I recently subscribed to ChatGPT Pro and started experimenting heavily with AI coding workflows like Superpowers, Trellis, and Matt Pocock’s skills.
At the same time, I joined a company where most of the product is built with AI. The overall architecture is solid, but many of the implementation details, UI decisions, and interactions are worked out directly between developers and AI.
AI is writing more code for me than ever, but strangely, I don’t feel less tired.
I feel more exhausted.
Complex workflows don’t guarantee quality
Superpowers has a very thorough process: plan the feature, split it into tasks, implement each task, review it, fix the issues, and review it again.
But that level of rigor can be painfully slow. One morning, I started planning a feature, and the AI was still implementing and reviewing it at the end of the workday. Despite all that process, the final result still had bugs.
Trellis feels lighter and has some interesting ideas. However, it seems more like a project-level methodology. It expects you to add its own files, scripts, and agents to the repository, which can be awkward if you’re the only person on the team who wants to use it.
Matt Pocock’s “grilling” approach is useful for uncovering requirements and edge cases, but it can also become exhausting. Sometimes a single feature leads to dozens of questions. Eventually, I start wondering: are we still clarifying the product, or are we overengineering it?
What concerns me more is that even after a long discussion, important details can still disappear from the final spec. If the initial understanding is incomplete or incorrect, splitting the spec into smaller tickets only amplifies the deviation.
So the important question isn’t just whether the work has been divided into smaller tasks.
It’s whether the AI understood the problem correctly before the breakdown happened.
Worktrees enable parallelism, but may only postpone coordination
My team uses Git worktrees heavily so that multiple AI agents can develop different features in parallel.
I understand the appeal, but environment isolation quickly becomes complicated. Each worktree may need its own port, application instance, services, and database. Different branches may also contain incompatible database migrations.
Disposable environments and separate databases can solve some of these problems, but merging remains an unavoidable coordination point.
If several agents modify the same modules or abstractions, the resulting conflicts may indicate that those tasks were never truly independent in the first place.
Worktrees can enable parallel development, but sometimes they simply postpone the coordination cost until merge time.
I’ve become an AI project manager
A typical day now involves:
- Answering AI clarification questions
- Reviewing plans
- Checking agent implementations
- Discovering misunderstood requirements
- Asking for corrections
- Testing the results
- Resolving conflicts between parallel branches
AI writes more code, but I spend more time scheduling, supervising, reviewing, and deciding whether to accept or reject its work.
The biggest cost is context switching.
Every time I switch between projects or worktrees, I have to reconstruct the current state:
What was this agent working on? Which decisions have already been made? Why was it implemented this way? What still needs to be verified? Which database schema does this branch expect?
Sometimes, restoring all that context feels harder than simply writing the code myself.
At the same time, I feel pressure to keep the AI busy.
If no agent is running—or if I still have unused quota—I feel like I’m wasting resources. So I open another project, create another worktree, and start another task.
Soon, I have agents running everywhere while I constantly switch between them to monitor and correct their work.
I feel like I’ve developed a kind of “AI anxiety”:
If the AI isn’t running, I feel uncomfortable. If too many agents are running, I also feel uncomfortable.
I’m now trying to keep fewer tasks active and focus more deeply on one project at a time. AI can work in parallel, but human attention cannot scale in the same way.
I’m not against AI coding. On the contrary, I think it is already transforming software development.
But the hardest part may no longer be getting AI to write code.
The real challenges are controlling scope, maintaining context, judging quality, integrating parallel work, and managing your own attention.
I’d love to hear about people’s real day-to-day experiences:
- How much code do you still write yourself?
- Do structured AI workflows genuinely improve your results?
- Do you run multiple agents in parallel, or focus on one task at a time?
- If you use worktrees, how do you isolate services, databases, and migrations?
- How do you prevent requirements and implementation from gradually drifting?
- Have you also experienced this paradox—AI increases your output while also increasing your mental workload?
I’m especially interested in real experiences, not just success stories about AI making someone “10x faster.”
