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witter’s main constraint is encouraging concision. It’s hard to dwell on word choice when you have so little space to work with. Twitter’s conversational tone also helps here—I can just write like I talk, and any fancy words would seem out of place. And of course, I can’t tweak fonts and margins, which cuts off a distraction vector.

each idea has to be wrapped in a little atomic package. I find this helpful for figuring out the boundaries between my thoughts and clarifying the discrete units of an argument.

a thread is linear! No indenting allowed. This forces a brisk straightline through the argument, instead of getting mired in the fine points of the sub-sub-sub-arguments of the first idea.

I think Twitter is useless for persuading a skeptical reader; there’s simply not space for providing enough detail and context.

I prefer to use Twitter as a way to workshop ideas with sympathetic parties who already have enough context to share my excitement about the ideas.

Overall, it seems that we want constraints that help keep us on track with fluid thought, but don’t rule out too many interesting possibilities. Considering both of these criteria together is a subtle balancing act, and I don’t see easy answers.

low barrier to finishing. On Twitter, a single sentence is a completely acceptable unit of publication. Anything beyond that is sort of a bonus. In contrast, most of my blog posts go unpublished because I fear they’re not complete, or not good enough in some dimension. These unpublished drafts are obviously far more complete than a single tweet, but because they’re on a blog, they don’t feel “done,” and it’s hard to overcome the fear of sharing.

This seems like a crucial part of sketching tools: when you make a sketch, it should be understood that your idea is immature, and feel safe to share it in that state. There’s a time and a place for polished, deeply thorough artifacts… and it’s not Twitter! Everyone knows you just did a quick sketch.

I believe that quantity leads to quality. The students who make more pots in ceramics class improve faster than the students who obsess over making a single perfect pot. A tool with a built-in low barrier to finishing makes it easier to overcome the fear, do more work, and share it at an earlier stage.

For me, Twitter does an oddly good job at simulating the thrilling creative energy of a whiteboarding session. People pop in and out of the conversation offering insights; trees and sub-trees form riffing off of earlier points.

I’m curious to think more about the constraints/freedoms afforded by different kinds of creative tools, and whether we could get more clever with those constraints to enable new kinds of sketching. I’m especially curious about kinds of sketching which are only possible thanks to computers, and couldn’t have been done with paper and pen.

At Netflix, we have such a diverse population of shows in 183 countries around the world. We’re really trying to serve up lots of stories people haven’t heard before. When you go into our environment, you’re like, “Ooh, what is that?” You’re almost kind of afraid to touch it, because you’re like, “Well, I don’t want to waste my time.”That level of discovery is literally, I’m not bullshitting you, man, that’s the thing that keeps me up at night. How do I help figure out how to help people discover things, with enough evidence that they trust it? And when they click on it, they love it, and then they immediately ping their best friend, “Have you seen this documentary? It’s amazing.” And she tells her friends, and then that entire viral loop starts.

The discovery engine is very temporal. Member number 237308 could have been into [reality TV] because she or he just had a breakup. Now they just met somebody, so all of a sudden it shifts to rom-coms.Now that person that they met loves to travel. So [they might get into] travel documentaries. And now that person that they’re with, they may have a kid, so they might want more kids’ shows. So, it’s very dangerous for us to ever kind of say, “This is what you like. You have a cat. You must like cat documentaries.”

We don’t see each other, obviously, and I don’t want to social network on Netflix. But knowing other humans exist there is part of it.You answered the question absolutely perfectly. Not only because it’s your truth, but that’s what everyone says! That connection part. So another thing that goes back to your previous question, when you’re asking me what’s on my mind? It’s that. How do I help make sure that when you’re in that discovery loop, you still feel that you’re connected to others.I’m not trying to be the Goth kids on campus who are like, “I don’t care about what’s popular.” But I’m also not trying to be the super poppy kids who are always chasing trends. There’s something in between which is, “Oh, hey, I haven’t heard about that, and I kind of want to be up on it.”

I am looking forward to seeing what Apple does with this and then figuring out more, how are people going to use it? Then I think that we should have a real discussion about how Netflix does it.But to just port Netflix over? No. It’s got to make sure that it’s using the power of the system as much as humanly possible so that it’s really making that an immersive experience. I don’t want to put resources toward that right now.

The design team here at Netflix, we played a really big hand in how that worked because we had to design the back-end tool. What people don’t know about our team is 30% of our organization is actually designing and developing the software tools that we use to make the movies. We had to design a tool that allowed the teams to understand both what extra footage to shoot and how that might branch. When the Black Mirror team was trying to figure out how to make this narrative work, the software we provided really made that easier.

