Podcast

A Motorcycle for the Mind

Naval

Last highlighted February 22, 2026 Source ↗ ← All highlights

Vibe Coding Democratizes App Creation
- “Vibe coding” lets non-programmers describe apps in English and have models build them end-to-end.
- This will create a tsunami of niche apps while top apps capture most users. Transcript: Naval Ravikant Been a shift, a marked pronouncement in the last year, and especially in the last few months, most pronounced by Claude Code, which is a specific model that has a coding engine in it, Which is so good that I think now you have Vine coders, which are people who didn’t really code much or hadn’t coded in a long time, who are using essentially English as a programming language, As an input into this code bot, which can do end-to coding. Instead of just helping you debug things at the middle, you can describe an application that you want. You can have it lay out a plan. You can have it interview you for the plan. You can give it feedback along the way, and then it’ll chunk it up, and it’ll build all the scaffolding. It’ll download all the libraries and all the connectors and all the hooks, and it’ll start building your app and building test harnesses and testing it, and you can keep giving it feedback And debugging it by voice, saying this doesn’t work, that works, change this, change that, and have it build you an entire working application without your having written a single Line of code. For a large group of people who either don’t code anymore or never did, this is mind-blowing. This is taking them from idea space and opinion space and from taste directly into product. So vibe coding is a new product management. Instead of trying to manage a product or a bunch of engineers by telling them what to do, you’re not telling the computer what to do. And the computer is tireless, the computer is egoless, and it’ll just keep working. It’ll take feedback without getting offended. You can spin up multiple instances. It’ll work 24-7, and you can have it produce working output. What does that mean? Just like now anybody can make a video or anyone can make a podcast, anyone can now make an application. So we should expect to see a tsunami of applications. Not that we don’t have one already in the App Store, but it doesn’t even begin to compare to what we’re going to see. However, when you start drowning in these applications, does that necessarily mean that these are all going to get used? No, I think it’s going to break into two kinds of things. First, the best application for a given use case still tends to win the entire category. When you have such a multiplicity of content, whether in videos or audio or music or applications, there’s no demand for average. Nobody wants the average thing. People want the best thing that does the job. So first of all, you just have more shots on goal. So there will be more of the best. There will be a lot more niches getting filled. You might have worn an application for a very specific thing, like tracking lunar phases in a certain context or a certain kind of personality test, or a very specific kind of video game That made you nostalgic for something. Before, the market just wasn’t large enough to justify the cost of an engineer coding away for a year or two. But now, best vibe coding app might be enough to scratch that itch or fill that slot. So a lot more niches will get filled. And as that happens, the tide will rise. The best applications, those engineers themselves are going to be much more leveraged. They’ll be able to add more features, fix more bugs, smooth out more of the edges. So the best applications will continue to get better. A lot more niches will get filled. (Time 0:02:24)
Model Tuning Is A New Programming
- Training and tuning models is a new form of programming that searches for programs inside data.
- Model design and hyperparameters shape which programs the system discovers. Transcript: Naval Ravikant The most obvious place that coding exists is in training these models themselves. There are many different kinds of models. There are new ones coming out every day. There are different ones for different domains. We’re going to see different models for biology or programming. We’re going to see pointed focus models for sensors. We’re going to see models for CAD, for design. We’re going to see models for 3D and graphics and games, models for video. You’re going to see many different kinds of models. The people who are creating these models are essentially programming them, but they’re programmed in a very different way than classic computers. Classic computing is you have to specify in great detail every step, every action the computer is going to take. You have to formally reason about every piece and write it in a highly structured language that allows you to express yourself extremely precisely. The computer can only do what you tell it to do. And then once you’ve got this very structured program, you run data through it and the computer runs the data and gives you an output. It’s basically an incredibly fancy, very complicated, meticulously programmed calculator. Now, when it comes to AI, you’re doing something very different, you are nevertheless programming it. What you’re doing is you’re taking giant data sets that have been produced by humanity, thanks to the internet or aggregated in other ways, and you’re pouring those data sets into a Structure that you’ve defined and tuned. And that structure tries to find a program that can produce more of that data set or manipulate that data set or create things off that data set. So you’re searching for a program inside this construct that you’ve designed. You’ve set up a model, you’ve tuned the number of parameters, you’ve tuned the learning rate, you’ve tuned the batch size, you’ve tokenized the data that’s coming, you’ve broken it Into pieces, and you’re pouring it inside the system you’ve designed, almost like a giant pachinko machine. And now the system is trying to find a program and could find many different programs. So your tuning really influences how good the program that you found is. And that program can now suddenly be expressive in different kinds of domains. (Time 0:06:55)
Keep Engineering Skills Sharp
- Learn software engineering fundamentals to patch leaky abstractions and optimize AI-generated code.
