L∞ps All the Way Down

There’s an old story about a scientist giving a public lecture on astronomy. Afterward, an elderly woman approaches and says the Earth sits on the back of a giant turtle. “And what is the turtle standing on?” the scientist asks. “It’s turtles all the way down,” she replies.

Working with AI is l∞ps all the way down.

Not one l∞p. Not a single feedback cycle you optimize and master. L∞ps nested inside l∞ps, each operating at a different timescale, each with its own rhythm and its own lessons. And the people who get the most from AI aren’t necessarily the ones running the tightest inner l∞p. They’re the ones who can see all the l∞ps at once.

Start at the smallest scale. The prompt l∞p is the one most people think about: you write something, the model responds, you adjust, you go again. This l∞p operates in seconds. It’s tactical. You’re steering toward a specific output—a function, a paragraph, a design decision. The skill here is speed and precision. Can you read a response quickly enough to know what to change? Can you articulate the delta between what you got and what you wanted?

But zoom out. The session l∞p is the arc of an entire working session—maybe an hour, maybe a whole afternoon. Within a session, you’re running dozens of prompt l∞ps, but the session itself has a shape. You start with a goal. You explore. You hit walls. You find unexpected paths. By the end, your understanding of the problem has shifted in ways you couldn’t have predicted at the start.

The session l∞p is where strategy lives. It’s where you decide whether to keep pushing on approach A or pivot to approach B. It’s where you notice that the model keeps misunderstanding the same thing and realize the problem isn’t the prompt— it’s your mental model. The session l∞p is slower and harder to optimize, because the feedback is ambiguous. A prompt l∞p gives you a clear signal: the output is right or wrong. A session l∞p gives you a feeling: this is going somewhere, or this isn’t.

Zoom out again. The project l∞p spans days or weeks. You ship something. You use it. You watch others use it. You learn what you got wrong, what you got right, and what you didn’t even think to consider. Then you take that knowledge back to the next session and start again. The project l∞p is where craft accumulates. It’s the difference between someone who’s been using AI for a year and someone who’s been using it for a week twelve times.

Each l∞p layer teaches different things. The prompt l∞p teaches you about language—how to say what you mean, how the model interprets ambiguity, what kinds of instructions produce what kinds of outputs. The session l∞p teaches you about architecture—how to decompose problems, when to break a task into pieces versus when to let the model handle it holistically. The project l∞p teaches you about judgment—what’s worth building, what actually works in practice, and where AI helps versus where it hurts.

And then there’s the outermost l∞p, the one most people never notice: the identity l∞p. Over months and years, your relationship with AI reshapes how you think about your own work. Your sense of what’s possible expands. Your definition of “my skill” shifts. You stop thinking of yourself as a person who uses a tool and start thinking of yourself as a person whose capabilities include the tool. The boundary between your thinking and the AI’s output gets blurry—not because you can’t tell the difference, but because the feedback l∞p between the two has become so tight that the distinction stops mattering.

The danger at every level is the same: mistaking the l∞p you’re in. Trying to solve a session-level problem with prompt-level tactics. Trying to fix a project-level failure by tweaking individual outputs. Running the inner l∞p faster and faster when what you actually need is to step back and run the outer l∞p once.

I see this constantly. Someone spends two hours refining a prompt to get a piece of code exactly right, when the real issue is that they’re building the wrong thing. The prompt l∞p is tight. The session is productive. But the project l∞p has a bug, and no amount of inner-l∞p optimization will fix it.

The inverse is equally common. Someone abandons an approach after one failed attempt because “the AI can’t do this,” when really they just needed three more iterations. They ran the project l∞p too early. They made an outer-l∞p decision based on inner-l∞p data.

The meta-skill—the one that sits above all the l∞ps—is knowing which l∞p to operate on right now. Sometimes you need to go deeper: tighten the prompt, be more specific, iterate faster. Sometimes you need to go wider: step back, reconsider the goal, throw away a day’s work and start fresh with new understanding.

It’s l∞ps all the way down. But the turtles at the bottom aren’t all the same size. And the one you’re standing on matters.