WEBVTT 00:00:00.760 --> 00:00:04.517 Hello again. If I keep doing this, I understand that 00:00:04.600 --> 00:00:09.837 there's vacancy at 60 minutes. Maybe I can If this investing thing doesn't work 00:00:09.920 --> 00:00:14.317 out, maybe I can do that. Another full disclosure, 00:00:14.400 --> 00:00:16.677 Sam is a friend. I'm also on the board 00:00:16.760 --> 00:00:19.437 of OpenAI, so 00:00:19.520 --> 00:00:23.797 I promise you I will not only ask him softball questions. I will channel my 00:00:23.880 --> 00:00:27.157 inner Brett Bear and and uh 00:00:27.240 --> 00:00:28.957 and Slink Gooper. 00:00:29.040 --> 00:00:31.557 So, Sam, as I said, 00:00:31.640 --> 00:00:35.397 CEO of OpenAI, one of the founders. 00:00:35.480 --> 00:00:39.197 He previously served as the president of Y Combinator, 00:00:39.280 --> 00:00:43.997 where he helped scale some of the world's most influential startup accelerators, 00:00:44.080 --> 00:00:48.037 and advise companies that will shape entire industries. 00:00:48.120 --> 00:00:49.877 At OpenAI, Sam has obviously been at the 00:00:49.960 --> 00:00:51.797 forefront of advancing artificial 00:00:51.880 --> 00:00:55.157 intelligence to the benefit of humanity, 00:00:55.240 --> 00:01:00.077 helping to bring cutting-edge AI tools to the hands of millions of people. How 00:01:00.160 --> 00:01:01.917 many people? Eight, 900 million people? 00:01:02.000 --> 00:01:05.757 >> More, yeah. >> and businesses around the world. 00:01:05.840 --> 00:01:10.437 So, Sam, let me start with what I think is the question that is on everybody's 00:01:10.520 --> 00:01:14.517 mind, okay? Which is 00:01:14.600 --> 00:01:16.917 where are we today 00:01:17.000 --> 00:01:19.317 in the world of AI? 00:01:19.400 --> 00:01:21.997 >> I I think at some point in the last few 00:01:22.080 --> 00:01:24.477 months, 00:01:24.560 --> 00:01:27.757 we really have crossed a threshold into 00:01:27.840 --> 00:01:30.477 major economic utility of these models. 00:01:30.560 --> 00:01:33.077 It may have happened a little bit earlier, but there was such an overhang 00:01:33.160 --> 00:01:37.117 before we figured out how to use these, and we had to not only continue to make 00:01:37.200 --> 00:01:40.997 the models get smarter, but figure out sort of the plumbing to make them easy 00:01:41.080 --> 00:01:43.557 to use. Where we're we're now in a world 00:01:43.640 --> 00:01:48.877 where the models are astounding people with the 00:01:48.960 --> 00:01:52.677 work they can do. Uh and I think this has been most 00:01:52.760 --> 00:01:54.797 noticeable in coding, yep, but it's also 00:01:54.880 --> 00:01:56.877 happening in science. It's happening in 00:01:56.960 --> 00:02:00.717 many fields of knowledge work. Uh 00:02:00.800 --> 00:02:04.037 at sort of with this orienting speed where people are saying like, "Man, 00:02:04.120 --> 00:02:09.237 these things that I thought were still years away are happening now and I have 00:02:09.320 --> 00:02:12.557 my job shifted from doing 00:02:12.640 --> 00:02:16.797 uh you know, direct technical work or uh you know, legal work to managing a 00:02:16.880 --> 00:02:19.077 team of agents doing this work." 00:02:19.160 --> 00:02:22.757 This is going to go much further. Uh I think we are at a very steep part of the 00:02:22.840 --> 00:02:23.797 curve 00:02:23.880 --> 00:02:27.557 and right now maybe you can trust say a 00:02:27.640 --> 00:02:32.077 AI software engineer to do a multi-hour task. Very soon it'll be a multi-day 00:02:32.160 --> 00:02:34.397 task and then a multi-week task. 00:02:34.480 --> 00:02:37.317 And not long after that, I think the 00:02:37.400 --> 00:02:39.637 paradigm will shift again and it'll feel 00:02:39.720 --> 00:02:44.517 like these AI systems are just connected to your life, to your company, whatever 00:02:44.600 --> 00:02:48.237 proactively thinking, working all the time. Uh 00:02:48.320 --> 00:02:52.157 and having full context on whatever they need to know and just sort of doing 00:02:52.240 --> 00:02:54.397 stuff like you would trust a senior employee to do. 00:02:54.480 --> 00:02:59.197 >> Mhm. And and you think that um companies have 00:02:59.280 --> 00:03:01.357 real have a real understanding of how 00:03:01.440 --> 00:03:03.637 these systems can help them and rein- 00:03:03.720 --> 00:03:07.157 reimagine how they do their businesses? 00:03:07.240 --> 00:03:11.157 >> Some do, some don't. Uh certainly the new generation of startups thinks 00:03:11.240 --> 00:03:14.277 differently than any generation of startups before. 00:03:14.360 --> 00:03:17.877 Uh it used to be that when we would talk to 00:03:17.960 --> 00:03:21.917 startups, they would talk about how many employees they needed. 00:03:22.000 --> 00:03:25.997 Uh now they generally don't want to hire a lot. They think that'll slow them down 00:03:26.080 --> 00:03:28.197 and they're all focused on how much compute they can get. 00:03:28.280 --> 00:03:31.117 >> Mhm. >> You know, can I reserve this much capacity? Can I do a cloud deal for 00:03:31.200 --> 00:03:33.877 that? Uh can I get this many tokens? 00:03:33.960 --> 00:03:38.317 And I think that is that that is a mental shift that bigger 00:03:38.400 --> 00:03:40.677 companies are going through more slowly 00:03:40.760 --> 00:03:44.277 but some are starting to do that. Uh one 00:03:44.360 --> 00:03:49.597 place I think you can see this happening is engineering orgs and product orgs 00:03:49.680 --> 00:03:54.037 talking about they're doubling, tripling what they're planning to ship this year. 00:03:54.120 --> 00:03:56.077 >> Mhm. And that has not happened before. 00:03:56.160 --> 00:03:57.117 >> Yeah. 00:03:57.200 --> 00:04:00.597 And you've been quite vocal in saying 00:04:00.680 --> 00:04:05.037 that um artificial general intelligence will 00:04:05.120 --> 00:04:06.997 come sooner rather than later. 00:04:07.080 --> 00:04:10.677 Want to share your views on how close we are and how soon it'll 00:04:10.760 --> 00:04:12.317 come? 00:04:12.400 --> 00:04:15.237 >> At at this point, I think the definition 00:04:15.320 --> 00:04:19.477 of AGI really matters. Some people would say we already got there. Some people 00:04:19.560 --> 00:04:22.517 say it's very close. Some people say we're kind of you know, it's maybe still 00:04:22.600 --> 00:04:28.517 a year away, but in any case, that word has ceased to have much meaning. 