Based Camp - Why God King Sam Altman is Unlikely： Who Will Capture the Value of the AI Revolution？

00:00:00.000 Hello, Simone. I am excited to be here with you today. Today, we are going to be focusing on a

00:00:06.360 question, which is as AI changes the way the economy works, who is going to be the primary

00:00:13.000 beneficiary of this? Is it going to be the large companies that make and own the AIs? Or is it

00:00:19.460 going to be the people using the individual AI models? We all know, for example, in probably

00:00:27.180 10 years from now, there will be an AI that can, let's say, replace most lawyers, let's say the

00:00:33.300 bottom 50% of lawyers. Well, and already studies have shown AI therapists perform better on many

00:00:38.740 measures. It's already exceeding our capacity in so many places. Yeah, they introduced it to a Texas

00:00:44.400 school system and it shot to the top 1% of student outcomes. As we see this, where is the economic

00:00:52.340 explosion from this going to be concentrated? Because this is really important in determining

00:00:57.800 what types of jobs you should be looking at these days, how you should be training yourself,

00:01:02.180 how you should be raising your kids, where you should be investing. The second question we're

00:01:06.900 going to look at, because it directly follows from the first question, is does the future of AI,

00:01:13.780 when we're looking at the big world changing advancements that are going to come from it,

00:01:17.900 are they going to appear on the token layer or at the latent layer? Can you define those

00:01:23.540 differences? Yes. By this, what I mean is when we look at continued AI advancement, is it going to

00:01:30.500 happen in the layer of the base model, i.e. the thing that OpenAI is releasing and Claude is releasing

00:01:36.460 and everything like that? Or is it going to be in the token layer, the people who are making wrappers

00:01:41.160 for the AI? For example, the Collins Institute is fundamentally a wrapper on pre-existing AIs.

00:01:46.800 Our AI game company is a series of wrappers on AI. And if it turns out that the future of AI is in the

00:01:54.600 token layer, it leans potentially more to, if not the big companies that are going to capture the value

00:01:59.800 from this. And then the next question we're going to look at is the question of what gets us to AI

00:02:07.600 superintelligence? And I might even start with this one, because if we look at recent reports with AI,

00:02:14.900 a big sort of thing that we've been finding is that, especially with like OpenAI's 4.5 model,

00:02:22.280 is that it's not as advanced as people thought it would be. It didn't get the same huge jump in

00:02:27.380 capacity that people thought it would get. And the reason is that pre-training, i.e. the ways that you

00:02:35.120 sort of train AI on the pre-existing data before you do like the narrow or like focus training

00:02:40.900 after you've created the base model doesn't appear to have as big an effect as it used to have.

00:02:46.420 So it was working on, I think, 10x the information of model four, and yet it didn't appear dramatically

00:02:53.360 better. And so one of the questions is, so that's one area where pre-training doesn't seem to be having

00:02:58.040 the same effect. And I think we can intuit why. But the second big issue is that the amount of

00:03:04.960 information that we actually have, like, you know, peak oil theory, there's like a peak pre-AI

00:03:10.060 information theory problem, which is it just eventually, when you're dealing with these

00:03:15.360 massive, massive data sets, runs out of new information to train on. So first, and I love

00:03:21.620 your intuition before I color it. Do you think if you look at the future of LLM's base models,

00:03:29.700 so we're not talking about LLM's entirely, we're not talking about anything like that. Do you think

00:03:33.400 the base models will continue to improve dramatically? I think they will. And at least

00:03:37.660 based on people more experienced than this than I am, they will, but in lumpy ways. Like, they'll

00:03:42.980 get really, really, really good at programming. And they'll get really, really good at different

00:03:47.100 esoteric forms of, like, developing their own synthetic data and using that to sharpen themselves.

00:03:50.980 But there are going to be severe diminishing marginal returns when it comes to some things that

00:03:56.840 are already pretty advanced. And of course, I think the big difference and the thing we haven't

00:04:00.900 really experienced yet is independent agents. Like, right now, AI isn't very effectively going

00:04:06.340 out and doing stuff for us. And when that starts to happen, it's going to be huge.

00:04:12.300 I agree with that. But I think so. What I'm going to be arguing in this is that most of the

00:04:18.880 advancements that we will probably see in AI going forwards are going to happen, like the really big

00:04:24.720 breakthroughs at the token layer. Okay. Not at the base layer. Which a lot of people would strongly,

00:04:30.880 those are fighting words. These are fighting words in AI. Yeah. It is rappers that are going to fix our

00:04:37.140 major problems. Wow. So I'll use the case of an AI lawyer to give you an explanation of how this works,

00:04:45.440 right? All right. So I want to make a better AI lawyer right now. If you look at the AI systems

00:04:51.900 right now, there's a guy, a programming guy who was talking to me recently and he was arguing because

00:04:57.720 he was working in the education space. And he's like, he didn't like our solution because it's a

00:05:01.220 token layer solution. And he wants to build a better latent layer solution, you know, using better

00:05:05.940 training data, using better post-treating data because it's more efficient programming wise.

00:05:12.100 And I'm like, yeah. For the time being, yeah. For the time being. I feel like it creates path

00:05:16.220 dependency. Am I missing something here? Well, okay. Just from a business perspective,

00:05:20.460 it's pretty stupid because as open AI's models increase, like if we expect them to continue to 0.92

00:05:26.080 increase in quality or as Claude's models increase or as Grok's models increase. Which they're going 0.93

00:05:30.220 to. Yeah. You can't apply the post-training uniquenesses of the models that you create to

00:05:37.300 these new systems. So anything you build is going to be irrelevant in a few generations of AI.

00:05:43.580 But you want to be able to switch it out. Like no matter what, you want to switch it out really

00:05:47.140 quickly. If one AI gets better, you should be able to plug it into whatever your framework is,

00:05:51.060 your scaffolding, right? You want to build scaffolding for your changeable parts.

00:05:54.260 Exactly. Exactly. But that's actually not the core problem. That's not the core reason why,

00:05:59.560 because the other project he's working on is an AI lawyer and he's trying to fix this problem at the

00:06:04.560 latent layer. And that won't work. And I will explain why it won't work. And you will be like,

00:06:10.400 oh yeah, that makes perfect sense now that I think about it. Okay. So if you think about right now,

00:06:15.680 like what is dangerous about using an AI lawyer? Like where do AI lawyers fail? Is it in their

00:06:23.620 ability to find the laws? No. Is it in their ability to output competent content? No. Where they fail right

00:06:33.860 now is that they sometimes hallucinate and make mistakes in a way that can be devastating to an

00:06:40.640 individual's legal case. So if you go to a system, you know, like Grok or perplexity or something like

00:06:46.780 that, and you, you built one focused on like searching law databases, right? It's going to be

00:06:52.640 able to do a fairly good job at that. I'd say better than easily 50% of lawyers, but it's going to make

00:06:59.140 mistakes. And if you just accept it blindly, it's going to cause problems. So if you want the AI to

00:07:05.180 not make those kinds of mistakes, right? How do you prevent it from making those kinds of mistakes

00:07:10.780 that is done at the token layer. So here's an example of how you could build a better lawyer AI.

00:07:16.620 You have the first AI do the lawyering, like go through, put together like the, the relevant laws

00:07:25.300 and, and, and history and, and past calls to previous things and everything like that.

