Ep. 380: ChatGPT Is Not Alive!  ## Metadata - Author: [[Deep Questions with Cal Newport]] - Full Title: Ep. 380: ChatGPT Is Not Alive! - Category: #podcasts - URL: https://share.snipd.com/episode/4462bcf2-add7-44c9-a86f-58c738c43e84 ## Document Notes ## Highlights - **Fluency Doesn’t Imply Human Mind** - Language models do much more than 'next-word' guessing; they internalize patterns, semantics, and domain knowledge during training. - This competence explains their impressive fluency without implying human-like cognition or intentions. Transcript: Cal Newport In some sense, right? Weinstein is right in how he starts the conversation, right? He's saying, look, we can't just saying language models predict the next word. That's too dismissive. So while it's true that, yeah, they literally do predict the next word, there is a lot of impressive understanding and processing that goes into actually figuring out what word to produce. In fact, writing The New Yorker recently, James Sommer argues that we can even think of the processing that goes into predicting next and language models as thinking. And I think he lays out a good argument for that. So Weinstein, he starts, I think I'm on board with him. But then as he continues in his discussion, he begins to argue that not only is AI doing impressive processing, but is rapidly evolving beyond our ability to control and that it is, quote, Like five minutes, end quote, away from starting to manipulate us. Weinstein implies that existing LLMs might already be conscious and that we have no real way of testing whether or not this is true. It's exactly this type of conversational turn where we shift from what's actually impressive about language models to impressive stories about sinister things these models might Be doing, that is the turn that I want to push back on in today's episode. I think it's just become really common in conversations about AI today to jump from real things about these models to fake things. And in doing so, I think it's distracting us from real problems, real issues that we really do need to be facing with this technology. So I want to get into all this today. What I'm going to do in the episode is I'm going to walk through some more of Weinstein's points. I'm going to play some more audio from that discussion. We're going to start on common ground, and then he's going to start to deviate from what I think is true, and I'm going to put on my computer scientist hat and argue why what he is saying Is deviating from what we actually know to be true about these machines all right um and then i'm going to turn to an actual computer scientist jeffrey hinton who seems to have been saying Sort of similar things to what we're going to hear and i'm going to deconstruct what he's saying if he actually knows why these machines work why is he saying something similar to Weinstein? I'm going to argue actually he's not. We're going to be more careful about deconstructing what actual computer scientists are saying. All right. So AI is interesting and scary enough on its ([Time 0:00:41](https://share.snipd.com/snip/93031894-bf65-447e-afc2-c71fa1ba2941)) - **Static Math, Not Dynamic Minds** - Deployed LLMs are static: a fixed table of parameters applied by matrix multiplications to produce tokens. - That static, sequential operation lacks online learning, drives, or spontaneous experimentation characteristic of brains. Transcript: Cal Newport There is a vast table of numbers that are often called parameters. These define a series of layers, each of which is a mix of something called a transformer and a neural network, roughly speaking. There's some other things like some embeddings early on as well. But it's a series of layers that you can imagine them as being arranged in a sequence. So they're defined by numbers with this giant table of numbers that just defines. The structure is always the same for these things, but what changes is like how we wire them up internally. And that's what those numbers define. So we have a vast table of numbers. The input to a language model is a sequence of numbers that are each representing the words of the input that is trying to expand with a particular new word. I'm being loose here. It's actually like an embedding of the underlying tokens in a semantic space that happens sort of separately. But just think of it as like a bunch of numbers that represents the text that's being implemented. These numbers, this sequence of numbers is going to be passed through those layers defined by the table of values sequentially. It goes through this layer. The output is in the input to the next layer. The output of that is in the input to the layer after that. What do I actually mean by pass through? Well, the actual algorithm that is running this, that's moving things through these layers, mainly what it's doing is multiplying numbers. So the way these various transformers and neural networks work is you can actually take a vector of values and a table that describes the structure of one of these layers. And you can do matrix multiplication. Actually, roughly is the main operation in simulating information moving through these layers. This is why GPUs are so critical to the AI revolution. GPUs or graphical processing units are specialized chips that are wired to do one thing fast, multiply matrices. And why? Because this is how you do high-end 3D graphics. You represent all of these 3D shapes with a bunch of points in space, and then you multiply them by each other to do transformations like to rotate things or to apply light to them. And