00:04:28.600 --> 00:04:31.917 >> Mhm. >> Uh there are maybe two thresholds that we 00:04:32.000 --> 00:04:33.597 could talk about that are interesting. 00:04:33.680 --> 00:04:35.677 >> Okay. >> Number one, when is there going to be 00:04:35.760 --> 00:04:40.077 more of the world's cognitive capacity inside of data centers than outside of 00:04:40.160 --> 00:04:45.677 them? And that to me feels like maybe it could happen. 00:04:45.760 --> 00:04:48.317 Huge error bars. I could be totally wrong, but maybe that could happen by 00:04:48.400 --> 00:04:51.237 like late 2028. >> Mhm. 00:04:51.320 --> 00:04:55.437 >> And that's an extraordinary shift in the world. 00:04:55.520 --> 00:04:56.677 Um 00:04:56.760 --> 00:05:00.477 the other one is when can 00:05:00.560 --> 00:05:03.157 a CEO of major company, a president of a 00:05:03.240 --> 00:05:05.437 major country, a Nobel Prize winning 00:05:05.520 --> 00:05:07.637 scientist, when can they not do their 00:05:07.720 --> 00:05:10.957 job without making heavy use of AI? This 00:05:11.040 --> 00:05:14.677 doesn't mean that there will be an AI CEO or an AI president, uh 00:05:14.760 --> 00:05:18.317 but it does mean that the the role of let's 00:05:18.400 --> 00:05:23.277 say a human CEO, when I think about my job, it's really quite different. You you 00:05:23.360 --> 00:05:25.277 still do need a person to 00:05:25.360 --> 00:05:27.277 stand behind decisions and kind of 00:05:27.360 --> 00:05:30.677 exercise human judgment and 00:05:30.760 --> 00:05:33.037 all of the understanding 00:05:33.120 --> 00:05:35.597 that we expect out of someone running an 00:05:35.680 --> 00:05:39.437 an important organization to do, but 00:05:39.520 --> 00:05:43.037 the actual parts of my role that I will 00:05:43.120 --> 00:05:47.477 increasingly have to rely on an AI to do because no human can. You know, no human 00:05:47.560 --> 00:05:51.477 can talk to no human CEO can talk to every employee at a company, every 00:05:51.560 --> 00:05:53.557 customer, be in every meeting, be an 00:05:53.640 --> 00:05:56.357 expert in every field. And so, 00:05:56.440 --> 00:05:59.317 more and more I think of these jobs will 00:05:59.400 --> 00:06:03.637 be supervising a bunch of AI, um providing 00:06:03.720 --> 00:06:06.237 oversight, deciding how to like trust 00:06:06.320 --> 00:06:08.037 the the outputs, how to provide 00:06:08.120 --> 00:06:12.317 guidance. And that threshold of when you 00:06:12.400 --> 00:06:16.637 really wouldn't want to be doing your job, 00:06:16.720 --> 00:06:20.877 running a large organization without heavily heavy reliance on AI, 00:06:20.960 --> 00:06:23.357 I think that's another sort of interesting threshold. That may take a 00:06:23.440 --> 00:06:26.277 little bit longer, but probably not a lot longer. 00:06:26.360 --> 00:06:29.077 >> And and as you do your job, how much are 00:06:29.160 --> 00:06:32.357 you finding yourself relying on 00:06:32.440 --> 00:06:36.117 some of the agents and some of the artificial intelligence that we're 00:06:36.200 --> 00:06:39.317 developing at OpenAI? >> Uh 00:06:39.400 --> 00:06:42.117 it's ramping incredibly 00:06:42.200 --> 00:06:45.757 quickly. Um if I have like a new idea 00:06:45.840 --> 00:06:51.520 for a business model, a strategy shift, a uh 00:06:51.680 --> 00:06:55.317 a product offering we should do, the very first thing I do before I even 00:06:55.400 --> 00:06:57.517 bounce it off somebody else is to ask 00:06:57.600 --> 00:07:02.517 our tools. And as they get more context, and I think this really is like 00:07:02.600 --> 00:07:06.157 the next big thing to happen is they can get close to full context of our 00:07:06.240 --> 00:07:10.717 company, access to all of our internal docs, communication, code, 00:07:10.800 --> 00:07:16.117 customer data, everything, the quality of the answers, thought, whatever you want to 00:07:16.200 --> 00:07:17.237 call it, gets better and better. 00:07:17.320 --> 00:07:21.437 >> Right. Okay. So, let's let's shift um a little bit. 2 00:07:21.520 --> 00:07:26.477 weeks ago, um you announced a $110 billion funding 00:07:26.560 --> 00:07:31.917 round. I asked ChatGPT, how does that compare compare to any 00:07:32.000 --> 00:07:34.717 other fundraising that's been done in the public markets? 00:07:34.800 --> 00:07:37.917 >> don't know. >> Uh four times as large, okay? The 00:07:38.000 --> 00:07:42.357 largest public offering ever done was roughly 25 billion that Aramco did 00:07:42.440 --> 00:07:45.477 several years ago. And the public markets are supposed to 00:07:45.560 --> 00:07:50.317 be the broadest and deepest sources of capital, okay? 00:07:50.400 --> 00:07:53.677 Three strategic partners, Amazon, 00:07:53.760 --> 00:07:56.997 Nvidia, and oh and SoftBank. 00:07:57.080 --> 00:07:59.477 Tell us a little bit about this. 00:07:59.560 --> 00:08:02.397 How is this an inflection point of the company? 00:08:02.480 --> 00:08:05.237 And one of the questions somebody asked me is, what are we spending all that 00:08:05.320 --> 00:08:08.797 money on? The 00:08:08.880 --> 00:08:12.357 There is many hard parts of this business, but one of the hardest ones is 00:08:12.440 --> 00:08:15.197 the infrastructure is so expensive. You 00:08:15.280 --> 00:08:20.360 need so much of it, and you have to commit so far in advance. 00:08:20.960 --> 00:08:25.757 I have never seen any other industry quite like this. I 00:08:25.840 --> 00:08:29.637 mean, there have been clearly many capital intensive industries throughout 00:08:29.720 --> 00:08:34.077 history, but as I look at the heat what's to come in 00:08:34.160 --> 00:08:35.637 front of us in the future in future 00:08:35.720 --> 00:08:41.117 years, if the ramp stays as steep as it looks like it is right now, the demand 00:08:41.200 --> 00:08:46.597 is growing as fast as it as it's growing, you have to do some pretty unusual things. 00:08:46.680 --> 00:08:49.157 Uh OpenAI does a lot of things that look 00:08:49.240 --> 00:08:51.237 weird. 