Based Camp - ＂Mid is Over＂ How Do We Protect AI From Those it Will Replace？ (With Brian Chau)

00:00:00.000 Of course, you see Sam Altman going to testify and he wants a licensing regime.

00:00:04.260 Of course, OpenAI will get a license.

00:00:05.900 Of course, OpenAI's competitors will not get a license.

00:00:08.420 Of course.

00:00:08.760 So if you're OpenAI, you know, and you've now secured billions of dollars in investment

00:00:14.900 in fundamentally these transformer models and, you know, the kind of hardware stacks

00:00:19.500 that are specific to replicating them.

00:00:22.240 And you get something that's out of left field or you get something that's limited

00:00:25.780 by a different factor, that's not limited by the advantages that OpenAI has over its competitors.

00:00:31.740 You know, maybe Foom is not the sound of AI accelerating,

00:00:34.520 but Foom is the sound of OpenAI's stock going to zero. 0.82

00:00:37.800 This is no longer a battle of, like, internet shitposters.

00:00:41.020 You know, this is a battle of real political interests 0.82

00:00:44.800 and the forces that drive the Democratic and Republican parties. 0.98

00:00:49.560 I also love that it's done such a good job of getting rid of these useless grifters, 0.97

00:00:54.280 like, artists and writers, because honestly, they had these pointless degrees. 0.95

00:01:02.160 They were a huge, I think, cause of our society's degradation.

00:01:07.380 And I'm so glad that AI has replaced them.

00:01:10.760 There's, okay, like, there's a real thing underlying that is that, like, mid is over, right?

00:01:16.660 And here, like, actually mid, I don't mean...

00:01:18.600 Would you like to know more?

00:01:19.520 Hello, everyone.

00:01:21.140 We are very excited to welcome Brian Chow for this episode of Basecamp.

00:01:25.400 We are going to be talking with Brian, not about his amazing podcast from the new world,

00:01:30.240 but rather a new venture of his, which is called the Alliance for the Future.

00:01:35.160 So I think that the number one thing to understand is that it's a non-trivial question,

00:01:42.440 whether you are able to use machine learning,

00:01:45.280 whether you are able to write machine learning, whether it will be illegal to log on to, you

00:01:49.400 know, chat.openai.com.

00:01:51.780 That is a real question that people have different answers to, that many people want for various

00:01:59.940 reasons to ban machine learning pretty much wholesale.

00:02:04.480 And this is, you know, the purpose of Alliance for the Future is to do everything we can to

00:02:11.440 make sure that does not happen.

00:02:12.460 Hmm.

00:02:14.200 So what are you saying to the AI apocalypticists who start immediately screaming at you and

00:02:21.700 flailing when you say things like this?

00:02:23.920 What's your take on LEIs or Yukowsky's positions and how do you respond to them?

00:02:28.300 Yeah, it's very funny.

00:02:29.420 This is very often the first question that people ask me when I mention this.

00:02:33.680 And they're, you know, striking the rogue data centers is among the, I think the less, you

00:02:42.660 know, the less dangerous versions of what people are trying to get.

00:02:47.800 Like this is something that maybe, maybe, I don't know, like there are people in, in

00:02:52.800 AFTF, in Alliance for the Future that I'm more worried about the EAs.

00:02:56.180 You know, I'm friends with many EAs and I've also seen the kind of regulatory environment

00:03:02.420 in, in Washington, in other countries, in the EU, you know, like the EU, the EU commission

00:03:09.520 are not EAs.

00:03:10.960 You know, the Chinese government is not EAs.

00:03:12.940 Chuck Schumer is not an EA.

00:03:14.620 Right.

00:03:14.760 I just don't think they're the, I just don't think they're the major threat.

00:03:18.380 They're like the closest thing to like a non-retarded version of the ban AI argument. 1.00

00:03:24.120 And that's why they get engaged with in like smart parts of Twitter.

00:03:27.360 It's because they're kind of like the, the, the smart representatives of this much bigger 0.99

00:03:33.500 target of people that I think most EAs would consider like stupid and misguided. 0.98

00:03:39.540 So, so I don't, I don't worry too much about the EAs. 0.98

00:03:42.180 I don't want this to be a conflict between, you know, like EAs and like accelerationists

00:03:48.960 or whatever. 0.67

00:03:49.560 I think that that's, you know, that's very silly.

00:03:51.820 That's getting caught up in like what I'm asking is what, so, you know, even our audience,

00:03:59.020 you know, we've got, I suspect some people in our audience who think AIs are going to

00:04:02.500 kill us all.

00:04:03.580 A lot. 0.99

00:04:04.600 Not a lot because we've taken very hard stances against that.

00:04:07.960 Well, very, so I don't know if you know our position on AI.

00:04:11.880 We take a position about variable AI risk, which is very different than absolute AI risk.

00:04:16.880 But I'm wondering, what is your stance?

00:04:18.840 Like if you're trying to calm somebody down, who's like, yeah, but it looks like genuinely,

00:04:24.560 I don't see how we remove this threat without restricting access.

00:04:30.200 How do you communicate to them?

00:04:32.700 So there's actually a difference between like Eliezer and like fans of Eliezer.

00:04:36.760 With fans of Eliezer, I just worry that they're driven too much by the current hype cycle.

00:04:42.060 Then, you know, they saw that it's cooling down a bit.

00:04:44.360 They saw, you know, like GPT-3 get released and then they saw GPT-4 get released.