so video games required these chips to do nothing but the matrix multiplication multiplications you need to do to do high-end 3d graphics and the ai revolution said oh we're also Multiplying uh matrices so let's take these chips that were made for video games and they'll allow us to to calculate moving through these layers much faster than if we just had like A traditional computer processor doing it. The output of the final layer then gets mapped onto a single word or part of word. And this is the output of the language model for that input, the word for which is trying to expand it. Critically, once one of these networks is trained, that vast table of numbers is fixed. It's static. It does not change. Like GPT-4, GPT-5, they're each defined by a static table of numbers that defines like what happens in their wiring. They were trained sometime in the past. And once the training is over, the training is done. And this algorithm that, you know, that moves things through the layers is very straightforward, right? It's just doing a lot of multiplication, right? Now what actually captured in those numbers could be all of these things we talked about before, like a lot of knowledge that was learned and logics and pattern recognizers. All of that is captured in those numbers. But the operation of applying that knowledge to produce the next word is very, it's static and sequential. It's very straightforward. We are systematically moving a vector of numbers through these layers by multiplying matrices. Boom, boom, boom, boom, boom, boom, boom, answer. All right. Complicated information is being pulled upon, but the computation itself is very predictable and static. This is very different than how a human brain thinks, and it's different than the type of thinking that Weinstein just summarized before in the clip I played. In particular, there's no spontaneous experiments in a language model. Static numbers, it does nothing until you call it and say generate a token, and then through the there's the token there's no learning once it's live once you've trained it once you're Using these models there's no learning it's just the same numbers every time it doesn't have an idea of learning the impact of its actions in the world like Weinstein talked about all It is trained to do during the training is to minimize loss on token prediction, right? So it's trained on real text. You cut out a word. You see how close that mechanical process comes to producing the right word. You adjust all the numbers so that in the future, its answer will be slightly closer. You do that a couple trillion times and you get a lot of interesting knowledge encoded in those numbers that if there's any want of these machines, it's just in training, minimizing Loss. That's it. Mathematically, we're minimizing loss. There's no notion of, as Weinstein talks about it, these machines learn how to get what they want or the impact of what they do upon the world. There is no world. There is no impact. It produces a number, a word. They adjust it so that word is closer to the right word. And then once it's live, it's completely static and non-spontaneous. All it can do is mechanically transform an input into an output. There's no evaluation of what's good. There's no evaluation of what's bad. There's no adjustments on the fly. ([Time 0:09:58](https://share.snipd.com/snip/cf874775-ee50-4933-8cc7-51a344832ec1)) - **Consciousness Needs Integrated Systems** - Consciousness seems tied to continuous, integrated systems with updating state, planning, and drives. - Isolated language-processing modules, however sophisticated, lack those integrated functions and thus aren't conscious. Transcript: Cal Newport I mean, to make things even worse, there is no even single machine where your language model is running because these tables of numbers are static. What really what you have is vast data centers of chips. And each chip is specializing on maybe just like one of the layers of this model. And it's like simultaneously handling all sorts of unrelated queries because all it's doing is like matrix multiplications. And so your query is getting outsourced to many different chips that are all like mixing in your multiplications interleaved with other multiplications and sharing these values Back and forth. There's never even a place where the language model is all in one memory or some sort of unified being that could even be conscious. It's because it's static. So we just have thousands of copies of different pieces of this and it's all spread out. So how do we get consciousness then? Well, we don't know exactly what causes human consciousness, but we do know that it seems to depend on a bunch of things. All right. So if we look at human consciousness and writing about human consciousness, we know that the human brain involves many different systems that do different things and are interconnected In complicated ways. We know that the human brain, and this seems to be key to consciousness, has dynamic ongoing computation. The human brain is constantly surveying its environment. It has an internal state that it can update. It plans, it simulates various futures based on what it sees around it and what it's learned before. It has a value system and drives to evaluate what's good or bad. It uses those simulations, those values to come up with different actions. It takes actions. It learns from that action and then updates all of its prior knowledge or understanding of how the world works. All of this type of computation, which happens in many different types of systems