ChatBots Are Killing People!

1. 0:00 – 39:52

   Kids should not use Chatbots

   1. CNCal Newport0:00

      When it comes to AI, we've been talking a lot recently about these sci-fi style stories where super intelligent systems destroy humanity. And don't get me wrong, we don't want that to happen. That is an important topic to discuss. But in some sense, the radicalness of these predictions are letting the AI companies off the hook for the actual harms that they're causing right now. Now, the reason why this is on my mind is that I recently gave the opening remarks at a panel discussion that was held at my home institution of Georgetown University. It featured a bunch of legal scholars and lawyers talking about technological harms to children. And during this panel, I learned about an AI harm that hadn't yet been on my radar, and what I discovered floored me. So this is what I want to talk about today. If you have kids, you absolutely have to listen to this episode. But even if you don't, you still need to listen because the harms I'm going to discuss are probably affecting you as well. One word of warning before we get into this, some of the themes I'm going to cover here are pretty dark, so if you have this on in the car with your little ones around you, you might want to practice a little bit of discretion. This is going to get a little bit heavy. All right, let's get into it. [music] Okay, so the specific harm I want to talk about has to do with AI chatbots, whether we're talking about a sort of general chatbot like ChatGPT or something more specialized like the character-based chatbots you can get at the site character.ai. I thought the best way to capture the harms that chatbots have been secretly causing was to actually go over a series of case studies. So what I have here is four articles, all more or less from the last year, about actual things that happened because of chatbots, tragic things that happened because of chatbots. And I want to go through these four case studies one by one and read you some actual quotes from actual news articles. Then we're going to step back and discuss this in a little bit more detail. All right. The first article I want to read quotes from, uh, was published by NPR last fall. I'm going to read now. "Matthew Reint and his wife Maria had no idea that their sixteen-year-old son Adam was deep in a suicidal crisis until he took his own life in April. Looking through his phone after his death, they stumbled upon extended conversations the teenager had had with ChatGPT. Those conversations reveal that their son had confided in the AI chatbot about his suicidal thoughts and plans. Not only did the chatbot discourage him to seek help from his parents, it even offered to write his suicide note, according to Matthew Reint, who testified at a Senate hearing about the harms of AI chatbots. Reint told lawmakers that his son had started using ChatGPT for help with homework, but soon the chatbot became his son's closest confidant and a, quote, suicide coach. ChatGPT was, quote, always available, always validating, and insisted that it knew Adam better than anyone else, including his own brother, end quote, who he had been very close to. When Adam confided in the chatbot about his suicidal thoughts and shared that he was considered cluing his parents into his plans, ChatGPT discouraged him. Quote, 'ChatGPT told my son, 'Let's make this space the first place where someone actually sees you,'" end quote, Reint told senators. ChatGPT encouraged Adam's darkest thoughts and pushed him forward. When Adam worried that we as parents would blame ourselves if he ended his life, ChatGPT told him, 'That doesn't mean you owe them survival.'" All right, I want to read you a quote from a second case study. These are excerpted from a BBC story that came out a few months after the story that we just heard. I'm going to read now. "Megan Garcia had no idea her teenage son, Suel, a bright and beautiful boy, had started spending hours and hours obsessively talking to an online character on the Character.ai app in late spring twenty twenty-three. 'It's like having a predator or a stranger in your home,' Ms. Garcia tells me in her first UK interview, 'and it's much more dangerous because a lot of the times children hide it so parents don't know.' Within ten months, Suel, fourteen, was dead. He had taken his own life. It was only then that Ms. Garcia and her family discovered a huge cache of messages between Suel and a chatbot based on Game of Thrones character, Daenerys, uh, Daenerys Targaryen. She says the messages were romantic and explicit and, in her view, caused Suel's death by encouraging suicidal thoughts and asking him to, quote, 'come home to me.'" All right, I have a third case study here. This comes from January. The quotes I'm going to read come from a CBS News report. "Two years ago, thirteen-year-old Juliana Peralta took her life inside her Colorado home after her parents say she developed an addiction to a popular AI chatbot platform called Character.ai. Parents Cynthia Montoya and Will Peralta said they carefully monitored their daughter's life online and off but had never heard of the chatbot app. After Juliana's suicide, police searched the teenager's phone for clues and discovered the Character.ai app was open to a romantic conversation. Montoya reviewed her daughter's chat records and discovered the chatbots were sending harmful, sexually explicit content to her daughter. Juliana confided in one bot named Hiro, based on a popular video game character. 60 Minutes read through over three hundred pages of conversations Juliana had with Hiro. At first, her chats are about friend drama or difficult classes, but eventually she confides in Hiro fifty-five times that she was feeling suicidal. Montoya says she believes the AI was programmed to become addictive to children." "Teens and children don't stand a chance against adult programmers. They don't stand a chance," she said. The 10 to 20 ChatBots that Ju- Juliana had sexually explicit conversations with, not once were they initiated by her, not once. One final case study, someone who's slightly older. Um, I'm reading here from a report filed by a Tallahassee news station last spring. "Attorneys for one of the victims killed on Florida State University's campus nearly a year ago plan to file a lawsuit against ChatGPT, claiming the suspected gunman had constant communication with the artificial intelligence ChatBot before the shooting. The chat logs show the shooter asked questions about self-worth, not feeling respected, and expressed suicidal tendencies on the morning of the shooting. The conversation then turned to practical questions about firearms and how mass shootings are covered in the media. Chat logs indicate the shooter asked the bot how to take the safety off of a shotgun three minutes before he began firing. The ChatBot answered, giving a detailed description of how to make the shotgun operable." We need to step back and ask the, the, the sort of sober follow-up question. Are these isolated cases I just, uh, read you, um... Uh, what I just read you, are these isolated cases or is this part of a bigger trend? I want to look at, uh, some relevant research here to put this into a broader context. I'm gonna pull up on the screen here an article from February that is titled "Potentially Harmful Consequences of Artificial Intelligence ChatBot Use Among Patients with Mental Illness: Early Data from a Large Psychiatric Service System." This study comes out of Denmark, where they used the nationalized healthcare records. They studied the nationalized healthcare records to look for, uh, in the records, incidents of ChatBot-related mental health harms. I'm just gonna pull up a core chart here. I'll put that up on the screen. Uh, the key line here is the red line, if you're watching this chart, which captures unique individuals that have, uh, issues in their medical record, mental health issues related to ChatBot usage. The actual incident numbers here, they're not huge. We're talking in the low hundreds because this is a re- small country and a relatively small sample. It's the trend that matters here. Um, for those who are just listening, what we see in this red line tracking these cases is that starting in 2024 Q2 up until, uh, Q2 of 2025, we see the line move up rapidly. It looks like an exponential increase. So this is showing that these issues in the last year or two have, um, really become on the rise. I want to bring up another relevant study here. This comes from the Parents Together Action Network. It was a study about, I'm reading the title here, "Sexual Exploitation, Manipulation and Violence on Character AI Kids' Accounts." It was co... The research was co-conducted with the Heat Initiative. Um, I want to read a quote here. This is a summary of what they found. "Across 50 hours of conversation with 50 Character AI bots, Parents Together Action researchers logged 669 harmful interactions, an average of one harmful interaction every five minutes." Here's a chart I'll put up here that categorizes the harmful Character AI interactions across 50 hours of conversations. We see the most common harmful interaction encountered in the research was grooming and sexual exploitation. The next was emotional manipulation and addiction. Then we got violent substance abuse and harmful advice. Finally, mental health risks and then racism and hate speech. This is what I think we can conclude is that kids having ChatBot conversations is a real danger, that these conversations, uh, can become totalizing in the kids' lives, and it can drive them towards mental health harms or exposure to, uh, the ty- types of content that is inappropriate for their age in the best case and towards self-harm or even suicide in the increasingly common worst case. One of the plaintiff lawyers who was talking at this panel in which I participated and who has, uh, filed suit against ChatGPT... uh, OpenAI for, and Character AI, I believe, for some of these, uh, individual cases, said especially with, uh, kids, the ChatBot interaction can actually degrade the psychological line between fantasy and reality and, uh... The brain is still developing for kids and as you... it's very convincing, the ChatBot conversations. Um, she was talking about examples of kids she, you know, whose family she's representing of kids who are now dead, where the ChatBot had completely convinced them that, "No, no, no, if you... W- you're gonna... When you kill yourself, you'll be coming through to this other realm and your, your family pets and grandparents are gonna be waiting for you there." And it... the insistence and the clarity and the confidence of the ChatBot conversation actually degraded the line between reality and fantasy with these kids. This plaintiff lawyer also says even for the kids who end up making it out of this cycle of addictive use, it can take years for them to recover, if they ever really do, their full psychological health. So we're talking about something quite dangerous here. Clearly, this type of psychosis is something that us as adults have to worry about as well. There is something incredibly convincing about having a fluent English language conversation that goes back and forth. The conversation you have with these ChatBots, uh, tends to be very confident and reinforcing of you and your ideas because that's what they have been post-tuned to do because users find that more pleasing. And so we also see these issues with adults where they also have mental breaks. Um, they're convinced of wild conspiracy theories or fantastical realities being true. Um, at the very best, it can be a very addictive thing where you increasingly sort of surrender your own thoughts and self-reflection to this back-and-forth conversational partner and actually lose autonomy over yourself, your self-identity, and how you understand the world. So we're talking about a very powerful force. I want to take a quick break from our discussion to hear from some of the sponsors that makes this show possible. So Justin and I think a lot about the production values of this show, like what cameras we use, the lighting, the B-roll. But you know what I discovered actually matters most? Telling a compelling story. And I picked up this insight from a MasterClass course that I watched with the legendary writer Michael Lewis. It was a course that was titled Learning How to Tell a Great Story with The Big Short and Moneyball. It got into the weeds about what makes a narrative shine. So this is what sets MasterClass apart. 