There is a scene that is becoming more and more common in software development right now.
You have one AI coding agent refactoring something. Another one is writing tests. A third is handling some annoying migration you did not want to touch yourself. You are bouncing between terminals, checking progress, answering questions, approving changes, and generally supervising a tiny army of extremely enthusiastic robot developers.
The problem is not that running multiple agents is automatically bad. Parallel work can be incredibly useful. The problem is that the second AI gives us more capacity, our first instinct is often to immediately fill that capacity with even more AI work.
Instead of asking, “What should I do with the time this just gave me?” we ask, “How much more stuff can I cram into this workflow?”
AI coding tools can make development dramatically faster for boilerplate, scaffolding, tests, utility functions, repetitive components, documentation, and migrations. So naturally, developers start doing more. Suddenly the amount of work you can start feels almost unlimited. The amount of work you can actually understand, review, and maintain is another story.
Speed Creates Its Own Problems
If you generate code five times faster, you can also lose track of what is happening five times faster. One of the strangest signs is reading an AI-generated summary of a change just to remember what your own project is doing. At that point, you are no longer really engineering the system—you are managing a queue of things being engineered around you.
Imagine you ask an agent to implement a permission system. The code looks clean, naming matches, tests pass—but your prompt never clearly defined how restrictive permissions should be. The model filled in missing details with something more permissive than you intended. Tests verify that permissions work, not that they match the security model in your head.
Passing tests does not automatically mean the software is doing what you actually intended.
AI Did Not Remove the Bottleneck — It Moved It
For a long time, one of the slowest parts of programming was turning an idea into working code. AI can blast through a lot of that. The new bottleneck is understanding and verification. Heavy AI adoption correlates with longer PR review times. Experienced developers in large codebases can feel faster while typing less, yet take longer overall because generated code must be carefully inspected against years of architecture.
We are delegating faster than we are learning how to verify. That creates verification debt: time saved during generation shows up later in review, debugging, or production incidents.
Why Do We Keep Opening More Agents?
Part technical, part human: there is a dopamine hit from watching agents crank through tasks. Visible output feels productive; reading architecture docs produces zero commits even if it saves ten hours later. Career anxiety around AI adds pressure to become the person running more agents—ironically risking the skills we will need most.
So What Should We Do With the Time AI Gives Us?
If AI saves you two hours, you do not necessarily need to spend them generating another two hours of code. Use some of that time for:
- Actually read the documentation — mental models improve prompts
- Write better specs before you generate — define what it should not do, security boundaries, edge cases
- Talk to humans — unclear requirements stay unclear no matter how many implementations AI produces
- Keep useful technical documentation — architecture decisions and tradeoffs help humans and agents
A Few Rules
- If you cannot explain the feature, do not prompt it yet
- Do not merge code you cannot explain
- Be extremely suspicious around high-risk code (auth, payments, DB mutations, encryption)
- Stop treating maximum parallelism as the goal
- Count review time as part of the task
- Ask the AI why — assumptions, alternatives, least confident parts
- Still write code yourself sometimes
Maybe You Do Not Need Another Terminal
When reducing parallel AI sessions, more time went into defining work before generating, reading code afterward, documenting decisions, and catching questionable assumptions. AI gives absurd leverage—but leverage works both ways. It can help you build faster, or help you create an enormous pile of code you barely understand at record speed.
Source: dev.to/sizzlebop/...