- Use engineers for high-performance or novel systems until models mature. Transcript: Naval Ravikant Software engineers, even the ones who are not necessarily tuning or training AI models, these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code. So they actually know what’s going on underneath and all abstractions are leaky so when you have a computer programming for you when you’ve clawed code or equivalent programming for You it’s going to make mistakes it’s going to have bugs it’s going to have suboptimal architecture so it’s not going to be quite right and someone who understands what’s going on underneath Will be able to plug the leaks as they occur so if you want to build a well-architected application if you want to be able to to even specify a well-architected application, if you want To be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do like a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain, when you have to write very high performance code, you’re running on architectures that are novel or brand new, when you’re actually Creating new things or solving new problems, then you still need to get in there and hand code it, at least until either there are so many of those examples that new models can be trained On them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own. Because given enough data points, there is some evidence that these AIs actually learn. They learn to a higher level of abstraction because the act of forcing them to compress the data forces them to learn higher level representations. (Time 0:10:18)
There Is No Demand For Average
- Markets reward the best product and leave no demand for average offerings.
- You must be the best at something or find a niche to dominate. Transcript: Naval Ravikant The average app, nobody wants it. At least as long as it’s not filling some niche, the app that is better will win essentially 100% of the market. Maybe there’s some small percentage that will bleed off the second best app because it does some little niche feature better than the main app or it’s cheaper or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three. Like in the famous Glenn Gary, Glenn Ross scene where Alec Baldwin says first place gets a Cadillac Eldorado, place gets a set of steak knives, and third place you’re fired. That’s absolutely true in these winner-take markets. That’s the bad news. You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where it said, become the best in the world at what you do, keep redefining what you do until this is true. And I think that still applies in this age of AI. (Time 0:13:11)
Don’t Obsess Over Prompt Tricks
- Don’t waste time mastering ephemeral prompt tricks; let models adapt to you as they improve.
- Focus only on bleeding-edge workflows if you need a competitive advantage now. Transcript: Naval Ravikant I don’t think it’s worth learning tips and tricks of how to work with these AIs. You’ll see, for example, on social media right now, there’s a lot of write-ups and books and tweets like oh i figured out this neat trick with the bot you can prompt it this way or you can Set up your harness this way or there’s like a new programming assist tool or layer that you can use on top of it to do this and that and i never bothered learning those i just sit there stupidly Talking to the computer because i know that this thing is now at the stage where it is going to adapt to me faster than I can adapt to it. It is getting smarter and smarter about how people want to use it. So it is learning, it is being trained, and tools are being built very quickly to make it easier for me to use it. So I don’t need to sit there and figure out some esoteric programming command. And this is what I think Andre Karpathy meant when he said, English is the hottest new programming language. I just can speak English. And for someone like me, who is relatively articulate with English and also has a structured mind, and I know how computer architectures work, and I know how computer programs work, And I know how programmers think, I can actually very precisely specify what I want just through structured English. I don’t need to go any further than that. The only reason to use these workflows and tool sets, which are very ephemeral and their longevity is measured in weeks, perhaps months at best, not in years, is if you’re building an App right now that needs you at the bleeding edge and you absolutely need every little bit of advantage that you can get because you’re in some kind of a competitive environment. But otherwise, I wouldn’t bother learning how to use an AI. (Time 0:15:32)
Selection Makes AI Useful To Humans
- Free-market pressures make deployed AIs useful because humans only spin up models that help them.