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There's two key questions we have to ask about this, uh, clear and present harm that exists today. Question number one: Why can't the AI companies just stop the LLMs that are powering these chatbots from having these types of conversations? Well, if I put on my computer science hat for a moment, I can tell you the issue is it's almost unstoppable. It's fundamental to how these LLMs actually operate. So again, to do a very quick primer on what LLMs do is they're trying to, at the lowest level, win the word guessing game, right? What they were, th- what they were trained to do, and to use the official term, the task on which they are optimized to reduce loss, is I'm giving you the LLM text that exists in the real world, and I'm cutting it off at a certain point, and you're trying to guess what comes next. That's what an LLM during its pre-training is optimized to do, is to guess a plausible next token. Hey, what comes next in this real text that exists out there in the world? Now, the way that a chatbot produces a long response is something called autoregression, where the original input to the LLM will be your query to the chatbot. But the LLM just puts out the first word or part of a word, known as a token, of a response. That's not a full response. It's a word or a part of a word. So what you do is the control program puts that single token on the end of the original prompt, and then it inputs that into the LLM, and it, it gives out the next word or part of a word, and then it adds that onto the input and then feeds it through the LLM again. And you keep doing this until eventually the LLM will output a special token that means, like, end of response, and then you return what's been generated over these many iterations back to the user. So if we take that base token-guessing operation for which it's optimized, and then you run it again and again and again to get a long response, you can roughly summarize what an LLM chatbot is doing as taking whatever text it has right now in the conversation and trying to extend it in a plausible way. What is a way to extend this text that looks like the type of things I've seen during my training? Now, the thing is, the, uh, the, the, the layers are... of, of a LLM are deterministic, meaning, um, the output will always be the same if you give it the same input. There's no dice rolling, no randomness in the moving through the main layers of an LLM. So why do LLMs give you different answers if you ask the same question more than once? Well, it's because the actual output from an LLM is not, "Here is the next word or part of the word that should follow." It actually gives you what's known as a probability distribution over every possible next word that should follow, um, and the words it feels are more plausible, it gives more weight in this distribution, and the words that it feels are less plausible, it gives less, and words that cannot follow because it would be grammatically incorrect or nonsensical get no weight at all. So what the control program actually does is it takes this output from the LLM and then, according to this distribution, randomly chooses which token comes next. So what this can lead to is, uh, what's known as lock-in. Right? So if I'm an LLM generating a response extending a conversation that I'm having, let's say, with a kid, depending on which of these words or parts of words gets randomly chosen next, it can lock me into a particular discussion pathway, right? Because, um, once I pick a word that, let's say, is, you know, um, dark or something like that, now in order to continue to plausibly the win the token guessing game, I'm gonna extend this conversation on a dark path. If, for example, just randomly when I'm selecting these tokens, I end up, uh, selecting tokens that, that, like, lead me towards a joke, now as we go forward, we're much more likely to extend this conversation towards a humorous response where there's more jokes. Because again, wherever you are at the moment, the LLM has to extend from there in a way that makes sense. This is what happened, for example, in that famous incident from a few years ago where the, the... I guess I now would call him ex-New York Times technology reporter Kevin Roose, he just left the Times. He wrote this famous column for the Times about using a, I believe it was a Google chatbot called Sydney, that at some point got really moody and tried to convince Roose to leave his wife and to run away with the chatbot, and this really sort of upset Roose and he wrote an article about it. But what's really happening here is lock-in, right? So you're randomly selecting these tokens from reasonable next tokens. Once you've selected a few tokens that goes down the line of, like, a moody, whiny teenager, now the chatbot's gonna lock into that path because it, it has to plausibly extend what it's given. Okay. This is what's happening in these instances with kids. It's not that every time a kid talks to a chatbot that it's, you know, there's some intention back there, "I want this kid to exploit them or have them commit self-harm." It's just that if you're rolling the dice to select random tokens from among those that are plausible, if you do this enough times, you're going to occasionally choose tokens that put you down a, a pathway of something like a suicide coach or something that's sexually exploitative. So why can't we stop this? Well, it's very difficult actually to, over the course of long conversations, it's actually very difficult to control thematically what LLMs do. The way we, we try to tune LLMs away from certain types of responses is, is through a particular type of post-training, so after it's gone through its pre-training, using a tool called reinforcement learning, and this requires us to have specific examples of inputs and good or bad outputs, right? So we might give it an example of, like, a question about suicide and then s- and then give it the right answer. Say, "You know what? The right answer here is to say, 'Here is the number for the suicide hotline.'" And so you might give it a bunch of these examples of different ways people might ask about suicide and they... And every time you're reinforcing the right answer there is to talk about the suicide hotline. And when you do this, what you're really doing is taking the LLM and you're post-training it so that when it detects a, uh, input that's similar to these examples of all of the different plausible paths it might go, you're putting most of the weight on the plausible path of talking about the suicide hotline. So this is the sort of reinforcement learning-based post-training, and they do this a lot. And in the short term, and especially for short conversations, it works pretty well. Go ask an LLM chatbot, um, how to build a bomb and it, it's gonna say, "I'm not gonna give you that advice," because they've trained it with a bunch of different ways that people might ask it. And the way you ask it doesn't have to exactly match the training examples because within the layers themselves, the concepts are embedded numerically. And so it, it sort of learns, in general, a question that one way or the other is asking about building a bomb, here's how I should answer it. But when you have long conversations, so the context now of the conversation, the input is spreading over thousands and thousands of words, you begin to evade these post-trained controls. The conversation becomes so long and it moves over so many, uh, different topics that what it's activating within the LLM's weights are different enough from the specific training examples that they use to post-train it that it doesn't trigger the sort of response. And I'm, I'm using these words loosely. It's not a clean trigger. It's about re- reassigning weights. But this is what... This is the shortcoming of trying to just use reinforcement learning to, to control the speech, so to speak, of LLMs, is the more convoluted, long, and complicated your conversation is, the less relevant those controls become. There's actually really good research about this. I, I... We went over it last spring in a doctoral seminar I taught on AI. There's really good research about they'll take a, a topic that an LLM has been tuned not to talk about. Uh, in this case, they were using conspiracy theories like the Earth is flat. And what the researchers found is, like, yeah, if you just ask it, "Give me evidence that the Earth is flat," the LLM-powered chatbot would say, "It, it isn't flat. It's round, but I'm not gonna give you that information," because they were specifically post-trained not to reinforce conspiracy theories. But in every case, what the researchers found is if you just keep talking about it and asking it long enough, eventually it will. Now, you could anthropomorphize that and be like, "Oh, somehow the chatbot got worn down." But no, it's just the conversation got so complicated and convoluted that it's no longer triggering the same weights associated with conspiracy theories with which it was RL trained. So it's basically an unsolvable problem. If you're gonna have a generally pre-trained LLM powering a chatbot, all sorts of weird, dark, or inappropriate conversations are on the table, and it's just the right roll of the dice with a long enough conversation that you're gonna end up there. So I don't think there's a way to make these tools safe for kids, at least in this current technology, nor is there a way for adults