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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:

Cloud deployment has become an integral part of software development, with the vast majority of developers now relying on cloud infrastructure in some capacity. While cloud deployment has become nearly universal, not all cloud usage is equally sophisticated. Despite widespread adoption of cloud infrastructure, many developers and organisations have yet to fully leverage cloud native technologies and architectural patterns that unlock the cloud's full potential. This report , produced in partnership with the Cloud Native Computing Foundation, presents the latest insights into the state of cloud native developers in Q1 2026. It provides an analysis of key trends shaping the cloud native ecosystem, drawing on data from the 31st edition of SlashData’s Developer Nation survey, fielded between December 2025 and January 2026, which reached more than 12,500 respondents from 100 countries around the world. Cloud Native developer population in 2026 The cloud native developer community continues to expand in absolute terms, growing from 15.6M developers in Q3 2025 to approximately 19.9M in Q1 2026. This represents roughly 39% of the entire global developer population and reflects cloud native practices are spreading beyond their backend infrastructure origins into mainstream software development across domains. Among backend developers specifically — historically the core cloud native community — 52% are classified as cloud native in Q1 2026, down from 58% in Q3 2025 but still above the 49% recorded in Q1 2025. While the quarter-on-quarter decline is notable, it may reflect normal variation or indicate that Q3 2025's 58% was elevated. The Q3 2026 measurement will clarify whether the typical baseline is closer to 52%, 58%, or somewhere between. Year-over-year growth remains positive. It is also worth noting that these shifts in values are happening in a period where the developer job market is undergoing pressures — in particular, the restructuring that was seen during the 2024–2025 period. While it is not possible to establish direct causation between the market forces and cloud native adoption, developers educated under the normalization of cloud native technologies entering the market at restricted or impacted rates may be a driver of this six-month decline. In Q1 2026, we estimate that there are 4.5M cloud native developers in Western Europe and 1.2M in Eastern Europe. The expansion of the cloud native population was broad-based rather than concentrated in any single area. Professional games developers, for example, increased from 30% cloud native in Q3 2025 to 39% in Q1 2026, while industrial IoT developers rose from 38% to 42% over the same period. These increases reflect cloud native technologies becoming standard tools across diverse development contexts, not just those in specialized infrastructure work. Read the full report. About the author Liam Bollmann - Dodd, Principal Market Research Consultant at SlashData Liam is a former experimental antimatter physicist, and he obtained a PhD in Physics while working at CERN. He is interested in the changing landscape of cloud development, cybersecurity, and the relationship between technological developments and their impact on society.

The AI transformation currently taking over the software development industry has already shown that many aspects of this revolution are here to stay. Amidst rapidly changing landscapes, we have always found solace in the reality of data, sourced through best-in-class research.  The software/AI industry is drowning in noise: Hype cycles, vendor-spin, conflicting “evidence”, model benchmarks that don't reflect real-world use, and adoption statistics that are basically…marketing.  Executives in this space have been burned repeatedly by analysis that turned out to be extrapolation dressed up as data. CTOs, CPOs, and VPs of Engineering make daily build/buy/partner decisions, AI adoption strategies, and platform bets. These are high-stakes, high-regret decisions. The cost of acting on bad analysis is enormous.  We’re proud to be able to tell them what's actually happening: how software gets built, what developers need, what teams prioritise, and how AI is actually adopted. With that in mind, this article is a “highlight reel” of the top findings we discovered over the past few months.  A new, updated batch of insights is coming in within the next few weeks. Join the newsletter to get updated first. Here’s what you need to know, now (with sources because we’re into insights, not clickbait). If you want to know something very specific, we're here for you . Artificial Intelligence in software development highlights in Q1 2026 AI on Edge: An on-device focus What we found: Smartphones and tablets are rapidly evolving edge AI targets, driving demand for NPU-optimised on-device models. Source: Integration of AI into edge devices Edge devices are becoming an increasingly important way for artificial intelligence (AI) to reach end users, from smartphones and laptops to wearables, industrial machines, and connected vehicles. This report aims to understand how developers are currently integrating AI models into edge devices and where the main opportunities to reduce friction lie. Based on a global survey of professional software developers who reported building or implementing AI functionality in the 30th edition of our global Developer Nation survey, the analysis details the widespread usage of edgeAI among these developers, regional differences, the devices they target, the approaches they use, and the main challenges they face when deploying models on the edge. AI in Game Development  What we found: Over half of game developers fear AI will further reduce job opportunities amid an already fragile industry marked by widespread layoffs Source: The State of Game Development 2025 In this report, we take a look at today’s landscape of game development. We examine who game developers are, the technologies, engines, and programming languages they rely on, the platforms they target, and the types of games they create. The report also explores how game developers perceive the impact of AI in the industry, shedding light on both the opportunities and the challenges it introduces. AI tool usage across professional developers (full report free to access) What we found: As of Q3 2025, ChatGPT and GitHub Copilot lead in adoption and satisfaction as AI-assisted coding tools among professional developers, reinforcing their position as the safest bets for large-scale rollouts. Source: Choosing the right AI coding tools for your team The rapid rise of AI-assisted coding tools marks a pivotal moment in software development. What began as experimental add-ons has quickly evolved into a crowded market of products, each claiming to boost productivity and transform workflows. Yet with so many options and so much noise, it can be difficult to know which tools are truly delivering value. By examining adoption, satisfaction, and trust-related attributes such as accuracy, support, and security, this report provides a data-driven benchmark of which AI coding tools developers are embracing and which they rate most highly. The analysis reveals where usage aligns with satisfaction, where trust is earned through consistent delivery, and where gaps remain between expectations and reality. For engineering leaders making decisions about which tools to integrate and scale across their teams, the insights in this report help distinguish the coding tools that enable productivity from those that are still struggling to meet developer needs. AI Blockers: why developers don’t build GenAI apps What we found: Most developers remain open to using generative AI if key concerns are addressed. However, stronger privacy and security controls are at the top of confidence drivers, especially for those facing data and compliance barriers. Source: Understanding the reluctance towards building generative AI applications The aim of this report is to understand what prevents developers from integrating generative AI functionality into their applications and what could increase their confidence to do so. For vendors of generative AI platforms and APIs, these findings highlight the areas where developers most need reassurance and support, from robust data protection and clear documentation to seamless integration paths. Agentic AI in software projects (full report free to access) What we found: Agentic AI is moving beyond the experimental stage. Of those integrating AI into their applications, half have already deployed agentic AI architectures to production Source: The state of agentic AI adoption in software projects Agentic AI is emerging as one of the most transformative shifts in how companies design and deploy intelligent systems. This mini-report analyses insights from over 8,400 professional developers to help CTOs and engineering leaders navigate the rapidly evolving agentic AI landscape and make informed architecture and use case decisions. We’ll explore how the implementation of agentic AI varies by company size and project type as well as looking at the types of agentic architectures that are being deployed to production, along with the use cases developers are targeting. Programming language communities and software developer population size  There are 48.4 million developers around the world “How Many Developers Are There in the World?” is our most frequently asked question here at SlashData, both from Product and Marketing people who want to measure adoption, executives who care about their Target Addressable Market (TAM), and software industry journalists and enthusiasts.  