00:08:51.320 --> 00:08:53.757 We spend a ton of money on 00:08:53.840 --> 00:08:56.837 infrastructure in advance of revenue. We 00:08:56.920 --> 00:08:59.517 do new business models like ads that 00:08:59.600 --> 00:09:04.477 seem like, you know, maybe not the most profitable thing we could do. Um 00:09:04.560 --> 00:09:08.237 a long list of other things, but we have this fundamental belief in abundance of 00:09:08.320 --> 00:09:12.357 intelligence. And that one of the most important things in the future is that 00:09:12.440 --> 00:09:16.997 we make intelligence, you know, to borrow an old phrase from 00:09:17.080 --> 00:09:19.757 the energy industry that didn't quite work, too cheap to meter. We want to 00:09:19.840 --> 00:09:21.997 flood the world with intelligence. We want people to just use it for 00:09:22.080 --> 00:09:24.997 everything. We want this to just be something that the future generation 00:09:25.080 --> 00:09:29.637 doesn't think about. They expect everywhere, and everybody has access to 00:09:29.720 --> 00:09:33.197 like geniuses, as many as they need in any 00:09:33.280 --> 00:09:37.240 area that they need. And 00:09:37.640 --> 00:09:41.917 this principle, which is one of our kind of like top guiding principles, does 00:09:42.000 --> 00:09:45.437 lead to a lot of behavior that would look 00:09:45.520 --> 00:09:49.197 less natural for other companies. And one of those is 00:09:49.280 --> 00:09:52.477 we really want to get out of this world that we have been in that we still think 00:09:52.560 --> 00:09:55.637 we're on a trajectory to stay on without changing what we do of always being 00:09:55.720 --> 00:09:58.237 capacity constrained. >> Right. 00:09:58.320 --> 00:10:01.477 And capacity constrained you mean compute? Yeah. 00:10:01.560 --> 00:10:06.917 And I've often heard you say a lot that compute is revenues. 00:10:07.000 --> 00:10:11.317 You want to talk a little bit about how you think how you think about that? 00:10:11.400 --> 00:10:14.797 >> Fundamentally, our business and I think the business of every other model 00:10:14.880 --> 00:10:19.237 provider is going to look like selling tokens. 00:10:19.320 --> 00:10:23.437 You know, they may come from bigger or smaller models which makes them more or 00:10:23.520 --> 00:10:26.917 less expensive. They may use more or less reasoning which also makes them 00:10:27.000 --> 00:10:29.997 more or less expensive. They may be running all the time in the background 00:10:30.080 --> 00:10:33.437 trying to help you out. Uh they may run only when you need them if you want to 00:10:33.520 --> 00:10:38.637 pay less. They may work super hard. You know, spend tens of 00:10:38.720 --> 00:10:41.877 millions, hundreds of millions, someday billions of dollars on a single problem. 00:10:41.960 --> 00:10:44.437 >> Right. >> That's really valuable. But 00:10:44.520 --> 00:10:46.997 we 00:10:47.080 --> 00:10:49.437 see a future where 00:10:49.520 --> 00:10:51.597 intelligence is a utility like 00:10:51.680 --> 00:10:56.517 electricity or water and people buy it from us 00:10:56.600 --> 00:11:00.957 um on a meter and use it for whatever they want to use it for. 00:11:01.040 --> 00:11:04.957 The demand that we see for that seems like it's going to continue to just go 00:11:05.040 --> 00:11:08.797 like this. And 00:11:08.880 --> 00:11:14.197 if we don't have enough, we either can't sell it or the price gets really high 00:11:14.280 --> 00:11:19.397 and it you know, it kind of goes to rich people or society makes a bunch of sort 00:11:19.480 --> 00:11:22.557 of central planning decisions that I think almost always go badly about, you 00:11:22.640 --> 00:11:25.997 know, we're going to use our limited compute supply for this and not that. 00:11:26.080 --> 00:11:30.397 So, the best thing to me throughout all the history of capitalism, innovation, 00:11:30.480 --> 00:11:32.637 whatever you want is to just flood the market. 00:11:32.720 --> 00:11:35.757 >> Yeah. And obviously a critical part of 00:11:35.840 --> 00:11:37.637 addressing that 00:11:37.720 --> 00:11:41.477 compute demand is Stargate. 00:11:41.560 --> 00:11:45.757 And this is an infrastructure conference and you know, you announced that several 00:11:45.840 --> 00:11:49.837 months ago, or almost a year ago, I guess it was, yeah. 00:11:49.920 --> 00:11:53.997 How's it going in the US? And then how is it going, because I 00:11:54.080 --> 00:11:59.680 also started in in Abu Dhabi. >> Yeah. Yeah. Um 00:12:00.240 --> 00:12:03.037 Honestly, there's many cool parts of the job. One 00:12:03.120 --> 00:12:06.317 of the coolest is getting to go visit these mega data centers under 00:12:06.400 --> 00:12:07.837 construction and operation. >> Yeah. 00:12:07.920 --> 00:12:11.317 >> Just the scale is, you know, of like these gigawatt campuses. It's it's it's 00:12:11.400 --> 00:12:15.237 really hard to explain and you see these photos and it's like, okay, it does look 00:12:15.320 --> 00:12:18.157 big. And then you go there and you're walking through it and building to 00:12:18.240 --> 00:12:21.597 building and it's just, you know, 10,000 people there, all these different 00:12:21.680 --> 00:12:24.197 skilled trades doing all these different things and 00:12:24.280 --> 00:12:28.797 you know, it looks like a spaceship inside. It's really quite incredible. Um 00:12:28.880 --> 00:12:32.557 we are training right now on the first site in Abilene, 00:12:32.640 --> 00:12:33.917 what I think will be the best model in 00:12:34.000 --> 00:12:38.797 the world, hopefully by a lot. And it is 00:12:38.880 --> 00:12:42.597 it's so amazing to have gone from, you know, many visits while it was under 00:12:42.680 --> 00:12:46.997 construction, and just really internalizing the scale and incredible 00:12:47.080 --> 00:12:49.277 complexity to the day that one 00:12:49.360 --> 00:12:51.277 researcher at OpenAI types in like one 00:12:51.360 --> 00:12:54.157 command and it pushes enter, 00:12:54.240 --> 00:12:58.557 unbelievable number of GPUs spin up and start doing this one huge computation 00:12:58.640 --> 00:13:02.517 all together. >> Yeah. It's very And and 00:13:02.600 --> 00:13:06.757 what have been the most pleasant surprises and what have 00:13:06.840 --> 00:13:11.237 been the hardest things about getting it up and running and scaling it going 00:13:11.320 --> 00:13:16.157 forward? >> Uh I mean, there have been all the expected 00:13:16.240 --> 00:13:19.517 challenges and then the unknown unknowns. Like there was a crazy weather 00:13:19.600 --> 00:13:22.677 event in Abilene that we hadn't, you know, was outside 00:13:22.760 --> 00:13:25.797 what we had planned for and that brought things down for a little while. 