How AI became standard in software development over three years, what effects it delivered, and why some metrics remain stable.
3.12.2025 This article is an adapted translation of the report “2025 State of AI-Assisted Software Development” by Google Cloud and DORA. The original is available via the link. Reading time: 20 min.
Derek DeBellis - quantitative user experience researcher, Google Cloud
In just three years, the use of AI in development has undergone a radical shift. If in 2022 a developer using
What once seemed unusual about AI is now normal: today, 90% of technical specialists use AI at work. Chapter "AI Adoption and Use"
shows that AI adoption has become almost ubiquitous, and this trend is confirmed far beyond DORA's data.
According to the Stack Overflow Developer Survey 2025, 84% of developers already use or plan to use AI in the development process, up from 76% a year earlier.
Everyday use has also become normal: 47% of respondents use AI daily.
This trend is also confirmed by Atlassian State of DevEx 2025: 99% of developers save time thanks to AI tools.
The growth in individual use is also reflected in corporate strategies.
According to the LinkedIn Corporate Communications report (2025), 88% of business leaders consider accelerating AI adoption a priority.
A McKinsey study shows that 78% of organizations regularly use AI in at least one business function.
Priorities also show up in budgets: AI Index 2025 (Stanford HAI) reports that in 2024 global corporate investment in AI reached $252.3 billion, 26% higher than a year earlier. But the labor market may reflect the new reality most clearly: the number of U.S. job postings mentioning generative AI skills grew by 323% in 2023 alone.
With interest in AI growing rapidly, it is important to ask: do the benefits keep pace with adoption?
Widespread AI adoption does not automatically mean that value becomes widespread as well.
It is important to keep in mind that adoption is often chaotic: it is frequently driven by hype and fear of missing out (FOMO), rather than a well-thought-out strategy.
In addition, adoption can be constrained and slowed by internal systems and organizational structure.
This was also noted in the DORA Report 2024, which showed that AI delivers promising results but at the same time increases software delivery instability and reduces development process throughput.⁷
The same study found a 1.5% decrease in software delivery speed and a 7.2% increase in software delivery instability for every 25% increase in AI use.
For example, a recent study by Model Evaluation & Threat Research (METR) showed a sharp gap between developers' perceptions and reality:
This underscores how subjective assessments of AI effectiveness can be.
Other independent studies show similar results: they point to a possible impact of AI on developers' thinking, focus, and overall well-being.
In other words, the industry is still only beginning to understand how AI actually affects professionals and workflows.
Nevertheless, when developers were asked to assess AI's impact across each aspect of the SPACE model, they reported mostly positive effects and only a small number of negative ones. Most participants in our AI adoption and use chapter also noted that AI improved their code quality and increased their individual productivity.
These conflicting signals show that more data-driven research is needed to truly understand AI's impact on product development, especially given the scale of investment and the speed of adoption.
We believe the developer community and employers should form realistic expectations. The first step toward that is to get a clear, objective understanding of how AI actually affects the development process.
Only then can expectations be managed responsibly and informed decisions about AI adoption be made.
To understand how key development outcomes depend on the level of AI use, we need to measure AI adoption accurately. This year, we developed a metric based on a few simple principles:
The metric should not favor any single role or function. For example, a developer should not automatically score higher just because they use AI to write code.
The metric should reflect an overall orientation toward using AI, regardless of role or task type, based only on real, meaningful use of AI.
As every year, we let the data lead the way, even when we have strong hypotheses.
The process includes exploratory factor analysis14, a method that depends less on prior expectations and helps reveal real structures in the data.
The approach to defining and measuring AI adoption should align with recognized methods for studying AI and be consistent with our qualitative observations.
The result was a factor made up of three closely related variables:
How much did you rely on AI at work over the past three months?
How much did you trust the quality of AI-generated results in your workflow over the past three months?
How often in the past three months did you automatically turn to AI when a work task or problem came up? Elements that form the AI adoption factor
The analysis showed that responses to these three survey questions follow very similar patterns.
