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).

Missed context - Because you’re not a full-time employee (even if you’re working 5 days a week) you may not be included on all-hands emails, announcements and so on and so you always have to work hard to gain the full context of a client. Tightly scripting a performance doesn’t leave room for new contexts to emerge during the performance. Instead there should always be room for new context to emerge and get integrated into the performance in real-time. Missed feedback - It’s not uncommon as a consultant to be the most proficient powerpoint user in the org (or at least your portion of the org). This has benefits but it also has the unintended consequence of making everything you touch look “finished”. And finished work gets very different feedback from people than raw materials and thinking. So sometimes it’s important to un-design and un-polish your work, to invite people onto the stage to co-create the performance - this way you ensure that you get the appropriate feedback.

“thinking on your feet” is about the balance between deflecting decisions for further analysis and providing the answer there and then.

learning to provide an answer that you believe in but leaves room for revision later is key. The real game that’s being played here is not one of being right or wrong - it’s the executive asking two questions at once - firstly “how much do you know?” and secondly “can you improv?” to understand how useful you’re going to be in the theatre of work.

There’s a fine line between reacting to a situation in the room and bullshitting. As a consultant this is especially hard to avoid. Your default mode of operating is the liminal space between industries, businesses and markets. A few times a year I’m forced to learn something new from scratch. This forces us to work in spaces where we’re often the least knowledgeable about a specific business (even if we are experts in the industry… And sometimes we’re experts at a discipline but neither knowledgeable about the business or the industry).

Forget trying to decide what your life’s destiny is. That’s too grand. Instead, just figure out what you should do in the next 2 years.

Visuals can stir up feelings or paint a scene in an instant. However, they may not always nail down the details or explain things as clearly as words can. Words can be very precise and give you all the information you need. Yet, sometimes they miss that instant impact or emotional punch.

For each visual you add to your presentation, you should ask yourself “What does it really say?” And then check: Does it enhance the meaning of my message, or is it purely decorative? Does it belong at this point in my presentation? Would it be better for another slide? Is there a better image that says what I want to say?

Computers don’t feel, and that means: they don’t understand what they do, they grow images like cancer grows cells: They just replicate something into the blue. This becomes apparent in the often outright creepiness of AI images.

AI is really good at making scary images. Even if the prompt lacks all hints of horror kitsch, you need to get ready to see or feel something disturbing when you look at AI images. It’s like a spell. Part of the scariness comes from the cancer-like pattern that reproduces the same ornament without considering its meaning and consequence.

Placing pictures next to each other will invite comparisons. We also compare images that follow each other. Make sure that you do not inadvertently compare apples and oranges.

When placing multiple images in a grid or on one slide after the other, ensure they don’t clash in terms of colors, style, or resolution. Otherwise, people will focus more on the contrast between the images rather than their content.

Repeating what everyone can see is bad practice. To make pictures and text work, they need to have something to say about each other.

Don’t write next to the image what people already see. A caption is not an ALT text.

The most powerful combination of text and image happens when the text says about the image what you can’t see at first sight, and when the image renders what is hard to imagine.

Do not be boring or overly explanatory. The visual should attract their attention to your words and vice-versa.

If a visual lacks meaning, it becomes a decorative placeholder. It can dilute your message, distract from what you want to say, and even express disrespect to your audience.

Both arts and athletics involve a lot of deft physical movement, and I could see why professionals in those fields would benefit from learning to resist overthinking so they can “just do it.”  Almost every profession involves some need for focus, however, so you can see why, over time, the idea of a flow state breached its original limits. Now, “flow state” has all sorts of associations—some scientific, some folk, and some a mix of both. For many, the term has just become a dressed-up version of focusing.

A 2023 study found, for example, that there is a huge range of barriers to flow—many of which aren’t just interruptions from coworkers. They categorized these as situational barriers, such as interruptions and distractions; personal barriers, such as the work being too challenging or not challenging enough; and interpersonal barriers, such as poor management and poor team dynamics.

A 2018 study found, in addition, that the most disruptive interruptions aren’t external—they’re internal. 81% of the participants predicted internal interruptions would be worse, but they were wrong. “Self-interruptions,” the researchers wrote, “make task switching and interruptions more disruptive by negatively impacting the length of the suspension period and the number of nested interruptions.”

But because no one literally interrupted your work, you might be unaware of the costs of that rote, mundane work. You might even castigate yourself over the day for not getting the work done: You fought for a distraction-free day, got it, and you have nothing to show for it. It can feel bad.

a seemingly individual problem, staying focused, is often downstream from an organizational problem.

