AI didn’t kill coding. It killed the reasons teams used to explain why nothing ships.
Output is cheap now. Accountability is not.
The thesis
AI didn’t replace developers. It replaced excuses.
When producing code becomes trivial, the bottleneck moves to judgment, ownership
and the discipline to say no.
What actually changed
Hard truth: code got cheaper. Thinking didn’t.
AI is good at generating plausible code. It is bad at choosing trade offs.
It does not understand your product, your users, your risks or the cost of being wrong.
That responsibility did not disappear. It became more visible.
- Speed: scaffolding, prototypes and refactors appear instantly.
- Surface area: more code written by more people at higher velocity.
- Risk: bugs that look correct and decisions that drift quietly.
Teams with strong habits move faster. Teams without them collapse sooner.
AI does not create problems. It removes the delay before you see them.
Good teams got better
In disciplined teams, AI is a multiplier. Not because it writes code
but because it removes friction from work that was already well understood.
AI does not make developers better. It makes systems unavoidable.
Strong teams already run on systems. Weak teams rely on hope.
Bad teams got louder
In chaotic teams, AI accelerates confusion. More code lands.
Fewer people understand what it does or why it exists.
Velocity looks high until it snaps.
- It compiles becomes the bar and the bar keeps falling.
- PRs get bigger because generating code is easy and reviewing it is not.
- Architecture drifts because nobody is steering.
- Confidence collapses because failures feel random and ownership is unclear.
If a team cannot explain what the code does, why it exists and how it fails,
then speed is an illusion. AI simply scales that illusion.
AI is a mirror
If your team lacks product and engineering discipline, AI will not fix it.
It will expose it faster.
AI produces output. It does not produce shared understanding.
Leaders now face a simple choice.
Build habits that turn output into quality or operate a factory that produces noise.
A practical checklist for CTOs and PMs
If AI is making things worse in your org, the fix is rarely the model.
Start with smaller PRs, stricter reviews, explicit ownership,
better specs and quality gates that do not negotiate.
FAQ
Should we ban AI in the codebase?
No. Bans fail. Treat AI like any powerful tool.
Define standards, require tests and train reviewers to detect plausible nonsense.
What is the fastest win?
Smaller PRs. If reviews become cheaper, AI becomes safer and actually useful.
What should CTOs measure?
Lead time, escaped defects, rollback rate, PR size distribution
and how long it takes new engineers to understand the system.