00:04:49.260 And they were like, what if it just keeps developing at the same rate that between GPT-3 and GPT-4

00:04:54.320 was released?

00:04:54.960 And of course, that did not happen.

00:04:57.180 That was, you know, that was the accumulation of lots of work and, you know, the effort across the entirety of OpenAI's existence.

00:05:07.640 And there are many technological factors that just slow development.

00:05:12.720 This is the trend you see is that you see, you know, like an accelerating technology.

00:05:17.560 You see it get adopted in early market versions.

00:05:20.840 And then you start to see that the speed of that development petering out.

00:05:24.740 I actually have a more specific argument going through all of the, so far I have the hardware part done.

00:05:31.400 This has been put on the back shelf a bit because of everything else I'm doing right now.

00:05:35.520 But I do think the first part of the article actually holds up very well in the past few months,

00:05:42.820 which is diminishing returns on machine learning one.

00:05:46.380 You can find that also at fromthenew.world, fromthenewworld.

00:05:50.460 It is the home of everything now, everything that I do.

00:05:54.100 And yeah, so to those people, I would say you are severely estimating the progress in artificial intelligence.

00:06:02.720 Many of them have, you know, they have this like, I did not come up with this word.

00:06:07.620 They came up with this word of like foom, which is basically like the idea that, you know, it goes foom.

00:06:16.380 It sounds like, this sounds like, this sounds like a straw man version of the argument.

00:06:22.460 But it is, it is actually their own argument that, you know, once it, once it starts accelerating,

00:06:27.480 it will just become, you know, extremely fast.

00:06:30.260 It will only get faster and faster.

00:06:31.900 It's like, this, this is just not, this is, you know, this is hype cycle fantasy.

00:06:37.260 In terms of like the longer term.

00:06:40.020 In terms of the longer term.

00:06:41.220 Explain why it's hype cycle fantasy.

00:06:42.700 And I, like, I, this is the main thing.

00:06:45.180 Look at, yeah, it's, it's not the mainstream view.

00:06:48.220 Like, like, maybe it's like, maybe it's the view of my, like, Twitter posters.

00:06:51.680 No, no, I mean, I'm among, like, EA Zoomers, I know.

00:06:54.480 Like, they, they genuinely, and I, I don't mean to be, we've tried really hard not to insult

00:07:00.680 specific people on this show.

00:07:02.860 But Eliezer Yukowski is the Greta Thornburg of AI apocalypticism.

00:07:07.320 No, no, you have to, you have to have the Straussian reading of Eliezer, right?

00:07:12.640 So, so the Straussian reading of Eliezer.

00:07:14.920 So, so like the surface level reading of Eliezer is that, like, he, he just wants to

00:07:18.540 create, you know, massive panic and for government to take control of everything.

00:07:23.680 You know, that, that's what he writes in his Time article pretty much.

00:07:26.780 It is.

00:07:27.040 Like, this is, this is like not even really an exaggeration.

00:07:29.720 So, so like the Straussian reading of Eliezer is, you know, like he, he doesn't actually

00:07:33.720 think that the world is like 90% that, that like the, that like the world is like absolutely

00:07:38.480 doomed, but he thinks that like no one will take it seriously, right?

00:07:41.880 He, he thinks that unless you, unless you turn the volume, everyone's wearing earmuffs,

00:07:46.240 so you better turn the volume up to a hundred and you better give, you know, and this is by

00:07:50.860 the way, like not necessarily an incorrect model of how, how the government works.

00:07:55.300 Like, this is what Fauci did as well.

00:07:57.260 You know, the, the reasoning was, you know, that you wouldn't give us power to do any

00:08:01.440 pandemic prevention measures unless we took very extreme positions on how dangerous the

00:08:08.100 virus was, on how confident we were about certain interventions.

00:08:12.780 And, you know, in terms of the politics, Fauci was correct.

00:08:18.040 Like that was actually correct.

00:08:20.440 He, he got the power.

00:08:21.700 It's very likely that he would not have gotten the power if he made, you know, like a

00:08:25.220 moderate case for the risks of COVID.

00:08:27.260 And I think Eliezer has learned that lesson.

00:08:29.660 So I don't know, like, like maybe, so like, I think Eliezer is like, not as, not as, not

00:08:36.720 as insane as, as it sounds.

00:08:38.460 Like the things that he advocates for are like truly insane.

00:08:40.980 But like, I think like Eliezer, the man, you know, is not necessarily that insane.

00:08:45.380 So your argument is just, he's, he's wildly overcorrecting.

00:08:49.720 And that in reality, AI is not going to accelerate as quickly as everyone thinks it is.

00:08:54.760 And therefore, as AI more linearly develops, people can develop safeguards as necessary.

00:09:03.820 Therefore, it doesn't present an existential risk.

00:09:06.000 And therefore we shouldn't be, be stifling its development with regulation and rules.

00:09:12.540 Is that correct?

00:09:14.100 Yes.

00:09:14.420 So it's mostly correct.

00:09:16.920 So, and I should say here, I'm speaking for myself, not for like the AF, for AFTF in

00:09:21.960 general, but so like the original version of the, the name of the original version of

00:09:29.300 EA that cared about AI was that long-termism.

00:09:32.260 And the reason why it was called long-termism is, you know, the idea is that in hundreds

00:09:38.980 of years, humanity, if you just look at, you know, population growth or like, I don't

00:09:43.820 know, you guys have a much more, much more pessimistic version of this, right?