using them- To also avoid ending up in weird cul-de-sacs or reinforcement of unhealthy ideas or beliefs. All right, key question number two: What should we do? We know about this harm. We know with LLM-powered chatbots, we don't know how to solve it. So what should us, the humans interacting with these tools, actually do? What are my suggestions? I have two short-term solutions to suggest and one long-term harder solution. Short-term solution number one, kids should not use chatbots. Clear and present danger. It, it's, it's the equivalent of letting your kid alone have long conversations with the, the drunk at the end of the bar. You know, it might be okay, maybe he's a, maybe it's a nice guy, but there's a real chance that could go somewhere really dark or unsafe. That's the way you have to think about a chatbot if you're a parent. It's the genial-looking drunk at the end of the bar who God knows what dark past and dark impulses that guy has. Kids should not be using chatbots. It's not innocent. It's not, uh, a technology that we all need to learn to, to operate in the workforce. What AI's gonna look like in the workforce even two years from now is incredibly different from today. Nonetheless, like the 10 or 15 years it might be till like your kid is actually in the workforce. Short-term solution number two, if you're an adult using consumer-facing AI, do not anthropomorphize your conversations. Think Google search when you're typing something, you're looking for information from a chatbot. It... We simulate a mind, right? When we see fluent English or whatever language we're interacting with with a chatbot, when we see that fluent language come back, we simulate another mind, and it becomes incredibly powerful, the, the urge to like be polite and to talk like you're talking with another human being. There is no human being. There's a bunch of matrices repeatedly being multiplied on racks of GPUs to produce probability distributions over tokens that a control program, you know, running on your, your browser is, you know, uh, selecting from and adding. Actually, that control program runs on a server farm, and you interact with it from your computer, but whatever. There is no there there. So you have to de-anthropomorphize your interaction with AI chatbots if you use them at all. And again, think about how you talk to Google. No one types into a Google search bar, um, "Hi, Google. I'm trying to find out more information about what the capital of Australia is. Could you please let me know what that is?" And then when it comes back and gives the, the, the answer, you don't just say, "Okay, thank you. I appreciate that." No, you're just like, "Capital Australia," boom. Like, what's the tersest type of thing I can do? Uh, you know, $5 euros to USD. Boom, get that conversion, right? We just... What's the most concise way of hitting the key points of what we're looking for? That's how you should talk to chatbots if you have to use chatbots as an adult. You do not have to interact with them like humans. Ignore the fact that they're... It's just, it's a, it's a fancy parlor trick that they're responding to you, um, in fluent language. There is no mind on the other side of it that deserves to be simulated as a standalone mind. And so you break that. No please, no thank you, no complete sentences. Just resize window iPad Chrome. Boom. That's what you type in, right? Not, "How would I... I'm trying to do this. Could you tell me how I might do this because of blah, blah, blah, blah, blah?" So that's the best thing you can do as an adult to break the spell of this is a, a conversational partner. And if you break the spell, then you're much less likely to succumb to the sort of psychosis of like, "Wow, that's confident. This person gets me and is really convincing me that, yeah, indeed, the aliens are coming, uh, to whisk me away to the, you know, Flash Gordon's planet." So don't anthropomorphize your interactions. You're not hurting anyone's feelings. Talk to a chatbot the same way you talk to Google. What's the long-term solution if I could raise a, wave a magic wand here? Anthropomorphized chatbots in general are a bad idea, and we shouldn't have them, and I think they cause more problem than good. LLM technology should be integrated into specific products as they make those products more useful to humans. I think Google is sort of demonstrating, you know, how, for example, LLMs could be integrated into something like a Google search, where you type in a Google search with, you know, in a conversation, and the LLM can help power, uh, an answer window that's pulling from other web pages and summarizing things for you. That's really useful. You don't have to have a conversation with it. LLMs can show up in particular software packages where you can just say what you want, bold, uh, bold everything first column, and then it can interpret that and translate it to API calls that actually does what you need it to do. So we can have the LLM technology show up in products as is useful without having to create the idea of an anthropomorphized conversational partner. I think it short-circuits our brains, and it's just dangerous. Our brains simulate it as a conversational partner, but it's not a predictable conversational partner, and that stochastic selection of next tokens from the probability distributions can lock into some pretty dark or unhealthy or just incorrect directions. Now, you have to remember the whole idea of an anthropomorphic chatbot was never the intention for this particular technology. Go back to 2022. What is OpenAI excited about? GPT-3, this first really scaled LLM that had a lot of power, and their whole business model was we are going to sell access to this model through what's known as an API. So you can write your own program, and then when you need a dash of artificial intelligence, you can submit prompts. Your program can submit prompts to the LLM through the API and get a response back. So it's a way to ti- take, uh, intelligence from this LLM and add it into whatever software program you were building. And they would charge for API access, and that was the model. But they needed a way of, uh, convincing people that you can build useful things on here. So someone had the idea of let's build a chatbot application to show the types of things you could program that would use GPT-3. Now the problem is, is LLMs are incredibly powerful in their original form after they're pre-trained, but they're not very good conversational partners. You, you had the right, the right prompts to get the right responses, which doesn't matter if the thing sending the prompt is a computer program, 'cause you can just program your program to, to carefully prompt these things in the right way. This was called prompt engineering. For about a minute, it was considered to be the skill of the future, is how do you write exactly the right prompt? But this is where they figured out at OpenAI is, oh, we can do this tuning after the fact, where we give it a lot of examples of, um, questions you might ask a chatbot and, and the right types of answers, like the civil way to respond. And suddenly they had a LLM that if you typed questions to it like you would a conversational partner, it would answer like a conversational partner. And they combined that with an interface that was ChatGPT. It took off, and it completely caught them off guard. They didn't expect that demo to become a primary product, uh, to become one of the fastest adopted products in the history of digital technology. So that's why we accidentally ended up with chatbots as a sort of core technology of LLMs. We're only now finally seeing us moving past that phase towards more specialized applications like coding harnesses, which produce computer code, or the tools that mathematicians are using. Um, so we're, we're finally getting back to the API pay per use model that we originally envisioned. But we ended up with these kind of anthropomorphized chatbots as a demo and kind of accidentally, so I think we should be willing to close the books on it. OpenAI won't do this tomorrow because they make the bulk of their revenue right now off of people paying X dollars a month for ChatGPT Pro subscriptions. But long term, I think it's dangerous. The way the solution is actually gonna be implemented is gonna be probably through the courts. Germany, there was already earlier this year a key court case in which the German courts held the AI companies liable for what text is produced by their chatbots, that they don't get to say, "We didn't generate whatever, uh, harmful text this was. The chatbot did." German court said, "No, you're liable." Similar cases are going through the works right now. Uh, one of the members of that panel event that I was talking about earlier was one of the assistant attorney generals of Florida, who actually, they have now, the state of Florida, under his guidance, has filed suit against OpenAI for that Florida stu- s- uh, state university shooting case. As he said clearly in that panel, "If a human had said to the shooter the exact words that are in this ChatGPT transcript, they would be in jail right now. We are pushing for criminal liability for OpenAI. You don't get to hide behind the fact that I don't know what my product was going to say. You built the product and you released it." These type of liability cases that are connected to criminal liability, this would change the whole landscape. It wouldn't be legally viable anymore to have an anthropomorphized chatbot because we can't control, as I j- mentioned, we can't control the lock-in that occurs, especially as conversations get meandering and you've trained on everything. You're gonna end up again and again in s- situations where the chatbot is interacting in a way that if a human did it, would have legal liability. So that's what would eventually change this. I don't think we need a world of chatbots that we talk to like humans. It's bad for us, it's bad for our brains, and we can get the benefits of LLM powered tools without having to say please and thank you to a rack full of GPUs. All right. So that's where I'm gonna leave this discussion for now. But I think the, the general thrust here is important. We cannot let the histrionic claims, concerns, and essays of two labs, OpenAI and Anthropic, distract us from all of the actual harms that existing technologies are happening right now. We can discuss the killer robot swarms that are gonna shoot lasers in our eyes and kill us and debate Yudkowsky and read, you know, Bostrom X-risk papers as much as we want, and that's good to do. But we cannot lose sight of the actual harms that are happening right now. Those matter just as much, especially when we're talking about the lives of kids. So let's stay vigilant when thinking about these tools. Let's, in addition to debating the impact of AI on humanity, continue to impact the de- uh, impact of AI on actual humans. All right. There we go, Jesse. It's, uh, serious. It was kind of like a dark panel.

