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You don’t need another AI hype post, so I will keep the intro part short and sweet. Too sweet, actually, because I will be sharing the freshest possible data from the 31st wave of our independent survey, which reached more than 11,500 respondents from 95 countries around the world. Based on the findings, we produce a 6-piece report series that delves into key developer trends for Q1 2026 and beyond. They're all free and available here, btw. In this post, we’ll look at 2: AI in the Developer Workflow The AI ROI Measurement Gap AI in the Developer Workflow Generative AI has entered the developer toolkit, but how deeply has it actually embedded itself into the work? This report examines how developers are using AI across their workflows: which tasks they are turning to AI for, how much of each task they are willing to hand over, and where the gap between adoption and reliance reveals the limits of current tooling. Code generation is the most widely used AI-assisted task, with 49% of developers using AI for it, yet fewer than half of those are letting AI handle the majority of the work. Drawing on survey data from developers actively using AI-assisted software development tools, the findings move beyond headline adoption figures to examine the texture of AI use across more than a dozen distinct development tasks. The result is a more honest picture of where AI is genuinely accelerating developer work, where it has found a willing audience for tasks developers are happy to offload, and where capability gaps are suppressing the trust needed for deeper reliance. For organisations evaluating or expanding AI tool adoption, the data offers a practical lens for calibrating expectations, informing governance, and identifying where human oversight remains essential. For those building the next generation of AI developer tools, it maps the frontier clearly: the tasks with high demand and low trust are not side thoughts; they should be the roadmap. Key Questions Answered in the AI in the Developer Workflow report What tasks are developers using AI tools to assist them with? How much of each task are developers handing over to AI tools? Which tasks do developers trust and rely on AI tooling for? Which tasks are the current suite of AI tooling falling short? The AI ROI Measurement Gap Artificial intelligence (AI) has long been embedded in technology organisations, powering systems such as search engines, recommendation algorithms, and fraud detection tools. However, it is now far more visible and strategically prioritised, with generative AI chatbots, coding assistants, and enterprise automation tools bringing it to the centre of business planning. As AI investment scales, a new pressure is emerging: the need to justify it. Boards want evidence, finance teams want numbers, and developers caught in the middle are discovering that believing AI works and being able to prove it are two very different things. The vast majority (80%) of developers in leadership roles (technology leaders) are using AI-assisted tools, and 75% rate them as valuable. This report examines how developers in leadership roles – hereinafter referred to as technology leaders – are experiencing and evaluating AI value today. We examine how they rate what it delivers, whether they measure it, and how rigorous those measurements are. The findings are drawn from 2,341 professional developers working in leadership positions in SlashData’s 31st global developer survey. This report provides an overview of the headline findings. For a full deep dive, including breakdowns by role, sector, region, and agentic AI maturity level, see the full report, The state of AI ROI measurement in software teams. Key Questions Answered in the AI ROI Measurement Gap What share of technology leaders are using AI-assisted tools? What share of those using AI-assisted tools are measuring their value or ROI? How is the AI ROI structured in terms of maturity, and how does that differ based on company size? How does AI ROI maturity level affect technology leaders’ evaluation of AI assistance? About the author Stathis Georgakopoulos, Head of Marketing at SlashData Stathis leads product marketing and loves building helpful content that turns complex research into practical decisions. He focuses on setting the table for launches and campaigns, and has a soft spot for content marketing.