00:09:47.280 But if we create a way, if we create a way to solve our population growth problems and

00:09:52.180 have the earth continue growing, then the number of humans in the future are just much

00:09:55.860 more than the number of humans in the present.

00:09:57.740 So you have to care about the long-term.

00:09:59.140 And in terms of the long-term, in terms of the timeframe of like hundreds of years, I

00:10:04.120 am not, you know, I'm not completely sure that AI will not be a problem in like a hundred

00:10:08.480 years.

00:10:09.080 That is something, you know, that, that I accept as, as a possibility.

00:10:13.760 The question is, you know, if you have a pace of technological growth, that is, you know,

00:10:20.860 what you would infer from every other technology that has ever existed pretty much from, you

00:10:27.940 know, like the history of technological development from, you know, early science to the industrial

00:10:32.320 revolution, to more recent cycles, the 2000 cycle, you know, the, the very recent, you

00:10:37.680 know, like 2020 to 2021 cycle of technologies.

00:10:41.320 You get that, you, you get this very known thing, very well-known thing called an S-curve.

00:10:45.300 You get an early acceleration and then it peters out.

00:10:47.840 And then that's where the hard work has to be done.

00:10:49.400 Actually getting the technology adopted in all these sectors of society.

00:10:53.360 And this is also like the mainstream economist view.

00:10:56.320 And, you know, I'm not like this, like the thing is, this is the mainstream view, right?

00:11:00.980 The mainstream view is that, you know, hype cycles happen, that techno technological progress

00:11:05.180 is good, but it is not necessarily something that should be, you know, that you should go

00:11:11.140 all in on, right?

00:11:11.880 You should, you know, you should invest in some tech stocks, but you should not, you know,

00:11:15.180 gamble your entire life savings on one tech stock and so on and so forth.

00:11:19.500 So I really do think this is the implementation of the normal, the normie, like non-hyper online

00:11:28.140 position on technology as policy.

00:11:31.000 That's how I would put it.

00:11:33.140 So functionally, your organization, if it succeeds, what's it doing?

00:11:39.300 What specific changes does it make in policy?

00:11:42.140 How do you achieve those?

00:11:43.640 What research are you outputting?

00:11:46.600 So Alliance for the Future is a completely new think tank.

00:11:49.760 The number one thing is just to balance the scales, because right now there is a lot of

00:11:55.140 funding either from the EA side, although I don't think that's the main problem, but even

00:11:59.520 more from existing political interests.

00:12:02.440 And of course, you see Sam Altman going to testify and he wants a licensing regime.

00:12:07.680 Of course, OpenAI will get a license.

00:12:09.320 Of course, OpenAI's competitors will not get a license.

00:12:11.680 Of course, you know, increase the barriers to entry.

00:12:15.140 Actually, so this is a thing that you're talking about briefly here, but I really want our audience

00:12:19.520 to understand this.

00:12:20.700 So in the business world, you know, what you want to do to maximize the value of your company

00:12:26.560 is to advocate for regulation.

00:12:29.060 Like a lot of people are really surprised that like Google would advocate for like internet

00:12:32.960 search regulation or like, but this is what you see with any large monopoly in a space

00:12:38.680 is they spend a huge chunk of their revenue advocating for regulation of their own company.

00:12:45.120 And the reason they're doing that is because it prevents new entrants from entering the market,

00:12:49.760 which protects their monopoly.

00:12:52.780 That is why people like Sam Altman are advocating for this regulation.

00:12:56.240 It's not because they're genuinely scared of AI.

00:12:59.300 It's because they're the first players on the market to continue.

00:13:02.160 This is something, this is actually a very important topic.

00:13:04.880 Okay.

00:13:05.160 Like this is a good venue to be like very autistic about this. 1.00

00:13:08.260 So there's, there is this, I think like he published most of his stuff in the 60s, 70s.

00:13:16.180 Economist, one of the, considered one of like the founding people of political economy,

00:13:20.920 Gordon Tullock.

00:13:21.720 Okay.

00:13:22.160 All of the GMU people love this guy because he was from GMU, I think.

00:13:25.880 And he has this idea called the Tullock rectangle.

00:13:29.820 And that idea is that, okay, you look at, you know, if you've looked at a supply and demand

00:13:34.600 curve, when there's regulations that interfere with the supply and demand curve, it can increase

00:13:38.200 the profit of, of an industry that has being interfered with because it stops.

00:13:43.740 And essentially like the number of new, new customers or like the, the amount of increased

00:13:48.120 profit from these regulations outweighs the amount of, the amount that you lose from missing

00:13:52.900 out on some traits.

00:13:54.560 The way that I really want to see this expanded is with firm dilution and not only with firm

00:13:59.860 dilution, but with dilution of the entire industry.

00:14:04.460 So, so, so what do I mean by this?

00:14:05.800 In an industry like machine learning, you have basically, you, you have a precedent that people

00:14:11.060 are not sure is optimal.

00:14:12.860 So you have, you know, right now we have transformers pretty much.

00:14:16.060 We have this paper from 2017.

00:14:17.880 There have been minor modifications to it.

00:14:19.820 The paper is called attention is all you need.

00:14:21.740 And this basically outlined the way in which all language models and many similar models

00:14:28.280 operate.

00:14:29.700 And we don't really like, but it's the best we got so far, but it's not like, it's not

00:14:36.940 like a mathematical proof.