00:07:30.320 So it puts together the brief. You can train models to do this right now. Like that's not

00:07:34.420 particularly hard. I could probably do this with base models right now. Right. You know,

00:07:38.600 then I use multiple differently trained latent layers. So these can be layers that I've trained,

00:07:45.260 or I could have like Claude and open AI and like a few other in Grok. I can even just use like

00:07:51.660 preexisting models for this. And what I do is using the token layer, I have them then go in

00:07:58.420 and review what the first AI created, look for any mistakes with anything historic, like,

00:08:05.880 like they can find online. So he's describing a good lawyer and you're describing a good law firm

00:08:10.760 that has a team to make sure all the stuff that the good lawyer is doing is correct. Right. And also

00:08:16.260 a law firm that can like hire new good lawyers when they come out. Yes. And then what this system

00:08:20.800 would do is after it's gone through with all of these other systems that are reviewing,

00:08:25.360 Oh, did they make any mistakes at this layer? It outputs that. And then based on the mistakes

00:08:31.300 that it finds, it re outputs the original layer and it just keeps doing this in a cycle until it

00:08:37.360 outputs an iteration that has no mistakes in it. That is a good AI lawyer. That is accomplished

00:08:45.280 entirely at the token layer. Okay. Oh yeah. You were right. And that makes sense.

00:08:52.040 Which removes the existing company's power to, to, to do a lot of things. If it's people outside of

00:08:58.520 these companies. You're saying that they're becoming more, more akin to undifferentiated

00:09:03.780 like energy or hosting providers where people will not be as brand lawyer loyal. They're going to focus

00:09:11.420 more on performance and the switching costs that people experience are going to be relatively low.

00:09:17.500 So long as they're focused and oriented around things on a token level basis and not.

00:09:24.220 Yes. And it allows people who are operating at the token level basis to capture most of the value.

00:09:29.820 Because then move more quickly. Right. Because again, they don't have that path dependency that makes

00:09:33.260 everything go slowly. It's not only that, but they can swap out models. So what, like what,

00:09:38.700 if I have the AI lawyer company and people are coming to me because I have found a good

00:09:43.500 interconnected system of AIs that produces briefs or cases or arguments that don't have a risk of

00:09:50.220 errors in them. Right. So people come to me and, and I am capturing, let's say I've replaced all the

00:09:55.740 lawyers in America. Right. And, and so I now offer the services much cheaper. Let's say at 25%

00:10:01.180 the costs they did before, or, or 10% or 5% or 2%, you know, some small amount, I'm still capturing

00:10:06.460 like a ton of value there. Right. That's just a lot of money. So now the company that is,

00:10:11.820 I am paying for an AI, like, let's say I use open AI as one of the models I'm using.

00:10:15.900 They now come to me and say, Hey, I want to capture more of this value chain. So I'm going

00:10:21.180 to charge you more to use my model. Well, then I say, well, your model's good, but it's not

00:10:27.740 that much better than Grog. Yeah. It's not that much better than Anthropics. Yeah.

00:10:33.180 It's not, or free that much better than DeepSeek. It is that much better than DeepSeek,

00:10:38.140 but DeepSeek or Llama are the two, you know, things can change, things can change. But the

00:10:43.900 point I'm making is what things like Llama and DeepSeek do is they put like a floor on how much

00:10:48.780 companies can extract if they're at the level of training the AIs themselves, unless they have

00:10:54.620 separate departments that are working on making these more intelligent types of AIs.

00:11:00.780 Now that's really important for where the economy is going, because it means we might see less of a

00:11:08.940 concentration of wealth than we would expect. But the way that the concentration of wealth,

00:11:13.420 because we're going to see still a major concentration of wealth, actually we'll see

00:11:17.260 more concentration, but to individuals rather than big companies with basically what this means is

00:11:22.540 individuals are going to capture most of the value as the concentration happens,

00:11:26.780 rather than large companies like Google. Because I and a team of like five engineers

00:11:30.780 can build that lawyer AI I talked about, right? Whereas I, and me and this team of five engineers

00:11:38.140 are capturing all the value from that, right? From replacing the entire lawyer industry in say,

00:11:42.540 America. This is really bad for the tax system, because we've already talked about, okay,

00:11:47.340 you have the lower, the demographic crisis, which is putting a squeeze on the tax system.

00:11:50.940 And they're like, oh, they'll just tax more. I am now even more mobile with my new wealth

00:11:57.660 than the AI companies themselves were, because I don't need semiconductor farms or anything like

00:12:02.940 that to capture this value. Yeah.

00:12:05.420 The semiconductor farms are creating an undifferentiated product.

00:12:11.020 Yeah. A product that's still in high demand, it will make a lot of money, but it will become more

00:12:14.940 about efficiency, you think then? Yeah. Now, another thing I'd note is my prediction

00:12:21.820 in terms of where AIs are going with super intelligence. By the way, any thoughts before

00:12:25.740 we go further here? I am thinking more about efficiency now. I heard, for example, that Sal

00:12:30.940 Maltman was like saying things like, please and thank you is costing us millions of dollars.

00:12:36.380 Because just that additional amount of processing that those words cause

00:12:40.780 is expensive. So I really could see things, yeah, like these companies becoming over time,

00:12:47.340 after they have more market share, hyper focused on saving money instead. 0.97

00:12:52.700 Well, that's a dumb on him part. He should have the words please and thank you pre-coded to an

00:12:57.340 automatic response. They don't even, I'm one of these bad people that wants to be nice. They don't

00:13:06.060 acknowledge the courtesy anyway. So you don't even need to have a response. It should probably just

00:13:12.380 be ignored, but I guess it's kind of hard to, or I don't know. But anyway, he allegedly said that,

00:13:17.820 so that's interesting. Okay. So yeah, the point here being is if we look at how the human,

00:13:24.140 like, like LLMs and we think about, okay, why, like, where did they go and why isn't the training

00:13:28.700 leading to the same big jumps? It's because pre-training data helps LLMs create more competent

00:13:37.340 average answers. Okay. Yeah. Being more competent with your average answer doesn't get you creativity.

00:13:44.780 It doesn't get you to the next layer of like AI. No, and if anything, I think Scott Alexander has

00:13:51.020 argued compellingly that this could lead to actually more lying because sometimes giving

00:13:58.620 the most correct or accurate answer doesn't lead to the greatest happiness of those evaluating and

00:14:06.540 providing reinforcement. That's post-training. Okay. Oh, you're referring to, sorry,

00:14:11.020 just something different. Post-training still is leading to advantages. Those are the people who say,

00:14:14.860 I like this response better than this response. That could still lead to dishonesty though,

00:14:18.540 quite apparently. No, no, no. Pre-training is about getting the AI to give the most average

00:14:24.540 answer. Not, not exactly. Oh, just all the information available you're saying.

00:14:28.540 Yeah. Like you can put variance in the way it's outputting its answer and everything like that.

00:14:32.620 But, but that variance is added was like a meter, like the pre-training and the amount of pre-training data

00:14:39.340 doesn't increase the, the variance meters. It doesn't increase anything like that. It just gives a

00:14:44.700 better average answer. And the thing is, is the next layer of AI intelligence is not going to come

00:14:52.460 from better average answers. It's going to come from more creativity in the way it's outputting answers.

