Ideas · Essay
“Almost right” is the most expensive answer an AI can give
A wrong answer gets thrown away. An almost-right one gets merged, debugged and paid for twice. The real cost of AI-assisted development lives in that gap.
By Nythrex team2 min read
Ask developers what annoys them most about AI tools and you rarely hear “it doesn’t work”. You hear “it nearly works”. The function compiles, the test passes, the explanation sounds confident — and something small and important is off.
That is not a minor irritation. It is the economic centre of AI-assisted development. A tool that is obviously wrong costs you a minute. A tool that is almost right costs you whatever it takes to find out — and if nobody finds out, it costs you an incident.
The case for “good enough”
The defence is reasonable. A draft that is 90% right is a head start; a senior engineer fixes the rest in minutes; across a team, the minutes add up to days. Developers keep choosing these tools for exactly that reason: in Stack Overflow’s 2026 survey, coding assistants and agents were the most common use of AI, and 73% of the people who use them do so every day.
Where it breaks
The same developers say the head start has a price. In Stack Overflow’s 2025 survey, 66% named “AI solutions that are almost right, but not quite” as a frustration — the most common one — and 45.2% said debugging AI-generated code takes more time. More respondents distrusted the accuracy of AI output (45.7%) than trusted it (32.7%). Only 3.1% trusted it highly.
A wrong answer gets thrown away. An almost-right one gets merged.
The 2026 survey shows how developers have adapted: 48% say they trust AI when they can easily validate its answers. That is a sensible rule, and an expensive one. Validation is work. It takes the time of people who know the system well enough to spot the near-miss — the same people who review, mentor and handle incidents.
Budget for checking, not just writing
If verification is where the cost lives, treat it as a cost.
- Count the work after the first draft: review rounds, follow-up fixes and reverts on AI-assisted changes.
- Point AI at work where checking is cheap — familiar code, strong tests — and say so when it is not.
- Compare cost per shipped change, not per generated line. Near-misses show up there, not in token counts.
Where Nythrex fits
Nythrex shows the cost of each change from AI session to commit, pull request, CI and deploy, by project and team, and every figure shows its basis. Extra commits and CI runs on the same pull request stay attached to that change, so the price of “almost right” becomes part of the number rather than a feeling. It does not judge whether code is correct, and it does not rate the people checking it.
Sources
Frequently asked questions
Do developers trust AI-generated code?
Mostly not without checking. In Stack Overflow’s 2025 survey 45.7% distrusted the accuracy of AI output and 32.7% trusted it; in 2026, 48% said they trust AI when they can easily validate its answers.
What is the biggest frustration with AI coding tools?
Answers that are almost right but not quite: 66% of respondents in Stack Overflow’s 2025 survey named it, and 45.2% said debugging AI-generated code takes more time.
Read next
- Our CFO asked whether AI coding tools pay for themselves. What do I actually show them?“The team feels faster” does not survive a budget review. The best-known controlled study found developers felt faster while they were slower. Here is the evidence a CFO will accept: the cost of shipped work, by project, before and after.Read
- Measure AI, not the people using itLeaders need to show what AI is worth, and the easiest metric is usage per person. Developers fear it, it pushes costs up, and Meta has already dropped it from reviews. The better unit is the change, not the engineer.Read
- How Nythrex works: from AI session to productionHow Nythrex links the task, AI session, commit, pull request, CI run, deployment and cloud into one unit of work with its own cost and the basis for every figure.Read
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