that are connected together, requires updatable and accurate models of the world. It requires the ability to plan and simulate the future. It requires drives, values, and motivations to help evaluate plans. It requires memories and, more generally, the ability to learn and to adapt and experiences in real time. You put all those functions together and something like human consciousness seems to arise. A language model, as we just described, is vastly simpler and vastly more constrained and has almost none of those features. Once we understand how they work, we see, oh, it has none of that stuff. It is static once it's trained. There's a simple mathematical process that even though it draws on complicated information that's been stored in these vast tables of numbers, the process is linear and static. Nothing gets updated. Nothing is learned there's no model of the world there's no planning there's no values there's no drive there's no ongoing dynamic computation or spontaneous experiments here's A vector multiply multiply multiply multiply multiply the word cat and this is spread out over hundreds of gpus they're doing lots of other stuff at the same time when we understand How language models work we can respect the fluency of the results they produce, but the operation matters. And we look at that operation, we say this has a fraction of the types of operations and behaviors you would need to even imagine something like the conscious or even the sort of manipulating, Hard-to types of minds that we would have to worry about. I think a good analogy is maybe to think about the language model like the language processing center of the human brain we actually have a couple of these centers but we can imagine kind Of combining them like there's a there's a mass of neurons in our brain that is very good at you you describe words to it and it can it has understanding of the words and it can map them into A more conceptual space and then that it can send that understanding to like all the other parts of the brains that didn't do stuff with it, right? So we can think of a language model as like you're taking the language processing part of the brain and put it in a vat. And maybe we wire up the input neurons to a machine so we can kind of like expose it to different words. That mass of neurons will do like a really good job of like, okay, know what this word means and this is similar to that word and i'm going to fire the neuron that corresponds to like the General concept of a door it does complicated processing those neurons have complicated information wired into them but we would never look at that language processing center in A vat and say that thing is a human that thing is alive that thing is conscious like oh no you have to hook that up these 20 other parts of the brain that all function in this complicated way Before you have human consciousness. Just take that part out. Yeah, it's really sophisticated. It's understanding of language, but that's all it does is understand language. That's not consciousness by itself. And yet that's kind of the way we're arguing about language models. ([Time 0:16:56](https://share.snipd.com/snip/bebd0322-e294-4b5e-93d4-e074717eb70e)) - **Judge AI By Its Design** - Focus on system design and mechanisms, not on surface stories that anthropomorphize AI. - Use mechanistic understanding to judge what AI can and cannot do instead of extrapolating fanciful narratives. Transcript: Cal Newport There's a bigger principle at play here that I want to emphasize that I think we need to keep in mind when talking about AI more generally. System design matters. The way a system is wired together, the algorithms that you run on the system, that determines what that system can and cannot do. These details are important. When a computer scientist looks at a language model, they look at specifically how they work. And a computer scientist can then say, all right, this is very different than how a brain works. It does just a fraction of the different things that a brain does. It's static and it's sequential. This is very good at producing words fluently, but it doesn't do the other types of stuff that a brain does. This is very different. When a non-technical critic like Weinstein looks at language models, here's what I think they're doing instead. They're observing from the outside the things that the model is doing. Oh, look at this language it's producing that's very fluent and shows understanding. They then write a story about what's happening inside the model that matches what they observed. Oh, this reminds me of like the type of thing a human child can do. So I'm going to write the story that maybe whatever's happening in this box, it's like a human child's brain. Then they extrapolate from that story, which they made up to make predictions about the future. Say, well, a human child brain eventually becomes conscious. So we're going to assume that the language models become conscious too. They wrote a story about what they observed, and then they extrapolated from that story to talk about what they think is going to happen. This is essentially the difference between a scientific and something like an ancient animist religion approach to understanding the world. The scientist tries to figure out the actual underlying mechanisms behind something they're observing. And then once they understand those mechanisms, they can use that to reason about what else might be possible or what else might happen. The ancient animist, by contrast, writes a story about what they observe. Oh, lightning