This suggests there is a single hidden characteristic that causes these variables to change in sync.
We believe this factor reflects three interconnected conceptual dimensions: behavioral, how often a person uses AI; operational dependence, how deeply AI is embedded in workflows; and attitudes and trust, how a person feels about AI quality and how much they trust it.
This combination fully aligns with both the research literature and our qualitative observations.
Most likely, this relationship is driven by a feedback loop.
Trust is a prerequisite for use, but use itself becomes the mechanism that builds trust.
This creates a strong loop: once users trust the system enough to use it, use itself increases dependence on it and raises trust even further, forming an adoption spiral.
This cyclical nature is what makes combining the variables into a single factor logical, especially since the survey captures only a snapshot in time rather than the full dynamic process.
AI adoption is a psychological process involving beliefs, intentions, and actions.
When we have a valid metric for the level of AI adoption, we can determine whether key outcomes change depending on how actively a person uses AI. The basic logic of the analysis looks like this: if we compare two people with the same characteristics, conditions, and processes, the person with the higher level of AI adoption will on average show {number} more or less on {result}.
Of course, almost no person, team, or organization is "average"
, but analyzing average effects helps reveal common patterns.
These patterns provide context for a deeper analysis in the DORA AI Capabilities Model chapter, where we examine the conditions under which AI adoption delivers the greatest, or least, benefit.
Each year, we choose and shape a set of outcomes that reflect the real goals and interests of the professionals reading this report.
In other words, we assess practices based on what truly matters to technology professionals.
These are the outcomes we examined this year:
A high-level indicator of overall organizational success, including factors such as profitability, market share, and customer satisfaction.
Team effectiveness A metric reflecting the team's perceived performance and its ability to interact and collaborate effectively.
Product effectiveness A metric that assesses the success and quality of the product or service the team is working on.
It considers characteristics such as the product's ability to help users complete important tasks, data security, and performance metrics such as latency.
Software delivery throughput A metric that reflects the speed and efficiency of the software delivery process.
This aspect is described in more detail in the second part of the study.
Software delivery instability A measure of the quality and reliability of the software delivery process.
This aspect is described in more detail in the second part of the study.
Reflects a developer's personal assessment of the quality of the code underlying the main application or service they work on.
Individual effectiveness Measures how productive people feel in their work and how highly they rate their own achievements.
Work value Measures the share of time a person estimates they spend on tasks they consider useful, meaningful, and worth the effort.
Friction / barriers Measures how strongly different obstacles interfere with work.
The lower the level of friction, the better.
Measures the level of work-related fatigue, exhaustion, and cynicism.
Lower values are considered a positive result.
The chart below shows how the level of AI adoption relates to key work outcomes, from code quality and effectiveness to team performance and delivery stability.
We estimate that, all else being equal, with the same conditions, environment, and processes, a person with a higher level of AI adoption will show: higher individual effectiveness; higher delivery instability; higher overall organizational performance; a larger percentage of time spent on truly valuable work; higher code quality; stronger product performance; higher delivery speed and efficiency;
higher teamwork scores; comparable burnout levels; comparable friction levels.
The remaining sections of the chapter will help explain why this is the case.
We build hypotheses based on research, interviews, expert opinions, and the experience of the DORA community.
Since last year, several metrics have continued to show a stable positive association with AI adoption level. These are: higher individual effectiveness; higher code quality; stronger teamwork; higher organizational performance.
These effects can now be considered baseline: they have repeated for a second year in a row and align with what many practitioners observe.
The reasons can vary greatly by company, team, and context. AI helps developers by automating routine actions, quickly suggesting solution options, generating contextual prompts, summarizing large amounts of information, and handling higher-level tasks from design to analysis.
These targeted improvements add up and reinforce one another, giving teams and organizations a tangible gain.
Some metrics show stable but not especially encouraging patterns for the second year in a row: no connection with job frustration levels; no connection with burnout levels; and greater AI use is linked to increased delivery instability.
These effects are tied not to AI itself, but to the system in which it operates.
