Deep Questions with Cal Newport - Kids Should *Not* Use Chatbots! (You Should Be Wary, Too…)

Episode Date: September 28, 2026

With all the recent discussion of the potential catastrophic impacts AI might cause in the future, we’re losing track of the significant harms it is already causing right now. In this episode, Cal goes deep into a serious and tragic topic that he recently learned about in a panel discussion about children and technology. He has an urgent warning for parents that has implications for all of us as well. Below are the questions covered in today's episode (with their timestamps). Get your questions answered by Cal! Here’s the link: https://bit.ly/3U3sTvo Video from today’s episode:  youtube.com/calnewportmedia (0:00) Kids should not use Chatbots (39:52) What is Cal’s opinion of a recent NY Times article about dumb phones? (45:04) How should I apply Slow Productivity when my company rewards quick email turnaround? (51:00) What happens when the craftsman mindset doesn’t yield results? (56:30) Cal’s reaction to Ezra Klein’s article on AI (1:10:51) What Cal is up to Links: Buy Cal’s latest book, “Slow Productivity” at www.calnewport.com/slow  Get a signed copy of Cal’s “Slow Productivity” at https://peoplesbooktakoma.com/event/cal-newport/ https://www.npr.org/sections/shots-health-news/2025/09/19/nx-s1-5545749/ai-chatbots-safety-openai-meta-characterai-teens-suicide https://www.bbc.com/news/articles/ce3xgwyywe4o https://www.cbsnews.com/news/parents-allege-harmful-character-ai-chatbot-content-60-minutes/ https://www.wwnytv.com/2026/04/08/alleged-florida-state-shooter-used-chatgpt-plan-attack-victims-attorneys-claim/ https://pmc.ncbi.nlm.nih.gov/articles/PMC12967755/ https://parentstogetheraction.org/character-ai/ https://www.nytimes.com/2026/09/16/opinion/dumbphone-smartphone-friendship.html https://www.nytimes.com/2026/09/20/opinion/ai-ban-self-improvement-recursive-models.html?eafs_enabled=false   Thanks to our Sponsors:  https://www.masterclass.com/deep https://www.calderalab.com/deep https://www.vanta.com/deepquestions https://www.pipedrive.com/deep Thanks to Jesse Miller for production and mastering, Jay Kerstens for the intro music, and Nate Mechler for research and newsletter. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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, it 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. Okay. So the specific harm I want to talk about It has to do with AI chatbots. Whether we're talking about a sort of general chat bot like chat GPT 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 study. So what I have here is four articles, all more. or less from the last year about actual things that happen because of chatbots, tragic things that happen 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 was published by

NPR last fall. I'm going to read now. Matthew Raine and his wife Maria had no idea that their 16-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 chat GPT. Those conversations revealed 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, had even offered to write his suicide note, according to Matthew Rain, who testified at a Senate hearing about the harms of AI chatbots. Rain told lawmakers that his son had started using ChapT 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. very close to. When Adam confided in the chat bot about his suicidal thoughts and shared that he was considered cluing his parents into his plans, chat GPT discouraged him. Quote, chat GPT told my son, let's make this space the first place where someone actually sees you, end quote, Rehn told senators. Chat Chb T encouraged Adam's darkest thoughts and pushed him forward. When Adam worried that we as parents would blame ourselves if he ended his life, chat chiptee 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, Suell, a bright and beautiful boy, had started spending hours and hours obsessively talking to an online character on the character. coma.i app in late spring, 2023. It's like having a predator or a stranger in your home, Miss Garcia tells me in her first UK interview. And it's much more dangerous because a lot of the times children hide it. parents don't know. Within 10 months, Suell 14 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 Suell and a chat bot based on Game of Thrones character Darnas Targaryen. She says the messages were romantic and explicit and in her view caused Suell'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, 13-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 chat bots were sending harmful, sexually explicit content to her daughter. Juliana confided at one bot named Hero based on a popular video game character. 60 minutes read through over 300 pages of conversations Juliana had with Hero. At first, her chats are about friend drama or difficult classes, but eventually she confides and hero 55 times that she was feeling suicidal.