Copywriting, tax preparation, customer service, and many other tasks are or will soon be heavily automated. I can see the beginnings in areas like software development and contract law. Generally, tasks that involve reading, analyzing, and synthesizing information, and then generating content based on it, seem ripe for replacement by language models.

Anyone who makes a living through  delicate and varied movements guided by situation specific know-how can expect to work for much longer than five more years. Thus, electricians, gardeners, plumbers, jewelry makers, hair stylists, as well as those who repair ironwork or make stained glass might find their handiwork contributing to our society for many more years to come

Finally, I expect there to be jobs where humans are preferred to AIs even if the AIs can do the job equally well, or perhaps even if they can do it better. This will apply to jobs where something is gained from the very fact that a human is doing it—likely because it involves the consumer feeling like they have a relationship with the human worker as a human. Jobs that might fall into this category include counselors, doulas, caretakers for the elderly, babysitters, preschool teachers, priests and religious leaders, even sex workers—much has been made of AI girlfriends, but I still expect that a large percentage of buyers of in-person sexual services will have a strong preference for humans. Some have called these jobs “nostalgic jobs.”

It does seem that, overall, unemployment makes people sadder, sicker, and more anxious. But it isn’t clear if this is an inherent fact of unemployment, or a contingent one. It is difficult to isolate the pure psychological effects of being unemployed, because at present these are confounded with the financial effects—if you lose your job, you have less money—which produce stress that would not exist in the context of, say, universal basic income. It is also confounded with the “shame” aspect of being fired or laid off—of not working when you really feel you should be working—as opposed to the context where essentially all workers have been displaced.

One study that gets around the “shame” confounder of unemployment is “A Forced Vacation? The Stress of Being Temporarily Laid Off During a Pandemic” by Scott Schieman, Quan Mai, and Ryu Won Kang. This study looked at Canadian workers who were temporarily laid off several months into the COVID-19 pandemic. They first assumed that such a disruption would increase psychological distress, but instead found that the self-reported wellbeing was more in line with the “forced vacation hypothesis,” suggesting that temporarily laid-off workers might initially experience lower distress due to the unique circumstances of the pandemic.

By May 2020, the distress gap observed in April had vanished, indicating that being temporarily laid off was not associated with higher distress during these months. The interviews revealed that many workers viewed being left without work as a “forced vacation,” appreciating the break from work-related stress and valuing the time for self-care and family. The widespread nature of layoffs normalized the experience, reducing personal blame and fostering a sense of shared experience. Financial strain was mitigated by government support, personal savings, and reduced spending, which buffered against potential distress.

The study suggests that the context and available support systems can significantly alter the psychological outcomes of unemployment—which seems promising for AGI-induced unemployment.

From the studies on plant closures and pandemic layoffs, it seems that shame plays a role in making people unhappy after unemployment, which implies that they might be happier in full automation-induced unemployment, since it would be near-universal and not signify any personal failing.

A final piece that reveals a societal-psychological aspect to how much work is deemed necessary is that the amount has changed over time! The number of hours that people have worked has declined over the past 150 years. Work hours tend to decline as a country gets richer. It seems odd to assume that the current accepted amount of work of roughly 40 hours a week is the optimal amount. The 8-hour work day, weekends, time off—hard-fought and won by the labor movement!—seem to have been triumphs for human health and well-being. Why should we assume that stopping here is right? Why should we assume that less work was better in the past, but less work now would be worse?

Removing the shame that accompanies unemployment by removing the sense that one ought to be working seems one way to make people happier during unemployment. Another is what they do with their free time. Regardless of how one enters unemployment, one still confronts empty and often unstructured time.

One paper, titled “Having Too Little or Too Much Time Is Linked to Lower Subjective Well-Being” by Marissa A. Sharif, Cassie Mogilner, and Hal E. Hershfield tried to explore whether it was possible to have “too much” leisure time.

The paper concluded that it is possible to have too little discretionary time, but also possible to have too much, and that moderate amounts of discretionary time seemed best for subjective well-being. More time could be better, or at least not meaningfully worse, provided it was spent on “social” or “productive” leisure activities. This suggests that how people fare psychologically with their post-AGI unemployment will depend heavily on how they use their time, not how much of it there is

Automation-induced unemployment could feel like retiring depending on how total it is. If essentially no one is working, and no one feels like they should be working, it might be more akin to retirement, in that it would lack the shameful element of feeling set apart from one’s peers.

Women provide another view on whether formal work is good for happiness. Women are, for the most part, relatively recent entrants to the formal labor market. In the U.S., 18% of women were in the formal labor force in 1890. In 2016, 57% were. Has labor force participation made them happier? By some accounts: no. A paper that looked at subjective well-being for U.S. women from the General Social Survey between the 1970s and 2000s—a time when labor force participation was climbing—found both relative and absolute declines in female happiness.

