and watching it. 00:22:06.640 --> 00:22:10.317 And the simplest rule for those of you who are CEOs is the only thing that 00:22:10.400 --> 00:22:13.197 matters in the CEO's life is revenue. 00:22:13.280 --> 00:22:17.917 Solves all problems. Now you're running I'm sorry. We became best friends. 00:22:18.000 --> 00:22:21.917 You're running Deote. How many people come into you and tell you bad news as 00:22:22.000 --> 00:22:23.437 opposed to people coming in to tell you 00:22:23.520 --> 00:22:25.677 good news? 00:22:25.760 --> 00:22:30.237 The ratio is undoubtedly higher good to 00:22:30.320 --> 00:22:34.877 bad, but not [clears throat] not as detrimental as you'd think. And in fact, 00:22:34.960 --> 00:22:37.117 I I actually think the number one 00:22:37.200 --> 00:22:40.077 dimension to effective leadership in an 00:22:40.160 --> 00:22:44.637 environment like we have today where there's so much disruption and so much 00:22:44.720 --> 00:22:47.037 uncertainty is creating a culture where 00:22:47.120 --> 00:22:51.757 there is a willingness to surface problems where you see a top- down 00:22:51.840 --> 00:22:53.437 command and control and there's a fear 00:22:53.520 --> 00:22:56.557 and all the dashboards are green or or 00:22:56.640 --> 00:22:58.877 that is a recipe for an organization 00:22:58.960 --> 00:23:01.437 that will be disrupted where you create 00:23:01.520 --> 00:23:03.917 a culture where people are actually 00:23:04.000 --> 00:23:08.957 willing to keep an open mind even if the strategy said X, the market is shifting, 00:23:09.040 --> 00:23:11.917 the environment is shifting, the technology is shifting, we're going to 00:23:12.000 --> 00:23:17.037 challenge our assumption, we're going to be sort of embodying a learning mindset. 00:23:17.120 --> 00:23:19.757 Those are the organizations that are actually going to thrive today. So you 00:23:19.840 --> 00:23:23.597 you need a lot more willingness to hear things that are unpleasant. And let me 00:23:23.680 --> 00:23:25.197 ask you about artificial intelligence 00:23:25.280 --> 00:23:27.917 this respect today in colleges when 00:23:28.000 --> 00:23:30.717 people get exams uh that say they're 00:23:30.800 --> 00:23:33.197 take-home exams uh and they write them 00:23:33.280 --> 00:23:35.837 up. How do they professors know that 00:23:35.920 --> 00:23:38.397 they're really not written by AI? And as 00:23:38.480 --> 00:23:43.117 I understand it, there is now some software that can determine whether it's 00:23:43.200 --> 00:23:47.037 been written by AI, but there's software that can determine that the how to fool 00:23:47.120 --> 00:23:51.037 the professor's software. How does that work? I've got two kids in college right 00:23:51.120 --> 00:23:53.917 now and this dominated the conversation 00:23:54.000 --> 00:23:57.037 over the winter break. The worst thing 00:23:57.120 --> 00:23:58.957 that an educational institution could 00:23:59.040 --> 00:24:03.117 possibly do is ban the usage of AI, ban 00:24:03.200 --> 00:24:07.837 the usage of the tool that's actually going to help make the individual more 00:24:07.920 --> 00:24:11.997 effective. And yet you have this huge dilemma because if all the student is 00:24:12.080 --> 00:24:16.317 doing is putting the assignment into the AI engine and turning in something that 00:24:16.400 --> 00:24:18.717 the LLM spit out, that is a useless 00:24:18.800 --> 00:24:20.717 earning learning exercise. And so how 00:24:20.800 --> 00:24:24.557 you actually allow for the usage but 00:24:24.640 --> 00:24:27.517 still demand the human overlay and the 00:24:27.600 --> 00:24:31.757 critical thinking and how did I take the output and use it to push myself. And 00:24:31.840 --> 00:24:34.157 frankly, these software programs that 00:24:34.240 --> 00:24:36.717 are detecting what percentage was 00:24:36.800 --> 00:24:41.757 actually derived by AI, they have some directional sense, but there's a lot of 00:24:41.840 --> 00:24:45.597 false positives. >> How has your job changed by AI? Do you 00:24:45.680 --> 00:24:48.237 use AI yourself? You go and chat GBT all 00:24:48.320 --> 00:24:50.637 the time to get answers to things. 