This is a transcript with the key highlights from the live webinar on software development Q1 2025 trends. You can watch the full presentation in the following video. Natasa Ljikar:  Welcome to today’s session. We’ll be going over insights on developer tools usage and measuring AI tools ROI. SlashData is a technology-focused analyst firm exploring the broader software development space as well as evolving AI technologies. The data we’ll be discussing comes from SlashData’s biannual Omnibus Global Developer Surveys, specifically the latest Q1 2026 Developer Nation survey . In addition to the topics we’re covering today, the survey also explores cloud, mobile, web, games, and other areas, capturing a snapshot of the broader software development ecosystem. The survey has a global reach, with over 12,400 valid responses from software developers in 95 countries . I’d like to pass the mic to Bleona Bicaj , Principal Research Consultant and Product Strategist at SlashData. The state of AI in software development in Q1 2026 Bleona Bicaj:  Today I’ll be sharing data about the state of AI and development. Over the past two years, AI has transformed from a technology developers were curious about into something embedded in the everyday workflows of the majority of professional developers. But using AI and quantifying its benefits are two very different things. That’s one of the things we wanted to explore in the most recent wave of our Developer Nation survey. I’m going to walk you through two separate research findings that paint a picture of where the developer community stands right now with AI, both in terms of what they’re actually doing with these tools and whether they can prove that these tools are working. By the end of this webinar, you’ll have three key takeaways: a clear picture of AI adoption among professional developers, evidence about the measurement gap that most organizations are facing, and an introduction to a new benchmarking product that directly addresses the questions technology leaders are asking about AI developer tools. AI adoption among professional developers This first report comes from our latest Global Developer Survey, where we asked over 10,000 professional developers  a straightforward question: Do you use or work with ML, AI models, tools, APIs, or services? And if so, in which of the following ways? The data gives us a clear snapshot of where the market stands right now. The majority of professional developers ( 75% ) are using AI-assisted tools in some form, and almost half ( 45%)  are adding AI functionality or developing AI models. Within that 75% using AI-assisted tools, there are three distinct ways developers are engaging with AI, and they represent fundamentally different relationships with the technology. First, 53%  of professional developers use AI-assisted tools outside the coding environment, such as AI chatbots or agents like ChatGPT, Claude, or other LLMs integrated into their workflow to get answers to coding questions. This is lightweight, low-friction adoption. Developers are not building with AI; they are mainly consulting with it. Second, 42%  of these developers are using AI-assisted development tools or agents integrated into the coding environment itself. We’re talking about GitHub Copilot, JetBrains AI, Amazon Q Developer — tools that live in the IDE, understand the code base, and surface suggestions in real time. This is a deeper level of integration because the AI is no longer just a side tool, but part of the development loop. Third, a quarter of developers are using AI tools to generate creative assets for projects such as images, diagrams, documentation, or other non-code assets. This is more niche, but it signals that AI is being used not just for code generation, but for the full spectrum of what goes into shipping software. Now, 45%  of developers report either adding AI functionality to their applications or developing AI models and infrastructure. That’s a different category entirely because these developers aren’t just using AI as a productivity tool; they’re actually building with it. Within this group, we also see another split. Around a third of developers are adding AI functionality to their applications either through fully managed AI services or APIs, or self-managed local AI models. Here, they’re leveraging the service provider’s model. They’re not training or hosting their own. This is fast, managed, and abstracts away a lot of infrastructure complexity. The remaining developers represent increasingly specialist activities such as customizing pre-trained AI models and fine-tuning hyperparameters. These are the developers building the foundation that the first group of tool users depend on. For the purposes of today’s discussion, we’re going to focus on this first, largest group: developers using AI-assisted tools, because this is the group experiencing the most immediate pressure to justify investment, the group most likely to be affected by organization-wide decisions around AI tooling, and the group we’ll be diving into with our new benchmarking product. The 75%  using these tools represent the market we need to understand in detail. Before we dive into those results, I want to show how we got to this adoption level. This chart tracks developer involvement with AI over the past two years, from Q1 2024 to Q1 2026 . The story is one of consolidation at the top and migration toward more sophisticated use cases. When it comes to using AI-assisted tools, this has grown from 61% in Q1 2024  to 75% in Q1 2026 . That is steady, consistent growth over two years. The trajectory has remained consistently steep, and each quarter we see incremental gains. We’re not seeing a phenomenon of early adopters jumping in and then plateauing. This continuous integration tells us that these tools are sticky and solving real problems for developers. Adding AI