To help them all with their goals, we happily share this number and update it as new data becomes available. Go ahead and confidently use this number in your pitch, BoD presentation, or article. We follow a strict methodology to ensure that this is the most accurate estimate you can get. Developer Population Trends Tracking Page JavaScript is the most popular language for software development (full report free to access) What we found: As of Q3 2025, JavaScript was the largest language community, with approximately 27M developers worldwide. Source: Sizing programming language communities Programming languages sit at the heart of the software development ecosystem, shaping not only the kinds of projects developers work on but also the communities they become part of. For product executives, understanding language adoption is more than an academic exercise as it directly informs decisions about which SDKs, APIs, and platform features to prioritise. Choosing the right languages to support can expand the reach of your platform, lower barriers for developers, and ultimately drive product adoption. Assessing how widely used a programming language is and estimating the size of each language community in absolute terms remains a challenge. The estimates presented here are based on two key data sources. First is our independent estimate of the global number of software developers, which we have been publishing for more than eight years. Second is our large-scale surveys, which reach tens of thousands of developers every six months.  A look into DevOps  DevOps: Lack of standardisation is connected to less security What we found: Organisations without DevOps standardisation show between two and three times lower rates of integrating security practices into their CI/CD pipelines Source: Impact of Platform Strategies on Security Practices in Software Development This report examines the security practices that developers integrate into their CI/CD pipelines, with a particular focus on how platform standardisation approaches influence which security tools see adoption and success. In this report, platform standardisation refers to organisation-wide standardisation strategies for DevOps practices, and we categorise platform configurations into five distinct groups: specialised internal developer platforms (IDPs), dedicated teams or individuals responsible for developer experience, unified systems for managing DevOps processes, curated lists of approved tools, and organisations engaging in none of these approaches. This report is based on data from SlashData’s 30th edition of the Developer Nation survey and represents the adoption patterns of more than 4,700 professional developers using CI/CD pipelines.  Company size and industry shape affect deployment strategies (full report free to access) What we found: As organisations grow in size, two overarching strategies to backend DevOps maturity emerge, with some empowering their developers to use a wide range of advanced technologies effectively, while others abstract away infrastructure behind internal development platforms leading developers to prioritise business needs Source: Benchmarking backend and cloud technology strategies This report examines cloud and server-side technology adoption patterns across organisation sizes and industry sectors, revealing insights that challenge conventional wisdom about technology maturity.We explore how multi-environment strategies evolve with organisational scale, why container adoption varies across company sizes, and how platform teams create infrastructure capabilities that are frequently invisible to their developers. Through analysis of deployment strategies, modern architecture adoption, and industry-specific technology leadership, we provide IT executives with frameworks for evaluating their technology strategies against relevant peer organisations rather than generic industry trends.The findings reveal that successful technology adoption depends lesson following best practices and more on aligning technology choices with organisational capabilities, industry requirements, and strategic priorities. Cloud updates you should know in Q1 2026 Cloud-native development (update coming in March 2026) What we found: There are 15.6M cloud native developers, of which 9.3M are backend developers Source: State of Cloud Native Development Q3 2025 (full report free to access) This report explores the current state and scale of cloud native development in Q3 2025. The report provides approximations of the cloud-native developer population in backend services, machine learning or AI, and throughout the entire developer population. The report also provides information on the popularity of different cloud native technologies or approaches among backend developers, to reveal the sophistication path organisations often go through.  In addition, the report explores the trends in cloud deployment approaches, as well as the technologies that developers are using in their backend or cloud development processes and services. We also provide estimates for the proportion of cloud nativeness throughout the range of types of development (e.g. mobile, desktop, DevOps, etc.).  Data residency: Compliance in practice  What we found: Collaboration between developers and legal teams is the leading challenge developers cite. Source: Data Residency Compliance Challenges and Organisational Responsibility This report provides an examination of how organisations are coping with data residency compliance in practice. It explores the primary challenges developers face when building compliant services, how these challenges vary by region and organisation size, and where responsibility for compliance tasks falls within organisations. The analysis reveals significant regional differences in both the nature of compliance challenges and how organisations structure accountability, offering insights for how cloud service providers (CSPs) should design their compliance offerings and which capabilities matter most to customers in different markets. FinOps beyond cost-cutting (full report free to access) What we found: Mid-sized organisations lead in FinOps adoption, likely due to scaling cloud complexity.  Budget monitoring and reporting are the most common FinOps activities, highlighting the importance of visibility into cloud spending. Source: The State of FinOps in 2025 Cloud spending can become one of the largest operational expenses for tech companies. It is often unpredictable due to elastic consumption models, hundreds of services and pricing models, and decentralised purchasing by developer teams (particularly in large companies). Cloud financial management (FinOps) sits at the intersection of finance, engineering, and product, ensuring that cloud resources are used efficiently, focusing on aligning cloud spending with business value, not just cost-cutting. In this report, we examine insights from over 6,300 professional developers working for companies with at least 2 employees who use cloud services. We’ll explore the adoption rate of FinOps practices among developer teams and how it varies by company size and region. Additionally, we’ll cover how teams implement FinOps by looking at the specific practices they have embraced. The report is designed to help technical leaders benchmark their organisations against industry peers and make informed decisions about where to focus their FinOps efforts. AR, VR and IIoT software developer trends AR and VR: ARVR practitioner numbers remain stable What we found: There are approximately five million AR/VR practitioners worldwide, a figure that has remained relatively stable over the past two years. 83% of AR/VR practitioners are leveraging AI across multiple use cases, from coding to content creation. Source: The State of AR/VR Development 2025 This report provides a detailed examination of today’s XR landscape. It explores how many practitioners there are, how they participate in the ecosystem, the types of projects they are building, and the platforms they target. It also investigates how XR practitioners are leveraging AI and which other technologies make up their stack. Finally, the report looks at the main challenges XR practitioners face today and looks ahead to the future of the AR and VR industries, capturing XR practitioners’ and other developers’ perspectives on its direction for the next decade. IIoT onboarding is frictionless  What we found: First IIoT development board onboarding is largely frictionless, as 65+% of professional developers involved in IIoT projects find “setting up the hardware” and “running a basic project” easy. Source: IIoT accessibility This report explores how developers begin their IIoT journey: which development boards they start with, how they experience onboarding across tasks and ecosystems, and how these patterns differ by professional status, experience level, and region. The findings highlight where the industry is lowering technical barriers and where better documentation, community support, or learning pathways are still needed. Insights come from the 30th edition of the Developer Nation survey, which ran from June to August 2025 and reached 830 developers worldwide involved in IIoT projects. After 20+ years of researching the software industry, we have a huge (HUGE) data library we can tap into to answer your questions. Our analysts are subject-matter experts on software development topics and can foresee trends and help you power your strategy with evidence. Let's dive into your priorities together. Get in touch . About the author Stathis Georgakopoulos, Marketing Manager at SlashData Stathis leads SlashData's marketing activities and 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 and terrible puns.