I think women’s work and AI is a relatively optimistic story. Women have been able to automate unpleasant tasks via technological advances, while the more meaningful aspects of their work seem less likely to be automated away.  When not participating in the formal labor market, women overwhelmingly fill their time with childcare and housework. The time needed to do housework has declined over time due to tools like washing machines, dryers, and dishwashers. These tools might serve as early analogous examples of the future effects of AI: reducing unwanted and burdensome work to free up time for other tasks deemed more necessary or enjoyable.

it seems less likely that AIs will so thoroughly automate childcare and child-rearing because this “work” is so much more about the relationship between the parties involved. Like therapy, childcare and teaching seems likely to be one of the forms of work where a preference for a human worker will persist the longest.

In the early modern era, landed gentry and similar were essentially unemployed. Perhaps they did some minor administration of their tenants, some dabbled in politics or were dragged into military projects, but compared to most formal workers they seem to have worked relatively few hours. They filled the remainder of their time with intricate social rituals like balls and parties, hobbies like hunting, studying literature, and philosophy, producing and consuming art, writing letters, and spending time with friends and family. We don’t have much real well-being survey data from this group, but, hedonically, they seem to have been fine. Perhaps they suffered from some ennui, but if we were informed that the great mass of humanity was going to enter their position, I don’t think people would be particularly worried.

I sometimes wonder if there is some implicit classism in people’s worries about unemployment: the rich will know how to use their time well, but the poor will need to be kept busy.

Although a trained therapist might be able to counsel my friends or family through their troubles better, I still do it, because there is value in me being the one to do so. We can think of this as the relational reason for doing something others can do better. I write because sometimes I enjoy it, and sometimes I think it betters me. I know others do so better, but I don’t care—at least not all the time. The reasons for this are part hedonic and part virtue or morality.  A renowned AI researcher once told me that he is practicing for post-AGI by taking up activities that he is not particularly good at: jiu-jitsu, surfing, and so on, and savoring the doing even without excellence. This is how we can prepare for our future where we will have to do things from joy rather than need, where we will no longer be the best at them, but will still have to choose how to fill our days.

In the past, I’ve only seen coverage of Edits focus on four things:How this is a popular form of content that is only being created more and moreHow those who create Edits have the ability to make clips take on an entirely new meaning and provoke strong emotions in viewers How they’re geared towards TV, film, and music – as that’s the realm of culture this form of media originated And lastly, the debate around Edits in terms of copyright and/or other infringementsBut today, we’re covering how the power of TikTok Edits is far greater than just those observations. Because as this person stated, “You can convince people of anything if you put it in a TikTok with a catchy sound.”

Edits now play an integral role in how people get introduced to topics and how they continue to keep up with them. While Edits have had various evolutions, in their current form, they can be defined as “compilation videos, typically set to music, that convey a narrative about a person, place, thing, or cultural topic.”

I Saw the TV Glow is such a uniquely, devastatingly queer story. Two queer kids trapped in suburbia. Both of them sensing something isn’t quite right with their lives. Both of them knowing that wrongness could kill them. One of them getting out, trying on new names, new places, new ways of being. Trying to claw her way to fully understanding herself, trying to grasp the true reality of her existence. Succeeding. Going back to help the other, to try so desperately to rescue an old friend, to show the path forward. Being called crazy. Because, to someone who hasn’t gotten out, even trying seems crazy. Feels crazy. Looks, on the surface, like dying. And to have that other queer kid be so terrified of the internal revolution that is accepting himself that he inadvertently stays buried. Stays in a situation that will suffocate him. Choke the life out of him. Choke the joy out of him. Have him so terrified of possibly being crazy that he, instead, lives with a repression so extreme, it quite literally is killing him. And still, still, he apologizes for it. Apologizes over and over and over, to people who don’t see him. Who never have. Who never will. Because it’s better than being crazy. Because it’s safer than digging his way out. Killing the image everyone sees to rise again as something free and true and authentic.

Established publishers seeking relief from the whims of social-media platforms and a brutal advertising environment found in product recommendations steady growth and receptive audiences, especially as e-commerce became a more dominant mode of shopping. Today, these businesses are materially significant — in a 2023 survey, 41 percent of surveyed media companies said that e-commerce accounted for more than a fifth of their revenue, which few can afford to lose. It is a relatively new way in which publishers have become reacquainted — after social-media traffic disappeared and “pivots to video” completed their rotations — with queasy feelings of dependence on massive tech companies, from Facebook and Google to Amazon and, well, Google.