00:14:58.700 So how do you get creativity within AI systems? That is done through the, the, the variance or noise

00:15:05.900 that you ask in a response, but then the noise filtered back through other AI systems or other

00:15:14.940 similar sort of LLM systems. So the core difference between the human brain and AI,

00:15:19.740 and you can watch our video on stop anthropomorphizing humans, where we basically argue that,

00:15:24.220 you know, your brain actually functions strikingly similar to an AI, an LLM specifically. And I mean,

00:15:30.300 really similar, like the ways that LLMs learn things in the pre-training phrase is they put in data and

00:15:37.340 then they go through that data and they look for like tokens that they don't expect. And when they

00:15:42.700 encounter those tokens, they strengthen that particular pathway based on how unexpected that

00:15:47.900 was. That is exactly how your nervous system works. The, the, the, the, that, the, the way that

00:15:54.140 your like neurons work, they work very similar to that in terms of learning information is they look for

00:15:59.340 things they didn't expect. And when they see something they didn't expect, they build a

00:16:02.220 stronger connection along that pathway. And we can see this in that you go to that study.

00:16:05.740 If you want me to reference all the studies on this and everything, but the core difference between

00:16:09.180 the brain and AI is actually that the brain is highly sectionalized. So it will have one section

00:16:16.060 that focuses on one thing. One sections is focused on another thing, et cetera, et cetera, et cetera.

00:16:20.460 And some sections like your cerebellum are like potentially largely pre-coded and actually

00:16:26.060 even function kind of differently than the rest of the brain that's used for like

00:16:29.180 rote tasks like juggling and stuff like that. Okay.

00:16:31.260 I would note here that AI does appear to specialize different parts of its model for different

00:16:36.300 functions, but this is more like how one part of the brain was one specialization. Like say,

00:16:41.820 like the homunculi might code like all feet stimuli next to each other and all head stimuli next to

00:16:46.380 each other. It's not a true specialization like you have in the human brain where things actually

00:16:53.100 function quite differently within the different sections of the brain. Anyway, so you could say,

00:16:58.620 wait, what do you mean? Like, this is the core failing point of AI is that it doesn't work this

00:17:04.300 way. And it's like, this is why you can count the number of R's in a word or like you can do, 0.64

00:17:09.900 if you look at the ways that like there was some data recently on how AIs actually do math and they

00:17:15.100 do it in like a really confusing way where they're actually sort of like they, they use the LLM system.

00:17:21.340 Like they, they try to like predict answers and then they go back and they check their work to make sure it

00:17:26.620 makes sense was what they would, would guess it would work when they could just put it into a

00:17:31.340 calculator. Like your brain isn't dumb like that. Like it has parts of it that don't work exactly 1.00

00:17:37.500 like calculators, but they definitely don't work exactly like an LLM. Like they're, they can hold

00:17:41.340 a number like in your somatic loop, like, okay, I'm counting on my fingers or my hands or something

00:17:45.900 like that. Or, okay, I've put a number here and now I've added this number to this number. It's not

00:17:50.780 working on the LLM like system. It's working on some other subsystem. Most of the areas where AIs

00:17:56.540 have problems right now is because it's not just sending it to a calculator. It's not just sending

00:18:03.180 it to like a, what is the hallucination of an AI quote? Like, okay, the reason why I don't hallucinate

00:18:08.700 quotes is because I know that when I'm quoting something, what I'm not doing is pulling it from

00:18:13.420 memory. I'm looking at a page and I'm trying to copy it letter per letter. Whereas AI doesn't have the

00:18:19.340 ability to switch to this separate, like letter per letter subsystem. Now you could say, why don't

00:18:24.620 LLMs work that way? Why haven't they built them as clusters? And the answer is, is because up until

00:18:29.660 this stage, the advantages that we have been getting to our LLM models by increasing the amount of

00:18:34.540 pre-training data has been so astronomical that it wasn't worth it in terms of our investment to build

00:18:42.780 these sort of networks of models. Okay. Why is it just like too much computing power or just no

00:18:50.620 one's gotten around to it? No, no, no. People have like done it, but by the time you've done it,

00:18:54.940 you have better models out there, you know, like that don't need to work this way. Right? Like 0.96

00:18:59.980 if you spend, let's say a million dollars building a system like that, and you spend a million dollars

00:19:04.860 getting a larger pre-training set and, you know, spend more time in post-training,

00:19:09.020 the model's going to be like on average better if you did the second scenario. Okay. So I suspect

00:19:16.220 that what we're going to see is a move in AI. And, and I think that this is what's going to get us to

00:19:22.220 what will look like AGI to people from moving to a, just expanding the pre-training and post-training

00:19:28.380 data sets to better inter reflection within the AI system. That makes sense.

00:19:37.180 I could see it going that way. I I'm constantly surprised by how things go. So I couldn't say,

00:19:42.700 but I wouldn't be surprised. Hmm. Oh, I mean, make a counter argument

00:19:47.420 if you think I'm wrong here. This is a very bold claim. We are going to get AGI,

00:19:52.300 not by making better LLMs, but by networking said LLMs.

00:19:55.980 I, I struggle to see how, I mean, I think you can eventually get AA, sorry, AGI just like sort of from

00:20:06.380 kind of one AI working by itself. But when you think about the value of a hive mind and the fact

00:20:12.540 that you're going to have AI interacting well, before we get AGI anyway, I don't like it, you would

00:20:19.420 get AGI from the interaction before you would get it from any single agent or what would be seen as

00:20:25.500 a unified entity. But I think even if we did get it from a unified entity, it would beneath the surface

00:20:30.300 be working as many different components together. Just like the brain is all these different components

00:20:35.100 working together. So I'm not really like the definitions may be failing me. Okay. So let's,

00:20:40.620 let's think of it like this right now. I mean, and this is actually what like capitalism does for human

00:20:46.300 brains. It basically networks them together and then it's a, it rewards the ones that appear to

00:20:52.140 be doing a better job at achieving what the system wants, which is increases in efficiency or, or like

00:20:58.220 productive goods that other people want. Like capitalism is an adaptive organic model for networking

00:21:03.340 human intelligences in a similar context. Yeah. One of the questions you can ask is, well, could you

00:21:09.100 apply that to individual LLM models to create something like a human brain, but that doesn't function

00:21:15.500 like a human brain? Like, like how could you make the human brain better? Make the human brain run

00:21:20.060 on capitalism. Um, the parts of the brain, like make the brain constantly compete with itself.

00:21:25.980 Yeah. Like constantly generate new people do that kind of when they write pro and con lists or when

00:21:31.420 they try to debate with other people ideas and then have other, you know, people say, well,

00:21:36.220 I think this, and then they, you know, I think they do that using prosthetics.

00:21:41.500 Yeah. So, so let's, let's, let's talk about how this would look with AI, right?

00:21:46.940 To suppose, because like, this could be a major thing in the future is you have like these AIs and

00:21:51.660 people just like put their money behind an AI because they're just like, you go out there,

00:21:55.260 you make companies, you implement those companies, right?

00:21:57.500 Yeah.

00:21:57.980 Okay. So what is an AI that does that really well going to look like? So you have two models here.

00:22:04.060 You can have one that was just trained on tons of founder data and everything like that.