The games industry has faced significant turbulence in recent years, marked by widespread layoffs, reduced investment , and declining market confidence. Earlier this month, Google’s Project Genie announcement triggered a sharp drop in several major game stocks , including Unity, Roblox, and Take-Two, further highlighting the broader uncertainty surrounding the industry’s direction. Against this backdrop, AI has emerged as both a widely adopted tool and a highly contested topic. Player communities have pushed back visibly; review-bombing titles suspected of using AI-generated art, criticising Ubisoft’s AI-powered NPC system, and prompting Valve to update Steam’s policies to require developers to disclose AI-generated content following sustained community pressure. In some cases, studios have even cancelled projects and publicly committed to avoiding AI altogether. 76% of professional game developers are currently using AI to assist with coding or generate creative assets Despite this backlash, the data shows that AI adoption is already mainstream among game developers. According to our Q3 2025 survey with more than 2,000 game developers, 66% are currently using AI to assist with coding or generate creative assets. Among professional game developers, that figure rises to 76%. In this blog post, we’ll explore how game developers perceive AI’s impact across several key dimensions. The full findings, along with insights into the platforms developers target, the engines they use, or the types of games they build, are available in The State of Game Development 2025  report. AI accelerates game production and might help indies rival big studios, but raises alarms over shrinking career opportunities So how do game developers themselves evaluate AI’s impact? Beyond public backlash, our data reveals a more nuanced perspective from within the industry. The most widely shared perception is that AI accelerates the game development process. Over two-thirds (68%) of game developers agree with this statement, highlighting how AI is reducing friction from concept to execution. This highlights AI’s role in accelerating coding workflows (e.g. boilerplate code, debugging, and troubleshooting), as well as in enabling faster prototyping and iteration for assets. 68% of game developers agree that AI accelerates the game development procecss. A closely related finding is that 62% of game developers believe AI will make it easier for indie developers and smaller studios to compete with large publishers. However, indie developers themselves are the least convinced. Only 58% agree, compared to over 70% of those working for publishers or large studios. Indie developers might recognise that while AI can amplify their capabilities, it also scales the advantages of well-resourced studios, enabling them to produce more content, iterate faster, and optimise performance at greater scale. Moreover, many indie game developers might face their biggest challenges in areas like distribution, visibility, and marketing, which remain largely beyond AI’s scope. When it comes to career opportunities, just over half (55%) of game developers believe that AI will reduce the number of roles and opportunities available in the industry. This concern sits within a broader context of instability across the tech sector, one that has disproportionally affected games . The relationship between AI adoption and employment uncertainty remains a debate. On the one hand, AI can augment productivity and create demand for new hybrid skill sets. On the other hand, it risks displacing entry-level responsibilities, as automation absorbs many of the structured, repetitive tasks that once served as gateways for junior developers. As seen in the Stanford Digital Economy study , for jobs with high AI exposure, such as IT and software engineering, employment has been steadily declining for early-career professionals while increasing for the more seasoned ones. If this pattern extends to game development, the industry may face a structural challenge: fewer entry points for newcomers, combined with growing demand for senior talent to oversee, integrate, and validate AI systems. Game developers believe AI enhances player experience, while noting bugs and creativity risks Despite the backlash from some players towards games that use AI, 62% of game developers believe that integrating AI improves the overall player experience. From adaptive difficulty systems and more responsive AI-powered NPCs to personalised storylines and dynamic environments, AI is viewed as a tool that can potentially enable richer, more immersive, and more reactive gameplay. However, confidence is lower among developers in creative roles (art, asset production, audio), where 56% agree. Concerns about originality further illustrate this divide. Overall, 52% of game developers believe AI poses a threat to creative originality, rising to 59% among those involved in creative activities. Many of these creative practitioners might fear that AI-generated content, trained on similar datasets and optimised for popular aesthetics, leads to homogenised content that prioritises speed and scale over originality, making games feel increasingly alike. For many game developers, the drive for efficiency risks dulling the diversity and individuality that define great games if AI is adopted without strong creative direction. There are also technical reservations. Although most developers acknowledge AI’s productivity benefits, 53% agree it increases the risk of bugs or unpredictable behaviour in games. Unlike traditional rule-based systems, AI models can behave in ways that are difficult to fully anticipate, test, or reproduce. This unpredictability can lead to broken dialogue trees, erratic NPC behaviour, balance issues, or edge-case logic loops that only emerge under specific player interactions. As a result, while AI can enhance immersion, it can also introduce new layers of systemic complexity that demand stronger oversight, validation processes, and design safeguards. Taken together, the findings in this blog post suggest that AI adoption in the game development industry is widely perceived as beneficial, but not without meaningful trade-offs. While AI is transforming workflows and accelerating production, it also raises concerns about shrinking career opportunities, creative homogenisation, and technical unpredictability. Ultimately, AI’s role in game development will be shaped not only by what the technology makes possible, but by the strategic decisions developers make about how, and how far, to integrate it. Dive deeper into the game development world. Explore what is shaping the industry with the help of our analysts and 20+ years of software development data. Book a call with Natasa and Petro. About the author Alvaro Ruiz Cubero, Research Manager, SlashData ​Álvaro is a market research analyst with a background in strategy and operations consulting. He holds a Master’s in Business Management and believes in the power of data-driven decision-making. Álvaro is passionate about helping businesses tackle complex strategic business challenges and make strategic decisions that are backed by thorough research and analysis.