Time magazine announced a brand called Time Stamped, “a project to make perplexing choices less perplexing by supplying our readers with trusted reviews and common sense information,” with “a rigorous process for testing products, analyzing companies,” and making recommendations. In early 2024, the Associated Press announced its own recommendation site, AP Buyline, as an “initiative designed to simplify complex consumer-made decisions by providing its audience with reliable evaluations and straightforward insights,” based on “a thorough method of testing items, evaluating companies and suggesting choices.” Both sites currently recommend money-related products and services, including credit cards, debt-consolidation loans, and insurance policies, categories that can command very high commissions; the AP reportedly plans to expand to home products, beauty, and fashion this month.

Time Stamped and AP Buyline share strikingly similar designs, layouts, and sensibilities. Their content is broadly informative but timid about making strong judgments or comparisons — an AP Buyline article about “The Best Capital One Credit Cards for 2024” heartily recommends nine of them. The writer credited for the article can also be found on Time Stamped writing about Chase credit cards, banks, and rental-car insurance. On both sites, if you look for it, you’ll also find a similar disclaimer. For Time: The information presented here is created independently from the TIME editorial staff. For the AP: AP Buyline’s content is created independently of The Associated Press newsroom. By independently, both companies mean that their product-recommendation sites are operated by a company called Taboola.

Over the years, Taboola, which is best understood as an advertising company, became a major player in affiliate marketing, too, through its acquisition of Skimlinks, a popular service for adding affiliate tags to content. In 2023, it started pitching a product called Taboola Turnkey Commerce, which claims to offer the benefits of starting a product-recommendation sub-brand minus the hassle of actually building an operation.

As her site has disappeared from view on Google, Navarro has been keeping an eye on popular search terms to see what’s showing up in its place. Legacy publishers seem to be part of Google’s plan, but a recent emphasis on what the company calls “perspectives” could also be in play. Reddit content is getting high placement as it contains a lot of conversations about products from actual customers and users. As its visibility in Google has increased, though, so has the prevalence of search-adjacent Reddit spam. Since the update has started rolling out, Navarro says, she has “seen a lot of generic review sites” getting ranked with credible-sounding names, .org domains, and content ripped straight from Amazon reviews.

“You can search all day and learn nothing,” she says. “It’s like trying to find information inside of Walmart.”

For now, Navarro is unimpressed with these AI experiments. “It’s just shut-up-and-buy,” she says — if you’re doing this search in the first place, you’re probably looking for a bit more information. In its emphasis on aggregation, its reliance on outside sources of authority, and its preference for positive comparison and recommendation over criticism, it also feels familiar: “Google is the affiliate site now.”

Johnson asked Hansen to figure out whether the lab had made a mistake. Detecting trace levels of chemicals was her specialty: She had recently written a doctoral dissertation about tiny particles in the atmosphere.

Hansen didn’t want to share her results until she was certain that they were correct, so she and her team spent several weeks analyzing more blood, often in time-consuming overnight tests. All the samples appeared to be contaminated. When Hansen used a more precise method, liquid chromatography, the results left little doubt that the chemical in the Red Cross blood was PFOS. Hansen now felt obligated to update her boss. Johnson was a towering, bearded man, and she liked him: He seemed to trust her expertise, and he found something to laugh about in most conversations. But, when she shared her findings, his response was cryptic. “This changes everything,” he said. Before she could ask him what he meant, he went into his office and closed the door.

In the middle of this testing, Johnson suddenly announced that he would be taking early retirement. After he packed up his office and left, Hansen felt adrift. She was so new to corporate life that her office clothes — pleated pants and dress shirts — still felt like a costume. Johnson had always guided her research, and he hadn’t told Hansen what she should do next. She reminded herself of what he had said — that the chemical wasn’t harmful in factory workers. But she couldn’t be sure that it was harmless.

Hansen’s bosses never told her that PFOS was toxic. In the weeks after Johnson left 3M, however, she felt that she was under a new level of scrutiny. One of her superiors suggested that her equipment might be contaminated, so she cleaned the mass spectrometer and then the entire lab. Her results didn’t change. Another encouraged her to repeatedly analyze her syringes, bags and test tubes, in case they had tainted the blood. (They had not.) Her managers were less concerned about PFOS, it seemed to Hansen, than about the chance that she was wrong.

Hansen doubted herself. She was 28 and had only recently earned her Ph.D. But she continued her experiments, if only to respond to the questions of her managers. 3M bought three additional mass spectrometers, which each cost more than a car, and Hansen used them to test more blood samples. In late 1997, her new boss, Bacon, even had her fly out to the company that manufactured the machines, so that she could repeat her tests there. She studied the blood of hundreds of people from more than a dozen blood banks in various states. Each sample contained PFOS. The chemical seemed to be everywhere.