00:14:38.300 It's not like a certain thing.

00:14:39.740 You know, we have no idea if this is the best way to do machine learning.

00:14:44.580 And in fact, many of the people who are more hyped about AI think that, you know, we're

00:14:47.680 just about to get another breakthrough in how we do, you know, how we do machine learning.

00:14:53.000 And so if you're open AI, you know, and you've now secured billions of dollars in investments

00:14:59.960 in, in fundamentally these transformer models and, you know, the kind of hardware stacks that

00:15:04.740 are specific to replicating them.

00:15:06.980 And you get something that's out of left field, or you get something that's limited

00:15:10.820 by a different factor.

00:15:11.840 That's not limited by the advantages that open AI has over its, its competitors.

00:15:16.480 You know, maybe foom is not the sound of AI accelerating, but foom is the sound of open

00:15:21.040 AI stock going to zero.

00:15:22.920 To this topic, just to shout out for listeners, cause I have a little request from a friend

00:15:28.600 here.

00:15:29.000 I know of this company that's found a way to create like much better using your own

00:15:34.220 models, like, like much tighter chip sets.

00:15:37.180 Anybody who's interested in investing in like a large fab, like, like, look, I'm talking,

00:15:44.020 you know, many millions of dollars, but it could make AI much less expensive to run.

00:15:48.820 I've got a company that's interested in doing that right now.

00:15:51.380 They've already developed all this stuff.

00:15:52.860 They're just looking for whoever they work with on the space.

00:15:54.860 Oh, this is fascinating.

00:15:56.460 So this is like not an open, open AI competitor, but like a TSMC competitor or something like

00:16:00.680 that.

00:16:00.820 Yeah.

00:16:02.100 Okay.

00:16:02.700 Interesting.

00:16:03.220 Interesting.

00:16:04.340 Another thing I wanted to dive into is before we started recording, you had alluded to this

00:16:08.360 not being really an EA style think tank.

00:16:12.760 And I thought that was interesting because you are trying to be effective in your altruism

00:16:18.340 through doing this, right?

00:16:19.980 Like I'm so, so tell me more.

00:16:22.020 Why do you think this is not an EA thing?

00:16:24.860 So like on the, on the center for effective altruism homepage, they have like this essay

00:16:29.540 about like, who is, who is an effective altruist.

00:16:33.020 And they basically say like everyone who wants the world to be better and who likes evidence.

00:16:38.540 And I'm like, okay, you know, I guess it's an EA think tank.

00:16:42.240 Sure.

00:16:42.540 Give us money.

00:16:43.880 Of course, you know, most EAs, you know, most EAs want more, more regulation in the, even

00:16:49.800 in the short term, or actually I'm not sure about like the, the people who are, you

00:16:53.720 know, most funded by EAs.

00:16:54.980 That's what they want.

00:16:56.000 I should say as well, this is an article that might be out by then, but there's also a very

00:17:01.340 strong EA case.

00:17:02.880 Even if you think that there, I actually, I say this as well.

00:17:06.520 Like the more you think that AI is happening soon, the more it's the flight 93 election.

00:17:16.500 The more you should be trying to make sure that there is no government control of AI because

00:17:23.040 there is one institution in all of human history that is guaranteed to be misaligned.

00:17:28.860 And that is the most powerful government in the world.

00:17:32.180 Why is that institution always guaranteed to be misaligned?

00:17:35.380 The answer is political economy.

00:17:37.380 It goes back to Tulloch.

00:17:38.440 It goes back to many of the factors we were talking about, but people have the idea.

00:17:42.280 Oh, you know, in markets, there's externalities and in markets, people will compete.

00:17:46.300 And the thing that makes them succeed will not always be the thing that's best for the

00:17:49.120 general population.

00:17:50.820 And, you know, when it comes to elections, you know, the thing that makes people succeed

00:17:54.120 in elections, the thing that makes them get the most votes is always going to be the

00:17:57.700 thing that is, you know, most rational and sane.

00:18:00.200 And of course, it's not true.

00:18:01.540 And you go further, you go further down the level, right?

00:18:04.840 So there are these like nesting, there are these layers of the onion as you go down like

00:18:09.760 the policymaking stack and people don't transfer their lessons.

00:18:13.720 So I think many EAs, maybe they've read like Brian Kaplan, they've read, they might even

00:18:18.340 have read Garrett Jones.

00:18:19.400 And they understand the flaws of the voter.

00:18:23.120 They understand the flaws of, you know, when you go and cast your ballot for Donald Trump

00:18:27.620 and Joe Biden, that that's not necessarily the best thing that, you know, they're not

00:18:32.860 necessarily going to be doing the best thing for the world.

00:18:35.540 But you go one level down the stack to the legislative level or to the administrative level,

00:18:41.200 and they do not see the exact same incentives in play.

00:18:44.580 Where when you pass a bill, for example, when you pass like, like, the authorization of the

00:18:49.820 use of military force, right?

00:18:51.760 Or when you pass like when you pass like the budget, the budget reconciliation bill, what

00:18:57.320 factors go into play in terms of getting your policy priority into that bill?

00:19:02.340 And what in what ways will be corrupted by the process by the requirements and the incentives

00:19:07.860 for it to be there in the first place?

00:19:09.760 And, you know, the short answer, this is something very funny that I posted about recently and

00:19:16.760 got a lot of support on Twitter for, is that like, there are some ideas that are so correct

00:19:21.060 that even the most strawman version of them is true.

