what if every police patrol car had a bank of cameras that scan not just every number plate but every face within a hundred yards against a national database of outstanding warrants? What if the cameras in the subway do that? All the connected cameras in the city? China is already trying to do this, and we seem to be pretty sure we don’t like that, but why? One could argue that there’s no difference in principle, only in scale, but a change in scale can itself be a change in principle.

Generative AI is now creating a lot of new examples of scale itself as a difference in principle. You could look the emergent abuse of AI image generators, shrug, and talk about Photoshop: there have been fake nudes on the web for as long as there’s been a web. But when high-school boys can load photos of 50 or 500 classmates into an ML model and generate thousands of such images (let’s not even think about video) on a home PC (or their phone), that does seem like an important change. Faking people’s voices has been possible for a long time, but it’s new and different that any idiot can do it themselves. People have always cheated at homework and exams, but the internet made it easy and now ChatGPT makes it (almost) free. Again, something that has always been theoretically possible on a small scale becomes practically possible on a massive scale, and that changes what it means.

This might be a genuinely new and bad thing that we don’t like at all; or, it may be new and we decide we don’t care; we may decide that it’s just a new (worse?) expression of an old thing we don’t worry about; and, it may be that this was indeed being done before, even at scale, but somehow doing it like this makes it different, or just makes us more aware that it’s being done at all. Cambridge Analytica was a hoax, but it catalysed awareness of issues that were real

if you just think about the business model of the internet as — there’s a box that you can upload some content into, and then there’s an algorithm between you and an audience, and some audience will find the stuff you put in the box, and then you put an infinity amount of stuff into the box, all of that breaks.

more and more of the stuff that you consume is designed around pushing you towards a transaction. That’s weird. I think there’s a vast amount of white space in the culture for things that are not directly transactable.

We constantly ask huge amounts of the population to do things that are very rote. Keep inputting this data on forms, keep filling out this tax form. Some lawyers arguing for the Supreme Court, a lot of them just write up various contracts. And that’s a good job in the sense that it pays well, it’s inside work, but it doesn’t ask you to be that full of a human being.

I think a lot of organizations are not set up for a lot of people to use judgment and discernment. They treat a lot of people like machines, and they don’t want them doing things that are complicated and step out of line and poke at the assumptions in the Excel doc. They want the Excel doc ported over without any mistakes.

I think a lot of organizations are not set up for a lot of people to use judgment and discernment. They treat a lot of people like machines, and they don’t want them doing things that are complicated and step out of line and poke at the assumptions in the Excel doc.

I distinctly remember life before computers. It’s an experience that I had quite viscerally. And that shapes my view of these tools. It shapes my view of these companies. Well, there’s a huge generation now that only grew up in this way. There’s a teenage generation right now that is only growing up in this way. And I think their natural inclination is to say, well, this sucks. I want my own thing. I want my own system of consuming information. I want my own brands and institutions.And I don’t think that these big platforms are ready for that moment. I think that they think they can constantly be information monopolies while they are fending off A.I.-generated content from their own A.I. systems. So somewhere in there all of this stuff does break. And the optimism that you are sensing from me is, well, hopefully we build some stuff that does not have these huge dependencies on platform companies that have no interest at the end of the line except a transaction.

these models in their most reductive essence are just statistical representations of the past. They are not great at new ideas.And I think that the power of human beings sort of having new ideas all the time, that’s the thing that the platforms won’t be able to find. That’s why the platforms feel old. Social platforms like enter a decay state where everyone’s making the same thing all the time. It’s because we’ve optimized for the distribution, and people get bored and that boredom actually drives much more of the culture than anyone will give that credit to, especially an A.I. developer who can only look backwards.

the idea is, in my mind at least, that those people who curate the internet, who have a point of view, who have a beginning and middle, and an end to the story they’re trying to tell all the time about the culture we’re in or the politics we’re in or whatever. They will actually become the centers of attention and you cannot replace that with A.I. You cannot replace that curatorial function or that guiding function that we’ve always looked to other individuals to do.

I think as the flood of A.I. comes to our distribution networks, the value of having a powerful individual who curates things for people, combined with a powerful institution who protects their integrity actually will go up. I don’t think that’s going to go down.