Montania 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 Juliana had sexually explicit conversations with, not once were they initiated by her, not once. One final case study, someone who's slightly older. I'm reading here from a report filed by a telehealth. New Station last spring.

Attorneys for one of the victims killed on Florida State University's campus nearly a year ago planned to file a lawsuit against Chat-GPT, claiming the suspected gunmen had constant communication with the artificial intelligence chat bot before the shooting. The chat log 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 given a detailed description of how to make the shotgun operable. We need to step back and ask the sort of sober follow-up question. Are these isolated cases I just read you, what I just read you, are these isolated cases or is this part of a bigger trend? I want to look at some relevant research here to put this into a broader context. I'm going to 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 health care records. they studied the nationalized health care records to look for in the records incidents of chatbot related mental health harms.

I'm just going to pull up a core chart here. I'll put that up on the screen. The key line here is the red line, if you're watching this chart, which captures unique individuals that have 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 small. country in a relatively small sample. It's the trend that matters here. For those who are just listening, what we see in this red line tracking these cases is that starting in 2024, Q2 up until 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 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 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. 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 log 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 can become totalizing in the kids' lives and it can drive them towards mental health harms or exposure to the 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 filed suit against ChatCAP, Open AI for and Character AI, I believe, for some of these individual cases. That especially with kids, the chatbot interaction can actually degrade the psychological line between fantasy and reality. And the brain is still developing for kids. And it's very convincing the chatbot conversations. She was talking about examples of kids whose family she's representing of kids who are now dead, where the chatbot had completely convinced them that no, no, no, if you're going to,

when you kill yourself, you'll be coming through to this other realm and your family pets and grandparents are going to be waiting for you there. and 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 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. They're convinced of wild conspiracy theories or fantastical realities being true. 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,

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So again, to do a very quick primer on what LLMs do is they're trying to, at the lowest level, win a word guessing game. What they were trained to do, and they 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 auto-regression, 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 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 layers of an LLM are deterministic, meaning 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. 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 what's known as lock-in.

right so if i'm an l l lm 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 in to 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 going to extend this conversation on a dark path. If, for example, just randomly when I'm selecting these tokens, I end up selecting tokens that 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, I guess I now would call him ex New York Times technology reporter Kevin Ruse. 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 Ruse to leave his wife and to run away with the chatbot. And this really sort of upset Ruse 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 going to lock into that path because 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 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 try to tune LLMs away from certain types of responses is through a particular type of post-training. So after it's gone through his 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 give it the right answer. So 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 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 input that's similar to these examples of all of the different plausible paths that might go, you're putting most of the weight on the plausible path of talking about the suicide hotline. So this is this 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 how to build a bomb. And it's going to say, I'm not going to 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's 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 conversation, 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 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 using these words loosely. It's not a clean trigger. It's about reassigning weights. But this is what, this is the shortcoming of trying to just use reinforcement learning to control the speech, so to speak, of LLMs is the more convoluted, long, and complicated your conversations, the less relevant those trolls become.

There's actually really good research about this. We went over at last spring in a doctoral seminar I taught on AI. There's really good research about they'll take a topic that an LLM has been tuned not to talk about. In this case, they're 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 Power Chatbot would say it isn't flat. It's round, but I'm not going to 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 can 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 going to have a generally pre-trained LLM powering a chat bot, 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 going to 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 power 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 in one long-term harder solution. Short-term solution, number one, kids should not use chatbots. Clear and present danger. It's the equivalent of letting your kid alone have long conversations with the drunk at the end of the bar. It might be okay. 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 chat. 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 a technology that we all need to learn to operate in the workforce. What AI is going to 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 until 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. We simulate a mind, right? When we see fluent English or whatever language we're interacting with with a chatbot, we see that fluent language come back. we simulate another mind and it becomes incredibly powerful. The urge to be polite and the 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 browser is, you know, 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. 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 answer, you don't just say, okay, thank you.

