I get to talk to people all the time about how they use AI in their work and in their lives, and also how it has changed them as people.

There's many different ways of knowing things, and many different ways of understanding things.

Computers, science, what both of those ways of seeing the world are trying to do is reduce the world into a set of really clean universal laws that apply in any situation. If X is true, then Y will happen.

And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique, very context specific ways to produce what comes next.

And what's really interesting about neural networks is the way that they think or the way that they operate is a lot like human intuition. Human intuition is also trained by thousands, and thousands, and thousands of hours of direct experience.

The reason I love that is because I hope that it makes more visible to us, the value and importance of intuitive thought.

My name is Dan Shipper, I'm the Co-Founder and CEO of Every, and I'm the host of the AI & I podcast.

Chapter 1, the limits of rationalism from Socrates to neural networks. I think rationalism is one of the most important ideas in the last, like 2,000 years.

Rationalism is really the idea that if we can be explicit about what we know, if we can really reduce what we know down into a set of theories, a set of rules for how the world works, that is true knowledge about the world, and that is distinct from everything else that kind of messes with our heads, messes with how we operate in society.

And you may not have heard that word, or maybe you have, but it is built into the way that you see the world. For example, the way computers work, or the way vaccines work, or the way that we predict the weather, or the way that we try to make decisions when we're, you know, thinking about, I don't wanna be too emotional about this. I want to get really precise about my thinking on this issue.

Even the way that we do therapy, a lot of therapy is about rationalizing, or rationalizing through what you think, and what you feel. All that stuff comes from an extensive lineage of ideas that started in Ancient Greece, really blossomed during the enlightenment, and now is like the bedrock of our culture, and the way that we think about the world.

I think the father of rationalism is Socrates, the philosopher. Socrates is one of the first people to really examine the question of what we know and how. What is true and what's not true. To be able to describe what we know, and how we know it, to make that clear and explicit so that only people that knew the truth, that knew how the world really works were the ones that were steering the state.

That really became the birth of philosophy, is this idea that if you inquire deeply into what is usually kind of like the in explicit intuitions that we have about the world, you can find, you can identify a set of rules, or a theory, about what the world is like, and what's true and what's not, that you can lay out explicitly, and that you can use to decide the difference between true and false.

I think that you can trace the birth of rationalism to this dialogue, Protagoras. And in the dialogue, it's a debate between Socrates on the one hand, and Protagoras. And Protagoras is what we call a sophist.

And it's where the term, sophistry which means like kind of, you know, someone who says really compelling things but is actually full of shit. What Protagoras and Socrates are debating is, can excellence be taught?

And excellence, the word is often translated in English as virtue, but I think a more appropriate translation is excellence. And in Ancient Greece, like that kind of excellence was really prized. It's sort of like a general ability to be good at important things in life, and in society.

And they approach it from very different angles. Protagoras believes that everyone has the capacity, every human has the capacity to be excellent, and he tells this big myth about how we, as humans, gain the capacity to be excellent. And Socrates is saying, no, no, no, I don't want any of that. What I want is I want a definition.

I want you to say explicitly what it is and what it's not, and what are the components of it. And that's a really big moment. At least the way that Plato writes it, Socrates kind of like takes apart Protagoras, and it's pretty clear by the end, that Protagoras doesn't know, doesn't have any way to define in a non-contradictory way what excellence is, what it means to be good.

And the implication is that then he doesn't know it. And that sort of set western society on this path of trying to find really clear definitions and theories for the things that we talk about, and to identify knowledge, the ability to know something, or whether or not you know something with whether or not you can really clearly define it.

And that idea became incredibly important in the scientific enlightenment. Thinkers on the philosophy side, like Descartes, and on the science side, like Newton and Galileo, took this idea, and used it as a new method to understand and explain the world.

So what it became is, can we use mathematics to explain and predict different things in the world? And from Socrates, to Galileo, to Newton, they continually reinforced this idea that in order to truly know something, you have to be able to describe it explicitly. You have to be able to have a theory about it. You have to be able to describe it mathematically ideally.

The world around us is shaped by this framework. So everything from smartphones, to computers, to cars, to rockets, to cameras, to electricity, every appliance in your house, vaccines, everything in our world is shaped with this idea, or this way of seeing the world. It's been incredibly impactful.

