Every AI coding assistant demo emphasizes the same number: how fast it produced a diff. Ninety seconds for a feature, thirty seconds for a bug fix, a full test suite in under a minute. It is an easy number to show off, and it is also the wrong number to optimize your tooling decision around. The number that actually predicts how much faster your team ships is how long a senior engineer spends reviewing, correcting, and re-explaining context before that diff is safe to merge.
Generation speed is half the equation
A tool that produces a fix in ninety seconds but needs twenty minutes of rework from a reviewer is slower, end to end, than a tool that takes five minutes and needs almost no correction. Generation speed only measures the first half of the pipeline. The second half, human review, is where the actual engineering hours get spent, and it is the half vendor demos almost never show.
Plausible-looking code makes this worse. An assistant can produce a diff that compiles cleanly and reads well on a first pass while still being wrong in a way that only a careful reviewer, applying the same standard they would to a human contributor's code review, will catch. If your reviewers are rubber-stamping AI output because it looks finished, you have not actually saved time, you have shifted risk downstream to whoever discovers the bug later.
What to actually track during evaluation
If you are comparing assistants, track review time as its own metric, not as an afterthought to generation time. For each task, log how long the initial draft took, then separately log how long a reviewer spent getting it merge-ready. Ask reviewers to flag any case where they had to re-explain the same architectural constraint more than once within a session. That repeated re-explanation is a strong signal the assistant is not retaining context well, and it matters far more on a multi-file change than on the small, single-function tasks most public benchmarks use to score tools.
task: fix intermittent timeout in export job
assistant_a: generation 90s, review 22min, 2 re-explanations
assistant_b: generation 6min, review 4min, 0 re-explanations
A table like that, even a rough one built from a handful of real tickets, tells you more about which tool will actually save your team time than any public leaderboard score.
Photo by Adrian Regeci on Unsplash
Review burden compounds across a team
The cost of a heavy review burden is not linear. A single engineer absorbing extra review time on one ticket is an inconvenience. A whole team absorbing it across every AI-assisted pull request becomes a hidden tax on velocity that nobody notices because it is distributed across many small interruptions rather than showing up as one big number. This is the same dynamic that makes unmanaged technical debt so easy to underestimate: each individual instance looks small, and the aggregate effect is what actually slows a team down.
Security review deserves its own line item
Review burden is not just about correctness. Some assistants will reproduce an insecure pattern if it appeared frequently enough in training data, and a reviewer needs to specifically watch for that rather than assuming a clean-looking diff is a safe one. Running at least one task from your evaluation set specifically to see whether the assistant flags an insecure pattern, checked against something like the OWASP Top Ten, tells you whether the tool has any security awareness at all, separate from whether it produces working code.
Why teams miss this in the first place
Most tool evaluations happen under time pressure, which pushes people toward whatever is easiest to measure. Generation speed is trivial to observe: you type a prompt, you watch a timer, you get a number. Review time requires actually tracking a second, less flashy metric across multiple people over multiple tasks, and it's the kind of measurement that gets skipped when a team is trying to make a decision quickly.
The irony is that review time is not hard to track once you decide to do it. It just requires treating it as a first-class part of the evaluation from day one, rather than something you notice informally after the tool is already in production and the standardization decision has already been made.
A simple way to start measuring it this week
You don't need a dashboard to start. Pick the next five AI-assisted pull requests your team merges and ask each reviewer to jot down two numbers: how long the generation step took, and how long they personally spent getting the change merge-ready, including any back-and-forth. A shared spreadsheet is plenty. After five tickets you'll already have a rough sense of whether your current tool's review burden is light or heavy relative to how fast it drafts.
Do this across every candidate tool you're comparing, using the same five or so ticket types each time so the comparison stays apples to apples. The pattern usually shows up faster than people expect. Some tools that look slow on paper turn out to need almost no correction, while some that look fast generate output that quietly eats a reviewer's whole afternoon.
What changes once review time is visible
Once a team starts tracking review burden explicitly, tool conversations shift. Instead of "which assistant is fastest," the question becomes "which assistant actually reduces the total time from ticket to merged pull request," which is a meaningfully different and more useful question. It also changes how teams talk to vendors, since a vendor selling on generation speed alone has a much weaker pitch once your team is asking about the number that actually matters to your velocity.
Build review time into your actual comparison
If your team is choosing between AI coding assistants, do not stop at whichever one demos fastest. Run a handful of real tickets from your own backlog through each candidate, using a shared Git branch as the common starting point, and time both the generation and the review for each. There is a longer breakdown of how to run that comparison, including how to score correctness against your own test suite instead of trusting an assistant's self-reported summary. This AI coding guide from 137Foundry covers the full evaluation process.
The tools that look best in a five-minute demo are not always the ones that make your team faster over a full quarter. Review time is the metric that tells you which is which, and it costs almost nothing to start tracking it on your very next AI-assisted pull request.
A quick reference for what to actually log
If you want to standardize how your team records this, a simple template works better than trying to remember every detail after the fact. Something like the following, filled out per ticket, is enough:
ticket_id, assistant, generation_time, review_time, re_explanations, merged_without_changes (y/n)
Five columns, filled in consistently across every AI-assisted pull request for a couple of weeks, gives you a genuinely useful dataset without requiring any new tooling. Most teams can maintain this in a shared spreadsheet, and the discipline of filling it in consistently matters far more than the sophistication of the format.
What this looks like once it becomes a habit
Teams that keep this kind of log for even a month start noticing patterns they'd otherwise miss. Maybe one assistant is consistently fast on isolated bug fixes but slow, in review terms, on anything touching more than two files. Maybe another produces test code that almost never needs correction but struggles with anything involving async logic. These patterns are specific and actionable in a way that a single leaderboard score never is, and they only become visible once review time is tracked as deliberately as generation time already is.
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