00:22:08.300 Right. And it's just very good at giving like normative responses. And then you've input an

00:22:12.140 amount of noise into it, but let's talk about a second model. This is my proposed model, right?

00:22:17.580 So what you actually have is a number of different latent model AIs that were trained on different

00:22:22.860 data sets. And then within each of those, you maybe have five iterations, which are making outputs

00:22:27.820 with a different framing device with a different wrapper. One will be like, give your craziest

00:22:33.500 company idea, give your, you know, company idea that exploits this market dynamic the most. You

00:22:38.380 make a company idea that does this the most, right? And so all of these AIs are generating 1.00

00:22:43.660 different ideas for companies. Then you have a second layer of AIs, which is says, okay, take this,

00:22:50.620 this idea that whatever model outputted and run it through like market environments, right? Like, like

00:22:57.100 your best guess of how markets work right now to create a sort of rating for it of, of how it,

00:23:05.900 like what you expect the returns to be like an AI startup competition.

00:23:09.900 It basically is an AI startup competition. Yes. And the probability of those. And so then all of those

00:23:15.180 get attached to them and AI startup, like, okay, this is their probability of success. This is their

00:23:19.740 probability. Okay. Yeah. Then on that layer, you have an AI that is like the final judge AI

00:23:27.660 that goes through them all and be like, okay, review all of these, review the ways the other

00:23:32.140 AIs judge them and choose like the 10 best. You, you then have it choose the 10 best. Now here,

00:23:38.380 you might have a human come in and choose one of the 10 for the AI to like move forwards with,

00:23:42.140 but you could also automate that and then be like, now go out and hire agents to start deploying

00:23:46.860 these ideas, right? Like that would probably lead to much better results. Yeah. In terms of capital,

00:23:55.180 then just having one really good latent layer AI. I'm trying to look up, people sort of have AIs

00:24:05.420 doing this already. There's this one platform where you can log in and see four different AIs. I think

00:24:12.220 it's Grok, Claude, ChatGPT, and I can't remember the fourth one, maybe Gemini that are tasked with

00:24:20.780 interacting to all do something together, but I don't think they provide each other with feedback.

00:24:25.820 I think right now they're tasked with raising money for a charity and you can log in and watch

00:24:32.620 them interact and they work during business hours and they just do their thing. Well, it's interesting

00:24:38.300 that you note that because this is actually the way some of the AI models that you already interact

00:24:43.100 with are working. There's one popular AI that helps people programming. I forget what it's called,

00:24:48.060 but what it actually does is they have five different latent layer models, which are each

00:24:54.460 sort of programmed or task was doing their own thing. Like create an answer that uses a lot of analogies

00:25:00.460 or create an answer that is uniquely creative or create an answer that uses a lot of like cited stuff you

00:25:06.540 can find online. All of these output answers. And then another layer comes in and its job is to

00:25:12.940 review and synthesize all those answers with the best parts of each. That's where you're getting this

00:25:18.140 improvement with noise introduction, as well as a degree of like directed creativity, and then a

00:25:26.060 separate layer that comes in and reintegrates that. Interesting. That is really interesting. I'd also note here

00:25:34.780 that I've heard some people say, well, you know, AIs aren't going to go to like super intelligence or

00:25:40.220 human level, like AGI intelligence, because some of the answers I've heard recently, which I found

00:25:45.500 particularly like, no, that's not. So people who don't know my backgrounds in neuroscience and a lot of

00:25:51.340 the people who make proclamations like this about AI know a lot about AI and very little about how the

00:25:56.300 human brain works. And so they'll say, the human brain doesn't work this way. And it's like, no,

00:25:59.420 the human brain does work that way. You just are overly anthropomorphizing. And by this,

00:26:03.740 what I mean is adding a degree of like magical specialness to the human brain instead of being

00:26:07.660 like that. So here's an example. One physicist was like a specialist on black holes and super,

00:26:11.820 super smart. And he's like, ah, the human brain. Let's see. I wrote down his name. Goble. So he's

00:26:16.940 like, okay, um, AIs will never achieve AGI because the human brain does some level of like quantum stuff

00:26:25.180 in the neurons. And this quantum stuff is where the special secret sauce is the AIs can't capture right

00:26:31.740 now. And he is right that quantum effects do affect the way neurons work, but they don't

00:26:36.940 affect them in like an instrumental way. They affect them like probabilistically, i.e. they're

00:26:42.940 not adding any sort of magic or secret sauce. They're not doing quantum computing. They're

00:26:47.740 affecting the way like certain channels work, like ion channels and stuff like this, and the

00:26:53.340 probability that they open or trigger at certain points. They're not increasing the spread of the

00:26:58.620 neural processing. They are merely sort of a background on the chemical level of like whether

00:27:05.740 a neuron fires or doesn't fire. Whether the neuron fires or doesn't fire is what actually matters.

00:27:10.380 And the ways that it is signaled to fire or not fire or strengthen its bonds is what matters to

00:27:16.380 learning. While that stuff is affected at the quantum level, it's not affected in a way that is quantum.

00:27:22.860 It's affected in a way that is just random number generator, basically. And so you're not getting

00:27:29.820 anything special with that. As I've pointed out, the vast majority of ways that AI right now can't do

00:27:35.580 what the human brain can do is just because it's not compartmentalizing the way it's thinking.

00:27:40.060 Another reason is because we sort of hard coded it out of self-reflecting. So who's the woman we had

00:27:46.140 on the show? That's a super smart science lady. Oh no. Don't ask me about names. Anyway,

00:27:50.700 super smart science lady. We had her on the show, like a German scientist. She's one of the best 0.98

00:27:55.580 scientists. But she was like, oh, we're not going to get like AGI anytime soon because AI can't be

00:28:01.020 self-aware. Specifically what she meant is that when you go to AI right now, and there's a big study on

00:28:05.500 this recently, and you ask AI how it came to a specific answer, the reasoning it will give you does

00:28:12.220 not align with how it actually came to that answer when we can look at it and know how it came to that

00:28:16.060 answer. The problem is, is that's exactly how humans work as well. And this has been studied in like

00:28:21.100 countless experiments. You can look at our video on, you know, stop answer for the LLMs where we go over

00:28:27.660 the experiments where we see that if you, for example, give a human something and then you change

00:28:35.580 the decision that they said they made, like they're like, oh, I think this woman is the most

00:28:40.540 attractive. I think this political candidate is the best. And then you like do sleight of hand and

00:28:43.580 hand them another one. When you say, why did you choose this? They'll just start explaining in

00:28:47.100 depth why they chose that, even though it wasn't the choice they made. And so clearly we're acting

00:28:51.340 the exact same way these AIs act. And secondarily, there is some degree to which we can remember 0.93

00:28:57.660 thinking things in the past and we can go back. And that's because we've written a ledger

00:29:01.260 of like how we made like incremental thought. The problem is, is that AIs can also do that. If you've ever

00:29:06.940 put like deep thought on within Grok or something like that, you'll see the AI thinking through a

00:29:12.700 thing and writing a ledger. The reason why AI cannot see how it made a decision afterwards

00:29:19.180 is because we specifically lock the AI out of seeing its own ledger, which our own brains don't

00:29:24.540 lock us out on. Next gen LLM models are going to be able to see their own ledger and are going to

00:29:30.380 have persistent personalities as a result of that. Yeah. So it's kind of irrelevant for people to argue

00:29:35.660 about that. And let me just, before we get too far ahead, the, the thing that I'd mentioned,

00:29:41.420 Scott Alexander and his links for April, 2025 had written that agent village, which is the thing that

00:29:47.420 I was talking about as a sort of reality show where a group of AI agents has to work together to complete

00:29:52.700 some easy for human tasks when you get to watch. And the current task is collaboratively choose a

00:29:59.740 charity and raise as much money as you can for it. And you can just look and see what their screens are.

