In January 2020, Jeffrey Katzenberg explained why Quibi would beat every other mobile video app. His videos cost up to $125,000 a minute. The videos on YouTube and TikTok cost next to nothing.
Quibi shut down later that year.
Katzenberg was making a linear assumption: that the best inputs would produce the most valuable outputs. TikTok was built on a nonlinear assumption: it was impossible to know what was best, and value could only be explained in retrospect. To produce the most compelling video, you had to start with a lot of videos, see what resonated, and amplify that. And you shouldn’t expect that to last for too long. In an adaptive environment, whatever works today may not work tomorrow.
AI intensifies this dynamic. It makes it possible to produce more content, more quickly, more cheaply. And it makes it easier to identify, scale, copy, and adapt whatever works. TikTok is now full of human and AI-made slop. The slop does not undermine the product; it powers it: TikTok’s production function runs on slop, and the app is designed to encourage people to upload more of it and provide rapid feedback on whatever everyone else uploaded.
In theory, TikTok could ultimately be overwhelmed by slop. But the same is true of any app in a world where content can be generated at the click of a button. TikTok’s advantage is that it is already designed for such a world.
AI is doing to software what Instagram did to photography and TikTok did to video: Anyone can create a decent-looking product, the abundance of supply is filtered through algorithms and social networks, and whatever ends up on top is cloned and improved on faster than ever. AI and social networks enable the same dynamic in the market for many other services and goods: They make it easier for people to design and build new things, market new and old businesses, and “specialize” in things they know little about. And they make it easier for anyone to copy whatever works and encroach on any territory that bears fruit.
The gist of Tyler Cowen’s Great Stagnation (2011) was that humanity had reached a “technological plateau” after exhausting the “low-hanging fruit” of innovation: We solved many of the most obvious problems that lent themselves to traditional scientific solutions. Nick Bloom et al’s Are Ideas Getting Harder to Find? (2020) corroborated the feeling that science required ever more resources for every step forward.
AI offers a way out of this plateau. As Anthropic co-founder Jack Clark pointed out last week, “If you pick different parts of science, you see the same pattern show up: Progress is accelerating, and it has started to accelerate in the last two years.”
AI does not make discoveries like an individual scientist would. It makes discoveries like a whole society would: by exploring multiple paths in parallel, including many paths that most reasonable humans would not bother with. Consider how OpenAI describes its approach to solving one of the most famous unsolved problems in mathematics, the Navier–Stokes Millennium Prize Problem:
“We used a system of coordinating agents powered by our internal model… the group that produced the Navier–Stokes resolution involved on the order of 10,000 concurrent agents… we prompted different groups of agents with different variants of the problem statement, covering all variants of the problem.”
If you squint, this is not too different from TikTok’s approach: Empower multiple “agents” to explore a territory, double down on whatever seems to work.
AI makes this approach available across more of the economy. It also makes it necessary. As I pointed out last week, cyber defense is a case in point: AI makes it easier for (swarms of) attackers to discover vulnerabilities and generate new attacks, forcing defenders to continually develop and deploy new responses. Finding something that works buys time, not a permanent solution.
The same is true in a growing number of domains: It is both possible and necessary for more people to try more things and discover (or create) new problems and solutions, new needs and products, new desires and services.
In such an economy, most attempts may produce nothing of value, and most value won’t last long. Just like on TikTok, the average attempt can become less valuable even as the output of the whole system becomes more valuable.
We are heading to a world in which humanity can thrive by empowering most people to fail most of the time. We need to design our institutions accordingly. And by “design,” I mean: to empower multiple approaches and scale whatever works, for as long as it works.
Have a great weekend.
👀 Check out the signals I’m watching this week.