00:13:25.880 --> 00:13:30.237 Um there's all the supply chain challenges. Anything at this scale is 00:13:30.320 --> 00:13:32.437 just like so much stuff goes wrong. Lots 00:13:32.520 --> 00:13:39.080 goes right, but the just try trying to build something around the clock 00:13:39.280 --> 00:13:42.397 with such complexity, there's all the stuff that goes wrong. 00:13:42.480 --> 00:13:46.757 One of the biggest surprises on the upside is how 00:13:46.840 --> 00:13:50.877 many different organizations had to come together to do this in an incredibly 00:13:50.960 --> 00:13:53.197 short period of time. Uh, and how much 00:13:53.280 --> 00:13:54.997 we all like ended up working together as 00:13:55.080 --> 00:13:58.557 one team uh, under a lot of pressure. 00:13:58.640 --> 00:14:03.077 >> And of course, the power demand part of it is the one that a lot of people have 00:14:03.160 --> 00:14:06.237 have focused on. Um, 00:14:06.320 --> 00:14:10.357 are you optimistic that we will solve that challenge 00:14:10.440 --> 00:14:15.077 first in the US and then we can talk about in in other places? 00:14:15.160 --> 00:14:19.077 >> Uh, in the long term, I am. 00:14:19.160 --> 00:14:23.477 >> Okay. >> Uh, I have no doubt we will figure out how 00:14:23.560 --> 00:14:27.797 to build huge amounts of power generation. AI will help, of course. 00:14:27.880 --> 00:14:31.437 Uh, but I think the portfolio the world has in front of it 00:14:31.520 --> 00:14:35.157 gas, solar, nuclear fission, nuclear fusion, more 00:14:35.240 --> 00:14:39.237 like we're I feel good about what what we will be 00:14:39.320 --> 00:14:41.397 capable of and what we will eventually do. 00:14:41.480 --> 00:14:42.157 >> Mhm. 00:14:42.240 --> 00:14:43.957 >> Um, 00:14:44.040 --> 00:14:48.637 given the demand growth we are seeing, 00:14:48.720 --> 00:14:52.997 I am sort of hoping for a miracle in terms of figuring out how we can get way 00:14:53.080 --> 00:14:54.957 more efficient per watt >> Uh, okay. 00:14:55.040 --> 00:14:59.877 >> models to give us time to build out all of this infrastructure. Now, 00:14:59.960 --> 00:15:03.477 the track record there has been incredible. Um, 00:15:03.560 --> 00:15:07.317 people cite whatever amazing statistic they like about how much more efficient 00:15:07.400 --> 00:15:10.877 our models have gotten over our industries models have gotten over time, 00:15:10.960 --> 00:15:14.477 but one that I think is incredible, 00:15:14.560 --> 00:15:18.237 our first reasoning model was called 01, came out 00:15:18.320 --> 00:15:22.917 like 16 months ago. Uh, 00:15:23.000 --> 00:15:28.800 and our latest model where we're now integrated reasoning is 5.4. 00:15:29.040 --> 00:15:31.357 To get the same answer 00:15:31.440 --> 00:15:34.437 to a hard problem from that first model 00:15:34.520 --> 00:15:40.800 to 5.4 has been a reduction in cost of about 1,000 x. 00:15:41.320 --> 00:15:42.997 Maybe I was a little bit wrong on the timeline. Maybe it was a little bit 00:15:43.080 --> 00:15:45.197 longer. But in any case, since 01 till now. 00:15:45.280 --> 00:15:50.317 >> Yeah. >> 1,000 x. Uh, that is unbelievable in a relatively 00:15:50.400 --> 00:15:54.677 short period of time. And 00:15:54.760 --> 00:15:58.397 the thing that that the two things that I think that points to 00:15:58.480 --> 00:16:02.277 one, we are still so early and in this 00:16:02.360 --> 00:16:05.597 paradigm and we're still so we have so much more to gain about our 00:16:05.680 --> 00:16:08.957 understanding of how to develop these models and train them and run them 00:16:09.040 --> 00:16:13.597 efficiently >> Mhm. >> that we have 00:16:13.680 --> 00:16:17.757 were then and still are doing things in dumb ways and will get better and 00:16:17.840 --> 00:16:21.437 better. And two, is that 00:16:21.520 --> 00:16:25.477 human ingenuity and the ability to operate 00:16:25.560 --> 00:16:29.877 in constraints and to find ways to solve problems almost always surprise you on the 00:16:29.960 --> 00:16:32.757 upside. So, it's not just that the models have gotten better, it's that we 00:16:32.840 --> 00:16:36.277 have figured out, you know, kernel engineers came to help figure out how to 00:16:36.360 --> 00:16:40.237 write more efficient kernels and like power engineers, the people that design 00:16:40.320 --> 00:16:43.077 data centers found more efficient ways to do that. So, people are answering the 00:16:43.160 --> 00:16:45.237 call well beyond just the model side >> Right. 00:16:45.320 --> 00:16:46.237 >> to make this more efficient. 00:16:46.320 --> 00:16:48.837 >> Right. And then of course, um, 00:16:48.920 --> 00:16:53.717 obviously OpenAI is a big customer of the Nvidias and AMDs of the world, but 00:16:53.800 --> 00:16:57.117 um, you know, I think we signed a big contract with Amazon. 00:16:57.200 --> 00:16:58.357 But we're building our own chip. 00:16:58.440 --> 00:17:01.517 >> Yeah. >> What's what's the thinking behind that? 00:17:01.600 --> 00:17:03.717 >> Uh, so the chip we're doing is inference only. 00:17:03.800 --> 00:17:08.037 >> Okay. >> And the thinking behind it is that on this, you know, come 00:17:08.120 --> 00:17:09.997 rise up to solve problems in front of 00:17:10.080 --> 00:17:12.317 us, um, 00:17:12.400 --> 00:17:15.957 we think that a specialized chip to be 00:17:16.040 --> 00:17:19.717 not necessarily the fastest inference chip, but the cheapest inference chip, 00:17:19.800 --> 00:17:23.597 the most efficient per watt given the constraints we see in front of us, is 00:17:23.680 --> 00:17:27.197 going to be important for all the agent demand we see in the future. So, it's a 00:17:27.280 --> 00:17:31.277 it's an opinionated bet, it's a limited chip, but the thing that it does, 00:17:31.360 --> 00:17:34.157 in a world where we're energy constrained, I think it'll be very important. 00:17:34.240 --> 00:17:38.197 >> Maybe you can explain because I am not sure everybody in the audience is 00:17:38.280 --> 00:17:43.197 AI proficient with the difference between an inference chip and a training chip. 00:17:43.280 --> 00:17:44.557 >> Sorry, I should have done that first. 