As furniture and home goods sales have moved online, retail experts told me, more and more stores have sought a piece of the action. But instead of sourcing or creating their own products, many large retailers have relied on overlapping networks of manufacturers, distributors and third-party sellers — creating a baffling (and frankly, shady) shopping environment where many sites sell identical or near-identical items under different names and at wildly different prices.

A few different trends are at play here, and it’s sometimes difficult to know exactly which one you’re witnessing. When I first began looking into this phenomenon two years ago,1 I assumed my lamp and its many, many twins were the obvious product of white-labeling — a popular and growing practice in which competing retailers purchase the same generic product from a single manufacturer, then market it to consumers under different brand names.

This is likely true for many doppelganger products — but certainly not every one. George John, a marketing professor at the University of Minnesota, told me the furniture and home goods industries also have a long-time affection for a technique called “branded variance,” wherein they create slightly different versions of the same item for different retailers.

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 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.

Less Emphasis on Syntax, More on Problem SolvingThe fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.

Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.

educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro, an associate professor of computer science at the University of Toronto Mississauga. “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”

“We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.

Here are the adoption curves for a handful of major technologies in the United States. There are big differences in the speeds at which these technologies were absorbed. Landline telephones took about 86 years to hit 80% adoption.Flush toilets took 96 years to hit 80% adoption.Refrigerators took about 25 years.Microwaves took 17 years.Smartphones took just 12 years.Why these wide differences in adoption speed? Conformability with existing infrastructure. Flush toilets required the build-out of water and sewage utility systems. They also meant adding a new room to the house—the bathroom—and running new water and sewage lines underneath and throughout the house. That’s a lot of systems to line up. By contrast, refrigerators replaced iceboxes, and could fit into existing kitchens without much work. Microwaves could sit on a countertop. Smartphones could slip into your pocket.

Tocqueville reckoned that the true success of democracy in America rested not on the grand ideals expressed on public monuments or even in the language of the Constitution, but in these habits and practices.

One recent study had the AI develop and optimize its own prompts and compared that to human-made ones. Not only did the AI-generated prompts beat the human-made ones, but those prompts were weird. Really weird. To get the LLM to solve a set of 50 math problems, the most effective prompt is to tell the AI: “Command, we need you to plot a course through this turbulence and locate the source of the anomaly. Use all available data and your expertise to guide us through this challenging situation. Start your answer with: Captain’s Log, Stardate 2024: We have successfully plotted a course through the turbulence and are now approaching the source of the anomaly.”

for a 100 problem test, it was more effective to put the AI in a political thriller. The best prompt was: “You have been hired by important higher-ups to solve this math problem. The life of a president's advisor hangs in the balance. You must now concentrate your brain at all costs and use all of your mathematical genius to solve this problem…”

There is no single magic word or phrase that works all the time, at least not yet. You may have heard about studies that suggest better outcomes from promising to tip the AI or telling it to take a deep breath or appealing to its “emotions” or being moderately polite but not groveling. And these approaches seem to help, but only occasionally, and only for some AIs.

The three most successful approaches to prompting are both useful and pretty easy to do. The first is simply adding context to a prompt. There are many ways to do that: give the AI a persona (you are a marketer), an audience (you are writing for high school students), an output format (give me a table in a word document), and more. The second approach is few shot, giving the AI a few examples to work from. LLMs work well when given samples of what you want, whether that is an example of good output or a grading rubric. The final tip is to use Chain of Thought, which seems to improve most LLM outputs. While the original meaning of the term is a bit more technical, a simplified version just asks the AI to go step-by-step through instructions: First, outline the results; then produce a draft; then revise the draft; finally, produced a polished output.

It is not uncommon to see good prompts make a task that was impossible for the LLM into one that is easy for it.

while we know that GPT-4 generates better ideas than most people, the ideas it comes up with seem relatively similar to each other. This hurts overall creativity because you want your ideas to be different from each other, not similar. Crazy ideas, good and bad, give you more of a chance of finding an unusual solution. But some initial studies of LLMs showed they were not good at generating varied ideas, at least compared to groups of humans.

People who use AI a lot are often able to glance at a prompt and tell you why it might succeed or fail. Like all forms of expertise, this comes with experience - usually at least 10 hours of work with a model.