The last three jobs I've held in the last 5 years have all felt like someone just handing me the keys to a sinking boat before they jump off. Every job is sold as having at least some greenfield development where you can "own" the domain and "lead" the direction of the project, but once you accept the offer and get on-boarded, you realize that the system is so brittle that any change will completely break and cause incidents, and there is a year's worth of backlog issues to address with duck-tape and glue before you could even consider fixing the fundamental problems.

Often the teams that built these systems are long gone, so there is nobody to ask for help when you're learning the rough edges, you're just on your own. The technology decisions are all completely set in stone because we could never justify the risk of making changes. There is so much tech debt and maintenance work, we don't really have time to do any new development with the current staffing levels. The job then becomes dominated by on-call responsibilities and fire-fighting. It's 90% toil, and almost zero actual system design and development work.

Being responsible for a whole system that you didn't build, that you know is brittle and broken, but which you cannot fix, is incredibly stressful. It's almost a hopeless situation.

what consequences has the rise of management consulting had for the organization of American business and the lives of American workers? The answers to these questions put management consultants at the epicenter of economic inequality and the destruction of the American middle class.

Managers do not produce goods or deliver services. Instead, they plan what goods and services a company will provide, and they coordinate the production workers who make the output. Because complex goods and services require much planning and coordination, management (even though it is only indirectly productive) adds a great deal of value. And managers as a class capture much of this value as pay. This makes the question of who gets to be a manager extremely consequential.

In the middle of the last century, management saturated American corporations. Every worker, from the CEO down to production personnel, served partly as a manager, participating in planning and coordination along an unbroken continuum in which each job closely resembled its nearest neighbor.

Even production workers became, on account of lifetime employment and workplace training, functionally the lowest-level managers. They were charged with planning and coordinating the development of their own skills to serve the long-run interests of their employers.

At McDonald’s, Ed Rensi worked his way up from flipping burgers in the 1960s to become CEO. More broadly, a 1952 report by Fortune magazine found that two-thirds of senior executives had more than 20 years’ service at their current companies.

Top executives enjoyed commensurately less control and captured lower incomes. This democratic approach to management compressed the distribution of income and status. In fact, a mid-century study of General Motors published in the Harvard Business Review—completed, in a portent of what was to come, by McKinsey’s Arch Patton—found that from 1939 to 1950, hourly workers’ wages rose roughly three times faster than elite executives’ pay. The management function’s wide diffusion throughout the workforce substantially built the mid-century middle class.

The earliest consultants were engineers who advised factory owners on measuring and improving efficiency at the complex factories required for industrial production. The then-leading firm, Booz Allen, did not achieve annual revenues of $2 million until after the Second World War. McKinsey, which didn’t hire its first Harvard M.B.A. until 1953, retained a diffident and traditional ethos

A new ideal of shareholder primacy, powerfully championed by Milton Friedman in a 1970 New York Times Magazine article entitled “The Social Responsibility of Business is to Increase its Profits,” gave the newly ambitious management consultants a guiding purpose. According to this ideal, in language eventually adopted by the Business Roundtable, “the paramount duty of management and of boards of directors is to the corporation’s stockholders.” During the 1970s, and accelerating into the ’80s and ’90s, the upgraded management consultants pursued this duty by expressly and relentlessly taking aim at the middle managers who had dominated mid-century firms, and whose wages weighed down the bottom line.

Management consultants thus implemented and rationalized a transformation in the American corporation. Companies that had long affirmed express “no layoff” policies now took aim at what the corporate raider Carl Icahn, writing in the The New York Times in the late 1980s, called “corporate bureaucracies” run by “incompetent” and “inbred” middle managers. They downsized in response not to particular business problems but rather to a new managerial ethos and methods; they downsized when profitable as well as when struggling, and during booms as well as busts.

Downsizing was indeed wrenching. When IBM abandoned lifetime employment in the 1990s, local officials asked gun-shop owners around its headquarters to close their stores while employees absorbed the shock.

In some cases, downsized employees have been hired back as subcontractors, with no long-term claim on the companies and no role in running them. When IBM laid off masses of workers in the 1990s, for example, it hired back one in five as consultants. Other corporations were built from scratch on a subcontracting model. The clothing brand United Colors of Benetton has only 1,500 employees but uses 25,000 workers through subcontractors.

The shift from permanent to precarious jobs continues apace. Buttigieg’s work at McKinsey included an engagement for Blue Cross Blue Shield of Michigan, during a period when it considered cutting up to 1,000 jobs (or 10 percent of its workforce). And the gig economy is just a high-tech generalization of the sub-contractor model. Uber is a more extreme Benetton; it deprives drivers of any role in planning and coordination, and it has literally no corporate hierarchy through which drivers can rise up to join management.