And you can find this too in the rest of the culture, like anytime you see, you know, a book, or a movie, or a blog post or whatever, talking about like the five laws of power, or like the five laws of negotiation. All that stuff is ways that physics has, and rationalism in general has like sort of seeped into the everyday way that we think about the world.

And to be clear, it's been super successful. But in areas of the world like psychology, or economics, or neuroscience, it has been really hard to make progress in the same way that physics has made progress.

I think if you look, for example, at the social sciences, a lot of the way that the social sciences are structured is inspired by physics. What we're trying to do is take very complex higher level phenomena, so like maybe psychology, or economics, or you know, any other branch of social science. And we're trying to reduce it down to a set of definitions, and a theory, and a set of rules for how things in that domain work.

And what's really interesting is if you look at those fields, so like psychology, for example, it's in the middle of a gigantic replication crisis. Even though we spent like 100 years doing psychology research, the body of knowledge that we've been able to build there in terms of its universal applicability, our ability to find, you know, universal laws in the same way that Newton found universal laws seems pretty suspect.

And we feel like we can't stop doing it because we have no better alternative. Another really interesting and important part of the world that this way of looking at things didn't work for in many ways is AI.

So this is usually the part of an explanation where I try to define it. And like, what is AI? And what's really interesting is, there's no universal agreed upon definition for this, in the same way that there's, we've struggled to come up with a universal definition for what it is to know something, or universal definition for what anxiety is, for example, in psychology is another really good example.

There are a lot of ways to kind of like gesture at what AI is. But really it's like obviously, or maybe not obviously, AI stands for artificial intelligence. And the AI's project is to build a computer that can think and learn in the same way that humans learn. And because of the way that computers work, for a very long time, that was a really hard problem.

AI started as a field in the '50s at Dartmouth, and you can like actually look at the original paper. They were very optimistic. They were like, you know, maybe like, you know, a summer's worth of work and we'll have nailed this. And the way that they defined it is to be able to reduce down human intelligence into a system of symbols that they could combine together based on explicit rules that would mimic human intelligence.

And so there's a really clear through line from Socrates's original project to the enlightenment, to the original approach that AI theorists took called symbolic AI. The idea that you could embody thinking in, essentially like logic, logical symbols, and transformations between logical symbols, which is, it's very similar to just basic philosophy.

And there were actually a lot of early successes. For example, the two founding fathers of AI, Herbert Simon and Alan Newell, built this machine that they called the general problem solver. And what's really interesting is it wasn't even built as a computer because computers were extremely expensive back then. They originally codified the general problem solver on paper and then executed it themselves by hand.

And the general problem solver, they kind of, they tried to reduce down, you know, complex real world situations into simple logical logic problems, that they look a little bit like games. And then they tried to see if they could build a computer that would solve some of those games. And they were actually quite successful at first. What they found was it worked really well for simple problems.

But as problems got more and more complex, the search space of possible solutions got really, really, really, really big. And so by representing the problem in that way, the systems that they built started to fail as soon as they moved away from toy problems to more complex ones.

I think a really interesting and simple example of this is thinking about how you might decide whether an email in your inbox is spam, or whether it's important. And you might say something like, if it mentions that I won the lottery, it's spam, right? And so that sort of like if then rule, is a lot like the kinds of rules that early symbolic AI theorists were trying to come up with to help you solve any problem, is to codify like if X, Y, Z is true, then here are the implications.

What happens is if you look at that really closely, there are always lots of little exceptions. So an example might be, if it says emergency, maybe you wanna put that at the top of your inbox, But very quickly you'll have spammers obviously being like, just put emergency in the subject line, and they'll shoot to the top. So then you have to kind of like create another rule, which is it's emergency, but only if it's from my coworkers or my family.

But computers don't really know what coworkers or family is. So then you have to define, okay, like how is it gonna know what a coworkers or what a family member is? So what you can do is maybe it's like a coworker is anybody from my company. And so if it says emergency, and it's from anybody in my company, put it at the top of my inbox. But what you may find is that there are certain people at your company who are annoying and want your attention, even if you don't really want them to contact you. And so they start putting emergency into their inbox, and now you have to create another rule which is like, don't let people who are abusing the privilege of getting to the top of my inbox, abuse it even if they're coworkers.

