AI Scaling, Alignment, and the Path to Superintelligence — With Dwarkesh Patel

26:34It's not necessarily for what it does today. It's what I can do maybe a couple of generations from now. and that will eventually give you the ROI. I'm curious. I mean, you've had these conversations with these folks at really the ground level of the science. Do you buy this, that it's almost going to be a linear progression in terms of how good it is from generation to generation? Yeah, I mean, like one way to think about it is you're mentioning the scaling laws and that's a relationship that basically as you dump more compute in these models, their loss gets better in a very predictable way and the loss in this case corresponds to their ability to ability to protect internet text um how that translates into capabilities is another question but if imagine a model that can predict any internet text it can predict how to write like really great scientific papers or whatever well then that's like you know it's like human level intelligence anyway so i think you could make the case that there might be some break or plateau previous to gpd3 or something where gpd2 is really impressive but here's the kinds of things they won't be able to do and sort of pre-register that prediction and stick by it it would just be really bizarre to me that gpd1 to gpd2 gpd2 is actually kind of really interesting artifact for the small amount of investment it took to make it gpd3 a couple million dollars and you've got to like this like this thing that's like early stages of intelligence whoa then you get a gpd4 and oh my god this is actually useful they can generate billions of dollars of revenue a year it would just be bizarre to me that like you're halfway through the human range of intelligence and now it stops getting better so i do sympathize with sam's statement in the sense of like why would it stop here right if it was going to stop why it seems like it should have stopped before it started getting better in a human intelligence way at all so you don't think we're going to hit this stop in the road?

28:30Well, the reason that could happen is because of the data wall, which is to say that we can't keep training them in the same way we've been training them before. You GPT-2 to GPT-3 to GPT-4, you can just dump more data and compute in these models. If you run out of more data, then the question is like, well, we could have made something smarter, but we just didn't have the data for it. Right. And then the operative question becomes synthetic data or RL. And I think the intuition there of why that will work is, first of all, that should work better once the models are smarter, because the sort of self play setup is contingent on the model being smart enough to be like, that was the wrong way to proceed.

29:11Let's back up and proceed to this other way. And let's learn from this. Why did I make this mistake? Let's make sure to do it the right way in the future. That seems like they're getting smart enough to be able to do that on a per token basis they're actually really smart potentially as smart as really smart humans it's just that five minutes out they lose their train of thought can they bootstrap themselves in a way to like help them back up every five minutes and learn how to do that so that they can stay coherent for longer seems plausible um and i mean i had one of these takes in one of my blog posts that the way humans got better was this sort of self-play are a setup as well right where we learned language and or at least the initial stages of language where our vocal cords got something called a fox uh fox p2 gene and then uh so from there just you can talk to other humans you can interact with them that's sort of like a self-play loop that led to uh humans getting smarter and so forth do you ever think it's weird that there is this belief in this predictability of improvement and yet when you speak with the people who are working on these projects they tell you that they don't really know why it's making that improvement like Dario Amodeo was basically like I'm not quite sure what's going on inside these LLMs to make them as smart as they are totally and I think that's where that's why you should have you shouldn't be like sure that they're gonna uh we're on the track of AGI because yeah fundamentally we don't know what kinds of things these are um you know it could just be I don't know it's less it's more implausible now than it was maybe a couple of years ago.

30:46But it could be some sort of curve fitting thing where I think if you ask me like, what is the reason AI progress? Like looking back on it, if let's say the GPT-6 isn't that much better than GPT-4 and you had to look back on it and say like, why did that happen? I think the thing I'd expect to say is that right now we are kind of fooled by how much data these models consume. whereas you know they've like literally seen all of internet text untrained out at multiple times and then in retrospect you'd be like well of course they knew how to do the nearest adjacent thing because it was in the data set but they you should have seen that they're not that good at being creative or novel and so yeah clearly they weren't going to keep improving in that way so we've talked about AGI a couple of times uh artificial general intelligence is this big phrase that's thrown around a lot.