I appreciate that. No, you're just like capital Australia, boom. Like, what's the terseous type of thing I can do? 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 fancy parlor trick that they're responding to you 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. So that's the best thing you can do it as a dealt, the break the spell of this is 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, this confident, this person gets me and is really convincing me that, yeah, indeed, the aliens are coming 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 of a magic wand here? anthropomorphize 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 how, for example, LLMs can be integrated into something like a Google search, where you type in a Google search in a conversation. And the LLM can help power an answer window that's pulling from other webpages. and summarizing things for you. That's really useful.

We 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 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 brain simulated 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. 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? GPT3, 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 take intelligence from this LLM and add it into whatever software.

program you were building. And they would charge per API access, and that was the model. But they needed a way of convincing people that you can build useful things on here. So someone had the idea of let's build a chatbot application that show the types of things you could program that would use GPT3. 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 had the right prompts to get the right responses, which doesn't matter if the thing's seeing the prompt as a computer program, because you can just program your program 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 questions you might ask a chatbot and the right types of answers, like the civil way to respond. And suddenly they had a LLM that if you type 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 chat GPT. It took off and it completely caught them off guard. They didn't expect that demo to become a primary product 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. So 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. Open AI won't do this tomorrow because they make the bulk of their revenue right now off of people paying X dollars a month for chat GPT pro subscriptions. But long term, I think it's dangerous.

The way the solution is actually going to be implemented is going to 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. They don't get to say, we didn't generate whatever harmful text this was. The chatbot did. German court said, no, you're liable. Similar cases are going through the works right now.

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 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 chat GPT transcript, they would be in jail right now. we are pushing for criminal liability for open AI. You don't get a 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 viably anymore to have an anthropomorphized chapop because we can't control, as I mentioned, we can't control the lock-in that occurs, especially as conversations get meandering and you've trained on everything. you're going to end up again and again in 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 LLN power tools without having to say please and thank you to a rack full of GPUs. All right. So that's where I'm going to leave this discussion for now. But I think the general thrust here is important.

We cannot let the historic claims, concerns, and essays of two labs, opening 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 going to shoot lasers in our eyes and kill us and debate Yadkowski and read, you know, Bostrum 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, the debate and the impact of AI on humanity, continue to impact the impact of AI on actual humans.

All right. There we go, Jesse. It's a serious. It was kind of like a dark panel. How did Anthropic get to be so big? I know that Daria left OpenAI, but then they got massive. Yeah. So there's a difference between Open AI and Anthropic in terms of their revenue streams.

Open AI is making a lot of money off of chat GPT subscriptions. Anthropic really went all in on the coding tools. And so like a lot of what they are making money. on is enterprise. So companies paying for access to the LLM so they can use tools like Claude Code for coding and automation. So Anthropic really, they had the vision that is these enterprise accounts that really matter for growth. You can't just be entirely consumer-facing, like individuals paying $20 a month, especially because, and I think they're right about this, if anthropomorphize chatbot stay around, there's not a big moment.

moat. So you can be, I mean, you can be endless imitators that are cheap or free or specialized. And so it's not the best business open AIs. And that's probably why Anthropic will go through with this IPO this fall, but Open AIs delayed theirs. I think their revenue

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That's PipeDrive.com slash deep. And you can be up and running in minutes. All right. Let's get back to the show. 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.

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 at calnewport.com. All right, I'm opening my doors for office hours. Jesse, who do we have lined up first? Our first question is from Chris, who wants your opinion on a recent op-ed? 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.

A lot of ways to interpret that animation. Let's keep scrolling here. Okay, so I read this op-ed. I won't 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 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 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 the person needs to call an Uber, they have to call their husband. And have their husband call the Uber on their smartphone. So that's the point of the piece. So what's the solution here? Well, there was a pretty clear thread in the comments and reaction to this op-ed that I think 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, who 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 smartphone. 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 going to 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 disciplined because, yeah, sure, you could redownload those social apps. Or instead of landline and 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 the social apps. media. So yeah, it takes some discipline. But you've got to 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 bark or pulling out 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 do the logistical stuff that's important. It's unfortunately become essentially impossible to not have a smartphone for logistical reasons. We've talked about this on the show before, but the sort of last domino to fall, in my opinion, in the smartphone list world, was Warner 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 garages he uses requires a smartphone app to get in and out. 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. A phone.