In effect, management consulting is a tool that allows corporations to replace lifetime employees with short-term, part-time, and even subcontracted workers, hired under ever more tightly controlled arrangements, who sell particular skills and even specified outputs, and who manage nothing at all.

the managerial control stripped from middle managers and production workers has been concentrated in a narrow cadre of executives who monopolize planning and coordination. Mid-century, democratic management empowered ordinary workers and disempowered elite executives, so that a bad CEO could do little to harm a company and a good one little to help it.

Whereas at mid-century a typical large-company CEO made 20 times a production worker’s income, today’s CEOs make nearly 300 times as much. In a recent year, the five highest-paid employees of the S&P 1500 (7,500 elite executives overall), obtained income equal to about 10 percent of the total profits of the entire S&P 1500.

as Kiechel put it dryly, “we are not all in this together; some pigs are smarter than other pigs and deserve more money.” Consultants seek, in this way, to legitimate both the job cuts and the explosion of elite pay. Properly understood, the corporate reorganizations were, then, not merely technocratic but ideological.

corporate reorganizations have deprived companies of an internal supply of managerial workers. When restructurings eradicated workplace training and purged the middle rungs of the corporate ladder, they also forced companies to look beyond their walls for managerial talent—to elite colleges, business schools, and (of course) to management-consulting firms. That is to say: The administrative techniques that management consultants invented created a huge demand for precisely the services that the consultants supply.

Consulting, like law school, is an all-purpose status giver—“low in risk and high in reward,” according to the Harvard Crimson. McKinsey also hopes that its meritocratic excellence will legitimate its activities in the eyes of the broader world. Management consulting, Kiechel observed, acquired its power and authority not from “silver-haired industry experience but rather from the brilliance of its ideas and the obvious candlepower of the people explaining them, even if those people were twenty-eight years old.”

A deeper objection to Buttigieg’s association with McKinsey concerns not whom the firm represents but the central role the consulting revolution has played in fueling the enormous economic inequalities that now threaten to turn the United States into a caste society.

Meritocrats like Buttigieg changed not just corporate strategies but also corporate values.

GM may aspire to build good cars; IBM, to make typewriters, computers, and other business machines; and AT&T, to improve communications. Executives who rose up through these companies, on the mid-century model, were embedded in their firms and embraced these values, so that they might even have come to view profits as a salutary side effect of running their businesses well.

When management consulting untethered executives from particular industries or firms and tied them instead to management in general, it also led them to embrace the one thing common to all corporations: making money for shareholders. Executives raised on the new, untethered model of management aim exclusively and directly at profit: their education, their career arc, and their professional role conspire to isolate them from other workers and train them single-mindedly on the bottom line.

American democracy, the left believes, cannot be rejuvenated by persuading elites to deploy their excessive power somehow more benevolently. Instead, it requires breaking the stranglehold that elites have on our economics and politics, and reempowering everyone else.

Under Raghavan, Google has become less reliable, less transparent, and is dominated by search engine optimized aggregators, advertising, and outright spam.

Google is the ultimate essential piece of online infrastructure, just like power lines and water mains are in the physical realm.

In April 2011, the Guardian ran an interview with Raghavan that called him “Yahoo’s secret weapon,” describing his plan to make “rigorous scientific research and practice… to inform Yahoo's business from email to advertising,” and how under then-CEO Carol Bartz, “the focus has shifted to the direct development of new products.” It speaks of Raghavan’s “scientific approach” and his “steady, process-based logic to innovation that is very different to the common perception that ideas and development are more about luck and spontaneity,” a sentence I am only sharing with you because I need you to see how stupid it is, and how specious the tech press’ accolades used to be. This entire article is ridiculous, so utterly vacuous that I’m actually astonished. What about Raghavan’s career made this feel right? How has nobody connected these dots before and said something? Am I insane?

Sundar Pichai, who previously worked at McKinsey — arguably the most morally abhorrent company that has ever existed, having played roles both in the 2008 financial crisis (where it encouraged banks to load up on debt and flawed mortgage-backed securities) and the ongoing opioid crisis, where it effectively advised Purdue Pharma on how to “growth hack” sales of Oxycontin. McKinsey has paid nearly $1bn over several settlements due to its work with Purdue. I’m getting sidetracked, but one last point. McKinsey is actively anti-labor.