- Personalization will make assistants feel more human and obsequious. Transcript: Naval Ravikant I think as AI evolves or co-evolves with us, it’s evolved by us according to our needs. The pressures on AI are very capitalistic pressures in the sense that it’s a free market for AI. As an AI instance, you only get spun up by a human if you’re useful to a human. So there is a natural selection pressure on these AIs to be useful, to be obsequious, to do what we want. And so it will continue to adapt towards us and I think will be quite helpful to us. That’s not to say that there’s no such thing as a malicious AI, but it’s malicious because the people who are using it are using it for malicious reasons. And like a dog that’s trained to attack, it’s actually being trained by its owner to go and do the owner’s malicious desires. Nivy So I don’t really worry about unaligned AI. I about unaligned humans with AI. So the selection pressure you’re saying is for AI to be maximally useful to people. Naval Ravikant Correct. And so if you find an AI to be very obsequious towards you, for example, how it’s always saying, oh, you’re right. Oh, that’s such a great idea. Oh my God, you’re so smart. That’s because that’s what most people want. And at least today, these AIs are being trained on massive amounts of users and massive amounts of data because you’re working with one size fits all models. But we’re going to quickly move into an era when you can personalize your AI and does begin to feel more and more like your personal assistant and it corresponds more to what you want, Which will, of course, anthropomorphize the AI even more. (Time 0:18:35)
Programmers Gain Extreme Leverage
- Programmers become far more leveraged by wielding fleets of AIs and can replace many incumbents.
- Intelligence and leverage are heavily skewed; few people will produce outsized outcomes. Transcript: Naval Ravikant Yeah, this is my point earlier, which is that programmers are becoming even more leveraged. So now a programmer with a fleet of AIs is, call it, 5, 10x more productive than they used to be. And because programmers operate in the intellectual domain, it’s a mistake to even say 10x programmers because there are 100x programmers out there. There are 1,000x programmers out there. There are programmers who just pick the right thing to work on and they create something that’s valuable and others who pick the wrong thing to work on and their work has zero value in That short time frame. Intelligence is not normally distributed. Leverage is not normally distributed. Programmability is not normally distributed. Judgment is not normally distributed. So the outcomes are going to be super normal. So what you have to really watch out for is there are programmers now who are going to come up with ideas that can replace entire industries. They will completely rewrite the way things are done and their intelligence can be maximally leveraged with all these bots and all these AI agents. I think every other job out there is going to get eaten up by programmers one way or another over the maximally long term. Obviously, it has to instantiate into robots, etc. But the good news is anybody who is a logical, structured thinker who thinks like a programmer and can speak any language that an AI can understand, which will be every language, will Now be on the playing field. They will be able to make anything they want obstructed only by their creativity, limited only by their imagination. So we are entering an era where every human, in a sense, is a spellcaster. If you think of programmers as like these wizards who have memorized arcane commands, you can think of AI as a magic wand that’s been handed to every person, where now they can just talk In any language they want, and they’re a wizard too. So it is more of a level playing field. I really do think this is a golden age for programming. (Time 0:20:20)
Use AI To Increase Agency
- Use AI as an ally when pursuing ambitious, entrepreneurial goals rather than fearing it.