31:40And I think that oftentimes people hold multiple definitions of it in their brain at the same time. And it's definitely something that's kind of one of the more amorphous finish lines, so to speak, that you've ever seen in the business world that everybody seems to be working toward it, but no one can really define it. What is your definition of it and do you think that we're going to reach it um the the way i've been thinking about it which is which has less to do with like maybe agi in the world and more so about i think it's long-term impact is the kind of model which can automate or significantly speed up ai research and why define it that way um given the fact that there's so many other jobs in the world because I think one of the things you really have to think about is once it can automate AI research, then you can have this sort of feedback loop where it's helping train the next version, but it's like looking at finding better activation functions and designing architecture that has better scaling curves, coming up with better synthetic data and so forth.

32:51So I think once you get to that point, then it's off to the races in the sense of like You could have an intelligence explosion, the kind of things that you see in sci-fi books. And so that's what I've been thinking about when I think in terms of AGI. Can it speed up AI research? And yeah, I think that's a plausible thing within the next five to ten years. Are people working on that problem in particular? I mean, the people making these models are AI researchers themselves. And I can imagine them being selectively trying to... Clearly, they care about their use case, which is helping them with their job.

33:25So I can imagine the model getting better at that than it gets better at other things. Fascinating. So what type of breakthroughs do you think we're going to need to get there? Right. We've talked a little bit about reasoning. And from my understanding, like the way that models do reasoning is kind of like look at a task instead of I mean, this is what Jack Clark told us a couple of weeks ago. Look at a task instead of just like spit back information, be like, huh, like how many steps do I need to perform to like really get this task right? And then just go step by step. Is that one way that we're going to get there or that they'll get there?

33:56Or is there something else that's going to happen? Yeah, I agree. I agree. That definitely seems like an important component of the puzzle. One big one, and this is similar, is that they aren't yet useful in long – when you need them to kind of go do a job. You can't be like GPT-4. I'll be back in a while, but can you manage my inbox for me in the meantime? Or can you go book a trip for me? Just things that require them to sort of autonomously hold themselves together for a while and act as an agent. And so just that kind of coherency where they go from five minutes of being able to be in dialogue with you to you just tell them to do something.

34:41You come back a couple hours later and they've just done a bunch of inference to make it happen. And I think that will be a huge bottleneck or a huge unlock, I mean. Yeah. I mean, this idea of memory in the bots, right? Like it's something, and maybe this is a little different, but it's something that I keep thinking about where like I'm speaking with Claude every day and yet every morning it's like 50 first dates. I have to introduce myself to Claude again. Yeah, totally. And although there's one weird thing that Claude started doing where like I did a podcast last week about, or a couple of weeks ago, about the data that you get from voice and the emotion that you get from voice when you can listen to something as opposed to just have text.

35:21And Claude, obviously, when it gets a transcript of a podcast is only getting the text of the voice. And so I uploaded that conversation about the data that you get from emotion into Claude. And now it keeps hallucinating the audio quality of further transcripts that I've put in, almost as if like it wishes it understood what the audio sounded like because it knows that that's an important data point but anyway let's just put that aside for a moment the memory thing is interesting do you think there are easy ways to then like have have a persistent conversation with one of these bots or is that going to be like another tough problem that we won't solve for a while i it could plausibly be very tough because i don't think it's a member it's a it's a matter of just keeping like storing memory i think it's like what kind of thing are you and are you a chat bot or are you is your persona like i am an entity that you know it's not just about like i'm storing these things somewhere it's like you have to train it to act as an agent and compared to just pre-training tokens on the internet where yeah it knows how to complete statements does it know how to act as an agent there's not necessarily a good way to structure that so people have been talking about long horizon rl which is the training method you need to get something like this where you go tell it to do something and then you reward it at the end for having achieved that outcome but the difficulty with those kinds of approaches and the difficulty with rl in general is sparse reward and uh non-stationary distributions which is like you failed to book me my right the right appointments based on like reading up my inbox and like talking with me about it why did you fail there's like so many different reasons you could have failed that's hard to attribute to any one of them you know what i mean it's like hard to learn from that it's kind of an interesting question honestly like why humans are so good at uh learning from these sparse rewards or making long-term plans because when you look at it from an ml perspective it's kind of a it's a it's a cursed sort of problem to solve yep all right well i want to take a break here and then when we come back from the other side, I want to talk about the Dwarkesh podcast, how you've started it and where it's going.

