The Tool Desk
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Use AI as an assistant, not an authority. It can help draft code, tests, or explanations, but whether it saves time or produces dependable work depends on the task and the review around it. Check its output against requirements, tests, security rules, and your team’s policies before relying on it.
What does the evidence say about AI in software development?
Adoption is not the same as trust, and reported experiences are not controlled measurements of accuracy or productivity. The findings below come from different surveys and one small randomized study, so their numbers are not directly comparable.
| Source and scope | What it found | How to interpret it |
|---|---|---|
| Stack Overflow’s 2025 Developer Survey | 84% of respondents said they use or plan to use AI tools. On output accuracy, 46% said they distrust it and 33% said they trust it; only 3% said they highly trust it. The accuracy question had 33,244 responses, or 67.8% of all survey respondents. | These are self-reported attitudes, not a test of how often AI-generated code is correct. |
| Stack Overflow’s 2025 Developer Survey | 66% of respondents reported frustration with solutions that were “almost right, but not quite”; 45% said debugging AI-generated code takes more time. | These responses point to review and repair as real parts of the workflow, but do not measure the time cost for every developer or task. |
| METR randomized study, July 12, 2025 | In a study of 16 experienced open-source developers completing 246 tasks in mature projects, task completion took 19% longer when early-2025 AI tools were allowed. Participants primarily used Cursor Pro and Claude 3.5/3.7 Sonnet. | This result applies to those developers, tasks, projects, and tools—not to software development in general. The authors said experimental artifacts could not be ruled out entirely, though their robustness checks suggested study design was unlikely to be the primary cause of the slowdown. |
| Stack Overflow’s 2025 Developer Survey | 76% said they did not plan to use AI for deployment and monitoring; 69% said the same for project planning. Among respondents asked about AI agents, 87% raised accuracy concerns and 81% raised security or privacy concerns. | These are stated preferences and concerns, not proof that AI can never assist with those activities or that every agent is unsafe. |
| GitHub survey, August 2024, updated April 15, 2025 | More than 97% of its 2,000 software-team respondents in the United States, Brazil, Germany, and India said they had used AI coding tools at work at some point. | The survey measured ever-use, not how often respondents used the tools. It also included a small number of data scientists and software designers. |
The productivity evidence is especially context-dependent. METR’s 2025 trial involved developers with an average of five years’ familiarity with the projects they worked on. Before using the tools, participants expected a 24% time reduction; afterward, they estimated a 20% reduction, despite the measured slowdown. That gap is a reminder that perceived usefulness and measured task time can diverge.
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Consider the consequences of an error and how easily you can check the result. A draft that is quick to reject or test is a different proposition from a change that could expose data, break a production system, or create an expensive incident.
#1 Best Overall
Lower-consequence, easy-to-check work
AI may be worth trying for bounded work when you can independently verify the result: for example, drafting a small helper function, suggesting test cases, explaining an unfamiliar error, or producing a first-pass summary of code you can inspect. These are possible uses, not guarantees of faster or better work. If the answer is wrong, you should be able to spot that before it causes harm.
High-consequence or hard-to-verify work
Be more conservative with deployment and monitoring decisions, security-sensitive changes, access control, data handling, and changes whose behavior is difficult to test. Stack Overflow’s survey reports reluctance around deployment and monitoring, as well as concerns about agent accuracy and privacy; those views reinforce the need to assess the specific task rather than treat an AI suggestion as authorization to act.
For any task, ask whether authoritative documentation, a known-correct example, or a meaningful test can confirm the result. If you cannot tell what the generated code does or how to validate it, do not merge or execute it on trust alone.
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Keep responsibility for the result with the person submitting it. Review the output as you would an untrusted contribution, including the parts that look plausible and the tests that accompany it.
Rank #3
- Set the boundaries. State the goal, constraints, relevant interfaces, and what must not change. Avoid sharing secrets, personal data, or confidential code unless your organization’s policy and the tool’s approved settings explicitly permit it.
- Inspect the proposed change. Read the full diff. Check whether it meets the actual requirements, handles edge cases, follows project conventions, and introduces unnecessary dependencies or permissions.
- Validate behavior. Run the relevant tests, linters, type checks, and security checks used by your project. Add or adjust tests for important cases rather than assuming generated tests cover them.
- Verify claims independently. Check APIs, language behavior, and security-sensitive details against authoritative documentation or another reliable source. A confident explanation is not evidence that the implementation is correct.
- Review integration and operation. Consider compatibility with the rest of the codebase, failure handling, logging, performance, and any deployment or rollback implications before accepting the change.
Generated tests also need human review. GitHub’s survey write-up specifically notes that developers should check AI-generated tests to ensure they cover the intended scenarios. A test suite can pass while missing the behavior that matters.
How can a team use AI without weakening its engineering process?
Tool choice alone does not make a workflow dependable. DORA’s 2025 report, based on survey responses from nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, characterizes AI as an amplifier of organizational strengths and dysfunctions. In practical terms, clear requirements, good tests, useful documentation, and disciplined review give a team more ways to catch mistakes; a weak process gives generated mistakes fewer chances to be caught.
Agree on policy before sensitive work
- Define what data and code may be entered into approved tools, and which tools or configurations are permitted.
- Specify when AI-assisted changes need additional security, privacy, or human review.
- Keep the same ownership and accountability for AI-assisted changes as for other code.
Evaluate the whole workflow, not just the first draft
When deciding whether AI helps a task, account for the time spent prompting, checking, debugging, and integrating its output—not only the time to generate a suggestion. Compare outcomes on work your team actually does, and pay attention to rework and defects as well as speed. A useful assistant on one task can be a distraction on another.
What is a practical rule for deciding whether to use AI?
Use it when the task is well-scoped, the information can be shared under policy, and you have a reliable way to verify the answer. Limit or avoid it when an error would be hard to detect or costly to reverse, or when you cannot safely provide the necessary context. In every case, review and test the result before relying on it.
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