00:19:24.380 So like the extreme strawman version of my opinion is like, or of this, like it really

00:19:30.140 like not my opinion originally, but like the field of political economy, the extreme strawman

00:19:35.440 of it is that like, whatever idea you want to get into law will be unrecognizably corrupted

00:19:40.760 once it is in law.

00:19:42.480 And even that like extreme strawman is like, pretty much true. 0.78

00:19:48.620 You just look at like,

00:19:50.020 I would call it extreme strawman.

00:19:51.660 You're like, this is what my opponent's like, but it's true. 0.98

00:19:53.920 Even the most insane of them. 0.56

00:19:56.160 Yeah.

00:19:56.460 Yeah.

00:19:56.840 That's, that's the best thing about it.

00:19:58.700 Right.

00:19:58.920 Like, like even, you know, even, you know, the most fervent supporters of law, you know,

00:20:04.160 you, you ask them, you know, you ask them like, what happens?

00:20:07.640 What do you think this law will look like after it's passed?

00:20:10.120 I'll say like, I, I don't know.

00:20:11.660 I just hope it's, this is also like a take that, that I want to, I want to like specifically

00:20:17.060 address.

00:20:17.680 If we're talking about addressing EA takes is that I've had multiple people that I really

00:20:22.680 respect say like, oh, we, we want the government to interfere because we'll just slow down

00:20:28.560 progress.

00:20:29.240 We just want it to like cause harm.

00:20:31.660 And as long as it's like, as long as it's like causing harm, it will reduce the probability

00:20:36.120 or the speed that we get AGI.

00:20:38.820 And this is also something that I don't necessarily think is true.

00:20:43.280 And the best counter example in recent memory is gain of function research.

00:20:47.600 Right.

00:20:48.060 So we have this phrase called like a narco tyranny.

00:20:50.680 Usually people talk about in the context of SF, which is like the bad actors are not punished

00:20:54.940 because they're outside of the system.

00:20:56.260 The only people who are punished are like the good actors in the system.

00:20:59.540 Right.

00:20:59.700 So, so you're, you're, you're punished for like protecting your convenience store, but

00:21:03.220 like the, the, the people who rob your convenience store are rewarded by the state.

00:21:07.520 And you see the exact same thing in regulatory capture.

00:21:10.460 You see the FDA, you know, very, very, you guys, you guys actually have had experience with

00:21:16.840 this, right.

00:21:17.320 With going after novel technologies that have some promise, but they will also, you know, this

00:21:22.780 is not the FDA, but the U S government will also fund gain of function research in Wuhan.

00:21:28.060 So it's, the question is not, you know, this like one singular lever of what it does to

00:21:33.460 the industry.

00:21:34.100 It is much more of the question of like how it influences the distribution of, of players.

00:21:42.120 And, you know, the most likely thing that will happen with the distribution of players

00:21:45.160 is that, Oh, actually it's just open AI, Google, Facebook, so on, or, or not really a so on.

00:21:53.140 It is really much just in this case, you know, in, in some regulatory capture cases, they're

00:21:58.960 morbid in this case, you know, it is, it is really just, you know, like the major named

00:22:03.200 players.

00:22:03.580 So let's say that you are successful with this beyond your wildest dreams, how will

00:22:09.640 the world be different?

00:22:10.660 Is it a scenario in which rather than there being like three players who are setting the

00:22:15.080 tone for AI, it's a little bit more distributed, like a lot of people are contributing, it's

00:22:19.180 more open source or, you know, what, what, what kind of environment or ecosystem are you

00:22:24.080 trying to create?

00:22:25.860 So many people, this is a very fascinating critique of political economy, because if you look at

00:22:31.560 these incentives, they, they rarely ever change.

00:22:33.680 So, so there are cases of just being like a doomer of people saying like, not, not an

00:22:38.020 AI doomer, but people like saying, Oh, like the regulatory crackdown is inevitable, but you

00:22:43.300 just look at the history of the United States and that's just not true.

00:22:48.060 The best example is you guys remember SOPA, like a stop or like something like online.

00:22:54.560 Yeah.

00:22:54.640 They were trying to stop the internet nonsense.

00:22:56.460 Oh my God, yes.

00:22:57.540 Yes.

00:22:57.840 Wow.

00:22:58.180 So like this, this was a case, I think it was the, it was, I forget, I forget which

00:23:05.220 agency there was, some, some agency was trying to pass, I forget the exact name of it, but

00:23:09.920 it may have been, okay, whatever.

00:23:12.960 But, but basically they were trying to do this restriction of content on the internet,

00:23:16.300 basically saying that, you know, like if any, if any like random commenter said, or like

00:23:21.560 linked to porn or, or something like that, then it would be, you know, then the entire

00:23:26.120 website would be subject to legal crackdowns.

00:23:29.220 Right.

00:23:29.660 And people, people just wrote in, people got, got media attention.

00:23:34.220 It became like, it became like this, this huge, like unification of the internet was,

00:23:39.060 was one way that it was described.

00:23:42.400 We are basically unanimously.

00:23:45.720 Everyone was like, this is a terrible idea.

00:23:47.040 Like, this is just, you know, this is just disastrous.

00:23:50.100 And we stopped it.

00:23:51.480 You know, we had, we had a W and there are instances where there are calls for regulating

00:23:59.980 an emerging industry.

00:24:02.140 And for one reason or another, it just doesn't happen.