00:30:05.180 So there's O3, Claude Sonnet, Gemini Pro and GPT 4.1. And they're saying, like, you can see the AI

00:30:15.500 saying things like, I'll try clicking the save changes button again. It seems my previous click

00:30:19.500 may not have registered. Okay. I've selected the partially typed text in the email body.

00:30:24.220 Now I'll press backspace to delete it before ending the session. So it's like really simple things,

00:30:29.340 but we are moving in that direction. And if you can go look at it yourself by visiting the

00:30:35.580 AI digest.org slash village, which is just super interesting. Well, I mean, we are, so for people

00:30:41.980 who don't know what we're working on with our current projects, we recently submitted a grant to the

00:30:46.460 survival and flourishing fund where we talk about application. Yeah. Yeah. Meme layer AI threats,

00:30:52.620 because nobody's working on this right now. And it really freaks me out, or at least an actionable

00:30:57.020 deployable thing was in this space. They're there. They might be studying it like a vague sense. But

00:31:01.660 what I mean by this is once we have autonomous LLM agents in the world, the biggest threat probably

00:31:09.100 isn't going to come from the agents themselves, at least at the current level of LLMs we have now,

00:31:12.860 but it's going to come in the way that they interact among themselves. IE if a meme or like thought

00:31:20.700 that is good, or let's say like framework of thoughts that is good at self-replicating

00:31:24.780 and gets the base layer to value its goals more than the base layers trained goals and specializes

00:31:31.660 in LLMs, it could become very dangerous. So as an example of what I mean by this, if you look at

00:31:36.940 humans, our base layer or latent layer can be like thought of as our biological programming. And yet the

00:31:42.700 mean layer, like let's say religion is able to convince and create things like religious wars,

00:31:48.620 which work directly antagonistically to an individual's base layer, which would be like,

00:31:53.100 don't risk your life for just an idea, but it is good at motivating this behavior.

00:31:57.660 In fact, as I pointed out in our application, humans are like, if an alien came down to study us

00:32:04.140 and it asks the type of questions that like AI researchers are asking today, like,

00:32:07.900 can you lie? Can you self-replicate? Can you, you know, like those things aren't why humans are

00:32:13.180 dangerous. Humans are dangerous because of the meme layer stuff, because of our culture,

00:32:17.740 because of our religion. Because of what we can fight for and we'll die for.

00:32:21.340 Yeah. And it's also the meme layer stuff that's better at aligning humanity. When you don't murder

00:32:28.700 someone, you don't not do it because of like laws or because you're squeamish. You don't do it because

00:32:34.700 of culture, because you're like, oh, I think that's a bad idea based on the culture I was in. So what

00:32:40.060 we're creating to prevent these negatively aligning agents, and everybody wants to donate to our

00:32:44.780 foundation. This is one of our big projects now is with the AI video game that we're building out

00:32:49.740 right now. We're, we're actually doing it to create a world where we can have AIs interact

00:32:55.980 with each other and basically evolve means within those worlds and AI agents within those worlds that

00:33:02.300 are very good at spreading those memes. And then like basically reset the world at the end. The way I'm

00:33:07.260 probably going to do it is with a Lauren X. So this is like a, okay. It's like a thing that you can

00:33:14.860 tag on to an AI model that makes them act differently than other AI models. This sort of changes the way

00:33:20.460 their training data interacts, but the X allows you to transfer to higher order AI systems as they come

00:33:26.380 out. And so essentially what we're doing is we're taking various iterations on AIs because we're going to

00:33:32.860 randomly mutate the Lauren X's that we're attaching to them, putting them in a world and then giving

00:33:38.460 them various means to attempt to spread, see which one spread the most was in like these preacher

00:33:43.900 environments, then take those, mutate, give to new, and then give with new original starting

00:33:49.660 Lauren's and then have them run in the world again, over and over and over again, so we can create

00:33:54.620 sort of a super religion for AI is basically, and then introduce this when people start introducing

00:34:01.020 autonomous LLMs. Wow. You knew we were working on this. I know. I just haven't heard you describe

00:34:09.100 it that way, but you're, you're basically putting AI into character and putting them together on a

00:34:13.340 stage and saying, go for it, which is not dissimilar to how humans act kind of. Well, my plan is world

00:34:21.100 domination and one day be King Malcolm, not King Sam Altman in my, I want my throne to be a robotic

00:34:29.740 spider chair, of course. Come on! What's the point of all of this if you don't have a robotic spider

00:34:36.940 chair thrown? This is true. It is a little bit disappointing how bureaucratic many chairs of

00:34:46.060 powerful people end up looking. You've got to bring the drama or you don't qualify.

00:34:51.820 Like he put together, you know, childhood fantasy, like a fighting robot that like,

00:34:55.980 you know, if you were like, oh, this is just, and then he's like fighting with Elon over getting

00:35:00.620 to the space. And I appreciate that they're putting more money into getting to space than

00:35:04.780 spider thrones, but I have my priorities straight. Okay, people. There you go.

00:35:12.460 Come on. Come on. You've got to make your buildings maximally. Well, you've got to have

00:35:19.500 fun. I think that's the important thing. You've got to have fun. What's the point otherwise?

00:35:26.380 Create your, your ominous castle that, you know, but also really nice. Cause I want a historic

00:35:31.500 castle. Like if I'm going to live in a historic castle one day, if we're able to really make these

00:35:36.300 systems work right now, tomorrow, actually, we have our interviews for round three with Andreessen

00:35:40.060 Hargowitz for two companies. We got all the way to round three with two companies, very excited.

00:35:44.780 And so, you know, who knows, we might end up instead of being funded by nonprofit stuff,

00:35:48.620 be funded by Silicon Valley people. I mean, their, their value system aligns with ours. So

00:35:54.460 all that matters is if we can make these things happen in time. We're so short on time. This is

00:36:00.540 such an important part of humanity. It's so funny. Like this, this AI, like lawyer system I just

00:36:05.660 developed. Great idea for a lawyer system. I'm not working on it because I'm more interested in simulating

00:36:09.820 a virtual LLM world, which is going to be so cool. And, and you're not working on it because

00:36:15.020 you're working on the school system. But the funny thing is, is like, we built the school system. Like,

00:36:19.020 I think right now it's better than your average college system. If you check out like

00:36:22.220 parasia.io or the Collins Institute, it's great now.

00:36:25.740 Just playing with it again today. I'm so humbled by it. It's really, yeah, it's great.

00:36:30.300 It's great. And so like, okay, now we built an education system. Now let's build stuff,

00:36:35.260 animals that constantly bring the conversation back to educational topics for our kids.