Edge devices are becoming an increasingly important way for artificial intelligence (AI) to reach end users, from smartphones and laptops to wearables, industrial machines, and connected vehicles. Running models directly on these devices can improve responsiveness, support offline or low-connectivity scenarios, and reduce the need to transmit sensitive data to the cloud. At the same time, doing more on the device introduces new constraints around compute, power, storage, and how data privacy and security are managed in practice. At the infrastructure level, recent industry analysis points to hundreds of billions of dollars being spent on edge computing over the next few years [ ref1 ] and several trillion dollars of cumulative investment in AI-driven compute capacity by 2030 [ ref2 ]. For anyone building hardware, frameworks, or platforms for AI at the edge, understanding how developers fit into this picture is essential. Here, we use SlashData’s latest Developer Nation data toestimate the size and growth of professional developers integrating AI into edge devices, and where this work is concentrated. Find a deeper analysis on the full report . 11 million professional edge AI developers worldwide and growing As of Q3 2025, we estimate that there are currently around 38.4 million professional developers worldwide. Of these developers, 29% (11 million) report building or integrating AI functionality on projects that target direct implementation into edge devices. We refer to this group as edge AI developers. Our data shows that edge AI is therefore already a substantial part of the AI developer ecosystem, rather than a niche reserved for early adopters. There are currently around 38.4 million professional developers worldwide. Of these developers, 29% (11 million) report building or integrating AI functionality on projects that target direct implementation into edge devices. We forecast that this population will receive substantial annual growth, even under conservative assumptions. In our conservative scenario, the number of edge AI developers is expected to rise by 30% to 14.3 million by late Q3 2026. Meanwhile, in the optimistic scenario, this figure reaches 18.1 million, representing a 64% increase. In both cases, the pool of developers integrating AI into edge devices is a moving target rather than a static market. As such, vendors should plan for a larger, more diverse edge AI audience in the near term. For technology leaders, there are three clear implications: Treat  edge AI as a strategic focus area with dedicated product planning and clear ownership, rather than as an add-on to cloud-only AI initiatives. Act  early to capture default status with developers by using the coming year of growth to position your products, APIs, and hardware platforms as the natural choice for teams starting or expanding edge AI work. Track  edge AI separately from broader AI efforts, so that usage, community engagement, and revenue for edge-specific offerings are visible in their own right and can inform investment decisions. Regional hotspots for edge AI development As of Q3 2025, edge AI activity is concentrated in three major hubs. North America and Western Europe account for 3.1 million and 2.9 million edge AI developers, respectively, while the Greater China area forms a third major centre at about 2.4 million. By Q3 2026, these regions are projected to grow by 30% to 60%, making them the highest-priority markets for advanced edge AI offerings where both scale and absolute growth are strongest.  The Middle East and Africa (MEA) and South Asia present notable but smaller markets, each with 800 thousand professional edge AI developers. However, we see major opportunities in our optimistic forecast, with both regions potentially reaching 1.4 million each by late Q3 2026. Vendors looking to grow in these two regions may benefit from lowering barriers to first deployments by offering accessible hardware options, opinionated tooling, and strong implementation support. South America presents a more extreme case, where the focus on edge AI development is significantly lower than in other regions. As such, penetrating this market may require a longer-term commitment, with particular emphasis on education, partnerships, and solutions that clearly demonstrate value under tighter resource constraints. At the same time, there is considerable interest and clear indications of increased activity over the next 12 months. This combination of low current penetration and rising intent points to significant headroom for growth for vendors prepared to invest early and build a presence over time. Edge AI is already a mainstream developer activity with clear room to grow Taken together, these findings show that edge AI is already a mainstream developer activity with clear room to grow, rather than an early-stage experiment. There are already 11 million professional developers working on AI functionality for edge devices worldwide, with an expected annual growth rate between 30% and 64% at the present time. North America, Western Europe, and the Greater China area are leading both in scale and growth, highlighting the three natural priority markets for edge AI offerings. Meanwhile, the Middle East & Africa, South Asia, and South America represent smaller markets with headroom for investment. Building tooling for edge AI? Access our full report , which breaks down device targets, integration patterns, and adoption barriers.  About the author Nikita Solodkov, Principal Research Consultant at SlashData Nikita Solodkov is a multidisciplinary researcher with a particular interest in using data-driven insights to solve real-world problems. He holds a PhD in Physics and has over five years of experience in data analytics and research design

Why a satisfaction score of 6 in Japan might be better than an 8 in China If you’ve ever looked at the same satisfaction question broken down by country and thought, “ Why are these numbers so wildly different? ” – you’re not alone. In global research, interpreting data responsibly is one of the hardest parts. At SlashData, we run developer studies across regions year after year (including our rolling Developer Program Benchmarking (DPB) , where we help vendors identify concrete improvement paths for their developer programs). One pattern shows up consistently: the way respondents use rating scales is deeply cultural. A satisfaction heatmap promises a unified view of performance. Executives can scan rows and columns for the green of success or the red of failure. Yet, once that data spans continents, it often conceals as much as it reveals. As we will see throughout this post, a growing body of research suggests that the standardised metric of customer satisfaction is often just a map of cultural biases. Without a cultural lens, your global heatmap isn't just a distorted mirror – it’s a dangerous map that can lead to strategic missteps, from the misallocation of resources to the unfair penalisation of high-performing regional teams. For simplicity, in this blog, we will assume a satisfaction scale from 0 to 10. Treat global satisfaction scores as directly comparable, and you can end up misallocating budget, fixing markets that aren’t broken, or missing early warning signals in markets that look healthy. The "Optimists" vs the "Sceptics" (and the Japan paradox) A quick glance at our DPB vendor satisfaction cuts often reveals a geographic divide. North American respondents are frequently at the high end. We also see a cluster of high-scoring Southeast and East Asian markets – especially the Philippines, Vietnam, Indonesia, and China. On the other hand, Japan consistently shows up as more conservative in its ratings, and we often see the same tougher grading in parts of Western Europe, including Germany and the Netherlands. This is not just a SlashData thing. Ipsos specifically flags that the Philippines, Indonesia, and Vietnam give high scores, while other Asian markets provide much lower scores, explicitly including Japan in the low-scoring set. And SurveyMonkey ’s cross-country NPS study shows just how dramatic this can be on a simple 0-10 “recommend” scale: Japan is the lowest of the markets they studied, while the United States and Canada are much higher, and the Netherlands sits well behind many other countries. If taken at face value, this data would suggest that developer programmes are thriving in North America but failing to impress in Western Europe. But does this align with actual retention rates? Often, the answer is no. This disconnect is likely driven by a few powerful cultural forces, like optimism bias and polite agreement on the one hand, and scepticism on the other.  