00:17:44.640 --> 00:17:45.517 So, 00:17:45.600 --> 00:17:49.957 there there are two main phases to uh 00:17:50.040 --> 00:17:53.477 AI workloads. Now, they're going to blend together eventually and it'll all 00:17:53.560 --> 00:17:55.837 be one continuous thing. But right now, 00:17:55.920 --> 00:17:58.757 first we train a model, 00:17:58.840 --> 00:18:00.717 you know, a gigantic number of GPUs 00:18:00.800 --> 00:18:03.157 crunch for weeks or months on a bunch of 00:18:03.240 --> 00:18:05.837 data and you can think of that like 00:18:05.920 --> 00:18:08.597 maybe it takes you 22 years in life to 00:18:08.680 --> 00:18:13.237 get your education and you learn a bunch of stuff starting when you're a baby and 00:18:13.320 --> 00:18:16.317 you drop things and see they fall and then eventually like in college physics 00:18:16.400 --> 00:18:20.157 understanding like at a very detailed level what's going on there. 00:18:20.240 --> 00:18:24.157 Um And then after that, if you ask a model 00:18:24.240 --> 00:18:26.637 to solve a physics question, it that's 00:18:26.720 --> 00:18:31.197 called inference uh and that's quite efficient. But this is true for people 00:18:31.280 --> 00:18:34.357 as well and when people talk about all these AI models are so 00:18:34.440 --> 00:18:37.557 efficient, they're usually comparing the 00:18:37.640 --> 00:18:40.597 22 years that a human takes to train to 00:18:40.680 --> 00:18:45.517 the 1 second that an adult takes to solve a physics problem. If you compare the 00:18:45.600 --> 00:18:48.717 you know, the model solving the physics problem to the human solving physics 00:18:48.800 --> 00:18:52.917 problem, actually the models already probably are more energy efficient. Um 00:18:53.000 --> 00:18:57.637 but training is this hugely massive amount of processing power that then 00:18:57.720 --> 00:19:01.037 produces really just like a file of numbers that 00:19:01.120 --> 00:19:03.877 you can then pose a question to and get a response. 00:19:03.960 --> 00:19:07.757 >> And are you optimistic about the progress we're making on the chip? 00:19:07.840 --> 00:19:12.037 >> Yeah, um we should have the first chips deployed at scale 00:19:12.120 --> 00:19:15.077 uh by the end of this year. We should have the first chips back just in a few 00:19:15.160 --> 00:19:18.237 months now. Um and looks like it'll be really good. 00:19:18.320 --> 00:19:21.877 >> Fantastic. So, you announced a new partnership this 00:19:21.960 --> 00:19:25.957 morning, actually, with the North American Building Trades Unions to 00:19:26.040 --> 00:19:30.357 expand training pathways for skilled construction, which has been a subject 00:19:30.440 --> 00:19:34.677 of my many of the discussions today. Can you tell us a little bit about what you 00:19:34.760 --> 00:19:38.517 and uh Sean McGarvey >> Yes. >> agreed and What's he And he's going to 00:19:38.600 --> 00:19:41.997 be on stage later on. So, let's hear your version of it and then see if his 00:19:42.080 --> 00:19:46.640 version is the same. >> So, 00:19:48.200 --> 00:19:52.557 we talked earlier and we've talked The world has talked many times 00:19:52.640 --> 00:19:53.997 in the past about the need for AI 00:19:54.080 --> 00:19:56.437 infrastructure, the need for the world 00:19:56.520 --> 00:20:00.357 to have the physical infrastructure, 00:20:00.440 --> 00:20:03.117 power plants, transmission lines, data 00:20:03.200 --> 00:20:05.997 center halls, chillers, 00:20:06.080 --> 00:20:10.477 obviously the racks and the GPUs and everything that goes inside of those. Um 00:20:10.560 --> 00:20:14.757 and I sort of wish everyone would go visit 00:20:14.840 --> 00:20:17.157 at some point one of these 00:20:17.240 --> 00:20:21.997 mega scale data centers because it it's hard, 00:20:22.080 --> 00:20:25.877 you know, when you ask ChatGPT a question, you get your answer back and it's really 00:20:25.960 --> 00:20:29.917 hard to visualize the scale of what it took to make that or what it takes to 00:20:30.000 --> 00:20:34.397 make that happen. Uh people talk about all of these different 00:20:34.480 --> 00:20:36.397 places that were limited. >> Mhm. 00:20:36.480 --> 00:20:38.477 >> We are limited in the, you know, number 00:20:38.560 --> 00:20:43.837 of turbines or now it's the voltage transformers or it's the memory fabs or 00:20:43.920 --> 00:20:46.557 it's building the data centers, whatever. All of these things have one 00:20:46.640 --> 00:20:49.757 thing in common, which is they are massively complex physical 00:20:49.840 --> 00:20:53.277 infrastructure that require skilled tradespeople 00:20:53.360 --> 00:20:57.077 >> Mhm. >> uh and a lot of them to do. And 00:20:57.160 --> 00:20:59.357 no matter where the choke point in the 00:20:59.440 --> 00:21:02.677 supply chain is at any one time, 00:21:02.760 --> 00:21:04.397 when I talk to people about what it 00:21:04.480 --> 00:21:07.597 would take to accelerate it, um 00:21:07.680 --> 00:21:11.997 it is more skilled trades workers to build out all of this infrastructure 00:21:12.080 --> 00:21:15.437 that we all depend on. I think this will be incredible jobs. 00:21:15.520 --> 00:21:19.997 Uh more than that, I will I think they will lay the foundation for the next 00:21:20.080 --> 00:21:23.837 generation of American infrastructure and economic prosperity. And we are 00:21:23.920 --> 00:21:28.197 thrilled to get to work uh together to to drive that faster. 00:21:28.280 --> 00:21:32.237 >> Uh just so you know, we at BlackRock share that view and we think it's really 00:21:32.320 --> 00:21:35.477 exciting that that you announced this partnership together. I think that's 00:21:35.560 --> 00:21:38.917 really really terrific. Uh I think another question that's on 00:21:39.000 --> 00:21:44.997 people's minds is competition with China. 00:21:45.080 --> 00:21:47.117 Where do you think that stands? 00:21:47.200 --> 00:21:51.397 Uh what what do we need to do to make sure I assume we're ahead, but I don't 00:21:51.480 --> 00:21:54.277 know. So, you might tell us whether we are or not. 00:21:54.360 --> 00:21:57.917 What do we need to do to make sure we stay ahead? 