- Preserve extreme agency; entrepreneurs direct tools toward hard, novel problems. Transcript: Naval Ravikant Their job. That one’s glib in multiple ways. First of all, being an entrepreneur isn’t a job. It’s literally the opposite of a job. And in the long run, everyone’s an entrepreneur. Careers got destroyed first, jobs get destroyed second, but all of it gets replaced by people doing what they want and doing something that creates something useful that other people Want. So no entrepreneurs are worried about an AI taking their job because entrepreneurs are trying to do impossible things. They’re trying to do very difficult things. Any AI that shows up is their ally and can help them tackle this really hard problem. They don’t even have a job to steal. They have a product to build. They have a market to serve. They have a customer to support. They have a creativity to realize. They have a thing that they want to instantiate in the world, and they want to build a repeatable and scalable process around getting it out into the world. This is so difficult that any AI that shows up that can do any of that work is their ally. If the AIs themselves are entrepreneurs, they’re likely going to just be entrepreneurs serving other AIs or they’re under the control of an entrepreneur. The thing that the AI itself is missing at the end of the day is its own creative agency. It’s missing its own desires and they have to be authentic, genuine desires. Unless you can pull the plug on an AI and turn it off and unless it lives in mortal fear of being turned off and unless it can actually make its own actions for its own reasons, for its own Instincts, its own emotions, its own survival, its own replication, it’s not quite alive. And even then people will challenge, is it alive? Because consciousness is one of those things that’s a qualia. It’s like a color. It’s like if you say red, I don’t know if you’re actually seeing red. You might be seeing what I see as green and I might be seeing what you see as red. But we’ll never know because we can’t get into each other’s minds. So the same way, even AI that’s completely imitating everything that humans do, to some people it’ll always be an imitation machine and to others it’ll be conscious. But there’ll be no way of distinguishing the two. We’re still pretty far from that though.. Right now, the AIs are not embodied. They don’t have agency. They don’t have their own desires. They don’t have their own survival instinct. They don’t have their own replication. Therefore, they don’t have their own agency. And because they don’t have their own agency, they cannot do the entrepreneur’s job. In fact, I would summarize this by saying the key thing that distinguishes entrepreneurs from everybody else right now in the economy is entrepreneurs have extreme agency. (Time 0:23:10)
AI Strengths And Fundamental Limits
- AIs compress massive data and learn higher-level abstractions, yet lack single-shot learning and full embodiment.
- They excel where humanity already has many examples but fail on truly novel physical tasks. Transcript: Naval Ravikant Don’t think these things are alive. I think they start out as extremely good imitators to the point where they’re almost indistinguishable for the real thing, especially for anything that humanity has already done Before en masse. So if the task has been done before, then it’s going to be automated and it’ll be done again. It may just be novel to you because you’ve never seen it, but the AI has learned it from somewhere else. That’s the first way in which it seems alive. The second way, which we talked about earlier, is where it does learn higher levels of abstraction. These are very efficient compressors. They take huge amounts of data and then they compress it down further. In the process of compressing it, they learn higher level abstractions. And then specific areas where they may not have learned those through the data themselves, they’re getting patched through human feedback. They’re getting patched through tool use. They’re getting patched from traditional programming becoming embedded And especially the AIs that are learning how to think in code, they have the entire library of all of human Code ever written to fall back on for algorithmic reasoning. In that sense, the set of things that they can do is getting broader and broader. However, what they lack still is a lot of core human skills, like single-shot learning. Humans can learn from just one example. The raw creativity of human beings, where they can connect anything to anything. They can leap across entire huge domains and search spaces and figure out an idea that just came out of left field. This happens a lot with the true great scientific theories. Humans also are embodied. They operate in the real world. They’re not operating in the compressed domain of language. They’re operating in physics, in nature. Language only encompasses things that humans both figured out and could articulate and convey to each other. That’s a very narrow subset of reality. Reality is much broader than that. (Time 0:30:35)
Superintelligence Is Not Beyond Human Grasp
- Superintelligence exists in narrow forms (e.g., calculators) but not as incomprehensible ideas beyond human understanding.
