iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
In some teams, probably yes, but the evidence does not show that it is true across the industry. Studies published from 2023 to 2025 point to a plausible mechanism: AI assistance can make code faster to produce while the work of checking it stays with the same people, and that load often lands on the most experienced engineers. At the same time, the studies that measured assistant-supported code and review directly reported favorable results in narrow, controlled tasks. Whether review has become your constraint depends on your team’s experience mix, review structure, and where delivery actually stalls.
Why review can become the constraint
Writing a function is only one step between an idea and a shipped change. Every change also needs a reader who understands the surrounding system, can judge whether the approach fits, and will still be willing to maintain it later. An assistant can shorten the first step without shortening the others. If more code arrives in the review queue per unit of reviewer time, the queue grows, and the people who can judge the riskiest changes become the limit.
That is the hypothesis behind the phrase “the bottleneck has shifted.” It is a reasonable model, but it makes a claim about where time goes, and that claim needs direct measurement in the setting where it is made.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
The open-source evidence: more review, less original work
The most direct evidence comes from a 2025 analysis of open-source projects by Feiyang (Amber) Xu, Medappa, Tunç, Vroegindeweij, and Fransoo. The paper, a peer-reviewed conference contribution, examined project activity after GitHub Copilot was introduced. Its headline finding is that experienced core developers reviewed 6.5% more code and their original-code productivity dropped by 19%.
#1 Best Overall
Those two figures describe a specific group, core developers in open-source projects, rather than all engineers or all teams. The cited summary does not state the number of projects or developers analyzed, so the effect sizes should not be carried over to commercial teams, other assistants, or other review structures. The authors’ own conclusion is more cautious than the headline:
“More broadly, this finding raises caution that productivity gains of AI may mask the growing burden of maintenance on a shrinking pool of experts.”
The useful part of this finding is the mechanism. Gains in visible output can coexist with a rising burden on the few people who must review and maintain that output. A team that measures only how much code is written will not see that burden.
PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteControlled studies that complicate the “AI code is worse” story
Two GitHub studies point in the opposite direction. They were run by the vendor of the tool, and both used small, controlled tasks, so they say little about long-running production work. They are still relevant because they test quality and review directly rather than assuming them.
A 2024 code-writing exercise with blind review
GitHub Customer Research (2024) recruited 243 developers with at least five years of Python experience for a randomized coding task. Of these, 202 submissions were valid and analyzed. The task was to build API endpoints for a fictional restaurant-review web server. Among those analyzed:
- The Copilot group was 53.2% more likely to pass all ten unit tests. This is a relative likelihood as reported by GitHub, not a 53.2 percentage-point difference in pass rates.
- The Copilot group scored better on several assessed quality dimensions.
- The Copilot group was 5% more likely to receive approval.
- A 25-person subset whose work passed all ten tests then performed blind code reviews.
This study measured functional correctness, reviewer assessment, and approval on one bounded task. It did not measure how the code fared after merge, how much review time it consumed, or how a team’s maintainers absorbed it over months.
Rank #3
A 2023 Copilot Chat review exercise
GitHub Customer Research (2023) involved 36 developers with five to ten years of experience who completed a controlled exercise that included both authoring and code review. Reviews assisted by Copilot Chat were reported as 15% faster, and almost 70% of participants accepted comments from reviewers using it.
The sample is small, the setting is controlled, and the source is vendor-reported. The result suggests that an assistant can speed up part of review in a task. It does not show a measured productivity change across an organization.
Comparing the evidence
The studies answer different questions, so their results are not directly comparable. The table below sets out what each one examined.
| Source | Setting and population | What was measured | Reported result |
|---|---|---|---|
| Xu et al., 2025 (open-source analysis) | Open-source projects after Copilot introduction; experienced core developers compared with peripheral developers. Sample size not stated in the cited summary. | Code reviewed, original-code productivity | 6.5% more code reviewed; 19% lower original-code productivity for experienced core developers |
| GitHub Customer Research, 2024 | Randomized, controlled Python API task; 243 recruited, 202 valid submissions; developers with at least five years of Python experience | Unit-test pass rate, quality scores, blind reviewer assessment, approval | 53.2% greater relative likelihood of passing all ten unit tests; 5% higher likelihood of approval |
| GitHub Customer Research, 2023 | Controlled authoring and review exercise; 36 developers with five to ten years of experience; Copilot Chat | Review speed, acceptance of reviewer comments | 15% faster reviews; almost 70% accepted comments from reviewers using Copilot Chat |
| DORA / Google, 2025 | More than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide | Organizational outcomes associated with AI adoption | AI acts as an amplifier of existing organizational strengths and weaknesses |
The differences in task complexity, project context, reviewer experience, incentives, and outcome definitions make direct comparison unsafe. Read the studies as evidence about plausible mechanisms: assistants can raise output and sometimes speed review, while the maintenance cost can concentrate on a small group of senior people.
Separate five measures that get conflated
“Faster coding” and “faster delivery” are different claims. Most of the cited evidence covers only a few of the measures a team needs.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →| Measure | What it captures | What the cited evidence shows |
|---|---|---|
| Authoring output | How much code is written or how fast a task is finished | Not measured as elapsed authoring time in the cited GitHub studies; the 2024 study measured test pass rates and quality scores |
| Review volume and reviewer time | How much code reviewers handle and how long they spend | Core developers reviewed 6.5% more code in the 2025 open-source analysis; Copilot Chat reviews were 15% faster in the 2023 exercise |
| Rework after review | Changes needed after comments, and churn after merge | Not measured in the cited studies |
| Approval and acceptance | Whether changes pass review and whether comments are adopted | 5% higher likelihood of approval (2024); almost 70% acceptance of reviewer comments (2023) |
| Team delivery throughput | How quickly working changes reach users | Not measured in the cited studies |
A team that sees faster authoring and higher approval rates has not yet shown that delivery is faster, and it has not shown that maintenance is cheaper. Those are the measures that decide whether the bottleneck has really moved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Organizational conditions matter more than the tool
Google’s DORA 2025 report, based on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide, frames AI’s role in these terms: “AI’s primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.”
In practice, a team with clear ownership, healthy review capacity, and small changes is more likely to absorb faster code creation. A team with unclear ownership or a few overloaded reviewers may find that the same assistant pushes its review queue further over the limit.
Signs that review has become your bottleneck
- Pull requests wait longer for a first review than they spend in development.
- A small number of senior engineers handle a growing share of reviews.
- Reviewers report spending more time on changes they did not write and cannot easily verify.
- Fixes after review and follow-up changes after merge increase while the number of merged changes also rises.
- Maintainers are reluctant to take ownership of code they did not write.
None of these signs proves the cause. Each one points to where to look next.
How to measure it in your own team
- Split review activity by reviewer experience and track how much of it falls to your most senior engineers.
- Measure time to first review and time from approval to merge, separately from development time.
- Track rework: follow-up commits after review and bug fixes or reverts within a defined window after merge.
- Compare end-to-end delivery time per change, from first commit to production, before and after adopting an assistant.
- Record which changes were AI-assisted and whether they are reviewed, reworked, or maintained differently from other changes.
Lines of code and task completion rates describe output. Reviewer load, rework, and delivery time describe whether that output is absorbed without strain.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