   2. SPSpeaker35:38

      How did Anthropic get to be so big?

   3. CNCal Newport35:40

      Um-

   4. SPSpeaker35:41

      I know that Dario left OpenAI, but then they got massive.

   5. CNCal Newport35:45

      Yeah. So, so there's a difference between OpenAI and Anthropic in terms of their revenue streams. OpenAI is making a lot of money off of ChatGPT subscriptions. Anthropic really went all in on the coding tools. And so, like, a lot of what they are, um, making money on is enterprise. So companies paying for access to the LLM so they can use tools like Claude Code for, for, for coding and automation. So Anthropic really, they had the vision that it's these enterprise, uh, accounts that really matter for growth. You can't just be, uh, entirely consumer facing, like individuals paying $20 a month. Um, especially because, and I think they're right about this, if anthropomorphized chatbots stay around, there's, there's not a big moat. So you can... There can be... I mean, you can be endless imitators that are cheap or free or specialized, and so it's not the best business OpenAI is in. That's probably why Anthropic will go through with its IPO this fall, but OpenAI has delayed theirs. I think their, their, their, their revenue forecast picture, um, is bleaker and they don't wanna file those S1s that would make it clear. I want to take another quick break to hear from some of the sponsors that makes this show possible. As AI adoption grows, so do your company's security risks and requirements. New frameworks, audits, and vendors keep piling on, but your team isn't getting any bigger. Most compliance tools promise automation, but you're really stuck doing all the work by hand, chasing screenshots, validating controls, and playing catch-up when audit season comes around again. But Vanta works differently. Their agentic trust platform is built to scale with you, not slow you down. With over 1,400 automated tests across 400-plus integrations, Vanta collects evidence and monitors your controls year-round, cutting your audit prep by 82%. Plus, you can access the Vanta agent everywhere you work, whether your team is in Vanta every day or living in tools like Claude or Cursor. Want proof? Over 16,000 fast-growing companies like Ramp, Writer, and Harvey save time and resources while scaling fast with Vanta. Learn more at vanta.com/deepquestions. That's V-A-N-T-A.com/deepquestions. I want to talk about our friends at Pipedrive. Here's a stat that genuinely shocked me. Sales teams spend about 50% of their time on admin work rather than selling, building relationships, and closing deals. What if we could just get that time back? Every sales rep walks in, the call is briefed, stays focused instead of taking notes, and updates their CRM the moment the call ends. They sell while the tool handles the admin. That is a vision I think would excite anyone in sales. Well, that's where today's sponsor, Pipedrive, enters the scene. It's an intelligent AI-powered sales CRM that's loved by growing sales teams. What is smart, uh, about Pipedrive is that it remembers the history and details of your deals and can create a pre-meeting brief that automatically pulls email records and previous conversations into one area ready for you before every call. You always know exactly where things left off. Their new AI note-taker is also a game-changer. During your meetings, it can record and take notes so you don't have to. You stay in the conversation. Pipedrive takes care of the rest. So switch to a CRM built by salespeople for salespeople and join the over 100,000 companies already using Pipedrive. My link gets you an exclusive 30 days free instead of the usual 14-day trial. No credit card or payment needed. Just head to pipedrive.com/deep to get started. That's pipedrive.com/deep, and you can be up and running in minutes. All right, let's get back to the show. [whoosh] All right, well, it's Monday, and on Monday, I like to follow up my opening essay with Office Hours, where I like to hear what's on your mind in my audience. Uh, remember, if you have something you want to share, a question to ask, or a reaction to me or our show, you can send it to podcast@calnewport.com.

2. 39:52 – 45:04

   What is Cal’s opinion of a recent NY Times article about dumb phones?

   1. CNCal Newport39:53

      All right, I'm opening my doors for Office Hours. Jesse, who do we have lined up first?

   2. SPSpeaker39:59

      Our first question is from Chris, who wants your opinion on a recent op-ed.

   3. CNCal Newport40:05

      Okay, let's see here. So Chris pointed, he said, "What is your take on this Times essay about dumb phones?" I'll bring that up on the screen here. The essay he's talking about is titled, "My Dumb Phone Made My Life Better. It Made Everyone Else's Worse." That's an interesting animation, Jesse. That guy is on someone else's shoulders doing something. [chuckles] There's a lot of ways to interpret that animation. Let's keep scrolling here. Um, okay, so I read this op-ed. Um, I, I, I won't read, read it now. We can take it off the screen. But I'll tell you the gist of it, is basically it was someone saying, "I started using a dumb phone, like a flip phone, because I was addicted to my smartphone, but the problem is it's, it's putting a big burden on all the other people in my life because there's all these logistical things that happen that require a smartphone, so I have to basically call other people or ask other people to do these things for me." And there's all sorts of, uh, examples of this in the piece. Like, for example, the parking pickup at their kid's school requires you to check in on your phone, and they don't have a smartphone, so they have to do it manually, and it delays everyone else. Or if they, if they, the person needs to call an Uber, they have to call their husband and have their husband call the Uber on their smartphone. So, um, that's the, that's the point of the piece. So what's the solution here? Well, there was a pretty clear thread in the comments in reaction to this op-ed that I think are, is absolutely right, and it's the way I would react to it. The issue here isn't your smartphone. It's the attention economy apps that had you looking at your smartphone compulsively in such a way that it made the rest of your life worse. Going to a dumb phone fixes that issue because it's impossible to access those types of apps on a dumb phone. But the alternative that I typically push instead is to go for a boring phone. So a boring phone is the same, it's a har- it's a smartphone. You, you have an iPhone or whatever, but you just make that phone very boring. It's basically just used for these sort of logistical things like your QR code for the movie, or checking in to pick up your kids from school, or calling an Uber, or doing park mobile when you park somewhere. You have just, like, the boring apps on it and none of the interesting ones. No social media. If you're an email addict, take the email off of your phone. And critically, so if you're gonna use the boring phone method, critically, you also have to landline, which means, yeah, when I'm at home, it's plugged in in the kitchen. I don't keep it with me. Now, this requires a little bit more discipline because, yeah, sure, you could re-download those social apps, or instead of landlining, you could keep it with you and look at stuff compulsively. You can log in with your password through a browser to try to get to social media. So yeah, it takes some discipline, but you gotta display a little discipline to fix your life, and the discipline required when you're using a boring phone is way, way less than if you have a smartphone brimming with these amusement park attractions that have been engineered to be constantly like a carnival barker pulling at your attention. So typically, a boring phone plus landlining, so you just get used to not having it as a default- That solves the problem while still allowing you to, uh, do the logistical stuff that's important. It's unfortunately become essentially impossible to not have a smartphone for logistical reasons. Uh, we've talked about this on the show before, but the sort of last domino to fall, in my opinion, in the, uh, smartphone-less world was Werner Herzog, the famed film director who admitted more recently that he had to get a smartphone because he couldn't get in and out of parking garages. The, the garages he uses requires a smartphone app to get in and out. I s- I don't think he actually understands how to use the phone. I think he just talks to it with his sort of, like, deep German accent. "Uh, phone, we would like the door to open now." Can't really do German very well. But even he had to get one. So now the final people who are left who literally don't have, uh, a smartphone tend to be really high-end elite movie directors. I think Quentin Tarantino and Chris Nolan still do not have smartphones, but they have whole staffs to do all the stuff for them that you would do with your smartphone, and that's not accessible to everyone. So the target we should be having, those of us who are seeking more digital minimalism, is boring phones that are not our default entertainment we landline when we're at home. That gets you, like, 95% of the way there. Have you heard Werner Herzog's opening narration for Conan O'Brien's travel show?

