Four days. A full professional application that would have taken two to three months of traditional development. Four days of co-creating with AI, and it was done.

The next project, I’ve been spacing out the phases. Every build before this one was a straight run from start to finish. Now I’m putting deliberate gaps between sessions because I know what’s coming.

The productivity gains from AI co-creation are real. But there’s a cost that nobody puts in the sprint summary. The process is cognitively brutal, and the aftereffect is something close to dread to go back into that mode of working.

A kind of builder’s block, where you know the output will be good but you also know what it takes out of you to get there.

How the process actually works

The way I build with AI isn’t casual prompting. It’s structured, phased, and deliberate.

• Start with a high-level plan: A couple of paragraphs describing what needs to exist and why, plus the specifics: language, version, modules, frameworks.
  
• Don’t build yet. Ask questions first: Get the AI to interrogate the design before writing any code. Walk through each component, how it should behave, how it should look, what the edge cases are. For a complex application, this conversation alone can take hours.
  
• Write out an implementation plan in phases: Phase 0 is setup. Phases 1 onwards work like sprints or MVPs, each one producing something testable.
  
• Test each phase as it lands: Check what was built, understand it, confirm the direction is right before moving on. This isn’t vibe coding.
  
• Generate documentation last: README, project config, licence, technical docs. All generated, all needing to be read and adjusted.

Every single step in that process is judgment work.

Where the fatigue lives

The AI handles the production. What it can’t do is handle the evaluation.

Each phase of the build requires reading through generated code, checking logic, catching subtle misunderstandings of intent, and deciding whether each section is right or just close enough.

That review is continuous high-stakes judgment, and it compounds across a session.

Reviewing something almost-right is harder than reviewing something clearly wrong. When the output is obviously broken, you reject it and move on. When it’s 90% correct, your brain has to engage deeply with every detail to find the 10% that needs fixing.

In human-computer interaction research, this is called the “seductive automation effect.” Plausible output demands more cognitive effort to evaluate than building it yourself would have.

The rate is relentless. You’re making dozens of micro-judgments per minute. Does this match my intent? Is this the right approach? Should I push back or accept this and move on?

Solo work lets you build incrementally and own the mental model as it forms. Co-creation compresses all of that judgment into a fraction of the time.

The research caught up

A Harvard and Boston Consulting Group study published in March 2026 surveyed over 1,400 workers and put numbers on this. About 14% reported “mental fog” after intensive AI sessions, describing difficulty concentrating, slower decision-making, and headaches.

The researchers called it “AI brain fry.”

The oversight findings were striking. Workers who spent their time monitoring and reviewing AI output reported 14% more mental effort, 12% greater mental fatigue, and 19% greater information overload compared to those in other roles.

Decision fatigue increased by 33%.

A study in Nature’s Scientific Reports found that AI collaboration improved immediate task performance, but the gains didn’t persist when people worked independently afterward.

The collaboration borrows against cognitive reserves rather than building them up.

The structural problem

When you build something yourself, you enter flow. Hours pass and you come out tired but satisfied.

Flow is restorative in a way that supervisory work isn’t.

Co-creating with AI puts you in a fundamentally different cognitive mode. You’re a reviewer, a director, a quality gate. That’s closer to project management than creative work, and it doesn’t produce the same mental payoff. You can sustain it for hours, but you come out depleted rather than accomplished.

Anthropic’s own research flagged the tension at the centre of this. AI delivers the biggest productivity gains on complex work, which is exactly the work that requires the most careful human oversight.

The harder the problem, the bigger the speed boost, but also the bigger the cognitive tax on the person reviewing the output.

The tools are optimised for output velocity, not for human sustainability.

The aftereffect

The part that surprised me was what happens between sessions.

Every project before this, I worked in a straight run. Start to finish, sustained intensity, done. The current one is different. I’m putting deliberate gaps between phases because I’m aware of how draining each session is going to be.

You have to be in the right frame of mind to go into that mode, and reaching that state takes longer than you’d expect.

Something like writer’s block, except it hits after the writing is done. The well needs time to refill before the next round.

What helps

Willpower won’t fix this. The fatigue is real and the research backs it up. But there are patterns that make it more manageable.

Invest heavily in the upfront design.
The hours spent walking through components and behaviour before any code is written aren’t wasted time. They’re the single biggest factor in reducing review burden later. The more precisely the AI understands your intent going in, the less adjudication you do on the way out.

Phase the build and test incrementally.
Reviewing a full application in one pass is overwhelming. Reviewing a single phase is manageable.

The phased approach isn’t just good engineering practice, it’s a cognitive load management strategy.

Time-box review sessions.
Resist the urge to review inline as the AI generates. Let output accumulate, then switch into review mode deliberately. Mixing creation and evaluation in rapid cycles is where the worst decision fatigue comes from.

Accept “good enough” consciously.
AI output is infinitely tweakable. You can always make it a little better. Set a threshold before you start reviewing, and stop when you hit it.

Alternate between AI sessions and solo work.
Some sessions you direct, some you build alone. The solo work rebuilds the flow state and deep focus that AI collaboration depletes.

Treat them as different kinds of work that need to be balanced across a week.

Where this leaves us

Four days instead of three months is a remarkable compression. But the cognitive cost of those four days was higher per hour than any stretch of traditional development.

The total hours went down. The intensity per hour went up. And the recovery time afterward was real.

I think this will improve as the tools get better at understanding intent and reducing the review burden. Right now though, if you’re working intensively with AI and finding yourself drained, struggling to start the next session, or working longer hours than the productivity gains should require, that’s not a failure of discipline.

It’s the cost of the current model. And it’s worth factoring in.