The ability of generative AI tools to analyse huge datasets can also be used to help spark creative inspiration. This can help us if we’re struggling for time or battling writer’s block. For example, if a social media manager is looking for ideas on how to engage alumni on Instagram, they could ask ChatGPT for suggestions based on recent popular content. They could then pick the best ideas from ChatGPT’s response and adapt them. We may use these tools in a similar way to how we ask a colleague for an idea on how to approach a creative task.

We may use these tools in a similar way to how we use search engines for researching topics and will always carefully fact-check before publication.

we will not publish any press releases, articles, social media posts, blog posts, internal emails or other written content that is 100% produced by generative AI. We will always apply brand guidelines, fact-check responses, and re-write in our own words.

We may use these tools to make minor changes to a photo to make it more usable without changing the subject matter or original essence. For example, if a website manager needs a photo in a landscape ratio but only has one in a portrait ratio, they could use Photoshop’s inbuilt AI tools to extend the background of the photo to create an image with the correct dimensions for the website.

You want to order from a local restaurant, but you need to download a third-party delivery app, even though you plan to pick it up yourself. The prices and menu on the app are different to what you saw in the window. When you download a second app the prices are different again. You ring the restaurant directly and it says the number is no longer in service. You go to the restaurant and order in person. You mention that their website has the wrong number and the woman behind the counter says they have to contact the company who designed the site for changes, which will cost them, but most people just order through an app anyway.

You want to watch the trailer for an upcoming movie on YouTube but you first have to sit through an ad. Then you sit through a preview for the trailer itself. Then you watch the trailer, which is literally another ad. When it ends, it cues up a new trailer, with a new ad at the start of it.

The first page of Google results are links to pages that have scraped other pages for information from other pages that have been scraped for information. All the sources seem to link back to one another. There is no origin. The photos on the page look weird. The hands are disfigured. There is no image credit.

You can’t read the recipe on your phone because it prioritises the ads on the page. You bring your laptop into the kitchen and whenever you scroll down, you have to close a pop-up. You turn AdBlock on and the page no longer loads, then AdBlock sends you an ad asking for money.

You buy a microwave and receive ads for microwaves. You buy a mattress and receive ads for mattresses.

What we have now is a profusion of well-cast, sleekly produced competence. We have tasteful remakes of familiar titles. We have the evidence of healthy budgets spent on impressive locations. We have good-enough new shows that resemble great old ones.

Put these two forces together — a rising level of talent and production competence on the one hand, the pressure to deliver versions of something viewers already like on the other hand — and what do you get? You get a whole lot of Mid.

MID IS NOT the mediocre TV of the past. It’s more upscale. It is the aesthetic equivalent of an Airbnb “modern farmhouse” renovation, or the identical hipster cafe found in medium-sized cities all over the planet. It’s nice! The furniture is tasteful, they’re playing Khruangbin on the speakers, the shade-grown coffee is an improvement on the steaming mug of motor oil you’d have settled for a few decades ago.

Mid is fine, though. It’s good enough.

Mid TV, on the other hand, almost can’t be bad for some of the same reasons that keep it from being great. It’s often an echo of the last generation of breakthrough TV (so the highs and lows of “Game of Thrones” are succeeded by the faithful adequacy of “House of the Dragon”).

As more people drop cable TV for streaming, their incentives change. With cable you bought a package of channels, many of which you would never watch, but any of which you might.

So where HBO used to boast that it was “not TV,” modern streamers send the message, “We’ll give you a whole lot of TV.” It can seem like their chief goal is less to produce standout shows than to produce a lot of good-looking thumbnails.

Cities are commonly mapped and surveilled like the internet, said to be made up of “networks” and clusters of “users.”

A City Is Not a Tree can provide us with some answers. As Alexander argued almost 60 years ago, our minds are inclined to categorize the world as a tree, but an organic society and city actually resembles a semilattice. And just like with a city, organizing the internet like a tree stifles it completely.

The internet hasn’t become a tree, but there are certainly those who would like it to resemble one. Both leading tech platforms and governments believe themselves to be capable of containing information and separating its parts. The process started in earnest after the Arab Spring (2010-2012), when it became clear that online activity could produce shocks with real-world consequences. A growing pessimism about technology in the hands of the public developed at the top, as the interests of both “public safety” and profit converged to more deliberately plan the internet and mediate its branches. Simply put, complex systems are easier to surveil when information is neatly siloed into branches. It also simplifies data collection for advertisers.