00:21:58.000 --> 00:22:02.157 >> So, a general framework thought first and 00:22:02.240 --> 00:22:06.200 then I'll answer the question more specifically. 00:22:07.240 --> 00:22:10.597 I think the discovery of deep learning is 00:22:10.680 --> 00:22:12.837 is closer to 00:22:12.920 --> 00:22:15.077 discovering an element or a fundamental 00:22:15.160 --> 00:22:18.077 property of physics than it is uh of 00:22:18.160 --> 00:22:22.117 like a secret technology. Um 00:22:22.200 --> 00:22:24.037 and that means that eventually, and 00:22:24.120 --> 00:22:26.157 eventually probably not being very long, 00:22:26.240 --> 00:22:31.157 the fundamental ideas that make a model so capable 00:22:31.240 --> 00:22:34.597 will be simplified. They'll be very well known. 00:22:34.680 --> 00:22:37.357 And just like we kind of understand how 00:22:37.440 --> 00:22:41.637 big parts of physics work, we will understand as a scientific principle how 00:22:41.720 --> 00:22:45.717 big parts of artificial intelligence work. Um 00:22:45.800 --> 00:22:50.197 this is this really we started to appreciate this with the scaling laws uh 00:22:50.280 --> 00:22:53.677 that OpenAI published maybe 7 years ago now. 00:22:53.760 --> 00:22:57.717 Um there was such a measurable 00:22:57.800 --> 00:23:01.477 beautiful correlation between the resources that go into a model and the 00:23:01.560 --> 00:23:03.277 intelligence of that model 00:23:03.360 --> 00:23:06.237 that it kind of felt 00:23:06.320 --> 00:23:10.437 like hair-raising but clear at the time that there was just something 00:23:10.520 --> 00:23:12.837 fundamental going on here as a scientific principle. 00:23:12.920 --> 00:23:14.757 >> Mhm. >> Now, there have been a lot of details 00:23:14.840 --> 00:23:18.677 we've discovered since. There will be more to come, but like other scientific 00:23:18.760 --> 00:23:23.437 frontiers, it is simplifying and becoming more clear over time. And 00:23:23.520 --> 00:23:27.757 eventually, this recipe will be well understood as a scientific principle. It 00:23:27.840 --> 00:23:32.957 will not be a trade secret in the sense that other things have been. 00:23:33.040 --> 00:23:38.397 Now, the analogy I like best historically uh from technological history is the 00:23:38.480 --> 00:23:41.877 transistor. >> Mhm. >> Transistor was also a sort of 00:23:41.960 --> 00:23:45.517 fundamental scientific breakthrough, very hard to 00:23:45.600 --> 00:23:49.797 uh discover, kind of chancy to discover, uh took us a little while to refine our 00:23:49.880 --> 00:23:53.397 discovery, but once we understood it, the scientific principle was clear to 00:23:53.480 --> 00:23:58.877 everyone. There were still massive amounts of operational knowledge that went around 00:23:58.960 --> 00:24:01.717 that. >> Uh TSMC >> can still do things that no one else in 00:24:01.800 --> 00:24:03.757 the world can do, and you know, I I 00:24:03.840 --> 00:24:06.517 expect the industrial process around 00:24:06.600 --> 00:24:09.117 this to have a lot of advantages, 00:24:09.200 --> 00:24:13.637 uh competitive advantages. I also expect that the integration in workflows and 00:24:13.720 --> 00:24:16.557 training data and other and usability of models, 00:24:16.640 --> 00:24:18.717 there will be a lot of differentiation there. 00:24:18.800 --> 00:24:21.797 Maybe most of all, I expect there will be differentiation on who has the 00:24:21.880 --> 00:24:26.597 infrastructure and how much of it. But the fundamental scientific principles 00:24:26.680 --> 00:24:28.757 I are going to be 00:24:28.840 --> 00:24:33.957 well known, and they will fit on a t-shirt, I think. Um 00:24:34.040 --> 00:24:38.237 In terms of where we are, uh the most capable models in the world, 00:24:38.320 --> 00:24:40.277 the frontier the US is leading on. 00:24:40.360 --> 00:24:43.477 >> Mhm. >> Um the cheapest 00:24:43.560 --> 00:24:48.237 inference usage for a two-generation earlier model, China is leading on. 00:24:48.320 --> 00:24:50.797 >> Mhm. >> Um infrastructure in the US is currently 00:24:50.880 --> 00:24:53.637 leading on, but China's moving much faster on. 00:24:53.720 --> 00:24:57.757 And that kind of industrialization, productization, whatever you want to 00:24:57.840 --> 00:25:01.637 call it. I The US is leading in closed source. 00:25:01.720 --> 00:25:04.957 China's leading in open source. Uh I think the US is probably leading 00:25:05.040 --> 00:25:09.237 overall. >> And you were recently in India. 00:25:09.320 --> 00:25:12.717 And um sounded like you were very excited about 00:25:12.800 --> 00:25:16.397 how India is thinking about the challenges and the opportunities. 00:25:16.480 --> 00:25:19.077 >> I was blown away uh talking to Indian 00:25:19.160 --> 00:25:25.000 startups and how they are using this technology. Um 00:25:25.160 --> 00:25:28.077 I like got there and someone like handed me a briefing sheet for India and it was 00:25:28.160 --> 00:25:30.237 like you know 00:25:30.320 --> 00:25:35.157 Codex usage in India has like 10x'd in some small number of months. And I was 00:25:35.240 --> 00:25:37.837 like that's got to be a bug. Like that >> It can't be right. 00:25:37.920 --> 00:25:43.517 >> It can't be right. Um but it was true. And the 00:25:43.600 --> 00:25:45.717 And then I started talking to these startups. 00:25:45.800 --> 00:25:48.560 And 00:25:49.320 --> 00:25:52.437 it's an even stronger version of what's happening in the US 00:25:52.520 --> 00:25:56.037 of people saying, "Hey, the world is different. Um 00:25:56.120 --> 00:25:59.317 you know, you talk about a one-person startup. I'm trying to build a 00:25:59.400 --> 00:26:02.357 zero-person startup. I'm trying to just write a prompt that's going to make my 00:26:02.440 --> 00:26:06.957 whole startup and you know, write my software, do my customer support, do my 00:26:07.040 --> 00:26:09.957 legal stuff, whatever. And then I want to go on vacation." 