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The art of the pivot, part 2: How, why and when to pivot
The art of the pivot, part 2: How, why and when to pivot
people mix up two very different types of pivots and that it’s important to differentiate which path you’re on: Ideation pivots: This is when an early-stage startup changes its idea before having a fully formed product or meaningful traction. These pivots are easy to make, normally happen quickly after launch, and the new idea is often completely unrelated to the previous one. For example, Brex went from VR headsets to business banking, Retool went from Venmo for the U.K. to a no-code internal tools app, and Okta went from reliability monitoring to identity management all in under three months. YouTube changed direction from a dating site to a video streaming platform in less than a week. Hard pivots: This is when a company with a live product and real users/customers changes direction. In these cases, you are truly “pivoting”—keeping one element of the previous idea and doubling down on it. For example, Instagram stripped down its check-in app and went all in on its photo-sharing feature, Slack on its internal chat tool, and Loom on its screen recording feature. Occasionally a pivot is a mix of the two (i.e. you’re pivoting multiple times over 1+ years), but generally, when you’re following the advice below, make sure you’re clear on which category you’re in.
When looking at the data, a few interesting trends emerged: Ideation pivots generally happen within three months of launching your original idea. Note, a launch at this stage is typically just telling a bunch of your friends and colleagues about it. Hard pivots generally happen within two years after launch, and most around the one-year mark. I suspect the small number of companies that took longer regret not changing course earlier.
ou should have a hard conversation with your co-founder around the three-month mark, and depending on how it’s going (see below), either re-commit or change the idea. Then schedule a yearly check-in. If things are clicking, full speed ahead. If things feel meh, at least spend a few days talking about other potential directions.
Brex: “We applied to YC with this VR idea, which, looking back, it was pretty bad, but at the time we thought it was great. And within YC, we were like, ‘Yeah, we don’t even know where to start to build this.’” —Henrique Dubugras, co-founder and CEO
·lennysnewsletter.com·
The art of the pivot, part 2: How, why and when to pivot
Companionship Content is King - by Anu Atluru
Companionship Content is King - by Anu Atluru

Long-form "companionship content" will outlast short-form video formats like TikTok, as the latter is more mentally draining and has a lower ceiling for user engagement over time.

  • In contrast, companionship content that feels more human and less algorithmically optimized will continue to thrive, as it better meets people's needs for social connection and low-effort entertainment.
  • YouTube as the dominant platform among teens, and notes that successful TikTok creators often funnel their audiences to longer-form YouTube content.
  • Platforms enabling deep, direct creator-fan relationships and higher creator payouts, like YouTube, are expected to be the long-term winners in the content landscape.
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).
·archive.is·
Companionship Content is King - by Anu Atluru
AI startups require new strategies
AI startups require new strategies

comment from Habitue on Hacker News: > These are some good points, but it doesn't seem to mention a big way in which startups disrupt incumbents, which is that they frame the problem a different way, and they don't need to protect existing revenue streams.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.
There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.
This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required
Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.
Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).
While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.
A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).
AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different
Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.
the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”
·longform.asmartbear.com·
AI startups require new strategies
Competition is overrated - cdixon
Competition is overrated - cdixon
That other people tried your idea without success could imply it’s a bad idea or simply that the timing or execution was wrong. Distinguishing between these cases is hard and where you should apply serious thought. If you think your competitors executed poorly, you should develop a theory of what they did wrong and how you’ll do better.
If you think your competitor’s timing was off, you should have a thesis about what’s changed to make now the right time. These changes could come in a variety of forms: for example, it could be that users have become more sophisticated, the prices of key inputs have dropped, or that prerequisite technologies have become widely adopted.
Startups are primarly competing against indifference, lack of awareness, and lack of understanding — not other startups.
There were probably 50 companies that tried to do viral video sharing before YouTube. Before 2005, when YouTube was founded, relatively few users had broadband and video cameras. YouTube also took advantage of the latest version of Flash that could play videos seamlessly.
