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).

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.”

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.

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?”