00:24:06.420 You can look at the internet, for example, you know, this is in the broader history of

00:24:10.580 the internet.

00:24:11.500 Mark Andreessen actually gave me this example, right?

00:24:13.400 So Mark Andreessen, you know, like he, he talked about, he talked about this on my podcast.

00:24:18.040 He's been on several others, but it should be, it should be out by the time, you know,

00:24:21.440 this, this releases.

00:24:23.180 And he talked about, you know, like nowadays, like, what if you try to call, what if you try

00:24:27.640 to ban the internet now?

00:24:28.980 We've created a successful political constituency.

00:24:32.360 If your industry is big enough, which I think machine learning will be, you know, I think

00:24:36.540 even though we won't get, you know, artificial general intelligence, we will get, you know,

00:24:39.900 many commercial applications.

00:24:41.160 We have many commercial applications now, right?

00:24:43.620 And once all of those are adopted, once all of those are, you know, regular parts of people's

00:24:47.500 lives, if it's big enough, like the internet is, and like, I think machine learning will

00:24:51.640 be, then you've created a political constituency.

00:24:54.560 You know, all you have to do is wait for the existing adoption curve to happen.

00:24:58.740 So you're, you're, you're saying that you're trying to broker that transition to like get

00:25:03.680 to a place where adoption is wide enough, where like the market will handle the protection

00:25:08.360 because they are dependent on it and huge fans of machine learning.

00:25:11.640 Right. It's not even the market.

00:25:12.360 It's just, you know, like if half your country is using chat GPT or using some form of LLM,

00:25:17.280 you know, you're not banning it.

00:25:19.220 I'm sorry.

00:25:19.700 You know?

00:25:19.960 Yeah. Like no one will get reelected if they support any legislation that does that. 0.94

00:25:23.340 So basically before that saturation has been reached, you were trying to ensure that we

00:25:28.860 don't preemptively make that impossible.

00:25:31.260 Right.

00:25:31.320 Yeah. Now, now is the most important reason for exact, or now is the most important moment

00:25:35.540 for exactly that reason is because it's, it's the only, you know, it's the period of time

00:25:40.620 in which it is most politically vulnerable, but most economically has the most economic

00:25:45.760 potential.

00:25:47.560 Yeah. That's meaningful.

00:25:50.460 Are you concerned about all the people?

00:25:52.860 Because it sounds like you're not really fighting against EA-ers who are making weird

00:25:56.300 arguments against AI.

00:25:57.360 You're more fighting against legislators who are like, well, but, you know, they might

00:26:02.240 have much more, what you might say, like normie arguments against it.

00:26:05.200 Right. So they're going to say, well, what about the fact that AI is going to take jobs

00:26:08.820 away? You know, what are you going to say to them?

00:26:11.160 I want to take an interlude between this.

00:26:14.160 Most of the people who are actually proposing like these, like legislative crackdowns are very

00:26:20.140 anti-EA.

00:26:21.700 You know, they are anti-EA.

00:26:23.640 Like, like one of these people, like Timnit Gebru, who is like this race grifter who is 0.64

00:26:28.100 now like focused on machine learning is they like, or like she absolutely like despises

00:26:34.340 EA for like, honestly, like pretty pathological reasons.

00:26:39.740 Like it's not helpful for her for in any way to dislike EA, you know, like they could easily

00:26:45.700 be, you know, like legislative allies, but, but she, like, you know, but, but she just absolutely

00:26:51.200 despises them, you know, loves calling them racist because they believe in IQ and they

00:26:57.040 believe in like.

00:26:57.820 Oh, okay.

00:26:58.420 So that general approach.

00:26:59.900 Yeah.

00:27:00.360 Yeah.

00:27:00.640 Like a lot of the most despicable people, like also hate EA and like, like this is, you know, 0.98

00:27:08.980 it's no longer just, this is no longer a battle of like internet shit posters. 0.95

00:27:13.380 You know, this is a battle of real political interests and the forces that drive the democratic 0.97

00:27:20.520 and republican parties.

00:27:22.600 What's your rebuttal to their arguments though?

00:27:24.480 Because those aren't EA arguments.

00:27:26.000 So what is your rebuttal to, this is going to take away jobs.

00:27:29.540 It's, it's dangerous and we don't need it.

00:27:31.420 So why should we support it?

00:27:33.240 Et cetera.

00:27:33.820 The jobs point is, is particularly interesting because it's, it's kind of like framed as a

00:27:39.120 republican concern and it's, it's very, it's very funny.

00:27:44.540 It actually relates to the other discussion that, that we've had or the discussion in the

00:27:50.600 future that I, that I have some premonitions about.

00:27:54.800 Yeah. 0.99

00:27:55.660 In many cases, AI, the things that AI are replacing are kind of bullshit jobs. 0.75

00:28:02.040 There are things that people already dislike, you know, people do not, there's this wonderful 0.63

00:28:07.040 tweet by Sam Altman that says, you know, today I've had one person tell me that they've used

00:28:14.580 ChatGPT to expand their bullet points into a long corporate email.

00:28:19.520 And another person tell me that they've used ChatGPT to condense a long corporate email

00:28:24.440 into five bullet points.

00:28:26.300 It's the future of communication. 0.98

00:28:28.340 I also love that it's done such a good job of getting rid of these useless grifters, like 1.00

00:28:33.840 artists and writers, because honestly, they had these pointless degrees. 0.96

00:28:41.240 They were a huge, I think, cause of our society's degradation.