00:26:10.040 --> 00:26:13.597 Um And the companies 00:26:13.680 --> 00:26:17.237 big companies in India they were just saying like, "How much capacity can we 00:26:17.320 --> 00:26:20.117 buy from you? How long can we reserve it for? Can we negotiate this right now? 00:26:20.200 --> 00:26:22.917 We're not going to like let you leave the room until you agree on this with 00:26:23.000 --> 00:26:24.917 us." That just the level of aggression 00:26:25.000 --> 00:26:28.397 and speed and sort of a belief that AI 00:26:28.480 --> 00:26:30.157 was going to 00:26:30.240 --> 00:26:33.037 reshape the business landscape in India 00:26:33.120 --> 00:26:37.560 was was really quite impressive. Um 00:26:37.760 --> 00:26:42.117 Yeah. >> Is is that different from when we when 00:26:42.200 --> 00:26:44.357 we talk to customers in the US? 00:26:44.440 --> 00:26:48.597 >> Uh it's it's the same vector, but they seem a little further 00:26:48.680 --> 00:26:51.957 along. They're moving faster. >> Yeah. The other The other comment you I 00:26:52.040 --> 00:26:55.877 think I saw you made was that there's a difference between autocratic and 00:26:55.960 --> 00:26:58.797 democratic AI. 00:26:58.880 --> 00:27:03.320 What did you mean by that and what what do you think is at stake? 00:27:05.920 --> 00:27:09.477 >> Once in a while, I think a technological shift comes 00:27:09.560 --> 00:27:12.077 along that 00:27:12.160 --> 00:27:15.277 reshapes society to such a degree that 00:27:15.360 --> 00:27:18.277 it does not decisions about it do not 00:27:18.360 --> 00:27:22.437 belong to the handful of companies that happen to be it. I am a huge believer in 00:27:22.520 --> 00:27:25.517 capitalism. I am a huge believer in the rights of companies. 00:27:25.600 --> 00:27:28.357 Um I am a 00:27:28.440 --> 00:27:31.077 huge believer in governments shouldn't 00:27:31.160 --> 00:27:35.840 interfere too much. But 00:27:37.240 --> 00:27:38.837 e- 00:27:38.920 --> 00:27:40.757 I think this is one of those exceptional 00:27:40.840 --> 00:27:44.237 times where society has a legitimate 00:27:44.320 --> 00:27:48.717 interest in what the impact of this technology 00:27:48.800 --> 00:27:51.277 is going to be. I think the internet was one of these times, too, and I don't 00:27:51.360 --> 00:27:56.317 think we got all of that right. But I'd like us to learn 00:27:56.400 --> 00:28:00.717 what we can do better. And 00:28:00.800 --> 00:28:02.597 if what 00:28:02.680 --> 00:28:05.237 the AI companies say, and I also believe 00:28:05.320 --> 00:28:09.637 it comes true, and that this is going to reshape the economy. This is going to 00:28:09.720 --> 00:28:12.797 reshape geopolitical power. This is going to 00:28:12.880 --> 00:28:15.037 change how we all live our lives. 00:28:15.120 --> 00:28:20.037 Um then I don't think it should be up to 00:28:20.120 --> 00:28:22.517 companies or 00:28:22.600 --> 00:28:24.757 a government to impose 00:28:24.840 --> 00:28:27.317 a particular will on how this is going 00:28:27.400 --> 00:28:30.877 to get used. Um I think that this 00:28:30.960 --> 00:28:32.997 belongs to the will of the people and 00:28:33.080 --> 00:28:35.157 working through the democratic process. 00:28:35.240 --> 00:28:38.040 And 00:28:39.120 --> 00:28:42.357 companies like ours, I think are quickly, more quickly than companies in 00:28:42.440 --> 00:28:46.197 previous generations have had to do, moving into a sort of critical 00:28:46.280 --> 00:28:47.797 infrastructure role. >> Right. 00:28:47.880 --> 00:28:52.757 >> Where we have to say um we create this technology. 00:28:52.840 --> 00:28:54.717 We are experts in it and we should have 00:28:54.800 --> 00:28:58.077 a real voice and we have a we have 00:28:58.160 --> 00:29:01.837 opinions and understanding of where its limitations 00:29:01.920 --> 00:29:05.757 are and where it's not ready to be used and where great harm could come. 00:29:05.840 --> 00:29:09.197 Uh but the rules, the limitations have to 00:29:09.280 --> 00:29:13.317 be agreed upon by society through this process and because the technology is 00:29:13.400 --> 00:29:16.037 moving so fast, it'd be great for that 00:29:16.120 --> 00:29:20.357 democratic process to run a little bit faster. But companies, 00:29:20.440 --> 00:29:24.837 governments, uh need to be able to depend on companies like ours to 00:29:24.920 --> 00:29:28.157 integrate and be able to use the the technology. 00:29:28.240 --> 00:29:33.597 >> Okay. So coming back to the global AI race, 00:29:33.680 --> 00:29:37.800 where where do you think the US is the most vulnerable? 00:29:44.680 --> 00:29:47.197 >> Three three things come to mind. Uh 00:29:47.280 --> 00:29:51.757 one, there's been a ton of noise made about 00:29:51.840 --> 00:29:55.637 the global supply chain dependence and US infrastructure. 00:29:55.720 --> 00:29:58.077 I don't have anything new or deep to say there, 00:29:58.160 --> 00:30:03.597 but I can't overstate how scary this is to me. 00:30:03.680 --> 00:30:06.437 >> Yeah. >> Um the if we fall behind on 00:30:06.520 --> 00:30:08.757 infrastructure and can't catch back up, 00:30:08.840 --> 00:30:15.117 um if if if globalization falls apart in any of the many ways it could, 00:30:15.200 --> 00:30:19.557 >> Mhm. >> uh and we are not able to fairly independently [snorts] 00:30:19.640 --> 00:30:24.597 keep building AI infrastructure, that 00:30:24.680 --> 00:30:29.437 that seems like a big vulnerability and I don't I don't hate, but I don't love our 00:30:29.520 --> 00:30:31.597 global position right now. >> Mhm. 00:30:31.680 --> 00:30:37.520 >> Um the second is if we don't 00:30:39.200 --> 00:30:42.637 it's a competitive world and if we don't move as quickly as other countries on 00:30:42.720 --> 00:30:45.157 economic adoption of this >> Mhm. 00:30:45.240 --> 00:30:50.077 then I think we will lose the advantage that we have from being the economic 00:30:50.160 --> 00:30:54.757 powerhouse that we are. And this is about how quickly companies 00:30:54.840 --> 00:30:58.237 adopt it. This is about how quickly our scientists adopt it. This is how quickly 00:30:58.320 --> 00:31:01.557 our government adopts it. This is like 00:31:01.640 --> 00:31:03.477 on the positive side, I think this is a 00:31:03.560 --> 00:31:09.077 once in many generation opportunity to really improve the economy, 00:31:09.160 --> 00:31:11.917 really rewrite some of the rules of society that aren't working in light of 00:31:12.000 --> 00:31:13.597 this new incredible wealth fountain we 00:31:13.680 --> 00:31:18.037 have. So I can see the world where 00:31:18.120 --> 00:31:21.637 this is not a disadvantage at all, but this becomes our biggest competitive 00:31:21.720 --> 00:31:26.157 advantage. I don't 00:31:26.240 --> 00:31:29.757 I don't think it's like super obvious that we're on the trajectory we 00:31:29.840 --> 00:31:32.957 want to be there now. Again, I don't hate it. I just I think it could move 00:31:33.040 --> 00:31:35.397 faster and you can see 00:31:35.480 --> 00:31:40.157 a bunch of potential headwinds, you know, like AI is not very popular in the 00:31:40.240 --> 00:31:44.677 US right now. Data centers are getting blamed for electricity price hikes. 