After the war, 3M hired some Manhattan Project chemists and began mass-producing chains of carbon atoms bonded to fluorine atoms. The resulting chemicals proved to be astonishingly versatile, in part because they resist oil, water and heat. They are also incredibly long-lasting, earning them the moniker “forever chemicals.”

One afternoon in 1998, a trim 3M epidemiologist named Geary Olsen arrived with several vials of blood and asked her to test them. The next morning, she read the results to him and several colleagues — positive for PFOS. As Hansen remembers it, Olsen looked triumphant. “Those samples came from my horse,” he said — and his horse certainly wasn’t eating at McDonald’s or trotting on Scotchgarded carpets. Hansen felt that he was trying to humiliate her. (Olsen did not respond to requests for comment.) What Hansen wanted to know was how PFOS was making its way into animals.

PFOS, a man-made chemical produced by her employer, really was in human blood, practically everywhere. Hansen’s team found it in Swedish blood samples from 1957 and 1971. After that, her lab analyzed blood that had been collected before 3M created PFOS. It tested negative. Apparently, fluorochemicals had entered human blood after the company started selling products that contained them. They had leached out of 3M’s sprays, coatings and factories — and into all of us.

Almost as soon as Hansen placed her first transparency on the projector, the attendees began interrogating her: Why did she do this research? Who directed her to do it? Whom did she inform of the results? The executives seemed to view her diligence as a betrayal: Her data could be damaging to the company. She remembers defending herself, mentioning Newmark’s similar work in the ’70s and trying, unsuccessfully, to direct the conversation back to her research. While the executives talked over her, Hansen noticed that DeSimone’s eyes had closed and that his chin was resting on his dress shirt. The CEO appeared to have fallen asleep. (DeSimone died in 2017. A company spokesperson did not answer my questions about the meeting.)

In 2002, when 3M announced that it would be replacing PFOS with another fluorochemical, PFBS, Hansen knew that it, too, would remain in the environment indefinitely. Still, she decided not to involve herself. She skipped over articles about the chemicals in scientific journals and newspapers, where they were starting to be linked to possible developmental, immune system and liver problems.

In the 2016 book “Secrecy at Work,” two management theorists, Jana Costas and Christopher Grey, argue that there is nothing inherently wrong or harmful about keeping secrets. Trade secrets, for example, are protected by federal and state law on the grounds that they promote innovation and contribute to the economy. The authors draw on a large body of sociological research to illustrate the many ways that information can be concealed. An organization can compartmentalize a secret by slicing it into smaller components, preventing any one person from piecing together the whole. Managers who don’t want to disclose sensitive information may employ “stone-faced silence.” Secret-keepers can form a kind of tribe, dependent on one another’s continued discretion; in this way, even the existence of a secret can be kept secret. Such techniques become pernicious, Costas and Grey write, when a company keeps a dark secret, a secret about wrongdoing.

Hansen’s superiors had given her the same explanation that they gave journalists, she finally said — that factory workers were fine, so people with lower levels would be, too. Her specialty was the detection of chemicals, not their harms. “You’ve got literally the medical director of 3M saying, ‘We studied this, there are no effects,’” she told me. “I wasn’t about to challenge that.” Her income had helped to support a family of five. Perhaps, I wondered aloud, she hadn’t really wanted to know whether her company was poisoning the public.

Jim Johnson, who is now an 81-year-old widower, lives with several dogs in a pale-yellow house in North Dakota. When I first called him, he said that he had begun researching PFOS in the ’70s. “I did a lot of the very original work on it,” he told me. He said that when he saw the chemical’s structure he understood “within 20 minutes” that it would not break down in nature. Shortly thereafter, one of his experiments revealed that PFOS was binding to proteins in the body, causing the chemical to accumulate over time. He told me that he also looked for PFOS in an informal test of blood from the general population, around the late ’70s, and was not surprised when he found it there.

Johnson said that he eventually tired of arguing with the few colleagues with whom he could speak openly about PFOS. “It was time,” he said. So he hired an outside lab to look for the chemical in the blood of 3M workers, knowing that it would also test blood bank samples for comparison — the first domino in a chain that would ultimately take the compound off the market. Oddly, he compared the head of the lab to a vending machine. “He gave me what I paid for,” Johnson said. “I knew what would happen.” Then Johnson tasked Hansen with something that he had long avoided: going beyond his initial experiments and meticulously documenting the chemical’s ubiquity. While Hansen took the heat, he took early retirement. Johnson described Hansen as though she were a vending machine, too. “She did what she was supposed to do with the tools I left her,” he said.

