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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →No: AI does not make software worthless. It can make producing a first draft of code cheaper or faster in some situations, but that is only one part of delivering software people can trust and maintain. The useful question is whether an AI-assisted workflow gets a correct, secure, maintainable change into service with less total effort—not how quickly it generates code.
What does “software cost” include besides writing code?
A generated change still has to meet the requirements, fit the existing system, pass appropriate tests, avoid security problems, and remain understandable to the people who will change it later. Someone must review it, find defects, resolve integration problems, and maintain it. If generation saves time on the first draft but adds enough review or rework, the end-to-end task may take longer.
That does not mean generated code is automatically poor, or that every AI-assisted change needs the same level of scrutiny. It means the cost of producing code and the cost of delivering and sustaining a working feature are different measures. The evidence does not establish one universal breakdown of software’s lifecycle costs.
Does AI coding improve developer productivity?
There is no single result that applies to every developer, task, project, or tool. Two 2025 studies found different outcomes in distinct settings:
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| Study and setting | Reported outcome | What the result does—and does not—show |
|---|---|---|
| Xu, Medappa, Tunç, Vroegindeweij, and Fransoo, 2025; studied open-source projects following GitHub Copilot adoption | Core developers reviewed 6.5% more code and had a 19% drop in their original-code productivity after adoption. | The authors report productivity increases concentrated among less-experienced peripheral contributors, alongside more rework. This is an analysis of the studied open-source projects, not a universal estimate for proprietary teams or all Copilot use. |
| Becker, Rush, Barnes, and Rein, 2025; randomized trial of 16 experienced open-source developers completing 246 tasks in mature projects they already knew, with early-2025 AI tools | Task completion time was 19% longer when AI tools were allowed. Participants had expected a reduction. | This small, specialized trial measured task completion time in that context; it does not predict outcomes for novices, greenfield work, later tools, or every measure of organizational value. The authors say experimental artifacts cannot be entirely ruled out. |
These figures should not be averaged: the studies examined different people, work, and outcomes. One points to a shift in who contributes and who bears review and rework; the other found slower completion in a particular trial. Neither settles whether AI improves productivity in a different team or workflow.
How should code quality be judged?
Code quality is more than whether a program runs once or looks plausible. At minimum, evaluate correctness against the requirements, complexity and maintainability, and security. A change that passes a narrow example can still fail other inputs, make the codebase harder to modify, or introduce a vulnerability.
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Liu, Tang, Luo, Zhou, and Zhang’s peer-reviewed 2024 study in IEEE Transactions on Software Engineering evaluated ChatGPT-generated code across algorithm and weakness scenarios, assessing correctness, complexity, and security. Its results are benchmark-specific:
- On the study’s coding benchmark, problems from before 2021 had a 48.14 percentage-point accepted-rate advantage over problems from after 2021. This is a difference between the tested problem groups, not a general performance increase for ChatGPT.
- The authors found relevant vulnerabilities in some tested scenarios. Their multi-round fixing process ultimately addressed more than 89% of vulnerabilities in the study’s vulnerability scenarios; that result does not mean first-pass code is secure or that the same rate applies in production.
- Results varied because generation was nondeterministic, and direct repair ability was limited in the tested setup. A successful result on one run is not proof that another run or a different task will behave the same way.
The study evaluated a particular model and set of scenarios in 2024. It is evidence that quality dimensions and repair behavior need to be tested, not a verdict on every current model or generated code in general.
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Why does an organization’s delivery system matter?
DORA’s 2025 State of AI-assisted Software Development report summarizes AI as “an amplifier, magnifying an organization’s existing strengths and weaknesses.” DORA’s report-level conclusion is that the greatest returns come from focusing strategically on the underlying organizational system, rather than on tools alone. That is not proof that every organization will get the same result, but it helps explain why identical tools can produce different outcomes.
A team with clear requirements, sound tests, effective review, and a manageable codebase has ways to detect problems and integrate useful changes. Where those practices are weak, faster code generation can also mean more output that is difficult to verify or maintain. The relevant unit of evaluation is therefore the workflow and its outcomes, not the coding assistant in isolation.
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How can a team tell whether AI actually saves time?
Compare AI-assisted and non-assisted work on representative tasks in the actual codebase. Define the acceptance criteria first, and track the effort and outcomes through delivery rather than stopping at generated code or the first draft.
| Measure | What to record |
|---|---|
| End-to-end time | Time from starting the task to an accepted, integrated change—not just time to generate a draft. |
| Correctness | Whether the result meets stated requirements and passes relevant tests, including important edge cases. |
| Security and other constraints | Whether the change satisfies applicable security and non-functional requirements. |
| Review and rework | Time spent reviewing, correcting, retesting, and resolving issues, including which roles absorb that effort. |
| Maintainability | Whether the change adds unnecessary complexity or makes likely future modifications harder. |
| Context | Task type, developer experience, project maturity, and delivery practices, so results are compared like with like. |
Look at the measures together. A faster draft is not a useful productivity gain if it fails acceptance criteria or creates enough downstream work to erase the time saved. Conversely, additional review may be worthwhile if the delivered outcome is better; whether it is worthwhile depends on the work and its consequences.
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Does cheaper code make software economically worthless?
No evidence cited here establishes that conclusion. These studies address particular coding, quality, and organizational outcomes; they do not provide an economy-wide, long-term estimate of software prices, vendor margins, labor demand, or the total value of software. Cheaper code production could change parts of software development, but the broader market effect remains unresolved.
For now, the defensible conclusion is narrower: AI can change the cost and distribution of coding work, while quality, review, rework, maintenance, and organizational capability remain part of the delivery equation. Code volume or typing speed alone cannot establish that software became cheaper to deliver—or that its value disappeared.
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