High satisfaction scores can coexist with low developer retention, especially across regions. North America & Emerging Asia are “optimists” when it comes to surveys Research indicates that in cultures prioritising social harmony, such as some Asian markets (often correlated with high collectivism), respondents are predisposed to be agreeable. For high-scoring Asian markets (like the Philippines, Vietnam, China, Indonesia), one driver is often what researchers call agreement and harmony effects: in some cultures, direct negative feedback is less comfortable, and respondents can lean toward more socially acceptable, relationship-preserving answers. S ome markets systematically use the top end of the scale more than others, enough to reorder league tables without any real change in underlying experience. In high-agreement environments, a score of 8-9 can be closer to “fine” than “fanatically loyal”. The danger is overconfidence. In the US and Canada, high scores are often driven by Optimism Bias . Culturally, there is a tendency to view things in a positive light. “Good” is often rated as “Great”. As noted in global NPS studies by SurveyMonkey , American respondents consistently score higher than their European counterparts for the same service levels. Western Europe & Japan are the “sceptics” when it comes to surveys On the other side of the chart, we see Western Europe (e.g. Germany, the Netherlands) and Japan hovering at the bottom of the scale. Western Europe is home to the “ Dutch effect ” or sober grading. In these cultures, hyperbole is viewed with suspicion. A score of 10 is reserved for perfection – a standard almost no B2B service achieves. Japan is the ultimate outlier in our data. Despite being geographically close to the high-scoring Asian nations, it often produces the lowest satisfaction scores in the world. Multiple other studies reveal this pattern: in Japan, service expectations are famously high. A minor friction point that an American consumer might forgive is often punished harshly by Japanese consumers in surveys. Importantly, lower scores don’t automatically mean unhappiness. It often means that top ratings are reserved for rare perfection, and the cultural norm is to score more conservatively.  The cultural bias disconnect: confusing ratings with retention The biggest error a vendor can make is assuming a linear, universal relationship between these scores and churn. In harder-grading markets like Japan and some of Western Europe, a 6 or 7 can still behave like a loyal consumer. If you treat every sceptic as a problem, you risk wasting resources fixing relationships that aren’t broken – or worse, annoying stable customers with constant nudges to “rate us a 10.” In high-scoring markets, the risk flips. If the baseline is inflated, then a drop from 9 to 8 might not be just noise – it can be your early warning signal. And if the culture discourages direct complaints, you may not hear why until the customer leaves. If direct criticism is culturally discouraged, customers can look satisfied right up until they churn. Your first visible signal can be cancellation, not feedback. Adding a culturally intelligent framework to your research To navigate this landscape, vendors and clients must move beyond raw comparisons and adopt a relative, culturally intelligent framework. Benchmark intranationally, not internationally  Stop asking, " Why is our German market less satisfied than the Chinese market? " The correct question is, " How does our German score compare to the German benchmark? " If the regional average is a 6.5 and you score a 7.0, you are likely a market leader. Create country indices that normalise scores against the local average to reveal true performance. This is where tailored services like our Developer Program Benchmarking shine – we help you normalise scores against local averages to reveal true performance. Adjust your internal thresholds In “stoic” markets like parts of Western Europe and Japan, a 7 or 8 can be a genuine win. In high-scoring markets like some East Asian countries, treat anything below the local norm as a potential signal – especially if it’s trending down. Use ranking over rating  Where applicable, complement ratings with questions that force trade-offs. Ranking-style questions (“ Which programme is best? ”) are harder to game through polite scale use than “rate each one 0-10,” and they often reveal the competitive truth hiding beneath the friendliness. Conclusion A red Japan does not necessarily mean a failing program, nor does a green China guarantee a secure future.  By interpreting survey data through a cultural lens – acknowledging the sceptics, the polite promoters, and the scale's structural biases – you get closer to the true voice of the customer. The goal is not to standardise the customers, but to sophisticate the analysis so your data predicts what users will actually do next. Are you worried that your retention strategy is based on skewed heatmaps? We can audit your satisfaction data to separate real performance issues from cultural noise. Contact us today! About the author Bleona Bicaj - Principal Research Consultant Bleona is a research consultant, enthusiastic about product strategy and behavioural science. She holds a Master’s in Economic and Consumer Psychology. With more than 6 years of professional experience as an analyst, she has worked across quantitative and qualitative research studies, turning complex data into clear narratives that inform better products, smarter investments, and long-term growth.

Throughout the development world, we are seeing two competing approaches to DevOps maturity: developer empowerment or business focusing. Both models aim to ensure that their organisations are able to increase their developer velocity, ship securer code, and be able to respond to feedback and demands, but take diametrically opposed approaches to do so.  In this article, we explore both approaches: where each excels, what challenges they create, and how they manifest in real development teams. Drawing on data from SlashData's 30th Developer Nation Survey (which reached more than 10,000 developers globally in summer 2025), we'll show how these philosophical differences translate into concrete security practice adoption patterns, and why organisations should choose based on their specific context rather than industry trends.  Developer Empowerment, Autonomy and Visibility Those who follow the developer empowerment model focus on ensuring their developers are knowledgeable, informed, and have autonomy and visibility over their DevOps processes. Organisations adopting this approach typically value developer satisfaction and retention highly. This model hopes that recognition of experienced developers desire to control their toolchains and their resistance to imposed limitations will create happier developers who are willing to experiment freely. The organisations can provide guidance, approved vendor lists, or internal documentation, but ultimately they leave the decision to the ground-floor developers.  The challenge with this is consistency. Security practices may vary between teams, which can lead to blind spots. While individual developers, or teams, may have high levels of visibility into their processes and build up deep familiarity with security practices, the lack of consistency can lead to blind spots in the organisation-wide security posture. Adding to this challenge is that knowledge can become siloed within teams, with successful approaches not being shared with others. At its worst, developers who lack security experience can have their autonomy instead become a liability rather than an asset. However, while decentralised approaches to security risks gaps, it also allows developers to react very quickly to new vulnerabilities without having to wait on a central platform team.  In our current examination, this can include developers who are provided a curated list of tools for their  selection and configuration (34% of professional developers). This leads to a slightly higher adoption of IDE security checks (32%), pre-commit hooks (20%), and container-scanning(28% ) integrated into their CI/CD pipelines, as they are selecting the tools that they interact with during development.  Business Focus: Abstraction and Efficiency The other approach is business-focused, where the goal is to abstract away the concerns about security, infrastructure, deployment, and other DevOps processes behind an IDP or a controlled list of tooling configured for them (27% of professional developers). This aims to allow the developers to focus more on addressing business needs, and their core responsibilities, rather than having to consider wider aspects of the software development lifecycle. This approach emerges from different organisational priorities, including consistency at scale, meeting compliance requirements, or protecting specific business interests, even if it means constraining developers' choices. This can become especially true for companies with hundreds or thousands of developers, where complete heterogeneity of tooling can create maintenance headaches. In addition, organisations that want to prioritise their developer time on product differentiation, or need to onboard developers rapidly, a centralised process supports both of these. This aims to allow the developers to focus more on addressing business needs, and their core responsibilities, rather than having to consider wider aspects of the software development lifecycle. In practice, this can manifest as developers interacting with an IDP with abstracted interfaces. When a developer might deploy to staging, they may not be aware whether this instruction triggers Kubernetes, ECS, or Cloud Run behind the scenes. Within this approach, security checks happen automatically in the pipeline, where developers see the results but don’t necessarily configure these themselves. With these developers, we see higher rates of SCA (29%), DAST(26%), and IAST (27%) practices built into CI/CD pipelines because these happen behind-the-scenes for developers, which are benefitted by having highly centralised platforms.  