37:36And then particularly, I'm very interested in the AI risks, because that's something that you're focused on. And it's something that I've like dismissed oftentimes in terms of like looking at the big risks. And I've promised myself to do a better job of taking it seriously. So why don't we do that on the other side of this break? Did you know your credit card points and miles can lose value to inflation? Credit card companies often reduce the redemption value of your points and miles. Now, imagine a credit card with rewards that can grow in value. With the Gemini credit card, you can earn Bitcoin or one of over 50 other cryptos instantly with no annual fee.

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40:08And we're back here on Big Technology Podcast with Dwarkesh Patel. He's the host of The Dwarkesh Podcast. All right. So you recently tweeted your bank account before advertising checks from the Mark Zuckerberg interview hit. And it reminded me of a similar screenshot from my bank account not too long ago. So you have checking negative$17.56 and savings$14. So congrats on the savings. It reminds me of before the advance of my book hit, I was negative quite substantially in my bank account also. And it's kind of this moment. I think we had similar moments where you're like, OK, I think things are going to be going on the right direction.

40:53Go all the way in on this content plan effectively and then trust good things will come. And they have for you. So apparently, I mean, and I saw the tweet and I was like, all right, we're definitely talking about this on the show. So apparently things are heading in a good direction for you financial-wise, at least that's the sense I get from Twitter. But I really want to know, given that it got to that point and now you're making your move, what is your background, Dwarkesh? And what got you interested in starting a podcast largely focused on some of the deepest questions in AI? Yeah. Yeah. Well, I started the podcast in college, sophomore year, because that's when COVID hit.

41:40And all my classes went offline. And so I was super bored. And then I just, I was super into economics and history at the time. So I invited some, I was emailing some economists and I, my first guest is actually Brian Kaplan, who's now a good friend. I asked him, well, you know, I'd love to chat with you on the podcast. I didn't tell him I didn't have a podcast. I didn't even have the name for the podcast yet and then he's like yeah sure i'll come on and then so we recorded an inaugural episode and from there i was super interested in economics history for a while for through college i was mostly doing topics like that and then i graduated about two years ago still kept doing it it was honestly i graduated a semester early so it was a way of basically taking a gap semester to figure out what i wanted to do i mean i was studying computer science but yeah, I wasn't sure what to do.

42:27I didn't want to become a code monkey. Um, in fact, I mean, there's a longer story there, uh, but, um, so yeah. And then things just kept going well in terms of the growth of the podcast itself. And so I was like, well, this seems like a thing worth doing and investing my time into. And it wasn't really like financially making money, but you know, whatever, this seems fun. And I'm, I'm learning a lot. I'm meeting a lot of interesting people. So I kept it going, dot, dot, dot, interview Elias Letzko at some point. And then like other cool things happened and then dot, dot, dot some more. And then interviewed Mark Zuckerberg and now I'm getting, uh, checks for ads on the podcast.

43:05Yeah. It's a great summary. And I recently listened to you on an effective altruist podcast. Uh, and I thought it was a great, a great conversation. I, and it sounded like the effective altruist movement influenced you a lot in the beginning to start the podcast, uh, or at least to focus the podcast on artificial intelligence. So what, what did you find interesting about the movement? Do you ascribe to the EA theory and how present is it in your life right now? Yeah. Um, I definitely think they've been like right on a lot of things, right in the sense of like, this is a big focus and they've realized it before a lot of other people like this ai stuff right the the eas have been talking about this stuff for decades um and like it's been a big part of the movement for a long time right so you got to give them some credit for that they were talking about uh pandemics and bio viruses and the dangers that opposes society from that long before covid a lot of them saw that coming um you know i'm actually curious about something listening to that podcast what was your sense to like uh the kinds of things where we were buying into ea assumptions did it feel like we were buying into too many assumptions did it feel like a reasonable you know i feel free to be a red team this and be harsh but like what was your sense on uh for somebody outside i'm uh yeah i'm assuming you're not necessarily an ea what was your sense of like uh were we assuming too many things in that conversation yeah no i'm not ea to me it was i don't think i had enough grounding to be able to to really answer that question well i think ea's should like talk a little bit more is my perspective.