happens. I'm going to write a story that lightning is because the gods are mad. And then they extrapolate from that story. If lightning is because the gods are mad, I assume that sacrifices make the gods happy. Ergo, we really need to be doing sacrifices to prevent getting hit by lightning. That's like an ancient way of thinking. You look at the stars, you look at the world, you write a story that makes sense to you, and then you build your understanding of the world off of extrapolations of that story. We have to sacrifice to keep the gods from hitting us with lightning. The scientist says, I don't care what story seems interesting to me. I want to actually try to understand the real mechanisms, and that greatly constrains what's actually possible. We cannot just say whatever we want to be true or what sounds interesting about systems like AI. The way the system's design matters. The fact that language models are using tables of static numbers that are being sequentially processed with a multiplication algorithm, that matters. ([Time 0:21:16](https://share.snipd.com/snip/9f4b1e76-a2ec-4854-9a33-040f67a835bb)) - **Hinton Fears Future Systems, Not Today's LLMs** - Jeffrey Hinton's alarm stems from faster-than-expected progress in token prediction, not from current LLMs being conscious. - His concern targets hypothetical future systems that combine language modules with goal-directed, updatable components. Transcript: Cal Newport So I want to try to resolve these discrepancies. Jesse, let's start by playing a clip of Hinton. This is from earlier this year, giving, you know, sounding the alarm. Speaker 2 And I think until quite recently, almost everybody thought that was just science fiction. We didn't really have to worry about AI becoming smarter than us and taking over from us. But I think now many people have come to realize that's a very real risk. So most of the experts believe that sometime between five and 20 years from now, AI will get smarter than people. And when it gets smarter, it won't get a little bit smarter, it'll get a lot smarter. And the problem is, we know very few examples of smarter things being controlled by less smart things. Cal Newport All right, so these are pretty dire warnings. But what is he actually saying? Well, we can get some more insight if we go back to a great profile of Hinton that was written by Josh Rothman in The New Yorker back in 2023. And if we look in that profile, we can figure out, first of all, what was it that in more recent years made Hinton suddenly afraid? He wasn't warning about AI 5, 10 years ago. But in the last few years, he's gotten worried about it. The first thing we can do is identify what did he see in new AI technology recently that got him worried. So I'm going to read now from the New Yorker profile a relevant piece of the answer to this question. All right, so here's Hinton from the profile. People say it's just glorified autocomplete. Now let's analyze that. Suppose that you want to be really good at predicting the next word. If you want to be really good, you have to understand what's being said. That's the only way. So by training something to be really good at predicting the next word, you're actually forcing it to understand. Yes, it's autocomplete, but you didn't think through what it means to have a really good autocomplete. All right, so this is an important point, and it's one I've made repeatedly, including in the introduction of this episode. What made Hinton originally impressed is that in winning the game of predicting the next word, which is what these models are trained to do, they got a lot smarter than he expected they Would. Because if you think about it, just like he argues here, if you really want to be good at winning the game of what word should come next, you have to really understand the text that you're Extending. And like in the example I gave earlier, if the text you're extending is like a joke and the next word is a key part of the punchline, well, like to win the game there, you have to understand Humor. And in fact, if you read longer in the Rothman profile, this was the thing, the specific type of functionality that really caught Hinton's attention is when he realized these models Are produced. They can produce text that's funny, they can understand what funny is and that's where he said oh the token prediction game if you train it on a big enough network long enough requires These models in those vast tables of numbers to define them to encode really complicated pattern recognizers and logics and knowledge um it gets and language understanding like it Gets pretty sophisticated. So there's a lot of understanding. That's what James Summers argued in his point as well, encoded in those vast table of numbers. And Hinton was caught off guard by, wow, just how much understanding a language model can build up. But here's the thing. Hinton is not worried about language models. Him seeing how much understanding language models built up to win at the prediction game got him, and this becomes clear in the profile, got him thinking, well, if we could solve that Problem so much more impressively and faster than I would have guessed, I'm going to assume that we could do that with other AI-related problems as well, right? Because he says, if you look at it closely, he's not worried about language models. He would completely disagree with Weinstein that language models as we have them today are conscious or that they're running experiments or that they have intentions. No, he knows how they work. He helped invent the technology. He