00:24:50.720 --> 00:24:54.077 >> Look, I I use it constantly in my 00:24:54.160 --> 00:24:57.997 personal life. I find it actually makes me much more effective at any variety of 00:24:58.080 --> 00:24:59.197 things that we come across in our 00:24:59.280 --> 00:25:03.357 day-to-day life. And I am embracing and 00:25:03.440 --> 00:25:07.357 trying to lead by example in terms of the tools, the demos, the proofs of 00:25:07.440 --> 00:25:10.077 concept, and then ultimately those things that look like they're going to 00:25:10.160 --> 00:25:15.357 scale that we're putting into production of using them myself, of providing 00:25:15.440 --> 00:25:18.877 feedback in terms of trying to help train the model so that where there are 00:25:18.960 --> 00:25:23.197 things that are valuable, feed that back and let's capitalize it. And also 00:25:23.280 --> 00:25:27.517 calling out the meter that things that look great on a screen and 00:25:27.600 --> 00:25:31.277 to an average uh recipient of the information appear to be very 00:25:31.360 --> 00:25:34.797 impressive, but to somebody who's an expert in the area is a bunch of 00:25:34.880 --> 00:25:39.037 gibberish. And that's the real dilemma right now. This stuff looks really 00:25:39.120 --> 00:25:43.837 impressive on the surface. And some of it is in substance and some of it's 00:25:43.920 --> 00:25:45.757 garbage. And it takes the human who has 00:25:45.840 --> 00:25:47.917 the domain knowledge to actually pick 00:25:48.000 --> 00:25:51.037 between the two. Eric, uh, bad news 00:25:51.120 --> 00:25:53.357 usually sells better than good news. 00:25:53.440 --> 00:25:57.917 People like to read bad news, it seems, and that's why CNN or the equivalent 00:25:58.000 --> 00:26:00.077 shows they when they have a crisis, it 00:26:00.160 --> 00:26:02.797 ratings go up and so forth. Um can you 00:26:02.880 --> 00:26:06.237 to lead finish this uh conversation by 00:26:06.320 --> 00:26:10.477 making people feel that AI is going to be a good thing for civilization not a 00:26:10.560 --> 00:26:13.037 bad thing and let people leave here 00:26:13.120 --> 00:26:17.517 thinking that if they know more about AI they'll be better off and living in a 00:26:17.600 --> 00:26:19.517 world with AI being more and more 00:26:19.600 --> 00:26:21.757 important is a good thing for everybody. 00:26:21.840 --> 00:26:26.397 >> So we have some choices as humans and 00:26:26.480 --> 00:26:28.237 those choices are really important to 00:26:28.320 --> 00:26:33.597 get right. So first place, this technology is going to happen. It's not 00:26:33.680 --> 00:26:36.717 going to get prevented. It's not going to get stopped. There's too many 00:26:36.800 --> 00:26:38.877 countries, too many people, too many 00:26:38.960 --> 00:26:41.277 incentives. It's going to happen. So 00:26:41.360 --> 00:26:43.997 what does this mean? It means great 00:26:44.080 --> 00:26:46.717 great solutions for healthcare, new 00:26:46.800 --> 00:26:49.437 drugs, better energy solutions, better 00:26:49.520 --> 00:26:51.597 power distribution, better physics, 00:26:51.680 --> 00:26:53.757 better chemistry, better sociology, 00:26:53.840 --> 00:26:57.037 better research. It also means that it 00:26:57.120 --> 00:27:01.277 can be used for bad. It can be used for example for oppression. It can be used 00:27:01.360 --> 00:27:03.437 to limit freedom in governments 00:27:03.520 --> 00:27:05.677 