Right now, I outsource about 80% of my execution to AI agents. Prototypes, code, copywriting—it’s all handed off. My primary job has shifted to “planning and judgment.”
It sounds like a dream setup. Until you realize what’s actually happening: You give AI a prompt. It spits out a complete, beautiful result. You don’t like it, so you say “tweak this.” It spits out another. You’re optimizing, but optimizing what?
You’re polishing a turd.
Adding more features to a broken foundation doesn’t fix the foundation. It just makes the turd shinier.
When you delegate 80% of execution to AI, its incredible speed becomes a liability. AI builds the mansion in seconds. But if the foundation is sand, you’re just decorating a trap. Faster execution paradoxically demands slower, deeper upfront thinking.
Let’s picture a scenario. You tell AI to build a booking tool. You say: “I want a calendar, ability to add, modify, and cancel appointments, clean UI.”
AI delivers. It has the calendar, the buttons, and it even threw in stats, customer tags, and a membership portal. Now you’re debating button colors, adding filters, and making it mobile-friendly.
But you forgot to ask the real questions. Does the client book themselves, or does the receptionist do it? Does an appointment occupy just the staff member, or the staff member AND a room? What happens if two people grab the same slot?
If you haven’t defined the problem before writing a single line of code, you aren’t managing a project—you’re just acting as a button-pusher for a very fast intern.
The real unlock isn’t prompting for better outputs. It’s using AI to explicitly separate known facts from unverified assumptions before any work begins.
You can’t know every answer upfront, but you can bucket your information: confirmed facts, assumptions to verify, and active scope decisions. If you write all three as a confident requirements doc, AI will treat your guesses as gospel.
Stop asking for pages. Start asking for alignment. Tell your AI: “Based on existing materials, reconstruct the user’s task flow. Separate confirmed facts, your inferences, and missing information. Give me two first-version plans. Do not generate the full page yet.”
Then, attach acceptance criteria to every single task. Don’t say “make the booking feature work.” Say: “Complete front-desk booking and cancellation using confirmed rules. Deliver a working version and actually test normal booking, double-booking, and slot release. Explain how to test each and list unverified items.”
If AI doesn’t know what to deliver, you have nothing to check. A task without acceptance criteria isn’t a task—it’s a wish.
When the AI says “Done,” tear that word apart. The page loading is one thing. The rules working is another. Whether a human can actually use it without wanting to throw their computer out the window? That’s the only metric that matters.
Don’t let AI evaluate its own work. That’s just an algorithm telling another algorithm it did a good job. Real validation requires putting it in front of humans in the real world.
When 80% of execution is outsourced, the 20% left to you is non-negotiable. You must know why you’re building it, what proves it’s done, and when to pull the plug on a broken direction.
Before you type “optimize this” to your AI, ask yourself: Am I fixing a specific problem, or am I dodging a decision that needs to be made from scratch?
If it’s the latter, rethink it. The time you spent is already gone. Don’t waste the next hour polishing a turd.
FAQ
Q: Isn't AI supposed to figure out the details so I don't have to?
A: No. AI accelerates execution, it doesn't replace strategy. If you don't define the goals, rules, and edge cases, AI will confidently build the wrong thing at lightspeed.
Q: How do I actually stop AI from guessing my project requirements?
A: Force it to separate facts from assumptions. Prompt it to map the user flow, explicitly list what is confirmed, what it is inferring, and what information is missing before it generates any actual product.
Q: What should I do when the AI says it's 'done' but the output feels wrong?
A: Stop saying 'optimize this.' Instead, write specific feedback: what happened, what was supposed to happen, and how to verify the fix. If the core premise is broken, scrap it and rethink. Don't polish a turd.