Why “vibing” with AI can lead to post-dopamine frustration, and what to do about it.
We’ve all been there. You fire up an AI assistant, type a sprawling ask, and watch it generate… something. It looks impressive. It sounds confident. But twenty minutes later, you’re staring at output you can’t use, unsure where things went sideways.
Here’s the uncomfortable truth: AI doesn’t have a “figure it out” mode. Treating it as it does is the fastest route to frustration.
The Three Faces of AI
Think of AI as a colleague who can show up in three different roles:
🧭 The Guide — When you’re exploring, not solving. You don’t need answers yet; you need better questions. AI helps you map the territory, surface possibilities, and sharpen your thinking.
🤝 The Peer — When you’re co-piloting. You know the direction, but you want a thought partner with bounded autonomy. AI handles specific pieces while you stay in the driver’s seat.
⚡ The Doer — When the problem is solved in principle, and you just need execution. Clear inputs, predictable outputs, minimal supervision required.
The magic happens when you pick the right mode. The frustration happens when you don’t.
The Problem with Undefined Problems
Here’s what we often forget: a prompt is just a problem wearing casual clothes.
And just like in traditional software development, undefined problems produce undefined results. We wouldn’t dream of building a complex system without decomposing it into sub-systems, components, and clear interfaces. Yet somehow, we expect AI to handle a rambling paragraph and return production-ready gold.
It doesn’t work that way.
AI excels at well-classified problems. Give it one clear problem class to solve, and it can work with surprising autonomy. Hand it a fuzzy mega-problem, and you’ve just delegated confusion. Now you can’t even evaluate whether the output is good. You never defined what “good” looks like.
The Dopamine Trap
Let’s talk about the elephant in the room: AI is fast, and speed is addictive.
That near-instant response creates a dopamine hit that sends us sprinting in twelve directions at once. We want to do more. We agree with what AI says (even when we shouldn’t). We make AI agree with what we say (it’s happy to oblige — sycophancy is baked in).
Before we know it, we’re deep in a conversation that feels productive but leads nowhere measurable.
Sound familiar?
The Product Mindset Fix
The antidote is surprisingly old-school: think like a product manager before you think like a prompter.
Before typing anything, ask yourself:
- What problem am I actually solving?
- Can I break this into sub-problems I understand well enough to evaluate?
- What class of problem is this? Are there known solution patterns?
- What are the trade-offs between approaches?
- How will I know if the output is good?
This is prompt engineering at its core. It is not clever phrasing or magic templates. It is the disciplined work of problem definition.
Agile vs. Waterfall (Yes, Even Here)
Here’s a useful mental model:
Waterfall mode: You know exactly what you want. The end-state is clear. Let AI run autonomously — it’s just execution.
Agile mode: You know the next milestone, not the final destination. Use AI to reach that interim state, then pause. Validate. Adjust. Repeat.
The key insight? Predictability improves when upstream risk is eliminated. Clear up assumptions before you hand off to AI, and the outputs become dramatically more useful.
If all the ambiguity lives in your prompt, all the ambiguity will live in your output.
The Bottom Line
AI isn’t magic. It’s a powerful tool that responds to how well you’ve thought through your problem.
| When you’re… | AI should be… | Your job is to… |
|---|---|---|
| Exploring possibilities | A Guide | Ask better questions |
| Building with oversight | A Peer | Define boundaries |
| Executing known patterns | A Doer | Specify clearly, then verify |
Set expectations straight — with yourself and with AI — and outcomes become remarkably more predictable.
Skip that step, and you’re just vibing. Which feels great until it doesn’t.
The same principles that make software projects succeed—clear requirements, sound architecture, iterative validation— also make AI collaboration succeed. There are no shortcuts. Just faster ways to do the right things.
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