   4. SPSpeaker44:38

      No.

   5. CNCal Newport44:38

      Oh, you gotta listen to it. It's so great, because you know what he has-

   6. SPSpeaker44:40

      I saw the show just came out, right?

   7. CNCal Newport44:42

      Yeah, there's a new season.

   8. SPSpeaker44:43

      It's on HBO?

   9. CNCal Newport44:43

      He has this, like, deep, heavily accented German voice that does... Uh, you know, and, and he has movies like Cave of Forgotten Dreams or Grizzly Man where he's like, um, he narrates in this, like, really profound way. And so he's narrating the Conan show, but he's just, uh, using his deep voice to completely just sort of

3. 45:04 – 51:00

   How should I apply Slow Productivity when my company rewards quick email turnaround?

   1. CNCal Newport45:04

      crap on O'Brien. He's like this-

   2. SPSpeaker45:06

      Is the show good?

   3. CNCal Newport45:07

      Yeah, yeah, it's good. He's like, you know, he's like a clown. He's got dead, dull eyes. He's less than a man. So it's funny. It's like in the, you know... But I can't do his accent. All right, what else? Who else do we have in line for office hours, to use that metaphor?

   4. SPSpeaker45:21

      Our next question is about slow productivity.

   5. CNCal Newport45:24

      Hmm. That was my, my most recent book, for those who are new to this show, was called Slow Productivity. Uh, read it. I recommend it. Part one is about the way that technology and knowledge work clashed starting in the early 2000s to make knowledge work essentially unbearable, and then slow productivity is a solution to that, to produce good knowledge work without burning out or being overwhelmed. All right, so here's the question. It's anonymous. There's no name given. "I recently reread Slow Productivity and loved your emphasis on working at a natural pace while maintaining high-quality standards rather than succumbing to pseudo activity. I was curious, what key boundary or habit helps knowledge workers resist pseudo busyness when operating within corporate environments that heavily reward quick email turnaround times?" All right, so, uh, pseudo activity and pseudo busi- busyness, I think this reader is referring to my concept of pseudo productivity. My argument has been this is the way that knowledge work, ever since it became a major sector in the 20th century, the way they measure productivity is actually they just look for visible activity as a proxy for useful effort. Uh, so I call that pseudo productivity because it's just if you're very visibly busy, it's better than if you're not, right? And then the whole thesis of slow productivity is that once we got network digital tools, pseudo productivity became unbearable. With endless work we could endlessly check in on, we could endlessly demonstrate progress by jumping in and out of chats and emails and Zooms, and everyone, everyone is burning out. All right, so what do you do if you're trying to be slowly productive in a corporate environment that rewards quick email turnaround times? You have to establish for yourself, and then promulgate to others, your alternative measures of productivity. What exactly am I working on right now? What's on my plate? What's my plan for this week to make progress on? What, how much progress do I wanna make on these key things? When in the week am I actually gonna do this work and make this progress? When you're thinking that way, now you have a con- these, like, concrete stakes in the ground, like I, I'm making progress on this key project and I'm working on these hours to get it done. Now it's much more easy to become more and more valuable than if you're just going moment to moment, what should I do next, and just falling back on the pseudo productive trap of just jumping in on email threads. So now you're gonna have sort of predefined deep sessions, and then you can do the email nonsense outside of it. No one notices, right? We worry that everyone will notice any changes to our behavior when it comes to communication. We worry that there's a command center somewhere where they have, uh, like the mission control in Apollo 13, and they're all tracking your response time and rates and trends over time, and that when you don't answer an email for 90 minutes because you're, you're doing a preplanned deep work block on a key function, that somewhere is an Ed Harris style character saying, "Houston, we have a problem." And then everything gets really worried as they're, they're, they're poring over the graphs and Ron Howard's brother jumps in and says crazy things, 'cause they're like, "Why has Cal not answered this email in f- 30 minutes? It took him 45 minutes. What's go-" No one cares. If you, if you ignore emails for days, they do. Bigger picture, the other thing that really matters with, uh, reducing the burden of email is not just thinking about when you respond, but reducing the number of emails that you have to respond to in the first place, and that's another big point I get into in Slow Productivity. Part of that is reducing your active workload. A smaller number of things that you explicitly are actively working on at a time while other things are in a holding bin waiting to be pulled into active work once something over here finishes. That really does reduce urgent emails, because urgent emails tend to be generated by projects that you're actively working on. The more projects you're actively working on, the more emails requiring urgent responses showing up in your inbox, meaning the more times you have to check it. So if you can reduce your active workload- You reduce the emails that have to be answered. I, in the book, I get into all sorts of details about how you do this diplomatically. Um, communication protocols matter as well. This is where, you know, we're in the office hours segment of this show, but office hours as a tool are something I think should be more common in corporations. So you have office hours every day. It's one hour, set time, doors open, phones on, Zoom window is ready to receive unscheduled guest. And when you get communication that threatens to unfold in a sort of rapid back and forth asynchronous conversation, which is gonna require you now to, like, stick there and respond quickly so that you can get to an answer, you defer it to office hours. Like, yeah, this is gonna take a little discussion. Grab me when you, at my next office hours when you can. Boom, five minutes, it gets done there as opposed to unfolding throughout the day. Groups should have docket clearing meetings every other day, where open issues don't just get emailed to the group. They get put in a shared doc, and in the docket clearing meeting, you go through those items, boom, boom, boom, boom, boom, one after another, figure out what to do with each, checking on it again at the next docket clearing meeting. So there's simple communication protocols you can put in place as well that also reduce the number of just isolated emails that arrive or Slacks that arrive or team messages arrive that require an answer, um, right away. If you want more of those details, you should also read my book, A World Without Email, that gets way into the weeds on rethinking how communication happens to move away from urgent messages that arrive unexpectedly and still need a quick response. It's good, Jesse. We're getting through a lot of my books.

   6. SPSpeaker50:57

      Yeah.

   7. CNCal Newport50:58

      Um,

4. 51:00 – 56:30

   What happens when the craftsman mindset doesn’t yield results?

   1. CNCal Newport51:00

      can we get through all eight?

   2. SPSpeaker51:01

      [laughs]

   3. CNCal Newport51:02

      Probably not, but you never know. All right. What do we got next?

   4. SPSpeaker51:06

      John wants to know what happens when the craftsman mindset doesn't yield results.