Overlap on the internet is made possible through search and indexing which has, in almost all cases, badly declined.28 Google, as the leading indexer, has been the prime target of enshittification despite its market dominance increasing.29 Additionally, most platforms are walled gardens that are not easily searchable, their content only being found because it was reposted in another walled garden. Platforms have an interest in making sure users stay in their domain as much as possible. This makes overlap especially difficult by design, and so much of the internet now exists as islands on the periphery as a result. Effectively, that which would make a semilattice of the internet dynamic and alive is being dismantled.

Like a city, the internet is a receptacle for life, and how it organizes itself has consequences for the psychological well-being of its users.

If you pledge to “vote blue no matter who,” promising Democrats your vote no matter who they nominate, what leverage will you ever have over the party? Once you give away your vote for nothing, how do you get any of what you want?

If the number of people who feel the same way as you grows large enough, eventually it becomes very politically expensive to ignore you. Your individual vote is worth very little. But if enough of you feel the same way - well, you can do things like vote en masse for George W. Bush despite your Democratic registration and hand him the presidency. Or you might eventually get the Democrats to implement a policy agenda that broadens their coalition and enables a 50-state strategy instead of piecing together coalitions of disparate groups that you hope turn out in sufficient numbers.

So here’s the question: once you’ve pledged your vote to a party in perpetuity without any qualifications and with zero expectation of getting anything in return… how do you make that party do what you want? You’ve already promised to give them the only thing they care about. Your vote’s already committed, so why on earth should they move in the direction of your values the slightest bit?

People love to say that there’s no other choice than a worse choice. But what if the Democrats and Republicans just keep getting worse in tandem? What if the Democrats remain one inch better than the Republicans, forever? How does actual progress happen? How do you get an actually-good option, instead of just “better than the Republicans,” which is the lowest of low bars? I have no idea. I don’t think the people who insist on “vote blue no matter who” have any idea, either.

I’m prepared for these questions to have answers that I don’t like. They do however strike me as very sensible questions, and yet Democrats often react to them with anger. And if we’re going to be in the business of condescending to each other, allow me to point out that for all of the post-2016 election recriminations the Democratic party has still not done essential work in figuring out what went wrong, which of its fundamental assumptions about politics had led it astray, and whether it really benefits them to treat left-wing voters with such unbridled aggression.

Hillary Clinton was a uniquely bad candidate who earned the nomination thanks to a massive amount of insider advantage, which she received because it was “her turn.”

If leftists voting third party amounts to support for Trump on consequentialist grounds, doesn’t voting and advocating for Hillary Clinton also amount to support for Trump on the exact same grounds?

Haptic sensations feel rewarding, so users feel compelled to repeat these actions.What's the optimal length for vibrations? Try 400ms

But what if you sell products on desktop?Instead of using vibrations, try animating products upon selection. Move items into the basketShake from side to sideGrow and shrink

In an online grocery store, customers bought more items when they felt vibrations while adding items to their cart (Hampton & Hildebrand, 2021).

Classical Conditioning. Vibrations often co-occur with social messages. Therefore, vibrations feel good because these sensations have been frequently paired with hits of dopamine (Hampton & Hildebrand, 2021).Perceived Ownership. Vibrations mimic touch, as if you are physically touching an item on your device. And touch is key to ownership (Li, Cowan, Yazdanparast, & Ansell, 2024; Peck, Barger, & Webb, 2013).

Boreing said: "your story is inaccurate to the point of being false," though he did not specify a single inaccuracy, nor did he deny that the Daily Wire had sought and obtained a gag order on Owens at the same time they were publicly posturing as wanting a debate with her. The confirmation we obtained of all these facts is indisputable. Boreing added: "I’m sure you can appreciate how fraught a high profile break-up like this is. For that reason, we are trying to resolve our issues with Candace privately."It certainly seems true that the Daily Wire is attempting to achieve all of this "privately." Nonetheless, Ben Shapiro has constructed his very lucrative media brand and persona based on his supposed superiority in debating, a reputation cultivated largely as a result of numerous appearances at undergraduate schools around the country where he intrepidly engages with students who are often in their teens or early twenties. Both Shapiro and the Daily Wire have also predicated their collective media brand on an eagerness to engage in free and open debate with anyone, and to vehemently oppose any efforts to silence people, especially those in media, from expressing their political views.