- Humans can interrogate and model any physically possible idea given time. Transcript: Naval Ravikant Explainers. Anything that is possible with the current laws of physics as we know them, a human can model in their own heads. Therefore, just by enough digging, enough questions, we could figure anything out. Related to that, we should discuss AI as a learning tool because I think the other place where it’s incredibly powerful is the most patient tutor that can meet you at your level and explain Anything to your satisfaction a hundred different ways, a hundred different times until you finally get it. I don’t think the AIs are going to be figuring things out that humans cannot understand. But intelligence is poorly defined. What is the definition of intelligence? There’s the G factor, which predicts a lot of human outcomes. But the best evidence for the G factor is predictive power. It’s that you measure this one thing and then you see people get much better life outcomes along the way in things that seem even somewhat unrelated to G. So I would argue, and I think it’s one of the more popular tweets, the only true test of intelligence is if you get what you want out of life. This triggers a lot of people because they go to school, they get their master’s degrees, they think they’re super smart, and then they don’t have great lives. They aren’t super happy or they have relationship problems or they don’t make the money that they want or they become unhealthy and this sort of triggers them. But that really is the purpose of intelligence for you as a biological creature to get what you want out of life, whether it’s a good relationship or a mate or money or success or wealth Or health or whatever it is. So there are people who I think are quite intelligent because you can tell they have high quality functioning lives and minds and bodies. And they’ve just managed to navigate themselves into that situation. It doesn’t matter what your starting point is because the world is so large now and you can navigate it in so many different ways that every little choice you make compounds and demonstrates Your ability to understand how the world works until you finally get to the place that you want. Now, the interesting thing about this definition that the only true test of intelligence is if you get what you want out of life is that an AI fails it instantly because an AI doesn’t want Anything out of life. The AI doesn’t even have a life, let alone that, but it doesn’t want anything. AI’s desires are programmed by the human controlling it. But let’s give it that for a second. Let’s say the human wants something and programs the AI to go get it. Then the AI is acting as a proxy for the human and the intelligence of the AI can be measured as, did it get that person that thing? (Time 0:33:30)
Use AI As A Patient Tutor
- Use AI as a patient, personalized tutor to learn any subject at your level and iterate until you understand.
- Have the AI generate diagrams, analogies, and stepwise explanations to cement comprehension. Transcript: Naval Ravikant I don’t think the AIs are going to be figuring things out that humans cannot understand. But intelligence is poorly defined. What is the definition of intelligence? There’s the G factor, which predicts a lot of human outcomes. But the best evidence for the G factor is predictive power. It’s that you measure this one thing and then you see people get much better life outcomes along the way in things that seem even somewhat unrelated to G. So I would argue, and I think it’s one of the more popular tweets, the only true test of intelligence is if you get what you want out of life. This triggers a lot of people because they go to school, they get their master’s degrees, they think they’re super smart, and then they don’t have great lives. They aren’t super happy or they have relationship problems or they don’t make the money that they want or they become unhealthy and this sort of triggers them. But that really is the purpose of intelligence for you as a biological creature to get what you want out of life, whether it’s a good relationship or a mate or money or success or wealth Or health or whatever it is. So there are people who I think are quite intelligent because you can tell they have high quality functioning lives and minds and bodies. And they’ve just managed to navigate themselves into that situation. It doesn’t matter what your starting point is because the world is so large now and you can navigate it in so many different ways that every little choice you make compounds and demonstrates Your ability to understand how the world works until you finally get to the place that you want. Now, the interesting thing about this definition that the only true test of intelligence is if you get what you want out of life is that an AI fails it instantly because an AI doesn’t want Anything out of life. The AI doesn’t even have a life, let alone that, but it doesn’t want anything. AI’s desires are programmed by the human controlling it. But let’s give it that for a second. Let’s say the human wants something and programs the AI to go get it. Then the AI is acting as a proxy for the human and the intelligence of the AI can be measured as, did it get that person that thing? Most of the things that we want in life are adversarial or zero-sum games. So for example, if you want to seduce a girl or get a husband, you’re competing with all the other people who are out there seducing girls or trying to get husbands. So now you’re in a competitive