Google and Facebook launched long after their competitors, but executed incredibly well and focused on the right things. When Google launched, other search engines like Yahoo, Excite, and Lycos were focused on becoming multipurpose “portals” and had de-prioritized search (Yahoo even outsourced their search technology).
·cdixon.org·
Competition is overrated - cdixon
Why corporate America broke up with design
Why corporate America broke up with design
Design thinking alone doesn't determine market success, nor does it always transform business as expected.
There are a multitude of viable culprits behind this revenue drop. Robson himself pointed to the pandemic and tightened global budgets while arguing that “the widespread adoption of design thinking . . . has reduced demand for our services.” (Ideo was, in part, its own competition here since for years, it sold courses on design thinking.) It’s perhaps worth noting that, while design thinking was a buzzword from the ’90s to the early 2010s, it’s commonly met with all sorts of criticism today.
“People were like, ‘We did the process, why doesn’t our business transform?'” says Cliff Kuang, a UX designer and coauthor of User Friendly (and a former Fast Company editor). He points to PepsiCo, which in 2012 hired its first chief design officer and opened an in-house design studio. The investment has not yielded a string of blockbusters (and certainly no iPhone for soda). One widely promoted product, Drinkfinity, attempted to respond to diminishing soft-drink sales with K-Cup-style pods and a reusable water bottle. The design process was meticulous, with extensive prototyping and testing. But Drinkfinity had a short shelf life, discontinued within two years of its 2018 release.
“Design is rarely the thing that determines whether something succeeds in the market,” Kuang says. Take Amazon’s Kindle e-reader. “Jeff Bezos henpecked the original Kindle design to death. Because he didn’t believe in capacitive touch, he put a keyboard on it, and all this other stuff,” Kuang says. “Then the designer of the original Kindle walked and gave [the model] to Barnes & Noble.” Barnes & Noble released a product with a superior physical design, the Nook. But design was no match for distribution. According to the most recent data, Amazon owns approximately 80% of the e-book market share.
The rise of mobile computing has forced companies to create effortless user experiences—or risk getting left behind. When you hail an Uber or order toilet paper in a single click, you are reaping the benefits of carefully considered design. A 2018 McKinsey study found that companies with the strongest commitment to design and the best execution of design principles had revenue that was 32 percentage points higher—and shareholder returns that were 56 percentage points higher—than other companies.
·fastcompany.com·
Why corporate America broke up with design
Generative AI’s Act Two
Generative AI’s Act Two
This page also has many infographics providing an overview of different aspects of the AI industry at time of writing.
We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.
We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.
Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.
generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”
·sequoiacap.com·
Generative AI’s Act Two
How to validate your B2B startup idea
How to validate your B2B startup idea
There are four signs your idea has legs:People pay you money: Several people start to pay for your product, ideally people you don’t have a direct connection toContinued usage: People continue to use your prototype product, even if it’s hackyStrong emotion: You’re hearing hatred for the incumbents (i.e. pain) or a deep and strong emotional reaction to your idea (i.e. pull)Cold inbound interest: You’re seeing cold inbound interest in your product
Every prosumer collaboration product, including Figma, Notion, Coda, Airtable, Miro, and Slack, spent three to four years wandering in the dark until they stumbled on something that clicked.
·lennysnewsletter.com·
How to validate your B2B startup idea
The algorithmic anti-culture of scale
The algorithmic anti-culture of scale
Ryan Broderick's impressions of Meta's Twitter copycat, Threads
My verdict: Threads sucks shit. It has no purpose. It is for no one. It launched as a content graveyard and will assuredly only become more of one over time. It’s iFunny for people who miss The Ellen Show. It has a distinct celebrities-making-videos-during-COVID-lockdown vibe. It feels like a 90s-themed office party organized by a human resources department. And my theory, after staring into its dark heart for several days, is that it was never meant to “beat” Twitter — regardless of what Zuckerberg has been tweeting. Threads’ true purpose was to act as a fresh coat of paint for Instagram’s code in the hopes it might make the network relevant again. And Threads is also proof that Meta, even after all these years, still has no other ambition aside from scale.
