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AI coding assistance can speed up some development tasks, but published studies do not establish that it turns a year-long project into a two-month one. That comparison is meaningful as a personal account only if the project timelines and scope are verified—and even then, AI may be one of several differences.
What changed when AI entered the workflow?
The title’s before-and-after comparison raises a reasonable question: did AI make the second project faster, or were the projects different in ways that mattered just as much? The studies available offer evidence about particular tasks and developer settings, not the calendar time for these two projects. Without verified records of the projects’ scope, dates, and workflow, the specific cause of the difference cannot be established.
A useful account should distinguish AI’s contribution from other factors. A smaller or more familiar project, steadier requirements, existing code to reuse, more available work time, a different framework, or prior experience can all affect elapsed time. The comparison also changes if “one year” and “two months” mean calendar time in one case but active coding or time to a usable release in the other.
What to compare before attributing the difference
- Timeline: Define when each project started and finished, and whether the duration includes deployment, testing, maintenance, and polish.
- Scope: Compare features, integrations, and release requirements—not just the amount of code written.
- Working conditions: Note available work time, team size, requirements stability, framework, project familiarity, and reusable code.
- AI overhead: Include time spent prompting, reviewing generated code, debugging, testing, and reworking it.
- Tools and timing: Identify the assistant and model versions used, when they were used, and which tasks they helped with.
If those records show meaningful differences beyond AI access, the strongest framing is a personal before-and-after account: AI was one possible contributor, not a proven sole cause.
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Does AI actually make developers faster?
There is no single speed effect that applies to every developer or project. Experiments have found faster task completion, more tasks completed, and slower completion, depending on the people, work, codebase, tools, and measurement used.
More completed tasks in three workplace experiments
A Microsoft Research summary of three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company reports 26.08% more completed tasks among developers given an AI coding assistant. The combined analysis covered 4,867 developers and reported a standard error of 10.3%. It measures task throughput during ordinary business operations—not a 26.08% reduction in whole-project calendar time. The summary also reports higher adoption and greater productivity gains among less experienced developers. Microsoft Research’s June 2025 summary
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Less time on one complex enterprise task
A randomized controlled trial involving 96 full-time Google software engineers estimated that AI shortened time on a complex enterprise-grade task by about 21%. The study used internal Google tooling in summer 2024, and the authors note that the confidence interval was large and that results may not generalize to other tools or times. This is evidence about one task setting, not a forecast for an entire project. Paradis et al., October 2024
Slower completion in mature repositories
In a randomized study of 16 experienced open-source developers completing 246 tasks in mature projects they had contributed to for years, allowing AI increased completion time by 19%. The developers primarily used Cursor Pro and Claude 3.5 or 3.7 Sonnet, tools from the early-2025 period. Participants had expected AI to reduce their task time; after the study, they estimated a 20% reduction. The authors say experimental artifacts cannot be entirely ruled out. The result is specific to these developers, repositories, tasks, and tools—not proof that AI slows all experienced programmers. Becker et al., July 2025
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A much faster result on a short coding task
An earlier controlled experiment found that participants with access to Copilot completed a JavaScript HTTP-server task 55.8% faster than the control group. That is a result for one bounded task from an earlier tool period, not an estimate of end-to-end project acceleration. Microsoft Research’s February 2023 summary
These results answer different questions. A percentage change in task time is not interchangeable with a change in task throughput, and neither directly gives the number of calendar months an entire project will take. The studies also differ in participant experience, codebase familiarity, task type, tooling, and period. Their results should be compared in context rather than averaged into a universal AI speedup.
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Can AI coding tools slow experienced developers down?
Yes, in at least one randomized setting: METR’s early-2025 study found longer completion times when experienced open-source developers could use AI on work in repositories they knew well. That result does not show that AI is generally harmful. Familiarity with a mature codebase, the nature of the tasks, and the tools in use can affect whether generating code saves time or adds review and correction work.
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Does faster coding mean better code?
Not necessarily, but a GitHub study found favorable results on a defined API-endpoint task. In November 2024, GitHub reported a randomized study with valid submissions from 202 developers, each with at least five years of experience. The Copilot group was 53.2% more likely to pass all 10 unit tests. In blind reviews, it produced 13.6% more lines of code per readability error; reviewer ratings also showed statistically significant differences for readability (3.62%), reliability (2.94%), maintainability (2.47%), and conciseness (4.16%). The study also reported a 5% higher likelihood of approval.
Those figures describe one task and its evaluation criteria. The review included a small blind-review subset, so they should not be treated as evidence that AI invariably improves production code or eliminates the need for testing and review. GitHub’s study, updated February 6, 2025
How much faster can you build a project with AI?
The cited studies do not support a reliable project-wide multiplier. Their outcomes range from substantially faster completion on one short task, to a modest reduction in time on a complex task, to greater task throughput in workplace experiments, to slower task completion in mature repositories. None measures the author’s year-versus-two-month project comparison.
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