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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.

To see something funny on the internet, immediately think of a friend who would love it, and then send it to said person is one of the nicest little things we have going in this troubling world. It’s beautiful to be on either end of that interaction. To send is to know that you’re brightening someone’s day. To receive is to know that someone out there is thinking of you, anticipating your smile.

“You don't say ‘Oh yeah, that meme is old’. You say "Oh that's a CLASSIC.’"

Trump realized modern GOP politics aren't about policies or governing well -- it's more akin to cutting a pro-Wrestling promo. His audience isn't waiting on a profound insight on the state of the republic, they're waiting to see who Trump will hurt and they'll cheer him on when it's the right people.

Americans can overwhemingly agree that Trump creates a negative tone but are drawn to it and support him. It's why in pro-wrestling the heel (or bad guy) can have the most dye hard fans. Trump is the modern Stone Cold Steve Austin and making a mockery of doing the equivalent of repeating "what? what? what?" when people talk -- thereby discrediting discourse itself, and finishing by never apologizing "that's the bottom line because I said so" is Trump's appeal.

Trump doesn't have to have policies, the GOP doesn't have to have a platform, there isn't any specificity of what they'll do with power, all that matters is they can own the libs.

A “pro” device that goes pro by getting thinner and lighter, not heavier and thicker, is not a non sequitur. Or at least not necessarily. What makes for a better iPad is simply orthogonal, in many regards, to what makes for a better Mac. Way back in 2010, when the iPad was new (and ran what was called iOS) and it felt like the Mac’s days might be winding down, I wrote, “It’s the heaviness of the Mac that allows iOS to remain light.” I meant that figuratively. But these new M4 iPad Pros mean it quite literally.