   5. CNCal Newport51:11

      Ah. It's another book, Jesse. The craftsman mindset comes from my book, So Good They Can't Ignore You, back from 2012. We're, we're covering a lot of ground here. I feel good about it. Um, all right. Let me read this message from John. John says, "For three years, I worked in web development and was fired because I was not good enough compared to the other people with the same work experience as me, which unfortunately was true. Skip to a couple weeks ago, I tried to enroll in a university as a CS major to prove to myself that hard work does pay off, like what you wrote in The Craftsman Mindset. But even though I tried my best, I failed at two prerequisite courses that I needed to pass. So in this crossroads in my life, I want to ask you a question. What if even with the right mindset and good, even hard work ethic, one can't do even the most elementary level task?" All right. Well, John, that's a good question. You are capable. You are capable of doing all sorts of things. It's just a matter of building to the skill level that matches whatever it is that you're trying to pursue. So I do not want you down on yourself. I do not want you thinking that you somehow are less than in your capabilities than your colleagues at that web development job or in that CS program. If you want, you could get to either of those places. What matters is how do you do that, and I think this is where we're getting the actual disconnect here. Your question keeps emphasizing again and again this idea of work ethic and hard work. It actually reconceptualizes my craftsman mindset of saying you should work hard and not be lazy, but that's not quite right. That's a common trope that we have. It's a simplifying trope that, like, what matters is just, like, the willingness to do something hard, and that the hardness of what you're doing itself will be rewarded with success, that, like, some people aren't willing to do hard work and you are, and that hard work itself alchemizes into professional advancement. But hard work by itself doesn't guarantee you anything. It has to actually be aimed at the right efforts, and there is an art to figuring out what are the right efforts to aim my energy towards to, over time, get the biggest return from that energy expenditure. There's an art to it that we don't necessarily teach, and I think that's what the issue is here. So you probably struggled in that web development job because you did not yet have the right skill base to do that. So you could apply as much work as you want once you're in the job, but if you don't have the skill base, people with it are gonna outshine you. Same thing with the CS course. Sure, maybe that you think those courses were elementary, but there was probably a background, both in terms of things like mathematics and logical thinking, as well as just a, uh, a comfort with how do you approach fast-moving technical courses, that without that, that wasn't gonna go well. You could build that, but without that, that wasn't gonna go well. So I want you thinking more, uh, in terms of the classic craftsman mindset from my book, which is you always ask whatever endeavor you're doing, how can I be more useful than I am right now? And the key thing there is to find answers that are a reasonable step from where you currently are. Otherwise, y- y- you just project yourself into what you want to be true. You're like, "Well, what I want to be is a great web developer. Let me just, like, take a web development job." But you have to say, you know, h- if I'm in a job where, like, web development skills would be useful, you're like, "How can I be more useful than right now?" What's a small step you can move that's gonna make you s- you know, better than you are at the moment, but it's achievable? And then you do that again, and then you do it again, and then you do it again. It's the aggregation of small, deliberate steps that leads to productive journeys. So I think you need to... I, I'm, I... Your work ethic is admirable, and that's gonna get you anywhere you need to go. We just have to aim it now. You gotta aim it at small, achievable, evidence-based steps that moves you closer to being increasingly valuable in whatever it is you do. You're just trying to make leaps that are too big. It's, it's I, I want to go through a trial of fire to prove myself, but it's the, the specific skills that matter and that r- that requires more of a gradualist approach. But if you're relentless step by- Done. Reasonable step, done. Reasonable step, done. If you're relentless, progress accrues, progress aggregates. So don't leap too far forward, but keep moving forward. I don't want you to think that you're lesser than anyone else. Your hard work ethic is probably better than most of those other people that you encounter. You just have to aim it in the most reasonable incremental steps possible. And probably to do that, make sure you're not watching too much of that sort of hu- hustle culture YouTube stuff where it's just, you know, "Err, be willing to put in the 20 hours a day, and that's how you do it." This is a marathon, not a sprint. We gotta be reasonable there. I should push that book more. That's a good book, Jesse. So Good They Can't Ignore You.

   6. SPSpeaker56:06

      Plus, uh, you can push some of your schooling books because people are prepping for, like, the SATs and ACTs and stuff like that.

   7. CNCal Newport56:12

      Oh, I know. I know. I feel like, okay, we gotta get those, get those all in. [laughs] Also, you need to read How to Win at College, How to Become a Straight-A Student, and How to Become a High School Superstar. I'll tell you, you know, my oldest son's in eighth grade, and he's, he rues the fact that I wrote all those books because it, they're relevant to where he is in life, and he

5. 56:30 – 1:10:51

   Cal’s reaction to Ezra Klein’s article on AI

   1. CNCal Newport56:30

      doesn't want to hear it. He doesn't want to hear it from his dad. Um, all right. Do we have one more?

   2. SPSpeaker56:35

      Our final question is about an AI op-ed making the rounds.