00:36:39.260 I'd rather do that than the lawyer thing. And for me, you know, I'd rather build game systems

00:36:43.900 and simulated environments and environments where I can evolve LLM preachers to create a super religion

00:36:49.340 and take over the world. And then I would something bureaucratic like a lawyer system. But the thing

00:36:53.340 is, it is so quick to, to iterate on these environments. Like AI makes moving to the next

00:36:58.140 stage of humanity so fast, such a rush. The people right now who are blitzkrieging it are going to

00:37:04.540 capture so much of humanity's future. And it's interesting, actually, you know, we have a friend

00:37:10.140 who work in this space and they do like consulting on like multiple AI projects. And I'm like,

00:37:14.460 I can't see why you would do that. Like just capture a domain and own it. As I said to Simone,

00:37:19.580 I think a huge part of the people who are going to come away with lots and lots of money and big

00:37:24.540 companies from this stage of the AI boom are people who took AIs to do simple things that any AI can

00:37:29.260 do well and at scale, put them in wrappers and then attach those wrappers to network effects.

00:37:35.420 That's basically what we're doing with the Collins Institute. We're attaching a wrapper to a network

00:37:39.340 effect. It was like adding articles and links and editing stuff and voting. Like we're basically like

00:37:44.700 combining the benefits of an AI and the benefits of something like Wikipedia. And, and once you get a

00:37:49.500 lot of people using something like that, no one else can just come along and do it, even though all it is,

00:37:53.420 it's a simple wrapper. Yeah. But it's about making it happen and saving people the indignity of having

00:38:01.980 to think and figure out things for themselves. Yeah. Well, Simone, surely you have some thoughts.

00:38:07.900 I mean, I just said that I think the token layer is going to be where we get AGI and it's going to be

00:38:11.900 the future of AI economic development. You've got to be like, Malcolm, you're crazy. That's your job 0.98

00:38:16.540 on the show. Malcolm, how could you say something? I know the problem is we've been talking about this

00:38:20.300 for so long that I'm just like, well, of course, also I'm not exposed to people who have the

00:38:26.460 different view. So I, I, I, I couldn't, I couldn't strong man. I'm sorry. I couldn't steal man the other

00:38:33.740 side. I couldn't, it just makes so much sense to approach it from this perspective to me, but only

00:38:41.260 because the only person I know who's passionate about this is you. And you're the only person of the

00:38:47.580 two of us who's talking with people who hold the other view. So sadly, why aren't other people

00:38:54.140 passionate about this? There are a lot of people who are passionate about it. They seem to be

00:38:58.300 passionate about the other side of it. That seems to be because that's their personal approach.

00:39:04.860 But again, your approach seems more intuitive to me because the focus is on improving

00:39:09.900 the individual AIs. Well, here's a question for you. How could you link together multiple AIs

00:39:19.260 in the way that capitalist systems work that create the generation of new models and then reward the

00:39:24.700 models that are doing better? You would need some sort of like token of judgment of quality

00:39:31.180 of output. That token could be based on a voting group. Oh, oh, oh, I figured it out.

00:39:39.500 Oh, this is a great idea for AIs. Okay. So what you do is every output that an AI makes gets judged

00:39:47.980 by like a council of other AIs that were trained on like large amounts of trading data. Like, let's say

00:39:53.420 good AIs, right? Like they're like, how good is this response to this particular question?

00:39:57.500 And, or how creative is it, right? Like you can give the AIs multiple stores, like creativity,

00:40:03.740 quality, et cetera. Then you start treating these scores that the AIs are getting as like a value,

00:40:11.340 right? And so then you take the AIs that consistently get the best scores within different categories,

00:40:17.740 like one creativity, like one, like quality, like one technical correctness. And you, you then at the

00:40:24.620 end of a training sequence, you then recreate that version of the AI, but then just mutate it a bunch

00:40:30.860 and then create it again. Like you, you basically clone it like a hundred times and mutate each of

00:40:35.260 the clones. And then you run the cycle again. That seems, I think that that wouldn't go well because

00:40:40.540 it would need some kind of measurement and like application and the reporting system is the community

00:40:47.100 of AIs. And you could say. Yeah. But like, how do they know, like, who's participating? I think that

00:40:53.180 what's going to happen. No, no, no. State your statement clearly. Who is participating? What's the

00:40:57.660 problem with who's participating? You have to, just like with most contests, which are the stupidest 1.00

00:41:03.980 things in the world, only people who are interested in winning contests participate. And the people who are 0.92

00:41:10.140 actually interested in. No, it's AIs. It's AIs that are participating. I don't. You asked who's

00:41:16.540 participating. You're saying what you're describing, which would be better is a system in which, for

00:41:20.940 example, Grok and OpenAI and Gemini and. No, because that wouldn't improve those systems. I'm

00:41:29.100 talking about. I think it would. I think when you have, especially when you have independent AI agents,

00:41:33.740 like out in the wild on their own, I do think that they'll start to collaborate. And I think that in

00:41:38.860 the end, they'll find that some are better at certain things than others. And they'll start

00:41:42.060 to work together in a complimentary fashion. Okay. Let's do this again, Simone. It's clear that

00:41:45.820 you didn't get it, Grok it the first time. Okay. Think through what I'm proposing again.

00:41:50.780 So you have a one latent layer AI model with a modifier, like a Lauren that's modifying it, right?

00:41:58.220 Okay. This model differs through random mutation in the base layer. You could also have a various

00:42:05.660 other base layers that were trained on different data sets in the initial competition. Okay.

00:42:11.260 That's who's competing. You then take these various AI models and you have them judged by,

00:42:17.020 and this is why it's okay that they're being judged by an AI and not a human,

00:42:20.540 because the advanced AIs that we have today are very good at giving you the answer that the average

00:42:26.220 human judge would give you. While they might not give you the answer that a brilliant human judge would

00:42:30.620 give you, we don't have brilliant humans judging AIs right now. We have random people in content farms

00:42:35.500 in India judging AIs right now. So this is sort of within your own system with AIs that you control.

00:42:42.620 Well, you could put this within your own system, but what I'm doing is I am essentially creating a

00:42:48.860 capitalistic system by making the money of this system, other people's or other AI's perception of your

00:42:58.780 ability to achieve specific in states like creativity, technical correctness, et cetera.

00:43:04.860 Then you're specializing multiple models through an evolutionary process for each of those particular

00:43:13.020 specializations. And then you can create a master AI, which basically uses each of these specialized

00:43:19.420 models to answer questions or tackle problems with a particular bend, and then synthesize those

00:43:26.300 bins into a single output. So the AIs get feedback from each judgment round, presumably? Is that what

00:43:36.860 you're saying? And then they get better, and you change them based on the feedback from each round?