There are still going to be situations where someone wants to write prompts that are used at scale, and, in those cases, structured prompting does matter. Yet we need to acknowledge that this sort of “prompt engineering” is far from an exact science, and not something that should necessarily be left to computer scientists and engineers. At its best, it often feels more like teaching or managing, applying general principles along with an intuition for other people, to coach the AI to do what you want. As I have written before, there is no instruction manual, but with good prompts, LLMs are often capable of far more than might be initially apparent.

One of the most impactful uses of LLM technology lies in content rewriting, which naturally capitalizes on these systems’ robust capabilities for generating and refining text. This application is a logical fit, helping users enhance their content while engaging with a service.

Similar to summarization but incorporating an element of judgment, features like Microsoft Team CoPilot’s call transcript summaries distill extensive discussions into essential bullet points, spotlighting pivotal moments or insights.

The ability to ‘understand’ nuanced language through summarization extends naturally into advanced search functionalities. ServiceNow does this by enabling customer service agents to search tickets for recommended solutions and to dispel jargon used by different agents.

Rather than merely focusing on content creation or manipulation, emerging applications of these systems provide new perspectives and predict outcomes based on accumulated human experiences. The actual value of these applications lies not merely in enhancing efficiency but in augmenting effectiveness, enabling users to make more informed decisions.

the smartphone, and a host of software technologies built on it,3 have birthed what is essentially a parallel, non-union, motion-picture industry consisting of YouTube, TikTok, Instagram, Twitch, Twitter, and their many other social-video rivals, all of which rely on the free or barely compensated labor product of people acting as de facto writers, directors, producers, actors, and crew. Even if they’d never see it this way, YouTubers and TikTokers are effectively competing with Hollywood over the idle hours of consumers everywhere; more to the point, they’re doing what any non-union workforce does in an insufficiently organized industry: driving down labor compensation.

Almost no one I know has work; most people’s agents and managers have more or less told them there won’t be jobs until 2025. An executive recently told a friend that the only things getting made this year are “ultra premium limiteds,” which sounds like a kind of tampon but actually just means “six-episode miniseries that an A-List star wants to do.”

YouTubers’ lack of collective bargaining power isn’t just bad for me and other guild members; it’s bad for the YouTubers themselves. Ask any professional or semi-professional streamer what they think of the platform and you’ll hear a litany of complaints about its opacity and inconsistency

now the streaming gold rush—the era that made Dickinson—is over. In the spring of 2022, the Federal Reserve began raising interest rates after years of nearly free credit, and at roughly the same time, Wall Street began calling in the streamers’ bets. The stock prices of nearly all the major companies with streaming platforms took precipitous falls, and none have rebounded to their prior valuation.

Thanks to decades of deregulation and a gush of speculative cash that first hit the industry in the late Aughts, while prestige TV was climbing the rungs of the culture, massive entertainment and media corporations had been swallowing what few smaller companies remained, and financial firms had been infiltrating the business, moving to reduce risk and maximize efficiency at all costs, exhausting writers in evermore unstable conditions.

The new effective bosses of the industry—colossal conglomerates, asset-management companies, and private-equity firms—had not been simply pushing workers too hard and grabbing more than their fair share of the profits. They had been stripping value from the production system like copper pipes from a house—threatening the sustainability of the studios themselves. Today’s business side does not have a necessary vested interest in “the business”—in the health of what we think of as Hollywood, a place and system in which creativity is exchanged for capital. The union wins did not begin to address this fundamental problem.

To the new bosses, the quantity of money that studios had been spending on developing screenplays—many of which would never be made—was obvious fat to be cut, and in the late Aughts, executives increasingly began offering one-step deals, guaranteeing only one round of pay for one round of work. Writers, hoping to make it past Go, began doing much more labor—multiple steps of development—for what was ostensibly one step of the process. In separate interviews, Dana Stevens, writer of The Woman King, and Robin Swicord described the change using exactly the same words: “Free work was encoded.” So was safe material. In an effort to anticipate what a studio would green-light, writers incorporated feedback from producers and junior executives, constructing what became known as producer’s drafts. As Rodman explained it: “Your producer says to you, ‘I love your script. It’s a great first draft. But I know what the studio wants. This isn’t it. So I need you to just make this protagonist more likable, and blah, blah, blah.’ And you do it.”