Today, AI mainly helps at the keyboard, speeding up code writing, improving quality, and increasing personal productivity.
But frustration, burnout, and delivery failures depend on processes, communication, team rituals, decision-making methods, and everything beyond a developer's individual work.
AI by itself does not change culture or processes. So despite productivity gains, frustration does not decrease, burnout does not disappear, and delivery instability increases until the system itself is adapted for working with AI.
Although it may seem that a tool that removes routine tasks would automatically reduce friction at work, the data show that friction is a far more complex phenomenon than simple automation of repetitive actions.
A number of studies indicate that a significant share of friction comes from processes that lie outside an individual developer's control.
A 2019 Microsoft study identified the main sources of friction in the workday: • unstable and slow infrastructure; • outdated documentation; • excessive administrative burden; • lack of time; • repetitive tasks.
One conclusion of the study is that leaders should first and foremost improve processes and tools.
Even if AI reduces friction at the individual level of work, for example by speeding up coding, inefficient processes can completely cancel out that effect, especially if the system is not ready for more changes and new ways of working. For example, increasing the volume of changes without updated rules, roles, and "golden paths" raises the cost of verification and coordination.
At the same time, friction does not disappear completely, it shifts.
Previously, it showed up as manual routine work; now it appears as the need to make decisions, verify results, refine prompts, and understand code that looks almost correct. As a result, overall friction may not decrease, even if
AI helps people do more meaningful work and automate some repetitive tasks. "We realized that AI works best when it amplifies the skills of strong engineers. By automating routine and repetitive tasks, AI frees developers for strategic work and real innovation."
It may seem that a tool that increases productivity should reduce burnout.
However, the results show that burnout is barely affected by technology.
It is influenced most strongly by the work culture around the person.
We see this in our own data across different years as well: burnout is closely tied to leadership quality, priority stability, and cultural maturity.
A 2017 meta-analysis identifies persistent factors that lead to burnout:
Even if AI could reduce burnout, the effect can easily be absorbed by cultural pressure.
There are also additional signals from our qualitative research. They align with the literature on "work intensification".
The core point is this: the productivity gains from AI have led some companies to raise expectations of employees. In other words, AI increases individual effectiveness, but the balance between demands and available resources remains the same, so burnout does not go away. "Software development has definitely changed because of AI, and I really felt that this year. I did not feel it last year."
I think that with the arrival of MCP servers (Model Context Protocol) and the ability to write code together with a model, a lot has changed: feature release, timelines, and the amount of work that can be completed in a given period. Stakeholders now expect more to be done in the product, faster. Deadlines and projects are under tighter time pressure, and that definitely changes how I work.
And that is a little worrying: management and product management have been given a pretty tight deadline for releasing the product." Software delivery instability Over 11 years of DORA research, it has become clear that technical practices and organizational processes are directly linked to software delivery performance. If these core capabilities are missing, they completely wipe out the benefits of AI adoption.
For example, a team may use AI heavily and still face high instability if it lacks a reliable delivery pipeline and depends on other teams. Our data show that AI does not fix instability; today, adopting it is even associated with higher instability. Classic technical practices may have become even more important and may require stricter adherence. But it may also be that this is no longer enough.
Everything points to a more radical conclusion: existing technical practices and metrics must evolve, be supplemented, or be replaced to fit the AI era. One argument is that increased instability is an acceptable price for the development speed AI provides. The logic is this: if delivery speeds up so much that errors are fixed almost instantly, the negative effect of instability may be smoothed out. However, the data do not support this hypothesis.
To test it, we examined whether a high level of AI adoption reduces the harm from instability for outcomes that have historically suffered from it. We found no evidence of such an effect. On the contrary, instability continues to worsen key outcomes, such as product quality and burnout. These effects can completely cancel out any gains in delivery speed.
We observed several notable shifts compared with the 2024 results: AI's relationship with the share of valuable work time moved from negative to positive; AI's effect on delivery speed became positive, after previously being negative; AI's effect on product quality moved from neutral to positive.