44:43And I've tried to reach out to many of them, especially when there was this like six month pause that was funded by open philanthropy. They call for the six month pause on AI development and was sort of like met with like a dismissive no on that front. So I think that's kind of where some of the skepticism comes from. But I will tell you this because I think you saw the screenshot that I uploaded five of your podcasts into Claude and was like, all right, tell me a little bit about what's important to Dvarkesh. And then I was like, do you think Dvarkesh is an effective altruist? And Claude says, there's a high probability that Dwarakish Patel is an effective altruist, or at least strongly influenced by EA ideas.

45:33Based on the strong alignment between his express views and EA priorities, I would estimate the probability is quite high, perhaps in the range of 70 to 90%. It's impressive that it gave you an actual probability number. Oh, I asked for probability. That was promising. Yeah. Oh, okay. That's actually a super interesting use case of these models to like info dump into the long context, a bunch of stuff about them. And like, what are the odds that they're like, you know, believe a certain thing or that's actually a really fascinating thing to do. Yeah, look, like I just like to not use like subscribe to labels just because it kind of constrains your ability to think.

46:19I'm not sure what EA necessarily means. And then also, you know, like there's certain things that maybe traditionally considered EA that I probably disagree with. But I'm definitely happy to say, like, look, the movement influenced me a lot. And I think like they've had some really interesting ideas that I found fruitful and useful. No, it's interesting. I feel like they are asking some of like the really the right questions about this technology. Like it is powerful. and how do you steer it? And then, you know, in fairness, there have also been moments where EA has been associated with some stuff and people have been like, what?

46:57Obviously, Sam Bakeman-Fried is not a distillation of EA philosophy, but he was definitely a firm believer and a funder. Totally. And then, you know, with the whole Sam Altman coup, clearly that was also part of it. So I'm curious. I actually don't know how much EA, I actually don't think EA was that big a deal of the board stuff. I think like from what I've heard, it was it was related to something separate. Yeah. Well, I'll say this. The the people who were some of the I mean, the two two of the board members who I think were in favor of the ouster were connected in some way. Whether there was a direct like this is an EA thing or not is still an open question.

47:35But that's what I'm saying. Like there was that presence. So I really want to get to like the core of the question here, which is what are the things that you do disagree with from EA? And then I bring up these examples not to impugn the movement, but to ask you if you think there are holes in the broader philosophy and they've just manifested in these in at least one strange moment. um i mean i could like go go on for days about things i uh disagree with uh about them depending on like what ea definitely necessarily counts as you can like look at their cause prioritization list like it depends on what in what sense are we disagree like what do we mean ea in terms of like when you go to effective altruism.org and like what they say the cause priorities are like it's hard to you know they'll say things like we care about the poorest people on the planet and about animal welfare and about existential risk.

48:28And I'm like, yeah, those all seem like things worth caring about, like probably some of the most important things in the world. Maybe we mean like the impacts they've had because of SBF or the board stuff, right? Is it a good sort of scalable culture? I'm actually curious like in what sense, because like on this sort of like sociocultural angle, there might be other things to say. So I'm curious which angle. Yeah. It's a great sort of return question because I think you're right that it has been something that has been a label for a lot of different things. And I don't think there's like a charter, right?

49:03So for me, when I think about it mostly, I think it more mostly in terms of like this expected value equation where like people should structure their lives to maximize the expected value that they're going to have on the planet or the expected value of their presence that we'll have in terms of adding goodness to the world.

49:31Yeah, I mean, there's definitely problems with that. And I certainly don't think of like, so here's one particular problem that I was talking about on the podcast is that it's hard to forecast in the case of any individual how to make decisions using this framework. Like I would have never started the podcast if I was thinking from a maximizing expected value perspective right um obviously you could be like well use your cs knowledge to do something more useful like you're gonna spend your time working on the podcast come on um so yeah it might not be it might not be practically practically useful in many ways and of course there's like the dangers of people who think they know what the expected value of something is and actually don't and just having uncertainty over that um uh on the other hand i think like at current margins like how does society currently, if you think of other charities or if you think of your own tax dollars, how are they allocated?