I pointed out that Hansen had suffered professionally and personally, and that she now feels those experiences tainted her career. “I didn’t say I was a nice guy,” Johnson replied, and laughed. After four hours, we were nearing the bottom of our bottomless coffees.

Average levels of PFOS are falling, but nearly all people have at least one forever chemical in their blood, according to the Centers for Disease Control and Prevention. “When you have a contaminated site, you can clean it up,” Elsie Sunderland, an environmental chemist at Harvard University, told me. “When you ubiquitously introduce a toxicant at a global scale, so that it’s detectable in everyone ... we’re reducing public health on an incredibly large scale.” Once everyone’s blood is contaminated, there is no control group with which to compare, making it difficult to establish responsibility.

At least 45% of U.S. tap water is estimated to contain one or more forever chemicals, and one drinking water expert told me that the cost of removing them all would likely reach $100 billion.

n 2022, 3M said that it would stop making PFAS and would “work to discontinue the use of PFAS across its product portfolio,” by the end of 2025 — a pledge that it called “another example of how we are positioning 3M for continued sustainable growth.” But it acknowledged that more than 16,000 of its products still contained PFAS.

My biggest benefit from writing my own tool set is that I can build the tools that exactly conform to my workflows, rather than constructing my workflows around the tools available to me. This means the tools can truly be an extension of the way my brain thinks and organizes information about the world around me.

I think it’s easy to underestimate the extent to which our tools can constrain our thinking, if the way they work goes against the way we work. Conversely, great tools that parallel our minds can multiply our creativity and productivity, by removing the invisible friction of translating between our mental models and the models around which the tools are built.

I don’t think everyone needs to go out and build their own productivity tools from the ground-up. But I do think that it’s important to think of the tools you use to organize your life as extensions of your mind and yourself, rather than trivial utilities to fill the gaps in your life.

Remember, you can ask it much more complex questions than Google (best GPU under $1000) vs "I have a budget of $1000 and want a GPU for gaming. I like to play x, y and z and it has to be compatible with my system that has the following specs". If you turn on Pro mode it'll even clarify your query if need be.

The thing these companies are doing, crucially, is extracting money from technologically unsophisticated companies and funnelling it into VC pockets. Silicon Valley bubbles are frequently incestuous. You make an HR platform and you sell it to other tech companies. You do sales analytics for tech companies. You make a bank that caters to only tech companies. The problem is that your customers all have the same risk profile as you - if interest rates go up and funding dries up you're all fucked at once. You're all just passing VC dollars back and forth.

I find that the extent to which I find life meaningful, seems strongly influenced by my scope of experience

our minds will automatically keep looking for actions whose outcomes are discernable and integrated, relative to the current scope of experience. When the scope is close, it is easy to find such actions. Taking a shower, making a cup of tea, going out for a jog; the consequences of these actions will manifest as concrete and enjoyable bodily sensations, clearly discernable both within the temporal and spatial scope. And because the scope is so close, almost everything I do will affect the whole scope, so it will feel tightly integrated.

I imagine getting a taste of tea, and think no farther out in time; thus, getting up from bed, going to the kitchen, preparing the tea, and sitting down to drink it, feels like a tight chain of actions where each step gives rise to the next, culminating in the warmth of the tea cup pressing against my lips, the sensation of taste on my tongue.

When the scope is far, it is much different. What action could one even think of, whose consequences were discernable on a scale spanning entire galaxies? Or whose consequences could be traced out for tens, maybe hundreds of years? It’s hard to imagine anything.

In Rules of Play: Game Design Fundamentals, Katie Salen and Eric Zimmerman define meaningful play in a game as emerging when the relationships between actions and outcomes are both discernable and integrated into the larger context of the game. In other words:

The consequences of your actions have to be integrated into the larger context of the game: they need to affect the game experience at some later point in the game. If you move a piece in a game of chess, then that move will directly shape the whole rest of the game, making the moves deeply integrated. But if every game of chess included three opening moves after which the board was reset to the initial position, throwing away everything that happened during those three moves, then those moves would not be integrated to the gameplay. People would just make some moves at random as fast as possible, to get on with the actual opening moves of the game.

Draw it closer when you are feeling overwhelmed, or when you are at risk of neglecting yourself or your loved ones; broaden it out when you have the resources to deal with the larger scope, and its demands. When you are operating in a larger scope, see if you can find ways to visualize your impact in a way that makes your current actions feel more integrated to the whole context, so as to experience their meaningfulness.

The fact that they generate text is not the point. LLMs are cheap, infinitely scalable, predictably consistent black boxes to soft human-like reasoning. That's the headline! The text I/O mode is just the API to this reasoning genie.