·garbageday.email·
The algorithmic anti-culture of scale
After “Barbie,” Mattel Is Raiding Its Entire Toybox
After “Barbie,” Mattel Is Raiding Its Entire Toybox
Just as Marvel had gone from ailing comic-book publisher to Hollywood behemoth, the toymaker could leverage its intellectual property at the multiplex. Kreiz told me, “My thesis was that we needed to transition from being a toy-manufacturing company, making items, to an I.P. company, managing franchises.”
She told me, “There are people who adore Barbie, people who hate Barbie—but the bottom line is everyone knows Barbie.” She wanted a film adaptation to confront those “sharp edges, ” but when she met with Kreiz she led with her desire to take the brand seriously.
Kreiz, meanwhile, hired a veteran of Miramax, Robbie Brenner, to head up the newly minted Mattel Films. Her first task: assemble a team of development executives to rummage through Mattel’s toy chest and identify I.P. that could be fodder for Hollywood studios. Mattel would help match properties with writers, actors, and directors; studios would provide all the funding. The brands, and audiences’ familiarity with them, were their own form of currency. Brenner told me, “In the world we’re living in, I.P. is king. Pre-awareness is so important.”
Jeremy Barber, an agent at U.T.A. who represents Gerwig and Baumbach, is close with Brenner, so he could be blunt. “Are you crazy?” he told her. “You should’ve come into this office and thanked me when Greta and Noah showed up to write a fucking Barbie movie!”
Barber told me that Mattel had figured out how to “engage with filmmakers in a friendly way.” Gerwig, meanwhile, was looking to move beyond the small-scale dramas she was known for. “Greta and I have been very consciously constructing a career,” Barber explained. “Her ambition is to be not the biggest woman director but a big studio director. And Barbie was a piece of I.P. that was resonant to her.”
Although Barber was pleased with the “Barbie” partnership, he was clear-eyed about its implications. “Is it a great thing that our great creative actors and filmmakers live in a world where you can only take giant swings around consumer content and mass-produced products?” he said. “I don’t know. But it is the business. So, if that’s what people will consume, then let’s make it more interesting, more complicated.”
The future of moviegoing now seems increasingly tenuous, and studios have leaned on pre-awareness as a means of drawing people to theatres: a nostalgia play like “Hot Wheels” is seen as a safer bet than an original concept. The box office has borne this out: the ten highest-grossing films of 2022 were all reboots or sequels. Disney’s much derided strategy of remaking “Aladdin” and other animated classics as live-action spectacles has largely paid off; by contrast, Pixar’s recent attempt at an original story, “Elemental,” bombed.
The mandate for audience recognition has pushed artists to take increasingly desperate measures—including scrounging up plotlines from popular snacks. Eva Longoria recently directed the Cheetos dramedy “Flamin’ Hot”; Jerry Seinfeld is at work on “Unfrosted: The Pop-Tart Story.”
creating “a story where there hadn’t been a story” felt like solving “an intellectual Rubik’s Cube.”
Whereas Scott’s “Monopoly” was shamed into nonexistence, advance screenings of “Barbie,” billed as “blowout parties,” are selling out. Nevertheless, the film’s slogan—“If you love Barbie, this movie is for you. If you hate Barbie, this movie is for you”—is indicative of the tightrope it has to walk. “Barbie” is somehow simultaneously a critique of corporate feminism, a love letter to a doll that has been a lightning rod for more than half a century, and a sendup of the company that actively participated in the adaptation.
When Robbie’s character ventures beyond Barbie Land, Gerwig explained, the film’s visual language also changes: “The way the camera moves and the way it feels is different once we’re in the real world.”