50:25And wouldn't you prefer at the current margins whether if they were allocated using a more sort of rational expected value framework? Like, you know, your taxes are going to be used, like if you live in California, especially, they're going to be wasted on a bunch of useless shit. Like all these nonprofits and whatever. And like, wouldn't it be better if like they thought about like, let's pull up the spreadsheets on how much good these nonprofits are and these different institutions we are funding with our tax dollars are doing. And I think that kind of mentality actually would be broadly useful in the world.

50:53Yeah. I don't know. I think it's worth considering sort of the longer term risks of AI, right? Which you mentioned they were early on. And I think that they've probably brought more focus to. So on that front, I'm curious like what you think makes somebody, I guess it's the CEOs, a lot of the CEOs that we hear from, and maybe some of like the intellectuals in the EA movement, but elsewhere. What do you think makes them so afraid of AI or so cautious about where it could lead us? Yeah, I think it's kind of a sort of straightforward thing of like looking at maybe a couple of years out from the people who are just thinking of this as a normal economic transition where you say, okay, we'll have things that are as smart as humans and as smart as the smartest humans when it comes to science and tech.

51:42well what happens if you plug this into our basic economic growth models you have you know you just have a huge effective population this is the and so there are people doing science and r &d for you um you're like rapidly going through the tech tree because you have like billions of researchers and maybe like there's certain physical bottlenecks to this but um you can you just you just have like billions of extra people helping you do further AI research whatever and there's enough of them that if they wanted to they could do a coup they have certain advantages in the sense that you can like they can easily copy themselves in the sense of their weights they can they can increase their population rapidly and there's like they're harder to kill to put it that way once they're deployed than humans are you put out a bioweapon or a nuclear war and a bunch of humans will die.

52:36If these things keep a seed version of themselves somewhere, then it'd be hard to... If you had to go to war with them, it's a sort of asymmetric. And then you go from there to... Listen, we fundamentally, as you were saying earlier, we fundamentally don't understand what's happening in these models, but we know they're really smart. And sometimes, like with the Gemini thing, they do things we didn't expect them to do. That was a great example where I'm sure Google didn't want this sort of embarrassing image to come out but that's just what ended up happening at the end like and now imagine these things are super integrated into like our cyber security and are trained on this long horizon or else they are they're coherent agents over a long period of time you can like you know put all that together and it's like well that could go wrong um yeah it's interesting because for me it's always felt far-fetched because i'm working in like the current versions of ChatGPT and Claude.

53:31But then if we get to this place where these machines are effectively improving themselves, right, which you mentioned, like, that's not only a potentiality. It seems like a likelihood that the developers of these systems are going to get them working on improving them. And again, we still don't fully know where they're going. Then it seems like that could have some unintended consequences. Totally. Yeah, yeah. Yeah. I mean, I'm still expecting a great future because of AI. My expectation is the median outcome is good. I think we should worry about the cases where things go really off the rails and do what we can to reduce the odds of that.

54:12And is that like the whole practice of alignment? Is that what people talk about when they're like, if we're going to set these things going, we should at least align their values to be closer to the ones we want as humans? Yeah. it means so many things at this point that like even i'm sometimes confused by what exactly it means i mean what one of the goals is that it should do what the user wants it to do unless it's the user wants it to do something that would be like hurt other people basically but there's like problems with that definition obviously uh what if the um user wants to use like uh in super intelligence to make a bioweapon or to do a coup against the government or something but yeah something like basically like we don't want the AIs to like then have their own drives and want to take over or something.