knows it's a static table of numbers and there's rote multiplications that moves information through. There's vast understanding and knowledge encoded in those numbers, but the actual operation of executing does not operate like a human brain. It can't be conscious. There's no intention, etc. That he began to worry about, well, what other AI-related technologies are going to start going faster as well. So what he's really worried about is hypothetical new machines that we haven't built yet, but now he's saying maybe we will build them faster than we thought. We could build a language model that understands humor when I thought that was impossible 10 years ago. Why can't we also build an artificial brain that can be conscious or have intentions or manipulate us or try to actually do things that are outside of our interest. Here's another quote from the New Yorker profile that gets at this reality that Hinton is worried about future machines that we haven't built yet. Here's the quote. Hinton warns that even a benign autonomous system could wreak havoc. You want a system to be effective, you need to give it the ability to create its own sub-goals, he says. Now the problem is there's a very general sub-goal that helps with almost all goals get more control. So what he's talking about here is not a language model. Language models don't have goals. Again, it's a static table of numbers. You multiply vectors. You get a word out on the other end. There's no goals, right? There's no intentions. There's no decisions being made like that. But he's thinking like, oh, what if we did build a system with goals? What if we built a system that could make decisions and plan and figure out what it wanted to do? Then that could be pretty dangerous. So Weinstein's talking about language models. Hinton's talking about AI artificial brains that we haven't built yet, but he's now more confident than he was before that they are buildable. That's different. ([Time 0:29:59](https://share.snipd.com/snip/bead85b1-34eb-4601-9f37-f3e6784e8b8a)) - **Use Agents Only For Simple Tasks** - Treat current AI agents skeptically: combine LLMs with simple control code and expect fragility and unpredictability. - Only deploy agents for simple, well-specified tasks until world models and planning improve. Transcript: Cal Newport So like the simplest thing we've been trying recently is what are known as like AI agents. And really what these are is like a control program, the straightforward program, you know, written in Python, just like normal code, a human wrote that has like linear logics we build Into it. And then it prompts language models to try to get ideas or plans, right? So like, Hey, language model, here's what I want to do. Give me a plan for this or tell me what I should do. And then it gets the answer from the language model. And then it goes and executes on its behalf. We talk about AI agents right now. That's what we're talking about. Those are okay, but they're not working that well. Like 2025 was supposed to be the year of AI agents where all of these programs that call language models, we're going to automate more and more of our work tasks. That was mainly a failure because it turns out that yes, language models have really sophisticated understanding baked into their vast tables of numbers. Like they really understand words and have a lot of logics. But there's also a lot of things these language models don't do because they weren't, that's not what they're supposed to do. The keeping models of the world, understanding how things work, planning, trying to figure out different scenarios for the future. They don't have any of those capabilities because again, it's a sequential static processing. It can just move a number through and they were trained on word prediction. So what they have encoded in there makes them good for predicting words, but they're not very good at like, let me make a plan about what to do for you in your office. That's going to make sense given your particular job and the people you know doesn't have any of that information. And it can't simulate the future. And it doesn't have a really good world model. We thought they might, but they don't have a very good world model. We now know this from research. So the agents we build that just ask language models, they're inconsistent and unpredictable. They're not that great. That's why we don't have a lot of these. Even though that was supposed to be the financial savior of open AI in 2025. They're just too unpredictable for anything but the simplest of tasks. And so that's why we haven't seen them used. ([Time 0:36:16](https://share.snipd.com/snip/c5242e77-8899-4b58-b286-5069059fec0e)) - **Language Is One Module Among Many** - Building an 'artificial brain' requires many modules beyond language: world models, policy networks, memories, and actuators. - Progress in language models doesn't automatically solve these orthogonal, hard problems. Transcript: Cal Newport But it's also going to need other modules to do all these other things that brains do. It's going to need a module that has like a really good model of the world. It's going to have to have something like a policy network that we encode sort of like its values and drives. They can evaluate potential actions to figure out what action is good based on what happens. It's going to need memories. It's going to need updatable state. It's going to need actuation, ability to try things, sees what happens, update its memories, update its state. It's going to have to hook these all together in some sort of