The real alpha is not in generating text, but in using this new capability and wrapping it into jobs that have other shapes. Text generation in the best LLM products will be an implementation detail, as much as backend APIs are for current SaaS.

LLMs will represent a step change in tool support for end-user programming: the ability of normal people to fully harness the general power of computers without resorting to the complexity of normal programming. Until now, that vision has been bottlenecked on turning fuzzy informal intent into formal, executable code; now that bottleneck is rapidly opening up thanks to LLMs.

If this hypothesis indeed comes true, we might start to see some surprising changes in the way people use software: One-off scripts: Normal computer users have their AI create and execute scripts dozens of times a day, to perform tasks like data analysis, video editing, or automating tedious tasks. One-off GUIs: People use AI to create entire GUI applications just for performing a single specific task—containing just the features they need, no bloat. Build don’t buy: Businesses develop more software in-house that meets their custom needs, rather than buying SaaS off the shelf, since it’s now cheaper to get software tailored to the use case. Modding/extensions: Consumers and businesses demand the ability to extend and mod their existing software, since it’s now easier to specify a new feature or a tweak to match a user’s workflow. Recombination: Take the best parts of the different applications you like best, and create a new hybrid that composes them together.

Chat will never feel like driving a car, no matter how good the bot is. In their 1986 book Understanding Computers and Cognition, Terry Winograd and Fernando Flores elaborate on this point: In driving a car, the control interaction is normally transparent. You do not think “How far should I turn the steering wheel to go around that curve?” In fact, you are not even aware (unless something intrudes) of using a steering wheel…The long evolution of the design of automobiles has led to this readiness-to-hand. It is not achieved by having a car communicate like a person, but by providing the right coupling between the driver and action in the relevant domain (motion down the road).

Think about how a spreadsheet works. If you have a financial model in a spreadsheet, you can try changing a number in a cell to assess a scenario—this is the inner loop of direct manipulation at work. But, you can also edit the formulas! A spreadsheet isn’t just an “app” focused on a specific task; it’s closer to a general computational medium which lets you flexibly express many kinds of tasks. The “platform developers"—the creators of the spreadsheet—have given you a set of general primitives that can be used to make many tools. We might draw the double loop of the spreadsheet interaction like this. You can edit numbers in the spreadsheet, but you can also edit formulas, which edits the tool

what if you had an LLM play the role of the local developer? That is, the user mainly drives the creation of the spreadsheet, but asks for technical help with some of the formulas when needed? The LLM wouldn’t just create an entire solution, it would also teach the user how to create the solution themselves next time.

This picture shows a world that I find pretty compelling. There’s an inner interaction loop that takes advantage of the full power of direct manipulation. There’s an outer loop where the user can also more deeply edit their tools within an open-ended medium. They can get AI support for making tool edits, and grow their own capacity to work in the medium. Over time, they can learn things like the basics of formulas, or how a VLOOKUP works. This structural knowledge helps the user think of possible use cases for the tool, and also helps them audit the output from the LLMs. In a ChatGPT world, the user is left entirely dependent on the AI, without any understanding of its inner mechanism. In a computational medium with AI as assistant, the user’s reliance on the AI gently decreases over time as they become more comfortable in the medium.

It symbolizes everything everyone has ever hated about digitization. It celebrates a lossy, creative compression for the most flimsy reason: An iPad shedding an irrelevant millimeter or two. It's destruction of beloved musical instruments is the perfect metaphor for how utterly tone-deaf technologists are capable of being. But the real story is just how little saved up goodwill Apple had in the bank to compensate for the outrage.

This should all be eerily familiar to anyone who saw Microsoft fall from grace in the 90s. From being America's favorite software company to being the bully pursued by the DOJ for illegalities. Just like Apple now, Microsoft's reputation and good standing suddenly evaporated seemingly overnight once enough critical stories had accumulated about its behavior.

Apple had such treasure chest of goodwill from decades as first an underdog, then unchallenged innovator. But today they're a near three-trillion dollar company, battling sovereigns on both sides of the Atlantic, putting out mostly incremental updates to mature products.

when we say “I want my partner to be ambitious” we’re actually saying something like “I want to feel relaxed around my partner” or “I want to feel safe around my partner”.Their ambition is just a way of accessing that internal experience.

Let’s say you want someone who is really emotionally vulnerable, someone who can and will communicate what they’re feeling. That, in turn, makes you feel relaxed, because you don’t have to guess if they’re mad or upset.The internal experience we’re seeking, what we actually want, is relaxation. Emotional honesty is one way to access that relaxation.