Mattel was sometimes uneasy with Gerwig’s interest in the brand’s missteps. In 1964, the company released a doll named Allan, whose packaging marketed him as “Ken’s buddy,” with the tagline “All of Ken’s clothes fit him!” Allan was soon pulled from shelves. When Gerwig learned about him, she found the ad copy both sad and amusing. In “Barbie,” Allan is played by Michael Cera, and much is made of the fact that his relationship to Ken is his main identifying feature. The company, Gerwig remembered, required some convincing: “There was just an e-mail that went around where they said, ‘Do you have to remind people that this was on the box?’ ”
Gerwig told me, “Barbie seems so monolithic, and there’s a quality where it just seems as if she was inevitable, and she’s always existed. I think all the dead ends are a reminder that they were just trying stuff out.” Although she understood why Mattel wanted “to protect Barbie,” she felt that “dealing with all the strangeness of it is a way of honoring it.”
A rival, Kenner, was having runaway success with “Star Wars” action figures, and Mattel scrambled to launch a science-fantasy saga of its own. Play-testing had revealed that young boys fixated on the notion of “power,” and that a muscle-bound hero was more appealing than the slighter action figures of the era. This intelligence yielded He-Man and the Masters of the Universe. When a retailer pointed out that kids would have no idea who these characters were—even then, pre-awareness was a consideration—Mattel hastily produced comic books that explained their backstories.
Brenner sat at the head of a long table while her right hand, Kevin McKeon, provided updates on various projects. His descriptions sometimes sounded like a Hollywood version of Mad Libs. A screenwriter, he informed the group, was at work on an American Girl script that would be “ ‘Booksmart’ meets ‘Bill & Ted.’ ” Jimmy Warden, the screenwriter of “Cocaine Bear,” had devised a horror-comedy about the Magic 8 Ball.
McKeon seemed most excited by Kaluuya’s Barney project, which would be “surrealistic”; he compared the concept to the work of Charlie Kaufman and Spike Jonze. “We’re leaning into the millennial angst of the property rather than fine-tuning this for kids,” he said. “It’s really a play for adults. Not that it’s R-rated, but it’ll focus on some of the trials and tribulations of being thirtysomething, growing up with Barney—just the level of disenchantment within the generation.” He told me later that he’d sold it to prospective partners as an “A24-type” film: “It would be so daring of us, and really underscore that we’re here to make art.”
Talk turned to a few recent pitches that had surprised the team. “Somebody just asked me about Bass Fishin’, which is, like, a toy fishing rod,” Bassin said. The pitch was for an “intense sports drama about this cheating scandal in competitive fishing”—an attempt, it seemed to me, to Trojan-horse a story that the writer actually wanted to tell into a conceit that might be green-lighted.
Gerwig’s “Barbie,” for all its gentle mockery of Mattel, has already paid dividends for the company. A fifty-dollar doll resembling Robbie as she appears in the film, unveiled in June, has sold out; so has a seventy-five-dollar model of Stereotypical Barbie’s pink Corvette.
·newyorker.com·
After “Barbie,” Mattel Is Raiding Its Entire Toybox
Isn’t That Spatial? | No Mercy / No Malice
Isn’t That Spatial? | No Mercy / No Malice
Betting against a first-generation Apple product is a bad trade — from infamous dismissals of the iPhone to disappointment with the original iPad. In fact, this is a reflection of Apple’s strategy: Start with a product that’s more an elegant proof-of-concept than a prime-time hit; rely on early adopters to provide enough runway for its engineers to keep iterating; and trust in unmatched capital, talent, brand equity, and staying power to morph a first-gen toy into a third-gen triumph
We are a long way from making three screens, a glass shield, and an array of supporting hardware light enough to wear for an extended period. Reviewers were (purposefully) allowed to wear the Vision Pro for less than half an hour, and nearly every one said comfort was declining even then. Avatar: The Way of Water is 3 hours and 12 minutes.