complex way. The language model might be in there, but it's one of like a bunch of different pieces we would need to have anything like an artificial brain that does more than like what these very simple Agents do. But we don't really know how to do that. You know, in fact, for an article I was working on earlier this year, I ended up interviewing some of the world's top roboticists, and they've been working on some of these problems in The world of robotics, especially building a good general world model you can use to plan actions. The advances in language models really have no impact on that challenge. That's its own challenge. The fact that we're better at language models doesn't help that challenge very much. And they have no idea how to do this well. They've been working on it for decades. There hasn't been any major breakthroughs recently, and they're continuing to work on it. And they're no optimism that like, oh yeah, we're just a year away from being able to model the world. That's like a completely separate challenge. ([Time 0:38:18](https://share.snipd.com/snip/baec562c-c820-4ec7-8b8c-f0b31290a268)) - **Prioritize Today's AI Harms** - Concentrate on present harms: cognitive erosion, truth decay, sloppy content, economic and environmental costs. - Ask who benefits from AI and why we tolerate its current real-world harms instead of chasing sci‑fi fears. Transcript: Cal Newport Problems like impacts on our mind or our relationships or our politics or environment or our very definition of truth. So when I look at AI, I think the right question is like, hey, who is asking for this? What good is this bringing us? Why are we tolerating all these harms that it's causing right now? What problems is this technology actually solving in our life? Why are we standing for all the real issues that it's creating? The question that's not interesting to me is when will it come alive and is it trying to trick us? I think that latter type of question is exciting to ask. It makes for good conversations on, you know, podcasts, but it distracts us from the real problems that AI is causing right now. And when you understand how these systems actually work, we can be much more specific about what they can and can't do. ([Time 0:42:46](https://share.snipd.com/snip/edc7daad-f20c-4e8d-a943-9f6d551741f3)) - **AI Progress Is Uneven Across Domains** - 'AI' is heterogenous: language models are distinct from biomedical, military, or robotic systems, which progress unevenly. - Progress in one domain doesn't imply imminent breakthroughs across all AI fields. Transcript: Cal Newport Here's what's important about this question. Like AI is a broad term. Often what people are talking about when they use that term today are language model based tools like chatbots, right? That's like what Brett Weinstein was talking about. But AI has been around for a long time. And there's lots of other tools that use something like AI that try to do something useful. Like when I first started at Georgetown, there's a group there that continues to work today, like very closely with the hospital at Georgetown. I do a lot of work with, you know, medical records, looking at films or test results and looking for patterns and helping people to figure out like, hey, do diagnoses or whatever. That's something to do with language models. Language models are very specifically for producing text. But, you know, they are working on that. Lots of people have been working on that. Radiology has been like a subject of AI interest for decades. Can we train models to be really good at looking at uh mri or x-ray films and finding things that like a human radiologist might miss like that i i think there's a there's a cancer over there That maybe like the human observer missed they're like you could do pretty good at this some of these things there's a lot of plateaus that they hit like jeff hinton famously said by like 20 whatever a date that already passed there would be no radiologists left there's just as many radiologists as they are we haven't actually cracked the problem of studying these films With with ai uh nearly as well as we thought we would be because there's like plateaus like you run out of data the techniques can only do so much there's a lot of different ai used in a lot Of different places they're not language models i think that's important because people often get this wrong or they mix it up. They mix all these things together and they assume because language models made rapid breakthroughs in a five-year period that these other AIs did as well. But there are different types of problems and different types of systems and they're not connected. You know, some people think, I hear this all the time, they think it's like the language models that we're using, that will be the key to like biomedical AI. Most biomedical AI has nothing to do with language models. Or the thing with language models is what we're going to use. Now our weapons will be smarter. No, that's a completely different type of AI that you're going to use in weapons. Or robots now. Look at Tesla's Optimus robot, which they basically canceled that project. Like, oh, because of language models, like now our robots will be real smart. No, that's completely different technology. They're not having comparable breakthroughs. There's a lot of AI technologies for a lot of areas, protein folding, playing games. These are all different types of AIs and they're all having advances at different speeds. None of them really are having advances at the speed of language models with like the