00:31:44.760 --> 00:31:48.957 Almost every company that does layoffs is getting 00:31:49.040 --> 00:31:52.797 is blaming AI whether or not it really is about AI. 00:31:52.880 --> 00:31:56.117 There is this real debate about the relative power between governments and 00:31:56.200 --> 00:32:01.280 companies going on. So So there's like a lot of 00:32:02.120 --> 00:32:03.557 There's a lot of stuff happening there. 00:32:03.640 --> 00:32:06.437 And then the third category is 00:32:06.520 --> 00:32:10.757 diffusion into the rest of the world. Is the world mostly going to build on the 00:32:10.840 --> 00:32:15.237 American AI tech stack? Chips, models, applications, whatever. Or are we going 00:32:15.320 --> 00:32:19.160 to enact a set of policies that make that harder? 00:32:19.360 --> 00:32:22.837 >> So it sounds to me like you think that AI could be 00:32:22.920 --> 00:32:26.237 the foundation of an immense 00:32:26.320 --> 00:32:28.237 productivity boom. 00:32:28.320 --> 00:32:31.637 If properly used and if if adopted. 00:32:31.720 --> 00:32:35.677 >> Yes, for sure. Although I think the way we measure that is going to have to 00:32:35.760 --> 00:32:39.720 change. Explain. 00:32:42.920 --> 00:32:45.757 I can see a world where we have an 00:32:45.840 --> 00:32:47.797 incredible productivity boom. 00:32:47.880 --> 00:32:51.237 Quality of life goes up and up. 00:32:51.320 --> 00:32:55.517 Most of the things that we think we we say we would care about get better and 00:32:55.600 --> 00:32:58.997 better. And yet GDP in the way we currently 00:32:59.080 --> 00:33:01.757 measure it goes down and down. 00:33:01.840 --> 00:33:04.477 Like deflation for a very long period of time. 00:33:04.560 --> 00:33:07.277 And 00:33:07.360 --> 00:33:09.997 I don't know what it means to like live 00:33:10.080 --> 00:33:12.877 in a forever deflationary world. 00:33:12.960 --> 00:33:17.397 I don't know what it means to think about GDP in a 00:33:17.480 --> 00:33:21.837 and GDP's correlation of quality to quality of life in a world where you 00:33:21.920 --> 00:33:25.757 know more of the economic capability or the sorry intellectual capability is 00:33:25.840 --> 00:33:29.637 inside of data centers than outside of them. But you know maybe we're going to 00:33:29.720 --> 00:33:34.317 find out and and I think there's going to be a lot of 00:33:34.400 --> 00:33:37.637 debate in the coming years about what the right 00:33:37.720 --> 00:33:41.757 thing to measure is. >> Do you think we're thinking about these 00:33:41.840 --> 00:33:46.117 challenges and issues the right way? Are we having that Are we starting to talk 00:33:46.200 --> 00:33:48.717 about that? 00:33:48.800 --> 00:33:52.117 >> I I think we yes, we are starting to. 00:33:52.200 --> 00:33:54.797 If there was an easy consensus answer >> [laughter] 00:33:54.880 --> 00:33:57.677 >> have done it by now. So I don't think anyone knows 00:33:57.760 --> 00:34:02.757 what to do. The all of these things that we've 00:34:02.840 --> 00:34:05.917 depended on for so long as a society are sort of 00:34:06.000 --> 00:34:08.117 coming into question at the same time. 00:34:08.200 --> 00:34:10.917 There was this quote that I saw on the internet a few weeks ago. It really 00:34:11.000 --> 00:34:14.677 stuck in my head and it was something like 00:34:14.760 --> 00:34:17.437 for centuries maybe millennia we have 00:34:17.520 --> 00:34:19.957 learned a lot about how to structure 00:34:20.040 --> 00:34:22.957 society to manage scarcity. 00:34:23.040 --> 00:34:29.840 Almost none of that helps us as we have to quickly learn towards managing abundance. 00:34:30.400 --> 00:34:35.077 So that's like a real change to how capitalism has worked. Um 00:34:35.160 --> 00:34:39.357 capitalism has also depended on this balance between 00:34:39.440 --> 00:34:41.157 somewhat of a power balance between 00:34:41.240 --> 00:34:44.357 labor and capital. But, if it's hard in 00:34:44.440 --> 00:34:48.677 many of our current jobs to outwork a GPU, then that changes. And I see we're 00:34:48.760 --> 00:34:52.597 out of time, but I had like a list of 10 things like that changing all at once. I 00:34:52.680 --> 00:34:55.917 I I'm not a long-term jobs doomer. I I 00:34:56.000 --> 00:34:59.997 think we will figure out new things to do. I'm also certainly not a long-term 00:35:00.080 --> 00:35:02.037 capitalism doomer. I believe in it very, 00:35:02.120 --> 00:35:04.477 very deeply. Um 00:35:04.560 --> 00:35:07.637 but I think the next few years 00:35:07.720 --> 00:35:11.637 are going to be a painful adjustment as we get to this future we all get to 00:35:11.720 --> 00:35:17.237 redefine of what the new system and just this incredible prosperity looks like. And 00:35:17.320 --> 00:35:20.077 there are going to be some very intense and uncomfortable debates on the way 00:35:20.160 --> 00:35:23.157 there. >> Okay, so thank you, Sam, for that very 00:35:23.240 --> 00:35:26.797 thoughtful discussion. Here's what I will say. 00:35:26.880 --> 00:35:31.557 Five years from now, we'll come back here 00:35:31.640 --> 00:35:36.877 and we'll see where we are and how we've navigated our way through this. 00:35:36.960 --> 00:35:38.757 Deal? >> Deal. I look forward to that. 00:35:38.840 --> 00:35:42.447 >> Don't drink. Thank you. >> Thank you. Thank you. 00:35:42.530 --> 00:35:44.550 >> [applause] 00:35:47.080 --> 00:35:48.837 >> We'll now take [music] a short break. 00:35:48.920 --> 00:35:50.517 Please join us in the East Green Room 00:35:50.600 --> 00:35:56.360 for refreshments and return to your seats in 15 minutes. Thank you. 00:35:59.560 --> 00:36:01.580 >> [music] 00:38:24.760 --> 00:38:26.880 >> Mhm. 00:39:00.400 --> 00:39:02.440 >> Mhm.