This sort of audience divide, not between those who love a show and those who hate it but between those who love it in very different ways, has become a familiar schism in the past fifteen years, during the rise of—oh, God, that phrase again—Golden Age television. This is particularly true of the much lauded stream of cable “dark dramas,” whose protagonists shimmer between the repulsive and the magnetic. As anyone who has ever read the comments on a recap can tell you, there has always been a less ambivalent way of regarding an antihero: as a hero

a subset of viewers cheered for Walter White on “Breaking Bad,” growling threats at anyone who nagged him to stop selling meth. In a blog post about that brilliant series, I labelled these viewers “bad fans,” and the responses I got made me feel as if I’d poured a bucket of oil onto a flame war from the parapets of my snobby critical castle. Truthfully, my haters had a point: who wants to hear that they’re watching something wrong?

Collective harmony and hierarchy are strongly idealised across East Asia. Communication is thus implicit and indirect. Conflict aversion and emotional suppression make it harder to learn what someone else really thinks. So what’s the solution?Alcohol reduces people’s inhibitions. This promotes social bonding and information-sharing. As argued in Edward Slingerland’s book “Drunk”, it benefits businesses! But this exact same cognitive shift also elevates risks of sexual abuse. Women may prefer to leave early. By doing so, they miss out on homosocial boozing and schmoozing.

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.”

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).

If I had to summarize it, I would say that the signal to noise ratio is what wore me down. We start companies to build products that serve people, not to sit in meetings with lawyers.  You need to be able to answer the "what have I done for our users today" question with "not much but I got promoted" and be happy with that answer to be successful in Corp-Tech.

being part of a Corporation means that the signal to noise ratio changes dramatically.  The amount of time and effort spent on Legal, Policy, Privacy - on features that have not shipped to users yet, meant a significant waste of resources and focus. After the acquisition, we have an extremely long project that consumed many of our best engineers to align our data retention policies and tools to Google. I am not saying this is not important BUT this had zero value to our users. An ever increasing percent of our time went to non user value creation tasks and that changes the DNA of the company quickly, from customer focused to corporate guidelines focused.

the salaries are so high and the options so valuable that it creates many misalignments.  The impact of an individual product on the Corp-Tech stock is minimal so equity is basically free money.  Regardless of your performance (individually) or your product performance, you equity grows significantly so nothing you do has real economic impact on your family. The only control you have to increase your economic returns are whether you get promoted, since that drives your equity and salary payments.  This breaks the traditional tech model of risk reward.

There are certain design principles you can apply to this like composition, hierarchy, color theory, and so on, but to the regular consumer, it’s the gestalt of all your design decisions that ultimately makes an emotional connection. We know emotion is so much of what drives purchasing behavior so the more nebulous goal of visual design is often pulling those levers in just the right ratio to elicit a desired connection to your product.

ven something as foundational as increasing white space in your design can instantly improve a customer’s perception of your brand’s worth when it’s done intentionally.

almost all clients agree that they need better look and feel in their digital experience, that they are looking to add some type of emotional signal that’s missing. But when it comes time to accept changes that address those problems, I’ve had several instances where clients are resistant to solutions that depart too significantly from what they’re already comfortable with. Usually that reservation is overcome when I correlate the visual changes to the ways in which the user experience is improved and the resulting impact on business performance. There will be also times when a client expresses to us that they’ve never been satisfied with their brand or website and they point to competitors that evoke certain emotional qualities that they are aspiring to capture. In those cases it’s quite rewarding to be able to translate those more nebulous feelings into concrete terminology that gives us specific visual principles to bring in or improve on.

As much as I wish circumstances were different, the stress and expense of this fight – coupled with the existing stress and expense of operating Omegle, and fighting its misuse – are simply too much. Operating Omegle is no longer sustainable, financially nor psychologically. Frankly, I don’t want to have a heart attack in my 30s. The battle for Omegle has been lost, but the war against the Internet rages on. Virtually every online communication service has been subject to the same kinds of attack as Omegle; and while some of them are much larger companies with much greater resources, they all have their breaking point somewhere. I worry that, unless the tide turns soon, the Internet I fell in love with may cease to exist, and in its place, we will have something closer to a souped-up version of TV – focused largely on passive consumption, with much less opportunity for active participation and genuine human connection.

I’ve done my best to weather the attacks, with the interests of Omegle’s users – and the broader principle – in mind. If something as simple as meeting random new people is forbidden, what’s next? That is far and away removed from anything that could be considered a reasonable compromise of the principle I outlined. Analogies are a limited tool, but a physical-world analogy might be shutting down Central Park because crime occurs there – or perhaps more provocatively, destroying the universe because it contains evil. A healthy, free society cannot endure when we are collectively afraid of each other to this extent.

