AI makes code feel cheap. Repair does not.
That is the part a lot of eng teams miss when they start adding agents into the workflow. The model can write fast, the IDE can autocomplete fast, the PR count can go up fast, but the system still has to find bad work, explain it, fix it, test it, and trust it again.
That is where the cost shows up.
The real question is not, can AI write code. It can. The better CTO/CIO question is, when does fixing AI code cost more than writing the work right the first time.
TeamStation wrote about that here:
https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it
The cheap part is not the whole system
Code output is only one step in the chain.
A useful eng system has a few other steps too:
- clear acceptance rules
- review depth
- test behavior
- architecture context
- ownership
- delivery telemetry
- rollback logic
If those parts are weak, agent speed does not lower cost. It moves cost downstream.
One loose prompt becomes three loose files. Then review gets noisy. QA finds a symptom. A senior eng has to rebuild the idea from scratch. The team says AI moved fast, but the system paid for the speed twice.
That is not an AI problem by itself. It is a reliability threshold problem.
Reliability threshold matters more than raw speed
In simple terms, a reliability threshold is the point where work is good enough to move forward without creating more cost than value.
If the threshold is clear, AI can help. The team knows what good looks like. Reviewers know what to check. Tests know what behavior matters. Telemetry shows where the work is drifting.
If the threshold is soft, AI creates fog. The code looks complete before it is actually safe. The team starts accepting output because it is fast, not because it is right.
That is how repair cost stacks up.
You do not just fix the code. You fix the misunderstanding behind the code. You fix the test gap. You fix the review miss. You fix the trust gap. Then you fix the planning model that allowed weak work to look done.
Telemetry is the control layer
This is why engineering telemetry matters in AI engineering.
A team needs signals that show:
- where AI-generated work gets rejected
- where review cycles expand
- where tests miss expected behavior
- where senior engineers keep rescuing the same class of issue
- where delivery speed turns into rework
Without those signals, leaders only see activity. They see commits, PRs, tickets, and demos. They do not see the hidden repair loop.
That hidden repair loop is where money goes.
For distributed eng teams, this gets louder. Work moves across time zones, async review, and handoffs. If the control layer is soft, every handoff adds cost. This is why TeamStation treats LATAM/distributed engineering as an operating system problem, not a staffing problem.
The point is not to slow AI down. The point is to put reliability, telemetry, and acceptance rules in front of scale.
Why this matters for CTOs and CIOs
AI engineering will make weak systems show their weakness faster.
That is good if the org can see the signal. It is bad if the org only sees speed.
The useful move is to ask better questions:
- Where does AI output fail review?
- Which work types create the most repair cost?
- Which teams can isolate bad output early?
- Which acceptance rules are still too vague?
- Which delivery signals prove the work is trusted?
Those questions matter bc they turn AI from a writing tool into a governed engineering workflow.
This TeamStation article explains the reliability threshold behind that. Read it if you are trying to understand when AI code stops being cheap and starts becoming a repair-cost machine.
https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it
Source article:
https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it