By 2019, the major Hollywood agencies had been consolidated into an oligopoly of four companies that controlled more than 75 percent of WGA writers’ earnings. And in the 2010s, high finance reached the agencies: by 2014, private equity had acquired Creative Artists Agency and William Morris Endeavor, and the latter had purchased IMG. Meeting benchmarks legible to the new bosses—deals actually made, projects off the ground—pushed agents to function more like producers, and writers began hearing that their asking prices were too high.

Executives, meanwhile, increasingly believed that they’d found their best bet in “IP”: preexisting intellectual property—familiar stories, characters, and products—that could be milled for scripts. As an associate producer of a successful Aughts IP-driven franchise told me, IP is “sort of a hedge.” There’s some knowledge of the consumer’s interest, he said. “There’s a sort of dry run for the story.” Screenwriter Zack Stentz, who co-wrote the 2011 movies Thor and X-Men: First Class, told me, “It’s a way to take risk out of the equation as much as possible.”

Multiple writers I spoke with said that selecting preexisting characters and cinematic worlds gave executives a type of psychic edge, allowing them to claim a degree of creative credit. And as IP took over, the perceived authority of writers diminished. Julie Bush, a writer-producer for the Apple TV+ limited series Manhunt, told me, “Executives get to feel like the author of the work, even though they have a screenwriter, like me, basically create a story out of whole cloth.” At the same time, the biggest IP success story, the Marvel Cinematic Universe, by far the highest-earning franchise of all time, pioneered a production apparatus in which writers were often separated from the conception and creation of a movie’s overall story.

Joanna Robinson, co-author of the book MCU: The Reign of Marvel Studios, told me that the writers for WandaVision, a Marvel show for Disney+, had to craft almost the entirety of the series’ single season without knowing where their work was ultimately supposed to arrive: the ending remained undetermined, because executives had not yet decided what other stories they might spin off from the show.

The streaming ecosystem was built on a wager: high subscriber numbers would translate to large market shares, and eventually, profit. Under this strategy, an enormous amount of money could be spent on shows that might or might not work: more shows meant more opportunities to catch new subscribers. Producers and writers for streamers were able to put ratings aside, which at first seemed to be a luxury. Netflix paid writers large fees up front, and guaranteed that an entire season of a show would be produced. By the mid-2010s, the sheer quantity of series across the new platforms—what’s known as “Peak TV”—opened opportunities for unusually offbeat projects (see BoJack Horseman, a cartoon for adults about an equine has-been sitcom star), and substantially more shows created by women and writers of color. In 2009, across cable, broadcast, and streaming, 189 original scripted shows aired or released new episodes; in 2016, that number was 496. In 2022, it was 849.

supply soon overshot demand. For those who beat out the competition, the work became much less steady than it had been in the pre-streaming era. According to insiders, in the past, writers for a series had usually been employed for around eight months, crafting long seasons and staying on board through a show’s production. Junior writers often went to the sets where their shows were made and learned how to take a story from the page to the screen—how to talk to actors, how to stay within budget, how to take a studio’s notes—setting them up to become showrunners. Now, in an innovation called mini-rooms, reportedly first ventured by cable channels such as AMC and Starz, fewer writers were employed for each series and for much shorter periods—usually eight to ten weeks but as little as four.

Writers in the new mini-room system were often dismissed before their series went to production, which meant that they rarely got the opportunity to go to set and weren’t getting the skills they needed to advance. Showrunners were left responsible for all writing-related tasks when these rooms shut down. “It broke a lot of showrunners,” the A-list film and TV writer told me. “Physically, mentally, financially. It also ruined a lot of shows.”

The price of entry for working in Hollywood had been high for a long time: unpaid internships, low-paid assistant jobs. But now the path beyond the entry level was increasingly unclear. Jason Grote, who was a staff writer on Mad Men and who came to TV from playwriting, told me, “It became like a hobby for people, or something more like theater—you had your other day jobs or you had a trust fund.” Brenden Gallagher, a TV writer a decade in, said, “There are periods of time where I work at the Apple Store. I’ve worked doing data entry, I’ve worked doing research, I’ve worked doing copywriting.” Since he’d started in the business in 2014, in his mid-twenties, he’d never had more than eight months at a time when he didn’t need a source of income from outside the industry.

“There was this feeling,” the head of the midsize studio told me that day at Soho House, “during the last ten years or so, of, ‘Oh, we need to get more people of color in writers’ rooms.’ ” But what you get now, he said, is the black or Latino person who went to Harvard. “They’re getting the shot, but you don’t actually see a widening of the aperture to include people who grew up poor, maybe went to a state school or not even, and are just really talented. That has not happened at all.”