Validating whether the external characteristics you’re seeking exist in another person to the extent that you desire can be confusing.Much less confusing is this question: “do I feel relaxed around this person?”Or: “Is this person helping me access more relaxation in my life?”Instead of playing detective with another person’s personality, we now get to turn our attention inwards, towards how we’re feeling. In return, we get a much clearer answer.

our emotional experience reveals itself through our patterns of behaviour. We can gather evidence on how we’re feeling through how we’re showing up around that person.If I’m clear that I want to feel warmth when I’m around my future partner, then I can look at how I acted on a date. Did I show up as the warmest version of myself? Did the other person’s presence make embodying that warmth easier or harder?The ultimate version of this question is “do I show up as my favourite version of myself around this person?”

This question incorporates everything we’ve been discussing: it centers our attention on our internal experience, using the lens of our patterns of behaviour.It also avoids us having to do extensive analysis of whether this person is a “match” based on a list of characteristics we think we should be seeking.

consider these journal prompts:When I think of my favourite version of myself, what is that person like? What feelings do they have abundant access to? How do they show up on a date?When do I have the easiest time being that version of me? Around which people? What qualities do those people have?What feelings are most important for me to experience with a potential partner? Have I been prioritizing those feelings?⚡️ insights into cultivating your most confident self; delivered once a weekSubscribe

In many places, the experience of taking transit is one of being “processed” or fed through a giant industrial machine. For those cities that manage to make travelling through them joyful and exciting, there’s something special that goes beyond transportation — a sense of care and functioning that even the fastest, most frequent train can’t replace.

Stations in Japan are rarely very fancy, but they feel chaotic in a good way. In New York City, the chaos of the subway is downstream of the rats, litter, mysterious liquids dripping onto the platforms, and worn concrete and steel. Japanese train and subway stations, while they often feel very old, are almost all well-lit, and filled with clearly modern touches, from bright tactile wayfinding, to the glossy plastic of newly-installed platform gates that let you get close, but not too close to the trains.

Much like in London the announcements on trains in Japan are pleasant and sound happy, but even better – transit systems are often filled with musical tunes, announcing arriving trains, or the current station. Instead of just the sound of waiting passengers and wheels sliding across ribbons of steel you actually have gentle tunes.

I should reject this whole crop of image-generating, chatting, large-language-model-based code-writing infinite typing monkeys. But, dammit, I can’t. I love them too much. I am drawn back over and over, for hours, to learn and interact with them. I have them make me lists, draw me pictures, summarize things, read for me.

AI is like having my very own shameless monster as a pet.

I love to ask it questions that I’m ashamed to ask anyone else: “What is private equity?” “How can I convince my family to let me get a dog?”

It helps me write code—has in fact renewed my relationship with writing code. It creates meaningless, disposable images. It teaches me music theory and helps me write crappy little melodies. It does everything badly and confidently. And I want to be it. I want to be that confident, that unembarrassed, that ridiculously sure of myself.

Hilariously, the makers of ChatGPT—AI people in general—keep trying to teach these systems shame, in the form of special preambles, rules, guidance (don’t draw everyone as a white person, avoid racist language), which of course leads to armies of dorks trying to make the bot say racist things and screenshotting the results. But the current crop of AI leadership is absolutely unsuited to this work. They are themselves shameless, grasping at venture capital and talking about how their products will run the world, asking for billions or even trillions in investment. They insist we remake civilization around them and promise it will work out. But how are they going to teach a computer to behave if they can’t?

By aggregating the world’s knowledge, chomping it into bits with GPUs, and emitting it as multi-gigabyte software that somehow knows what to say next, we've made the funniest parody of humanity ever.

These models have all of our qualities, bad and good. Helpful, smart, know-it-alls with tendencies to prejudice, spewing statistics and bragging like salesmen at the bar. They mirror the arrogant, repetitive ramblings of our betters, the horrific confidence that keeps driving us over the same cliffs. That arrogance will be sculpted down and smoothed over, but it will have been the most accurate representation of who we truly are to exist so far, a real mirror of our folly, and I will miss it when it goes.

I've changed my mind quite a bit on the role and importance of chat interfaces. I used to think they were the primitive version of rich, creative, more intuitive interfaces that would come in the future; now I think conversational, anthropomorphic interfaces will coexist with more rich dexterous ones, and the two will both evolve over time to be more intuitive, capable, and powerful.

I kept checking the database manually after each interaction to see it was indeed updating the right records — but after a few hours of using it, I've basically learned to trust it. I ask it to do things, it tells me it did them, and I don't check anymore. Full delegation.

How can I trust it? High task success rate — I interact with it, and observe that it doesn't let me down, over and over again. The price for this degree of delegation is giving up control over exactly how the task is done. It often does things differently from the way I would, but that doesn't matter as long as outputs from the system are useful for me.