00:28:46.440 And I'm so glad that AI has replaced them.

00:28:49.800 There's okay.

00:28:50.740 Like there's a real thing underlying that is that like mid is over.

00:28:55.380 Right.

00:28:55.600 And here, like actually mid, I don't mean, you know.

00:28:57.880 Yeah, yeah, yeah, yeah.

00:28:57.900 It is over.

00:28:59.060 I like that way of putting it.

00:29:02.280 It's very funny because people portray like the most famous, people portray like Drake

00:29:12.020 being replaced by AI.

00:29:13.760 It's like, no, he has, he still has interesting things going on.

00:29:18.500 Or like Taylor Swift.

00:29:19.460 Look at Taylor Swift's like ticket sales, you know, like she's not worried about this.

00:29:22.620 The thing, like the top, like the people who are actually contributing to, you know, the

00:29:31.220 culture that we consume every single day, they're, they're just going to be fine.

00:29:36.860 And in fact, they're going, like the next generation, Sam Woods was on my podcast.

00:29:40.940 He had this wonderful line, which is your job's not going to be replaced by ChatGPT.

00:29:45.760 It's going to be replaced by someone using ChatGPT.

00:29:47.760 I think that, you know, in the future, like the meta, the meta of art will be very much.

00:29:54.120 And I think like the top artists today will be able to adapt.

00:29:58.160 They have that kind of like entrepreneurial focus.

00:30:00.420 Taylor Swift is once again, a great example.

00:30:02.840 Like, I think she'll really enjoy, you know, playing and her team will really enjoy using

00:30:07.540 the new tools to discover like the frontier of music.

00:30:10.800 But I think what you're discounting here.

00:30:12.220 That people never want to work.

00:30:13.600 She represents 1% of people, like the vast, vast majority of people have not done anything.

00:30:19.580 Grimes has adapted.

00:30:21.280 Yeah, exactly.

00:30:22.040 Exactly.

00:30:22.520 I think that you are overestimating the competence and the aggressiveness of this top, you know,

00:30:30.500 1% of society.

00:30:31.960 And that for a long time, they haven't been pushed out.

00:30:34.980 Like Quentin Tarantino, like digital cameras come along.

00:30:37.580 He's like, I'm not going to touch them.

00:30:38.740 Even still, he doesn't do them.

00:30:39.660 A lot of the, throughout most of our history, you've been able to get away with that kind 0.65

00:30:43.180 of BS, that kind of arrogance.

00:30:45.640 But I don't think you're going to be able to now.

00:30:48.740 Yeah, there is, you know, there is the innovator's dilemma.

00:30:52.060 I think you're right, actually.

00:30:53.440 I'll say more like, I think that non-zero of the current top artists will adapt.

00:30:59.040 But in terms of like one specific one, yeah, like, you know, I'm not 100% sure that Taylor

00:31:04.740 Swift will be, you know, will be the one who is like, yeah, who is taking up all of these

00:31:09.180 new tools.

00:31:10.220 But I think that, I think that it will be, you know, it will be a hybrid.

00:31:13.260 That I'm very confident about.

00:31:14.920 That's, you know, the mainstream of art, the mainstream of culture, the mainstream of

00:31:19.420 film.

00:31:20.420 Those will all, you know, it won't be, it won't be completely AI generated.

00:31:23.700 It won't be completely human generated, created from scratch.

00:31:27.820 It will be some combination of the two.

00:31:30.280 Just like, you know, we had digital cameras, people, you know, people are adapting to digital

00:31:33.900 cameras.

00:31:34.480 We had the internet, we had social media, we now have a mix, right?

00:31:38.020 You can think of Netflix as a mix between the original film model and YouTube, right?

00:31:43.560 Garrett Jones has this amazing term like spaghetti and spaghetti assimilation, which is he uses

00:31:49.700 this in the context of immigrants.

00:31:50.940 So like Italians come to America and like, or like they come to New York and they make 1.00

00:31:55.220 New York, you know, they become more like New Yorkers, but New York's culture also, you

00:32:00.480 know, they start eating pizza and they start eating spaghetti.

00:32:03.400 It becomes more Italian.

00:32:05.140 And I think the same is true of AI, you know, our current culture will become more like AI.

00:32:10.740 It will, there will be, you know, I think like Sam would put it best, you know, the replacement

00:32:16.220 of jobs is not really going to be, you know, like vertical.

00:32:19.540 It's not really going to be like people being completely replaced by AI.

00:32:23.100 It's going to be a new skillset.

00:32:25.440 People are learning to do something better.

00:32:28.180 And, you know, the people who will do that better are going to be the people who use AI.

00:32:33.700 I think though, that what's understated is that a huge portion of knowledge workers, and

00:32:40.700 in that I include people like sales, marketing, writers, artists, designers, salespeople,

00:32:47.780 like website people have been doing work that isn't actually used.

00:32:52.380 Like, I think we've had this period of inertia where people are still hiring and paying and

00:32:57.040 thinking that they need these people even before AI.

00:32:59.280 And like actually not using the vast majority of work they do, and that there are busloads

00:33:04.720 of graduating classes that believe that their job is to sit behind a computer and write strategy

00:33:09.760 documents and analyze things, but not actually build or create anything.

00:33:13.620 And that these people are going to get laid off.

00:33:17.400 They're getting laid off in droves.

00:33:18.700 They're not going to get rehired.