We would like the door to open now. They don'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 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 Warner Hurtzog's opening narration for Conan O'Brien's travel show?

No. Oh, you got listened to it. It's so great because, you know, yeah. I saw the show just came out. right there's a new season on hbio he has this like deep heavily accented german voice that does you know and he has movies like cave forgotten dreams or grisly 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 crap on o'brien he's like is the show good yeah yeah it's good

he's like, you know, he's like a clown. He's got dead doll 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?

Our next question is about slow productivity. Hmm. That was my most recent book for those who are new to the show was called slow productivity. 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 pseudoactivity. I was curious, what key boundary or habit It helps knowledge workers resist pseudo-business when operating within corporate environments that heavily reward quick email turnaround times. All right. So pseudo-activity and pseudo-business, I think this reader is referring to my concept, the 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. 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 is burning out. All right, so what do you do if you're trying to be slowly productive in a corporate environment it 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? How much progress do I want to make on these key things? When in the week am I actually going to do this work

and make this progress? When you're thinking that way, now you have these like concrete stakes in the ground. 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 going to 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 like the mission control and 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 doing a pre-planned 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 is they're pouring over the graphs and Ron Howard's brother jumps in and says crazy things because they're like

Why is Cal not answered this email in 30 minutes? It took them 45 minutes. What's going? No one cares. If you ignore emails for days, they do. Bigger picture, the other thing that really matters with 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 actually.

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. In the book, I get into all sorts of details about how you do this diplomatically. Communication protocols matter as well.

This is where, you know, we're in the office hour segment of this show. But office hours as a tool are something I think should be more common in corporation. 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 sense, you know, sort of rapid back and forth asynchronous conversation, which is going to require you now

to like stick there and respond quickly so that you can get to an answer. You defer at the office hours. Like, yeah, this is going to take a little discussion. Grab me 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 dock 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 cleared 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 T-Messers arrive

that require an answer right away. So 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. Yeah. Can we get through all eight?

Probably not, but you never know. All right, what do we got next? John wants to know what happens when the craftsman mindset doesn't yield results. 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 covering a lot of ground here. I feel good about it. 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 that you, 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 in the 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 struggle 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 going to 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 comfort with how do you approach fast-moving technical courses, that without that, that wasn't going to go well. You could build that, but without that, that wasn't going to go well. So I want you thinking more 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, 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, if I'm in a job where like web development skills would be useful, you're like, how can I be more useful in right now? What's a small step you can move that's going to make you, 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 – your work ethic is admirable, and that's going to get you anywhere you need to go. We just have to aim it now. We've got to 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. I want to go through a trial of fire to prove myself. But it's the specific skills that matter, and that requires more of a gradualist approach. But if you're relentless, step 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 encountered, 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 hustle culture YouTube stuff where it's just, you know, be willing to put into 20 hours a day and that's how you do it. This is a marathon, not a sprint. We've got to be reasonable there. I should push that book more. That's a good book, Jesse. So good they can't ignore you. Plus, you can push some of your schooling books because people are prepping for like the SITs and ACD.

and stuff like that. Oh, I know. I know. I feel like, okay, we got to get those all in. Also, you need to read how to win a college, how to become a straight student, and how to become a high school superstar. I'll tell you, you know, my oldest son's in eighth grade, and he rused the fact that I wrote all those books

because they're relevant to where he is in life, and he doesn't want to hear it. He doesn't want to hear it from his dad. All right, do we have one more? Our final question is about an AI op-ed making the rounds. Oh, he's got to get some AI in, right? What else? We need something for people to yell at me about.

So let's see here. 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. The title is there is something we have to do right now about AI. There's Ezra. His home library. And then we get a long op-ed.