functionality or developing AI models also shows an incline. It started at about 32%  two years ago and is now at 45% . The one thing that is declining is the share of developers saying that they don’t use or work with ML, AI models, tools, APIs, or services at all. Two years ago, that was about 28%  of the market, and today it’s down to 12% . The holdouts still exist, but they’re clearly a shrinking minority. Measuring AI ROI This brings us to the second report and the harder question of how technology leaders know that this investment is actually working and bringing productivity gains. AI has long been embedded in technology organizations, either through powering search engines or recommendation algorithms. But something has shifted in the past two years because it’s no longer a background technology. It’s visible, strategic, and expensive. Generative AI chatbots, coding assistants, and enterprise automation tools have moved to the center of business planning. As AI investment scales, pressure is emerging: the need to justify it to the board and to finance. Boards want evidence, finance teams want numbers, and developers caught in the middle are discovering that believing AI works and being able to prove it are two very different things. The second report examines how developers in leadership roles — or technology leaders — are experiencing and evaluating AI value: how they rate what it delivers, whether they measure it, and how rigorous or formal those measurements are in practice. The findings come from more than 2,000 professional developers working in leadership positions , drawn from SlashData’s 31st Global Developer Survey . Today we’re providing an overview of the headline findings. For a full deep dive, including breakdowns by role, sector, region, and agentic AI maturity level, we prepared a more comprehensive premium report called The State of AI ROI Measurement in Software Teams . Since we’re now focusing only on professional developers in leadership roles, I want to set the scene by saying that 80%  of them use AI-assisted tools, which is five percentage points higher than professional developers overall. From that group, 75%  rate AI tools as valuable or extremely valuable relative to the cost and effort required. This is a big number, so it deserves both celebration and scrutiny. On one hand, it reflects a genuine shift in the market. It confirms that AI tooling has become core infrastructure for most software engineering organizations, and the people close to that transition — the engineering leaders using these tools every day — are overwhelmingly positive. 27%  even describe the benefits as far exceeding the cost and effort. But confidence and evidence are not the same thing, and the broader market context makes that distinction urgent. For example, a study from S&P Global  found that the share of companies abandoning the majority of their AI initiatives before reaching production surged from 17% to 42% in a single year . Somewhere between pilot and production, the reality of the work (data quality challenges, integration complexity, and unclear ROI) caught up with the enthusiasm. Gartner  also predicted that at least 30% of generative AI projects  will be abandoned after proof of concept, citing three primary causes: poor data quality, unclear business value, inadequate risk controls. This unclear business value is the issue we’re examining today. When we asked technology leaders directly, “Do you measure the impact or ROI of the AI tools, models, or services that your teams use?” , 88% said yes . On the surface, that’s reassuring. An overwhelming majority is actively tracking AI value, and only 12%  seem to be making decisions about expensive tools based on trial and error. But when you look past this binary of measuring versus not measuring, a more complicated picture emerges. We can see that 39%  of technology leaders who are measuring AI ROI are doing so through formal or automated processes: things like regular KPI tracking, integrated dashboards, and automated reporting systems. This is the gold standard. Metrics are being collected continuously, tracked systematically, and when there’s a question about AI value, the data is already there. Another 41%  describe their approach as defined but manual. They have structure — quarterly reviews, internal surveys, even periodic conversations between developers and engineering leads. There’s a framework, but it depends on someone to trigger it. So when it’s time for the quarterly review, someone organizes the conversation, gathers the feedback, and documents the outcome. It’s rigorous in intent, but episodic in execution. Then we have 17%  operating informally or ad hoc, through occasional discussions and subjective impressions. There’s no consistent tracking. With this, I want to reframe that 88%  headline. Eighty-eight percent of organizations believe they are measuring AI value, but only 39%  are doing so through processes that don’t require someone to initiate measuring. If your organization sits in that manual middle — and many do — it’s worth understanding what this means operationally. Manual measurement, at 41% , often involves structured efforts like quarterly reviews or team surveys. But it also carries structural weaknesses that make it a poor foundation for high-stakes investment decisions like AI tooling. First, it is vulnerable to deprioritization. When delivery pressure rises, when teams are shipping a critical feature or fighting a production issue, quarterly AI review is often the first thing that slips. Measurement cadence breaks down precisely when it’s most needed (during periods of change or uncertainty) when the organization is deciding whether to double down on AI or pull back. Second, it is susceptible to recency bias. Informal and periodic processes tend to weigh the most recent and most visible interactions disproportionately. A