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Hello, fellow humans! Following up on yesterday’s piece on the dangers of AI speed, today, we’re talking about how to make speed and the delete key your ally.

"In writing, you must kill all your darlings." — Sir Arthur Quiller-Couch (popularized by William Faulkner)

We've entered a strange new world. For the first time in software history, AI will build almost anything you ask for without pushback. No engineering manager saying "that's technically impossible." No architect warning about scalability issues. No sprint planning meeting forcing you to defend your priorities.

Just you, Claude, and infinite possibility.

Yesterday, I wrote about the speed trap—how AI's frictionless building removes the economic barriers that used to force strategic thinking. Today, I want to address the harder question: now that we can build anything, what should we build?

It's not about building better. It's about learning to let things die.

Because when you throw away code, you're not wasting time. You're collecting something AI cannot give you: wisdom.

Here's the mental shift that changes everything: Don't use AI to build products faster. Use it to kill bad ideas faster.

The old math: Killing an idea after three months of development was catastrophic.

The new math: When you can build a functional prototype in two days, throwing it away doesn't hurt—it teaches.

The disposable prototype isn't a compromise; it's the goal. You can now test ten ideas in the time it used to take to build one. Nine will fail. That's not a bug; that's the entire point. Each failure eliminates a wrong path and narrows your search space for the right one.

The critical question shift:

Here's what validation at the speed of thought actually looks like.

Remember the painted door test from yesterday? AI makes it radically more powerful.

How it works:

The payoff: You've eliminated nineteen wrong directions before writing a single line of production code. In the old world, this would have required a design team, a front-end developer, and a week of work per variation. Now it's a Tuesday afternoon project.

Make the front end look finished, but power it with humans behind the curtain.

The setup:

What you're actually testing: Not whether AI can solve the problem, but whether solving the problem matters. If users don't get value from perfect output delivered by humans, automation won't save you.

The strategy: Run this with your first ten customers. If they love it, you've validated demand before building the hard parts. If they're lukewarm, you've saved yourself six months of engineering on a product nobody wants.

Process real user data in the background without showing them results.

Example scenario: You think users need an AI-powered "smart summary" of their activity.

The validation process:

Why it works: You're testing the value of the output before investing in the interface. If the summaries are obvious or useless, you've learned that in a day instead of three months after launch.

Here's the discipline that separates winners from losers in the AI era: Differentiate between "Build to Learn" and "Build to Keep."

These are not the same thing.

Your goal: Learning, not clean code.

What this looks like:

The mindset: Expect to throw it away. That's not failure; that's the process working correctly.

The trap to avoid: I've watched founders agonize over architecture decisions in validation prototypes. They're optimizing the wrong variable. In this phase, the only metric that matters is "How fast can I learn whether this is worth building?"

When you enter this phase: Once you've validated demand, throw away the prototype and rebuild it properly.

Why this matters: This is the step most teams skip, and it kills them. They ship the validation prototype as the production product. Six months later, they're drowning in technical debt, struggling to add features, unable to scale.

What you need now:

The golden rule: The discipline is knowing which phase you're in. Validation code is disposable. Production code is permanent. Treat them accordingly.

Yoda was right: "Fear of loss leads to the dark side."

The hardest part of the AI era isn't building. It's deleting. We've built something that works. It took two days, but still—it's ours. We're attached. But being able to step back and accurately assess whether customers will appreciate and value it is legitimately difficult and valuable.

This is where discipline separates strategic leaders from builders who got fast tools.

When you delete validation code, you're not losing progress. You're crystallizing learning. Each abandoned prototype teaches you:

The asset AI cannot give you: Time spent building to learn is time invested in your own wisdom. AI can write code, but it cannot tell you what's worth building. That requires judgment, and judgment comes from accumulated experience with failure.

The companies that win will:

Your new mantra: The best code is the code you chose not to write.

AI has given us the ability to build anything. That doesn't mean we should build everything. It means we can now afford to be wrong quickly, repeatedly, and cheaply.

The new discipline:

Why it matters: The wisdom you accumulate from dead projects is worth more than the code from living ones.

In an age where anyone can build anything, knowing what to kill becomes your most valuable skill. Master that, and speed becomes your ally instead of your trap.

Your next step: Next time you finish a prototype, ask yourself: "Did this teach me something worth knowing?" If yes, you've won—even if you delete every line of code.

It might not feel like it, but it’s not wasteful; it’s gaining wisdom. And wisdom is something that AI will never be able to replicate.