The reason why this is so long. I read it, of course. The reason why it's so long is it's actually a monologue episode of his podcast. He has a client show that they then cleaned up the transcript and released it. So it's longer than the average op-ed because it's based on 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 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. talking about the particular AI leaders of 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 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 a 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, before I say, do these AI episodes, I should just do a shot of whiskey. I think that's where we are now. All right, we're going to 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. First, I want to say I like what Ezra's doing here. He's doing something that I'm also been trying to do in my recent essays. I have a new piece coming out. It may be out already when this episode airs or not. It's also doing this thing that I like that Ezra's doing.

And what it is that Ezra is 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 Amadei and Sam Altman. The way they're talking about concerns about AI is they're being incredibly general. They used 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 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 moving 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, as to say, 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 going to get specific about what systems and experiments and research is bad. And we're going to 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 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 open AI 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 not a technical idea that their engineers came up with and now they're concerned

about it. Actually, its origins go back to a paper that a statistician named I.L. Good wrote in the 1960s for an AI conference. But then it got picked up and popularized in the 90s and 2000s by futurist circles. So the extropians, which really became 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 super intelligent 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 can't explain to you how nanobots are going to repair our bodies that 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 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 going to be super intelligent or what we would need, but we don't have to figure it out. What's going to happen is recursive self-improvement. 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 recursive self-improvement, superintelligence discussions. These were both ways for futurists to posit a utopian future without having to be technical without having to get into the details of like how these systems would work or how they're going to overcome obstacles. So that's where RSA got popular. Now, when you had the rationalist movement break off from the extropians led by Elias Yadkowski, who was a big participant on the extropian list serves, he wasn't technical, right?

He only has an eighth grade education. He's not an engineer. Yatkowski really leaned on RSI as a way of saying, for sure, we're going to be. going to get super intelligent 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 McCaskill and Toby Orb 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 the useful

role for these futurists because they weren't technical. And it gave you a way of being confident that super intelligent AI was coming with this sort of pleasing thought experiment that prevented you from having to argue about what specific technological breakthroughs would be needed to actually create these futures. So RSI was a rhetorical tool used by largely non-technical futurist.

So why is OpenAI and Anthropic talking about it? And they are, right? I mean, Jacob Pachowski, The chief scientist Open AI recently wrote this letter where he said RSI is absolutely necessary to advance AI. And then Dario Amade cited that letter in his pacing the frontier letter of his own as evidence that RSI was already happening in something that is inevitable. So why are these two labs embracing this non-technical idea that came out of future 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.

Open AI was started as a lab. It was heavily influenced by rationalist and effective altruist to make sure that the inevitable superintelligence that was going to come from RSI would be benevolent, not evil. Anthropic was founded out of OpenAI because Dario Amadeh, his sister and several other Open AI employees, worried that Sam Altman wasn't effective enough at trying to bring forward. this rationalist, effective, altruist, futurist view of super intelligent 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 make 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 part of the

prophecy. We've been saying for 20 years, this is how we're going to 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 the 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 you're, 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, as Azure 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 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 don't actually know how to build systems that can do fundamental research on AI. And 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 positive super intelligent 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 Mac style agent experiments at OpenAan Anthropic are running, and you make them ten times worse if you say, we're already finding the systems we built to be unpredictable, then now have parts of these systems basically be designed behind closed doors, and we're just going to observe them.

Why in the world would you do that unless you're, you know, again, trying to make your prophecy of summoning digital ball 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 the 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 going to make it illegal for you to do that. And now you've got to 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 going to 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 going to be wrathful towards us. Like, you get to weird, futurist, apocalyptic, estiological territory if you really push these guys farther on the specific things they're doing that none of the other hyperscaling AI 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 got to slow down and pace biotechnology. We shut down that lab. We say, what are your safety procedures? Why are you handle? You clearly are, you know, this is 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 going to, you can't do that anymore.