Developers gained an extra year to learn AI; organizations gained a year to redesign processes; vendors gained a year to improve models and user experience.
In many tests, the quality of AI solutions has improved.
Fine-tuning models on your own data no longer requires deep ML expertise, because platforms have made the process accessible.
Cloud providers offered secure ways to connect internal data, such as customer databases, documentation, and repositories, to the fine-tuning process without sending it to external services.
AI is getting new roles and operating rules:
Such feedback loops reduce risk and help tools work more reliably.
Developers better understand where AI adds value.
Routine and repetitive tasks are handed to AI, while people focus on analysis, design, and problem solving.
This explains why the share of valuable work time increased and delivery throughput accelerated.
When AI takes on the technical "rough draft" work, templates, boilerplate code, and routine transformations, developers free up time for releases, which increases throughput and improves product performance.
At the same time, organizational systems are becoming more AI-friendly: teams are adjusting processes, building new protocols, and creating conditions in which the benefits of AI extend not only to individual employees, but also to the product and the company as a whole.
We also examine these limitations and growth areas in the AI Capabilities Model and AI Mirror chapters.
Why did some metrics improve while others did not, such as delivery instability?
It is likely a matter of where teams direct their efforts, which system constraints are most visible, and how complex individual problems are.
"If AI does in 30 minutes work that would have taken me two hours or more, that's good. I get free time, and I can do something else, take on another task, get more done. It speeds up my professional growth: I learn faster and move forward faster."
AI has a positive effect on most key metrics, but there are important exceptions: it does not reduce burnout or friction, and it remains associated with increased software delivery instability.
Comparing 2025 results with last year makes it clear that models, tools, and workflows are adapting alongside people and organizations.
Practitioners have found ways to use AI to free up time for higher-value work, and we are beginning to see how AI adoption helps improve delivery speed and product quality.
The persistence of effects such as rising instability, no progress in reducing friction, and burnout points to something else: it is not only about the tools, but also about how the work system itself is designed.
If an organization does not change its processes and practices, AI capabilities run into existing constraints. In our view, AI’s value is unlocked less by the models themselves and more by changes to the work system into which those models are embedded.
This conclusion is explored further in the next parts.
The environment in which developers work directly shapes how they perceive their work and how they feel in the profession. AI is changing that environment: companies are revising priorities, leaders are looking for new opportunities for innovation, and AI is becoming more deeply embedded in workflows.
This is gradually changing both the tasks themselves and the nature of developers' work.
History shows that technological shifts can change our sense of what is normal.
Before Uber, the idea of getting into a stranger’s private car for money seemed impossible.
Developers are at the forefront of a similar AI-driven shift, and our task is to capture these changes as they happen. In the study, we focused on six sociocognitive aspects that reveal how developers perceive themselves and their work:
(interest in challenging tasks)
Psychological ownership of the product Redistribution of skill importance The effect of AI adoption level on key metrics
Pride is a basic emotion that arises when a person attributes achievement to their own efforts. Like a runner who feels satisfied after a marathon, knowing how much training went into it. We feel uplifted when we master a difficult task or make progress. Why is it important to measure pride in the context of AI? There are two opposing assumptions.
First, if a person relies too heavily on AI and automates a large share of the work, the space for achievement through personal effort may shrink. Second, AI frees up time, making it possible to work on more substantive tasks, ones a person can genuinely be proud of. The data support the second view. Higher AI usage is associated with a stronger sense of genuine pride.
The mechanism is clear as well: the higher the AI adoption, the more time a person has for tasks they consider valuable. And the larger the share of such work, the higher the level of professional pride. This shows an important effect: when developers hand routine duties over to AI, they gain the most valuable thing, time, and can invest in projects and ideas that truly matter to them.
Meaningful work is the human desire to do what feels important and significant. Research shows that when people find meaning in their work, their well-being and satisfaction increase. We wanted to understand whether AI adoption affects how meaningful developers consider their work. Two scenarios were possible: AI takes over tedious routine tasks and increases the share of truly valuable work, in which case the sense of meaning grows.