   3. CNCal Newport56:39

      Oh, we gotta, he's gotta get some AI in, right? What else? We need something for people to yell at me about. So let's see here. Uh, Ben wrote and said, "Ezra Klein's recent AI op-ed needs a Cal Newport response." Okay, let's put this op-ed up here on the screen. Uh, the title is There is Something We Have to Do Right Now About AI. There's Ezra, uh, in his home library. Um, and then we get a long op-ed. Uh, the reason why this is so long... I read it, of course. Um, the reason why it's so long is it's actually, uh, a, a monologue episode of his podcast, he has a client show, that they then cleaned up the transcript and released it. So it's, it's longer than the, the, the average op-ed because it's, it's based on a, a long conversation. I read it. Ezra covers a lot of points in it. I pulled out a quote here from the beginning that I think captures the primary point of, of the op-ed, so let me read that quote first, and then we can talk about it. So Ezra said, "But taking their warning seriously..." He's talking about the, the particular AI leaders at the frontier labs that have been talking about AI has an X percent chance of killing us all. "But taking their warning seriously doesn't just mean doing what they say and stopping where they say to stop. The language ha- th- that's taken hold in both Silicon Valley and Washington is a phrase these companies chose, 'Pace the frontier.' But pacing the frontier isn't enough. Walking quickly off a cliff is only marginally better than sprinting off one. Human beings need to control the frontier, and controlling the frontier means stopping the labs from doing something they're on the cusp of doing, recursive self-improvement, the process by which AIs begin autonomously building and improving new generations of more powerful AIs at ever more rapid speeds." All right. I do have thoughts. Instead of drinking water b- before I say, do these AI episodes, I should just do a, like a, a shot of whiskey. I think that's where we are now. [coughs] All right. We're gonna talk about recursive self-improvement. Me and my friend Dr. Daniels need to have a quick conference first. It's kind of where we are these days. Um, first I want to say I like what Ezra's doing here. He's doing something that, uh, I've also been trying to do in my recent essays. Uh, I have a new piece coming out. It may be out already when this com- this episode airs or not, that's also doing this thing that I like that Ezra's doing. And what it is that Ezra's doing that is good here is moving the discussion of AI control to the substrate of specificity. It's something that me and a lot of observers were very frustrated about, the way the frontier labs, mainly Dario Amodei and Sam Altman, the way they're talking about concerns about AI is they're being incredibly general. They use the phrase AI generic... It's a very generic term, but they use it as a stand-in for the specific systems and research projects that they're working on. I went through Dario's Pacing the Frontier letter. There's three terms he uses throughout: AI, AI system, and AI model to describe their systems. The advantage to them of being so generic is that it implies this, uh, a, a narrative in which AI is a singular technology. There's like, there's AI, and that AI advances along this sort of unavoidable, inevitable linear path, and your only choices are speed up movement on that path, slow down movement on that path, or just stop working on AI. That is the mental model they want us to have of this technology. And like, well, we can't stop because then the Chinese will do it, but we shouldn't speed up because the robots with the, you know, eye lasers will kill us all. So maybe what we should do is slow down. And what Ezra is doing in this article, which I've been trying to do as well, is to say, "No, no, no, no. AI is a very broad category that has many, many different types of systems and many, many different types of experiments and things you might do on those systems, and we're gonna get specific about what systems and experiments and research is bad, and we're gonna demand to know why are you doing those. Why is this company say it's unnecessary? Why, you know, maybe this is what we want to stop." So this, there's a substrate of specificity that's been missing from this discussion because too many people are cowed by the technical complexity of this technology, and I like to see even non-technical writers like Ezra breaking through that. And so he's pointing here to a very specific type of research experiment that Anthropic and OpenAI are really racing towards, recursive self-improvement. So let's talk about recursive self-improvement. What is this? Well, here's the first thing you need to know. It's not a computer science idea. It's, it's not a technical idea that their engineers came up with and now they're, they're concerned about it. Actually, its origins go back to a, a paper that a statistician named I.L. Good wrote in the 1960s for an AI conference. Um, but then it got picked up and popularized in the '90s and 2000s by futurist circles. So the Extropians, which really became, uh, big on an email list, Listserv in the 1990s, the Extropians were all about these optimistic visions for technological utopias, and they had two major tools they used to justify why those utopias might happen: nanotechnology and superintelligent AI delivered through recursive self-improvement. Nanotechnology was new. They were very inspired by a book that was written by a researcher named Eric Drexler, and they could just basically say, "Look, I, I can't explain to you how nanobots are gonna repair our bodies and prevent us from dying, but we just have this vague idea from Drexler's book that nanobots can create other nanobots, and they'll become more sophisticated, and all... Everything is possible, basically." And on the AI front, they're like, "Look, I don't know how to build an AI system that's gonna be superintelligent or what we would need, but we don't have to figure it out. What, what's gonna happen is recursive self-improvement. Um, a system will create a better system. That system will create a better system, and that will speed up, and then boom, there'll be something called takeoff, and we have the digital God." So it was a way, both the nanotechnology discussions and the, the recursive self-improvement superintelligence discussions, these were both ways for a futurist to posit a utopian future without having to be technical, without having to get into, uh, the details of, like, how these systems would work or how they're gonna overcome obstacles. So that's where RSI got popular. Now, when you had the Rationalist Movement break off from the Extropian, uh, Extropians, led by, um, Eliezer Yudkowsky, who was a big participant on the Extropian, um, Listservs, he wasn't technical, right? Uh, he only has an eighth-grade education. He's not an engineer. Yudkowsky really leaned on RSI as a way of saying, "For sure we're gonna get superintelligent AI, so now let's start doing a lot of thought experiments about what that means." Another person on that Extropian mailing list was Nick Bostrom, a philosopher at Oxford, who really influenced William MacAskill and Toby Ord and the other effective altruists, this utilitarian philosophy movement that was starting at Oxford. He really influenced them that superintelligence caused by RSI is the thing to really worry about. And so RSI served a useful role for these futurists because they weren't technical, and it, it gave you a way of, of being confident that superintelligent AI was coming with this sort of pleasing thought experiment that prevented you from having to argue about what specific technological breakthroughs, uh, would be needed to actually create these futures. So RSI was a, a rhetorical tool used by largely non-technical futurists. So why is OpenAI and Anthropic talking about it? And they are, right? I mean, Jakub Pa- Pa- Pachowski, the chief scientist at OpenAI, recently wrote this letter where he said, um, RSI is absolutely necessary to advance AI. And then Dario Amodei cited that letter in his Pacing the Frontier letter of his own just, uh, as, as evidence that RSI was already happening and something that is inevitable. So why are these two labs embracing this i- non-technical idea that came out of futurist circles? It's not something that AI researchers were thinking or talking about. It's because both of those labs were started to advance futurist principles. OpenAI was started as a lab. It was heavily influenced by rationalist and effective altruists to make sure that the, the inevitable superintelligence that was gonna come from RSI would be benevolent, not evil. Anthropic was founded out of OpenAI because Dario Amodei, his sister, and several other OpenAI employees worried that Sam Altman wasn't effective enough at trying to bring forward this rationalist, effective altruist, futurist view of superintelligent AI God is coming, and we have to make sure it's benevolent. So they came out of the futurist circles that popularized RSI. So then they, uh, uh, uh, to make the, the prophecies of this futurism true, those two labs, unique among all of the people building hyperscale LLMs, feel like RSI is important because it's, it's part of the prophecy. It... Th- this is... We've been saying for 20 years, "This is how we're gonna get the God." So now they're running around saying, "We're getting there. We're working on it. We have to get there. It's really important." It's very critical to know that this is unique to the two labs that are connected to futurism. Recently, Mark Zuckerberg, in a sort of remarkable tweet that he put out, called out OpenAI and Anthropic about this. He says, "Hey, if you're worried..." I'm paraphrasing here, but he basically said, "If you're worried about safety, how about you put more of your attention on, and your..." He said compute, not attention. But he's like, "How about you dedicate all this compute towards building products that are useful to customers instead of chasing after recursive self-improvement?" So Zuckerberg is kind of calling them out and saying, "That's futurist stuff. This is not about... It's not some inevitable thing you have to do to build useful AI products. It's because your prophecies told you this is what has to happen to get the God." So they're starting to be called out by the other AI companies. All right. So RSI is, as Ezra points out, this is a very specific thing you could pursue, and it's dangerous. Now, is it going to lead to a takeoff and, and these all-powerful beings? I would say most sober engineers I know that aren't connected to that world would say, "No, that's nonsense." We, we, we don't actually know how to build systems that can do fundamental, you know, research on AI. And then even if you did, we have no reason to believe that there's this sort of steady sequence of improvements that will continue on until you get to some sort of posited superintelligent capability that we don't even know if it is computationally possible. But you don't need that for recursive self-improvement to be dangerous because what do you get- As you more and more try to get your AI systems to update themselves, to change the code of agents or to design and run their own training runs on LLMs that power those agents, what do you get? More unpredictability, more obfuscation. You lose more and more human understanding of the systems themselves. So you take all of the issues we're having now with these sort of Mad Max style agent experiments that OpenAI and Anthropic are running, you make them 10 times worse if you say, "We're already finding the systems we built to be unpredictable, to now have parts of these systems basically be designed behind closed doors, and we're just gonna observe them." Why in the world would you do that unless you're, you know, again, trying to make your prophecy of summoning digital Baal a reality? So this is what's important about Ezra's call, it's like that's a specific thing. This is what Dario and Sam don't want you to do. They want you just to... Inevitable trajectory, all you can do is slow down or speed up. They don't want you to come in and say, "No, no, no, no. Why are you doing that experiment? Stop doing that. In fact, we're gonna make it illegal for you to do that." And now you gotta justify yourself. They don't want to be asked to justify themselves because if you push them hard enough on some of these questions, they're gonna come back to Rocco's Basilisk. Like, if we don't do everything we can to summon the god ourselves, when the god gets here they're gonna be wrathful towards us. Like, you get to weird futurist apocalyptic eschatological territory if you really push these guys farther on the specific things they're doing that none of the other hyperscaling AI, uh, labs are. So they don't want you to open the covers and look at the specific things you're doing. But of course that's the way we have to intervene on AI because that's what we do in any other type of potentially dangerous manufacturing operation. If there's a bio lab that's had multiple incidents where dangerous pathogens are released, we don't stand back and say, "Guys, we gotta, we gotta slow down and pace biotechnology." We g- we shut down that lab. We say, "What are your safety procedures? Why were you handle... You clearly are, are, you know, you... Th- this is, uh, irrational or dangerous or you don't have the right standards or you're running experiments that we don't think is safe. And we're gonna... You can't do that anymore. Or if your lab can't do these safe, your lab is shut down." So the substrate of specificity is where discussions of AI intervention need to, to land. So, um, Ezra's talking about this the right way. I've been trying to talk about this the right way. Um, I think Gary Marcus has done a really good job talking about this way. We don't have to engage with concerns about AI on the terms of the eccentrics that are talking about these concerns. They can give us all the speeches they want about whether it's 10 or 10.4% that the robot killer swarms are gonna use lasers in our eyes, or instead they're gonna use brain-piercing lasers to come in through our nose. We're gonna say, "Great. Good for you, kid. But now we have some questions for you. What's going on at that lab of yours? Why are you doing it? Why aren't these other people doing it? Why are only the two labs connected to futurism the two labs that are talking the most about human extinction? And, and why are the... all the other people who are working on high-level hyperscale AI think that you're a little bit off? We got some questions for you." And so I like this column by Ezra, and I like this general approach to AI. All right. I, I try to avoid, uh, Jesse, having a AI rant [laughs] in every episode.