high-profile failure shortly before a review, such as a hallucination in an AI suggestion, shapes the assessment more than months of quiet incremental productivity gains that were never explicitly tracked. Third, manual reviews rarely produce the evidence that finance teams find convincing. We’ve been running interviews with software engineering leaders about the future of developer teams and how ROI measurement is happening within teams. We find that formality, actual numbers, and longitudinal data are often lacking. Pressure from the board keeps increasing, so it’s paramount that teams are able to provide that data. Gartner noted that a major challenge for organizations is justifying substantial investment for productivity enhancement, which can be difficult to translate directly into financial benefits. Historically, CFOs have not been comfortable investing in indirect future value. Without longitudinal data connected to business outcomes, that investment is nearly impossible. Among teams with no measurement in place, 59%  rate AI as valuable and 13%  rate it as not valuable. Among teams that are measuring in some form, 78%  rate it as valuable, compared to 4%  finding it not valuable. So the difference between non-measurers and measurers is a 19 percentage point gap  in perceived value. When we isolate teams measuring formally, those with automated processes, the figure rises further to 85%  rating AI as valuable. The gap is huge. The intuitive explanation is straightforward: measurement captures value. Teams that track metrics are able to see what AI is actually delivering, so they rate it more highly. But there might be something deeper happening. We pose another hypothesis: measurement doesn’t just capture value, it helps create it. When teams are tracking metrics consistently, they also use AI tools more deliberately. They start routing tasks toward AI where it demonstrably helps and avoiding it where it doesn’t. They start to develop better practices. They correct for the asymmetry between the spectacular failure that sticks in memory and the hundreds of routine successes that don’t. Systematic tracking also creates feedback loops. You measure, you see what’s working, you adjust, and then you measure again. The process gets tighter and the value increases. Measurement doesn’t just answer the question, “Is AI working?” It also changes team behavior in ways that make the answer more likely to be yes. Organi s ation size and measurement maturity We also find that the clearest organization-size-related divide in our data set sits at the informal end of the measurement spectrum. Among freelancers and organizations with up to 100 employees , 25%  rely on occasional discussions and subjective impressions. That’s more than double the rate of enterprises with more than 1,000 employees  and notably higher than midsize firms. The flip is true at the formal end. 46%  of enterprises have KPI tracking, dashboards, or automated monitoring in place, compared to 41%  of midsize firms and only 30%  of smaller organizations. That’s a 16-point gap  between the smallest and largest organizations. We also see that every organization type has landed in the manual middle in roughly equal proportion, regardless of resources or scale. That consistency tells us that this manual tier doesn’t function as a stepping stone toward formal measurement. It is more like a default state that organizations settle into and tend to stay in. For smaller organizations, the practical priority is not building dashboard infrastructure from scratch, but escaping the informal tier entirely. Moving from ad hoc impressions to even one defined manual process closes the most consequential gap in the data. For midsize organizations already in the manual tier, the formal measurement gap relative to enterprises is the number worth closing, because it connects directly to the value perception gap between those groups. If that gap is closed, the organization is not just improving its measurement process, but likely also improving how senior leaders perceive the value of AI investments. What this means for technology leaders The question is no longer whether to adopt AI. The question is whether organizations have built the internal capabilities to know what that adoption is actually worth. The data suggests that most have not done it in a formal way. The gap between claiming to measure and measuring formally and rigorously is wide, and it carries real consequences for the quality of evidence available to senior leaders. When a board or CFO asks, “Is this AI investment paying off?” , the answer depends almost entirely on whether the organization has systematic data or quarterly impressions. When AI initiatives start being abandoned across the industry, boards will ask even harder questions. Organizations with measurement frameworks in place will have answers. Others will be scrambling. Without systematic tracking, it’s difficult to tell whether Copilot is saving more time than Q Developer, whether investment in an agentic AI platform is actually reducing toil, or whether the organization is paying for a tool that nobody is using effectively or to its full potential. The key point is that knowing how your measurement practices compare to the market is the starting point for building that capability. AI Developer Tool Benchmark We’ve been listening to questions from the market about which tools are actually delivering, what’s worth the investment, how they’re being used, and what’s driving adoption. We’ve developed something that is a direct response to that. We’ve recently introduced the AI Developer Tool Benchmark , a new research product specifically designed to give technology leaders the data they need to make informed decisions about AI developer tooling. Whether you’re a vendor building and selling AI products or a buyer looking to choose the right tool