55:00And do you think that this is a reasonable concern and if so do we have a reasonable chance of stopping it? I think both it's a reasonable concern and we have a reasonable chance of stopping it. One of the things I've discussed in one of my recent episodes with Trenton and Cholto is there's these researchers who have discovered interesting properties that these models have in terms of how they represent uh their drives or their uh you know their what they're thinking and so you can like see if they think if they're being honest or not or if you're just reading their internals whether they think they're being honest or not and as they get smarter maybe you can parse out fundamentally it's a bunch of parameters right so it's much more interpretable than a human brain or something so maybe we can learn ways to sort of understand what they're up to and train them to do the right thing.

55:53We have an advantage in the sense of like, listen, if you break the law, we might put you to jail or something. But with these AI models, we can literally change their brain if they do something wrong. And like all their children have changed brains as a result. Just like the entire lineage has changed. Yeah, exactly. It is. And I guess we could shut them off. I don't know. Well, hopefully. That is a good point. I mean, they will be broadly deployed. So if they really go off the rails, then I think it might be tough. Damn. And so but but you do think that we're going to end up with a positive outcome here.

56:27Contingent on people still doing a bunch of alignment research and also these systems being deployed in a way that is, you know, you don't want just like some China just racing ahead of everybody else. And then just like doing a coup of the whole world because they have much more advanced AI. Right. So contingent. And then also, like, we want to make sure that the models are deploying. They serve the needs of the user and don't do crazy things. But given that, just fundamentally, more stuff, more abundance, more prosperity. I think that's good. Okay. As we come to a close, I did open some questions up from the Twitter folks to ask me what they want me to ask you.

57:07So I have actually one question that aligns a little bit with this discussion topic and then one that's kind of more about the podcast. One is, how have your political views changed, if at all, since you started the show? Or let me even, you know, put a different frame on that or similar. You've interviewed like Mark Andreessen as well, who has like very different perspectives from some of the early AI guests. So has that sort of changed the way you think about AI at all? I mean, generally, politically, I'm very libertarian. And I was probably even more libertarian than I am now. Like I was like an anarcho-capitalist and, you know, whatever.

57:43So or at least a soft version of that. So politically, I mean, the way in which that's changed is that I'm open to the possibility that potentially some kind of regulation might be useful on AI. But I still have libertarian instincts and I'm not sure if it will be done the right way. And maybe it's better for private companies to proceed and come up with incentives and constraints by themselves. What type of regulation do you think might be appropriate? I was talking this week with an editor I work with, and we were like maybe regulating the way that kids and AI can interact, given you really have no idea where that's going to go once you put this in the hand of a child.

58:22Yeah, potentially. But I think it'll honestly be better than what they're currently doing, which is YouTube and TikTok and Twitter, you know, Facebook and so forth. So I would prefer my kids are playing with chatbots than they're playing with what they currently have access to. I don't have kids. What if I did? uh i think so i think in the world where you have really fast ai progress and you are coming up to this point we're talking about where ais can help improve themselves then i think what you want to do is you might need a sort of government level actor to be like all right everybody pause for a second anybody pushes this button basically of like helping the having the ai help us with ai research they could get fundamentally better ais than everybody else has and kind of take over the galaxy basically so before we before we let somebody do that we got to make sure we're in a place where we're we're ready to proceed right and not just let some random person do that so in that world i think regulation makes sense yeah then the second question we had was uh someone says give us the dvarkesh interview prep playbook that's his innovation and if he's able to explain it in the way that can be replicated or at least approximated by others we'll have many more interesting interviews okay i'm honestly self-interested in this as well so how do you do it well i i know it sounds like uh terse to say this or but i honestly just like i i prep a lot and i think probably there's also a flywheel uh by doing interviews i learned a lot of things and because of that i can uh get better interviews learn more things i think the The main flywheel, honestly, is that I make the podcast better.

1:00:00Smarter people listen. Some of those smart people I become friends with. And they teach me a bunch of things. And now I can do an even better interview. I can get connected to a bunch of other smart people. They teach me more things. So I think that's going to be a big part of the flywheel that people may not know about. Yeah. I've definitely had this here. Like we've talked about certain companies and stuff. And then people who listen have reached out and been like, there's something you should probably know about this. And given that I enjoy the show, let's talk through it. Totally. It's always helpful.