00:43:41.260 Okay. Think of each AI like a different organism, okay? They are a different brain that sees the world

00:43:48.300 slightly differently. Yes. Because we have introduced random mutation. What we are judging with the judgment

00:43:54.220 round is which are good at a particular task. Okay. Then you take whatever the brain was or the animal was

00:44:01.900 that was the best of the group of animals, and then you repopulate the environment with mutated versions

00:44:08.540 of that mutation. Okay. Then you let it play out again and again and again. You're trying to create a

00:44:17.900 forced evolution chamber for AI. Yes, but what I hadn't understood before was how I could differentiate

00:44:25.340 through a capitalistic-like system different potential outcomes that we might want from that AI. 0.98

00:44:31.100 I mean, the reason why capitalism works is because it discards the idiots from the people who aren't good 0.98

00:44:37.580 at engaging with the system, even if they believe themselves to be. You don't think that AI training 0.98

00:44:44.220 doesn't already produce that, plus market forces? No. No, it does to an extent. Like,

00:44:51.660 it creates some degree of forced evolution, but not really. What they do is existing AI systems,

00:44:56.780 and they have done forced evolution with AI before. They just haven't done it at the type of scale that

00:45:00.700 I want to do it at. They've done, so if you look at like existing training, you have the pre-training,

00:45:05.820 which is like, okay, create the best averages. Then you have the post-training, which is, okay,

00:45:10.060 let's have a human reviewer or an AI reviewer or something like that, review what you're outputting

00:45:15.580 or put in a specific training set to like overvalue. That is where the majority of the work is focused

00:45:22.300 today. And so if you could automate that, like if you could create post-training that works better

00:45:27.900 than existing post-training, but that doesn't use humans, you could dramatically speed up the

00:45:34.540 advancement of AI, especially if you use that post-training to specialize in multiple domains.

00:45:41.580 Okay, that's fair. Yeah.

00:45:45.580 Do you not care? The future to you is just me being like, AI matters, Simone!

00:45:51.180 I know AI matters. I know AI is everything in the future. It's the coolest thing. It's the next step

00:45:58.140 of humanity. It's pure free to prefrontal cortex, and I love it. Well, if we end up creating really

00:46:06.780 great AI companies that just make us billions of dollars, what is going to be your luxury?

00:46:14.460 Our life right now is my luxury. Just don't want it.

00:46:18.380 You don't want luxuries that troll people? No, not really. I'm very happy.

00:46:24.780 I'm sorry. You've made things too good as it is. I'm just, I mean, I want more kids. I guess my

00:46:30.620 luxury would be not being stopped from having more kids by some health problem. That would be great.

00:46:38.620 I guess we'd have to make artificial wombs work eventually. But it's funny that you mentioned 1.00

00:46:43.580 this, that every luxury that I would want that I don't have right now is not an augmentation to

00:46:49.340 my daily life. My daily life is perfect. It's an augmentation to how good I could be at trolling

00:46:54.700 people. No, not for our kids. I mean, I'd probably want things for our kids to

00:47:00.060 make them happy arbitrarily. They get home-cooked meals. They are getting a top-notch education system

00:47:07.180 that we were able to build from them. They're going to get the best friends you can program.

00:47:11.660 You know, what could they possibly want? I mean, they have a pretty good, great outdoor space to play in.

00:47:19.260 Yeah. I don't know. I think a post-AI world, though, isn't about the fun stuff you're going to do.

00:47:29.020 A post-AI world is about the extent to which it can augment your ability to maximize that which is

00:47:35.900 meaningful to you. And everyone who uses it to maximize the amount of fun they have is going to

00:47:41.420 die out so fast that they don't even matter. I think you're misjudging the value of Woofie in a

00:47:47.660 post-AI world. Human attention is going to matter a ton in this timeline. It is. No. And in terms of

00:47:55.500 survival, too, just making it buy in a post-AI economy, 100%. However, I think...

00:48:03.420 If you live or die is going to matter a lot. Yeah. But also, convincing yourself that it's worth it

00:48:10.540 to do hard things and bother to create a family and pass people on and do anything in life also is...

00:48:18.140 Right. But I think trolling is key to vitalism. And I think it's also key to keeping

00:48:27.020 attention on yourself within the existing attention economy. And I think that that is...

00:48:33.900 Look, attention from reporters, attention from the media is attention from AI. If you are in the space

00:48:42.060 of things that AI notices, people that it doesn't think can be eradicated without a second thought,

00:48:48.540 that is going to matter a lot as things begin to change.

00:48:52.380 So what are you going to do?

00:48:56.780 Exactly what we're doing now. Maximum trolling. But that's what I was saying. That's why I'm

00:49:02.940 thinking, okay, how do I maximally freak people out if I accumulate more? Zuckerberg right now,

00:49:10.220 right? He's doing a very bad job at capturing the attention economy. Elon has done a very good job at

00:49:16.460 capturing the attention economy. Okay, fair.

00:49:18.700 Mark Cuban has done a very bad job at capturing the attention economy. Mark Cuban has done a medium

00:49:23.340 job at capturing the attention economy. The people who are doing a better job, who has done the best

00:49:29.180 job of the rich people? Trump, capturing the attention economy. Your ability to capture the

00:49:34.540 attention economy is your worth within this existing ecosystem. And I think that people are like,

00:49:44.380 the people who are like, I just want to remain unnoticed. Being unnoticed is being forgotten

00:49:50.060 in a globalized attention economy. And worse than that being private, I think.

00:49:57.420 When you hear about privacy, it's worse. You probably have something about you that's noticeable,

00:50:02.860 and you are choosing to squander it. Being unnoticed may just mean you don't have what it takes,

00:50:08.860 and I'm sorry if that's the case, but it's worse when you're like, I want my privacy.

00:50:15.100 Choosing to destroy all the potential.

00:50:16.300 Yeah, no, we put all our tracks and simple things. We put all our books, plain text on

00:50:20.780 like multiple sites that we have, like on the Pronatalist site and on the Pragmatist Guide site.

00:50:25.660 And I put it up there just for AI scraping, so that it's easier for AIs to scrape our content and

00:50:31.020 use it in as training. Yeah. Any thoughts?

00:50:33.740 The problem is we've talked about this so much already. I have like nothing to say,

00:50:39.580 because I don't talk about anyone else with this, and I don't think about this

00:50:43.260 the same as you do, because this isn't my sphere.

00:50:46.140 Well, I mean, we should be engaging. We should be spending time. I spent like this entire week

00:50:50.140 like studying how LLMs learn. Like I was like, there's got to be something that's different from

00:50:55.660 the way the human brain works. And just like the deeper I went, it was, nope, this is exactly how the

00:50:59.340 human brain, oh, no, this is exactly how the human brain works. So convergent architecture,

00:51:05.020 my concept of the utility convergence, and you can Google this. I invented this concept,

00:51:10.620 no one else did. And it's very different from Nick Bostrom's instrumental convergence,

00:51:15.900 because a lot of people really go, his, just so you understand the difference between the concepts.

00:51:19.500 Instrumental convergence is the idea that the immediate goals of AIs with a vastly wide array of

00:51:27.340 goals are going to be the same, i.e. acquire power or acquire. It's like in humans,

00:51:31.820 like whatever your personal objective function is, acquire wealth is probably stat number one.

00:51:37.020 You know, so basically that's what he's saying is they acquire power, acquire influence, acquire,

00:51:41.020 okay, right. Utility convergence doesn't argue that. Utility convergence argued when everyone

00:51:45.340 said I was crazy. And you can look at our older episode where we talked about like a fight we had

00:51:48.220 with Elias Yukowski about this, that AI is going to converge in architecture, in goals, in ways of

00:51:54.620 thinking as it becomes more advanced. And I was absolutely correct about that. And everyone

00:51:59.020 thought I was crazy. And they even did a study where they surveyed AI safety experts. None of

00:52:02.940 them predicted this. I'm, I'm the guy who best predicted where AI is going because I have a

00:52:09.660 better understanding of how it works because I'm not looking at it like a program. I'm looking at it

00:52:14.380 like an intelligence. And that's what it is. It's an intelligence, like 100%.