Guadagnino brought them Challengers, which will be released this month. Reznor said, “He started us down a path, saying, ‘What if it was very loud techno music through the whole film?’ ” (This is exactly what it turned out to be.)“I wish I had his notes,” Ross said of Guadagnino. “His notes were so fucking funny on what each piece was meant to do.”“Oh, yeah,” Reznor said. “ ‘Unending homoerotic desire.’ It was all a variation on those three words.”

“The way he described ‘Challengers’ was on a one-sentence email,” recalls Ross. “‘Do you want to be on my next film? It’s going to be super sexy.’ Two x’s.”  “Luca said, ‘What if all the music was driving, thumping techno, like a heartbeat that makes the movie fun?’” adds Reznor.

Tashi Duncan is exactly what we have been missing from our screens; a selfish, messy, calculated woman who wields her intelligence and sensuality as weapons. She’s a cheater, a woman unable to see beyond her own needs, who refuses to lie even when she could save someone the heartbreak of the truth — “unlikeable” by all standards. When her golden retriever malewife of a partner Art tells her he loves her, she simply looks over and purrs “I know” in response. It is coolly dismissive and self-assured in a way that we do not usually get to see Black women behave onscreen.

Tashi Duncan is too formidable a character to be pushed around, least of all by white stans who don’t know what it means to strive beyond the approval of men. The story underneath all the tennis is about Tashi’s heartbreak, the mourning over the loss of her career and the identity it gave her and how she forges ahead anyway.

in every charged moment we see between Patrick and Art, Guadagnino makes it clear that the center of their connection is Tashi. Even the now infamous churros scene revolves almost entirely around Tashi. Patrick says that he likes seeing Art riled up, that he enjoys seeing him willing to connive and fight for something — even if that thing is his girlfriend. Even their lust for one another is moderated by her, unleashed by her. They would have stayed perfectly content meandering in suggestion if she had not forced them to confront their attraction for one another tongue-first in that bedroom scene.

Tashi Duncan is a woman so in control of herself that even in her greatest moment of heartbreak — sitting alone under a tree with an injury that has ruined her chance at doing the only thing she knows how to do — does not allow herself to break. Zendaya’s portrayal of her is glorious here. You watch the hurt, pain, grief, anger, and then finally resolute determination pass over her face as she makes her choice. She will not mope. That moment makes the woman we meet years later, who tells her husband that her love is absolutely conditional on his success as a player.

Tashi Duncan is utterly unique, a manifestation of a particular kind of female rage that makes her hard to forget or even fully hate. It’s what makes the boys so obsessed with her.

Challengers is filled with details like that, people and places that may not matter to the drama but are integral to dropping us into a world that feels authentic and joyful. Joyful because Guadagnino’s delight in them is contagious; you can feel his affection for every cheap hotel and country club tennis court and all of the unimportant people whose lives are built around them. There are a lot of wonderful, trashy American locations in this movie and every one of them Guadagnino films as if it were a beautiful villa somewhere in Northern Italy.

And you always get the sense that they love what they’re doing, leaning deep into their material and capturing it with a cinematic verve that is often unexpected but never pretentious. Even when I feel totally lost to the plot of Guadagnino’s Suspiria, I can feel his love for the theatricality of Tilda Swinton’s old age makeup or the fountaining blood pumps that end the film.

inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”

Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.

We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.

The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.

a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.

we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.

We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.

My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.

Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.

Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.

Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.

All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.

At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.

all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.

We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.

What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.

High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.

Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.

Challengers is filled with details like that, people and places that may not matter to the drama but are integral to dropping us into a world that feels authentic and joyful. Joyful because Guadagnino’s delight in them is contagious; you can feel his affection for every cheap hotel and country club tennis court and all of the unimportant people whose lives are built around them. There are a lot of wonderful, trashy American locations in this movie and every one of them Guadagnino films as if it were a beautiful villa somewhere in Northern Italy.

These movies could exist without the guys who directed them…but they would be shells of what they are now. Demme and Guadagnino bring their love for character, for actors, for bodies, for genre, for the camera, for music, for landscape, for Applebee’s, and suddenly what could have been just very good becomes truly great.