AI-assisted software development

Why it matters

The value of AI-assisted development depends on the task, not the tool. Repetitive code, glue code and well-worn patterns speed up clearly, while new architecture, tricky domain logic and debugging an unfamiliar system move only a little. That is why an average “productivity percentage” across a whole organisation rarely says anything useful. What matters is where you point the AI.

The scale is large. Google and DORA’s 2025 report, State of AI-assisted Software Development, published in September 2025, gathered responses from nearly 5,000 practitioners. Of those, 90% said they use AI at work and more than 80% felt it had raised their productivity. The same report names the flip side: AI use correlated positively with delivery throughput but negatively with delivery stability. Speed alone is not enough. What decides the outcome is how well a team’s testing, version control and feedback loops hold up under a rising flow of changes.

What does AI-assisted software development mean?

AI-assisted software development means building software with AI involved across the whole workflow, from generating and refactoring code to reviewing and testing it. It is an umbrella term that spans the full range: code assistants embedded in the editor that complete a line at a time, and more autonomous coding agents that take a task and work it through in several steps. The common thread is that a person sets the goal and owns the result, and the AI produces suggestions within that.

The term says nothing about how much responsibility is handed to the AI. It covers both cautious use, where every line is reviewed, and a looser style, where suggestions are accepted quickly. Those extremes lead to very different outcomes, which is why neighbouring terms such as vibe coding, agentic coding and AI-native development exist to pin down which style is meant.

How is it done in practice?

The day-to-day work splits into a few modes that often combine within the same day. A code assistant completes code in the editor as suggestions the developer accepts or rejects; GitHub Copilot and Cursor are the best known. Chat-based use means the developer asks the model for an explanation, a draft or a fix and moves parts of it into the code. A coding agent is handed a whole task and works through it in stages: it reads the codebase, proposes changes, runs tests and corrects itself.

AI is not limited to writing code. It is also used for review (going through a change and flagging issues before merge), generating tests, writing documentation and explaining old code. In all of these, quality depends on the instructions: the more precisely the task, context and constraints are described (prompting), the more useful the suggestion. The decisive step is still the same as it was before AI. A person reads, understands and approves the change.

What does the research say about productivity?

The evidence cuts both ways, and it is best read as a whole. In a controlled experiment by GitHub researchers (2023), developers who wrote an HTTP server in JavaScript with Copilot finished the task about 55.8% faster than a control group (95% confidence interval 21–89%). That result was for a bounded, familiar task, exactly where AI is at its strongest.

From the field the picture is more sober. A randomised trial published by METR in July 2025 followed 16 experienced open-source developers working in their own repositories, which they had worked on for five years on average (246 real tasks). Surprisingly, the developers were 19% slower with AI, even though they estimated they had been 20% faster. The gap between measured and perceived is the study’s central warning: your own sense of speed is not a reliable gauge. The authors stress that the result applies to experienced developers in familiar, high-quality codebases and does not generalise to all developers.

In large survey data the direction is similarly split. Google and DORA’s 2024 report found that more than 75% of respondents leaned on AI for at least one daily task, but 39% had little or no trust in AI-generated code. The same report estimated that a 25% increase in AI use was associated with a 1.5% drop in delivery throughput and a 7.2% drop in delivery stability. Faster code production does not automatically mean faster or steadier delivery.

Code quality and technical debt

Faster production shifts the load onto review and maintenance. GitClear’s analysis of 211 million changed lines of code (2020–2024) found that the share of copy-pasted code rose from 8.3% to 12.3%, while refactoring, the reorganising of existing code, fell from around 25% of changed lines to under 10%. In other words, more code is being produced but less of it is being tidied, and the number of duplicated blocks grew clearly. That is technical debt, and it shows up only later as the cost of maintenance.

The same pattern appears in DORA’s finding on falling stability. When AI makes it easy to produce large batches of change, the risk grows too, unless testing and small batch sizes keep up. A separate but related problem is comprehension debt: when code is accepted without being read closely, you end up with code that nobody on the team fully understands. It comes due when that code has to be fixed or extended.

Risks and pitfalls

The biggest pitfall is a suggestion that looks right but is not. In Stack Overflow’s 2025 developer survey, 66% of respondents named AI solutions that are “almost right, but not quite” as their leading frustration, and 45% said debugging AI-generated code takes more time than usual. In the same survey AI use was widespread (84% use it or plan to, up from 76% in 2024), but trust in the accuracy of the results had fallen from before. Only about a third trusted the accuracy of what the AI produced.