And what you find is anytime you try to create rules to define these things, you always run up against exceptions. If you wanna, for example, define what an important email is, you have to define pretty much everything about the world. You have to create a world full of definitions. And that project of making the entire world explicit in definitions just didn't work. It's too brittle, it's too hard, there's too much computational power required to loop through all the different definitions to decide, you know, if this email is important or not. And it's just, there are too many definitions to create. It's just, it's too big of a project.

The alternative, which originated around the time that AI itself originated, but really wasn't taken that seriously until probably the '80s and '90s is what's called a neural network. And a neural network, it is inspired by the way our brains work. It doesn't work exactly the same way, but it is inspired that way. It is inspired from brains. And it basically consists of layers of artificial neurons that are connected to each other.

And what you can do with a neural network is you can get it to recognize patterns by giving it lots of examples. You can, you know, for example, if you want it to recognize like whether an email is important, what you can do is you can give it an example, say like, you know, here's an email from a coworker, and have a guess the answer. And if the answer is wrong, what we've done is we've created a way to train the network to correct its wrong answer. And what happens is over many, many, many, many iterations, and many, many different examples, what we find is without any explicit set of definitions, or explicit rules about like, you know, this is a important email, or this is a cat, or this is a good move in chess. The neural network learns to recognize patterns, and is able to do a lot of the more complex thinking style tasks that early symbolic AI was unable to do.

Language models are a particular kind of neural network that operates by finding complex patterns inside of language and using that to produce what comes next in a sequence. So what we've done with language models is fed them basically, you know, all of the text on the internet.

And language models learn that there are many, many thousands of partially fitting rules that they can apply based on the previous history of texts they've seen to predict what comes next. And all of those rules are inexplicit. They're kind of like, you can observe them in the overall behavior of the network, but they don't exist anywhere in the network. You can't go and look inside of a neural network, and find like this is exactly, this is the entire set of rules that it has. You may be able to find a couple, but you can't find a definitive list. In the same way that if I like took a microscope and looked in your brain, I would not be able to find that. I would not be able to find the list of rules that you use, for example, to, you know, recognize a cat, or do the next move in chess.

And what's really interesting about neural networks is the way that they think, or the way that they operate is a lot like, it looks a lot like human intuition. Human intuition is also trained by thousands, and thousands, and thousands of hours of direct experience. Often our best metaphor for our minds are the tools that we use. So a really good example is Freud, has one of the most impactful models of the mind. And the way that he came up with that, is he used the steam engine as a metaphor, so it's an explicitly steam engine-based idea. In the 20th century, the metaphor for our minds moved into being like a computer that became the kind of thing that we all wanted to be like, we wanted to be logical and rational, and operate like a machine to make the best decisions possible.

And I think one of the most interesting things about that way of thinking is it makes invisible to us. And this, I think, relates a lot to the like sort of Socratic enlightenment type of thinking as well. It makes invisible to us the importance of our intuition in being the foundation of everything that we do, everything we think, everything that we know. In a lot of ways you can think of rationality as emerging out of intuition. So like we have this sort of like squishy inexplicit, intuitive way of understanding what's going on in the world. And our rational thought comes out of that, and is able to, once intuition sort of sets the frame for us, is able to go in, and sort of manipulate things in a more methodical, rational, logical way. But you sort of need both.

And neural networks are the first technology we've ever invented that works a lot like human intuition. And that actually loops back and takes us all the way back to Protagoras, and is sort of the thing that we lost in this birth of rationalism, and back in Callias's house, because Protagoras is arguing that everyone teaches us excellence all the time. He's arguing, he's using stories, and myths, and metaphor to help understand something that he knows from his own personal experience. And Socrates is saying, well, if you can't define it, if you can't tell me exactly the rules by which you know something, then you don't know it. And that way of thinking about the world has been very successful for us, but it also kind of blinded us to how important that idea that everyone teaches us to be excellent, that stories and personal hands-on experience give us a way of knowing about things that we may not be able to explicitly talk about, but we still know, just as much as we know things that we can explicitly say. And it was only when we began to embody that way of being in the world or that way of knowing things, that way of thinking into machines that we started to get actual artificial intelligence.

History shows that it is better to be open to more ways of seeing and working with the world. And that in this particular era, it's very important to be able to work with things that are a little bit mysterious, and be comfortable with that.