Meta’s singular strategic objective is to escape second-tier status and, like Apple and Alphabet, control its distribution. And its path to independence runs through Apple Park. Zuckerberg is spending the GDP of a small country to invent a new world, the metaverse, where Apple doesn’t own the roads or power stations. Vision Pro is insurance against the metaverse evolving into anything more than an incel panic room.
The only product category where VR makes difference is good VR games. Price is not limiting factor, the quality of VR experience is. Beat Saber is good and fun and physical exercise. Half Life: Alyx, is amazing. VR completely supercharges horror games, and scary stalking shooters. Want to fear of your life and get PTSD in the comfort of your home? You can do it. Games can connect people and provide physical exercise. If the 3rd iteration of Vision Pro is good for 2 hours of playing for $2000 Apple will kill the console market. Playstations no more. Apple is not a gaming company, but if Vision Pro becomes better and slightly cheaper, Apple becomes gaming company against its will.
·profgalloway.com·
Isn’t That Spatial? | No Mercy / No Malice
When to Design for Emergence
When to Design for Emergence
In complexity science, ‘emergence’ describes the way that interactions between individual components in a complex system can give rise to new behavior, patterns, or qualities. For example, the quality of ‘wetness’ cannot be found in a single water molecule, but instead arises from the interaction of many water molecules together. In living systems, emergence is at the core of adaptive evolution.
Design for emergence prioritizes open-ended combinatorial possibilities such that the design object can be composed and adapted to a wide variety of contextual and idiosyncratic niches by its end-user. LEGO offers an example — a simple set of blocks with a shared protocol for connecting to one another from which a nearly infinite array of forms can emerge. Yet as we will see, design for emergence can generate value well beyond children’s toys.
In contrast to high modern design, user-centered design takes a more modest position; the designer does not inherently know everything, and therefore she must meticulously study the needs and behaviors of users in order to produce a good design. User-centered design remains the dominant design paradigm today, employed by environmental designers, tech companies, and design agencies around the world.
In this paradigm, design is about gaining knowledge from the user, identifying desirable outcomes, and controlling as much of the process as possible to achieve those outcomes. ‘Design’ remains synonymous with maximizing control.
But consider even the ‘desire path’ example pictured above. The modal user may be well supported by paving the desire path indicated by their behavior, but what good is a paved path leading to stairs for a wheelchair user? In practice, user-centered design tends to privilege the modal user at the expense of the long-tail user whose needs may be just as great.
User-centered design has a better track record than high modern design, but it still exerts a homogenizing effect. The needs of the modal user are accommodated and scaled through software or industrial manufacturing, while power users and those with edge cases can do nothing but actively petition the designer for attention. In most cases, diverse users with a wide variety of niche use cases are forced to conform to the behavior of the modal user.
In design for emergence, the designer assumes that the end-user holds relevant knowledge and gives them extensive control over the design. Rather than designing the end result, we design the user’s experience of designing their own end result. In this way we can think of design for emergence as a form of ‘meta-design.’
In other words, to address the long-tail problem, the tool must be flexible enough that it can be adapted to unexpected and idiosyncratic problem spaces—especially those unanticipated by the tool’s designer.
In contrast to user-centered design, design for emergence invites the user into the design process not only as a subject of study, but as a collaborator with agency and control.
What all these tools have in common is support for open-ended adaptation to highly contextual problems without the need for technical knowledge. Rather than building a static, purpose-built solution to a single common problem with lots of users (and lots of competitors), they’ve won robust user bases by supporting a broad swath of long-tail user needs.
Design for emergence is composable. It provides a limited ‘alphabet’ and a generative grammar that’s easy to learn and employ, yet can be extended to create powerful, complex applications. As Seymour Papert once remarked, “English is a language for children,” but this fact, “does not preclude its being also a language for poets, scientists, and philosophers.”
·rhizomerd.substack.com·
When to Design for Emergence