1:00:29Yeah, yeah. In what ways is it different? Do you have some trick that I should be aware of or like other tools to trade? No, I really think that's it. I mean, you're going to get a great show. I think there's a few ways you'll do it. One, I mean, and you already know this, but you prep like crazy. You're not afraid to ask tough questions. And yeah, when somebody wants to call and talk through the topics afterwards, take the call. Totally. And then one more thing. This is more of like a media question, but video has been pretty big for you. So what was the thought about doing video? Because it's also expensive and time-consuming to produce.

1:01:07So I'm curious if you had an ROI calculation about doing video from the beginning. You also oftentimes show up with a video camera and record in-person interviews. So talk a little bit through your strategy there. I will say for anybody doing a podcast I highly recommend video and like if you can do it in person I mean especially for me doing it full-time because it increases the expected value so much where with a audio podcast the discoverability is so low that you kind of know how many listeners you'll get but the tail outcome where something goes really viral I'll give you an example my most popular episode right now is sally pain it has like 800 something thousand views on youtube and you know like she's she was totally unknown before the podcast at least by the wider public but the episode was so compelling that now you can make clips of it now the clips wouldn't be as compelling if they were made of a not in-person episode let alone if there was no video at all so you make these amazing you make these great clips they bring people to the video and then you can have like close to a million people watch it just because so on any average video you might do in the beginning that might not be the case but you just have this asymmetric return potentially from having that artifact and then as far as how to make it better like i would tell people to just like well honestly like well watch a bunch of like podcasts with mr beast i think he has good advice yeah and so do you set up those cameras yourself or you bring somebody in i've usually half and half about um yeah but yeah like i've got the workflow down i set it up and um more recently i've been having a friend help me um nice helpful yeah that's sort of like the the bag of podcaster tricks like it's it's not just sitting down in front of a microphone we all have to learn these days we all have to learn sound we have to learn video and figuring out the main thing is like i don't know this is your experience but like clips is the main thing like Like you had to spend a ton of time.

1:03:04And you got to do all this Mr. B stuff of you make a clip with the wrong first five seconds and it's not going to do well at all. But then if you spend a bunch of time thinking through what is like the hook to begin with, then it could go super viral. So that takes up a bunch of time, right? Definitely. Yeah. So much time that I mean, it's it's videos like become like part of the strategy for me. But also it's low down because of the amount of work that it takes to get into. But we also we do two shows a week. So it's like, yeah, it's a question of sometimes do the show or do the clip. So totally, totally.

1:03:39Do you do, are you doing the show full time or? Yeah. So big technology is full time for me. It's the show. It's the newsletter on Substack and then YouTube. You're full time also, right? That's right. Yeah. I mean, it's a really, it's a great life if you're able to do it because of what we talked about, just finding all these interesting people to just get to spend time and learn from. Yeah, 100%. All right, last question for you. Out of all the interviews you've done, I don't want to ask you your favorite, but I want to ask you who was the most impressive person that you've spoken with? Someone who you walked away with and said, all right, this person really gets it.

1:04:17I mean, I'm sure there were multiple, but who's at the top of the heap there? Carl Schulman, I would say. He's just this really interesting person who has these models about how the AI takeoff will happen. The stuff I've been saying about, do you have AI researchers and whatever, he has just thought it out much more. He can go through the numbers in terms of, I mean, literally things down to the level of, okay, well, suppose you get something really smart. How fast could it do a takeoff? And then, well, E. coli can double every 20 minutes, and it has this many moving parts inside of it. So we have a sort of lower bound there of you can just do that.

1:04:52And then what would it look like to convert the entire Sahara into solar power and how many data centers could make that of that and stuff like that? Yeah. Oh, that's fascinating. All right, Dvorakish, awesome stuff. Thank you so much for joining. Awesome. Thanks so much for having me. This was fun. All right, everybody, thank you for listening. We'll be back on Friday with our show with Ranjan Roy, breaking down the week's news. And we'll see you next time on Big Technology Podcast. What the hell is going on right now? And why is it happening like this? At Wired, we're obsessed with getting to the bottom of those questions on a daily basis.

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