Every few weeks, the same analogy resurfaces across my feed:

“AI is like a snake eating its own tail. It learns from human work, fills the internet with AI-generated content, and eventually trains on its own output until it slowly decays.”

It’s a compelling, almost warnings-of-doom image. But the more I think about it, the more I believe AI isn’t a snake.

It’s a library. And more importantly, it is a garden that depends entirely on us for its seeds.

The Largest Library Humanity Has Ever Built

When people say AI is trained on “the internet,” it sounds dismissive—as if the training corpus were just a chaotic dump of social media posts, reddit threads, and random blogs.

But the internet is much more than that. It is the digital inheritance of humanity’s collective struggle and curiosity:

  • Academic papers and medical discoveries.
  • Entire programming language compilers and open-source ecosystems.
  • Engineering postmortems explaining why a bridge collapsed or a database failed at scale.
  • Decades of human programmers explaining to each other why one line of code works better than another.

An AI model doesn’t invent this library. It inherits it. It stands on a mountain built by millions of individuals who experimented, failed, and discovered something worth writing down.

Tomatoes vs. Jackfruits: The Seed Analogy

To understand why the “snake eating its own tail” theory of model collapse is flawed, we have to divide knowledge into two categories: optimizing what we know, and discovering what we don’t.

Think of it as the difference between growing tomatoes and growing jackfruit.

1. Cultivating the Tomato Seeds (Optimization)

If you give an AI system a defined domain with established rules—like mathematics, a Python compiler, or the game of Go—you have effectively handed it tomato seeds.

Once the AI has these seeds, it doesn’t need humans anymore to grow a massive harvest. Through reinforcement learning and verifiable self-play, it can generate 10,000 math proofs or code routines, run them through a formal validator (like a compiler or a math checker), discard the failures, and train on the successes.

This isn’t “toxic synthetic data” decay. It is distilled, mathematically verified logic. Within the boundaries of the tomato garden, the AI will learn to grow the perfect fruit at 1/100,000th of the cost and 1,000x the speed of a human gardener. It is solving the problems we mapped out.

2. Hunting for the Jackfruit Seeds (Discovery)

But what happens if we need to grow jackfruit? What if humanity encounters a completely new paradigm?

  • A brand new, undocumented programming language.
  • An unexpected biological anomaly.
  • A new dimension of physical reality.

The AI cannot bootstrap its way to a jackfruit from its tomato seeds. No amount of self-play or parameter optimization will help it write code in a language whose compiler parser rules it has never seen.

To grow jackfruit, we must go back to the wild. A human has to step into the fresh, unmapped reality, struggle with the compiler, observe the anomalies, coin the new vocabulary, and bring the first “jackfruit seed” back to the market. Only then can the librarian catalog it; only then can the garden cultivate it.

Reality is the Teacher, and “Life” is the Motivation

Why can’t the AI just go fetch the seeds itself? Even if we give AI robotic bodies, high-resolution cameras, and direct access to physics simulators, a vital element is missing: Life.

An AI operates on data, but humans operate on experience. A scientist runs an experiment because they are driven by curiosity or the desire to cure a disease. An engineer spends six agonizing hours tracking down a race condition because they care about their system’s reliability (and their own sleep). A doctor notices a weird symptom because they care about saving a life.

Without “life”—mortality, pain, desire, and care—there is no such thing as a “problem.” To a machine, a universe where cancer is cured and a universe where cancer runs rampant are simply different arrangements of atoms. Both obey the laws of physics. The machine has no intrinsic reason to declare one state better than the other.

We coin the words, define the languages, and identify the problems because we are the ones who care. The AI can calculate the gravitational acceleration of a falling apple with perfect accuracy, but it was Newton who felt the curiosity about why it fell.

The New Role of the Human Creator

For the past few years, the dominant question has been: “Can AI replace the programmers, the writers, and the scientists?”

If we look through the lens of the library and the seeds, the answer changes.

AI will commoditize the mechanics of solving problems. If a problem can be solved by brute-forcing calculations or writing code within known frameworks, the AI will do it faster and cheaper than any human ever could.

But this shift makes human curiosity more valuable, not less.

The bottleneck of progress will no longer be the speed at which we can write the code or compile the data. The bottleneck will be the seeds.

  • Who will explore the unmapped boundaries of physics?
  • Who will coin the new concepts that allow us to think about new problems?
  • Who will decide what is actually worth building in a world of infinite, free software?

The smarter the AI becomes, the more we need human beings to live in the real world, stumble over undocumented failures, and write down the answers.

No matter how intelligent the librarian becomes, it cannot catalog books that were never written. And no matter how efficient the garden becomes, it cannot grow fruits for which we have never brought the seed.