for the team or for your products, this data will be useful. We launched a pilot version in Q4 last year  to pressure-test the product in the market and gathered extensive feedback from clients. Within April, we’ll be launching the official first benchmark with more than 2,400 professional developers worldwide . The benchmark covers: an overview of 20 AI developer tools  scored on adoption, usage intensity, and satisfaction among 2,400 professional developers , how developers actually work — whether they use a single tool or stack multiple tools together, how they make tooling decisions, and what’s driving preference, task priorities and satisfaction, including code generation, debugging, and documentation, cost and pricing evaluation, including how developers perceive value relative to cost, decision drivers and trust perceptions, productivity and measurable impact, including whether developers perceive AI tools as making them more productive in the sense of saving time, and a deep-dive section that changes each wave based on client feedback and market interest. This time, we decided to focus on AI agents , mainly the agentic form of the AI coding tools that we’re asking about. This is fundamentally different from using them simply as coding assistants. We built this section to understand how teams are experimenting with agents, the level of autonomy they’re providing, what blockers they’re hitting, and where they see opportunity. Q&A Natasa Ljikar:  You mentioned that 42% of companies are abandoning AI initiatives , but 80% of tech leaders say that AI is valuable . That seems contradictory. Are the leaders saying it’s valuable the same ones whose companies are abandoning initiatives? Or is there a different group of people making those decisions? Bleona Bicaj:  The 42% abandoning  is coming from an external source, and the 80%  is coming from ours. The 80%  are engineering leaders and developers who are using AI tools and finding them valuable in their day-to-day work. The 42% abandonment rate  that S&P Global is tracking refers to projects that got greenlit at the board or executive level, often broader AI transformation initiatives, not just developer tools. So you can have engineering leaders genuinely finding Copilot valuable while the company’s enterprise AI data pipeline project — something much larger and more complex — gets abandoned because the data quality wasn’t there or the business case fell apart during implementation. Developer-level adoption and enterprise-level success are two different problems. That’s exactly why measurement matters. If you’re only measuring at the team level — “my engineers like this tool” — you’re missing the executive or strategic question of whether this is solving the business problem at scale. And that’s where the abandoned projects struggle. Natasa Ljikar:  You talked about how manual measurement is vulnerable to deprioritization and recency bias. But doesn’t the act of doing a quarterly review, even if it’s manual and imperfect, create accountability? Isn’t that better than nothing? Bleona Bicaj:  Manual measurement is genuinely better than nothing. That quarterly review does create accountability internally. The problem isn’t that it exists, but what you can’t see with it, and where the gap between the manual and formal process lies. When you’re doing quarterly reviews, you’re documenting past impressions. What you actually need is a more real-time signal about what’s working and what isn’t — more up-to-date data that you can adjust now and not in three months when the next review comes in. Teams in the manual tier often end up doing the measurement work twice. They do the quarterly review, report what they find, and then when the CFO asks follow-up questions three months later, they scramble to do a special investigation because the quarterly review data wasn’t structured to answer those new questions. Formal measurement systems are expensive upfront, but they answer both the questions you knew you’d have and the ones you didn’t. Natasa Ljikar:  How is value typically measured in organizations? Bleona Bicaj:  This is something we’re trying to answer through interviews. In our AI Developer Tool Benchmark study, we typically ask developers to tell us about time saved, for example, or PR merge — things that are easier to quantify and that people are measuring through KPIs. But value is very subjective. From the discussions we’ve been having with software engineering leaders, many are trying to include the subjective, human factor as well. They are trying to understand how developers themselves perceive the help they’re receiving from these AI developer tools — not just in terms of productivity, but also how they feel about them potentially replacing their roles. So this is something that may affect psychological safety, which we keep hearing about in interviews. It is being interpreted differently across organizations. There isn’t one exact answer yet, but it’s something we’re exploring through the Future of Developer Teams  interviews, with the aim of getting both the quantitative and qualitative sides. Natasa Ljikar:  Have you run any causal impact studies for measuring impact of AI on coding productivity? Bleona Bicaj:  That is what we’re trying to get with the AI Developer Tools Benchmark, and that is a quarterly product that we keep trying to improve. If someone is interested in something that the benchmark doesn’t carry in a given quarter, we can add questions in other quarters and make it a richer product. In terms of trust- and performance-related metrics, we’re able to have an answer about the AI developer tools — the 20 tools  that we’re measuring. Natasa Ljikar:  If any other questions arise, please feel free to reach out to us. You will also be receiving the recording of this session and the report when it’s ready in your inbox. Bleona Bicaj:  Thank you.