hopefully not in the west. Uh it can be 00:27:05.760 --> 00:27:08.957 used in war. Um the technology itself is 00:27:09.040 --> 00:27:12.877 so addictive that it can affect our young people and we should make sure 00:27:12.960 --> 00:27:15.837 that our young people are protected from some of the worst parts of the 00:27:15.920 --> 00:27:18.717 technology. I could go on. But we face 00:27:18.800 --> 00:27:20.797 choices. I've written two books on this. 00:27:20.880 --> 00:27:23.997 We face choices now about how we want to 00:27:24.080 --> 00:27:27.837 deal with this incredibly powerful technology. I will tell you and it's 00:27:27.920 --> 00:27:31.917 really important to understand that we are living through a moment that will be 00:27:32.000 --> 00:27:34.557 in history for thousands of years. It is 00:27:34.640 --> 00:27:37.757 the moment in history when a nonhuman 00:27:37.840 --> 00:27:42.797 intelligence arrived and it was a competitor to us. Right? How we use 00:27:42.880 --> 00:27:46.477 that, how we shape it, right? How we guarantee alignment will determine the 00:27:46.560 --> 00:27:48.797 outcome of your >> final question. If you had the chance to 00:27:48.880 --> 00:27:53.277 rename artificial intelligence and give it a name that's more appropriate, would 00:27:53.360 --> 00:27:56.317 you change the name or would you just say it's better? The best name is 00:27:56.400 --> 00:27:59.277 artificial intelligence. >> The the best name is artificial 00:27:59.360 --> 00:28:03.837 intelligence because it is intelligence and it's artificial. Remember that its 00:28:03.920 --> 00:28:08.237 intelligence is not the same as human intelligence. It doesn't have the same 00:28:08.320 --> 00:28:10.397 values. It doesn't think the same way. 00:28:10.480 --> 00:28:12.717 It doesn't think at all. It's a it's a 00:28:12.800 --> 00:28:15.517 different kind of intelligence. When we 00:28:15.600 --> 00:28:18.077 work with it, we will be more powerful. 00:28:18.160 --> 00:28:21.037 When it runs us, we will be very unhappy. 00:28:21.120 --> 00:28:25.597 >> Is it harder to learn artificial intelligence or pass the CPA exam? 00:28:25.680 --> 00:28:28.317 Well, those two are arguably becoming one and the same. That's one of the 00:28:28.400 --> 00:28:30.157 tests that's been used to define the 00:28:30.240 --> 00:28:32.637 efficacy of the technology. But we do 00:28:32.720 --> 00:28:37.037 have a real issue of education around 00:28:37.120 --> 00:28:41.037 the societal benefit. And there's some recent polling where those who are 00:28:41.120 --> 00:28:43.437 actually using the technology themselves 00:28:43.520 --> 00:28:46.557 are far more positive on the potential 00:28:46.640 --> 00:28:49.037 impact than those who aren't using it. 00:28:49.120 --> 00:28:51.357 And I I do worry, Eric, the technology 00:28:51.440 --> 00:28:54.157 is coming. it is going to happen. But 00:28:54.240 --> 00:28:56.237 we're in this powder keg of a moment 00:28:56.320 --> 00:28:58.797 society where you see the sort of the 00:28:58.880 --> 00:29:01.197 populist concerns and the fears over the 00:29:01.280 --> 00:29:03.757 future and then you layer this onto it 00:29:03.840 --> 00:29:06.397 and all the worries over job disruption. 00:29:06.480 --> 00:29:10.477 Our number one job as leaders is to actually go out and tell the story and 00:29:10.560 --> 00:29:14.637 make this real in terms of how this is going to improve the quality of human 00:29:14.720 --> 00:29:17.437 life. >> Thank you both for a very interesting 00:29:17.520 --> 00:29:20.797 conversation. How many people here feel they know a lot more about AI than they 00:29:20.880 --> 00:29:23.997 did a half hour ago? >> Okay, thank you very much. 00:29:24.080 --> 00:29:27.800 >> Thank you, Dave. Thank you.