situation. The AI has to outmaneuver the other people. Or if you say, hey, AI, go trade on the stock market for me and make me a bunch of money, that AI is trading against other humans and other trading bots. It’s an adversarial situation. It has to outmaneuver them. Or if you say, hey, AI, make me famous, write me incredible tweets, write me great blog posts, record me great podcasts in my own voice and make me famous, now it’s competing against all The other AIs. So in that sense, intelligence is measured in a battlefield arena. It’s a relative construct. I think the AIs are actually going to fail mostly in those regards, or to the extent that they even succeed, because they are freely available, they will get out-competed away. And the alpha that will remain would be entirely human. As a thought exercise, imagine that every guy had a little earpiece where an AI was whispering to him, a Cernod de Bergerac kind of earpiece, telling him what to say on the date. Well, then every woman would have an earpiece telling her to ignore what he said or what part was AI generated, what part was real. If you have a trading bot out there, it’s going to be nullified or canceled out by every other trading bot until all the remaining gain will go to the person with the human edge, with the Increased creativity. Now that’s not to say that the technology is completely evenly distributed. Most people still aren’t using AI or aren’t using it properly or aren’t using it all the way to the max or it’s not available in all domains or all contexts or they’re not using the latest Models. So you can always have an edge like people who early adopt technology always do if you adopt the latest technology first. This is why I always say to invest in the future you want to live in the future. You want to actually be an avid consumer of technology because it’s going to give you the best insight on how to use it and it will give you an edge against the people who are slower adopters Or laggards. Most people hate technology. They’re scared of it. It’s intimidating. You press the wrong button, the computer crashes. You lose your data. You do the wrong thing, you look like an idiot. Most people do not have a positive relationship with complex technology. Simple technology, embedded technology, they’re fine with. You throw on a light switch, light turns on. That used to be technology. It’s so simple now you don’t think of this technology anymore. You get in a car, you turn the steering wheel left to a caveman, that would be a miracle. The car turns left. It’s no longer technology to you. But computer technology in particular has had very complex interfaces and been very inaccessible and very intimidating to people in the past. Now with the AIs, we’re getting the chatbot interface, which is you just talk to it, you type to it. And one of the great things about these foundational models, what truly makes them foundational, is you can ask them anything and they’ll always give you a plausible answer. It’s not going to say, oh sorry, I don’t do math or I don’t do poetry or I don’t understand what you’re talking about or I can’t give relationship advice or anything like that. Its domain is everything that people have ever talked about. In that sense, it’s less intimidating. It can be more intimidating because it anthropomorphizes so much. If you think Claude or Chad GPT is a real person, then it can be a little scary. Am I talking to God? This guy seems to know so much. He knows everything. He’s got an opinion on everything. He’s got every piece of data. Oh my God, I’m useless. Let me start talking to it and asking it what to do. And you can reverse the relationship and fool yourself very quickly. That can be intimidating. Overall, I think these AIs are going to help a lot of people get over the tech fear, but if you’re an early adopter of these tools, like with any other tool, but even more so with these, you Just have a huge edge on everybody else. I remember early on when Google first came out, I used to use it a lot in my social circle. People would ask me basic questions and I would just go Google it for them and look like a genius. Eventually this hilarious website came along, something like lmgtfy.com and it stood for let me Google that for you. Someone would ask you a question, you would go type the question into this website and it would create like a tiny little inline video showing you typing that question into Google and Giving the Google results. And I feel like AI is in a similar domain right now where I will sit around in social context and people will be debating some point that can be easily looked up by AI. Now you do have to be very careful with AI. They do hallucinate. They do have biases in how they’re trained. Most of them are extremely politically correct and taught not to take sides or only take a particular side. I actually run most of my queries, almost all actually, through four AIs, and I’ll always fact check them against each other. And even then, I have my own sense of when they’re bullshitting or when they’re saying something politically correct, and they’ll ask for the underlying data or the underlying evidence. And in some cases, I’m finally dismissing it outright because I know the pressures that the people who trained it were under and what the training sets were. However, overall, it is a great tool (Time 0:33:56)
Solving Problems Isn’t Always Creativity
- AI can assemble embedded knowledge to solve math problems, but that isn’t necessarily human-level creativity.