exception of some of DeepMind's work. You know, they were having some big breakthroughs, I think on games, and the protein folding was a big breakthrough, but those are like bespoke systems. But for the most part, these are all separate AIs that require separate breakthroughs, and they're all moving at their own speeds. Most of these AIs have been around for a long time. The technologies, there's nothing really new. So if you weren't afraid five years ago of these technologies becoming super intelligent, then like you shouldn't be afraid of that today. There hasn't been any major change for most of these AIs and how they operate it's much more incremental trying to break through plateaus so no these are all like systems we can understand That operate in specific ways and are really hard to get to work well and they're working to try to get them to work well and the gap between how do we get better at noticing abnormalities On MRI scans and Skynet is so vast in all these technologies that no, they don't. It's just the wrong question. It's just the wrong question. Like, are these things going to get out of our control? It's just the wrong question. We're trying so hard right now just to get them in these bespoke, fragile systems, just to like do what we're asking to do, you know, in a way that's useful to us. Like, that's what we're working on with most AI systems. Yeah, there's a lot of AI. Most are not language models. They all make progress on their own pace. ([Time 0:49:17](https://share.snipd.com/snip/c068fca6-8b50-4078-8bca-486d01648809)) - **Use AI To Raise Skill, Not Just Speed** - Learn which AI tools genuinely improve your craft and use them to become better, not just faster. - Ignore tools that only speed work without raising the quality of what you produce. Transcript: Cal Newport You got to balance two things with AI. Places where it could help you and places where it's just like your management team is like, I read a bunch of, you know, I listened to the interview on Rogan and we need AI. Like we just do AI, right? You got to walk that line between useful AI and non-useful AI. So keep abreast of new tools as a developer. See which ones make you better. Use them. See which ones don't. Ignore them. The key thing I would say is the, and this is in programming, there's a lot of tools that can make things easier and better. You have to use those tools to raise your level. So if what you're doing is just great this can automate other parts of what i do so now i can like get my work done faster not have to work as hard that's dangerous if ai as a programmer can Help you do certain things faster use that to become a better programmer like oh now i am a better programmer than i was before this tool not a faster programmer a better programmer if If can handle these things for me, now maybe I can build more sophisticated systems. Or if I can get help from AI in figuring out these complicated library calls, I can now use these libraries that intimidated me before. And now I'm open to like, I can build much more complicated stuff. So the AI that helps you use it to make you better, not just faster. And the AI that doesn't, don't worry about it. Ultimately, the value of what you produce is what matters. ([Time 1:05:13](https://share.snipd.com/snip/09bc12dc-b1d6-4392-bb15-fc9abb2f8721)) - **Zettel Toffel: A Physical Productivity System** - Ernst built a physical Zettelkästen + Kanban 'Zettel Toffel' board with daily time-blocking and three active tasks. - Cal praises the tactile system for reducing digital distractions and clarifying priorities. Transcript: Cal Newport All right, so here's the system for those who are watching, so just listening. How do we pronounce that, Jesse? He calls it Zettel Toffel. Speaker 2 Yep. Zettel Toffel. Cal Newport It says at the top it is Cal Newport inspired. There we go. All right, let's see what we got here. What is Zettel Toffel? Once upon a time, a Kanban board and a Zettel cast and got married, had a baby, and the Zettel Toffel was born. It handles the configure and control portion of productivity management in a clear, tactile, and analog manner. All right, and he specifies here that he's a CFO for hire. So he's like a entrepreneur or knowledge worker. All right. There's a board here we're looking at labeled Zettel. Oh my gosh. Complicated, Jesse. Oh my. All right. All right. There's pending tasks and those are, there's rows for different pending tasks like budget, general finance, loans. And then I like this. There's post-it notes under each of these sections that describe individual tasks. Then there's in progress for each of these categories. So that way you can keep track of, like, all the finance stuff you need to do, but here are the two things he's actually working on at the moment. And then there's waiting. So I assume what happens there is if he's waiting to hear back from someone about some task, he can move at the waiting so he doesn't forget and then move something else in progress to keep Making progress. So that's kind of like classic Kanban, which I think is great. You're being very clear about your workload. You're not forgetting things, but you're also being careful about how much you're actively working on. So I think that's pretty cool. I don't quite know what's happening over here. He has my day. Oh, I see. I think he's moving these tasks over to his day. He says he actively works on at most three things per day. He moves those post-it notes over to a slot labeled one, two, three. So he kind of decide like which of these things am I working on today? And