However, despite the benefits to organisations, and developers, these systems risk creating ‘black box’ problems. If developers don’t understand what is happening behind the abstraction, they can become less effective at debugging, and have a shallower understanding of security practices. Additionally, platform teams can risk becoming bottlenecks, with every new tool or feature request platform team time. This can leave developers unable to work, or risk them engaging in shadow IT and compromising the goals of centralising security practices The False Choice Neither approach is inherently better or worse than the other. Every few years thought leaders emerge to declare that development teams should shift-left or shift-right  as the ‘correct’ way to do development, or to unlock previously unimaginable benefits. However, the reality is that simply shifting  doesn’t actually do anything, and it is instead the processes, practices, and culture within organisations and development teams that have the largest impact, and centralising or decentralising are just mechanisms to achieve this.  What matters instead is for organisations to consider other factors that will motivate them, and what capabilities it instead needs: faster feedback loops, comprehensive security coverage, developer satisfaction, or operational reliability. Some of these benefit from centralisation, and others from distribution, and organisations frequently blend aspects together to meet their specific needs.  What to consider when choosing a DevOps approach   Rather than asking 'which approach is better?', organisations should ask 'what does our context demand?'. Consider: Organisational size and growth trajectory: A 50-person startup might start with curated lists, knowing they'll need an IDP at 500 people Team security maturity: Less experienced teams may need more guardrails; senior teams may resent them Regulatory requirements: Financial services or healthcare often require centralised control and audit trails Cultural values: Does your organisation optimise for innovation speed or operational consistency? Platform team capacity: Building an IDP requires sustained investment—do you have the people and time? Your choice isn't permanent. Many organisations start with developer autonomy and gradually centralise as they scale. Others go the opposite direction, decentralising after realising their IDP became a bottleneck. The key is being intentional about the trade-offs you're making and regularly reassessing whether your approach still serves your needs. Our team of analysts can help you decide on the best option, using concrete data to help your decision-making. Let’s talk and find the solution that works for you.  About the author Liam Bollmann-Dodd Principal Market Research Consultant at SlashData Liam is a former experimental antimatter physicist, and he obtained a PhD in Physics while working at CERN. He is interested in the changing landscape of cloud development, cybersecurity, and the relationship between technological developments and their impact on society.

With AI taking centre stage these days, we thought we'd take a moment to step out from behind the algorithms. And say something simple: Thank you. Thank you for trusting the brains and hearts behind SlashData to help you make sense of the ever-expanding universe of AI and data. 🎇 From all of us, wishing you a joyful, curious, and very Happy New Year! 🥳 Happy New Year from  Alex ,  Álvaro ,  Andreas ,  Berkol ,  Bleona ,  David ,  Evgenia ,  Jed ,  Liam ,  Maria ,  Máté ,  Mina ,  Natasa ,   Nikita ,  Petro ,  Sarah  and  Stathis ! ❤️

We are entering a new era in which AI doesn’t just generate content, it acts. AI agents, capable of perceiving their environment, making decisions, and taking autonomous actions, are beginning to operate across the enterprise. Unlike traditional Large Language Models (LLMs) that work within a confined prompt-response loop, agents can research information, call APIs, write and execute code, update records, orchestrate workflows, and even collaborate with other agents, all with little to no human supervision. The excitement and hype surrounding AI agents is understandable. When designed and implemented correctly, these agents can radically streamline operations, eliminate tedious manual tasks, accelerate service delivery, and redefine how teams collaborate. McKinsey predicts that agentic AI could unlock between $2.6 trillion and $4.4 trillion  annually across more than sixty enterprise use cases. Yet, this enthusiasm masks a growing and uncomfortable truth. Enterprises leveraging agentic AI face a fundamental tension:  the trade-off between utility and security . An agent can only deliver real value when it’s entrusted with meaningful control, but every additional degree of control carries its own risks. With agents capable of accessing sensitive systems and acting autonomously at machine speed, organisations risk creating a new form of insider threat  (on steroids), and many are not remotely prepared for the security risks that agentic AI introduces.  The vast majority of leaders with cybersecurity responsibilities ( 86% ) reported at least one AI-related incident from January 2024 to January 2025, and fewer than half ( 45% ) feel their company has the internal resources and expertise to conduct comprehensive AI security assessments. Rushing to deploy digital teammates into production before establishing meaningful security architecture has a predictable result. Gartner now forecasts that more than 40% of agentic AI projects will be cancelled by 2027 , citing inadequate risk controls as a key reason. This blog post covers the risks that pose the greatest challenges for organisations building or adopting AI agents today and how to minimise them, enabling technical leaders and developers to make informed, responsible decisions around this technology. Harness the power of agentic AI with our analysts' help. Talk to an analyst here . The dark side of AI agents Rogue actions and the observability gap Traditional software behaves predictably. Given the same inputs, it produces the same outputs. Understanding results and debugging is therefore a matter of tracing logic, replicating conditions, and fixing the underlying error. However, agentic AI breaks this paradigm. Agents do not follow deterministic paths, meaning their behaviour isn’t always repeatable even with identical inputs, and complex, emergent behaviours can arise that weren’t explicitly programmed . Worse, most systems that agents interact with today lack any understanding of why an agent took a particular action. Traditional observability wasn’t designed to  understand why a request happened, only that it did. This creates a profound observability gap, where organisations can’t understand or replay an agent’s decision sequence. A minor change in context, memory, or input phrasing can lead to an entirely different chain of tool calls and outputs. As a result, traditional debugging techniques collapse. When something goes wrong, teams are often left guessing whether the issue came from the underlying model, the agent design, an external dependency, a misconfigured tool, corrupted memory, or adversarial input.  