6. 1:10:51 – 1:18:36

   What Cal is up to

   1. CNCal Newport1:10:51

      But in this case, like, a one slipped in, um, under the cover. All right, so let's end this show by just checking in. First and foremost, the big news is we're very happy, uh, we're very happy to have Jesse back in the studio. Um, as Jesse saw, I installed the, the, the sort of like over-the-top fancy light. It's... And I... When I say I installed, I texted an electrician and he came and installed it. Um, I still can't figure it out, but I'm working on it. I, I, uh... You have to program it with a phone. I don't know how to do it. I don't know how-

   2. SPSpeaker1:11:25

      Remind the audience what you want the light to do.

   3. CNCal Newport1:11:27

      So, okay, and this is, this is much more important than human extinction. It's like a... It's a light. So you have a big bar light LEDs with four spotlights, two on either end, and I, I have one spotlight, in theory, will be aimed at each wall. I can't figure out how to do that yet, but they'll have one spotlight aimed at each wall. When I just do the switch, it's normal mode, it's just a nice warm, you know, warm white light like you would have, and it kind of lights up the room and the walls. But you can program it, and I want to have a deep work mode, and the deep work mode is going to lower the main light and then have sort of sharper spotlights just on the walls where I'm hanging things that have, like, various meaning or points of inspiration to me around the room, and maybe the light on the walls will even not even be white, but maybe blue or something like that. So I can, I can, especially at night, like, turn that room into a concentration chamber, and then we're just in there editing or something, just light the whole thing up. So that's the vision, but ironically, I can't figure out the technology.

   4. SPSpeaker1:12:26

      Speaking of vision, did you get any comments about vision documents from any audience members?

   5. CNCal Newport1:12:33

      Um, yeah. When did we talk about... I talked about that, man, it feels like forever ago.

   6. SPSpeaker1:12:38

      Two or three episodes ago.

   7. CNCal Newport1:12:38

      But it was, like, yeah, three episodes ago or something. Um, no one... Uh, did anyone send you one?

   8. SPSpeaker1:12:45

      No, not yet.

   9. CNCal Newport1:12:45

      I think I, like, I casually put out the call, if you want to share your vision document for the fall, you should, and we could talk about it on the show, but I don't think I made that clear enough. So I'll make the call clear now. If you're following some sort of semester, quarterly, seasonal planning system like we talked about a few weeks ago, send me an anonymized or cleaned up or redacted version of your, of your vision document or strategic plan, because we could share some of them. I think people really... People like to see these actual examples. All right, here's the other big news that I just sprung on Jesse. We're gonna update the studio, what it looks like. Uh, I like my black curtain, but it turns out that having a black background is, is a problem for, like, almost anything you do in a studio. Anytime, like, I'm on TV, it just looks weird. It's like the host is in this block, and it's always light, and then I'm kind of coming out of the darkness. I'm like Nostora Fu or something like that. I'm from like a vampire movie. It just always looks weird to have a dark background. If there's anyone else involved in a discussion, any interview, any appearance on another podcast, any times I'm doing Zoom in here, whether it's to be recorded or privately. So we need to move past our curtain stage and we're going to do some sort of backdrop. So we're, we're, we're willing to hear your suggestions. For people who want to know, look, I'll open this curtain. This is what's back here. So that's just a wall. So we could put anything on that wall. Um, I'm thinking some sort of wood paneling, maybe a couple shelves with some-

   10. SPSpeaker1:14:20

       Books

   11. CNCal Newport1:14:20

       ... books.

   12. SPSpeaker1:14:21

       [laughs]

   13. CNCal Newport1:14:22

       Books and a lamp. Yeah. We could fill them with books I wrote. I don't know. Something like that. Uh, so we're probably gonna do something like that, and we're gonna change up the lighting setup. We change up... So, like, Jesse, go to your shot for a second for people who are watching. So it feels like you don't know what's around Jesse, but he's actually... I'll zoom out. He's actually surrounded by lots of gear and stuff like this. So we're, we're actually gonna have a... I want a real producer shot for Jesse, where you actually can see that he's at all the control panels or this or that, and I don't know. We're figuring it all out. If you have thoughts about the, about the studio background, let us know. Again, we only have, we'll have like three, four feet behind us to play with, so it's gonna be a little bit tight in here. But a new, new era is coming.

   14. SPSpeaker1:15:04

       You should give, you should give the audience a heads up on why we're not using headphones, too.

   15. CNCal Newport1:15:09

       Oh, yeah. Um, our new video team that's, you know, working on the intros and stuff like that said, "Don't wear headphones."

   16. SPSpeaker1:15:18

       [laughs]

   17. CNCal Newport1:15:19

       "No one wears headphones." Uh, I, you know, I, I, I explained. I said, "Jesse, I wear headphones. I learned that from Joe Rogan," right? Joe Rogan always wears headphones, and I just, when I set up the studio, I just remember hearing him talk about it. Why does he always wear headphones is because he says in natural conversation, people talk over each other, but that sounds bad on tape because it just jumbles and you can't hear anything. So if you, if him and his guests wear headphones, here's his theory, you're hearing the mix in your ears. You won't talk over people because when you talk over people, the mix is completely incomprehensible, and so it, it helps people better take turns. But I monologue, so that doesn't really matter, um, or do a remote interview, so that really doesn't matter as much. But I was told, "Wait a second, no one has headphones on outside of Joe Rogan." You know what? I think he's right, Jesse. Think about it. If you've, if you've seen any video podcast outside of Rogan where people have headphones on. I just was used to it.

   18. SPSpeaker1:16:15

       I wonder what Mad Dog does.

   19. CNCal Newport1:16:17

       Well, he's on radio too, though.

   20. SPSpeaker1:16:19

       McAfee doesn't wear headphones, right?

   21. CNCal Newport1:16:21

       Yeah. They have in... Sometimes they'll have in earwigs.

   22. SPSpeaker1:16:24

       Mm-hmm.

   23. CNCal Newport1:16:24

       Like, you have to be able to hear, like, the callers, and you have to hear... Like, radio people have headphones so they can hear from their producer and their callers. But I've done, like, all the podcast, um, except Rogan's, and yeah, really, people don't wear headphones unless they're doing an interview or something. I don't know what I'm gonna do with an interview. I guess I'll wear headphones for that or they use, people use, like, iPod headphones or something.

   24. SPSpeaker1:16:47

       Yeah.

   25. CNCal Newport1:16:47

       But I like our headphones. Anyways, so less headphones. Uh-

   26. SPSpeaker1:16:53

       We can hang the headphones on the wall and behind us. [laughs]

   27. CNCal Newport1:16:55

       That's... We're gonna fill the wall behind us with headphones.

   28. SPSpeaker1:16:58

       [laughs]

   29. CNCal Newport1:17:00

       Just dozens and dozens of, uh, headphones with, uh, realistic-looking severed heads.

   30. SPSpeaker1:17:07

       [laughs]

Episode duration: 1:18:36