If AI interferes with the key elements of the profession, then the sense of significance declines. The results did not show any effect. Developers did not see their work as either more or less meaningful. We are probably trying to capture such shifts too early, because the transformation is only beginning.
The arrival of AI gave people a simple way to reduce cognitive load. Research shows that student developers sometimes use AI to literally switch their brains off. And while many enjoy working through hard problems, there is a risk that easy access to ready-made answers will reduce the satisfaction of mental effort: even complex problems can now be solved instantly. But the study data showed that AI adoption did not change developers' need for mental effort.
Existential connection is the feeling that there is a bridge between your inner world and another person's experiences. Philosopher William James noted that it is the gap between people's inner experiences that makes understanding one another so difficult. This feeling reflects our ability to build deep human connections and feel less alone in our views. We studied this factor because growing AI use may change the nature of workplace interactions.
AI provides fast, personalized answers, reducing the need to ask colleagues for help. This speeds up work, but it can reduce the number of live conversations and joint problem-solving. We assumed that the more developers rely on AI, the weaker their connections with colleagues might become. However, the data showed no link between the level of AI adoption and the feeling of existential connection. There are two possible explanations.
Either we are too early in the adoption cycle to see the effect, or AI, even while replacing part of communication, is simultaneously creating new points of contact by helping people share knowledge, speed up discussions, and expand the common base of experience.
Psychological ownership is the feeling that something belongs to you, even if it does not legally.
This feeling can arise both toward physical objects and toward intangible things, such as one's own ideas.
Many developers feel a sense of ownership over the code they write, so we asked whether using AI might weaken that feeling.
Our results show, with 78% probability, that AI adoption does not reduce a sense of personal contribution to the outcome.
Simply put, developers do not see code as any less their own, even if it was written with AI.
This supports the interpretation that modern AI tools are seen more as advanced assistants than as autonomous coauthors.
Developers psychologically place AI in the category of tools, like a compiler or linter.
They use it, but they do not share authorship with it.
Nevertheless, there is a small but noticeable probability (21%) that AI can reduce the sense of ownership.
When a developer writes code entirely by hand, authorship is clear.
But when AI enters the process, some people find the boundaries of authorship blur: their sense of personal investment and control decreases, and those are what create a feeling of ownership over a task or outcome.
As developers increasingly work alongside AI, discussion is intensifying about how this may change the value of different skills.
We decided to test whether AI adoption affects which skills developers consider more or less important for their work.
We assumed that, in the context of AI, skills related to interacting with people and with AI itself may grow in importance, while skills related to writing code may decline.
We asked participants to rank eight skills by importance:
Problem-solving skills Prompt engineering
Memorizing programming language syntax
Understanding your team's codebase
As expected, AI adoption has increased the importance of prompt engineering.
Unexpectedly, it also increased the importance of memorizing syntax.
This is an interesting observation: syntax would seem to be the first thing to take a back seat in the age of AI.
Most surprisingly, AI did not affect perceptions of the importance of the other skills.
. There are many possible explanations: developers may be in a period of adapting to new tools, or they may believe that their professional expertise will remain indispensable anyway.
Taken together, the results show that AI adoption has not yet had a noticeable effect on how developers perceive their work.
In the meantime, organizations should give developers more room to do the work they consider valuable.
It is useful to create conditions that help them learn to use AI effectively, eliminate routine tasks, and free up time for truly important work.
To avoid reducing the sense of ownership over outcomes, it is important to treat AI as a tool that works for the developer.
Even as technologies become more autonomous, a human must remain in control.
To understand how organizations can realize the true value of AI, and which technical and cultural mechanisms strengthen or weaken its impact, move on to the fifth part of the study. It presents DORA's first AI capabilities model.
It shows that AI's effect depends not on the tools themselves, but on the environment in which they operate, and explains which elements of the development system need to be redesigned for AI to deliver sustained results.