Chapter 2, seeing the world like a large language model. I've always been like a real huge note-taking nerd. I love taking notes, especially because when I started my first company, I started my first company in college. And I ended up selling it. I flew from my college graduation to Boston to finish negotiating the deal to sell it. So that whole situation for me was this trial by fire of like, I felt like I had to, I was like an information firehose. I had to learn so much in order to successfully run a software company as a, I guess I was 20, 21, 22. And the way that I felt like I could do that best was to start taking notes, is to be like, okay, I learned this thing from a book, and it's about for how to hire people for example. And I know, I think it'll be relevant for me, but I don't know when it's gonna be relevant. So I'm gonna write it down and I'm gonna try to create like the perfect organizational system to categorize all this stuff so it'll come back to me when I need it.

And if you really take seriously that question of like how do you build the perfect note taking or organizational system, you actually run into the same problems that early symbolic AI theorists run into, and philosophers have been running into for a long time, which is how do we create the perfect system to organize reality? How do you know, you know, where to put a particular note?

And so when I first bumped into the language models, I realized that they had this ability to be sort of flexible and contextual in a way that meant that I didn't have to create the like perfect organizational system to teach a computer how to like organize my notes. It operated in this way that was ruleless, and fuzzy, and flexible.

Computers, science, what both of those ways of seeing the world are trying to do is reduce the world into a set of really clean universal laws that apply in any situation. Is to say if X is true, then Y will happen. Like really clear cause and effect, really clear chains that are universal and context-free. And what language models see instead is a dense web of causal relationships between different parts of the world that all come together in unique very context-specific ways to produce what comes next.

Where, you know, for example, previously on the internet, you could get an answer that was written by someone for like a very general reader or a very general situation, and maybe you'd have to like hunt through a Wikipedia page to find like the one sentence that answers your question. Language models go one step further, which is they reduce down their response to you to be written for you in your context, in your place, and in your time.

That sweep of the history of AI, in a lot of ways is speed running the history of philosophy. So philosophy started with this attempt to make explicit what it is to know something. Now we're in this place where it's like actually, like it's all kind of like fuzzy, and pattern matching, and it's very, very contextual and relational.

And so in a lot of ways, machine learning and AI's speed running philosophy, and it's gone a little bit of a step further, because it's built something with it that you can do. So moving from this way of thinking about knowledge, which is about making everything explicit, finding theories, and definitions, and rules for how to understand the world, to a more balanced appreciation for both that, and the way that a more intuitive relational fuzzy pattern matching type experiential, contextual type way of knowing about the world has to be underneath the like the rational stuff in order for the rational stuff to work at all.

And it sort of begs the question of like, should we stop looking for general theories? And for example, should we, you know, not be trying to unify quantum physics with Newtonian mechanics? I definitely think that it's awesome that we're trying to unify those things, and trying to build a universal theory. But also, there are many, many, many, many parts of the world that it won't touch at all. That we still, even if we have a universal theory of physics, like that probably won't filter into our understanding of depression.

We've been searching for those things for a really, really long time. And we've had a lot of like different answers. And we keep trying to do that. We keep trying to find that kind of like universal theory, that explanation that says, well, if X, then Y, if you have this going on in your life or in your brain, then you're gonna get depressed. Or if you take this medication, then depression will go away. We've been trying to find that for a really long time, because we felt like we had no other options, because normally in order to predict an outcome, to know, oh, if I do this, then it'll cure someone's depression. To predict it, you have to have an underlying scientific explanation, you have to have a theory about it.

And I think AI actually changes this. So with AI, what you can do is, and people are really already starting to do this, you can train neural networks that are, for example, able to identify who is depressed or who will get depressed. You can train neural networks who will be able to predict which interventions might for which people and which circumstances in a very contextual, hyper-personalized kind of way without having to discover beforehand any scientific explanation for the underlying phenomena that we're trying to predict.

The reason why I think that's so valuable is, one, it allows us to make progress immediately, 'cause we turn what used to be a scientific problem into an engineering problem. And then, two, it like really changes how we should conduct science, how science should be done. It changes our view of that, because right now, if you're a scientist, and you wanna like figure out depression, or any number of things in the field of psychology, what you're gonna want to do is like, a really small scale study where you're like, I'm gonna take 16 undergrads. What AI does is it kind of helps us, I think, to me, it helps us realize that there's a better way, which is rather than have, you know, random academics doing small scale studies, what we should do is have like the, you know, Apples, and Metas, and Googles of the world donate their data to science and data trusts so that scientists can access 'em to train models.