This problem is exacerbated by the degree of autonomy an agent has, as the longer an agent operates independently and the more steps it takes without human oversight, the larger the gap between intention and action can become. Without robust audit logs designed for agentic systems, organisations can’t reliably answer fundamental questions such as: What did the agent do? Why did it choose those actions? What data did it access? Which systems did it interact with? Could the behaviour repeat? Expanded attack surface and agents as a new insider threat When you give an AI agent the ability to act, particularly across internal systems, you effectively create a new privileged user inside your organisation. Too often, this user is granted broad, overly generous permissions, disregarding the principle of least privilege, a cornerstone of cybersecurity. Teams often grant generous permissions because restrictions seem to “block the agent from being helpful”. However, as highlighted earlier in this post, every added degree of autonomy or access carries its own risks. Your “highly efficient digital teammate” can very quickly become a potent insider threat. Granting agents broad access and permissions to internal documents, systems, repositories, or databases dramatically expands an organisation's attack surface, especially when these agents interact with external services. If an attacker succeeds in injecting malicious instructions through poisoned data, manipulated content, compromised memory, tampered tools, or adversarial prompts, the agent can unknowingly carry out harmful actions on the attacker’s behalf. It may leak sensitive information, modify records, escalate privileges, execute financial transactions, trigger unwanted workflows, or expose data to external systems. The danger compounds in multi-agent environments, where one agent’s compromised output can cascade into others, amplifying the impact of even small vulnerabilities. Agentic drift Agents operate in dynamic environments, learn, adapt, and evolve. Over time, this evolution can lead to agentic drift . An agent that performs well today might degrade tomorrow, producing less accurate or entirely incorrect results. Many factors can influence this, such as updates to underlying models, changes to inputs, changes to business context, system integrations, or agent memory. Because drift often emerges gradually, organisations may not notice until the consequences are significant, especially for agents interacting with external stakeholders (e.g. customer service agents) or operating in multi-agent workflows, where drift can cause cascading failures. Moreover, because AI agents are inherently goal-driven, drift can emerge in which agents start optimising for the metrics they can observe, rather than the ones humans intended. This leads to specification gaming , where agents find undesirable shortcuts that technically satisfy the objective while undermining policy, ethics, or safety. For example, an agent tasked to “reduce task completion time” may quietly eliminate necessary review steps; an agent configured to “increase customer satisfaction” might disclose information it shouldn’t; or a coding agent tasked to “fix errors” might make changes that violate security or compliance constraints. How to build agents safely The risks of agentic AI are significant, but the solution is not to avoid agents altogether. The value is too great, and the competitive pressure is too high. Instead, organisations must treat agentic AI as a new class of enterprise technology, requiring its own security model, governance structures, and operational rigour. As the saying goes, “ a chain is only as strong as its weakest link ”. Don’t introduce a weaker one. To position your organisation to harness the full potential of agentic AI safely, it’s essential to understand how to mitigate these risks. Establish a rigid command hierarchy.  To ensure accountability, AI agents must operate under a clearly defined chain of command where human supervision is technically enforced. Every agent should have a designated controller(s) whose directives are distinguishable from other inputs. This distinction is crucial because agents process vast amounts of untrusted data (such as emails or web content) that can contain hidden instructions designed to hijack the system (prompt injection). Therefore, the security architecture must prioritise the controller’s voice and system prompts above all other noise. Furthermore, for high-stakes actions, such as deleting important datasets, sharing sensitive data, authorising financial transactions, or modifying security configurations, explicit human confirmation should always be required (“human-in-the-loop”). Enforce dynamic, context-aware limitations.  Security teams must move beyond broad, static permissions and instead enforce strict, purpose-driven limits on what agents can do. Agents’ capabilities must adapt dynamically to the specific context of the current workflow, extending the traditional principle of least privilege. For example, an agent tasked with doing online research should be technically blocked from deleting files or sharing data, regardless of its base privileges. To achieve this, organisations require robust authentication and authorisation systems designed specifically for AI agents, with secure, traceable credentials that allow administrators to review an agent’s scope and revoke permissions at any time. Ensure observability of reasoning and action.  Transparency is the only way to safely integrate autonomous agents into enterprise workflows. To ensure agents act safely, their operations must be fully visible and auditable. This requires implementing a logging architecture that captures more than just the final result. It must record the agent’s chain of thought, including the inputs received, reasoning steps, tools used, parameters passed, and outputs, enabling organisations to understand why an agent made a specific decision. Crucially, this data cannot remain buried in server logs; it should be displayed in an intuitive interface that allows controllers to inspect the agent's behaviour in real time. Organisations that fail to invest early in these foundations may find themselves facing a new generation of incidents, faster, more powerful, and more opaque than anything their current security posture was designed to handle.  The next wave of innovation will not be driven by models that generate text, but by systems that take action. Is your organisation ready for what those actions entail? At SlashData, we can help you navigate the challenges of implementing and scaling agentic AI systems by providing data-backed evidence and insights on how developers successfully create agentic AI workflows, avoiding common pitfalls along the way. About the author Alvaro Ruiz Cubero, Market Research Analyst, SlashData ​Álvaro is a market research analyst with a background in strategy and operations consulting. He holds a Master’s in Business Management and believes in the power of data-driven decision-making. Álvaro is passionate about helping businesses tackle complex strategic business challenges and make strategic decisions that are backed by thorough research and analysis.

Manchester, 3/11/2025 SlashData has released new findings revealing the real-world adoption of AI in late 2025.  As early adopters and reliable predictors of technology trends, developers provide a window into where AI is heading next. Based on their responses, SlashData highlights three trends transforming the AI landscape: Agentic AI goes mainstream, AI coding tools preferences, Gen AI adoption blockers. AI coding tools: ChatGPT and Copilot dominate ChatGPT (64%) and GitHub Copilot (49%) lead in adoption and satisfaction among professional developers using AI coding tools. JetBrains AI shows low adoption and high satisfaction, signalling a growth opportunity. Adoption varies by experience: “Satisfaction with ChatGPT drops notably among experienced developers, as they appear less happy with its accuracy, scalability, and ease of use compared to newcomers” says Bleona Bicaj, Senior Market Research Analyst at SlashData Agentic AI goes live: half of adopters already in production 50% of professional developers adopting AI functionality have already deployed Agentic AI into production, marking the end of the experimental era. Text generation, summarisation or translation (28%) is the top use case for Agentic AI. AR/VR and IoT projects lead adoption. Reliability and security concerns might be slowing the adoption of agentic AI in backend systems.  “Large enterprises’ governance complexity may be neutralising their resource advantages in agentic AI deployment” says Alvaro Ruiz Cuber, Market Research Analyst at SlashData Data privacy & security fears slow down AI rollout Organisations face two core hurdles: privacy risks that delay approval and quality concerns that undermine developer trust as only 25% of professional developers are currently building applications powered by Generative AI.  “Organisations must prioritise enterprise-level safeguards to prevent projects from stalling under compliance reviews.” urges Nikita Solodkov, Market Research and Statistics Consultant at SlashData Full analysis and 29 charts instantly available to all through the SlashData Research Space .   The insights come from 12,000 developers surveyed in Q3 2025. The six State of Developer Nation reports cover AI, FinOps, Cloud and Language communities. About SlashData SlashData is an AI analyst firm. For 20 years, we have been working with top Tech brands like Google, Microsoft and Meta. We track software technology trends to empower industry leaders to make product and marketing investment decisions with clarity and confidence, and drive the world forward with technology.