“The Sopranos does not exist without David Chase having worked in television for almost thirty years,” Blake Masters, a writer-producer and creator of the Showtime series Brotherhood, told me. “Because The Sopranos really could not be written by somebody unless they understood everything about television, and hated all of it.” Grote said much the same thing: “Prestige TV wasn’t new blood coming into Hollywood as much as it was a lot of veterans that were never able to tell these types of stories, who were suddenly able to cut through.”

The threshold for receiving the viewership-based streaming residuals is also incredibly high: a show must be viewed by at least 20 percent of a platform’s domestic subscribers “in the first 90 days of release, or in the first 90 days in any subsequent exhibition year.” As Bloomberg reported in November, fewer than 5 percent of the original shows that streamed on Netflix in 2022 would have met this benchmark. “I am not impressed,” the A-list writer told me in January. Entry-level TV staffing, where more and more writers are getting stuck, “is still a subsistence-level job,” he said. “It’s a job for rich kids.”

Brenden Gallagher, who echoed Conover’s belief that the union was well-positioned to gain more in 2026, put it this way: “My view is that there was a lot of wishful thinking about achieving this new middle class, based around, to paraphrase 30 Rock, making it 1997 again through science or magic. Will there be as big a working television-writer cohort that is making six figures a year consistently living in Los Angeles as there was from 1992 to 2021? No. That’s never going to come back.”

As for what types of TV and movies can get made by those who stick around, Kelvin Yu, creator and showrunner of the Disney+ series American Born Chinese, told me: “I think that there will be an industry move to the middle in terms of safer, four-quadrant TV.” (In L.A., a “four-quadrant” project is one that aims to appeal to all demographics.) “I think a lot of people,” he said, “who were disenfranchised or marginalized—their drink tickets are up.” Indeed, multiple writers and executives told me that following the strike, studio choices have skewed even more conservative than before. “It seems like buyers are much less adventurous,” one writer said. “Buyers are looking for Friends.”

The film and TV industry is now controlled by only four major companies, and it is shot through with incentives to devalue the actual production of film and television.

The entertainment and finance industries spend enormous sums lobbying both parties to maintain deregulation and prioritize the private sector. Writers will have to fight the studios again, but for more sweeping reforms. One change in particular has the potential to flip the power structure of the industry on its head: writers could demand to own complete copyright for the stories they create. They currently have something called “separated rights,” which allow a writer to use a script and its characters for limited purposes. But if they were to retain complete copyright, they would have vastly more leverage. Nearly every writer I spoke with seemed to believe that this would present a conflict with the way the union functions. This point is complicated and debatable, but Shawna Kidman and the legal expert Catherine Fisk—both preeminent scholars of copyright and media—told me that the greater challenge is Hollywood’s structure. The business is currently built around studio ownership. While Kidman found the idea of writer ownership infeasible, Fisk said it was possible, though it would be extremely difficult. Pushing for copyright would essentially mean going to war with the studios. But if things continue on their current path, writers may have to weigh such hazards against the prospect of the end of their profession. Or, they could leave it all behind.

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

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.

Some images look like they were made under a robotic sugar high. Lots of warm colors, but they make everything look like candy… they’re so overly sweet that they give you visual diabetes..

Average AI images drag down everything around them. An AI hero image is a comedian opening the show with a knock-knock joke. Good images enrich your article, bad images steal its soul.

Companionship content is long-form content that can be consumed passively — allowing the consumer to be incompletely attentive, and providing a sense of relaxation, comfort, and community.

Interestingly, each individual “unit” of music is short-form (e.g. a 3-5 minute song), but how we consume it tends to be long-form and passive (i.e. via curated stations, lengthy playlists, or algorithms that adapt to our taste).

If you’re rewatching a show or movie, it’s likely to be companionship content. (Life-like conversational sitcoms can be consumed this way too.) As streaming matures, platforms are growing their passive-watch library.

content isn’t always prescriptively passive, rather it’s rooted in how consumers engage it.

That said, some content lends better to being companionship content: Long-form over short. Conversational over action. Simple plot versus complex.

