We live in the age of the endless video stream — TikTok, Instagram Reels, YouTube Shorts, videos on X. This now dominant form of media consumption has influenced many aspects of society, and it’s changing radically how startups work.
A recent article by Derek Thompson has an apt description for what’s happening to our media diet. Everything is turning into television — or rather its modern form: a continuous stream of short, emotional, interchangeable clips, algorithmically arranged into feeds. The individual piece matters less; what matters is that the feed never stops. The grammar of this world is immediacy, brevity, spectacle and emotion. It doesn’t invite you to think in paragraphs and coherent arguments. It trains you to think in clips and emotionally impactful hooks.
Netflix and other media outlets have already changed their content strategy to accommodate this kind of media diet. Screenwriters have been instructed to write simpler plots that work for viewers with short attention spans who probably are on their phones while simultaneously watching a show.
Founders turn into creators
What happens when this logic spills over from media into the startup world, and especially into AI? More and more, it feels like we are building, funding and evaluating startups as if they were shows, or even just clips in an endless feed of superficial messages.
Founders have always needed to tell a story. The difference is how deep and nuanced this story is. Ten or fifteen years ago, you pitched investors in a room, maybe spoke at a conference, wrote a long blog post. Today, in some sectors, your entire company exists in feeds: customers meet you through short demo videos, talent discovers you on TikTok and YouTube, journalists get their first impression from a launch trailer, investors might scroll past your product long before they see a deck.
The founders who win attention are those who behave like creators. They know how to compress a complex idea into 30 seconds of feed magic. They know how to craft launch videos, how to pick a fight for attention, how to deliver a simple, compelling narrative (“Look, we went from 0 to $50M ARR in just two weeks!”). The line between “startup founder” and “creator of a TikTok channel” is getting blurry.
Demo theatre and benchmark fluff
You can see this very clearly in the current AI wave. There is a whole category of products that are, if we’re honest, mostly “demo engines”: an LLM with a thin wrapper around it, designed first and foremost to look impressive on video. The core asset is not the depth of the product, but the founder’s ability to generate spectacular clips that win on Product Hunt and X. The success metric is not retention or gross margin; it’s how often the demo gets reposted.
The same dynamic shows up in how we judge technology. Television cares about spectacles and scores. AI now has its own versions: dramatic launch videos and cherry-picked benchmark charts. A new model or product appears, accompanied by glossy footage of mind-blowing use cases and a few carefully chosen numbers that place it on top of some leaderboard. Some companies deliberately optimize their product to make it to the top of a chart. What rarely survives the journey into the feed are the boring questions: what are the assumptions behind these evals, how robust is this in real-world conditions, what does inference actually cost at scale, who owns the data, where are the safety rails?
In other words: we get demo theatre and benchmark theatre. The point is not to show how the system behaves in the real world. The point is to deliver a memorable scene with a clear score.
You can feel the pressure this creates on product teams. If your distribution is video-first and your growth depends on staying in other people’s feeds, your roadmap starts to tilt towards features that look good on camera. Meanwhile, the unglamorous work that actually creates defensibility — reliability, proprietary data, robust tooling, deep integration into existing workflows — is hard to film and impossible to explain in 30 seconds. It’s the first thing that gets pushed down the backlog. Short term, the spectacle wins. Long term, the debt piles up.
Thinking, fast and faster
All of this sits on top of something deeper: what this media environment does to our thinking in tech, how it may distort how we reason as founders and investors.
Thompson connects feed logic to declining literacy and eroding deep thinking capabilities, arguing that when everything becomes a stream of short clips, we lose the ability to sit with long-form arguments. Neil Postman warned that a TV-driven culture would start to treat politics, science and news as performance rather than discourse. That sounds uncomfortably close to where parts of the AI conversation are today: continuous drama about “AGI is near” vs. “everything is a toy”, daily outrage cycles (even driven by online spats among billionaires), tribal fights between doomers and accelerationists, breathless coverage of every new model drop.
If you live in that stream all day, your own thinking becomes episodic. It jumps from one extreme to the other in response to whatever is trending, instead of building a stable mental model of where value might actually accrue in five or ten years. That doesn’t just affect journalists or Twitter personalities; it affects founders and investors as well. It’s much easier to react to the latest spectacular demo than to spend an afternoon reading a dense research paper and tracing through what it really implies for your product or your portfolio.
