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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAI can help developers produce code faster, but generated code still has to be understood, reviewed, tested, and checked for security before it is ready to ship. Recent surveys suggest that verification work is a significant constraint for many teams—not that every team now spends more time checking code than writing it, or that AI reliably shortens delivery from idea to production.
Why code generation is not the same as delivery
A code assistant produces a proposed change. Production-ready software requires confidence that the change does what it should, fits the surrounding system, avoids unacceptable security risks, and can be maintained. That work can include peer review, functional and integration tests, security checks, quality analysis, and rework.
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So “proving” code is shorthand, not a claim that software correctness can be established with absolute certainty. Tests and analysis reduce uncertainty; they do not guarantee that every defect has been found. The amount of checking a change needs depends on its context and risk.
AI can increase the volume of code arriving for review, while leaving reviewer capacity and testing resources unchanged. In that situation, the constraint may shift downstream: less time spent drafting does not automatically mean less time to deliver a safe, useful change.
What developer surveys say about the verification gap
Sonar’s 2026 State of Code Developer Survey was based on an online survey of 1,149 professional developers globally. Fieldwork took place in October 2025, and respondents had used AI at work during the preceding year. The figures below are respondents’ reports and perceptions, not measurements taken from their repositories.
- 42%: respondents said AI generated or significantly assisted the code they committed. This is a reported share, not an independently audited measurement of committed code.
- 96%: respondents said they did not fully trust AI-generated code to be functionally correct.
- 48%: respondents said they always checked AI-assisted code before committing.
- 38%: respondents said reviewing AI code took more effort than reviewing human-written code. This is not a measure of the share of their working hours spent reviewing.
These results describe a gap between code assistance and confidence in the output. They do not show that all AI-generated code is unreliable, or that checking it always takes longer.
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Black Duck, working with UserEvidence, surveyed 831 software engineering and DevOps professionals in March 2026. The report says the companies’ and UserEvidence’s customers were not excluded, so its results should not be treated as a fully representative probability sample. Respondents named manual review (52%), security testing (51%), and code rework (48%) among the leading bottlenecks for AI-generated code.
The same survey found that 92% of respondents reported increased productivity and velocity from AI coding assistants, with 58% describing a major improvement. Those reports can coexist: respondents can feel that assistants help them move faster while also reporting that review, testing, or rework remains difficult.
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Does AI coding make software teams faster overall?
The available survey findings do not establish one universal effect on end-to-end delivery time. A faster first draft is not the same as a completed, reviewed, tested, and deployed change. To assess whether AI is helping a particular team, measure outcomes across that whole path rather than treating generated-code volume or drafting speed as the result.
Perceptions also differ by role. In Black Duck’s survey, 74% of C-suite respondents said productivity and velocity improved greatly, compared with 38% of technical contributors who reported a major improvement. These are survey responses, not directly measured differences in team output.
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Team conditions matter, too. DORA’s 2025 report draws on nearly 5,000 technology professionals and more than 100 hours of qualitative data. Its summary describes AI as an amplifier of organizational strengths and dysfunctions. That is a useful way to understand why the same kind of assistant may help a team with clear standards and functioning review while adding friction to a team with overloaded reviewers or weak delivery practices. It is not a numerical estimate of AI’s causal effect.
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Verification is not a single final gate. Black Duck describes work distributed across pre-commit, post-commit, quality assurance, and testing. Its leading bottlenecks include manual review, security testing, rework, and prompt iteration. For respondents whose AI-generated code volume had grown by more than 50%, 57% cited additional security testing and vulnerability remediation as a major bottleneck. That figure applies to that survey segment, not to all AI-using teams.
eu-LISA’s July 9, 2026 report says coding assistants may support productivity gains, while emphasizing system security and quality, regular evaluation, and adequate resources to review generated code. Together, these findings point to a practical issue: generating more code can increase the work required to establish that it belongs in the product.
How to manage verification without turning it into a rubber stamp
- Review the change as a proposal. Ask what the code does, why it is needed, and how it interacts with the surrounding system. Do not treat an assistant’s explanation as evidence that the implementation is correct.
- Match checks to risk. Use functional and integration tests for behavior, and security checks where the change touches sensitive data, permissions, dependencies, or exposed interfaces. A test passing is evidence about the cases it covers, not proof that no defect remains.
- Protect reviewer capacity. If code generation increases the volume of proposed changes, track whether review queues and review quality are keeping pace. Faster drafting can otherwise move the delay rather than remove it.
- Evaluate tools and outputs regularly. Track whether changes are correct, secure, maintainable, and useful in your team’s context—not merely how much code an assistant produces.
- Compare like with like. Separate routine internal applications from customer-facing or business-critical systems, where the consequences of defects differ. Distinguish individual contributor experience from leadership perceptions, and survey impressions from observed delivery outcomes.
Black Duck reports that 30% of respondents had a fully governed approach to AI-assisted development, and that this group more often reported major efficiency improvements. The association does not show that governance caused those gains. It does, however, reinforce the value of measuring how a team’s process and oversight relate to its own outcomes instead of assuming that tool adoption alone determines them.
What the evidence does—and does not—show
The surveys provide useful snapshots of professional perceptions and reported workflows, but they are not a single controlled, causal measurement of AI’s effect on software delivery. Sonar’s findings come from developers who had recently used AI at work; Black Duck’s sample includes its and UserEvidence’s customers. Their percentages should be read with those methods and dates in mind.
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The strongest supported conclusion is narrower than “AI makes software slower” or “review is now the biggest bottleneck everywhere”: developers report both productivity gains and substantial verification work. Whether the bottleneck has moved for a particular team depends on how much code it generates, the risks of that code, and whether its review, testing, and security practices can keep up.
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