At KubeCon + CloudNativeCon EU 2026 in Amsterdam, theCUBE’s Rebecca Knight and Rob Strechay sat down with Liam Bollman-Dodd, Principal Market Research Consultant at SlashData, and Bob Killen, Senior Technical Program Manager at CNCF, to discuss the newly released State of Cloud Native report . The full interview is at the end of the article. The conversation was framed around one of the report’s headline findings: the cloud native ecosystem now includes nearly 20 million developers, a sharp increase that raises a bigger question than simple growth alone can answer — who counts as a cloud native developer now, and what does that say about where the industry is heading? That was where Liam Bollman-Dodd made some of the most important points in the discussion. Cloud native growth is more than an adoption story Rather than treating the growth figure as a straightforward adoption story, Liam explained that part of the jump comes from a broader and more accurate view of the developer population. In earlier waves of research, cloud native was largely associated with backend and infrastructure specialists. But as SlashData and CNCF expanded the lens to include developers working across cloud-based environments more broadly, the picture changed significantly. Many developers are now building on cloud native foundations without directly configuring the infrastructure themselves. That distinction matters. Liam’s argument was that cloud native has moved beyond its original identity as a specialist infrastructure domain. Developers may not be managing Kubernetes clusters by hand, but they are still using observability stacks, cloud services, APIs, telemetry tools, and platform abstractions that place them firmly inside the cloud native ecosystem. In that sense, the report is capturing a structural change in software development, not just a bigger version of the same community. How AI is connected to cloud native growth He also connected that shift directly to AI. One of Liam’s most notable observations was that cloud native tooling is increasingly becoming the operational backbone for AI inference and machine learning workflows. His point was not that these tools were built specifically for AI, but that they are well-suited to the scale, networking, data movement, and orchestration demands that AI creates. As a result, teams pursuing AI are often becoming cloud native by necessity, even if that was not their original strategic goal. Organisational structure Another key theme Liam raised was the growing divide in how organisations structure development work. Some companies still want deeply empowered developers who understand the full toolchain and can work close to infrastructure. Others are moving in the opposite direction, using platform engineering and internal abstraction layers so developers can focus more narrowly on product and business problems. Liam’s view was that both models are valid responses to different organisational needs, but they produce very different relationships with cloud native technology. That, in turn, affects how adoption should be interpreted. He also pointed to what may become one of the more consequential findings over time: the long tail of organisations that remain underrepresented in industry narratives. Liam noted that many capable developers operate in environments with low budgets, limited internal leadership, compliance constraints, or delayed access to newer tooling. In his telling, the cloud native market is not only being shaped by advanced platform teams and large-scale adopters, but also by organisations that are arriving later and more gradually. That makes the ecosystem broader, but also more uneven than headline numbers alone suggest. By the close of the interview, Liam’s broader message was clear: the meaning of “cloud native developer” is evolving, and the community may expand even faster than the raw numbers suggest as more people begin to recognise that the tools and workflows they already use fall under that label. In that respect, the interview was less about celebrating a large number in and of itself, but about redefining the boundaries of a maturing ecosystem. The full interview:

Key Questions Answered How has engagement with AI changed amongst professional developers over the past 2 years? Where do open and open-source models stand when it comes to embedding AI functionality into applications? Click to expand ACCESS THE FULL REPORT Methodology The 31st edition of the Developer Nation survey reached more than 11,500 respondents from 95 countries around the world. This research report series delves into key developer trends for Q1 2026 and beyond. Contact us First name* Last name* Work Email* Company * Role* Message I agree to SlashData's Privacy Policy and I want to be contacted * SUBMIT All Reports Questions? Let's talk! Fill the form. Natasa and Petro will help you drive developer adoption: Name Email I have read and agree to SlashData's Privacy Policy and I want to be contacted. GET IN TOUCH WITH ME Frequently asked questions (F.A.Q.) Who benefits from these reports? Leaders who need to: Make or defend a high-stakes decision Secure budget Enter a market Reposition a product Prove ROI Reduce uncertainty Build a high-adoption product roadmap If you’re under pressure to justify a strategic move with evidence, that’s where our insights help most. What makes SlashData different from other research providers? We focus on developers as the deciders of the future. They are the early adopters and signal the trends to come. Our methodology is rigorous and airtight, ensuring our data is of the highest quality and exceeds industry standards. Our research connects: Developer awareness, adoption, usage and satisfaction Feature importance and pricing sensitivity Competitive positioning and switching behaviour So you don’t just get numbers. You get decision context: what the data means for your roadmap, budget, or go-to-market. How reliable is the data? We run large-scale, independent, global developer research surveys with consistent methodology across waves. Our methodology is rigorous both at clearing our fraudulent (or inaccurate) data and in weighting for proper distribution and reduced margin of error. We take pride in our supreme data quality, and we can prove it. Our results are defensible evidence you can take to an exec review without thinking twice. Can this support a business case or budget proposal? Yes. Many clients use our data to: Justify entering a new developer segment Prioritise features based on quantified demand Secure budget for DevRel, AI tooling, or marketing Validate pricing or packaging changes We translate population estimates and sentiment data into clear, actionable foresight, not charts. How do you ensure credibility with our executive team? We take pride in our rigorous methodology. Our work is: Global and independent Methodologically rigorous Focused on clean data Transparent in assumptions Clear about weighting and limitations. We are confident our insights can stand up to any CXO or Board scrutiny. Do you replace internal research or complement it? We complement it. Internal Analytics tells you what your users are doing. We tell you what the market is doing, including non-users and competitor users. That combination gives you: Internal performance clarity External positioning confidence Together, it reduces blind spots and uncovers opportunities I don’t see my company/product on the list. Can you benchmark us against our competitors? Most probably yes. Even if you don’t see your product in a report, we can pull extensive data for you on awareness, trial, usage, satisfaction, and feature perception. Please get in touch and we can dive in it together. Is this relevant if we sell AI tooling? Absolutely. For AI vendors, we support: Developer adoption tracking Productivity gains Tool benchmarking on awareness, engagement and more Enterprise buying criteria insights Security and compliance sentiment ROI perception We help you understand both AI tool builders demand and buying friction. How long do projects typically take? It depends on complexity. Some projects are focused and fast. Others involve deeper segmentation or spread across multiple geographies. Depending on the request, we will share a roadmap early on so you know when to expect what. Progression of how developers engage with AI Trends amongst professional software developers, Q1 2024 to Q1 2026 Access the Full Report About this Report This report examines how professional software developers’ engagement with artificial intelligence (AI) has evolved between Q1 2024 and Q1 2026. We first look at the overall state of AI engagement in Q1 2026 and then assess how that picture has changed over time. We then focus on developers who are adding AI functionality to applications to understand what types of models they are using and what this may indicate about the direction of the market. Results are based on data collected from a combined pool of over 47,000 professional developers over six waves of our global Developer Nation survey1. More information about how the survey was conducted can be found in the Methodology section at the end of this report. 1Sample sizes per wave: 7,380 (Q1 2024); 7,567 (Q3 2024); 4,933 (Q4 2024); 8,175 (Q1 2025); 9,540 (Q3 2025); 10,278 (Q1 2026).