In recent years, it seems like the whole world has become more ornery. Maybe that has something to do with the pandemic, or with political disagreements. Whatever the reason, people have become faster to attack, and slower to recognize each other’s shared humanity. One aspect of this has been a constant barrage of attacks on communication services, Omegle included, based on the behavior of a malicious subset of users. To an extent, it is reasonable to question the policies and practices of any place where crime has occurred. I have always welcomed constructive feedback; and indeed, Omegle implemented a number of improvements based on such feedback over the years. However, the recent attacks have felt anything but constructive. The only way to please these people is to stop offering the service. Sometimes they say so, explicitly and avowedly; other times, it can be inferred from their act of setting standards that are not humanly achievable. Either way, the net result is the same.

I didn’t really know what to expect when I launched Omegle. Would anyone even care about some Web site that an 18 year old kid made in his bedroom in his parents’ house in Vermont, with no marketing budget? But it became popular almost instantly after launch, and grew organically from there, reaching millions of daily users. I believe this had something to do with meeting new people being a basic human need, and with Omegle being among the best ways to fulfill that need. As the saying goes: “If you build a better mousetrap, the world will beat a path to your door.” Over the years, people have used Omegle to explore foreign cultures; to get advice about their lives from impartial third parties; and to help alleviate feelings of loneliness and isolation. I’ve even heard stories of soulmates meeting on Omegle, and getting married. Those are only some of the highlights. Unfortunately, there are also lowlights. Virtually every tool can be used for good or for evil, and that is especially true of communication tools, due to their innate flexibility. The telephone can be used to wish your grandmother “happy birthday”, but it can also be used to call in a bomb threat. There can be no honest accounting of Omegle without acknowledging that some people misused it, including to commit unspeakably heinous crimes.

As a young teenager, I couldn’t just waltz onto a college campus and tell a student: “Let’s debate moral philosophy!” I couldn’t walk up to a professor and say: “Tell me something interesting about microeconomics!” But online, I was able to meet those people, and have those conversations. I was also an avid Wikipedia editor; I contributed to open source software projects; and I often helped answer computer programming questions posed by people many years older than me. In short, the Internet opened the door to a much larger, more diverse, and more vibrant world than I would have otherwise been able to experience; and enabled me to be an active participant in, and contributor to, that world. All of this helped me to learn, and to grow into a more well-rounded person. Moreover, as a survivor of childhood rape, I was acutely aware that any time I interacted with someone in the physical world, I was risking my physical body. The Internet gave me a refuge from that fear. I was under no illusion that only good people used the Internet; but I knew that, if I said “no” to someone online, they couldn’t physically reach through the screen and hold a weapon to my head, or worse. I saw the miles of copper wires and fiber-optic cables between me and other people as a kind of shield – one that empowered me to be less isolated than my trauma and fear would have otherwise allowed.

By Katharine Schwab4 minute ReadOver the last year, Ideo’s philosophy of “design thinking“–a codified, six-step process to solve problems creatively–has come under fire. It’s been called bullshit, the opposite of inclusive design, and a failed experiment. It’s even been compared to syphilis.Ideo as an institution has rarely responded to critiques of design thinking or acknowledged its flaws. But at the Fast Company Innovation Festival, Ideo partner and leader of its Cambridge, Massachusetts, office Michael Hendrix had a frank conversation with Co.Design senior writer Mark Wilson about why design thinking has gotten so much flack.“I think it’s fair to critique design thinking, just as it’s fair to critique any other design strategy,” Hendrix says. “There’s of course many poor examples of design thinking, and there’s great examples. Just like there’s poor examples of industrial design and graphic design and different processes within organizations.”Part of the problem is that many people use the design thinking methodology in superficial ways. Hendrix calls it the “theater of innovation.” Companies know they need to be more creative and innovative, and because they’re looking for fast ways to achieve those goals, they cut corners.“We get a lot of the materials that look like innovation, or look like they make us more creative,” Hendrix says. “That could be anything from getting a bunch of Sharpie markers and Post-its and putting them in rooms for brainstorms, to having new dress codes, to programming play into the week. They all could be good tools to serve up creativity or innovation, they all could be methods of design thinking, but without some kind of history or strategy to tie them together, and track their progress, track their impact, they end up being a theatrical thing that people can point to and say, ‘oh we did that.'”

“If you make something rigid and formulaic, it could absolutely fail,” he says. “You want to rely on milestones in the creative process, but you don’t want it to be a reactive process that loses its soul.”

“There is a real need to build respect for one another and trust in the safety of sharing ideas so you can move forward,” Hendrix says. “Knowing when to bring judgments is important. Cultures that are highly judgy, that have hierarchy, that are rewarding the person who is the smartest person in the room, don’t do well with this kind of methodology.”

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?”