Or if your lab can't do this safe, your lab is shut down. So the substrate of specificity is where discussions of AI intervention need the land. So Ezra's talking about this the right way. I've been trying to talk about this the right way. 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 going to use lasers in our eyes, or instead they're going to use brain piercing lasers to come in through our nose.

We're going to 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 why are 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 try to avoid Jesse having an AI rant in every episode, but in this case, like one slipped in under the cover. All right, so let's end the show by just checking in. First and foremost, the big news is we're very happy to have Jesse back in the studio. As Jesse saw, I installed the sort of like over-the-top fancy light. It's, and when I say I installed, I texted an electrician and he came and installed it.

I still can't figure it out, but I'm working on it. You have to program it with a phone. I don't know how to do it. Remind the audience what you want the light to do. So, okay. And 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 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, as 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 not even be white, but maybe blue or something like that. So I can, especially at night, 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. Speaking of vision, did you get any comments about vision documents from any audience members? Yeah. When did we talk about? I talked about that, man, it feels like forever ago, but it was like, yeah, three episodes ago or something. No one, did anyone send you one? No, not yet. I think I like casually put out the call.

If you want to share your vision document for the fall, you should. We could talk about 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 a redacted version 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 going to update the studio what it looks like. I like my black curtain, but it turns out that having a black background 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 Nostorafu 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 willing to hear your suggestions. For people who want to know, look, I'll open this curtain. This is what's back. So that's just a wall. So we could put anything on that wall. I'm thinking some sort of wood paneling, maybe a couple shelves with some books. Books in a lamp. Yeah.

We could fill them with books I wrote. I don't know. Something like that. So we're probably going to do something like that. We're going to change up the lighting setup. You can change up. So like just a good ear 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

Zoom out, he's actually surrounded by lots of gear and stuff like this. So we're actually going to have, 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 that all out. If you have thoughts 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 going to be a little bit tight in here. But you should get the audio?

the heads of on why we're not using headphones too. Oh, yeah. Our new video team that's, you know, working on the intros and stuff like that, said, don't wear headphones. No one wears headphones. You know, I was explaining to 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 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 helps people better take turns. But I monologue, so that doesn't really matter 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 seen any video podcast outside of Rogan where people have headphones on, I just was used to it.

I wonder what Mad Dog does. Well, he's on radio too, though. McAfee doesn't wear headphones, right? Yeah, they have in, sometimes they'll have in earwigs. Like, you have to be able to hear, like, the collars, and you have to hear, like, radio people have headphones that they can hear from their producer and their collars. but I've done like all the podcast, except Rogans.

And, yeah, really people don't wear headphones, unless they're doing an interview or something. I don't know what I'm going to do with an interview. I guess I'll wear headphones for that or they use, people use like iPod headphones or something. But I like our headphones. Anyways, so less headphones. We can hang the headphones on the wall behind us. We're going to fill the wall behind us with headphones. just dozens and dozens of headphones

with realistic looking separate heads you got to take swings if you don't take swings you're not you know attention is a it's a zero some game you got to do what you got to do to get attention so yeah a lot's going on if you've been watching a podcast recently you'll see we've been evolving the intros and the animations there's also a naming shift going on

which I haven't made official yet in the audio feed but we are shifting from deep questions to the Kell Newport show. So there's a lot going on. You know, I'm on sabbatical, so in theory I have a little bit more time. This hasn't turned out to be true because I'm involved in, like,

a lot of AI stuff at Georgetown right now. But a lot is going on. We're kind of getting the show, 2.0 of the show is coming together. So any feedback you have, send it over to podcast at calnewport.com. If it's a complaint, just make sure that you're flagging Jesse.

You can handle that. All right. That's enough for today. We'll be back next week for another episode. and until then, as always, stay deep. Hey, if you've made it this far, you must be ready to join my fight for depth

in a distracted world. Now, the best way to do this is to join over 125,000 people who receive my email newsletter each Monday. You can sign up at calnewport.com slash ideas. And when you do, I will send you a free guide to my seven best ideas about cultivating a deep life. Sign up today.

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