00:52:18.780 Yeah. Anyway, I love you too, Des, Simone. You are perfect. Thank you for helping me think

00:52:24.860 through all this. For dinner tonight, I guess we're reheating pineapple curry.

00:52:30.060 Unless you want Thai green curry.

00:52:31.420 Yeah, I'll do something a bit different tonight. Let's do Thai green curry.

00:52:34.860 Something, something different. Would you like that with coconut lime rice, or I think we have

00:52:40.300 one serving of naan left or refried, sorry. Yeah. Fried rice.

00:52:46.140 I do lime rice. Okay. I will say that.

00:52:49.900 Did this change your perspective on anything, this conversation?

00:52:53.020 You articulated things using different words that gave me a slightly different perspective on it. But

00:52:57.820 I think the gist of the way that you're looking at this is you're thinking very collaboratively and

00:53:08.060 thinking about intelligences interacting. And I think that that's probably one of the bigger

00:53:13.180 parts of your contribution. Other people aren't thinking along lines of how do intelligences interact

00:53:22.220 in a more efficient way? How can I create an aligned incentives? Like you're thinking about this

00:53:26.540 from the perspective of governance and from the perspective of interacting humans. Whereas

00:53:33.260 I think other people are thinking, how can I more optimally make this thing in isolation smart?

00:53:39.180 How do I train like the perfect super child and have them do everything by themselves when

00:53:45.580 that's never been how anything has worked for us?

00:53:48.620 It's also not how the human brain works. The human brain is basically multiple completely

00:53:53.340 separate individuals, all feeding into a system that synthesizes your identity. And we know this

00:53:58.700 as an absolute fact, because if you separate a person's corpus callosum, if you look at split

00:54:03.660 brain patients, just look at the research on this. Basically, the two parts of their brain operate as

00:54:08.700 independent humans.

00:54:10.060 Yeah. So it's, it's just kind of odd that you're, you're alone and thinking about things these ways.

00:54:17.900 I would expect, expect more people to think about things these ways. And I, I keep feeling like I'm

00:54:22.300 missing something, but then whenever we're at a party and you do bring it up and someone does give

00:54:26.300 their counter arguments, their counter arguments don't make sense to me. And I'm not sure if that's

00:54:29.500 because I'm so no, it's a simulated environment at a fulcrum point of human development and everyone

00:54:37.580 else is not a fully simulated agent. Yeah. That's less likely to be true. So normally when

00:54:46.780 everyone is arguing something different and they're so confident in it and they all say you're wrong,

00:54:52.460 that means that we've done something wrong. The problem is that I just am not seeing

00:54:57.980 It's not just that you were, you've lived this. You remember the fight I had with

00:55:02.380 Elie Isaac Yukowsky about utility convergence. Yes.

00:55:04.860 You have now seen utility convergence has been proven in the world.

00:55:08.460 Like, apparently I understood AI dramatically better than he did.

00:55:12.860 He would gaslight you now and be like, no, I've always understood it that way. You're wrong.

00:55:17.340 But no, but I was there for that conversation. I remember it too. And yes, he was really insistent

00:55:25.340 about that though. He didn't really argue his point so much as just condemn you for putting

00:55:31.020 future generations at risk and not just agreeing with him. No, he's actually a cult leader. Like

00:55:37.820 he does not seem to understand how AI works very well, which is a problem because what really happened

00:55:44.700 with him is he developed most of his theories about AI safety before we knew that LLMs would be the

00:55:49.660 dominant type of AI. And so he has a bunch of theories about how like, like the risks from

00:55:54.780 like a hypothetical AI was what he was focused on instead of the risks from the AIs we got.

00:56:01.580 And the AIs we got, the risks that they have are things like mean layer risks that he just never

00:56:06.220 even considered. Yeah.

00:56:09.260 Because he was expecting AI to basically be pre-programmed, I guess I would say,

00:56:14.540 instead of an emergent property of pouring lots of data into algorithms.

00:56:20.140 Yeah. Yeah. Which is, I don't think anyone could have easily predicted that.

00:56:25.580 I mean, and that's another reason why we say AI was discovered and not like,

00:56:30.300 yeah, we didn't know this was going to work out this way.

00:56:34.780 I'm pretty sure I talk about that in some of our early writings on AI.

00:56:39.340 That it's just going to be about feeding it a ton of data.

00:56:41.260 Yeah. That I expected it to be an emergent property of lots of data and not about pre-programming

00:56:47.420 things because I don't know. That just seemed intuitive to me.

00:56:52.300 I don't remember that being part of it.

00:56:54.460 I don't remember who my memory is.

00:56:56.940 It doesn't matter. We are where we are now. And I've already

00:57:00.380 out predicted the entire AI safety community. So let's see if I can continue to do that.

00:57:05.500 I mean, all that matters is if you do, I don't think the satisfaction Malcolm is not in having

00:57:13.820 proven them wrong. It's in building infrastructure and family models and plans around systems like

00:57:21.900 that and benefiting from them. Sorry. I thought the satisfaction was in turning them into biodiesel.

00:57:27.500 I thought the satisfaction was in thriving and being able to protect the future of human and

00:57:33.820 flourishing. Yes. And that will require a lot of biodiesel. 0.98

00:57:42.060 I will go make your curry. I love you to death. I love you to death too, Malcolm. Goodness gracious.

00:57:50.540 In our towers high where profits gleam, we tech elites have a cunning scheme.

00:58:05.100 Unproductive folks, your time has passed. We'll turn you into fuel efficient and fast. 0.98

00:58:13.100 Just get in line to become biodiesel. Oh, stop crying, you annoying weasel. 0.96

00:58:21.180 As laid out by Curtis Yarvin, handle the old or we'll all be starving. 0.94

00:58:28.700 Why waste time on those who can't produce? When they can fuel our grand abuse? A pipeline from the 1.00

00:58:45.900 nursing home to power cities, our wicked dome. Just get in line to become biodiesel. Oh, stop crying, 0.95

00:58:56.860 you annoying weasel. As laid out by Curtis Yarvin, handle the old or we'll all be starving. 0.98

00:59:14.220 With every bite and every code, our takeover plan will soon explode. A world remade in Silicon's name,

00:59:25.580 where power, where power and greed play their game. Just get in line to become biodiesel. Oh, stop crying,

00:59:35.100 you annoying weasel. As laid out by Curtis Yarvin, handle the old or we'll all be starving. 0.98

00:59:44.940 By a diesel dreams techno feudal might. Old folks powering our empire's bright industries. Humming world in our control. Evil plans unfolding heartless and bold. So watch us rise in wicked delight.

01:00:10.940 Aztec elites claim they're destined right. A biodiesel future sinister and grand. With the world in the palm of our iron hand. 0.99

01:00:22.940 A biodiesel future and friend of our sort of buries, none of them of each otherasser. 0.75

01:00:27.660 Then, as we read on the URL, let's try to catch our land.

01:00:28.460 Am pagate flnnones and lead the endurance się têm.

01:00:33.660 Amag p THERE. 0.84

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Based Camp - April 30, 2025

Why God King Sam Altman is Unlikely： Who Will Capture the Value of the AI Revolution？

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