All of this is amplified in AI because the underlying technology really is spectacular. When an LLM or a video model does something surprising, it’s genuinely fascinating — and extremely easy to clip. Sora clips, synthetic influencers, AI agents independently completing little tasks: they are made for television. The risk is that we start to mistake “AI that makes for a good clip” for “AI that makes good businesses.”
TikTok time vs VC time
One place where this is very visible and, frankly, quite worrying is venture capital. VC is structurally a long-duration game: you write a seed cheque in year zero and, if things go well, see real cash returns somewhere around year eight to twelve. Strong exits have become slower and rarer, not faster. On paper, we can mark things up along the way (and LPs reward that), but actual DPI still arrives on decade-long timescales.
Now overlay that with a market psychology trained by TikTok. Attention cycles compress into weeks, sometimes days. The half-life of excitement around a company is often one launch video or one funding announcement. Even large AI rounds feel like content objects in the feed more than milestones on a long operational journey. Everyone moves on to the next “hot” model, agent, or device before the previous one has even had a chance to prove whether it works outside controlled conditions.
That creates a fundamental tension: how do you run a ten-year strategy in a market that mentally resets every ten days? One obvious answer is that VCs start behaving like momentum traders. They surf narrative waves, invest into companies that are good “marks” for the next round, and they hope to offload risk to a later-stage investor or a strategic buyer before anyone looks too closely at retention, unit economics, or real moats.
Celebrity-driven investing
Maybe as a consequence, fundraising feels increasingly detached from tangible technical substance or revenue traction. Celebrity is becoming the product. If a luminary like Mira Murati or Ilya Sutskever starts a company, capital in the billions lines up before the product exists, because the “IP” is the person and their narrative.
You can see similar effects with younger, far less accomplished founders who have managed to get their 15 seconds of fame. That’s rational to some extent — these people can attract talent, capital and distribution — but in a feed-logic market the pendulum often swings too far. Founder–audience fit starts to overshadow founder–market fit.
Sure, many of these companies generate real revenue, often growing at unprecedented rates. In today’s AI market, hype helps winning paying customers because everybody wants to try out the latest cool toy. But once you look behind the curtain at unit economics and churn rates, the picture gets a lot darker.
We will see what the returns on these hype-driven investments are going to look like in the long run. If history is any guide, there’s not a lot of reason for optimism, apart from — as usual — a few outliers.
Every wave and hype cycle has its stars that deliver outsized returns, but only very, very few make it big. Sometimes fame correlates with financial success, but much more frequently it doesn’t. Yes, we all know Elon Musk, Mark Zuckerberg and Jeff Bezos. But do you know Kim Polese, Louis Borders or Joe Kraus? They were big media stars in the dot-com boom, running some of the sexiest startups. But their companies failed or delivered mediocre outcomes, and they declined into obscurity again. And their investors for the most part lost a lot of money.
This echoes patterns that we have seen historically in the creator industry. For every Mister Beast or Charli D’Amelio, there are countless once-famous creators that have lost their audience (remember Michelle Phan? Smosh? Ryan Higa?). Fame is very ephemeral, particularly in the age of the short-form video feed. Startups that rely on the sugar high that comes from feed fame might very quickly find themselves on the wrong side of the next hype wave.
Finding balance in the age of spectacle
So what do we do with this? I don’t think we can or should roll back the clock. Video is a powerful way to communicate complex ideas (I should know; my last own startup focused on video for a reason). Founders with strong public personas can be a real asset to their companies. Attention is not fake, it’s a real input into distribution and hiring.
But if everything is quietly becoming a TikTok feed, we need to be much more explicit about when we are optimizing for the show and when we’re building the substance behind it. We need to notice when we’re funding narrative arcs rather than unit economics. We need to catch ourselves when we’re judging companies by how they look in the feed instead of how they perform in the dark, when nobody is watching.
In ten years, cap tables won’t remember who had the best launch trailer. They’ll remember who quietly shipped boring infrastructure that never broke.
Good TV and good companies are not mutually exclusive. But they are not the same thing.