The other risks are concrete. Security: generated code can introduce vulnerabilities or outdated dependencies if it is not checked. Extra rework: a seemingly finished solution can need more fixing than writing it from scratch. Skill erosion: if the basics are delegated wholesale to the machine, understanding of your own codebase thins out. The same thing holds in every case. Responsibility stays with the person, and the focus simply moves from writing to judging.

How does it differ from neighbouring terms?

AI-assisted software development is the umbrella, and the neighbouring terms sit under it according to how much responsibility the person gives up.

• Vibe coding means accepting the AI’s suggestions without thorough review. It is sketching, where the feel of the result is what counts. The term was introduced by Andrej Karpathy in February 2025. It suits experiments and prototypes, but not production code, where understanding the code is essential.
• Agentic coding refers to AI acting more autonomously, taking a task and working through it in several steps (reading, editing, running tests, fixing). The emphasis is on the degree of autonomy.
• AI pair programming stresses the interaction: the AI acts as a pair that suggests and explains, while the person steers and decides.
• AI-native development means the broader approach where the whole process and toolchain are designed AI-first, rather than bolted on afterwards.

In practice these overlap. The same developer can vibe-code a prototype in the morning and carefully review an agent’s change in the afternoon.

How to adopt it

Start by targeting, not by spreading it everywhere. Pick the task types where the benefit is proven (repetitive code, tests, documentation, explaining old code) and keep human review a mandatory part. Measure per task type, not as a single organisation-wide average: one team’s CRUD work may speed up clearly, while a complex data migration does not move, because the bottleneck was never the writing. Keep batch sizes small and testing solid, so that faster production does not turn into weaker stability.

Frequently asked questions

Does AI speed up software development?

It depends on the task. On a bounded, familiar task the speed-up can be large. In GitHub’s experiment the Copilot group finished about 55% faster. On real, complex projects the effect can go the other way: in METR’s trial experienced developers were 19% slower, even though they felt faster. An average across a whole organisation says little; what decides it is where the AI is pointed.

Can you trust AI-generated code?

Only through review. In Stack Overflow’s 2025 survey, 66% of developers named solutions that are almost but not quite right as the biggest problem, and trust in the accuracy of AI code had fallen. The practical rule: the AI produces a suggestion, the person owns the result.

What is the difference between AI-assisted development and vibe coding?

Vibe coding is the light extreme of AI-assisted development, where suggestions are accepted without thorough review. AI-assisted development also covers the careful style, where every change is understood and tested. Vibe coding suits experiments, not production code.

Does AI make code quality worse?

It can, if review and refactoring fall away. In GitClear’s data the share of copy-pasted code grew and the share of refactoring dropped clearly over 2021–2024. Quality does not fall because of AI itself, but because code is produced faster than it is tidied and understood.

Will AI replace software developers?

Not so far. The evidence shows AI shifting the focus from writing code to judging, instructing and understanding it. Responsibility for design, review and the result stays with the person, and it is precisely in the hardest tasks that AI’s benefit is smallest.

Sources

• Peng et al.: The Impact of AI on Developer Productivity — Evidence from GitHub Copilot (2023) – controlled experiment: Copilot group ~55.8% faster on an HTTP-server task (95% CI 21–89%).
• METR (2025): Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity – randomised trial, 16 experienced developers / 246 tasks; 19% slower, measured vs perceived gap.
• Google / DORA (2025): 2025 DORA Report — State of AI-assisted Software Development – nearly 5,000 respondents; 90% use AI, over 80% report productivity gains; throughput up, stability down.
• Google / DORA (2024): Announcing the 2024 DORA Report – over 75% rely on AI daily, 39% little/no trust; 25% increase associated with −1.5% throughput, −7.2% stability.
• Stack Overflow (2025): Developer Survey, AI section – 84% use or plan to (76% in 2024); 66% “almost right but not quite”; 45% debugging takes more time; trust fallen.
• GitClear (2025): AI Copilot Code Quality – 211M changed lines; copy-pasted code 8.3% → 12.3%, refactoring ~25% → under 10% (2021–2024).
• Wikipedia: Vibe coding – term introduced by Andrej Karpathy in February 2025; contrast with careful AI-assisted development.
• Wikipedia: AI-assisted software development – overview of the umbrella term and sources.