And what I think is even more interesting is once you have, once you've trained models that can, for example, predict depression really well, what you may be able to do is, models are actually easier to interpret and understand than brains are. And so if you have a good enough predictor, what you can do is just go into the neural network and try to figure out how it's wired. And it may be that the explanation for what depression is is like too big and too complicated, and you can't figure it out. But mechanical interpretability is good enough that you may be able to like find what is a solid theory for depression in the weights of a trained neural network.

You know, like I think we've been pursuing explicit definitions and scientific explanations for things for a long time, because if you can write it down, you can spread it. But if you're dealing with parts of the world that you can't write down explicitly, there's been no good way to collaborate on them or make progress on them. And what neural networks allow us to do, is to take some of that intuitive experience, or intuition that we might have built up ourselves, and put it into a machine that we can pass around. But language models and AI in general allows us to put that kind of intuition into a tool that will allow anyone in the world to access, for example, the best clinician in the world.

Chapter 3, will AI steal our humanity? I think AI is an incredible mirror. Like I understand so much more about myself just from being able to talk to ChatGPT. So we're moving from this metaphor of our minds, as like in an ideal world, this like logic rule-based explicit computer, to a much squishier, contextually-sensitive pattern-matching, experience-driven language model that I think is a really good metaphor for the more intuitive parts of our mind.

And I think in a lot of ways, the like, will it take our humanity? It makes two errors. The first error is to think that you can like pin down what it is to be human into like one unchanging thing. And I think the second error is to confuse what we are- It's sort of like saying that what you're unfamiliar with is bad. For me, or for people who are even younger than me, texting like can feel very, very intimate.

I think the people who are super afraid of AI, it is actually, it sort of goes back to this rationalist idea that we've been talking about, which is if you can't explicitly define, and prove 100% that a thing is safe, then it's dangerous. And that sort of, to me at least, like takes away a lot of the magic of AI. Like the thing about it that makes it actually powerful is that it works on probability, it works on many, many, many thousands of correlations coming together to figure out what the appropriate response is in this one very unique, very rich context.

But for people like me, people who build stuff, solving the problems in practice actually is a better way to do things than solving them in theory. What we're doing every single day is we are training these models on human preferences. The fact that we don't have those guarantees from language models is what makes them so powerful.

I think there's a really big question about how AI may change creative work. One of the metaphors that I like to use is this sort of difference between a sculptor and a gardener. So creative work ordinarily is a lot like sculpting. If you're a sculptor, every curve in line in that finished product is something that you decided to do like with your own hands. And I think that working with AI is actually, it's a bit different. It's a lot more like gardening. If you're a gardener, you don't like pull the plants up from the ground by the roots to try to make them grow. But what you can do is you can create the conditions for the plants or the garden that you're making to flourish in a particular kind of way.

And this actually gets me to like another metaphor that I really like, or another idea for understanding this wave of technology in a way that I think is really helpful, which is this idea that we're moving from a knowledge economy to an allocation economy. In a knowledge economy, you are compensated based on what you know. In an allocation economy, you're compensated based on how well you allocate the resources of intelligence.

And there's a particular set of skills that are useful today but are not particularly widely distributed that I think will become some of the main skills in this new economy, in this new allocation economy. And that is the skills of managers. Things like knowing what you want, being able to articulate what you want, being able to break down a complex task or a complex project into a set of smaller, achievable subtasks that you can then give to the right person. And I think the same thing is true of being a model manager.

And if we're looking for a definition of intelligence, one of the ones that makes a lot of sense to me is the idea that intelligence in a lot of ways is like a form of compression. So brains contain like an extraordinary compression of like all of the situations that we've faced.

And I think there's also something interesting and beautiful about thinking about things in the world as all having a little bit of consciousness, like a sort of panpsychic perspective. And the reason I like that is it encourages us to treat things in the world as if they were conscious. I always say please and thank you to ChatGPT 'cause you never know when the machine apocalypse is gonna come.