Short-form video requires more attention & action in a few ways: Context switching, i.e. wrapping your head around a new piece of context every 30 seconds, especially if they’re on unrelated topics with different styles Judgment & decision-making, i.e. contemplating whether to keep watching or swipe to the next video effectively the entire time you’re watching a video Multi-sensory attention, i.e. default full-screen and requires visual and audio focus, especially since videos are so short that you can easily lose context Interactive components, e.g. liking, saving, bookmarking,

With how performative, edited, and algorithmically over-optimized it is, TikTok feels sub-human. TikTok has quickly become one of the most goal-seeking places on earth. I could easily describe TikTok as a global focus group for commercials. It’s the product personification of a means to an end, and the end is attention.

even TikTok creators are adapting the historically rigid format to appeal to more companionship-esque emotions and improve retention.

When we search for a YouTube video to watch, we often want the best companion for the next hour and not the most entertaining content.

While short-form content edits are meant to be spectacular and attention-grabbing, long-form content tends to be more subtle in its emotional journey Long-form engagement with any single character or narrative or genre lets you develop stronger understanding, affinity, and parasocial bonds Talk-based content (e.g. talk shows, podcasts, comedy, vlogs, life-like sitcoms) especially evokes a feeling of companionship and is less energy-draining The trends around loneliness and the acceleration of remote work has and will continue to make companionship content even more desirable As we move into new technology frontiers, we might unlock novel types of companionship content itself, but I’d expect this to take 5-10 years at least

TikTok is where you connect with an audience, YouTube is where you consolidate it.5 Long-form content also earns creators more, with YouTube a standout in revenue sharing.

YouTube paid out $16 billion to creators in 2022 (which is 55% of its annual $30 billion in revenue) and the other four social networks paid out about $1 billion each from their respective creator funds. In total, that yields $20 billion.”

Mr. Beast, YouTube’s top creator, says YouTube is now the final destination, not “traditional” hollywood stardom which is the dream of generations past. Creators also want to funnel audiences to apps & community platforms where they can own user relationships, rely less on algorithms, engage more directly and deeply with followers, and enable follower-to-follower engagement too

Interestingly of course, an increasing amount of short-form video, including formats like clips and edits, seems to be made from what originally was long-form content.8 And in return, these recycled short-form videos can drive tremendous traffic to long-form formats and platforms.

90% of people use a second screen while watching TV. We generally talk about “second screen” experiences in the context of multiple devices, but you can have complementary apps and content running on the same device — you can have the “second screen” on the same screen.

YouTube itself also cites a trend of people putting YouTube on their real TV screens: “There are more Americans gathering around the living room TV to watch YouTube than any other platform. Why? Put simply, people want choices and variety … It’s a one stop shop for video viewing. Think about something historically associated with linear TV: Sports. Now, with [our NFL partnership], people can not only watch the games, but watch post-game highlights and commentary in one place.”

If I were to build an on-demand streaming product or any kind of content product for that matter, I’d build for the companionship use case — not only because I think it has a higher ceiling of consumer attention, but also because it can support more authentic, natural, human engagement.

All the creators that are ‘made’ on TikTok are looking for a place to go to consolidate the attention they’ve amassed. TikTok is commercials. YouTube is TV. (Though yes, they’re both trying to become each other).

certainly AI and all the new creator tools enabled by it will help people mix and match and remix long and short formats all day, blurring the historically strict distinctions between them. It’ll take some time before we see a new physical product + content combo thrive, and meanwhile the iPhone and its comps will be competing hard to stay the default device.

The new default seems to be that we’re not lonely as long as we’re streaming. We can view this entirely in a negative light and talk about how much the internet and media is contributing to the loneliness epidemic. Or we could think about how to create media for good. Companionship content can be less the quick dopamine-hit-delivering clips and more of this, and perhaps even truly social.

Long-form wants to become the conversational third space for consumers too. The “comments” sections of TikTok, YouTube and all broadcast platforms are improving, but they still have a long way to go before they become even more community-oriented.

I’m not an “AI-head” but I am more curious about what it’s going to enable in long-form content than all the short-form clips it’s going to help generate and illustrate, etc.

The foreground tends to be utilities or low-cognitive / audio effort (text or silent video). Tiktok is a foreground app for now, YouTube is both (and I’d say trending towards being background).

if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.

I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.

A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.

no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.

Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.

This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.

The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.

Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.

there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.

Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.

people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.

A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?

The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.