The Leverage Mindset: Why Most People Use AI Backwards

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Most people open a chat window and ask the model to do a task. Write the email. Summarize the doc. Fix the function. It works, and it feels like progress — which is exactly the trap.

Doing a task faster is the smallest possible win AI offers. The operators — the people who actually compound — start one level up. Before automating anything, they ask a colder question: where is the constraint, and can I remove it entirely instead of speeding it up?

Eliminate before you automate

There's an order of operations that almost nobody follows:

1. Eliminate — does this work need to exist at all?
2. Automate — if it must exist, can a machine do the boring 80%?
3. Delegate — if a human is still needed, can it be a cheaper, scoped one?

AI makes step 2 intoxicating, so people skip step 1. They build a beautiful pipeline to generate reports nobody reads. The leverage was never in faster reports — it was in killing the report.

The function breakdown

When a task genuinely should be automated, don't hand the whole thing to a model and pray. Break it into functions. Automate the smallest reliable piece. Verify it. Then chain the next piece onto proven ground.

Task:   "Turn support tickets into a weekly insights digest"
Bad:    one giant prompt → hope
Good:   classify → cluster → summarize-per-cluster → rank → format
        (each step testable in isolation)

Boring is beautiful here. A workflow you can inspect beats an agent you have to trust.

The curiosity rule

Never ship output you don't understand. If the model produced something and you can't explain why it's correct, you haven't saved time — you've borrowed it at interest. The leverage compounds only when you stay in the loop enough to steer.

That's the whole mindset shift: stop asking AI to do your work, start asking it to delete your work. The rest is technique.