- True creativity produces unforeseeable answers far outside prior elements. Transcript: Nivy Talk about the epistemology of AI, I think the next big misconception is AI is already starting to solve some unsolved basic math problems that a human probably could solve if they cared To, but they haven’t been solved yet. Like Erdos problem number whatever. Now, I think people are taking that or will take that as an indicator that the AI is creative. I don’t think it’s an indication that the AI is creative. I actually think the solution to the problem is already embedded somewhere in the AI. It just needs to be elicited by prompting. Naval Ravikant There’s definitely that element to it. And then the question is, what is creativity? It’s such a poorly defined thing. If you can’t define it, you can’t program it. And often you can’t even recognize it. So this is where we get into taste or judgment. I would say that the AIs today don’t seem to demonstrate the kind of creativity that humans can uniquely engage in once in a while. And I don’t mean like fine art. People tend to confuse creativity with fine art. They’re like, oh, paintings are creative and AIs can paint. Well, AIs can’t create a new genre of painting. AIs can’t move humans with emotion in a way that is truly novel. So in that sense, I don’t think AI is creative. I don’t think AI is coming up with what I would call out of distribution. Now, the answer to the Erdős problems that you mentioned may have been embedded within the AI’s training data set or even within its algorithmic scope, but it was probably embedded In five different places in three different ways, in two different languages, in seven different computing and mathematical paradigms. And the AI sort of put them all together. Now, is that creativity? Steve Jobs famously said, creativity is just putting things together. I actually don’t think that’s correct. I think creativity is much more in the domain of coming up with an answer that was not predictable or foreseeable from the question and from the elements that were already known. It was very far out of the bounds of thinking. If you were just searching it with a computer or even with an AI and making guesses, you’d be making guesses till the end of time or until you arrived upon that answer. So that’s the real creativity that we’re talking about. But admittedly, that’s a creativity that very few humans engage in and they don’t engage in it most of the time. It becomes harder and harder to see. So we are probably going to get to where if you have a giant list of math problems to be solved, and AI starts going through and picking, okay, this one out of that set of 1 million I can solve, And this set out of 300,000 I can solve, and I need a person to prompt me and ask the right questions, that’s a very limited form of creativity. There’s another form of creativity where it starts inventing entirely new scientific theories that then turn out to be true. I don’t think we’re anywhere near that, but I could be wrong. The AIs have been very surprising, so I don’t want to get too much in the business of making prophecies and predictions. But I don’t think that just throwing more compute at the current AI models short of some breakthrough invention is going to get us there. (Time 0:44:37)
Cure AI Anxiety With Action
- Overcome AI anxiety by taking action: learn how it works and experiment with it.
- Understanding internals helps you use AI better and judge legitimate risks. Transcript: Naval Ravikant When new paradigms and new tool sets come out, there is a moment of enthusiasm and change. And this is true in society. And this is true as an individual. If you ride the moment of enthusiasm in society, that’s exciting and you can learn new things, you can make friends and you can make money. But there’s also a moment of enthusiasm in an individual. When you first encounter AI and you’re curious about it, and you’re genuinely open-minded about it, I think that’s the time to lean in and learn about the thing itself. Not just to use it, which of course everyone will, but to actually learn how it works. I think diving into and looking underneath the hood is really interesting. If you encounter a car for the first time in your life, yes, you can get it and drive it around, but that’s the moment you’re also going to be curious enough to open up the hood and look how It’s structured and designed and figure it out. I would encourage people who are fascinated by the new technology to really get into the innards and figure it out. You don’t have to figure out the level where you can build it or repair it or create your own, but to your own satisfaction, because understanding what’s underneath the abstraction, What’s underneath that command line, it’s going to do two things. One is it’ll let you use it a lot better. And when you’re talking about the tool that has so much leverage, using it better is very helpful. Second is it’ll also help you understand whether you should be scared of it or not. Is this thing really going to metastasize into a Skynet and destroy the world? Are we going to be sitting here and Arnold Schwarzenegger shows up and says at 429 AM on February 24th is when Skynet became self-aware, right? Or is it more that, hey, this is a really cool machine and I can use it to do A, B, and C, but I can’t use it to do D, E, and F, and this is where I should trust it, and this is where I should be suspicious Of it. I feel like a lot of people right now have AI anxiety. And the anxiety comes from not knowing what the thing is or how it works, having a very poor understanding. And so the solution to that anxiety is action. The solution to anxiety is always action. Anxiety is a non-specific fear that things are going to go poorly and your brain and body are telling you to do something about it, but you’re not sure what. You should lean into it. You should figure the thing out. You should look at what it is. You should see how it works. And I think that’ll help get rid of the anxiety. That action of learning, that pursuit of curiosity is going to help you get over the anxiety. (Time 0:49:37)