then I guess he puts it back if he finishes it, there's a done column over here. And then there's other room in here to keep track of like meetings and some other stuff as well. So this is pretty cool. This is like a highly structured task management system that he could use to keep track of what's going on and its status all in one physical analog visual form. You could do this in a Google Doc or Trello, but I like the way he's doing it here. It's probably nice to actually move these post-its around. There's some more details. I won't get into all of this, but I love the fact that he has my time block planner. So he's time blocking throughout his day. Way to go. And he moves stickies from the planner to Zettelcast on the wall. Okay. So when he has things to capture, instead of just writing them in the task capture part of the time block planner, he puts it on a post-it note that's stuck on his planner, and then he can Move it from the planner to where it should go on his wall, the Zettelkasten wall. So that's pretty cool stuff as well. Some more details about capture. Oh, I see. So now he's breaking down the details we just looked at all right um that's pretty cool there's some other details in here as well i won't go through all of them but what i like about this Example is it shows the level of intention you can have about how you manage your workload in this current age of like constant digital distractions and context switching having some Sort of uh set way of keeping track of what you need to do, what status it is, getting out of your head, making smart decisions about what to do with your time. It really makes a difference. And for some people, this level of detail, I think is really powerful. Other people, you could do something much simpler, but I think that's pretty fun, Jesse, to see the degrees to which people push this, especially with their solopreneurs and they can Completely control their day. ([Time 1:07:02](https://share.snipd.com/snip/1e676700-19d0-4c18-98ee-6cdb67a7b8c7)) - **Prompt Your Long Thinking Sessions** - Use a focused prompt for notebook thinking: bring a clear question when you sit down to write or walk and reflect. - Capture spontaneous insights into the notebook so the loaded neural networks generate more useful ideas. Transcript: Cal Newport Asking questions and answering, making lists, free writing. Sometimes I get stuck because I can't deliberately think, but thinking or ideas come up randomly while doing other tasks. The moment I sit down to write think, it is blank up there. Usually when I'm doing specific long thinking with a notebook, there's a particular problem I'm trying to solve. Like what is going to go into this chapter I'm writing? How do I want to organize my argument for this part of the book? Or, hey, this part of my life is, why am I struggling in this part of my life? Like, what's going on here that's not right? And what do I think I need to do to fix it? So I have a particular prompt that I'm trying to answer. And then I write ideas that help me answer that prompt into the notebook as they come up. If I'm doing something else, I'll go get the notebook and write it there, or I'll go on a walk and be like, let me specifically try to come up with answers about this, ideas that help me figure Out this prompt, and then I'll write them in the notebook. If you go on a walk and you're just thinking about that prompt, what's going to happen is your brain is going to load up all the relevant neural networks, and you're going to find that you Have more insights because now your brain has all the right stuff loaded up and you're more likely to come up with new ideas. ([Time 1:13:22](https://share.snipd.com/snip/6d7636b1-dafa-4192-9fa7-6581e57a3223)) - **Let Format Follow The Thinking Task** - Don't rigidly separate research and notebook notes; use the format that best helps your long thinking and proofs. - Let the task determine whether you write ideas in a notebook or a word processor. Transcript: Cal Newport This one comes from Lori Loesch, 7440. It says the following, for this long thinking method notebook, is research material allowed or would that then make it deep work? Like should the notebook writing only be for our thoughts? As I'm writing this, the answer is coming. Seems to be a yes. The notebook content is only straight from our minds. Please correct me if I'm wrong. Thank you. And I'm glad I just found your channel, even though I'm trying to get offline more and more, you know, it depends on what you're doing and your preference. Like I want it, if I'm writing an article or something, the notebook would be for like ideas about that article. And I would write it in the tool. I want to write the article in where it's a word processor, it's Scrivener, it's on my computer. There's other things I do though, where I'm actually putting in my notebook pretty completed thoughts. So this would be the case for mathematical proofs. I would go for a walk to try to make progress on the proofs. I'm going to write out theorems and do the equations right there in the notebook. There's many a case where when I was writing an academic theory paper, I had my notebook open and then I'm copying over equations into it. Like, oh, I did this math over here. Let me copy it in here and clean it up. So it just depends on the type of thinking you're doing and what format it's eventually going to end up in. So don't have rules about it. Whatever is helping you do better long thinking, do that. ([Time 1:14:44](https://share.snipd.com/snip/58e33bf5-2b9f-4d31-91f5-7429ab7c29cf))