Industry and technology market reports that are free to access and download and share key insights on the trending technology and software development trends. Insights with instant access for your decision-making . Analyst insights powered by developers around the world. Each insights report dives into a key trending topic. Explore our latest research 12 May 2026 Cloud Repatriation: Not the Story We've Been Told MORE 30 April 2026 Progression of how developers engage with AI MORE 21 April 2026 AI in the Developer Workflow MORE 20 April 2026 Sizing programming language communities MORE 17 April 2026 The AI ROI Measurement Gap MORE 2 April 2026 Where developers go to learn, connect, and stay informed? MORE 31 March 2026 State of Cloud Native Development Q1 2026 MORE 25 March 2026 CNCF Technology Radar Q1 2026 MORE 11 December 2025 AI Coding Tools Benchmark MORE 20 November 2025 Developers in the age of AI MORE 14 November 2025 Barriers to integrating generative AI in applications MORE 14 November 2025 The state of agentic AI adoption in software projects MORE 14 November 2025 Choosing the right AI coding tools for your team MORE 11 November 2025 CNCF Technology Radar Q3 2025 MORE 10 November 2025 State of Cloud Native Development Q3 2025 MORE 21 October 2025 The State of FinOps in 2025 MORE 21 October 2025 Sizing programming language communities MORE 21 October 2025 Benchmarking backend and cloud technology strategies MORE 12 September 2025 2025 Cloud Landscape in Europe and the US MORE 7 May 2025 Usage of AI assistance between DORA performance groups MORE 7 May 2025 Challenges organisations face in software development projects MORE 6 May 2025 The developers behind generative AI applications MORE 6 May 2025 Sizing programming language communities MORE 6 May 2025 How and why developers engage with emerging technologies MORE 6 May 2025 How technology practitioners use social media MORE 16 April 2025 The state of cloud operations and management in 2025 and the impact of AI MORE 4 April 2025 CNCF Technology Radar MORE 10 March 2025 Generative AI for Business: Success, Challenges and the Future MORE 11 February 2025 State of Development Environments MORE 1 December 2024 Profiling of technology professionals working at startups MORE 29 November 2024 CNCF Technology Landscape Radar MORE 1 November 2024 The rise of AI-chatbots for problem-solving MORE 1 November 2024 Network APIs: The new oil in the 5G economy MORE 1 November 2024 Sizing programming language communities Q3 2024 MORE 1 November 2024 What developers think about their teams MORE 1 November 2024 How developers build AI-enabled applications MORE 1 May 2024 How and why developers engage with emerging technologies MORE 1 May 2024 Threats in software supply chain management MORE 1 May 2024 How happy are developers with their jobs? MORE 1 May 2024 How developers interact with AI technologies MORE 1 May 2024 Profiling of new ML/AI developers MORE 1 May 2024 Sizing programming language communities Q1 2024 MORE 1 April 2024 State of Continuous Integration and Continuous Delivery Report 2024 MORE 13 March 2024 How Silicon Developers help developers build AI solutions MORE 1 February 2024 Maturity of Software Supply Chain Security Practices 2024 MORE 1 November 2023 25th edition - State of the Developer Nation MORE 1 September 2023 Developer Perceptions of Distributed Cloud MORE 1 September 2023 The State of WebAssembly 2023 MORE 1 July 2023 Designing for success MORE 1 June 2023 2023 state of data management solutions for digital natives MORE 1 June 2023 The state of developer happiness MORE 1 May 2023 24th edition - State of the Developer Nation MORE 1 May 2023 State of Continuous Delivery Report 2023 MORE 1 May 2023 Building and Developing on Salesforce Report 2023 MORE 1 February 2023 Securing the enterprise MORE 1 November 2022 NGINX State of App and API Delivery Report MORE 1 November 2022 Developers & Shift-left Security MORE 1 October 2022 23rd edition - State of the Developer Nation MORE 1 May 2022 AI Coding Tools MORE Can’t find what you are looking for? Get in touch and we will be happy to help LET'S TALK Case Studies Explore real life scenarios, and how we helped our clients access key market information. We include the process, what SlashData brought to the table and the results they achieved. CASE STUDIES

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