00:33:19.960 They're going to have to figure out their own way.

00:33:21.400 Do you think that those groups are capable of building new lives for themselves when

00:33:26.780 they've been conditioned to do something that is completely different?

00:33:33.560 Like, so this is an interesting question.

00:33:38.100 This is an interesting, like, economics question.

00:33:41.620 But I do want to, like, mess with the framing a little bit.

00:33:45.820 Go ahead.

00:33:46.200 I think, like, this is not that related to AI.

00:33:49.200 This is, like, related to low interest rates more than it is related to AI.

00:33:54.320 Like, it is kind of like, it is like these things happening in the same time.

00:33:59.060 But, like, you know, you could easily see a world where, you know, open AI just develops

00:34:04.700 or, like, all open AI and its competitors just develop and release everything, like, a few

00:34:09.900 years earlier.

00:34:10.780 And we have AI being released to broader society in, like, a, you know, user-friendly way.

00:34:17.960 At the same time as, like, the crypto hype bubble.

00:34:22.520 What would the vibes be around AI then?

00:34:25.480 You know?

00:34:25.960 If it's, like, if it's, like, the tail end of the lockdown, crypto stocks are going crazy,

00:34:31.700 you know?

00:34:32.160 And then open AI publishes, like, current level chat GPT.

00:34:36.840 What are the vibes then?

00:34:39.080 And I think it will be a vibe of, like, just much more optimism.

00:34:42.080 It won't be a vibe of, like, complaining.

00:34:43.860 It won't be a vibe of, like, you know.

00:34:45.600 And, of course, this is not really an argument for my position.

00:34:49.160 But it's an argument against, you know, I think it's an argument against much of the

00:34:53.060 contemporary.

00:34:54.140 This is, like, not the EAs, right?

00:34:55.460 I think the EAs would still be worried.

00:34:57.260 But for, like, the people worried about jobs, for the people who are worried about, you know,

00:35:01.660 like, automation, for the people who are worried about, you know, basically collapse, I think

00:35:06.180 that that's much more kind of absorbing the more general economic environment than it is an

00:35:10.660 actual concern about AI.

00:35:11.920 Well, what I like about your view is that it's actually quite optimistic, which is super

00:35:17.600 not Gen Z.

00:35:18.460 Like, I really like that.

00:35:19.320 It's eminently reasonable.

00:35:20.580 It's like, actually, this problem will solve itself.

00:35:23.140 We're going to have the critical mass of machine learning users, essentially, that are

00:35:26.460 going to make sure that it doesn't get, you know, walled off and made very difficult to

00:35:30.580 open source and, you know, collaboratively develop.

00:35:33.660 I'm just going to help to bridge the gap with this.

00:35:35.840 And I think that makes me super intrigued to see how it goes for the Alliance for the Future.

00:35:39.660 I'm really glad that you are in its foundational team and doing this work.

00:35:44.760 And I'm keen to see how it goes.

00:35:45.800 So to our listeners, if you're interested in this, check out the Alliance for the Future.

00:35:49.920 Check out Brian's podcast as well.

00:35:52.660 Yeah, affuture.org is how to find us.

00:35:54.660 We would really, really appreciate donations at this early stage.

00:35:59.560 And yeah, you can check out all of my writing.

00:36:01.640 You can check out my writing on AI specifically at pluralism.ai.

00:36:05.300 And you can check out all of it, including the podcast at fromthenew.world.

00:36:10.840 Thank you so much, Brian.

00:36:12.380 This is, it's always really fun to talk with you.

00:36:15.100 Awesome.

00:36:15.580 This was very fun.

00:36:16.940 It was not, you know, it wasn't four hours, but it was, you know.

00:36:20.560 See, we don't have attention spans for that.

00:36:22.560 We're like fast, but you know.

00:36:24.400 Yeah, yeah, yeah.

00:36:25.080 You guys were on the podcast for four hours.

00:36:29.320 So for people who want to see our podcast with him, we talked to him for like four hours each.

00:36:34.020 And I was hammered when I talked to him.

00:36:37.060 Completely hammered.

00:36:37.780 He got me at like 9 a.m. at night, going on until like 1 a.m. in the morning.

00:36:43.780 It was enjoyable.

00:36:45.880 It was more like, I think it was like 3 until 8 p.m.

00:36:48.780 But for Malcolm, whose day starts at 2 a.m., that is like extremely late.

00:36:54.000 No, no, no.

00:36:54.440 It was late.

00:36:55.580 No, I went past midnight, I think, was recording.

00:37:00.440 It went pretty late.

00:37:01.900 I don't think.

00:37:02.880 Yeah, I'm not sure if it was midnight.

00:37:05.080 In my brain, it was past midnight.

00:37:07.980 We'll see.

00:37:08.420 We'll see.

00:37:08.900 Okay, okay.

00:37:09.680 But our viewers, check it out.

00:37:11.580 He has a great show.

00:37:13.340 He really knows how to pull things out of people.

00:37:15.520 Yeah.

00:37:16.660 Yeah.

00:37:17.060 You listed amazing questions.

00:37:18.080 You're an amazing interviewer.

00:37:19.320 So yeah, check out From the New World, but also The Alliance for the Future.

00:37:22.780 Thanks again, Brian.

Based Camp - August 17, 2023

＂Mid is Over＂ How Do We Protect AI From Those it Will Replace？ (With Brian Chau)

Episode Stats

Summary

Transcript