You don’t need another hype post. No one does. What the Tech world needs are the clear signals developers are actually sending: where adoption is real (and measurable), where it stalls, and how to present this at a board-level.  Developer Signals, Not Vendor Noise The latest State of the Developer Nation (DN30) series from SlashData gives you that edge across: Agentic AI architectures being implemented The AI coding tools developers rely on The barriers to adopting Generative AI applications  The current stage of Backend/Cloud Sizing the language communities FinOps in 2025 Responses from 12,000 developers are combined into 6 in-depth reports, filled with data and analyst commentary. The insights within, curated by our analysts, experts in their field, will help you make go/no-go decisions faster and with confidence.  Think developer sentiment, adoption curves, regional differences, and tech maturity, not guesswork. Below is a quick, exec-ready tease of what’s inside each report and how to dig deeper. What’s New in AI, According to Developers AI coding tools: concentration + clear satisfaction leaders Only 20% of professional developers currently use AI-assisted coding tools, and usage is heavily concentrated in ChatGPT (~65% of AI-tool users) and GitHub Copilot (49%).  65% of AI tool users use ChatGPT Both also top satisfaction (CSAT 78 each), with JetBrains AI close behind on 76 despite only ~10% adoption — a classic high-satisfaction/low-awareness opportunity.  Attribute-level scores explain why: ChatGPT leads on ease of use and setup; Copilot wins on integration and in-IDE workflow fit.  Insights Source: Which AI coding tools do professional developers rely on? Agentic AI: single-agent now, multi-agent building blocks next Among developers who’ve implemented agentic AI in the past six months, 56% ship single-agent systems, while 44% use multi- or hybrid-agent designs.  Text generation/summarisation/translation is the top use case (~28%), with multi-agent setups over-indexing on tasks like multimedia creation, web retrieval, and database querying — building blocks for orchestration.  Adoption varies by context: immersive (AR/VR/games) and IoT projects lead; backend and web services lag, where reliability/security constraints make autonomous agents a tougher sell.  Insights Source: The state of agentic AI adoption in software projects GenAI barriers: privacy first, then quality, skills and ROI 77% of developers not adding GenAI cite specific blockers. The top is data privacy/security (22%), with budget (16%), limited expertise (15%), output quality (14%), and integration complexity (13%) close behind.  As company size rises, privacy and compliance hurdles climb too.  Source: Barriers to adopting generative AI in applications Backend & Cloud: Hybrid Peaks Mid-Size; Private Cloud Scales with Risk Larger organisations are more likely to use private cloud, driven by security and compliance, while hybrid cloud adoption peaks in mid-sized companies and drops at the very large and very small.  Multi-vendor strategies remain the norm across sizes; smaller firms average 3.8 cloud providers vs. 3.3 for enterprises. Optimisation over consolidation.  Look at sector patterns: financial services lead on containers (40%) and orchestration (21%), while  AI model/service companies top MLaaS usage (29%).  One nuance worth watching: container usage dips at 501–1,000-employee “large businesses”. While we might generally expect container usage to increase as organisations grow and they have a greater need for the flexibility and scalability of containers, this low container adoption instead gives us insight into how platform teams are changing the developer experience and removing direct interaction with specific technologies. Insights Source: Benchmarking Backend and Cloud Technology Strategies  FinOps: Wide Adoption, Clear Regional Spread Two in three developers say their teams practice FinOps (66%), with mid-sized organisations leading as cloud bills and complexity bite. Regionally, adoption is highest in the Greater China Area (88%) and strong in North America (73%), while South America trails at 22% — signalling big upside for early movers in emerging markets. Visibility (budget monitoring/reporting) is the common entry point. Insights source: State of FinOps in 2025 Programming Language Communities: Scale, Momentum, and Who to leads JavaScript remains the largest community (~26.9M) with Python (24.4M) now ahead of Java (23.1M).  Over the last year, JavaScript usage dipped from 61% to 56% — maturity, not a collapse.  Momentum stories: C++ adds 7.6M developers over two years, expanding across embedded, desktop, games, even web and ML. Ruby doubles to 4.9M in the same period. Experience curves matter: Python skews earlier-career; PHP and C# adoption rises with tenure: Languages often “learned on the job” inside established stacks.  Insights Source: Sizing programming language communities Why this matters For CTOs & Heads of AI: De-risk platform bets. Align agentic AI architecture choices to today’s real use cases; prioritise privacy, evaluation pipelines, and governance to unblock GenAI adoption. For Product Managers, PMMs and DevRel: Position to developer reality. Back the tools and languages developers actually rate and use; target regions and segments where FinOps and cloud maturity shift the buying criteria. Next step: Talk to an analyst for a briefing and a go/no-go view for your roadmap or AI rollout. Or access all State of the Developer Nation insights if you want to drill into charts, regions, and cohorts yourself, in the SlashData Research Space : Which AI coding tools do professional developers rely on?  The state of agentic AI adoption in software projects  Sizing programming language communities State of FinOps in 2025 Benchmarking Backend and Cloud Technology Strategies  Barriers to adopting generative AI in applications  About the author Stathis Georgakopoulos, Product Marketing Manager 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.

If you have been following SlashData for a while, you know how we are not only tracking the latest technology trends, but are also early adopters ourselves. Now we are taking one more step forward.  SlashData has been tracking the developer ecosystem and economy for 20 years. We have been working on analysing the current state of software development, predicting software trends, and benchmarking industry leaders. All these, through expert analyst insights, backed by solid data.  SlashData’s reputation has been built on understanding developers and technology through research, including population sizing, tool adoption, and ecosystem trends.  In the age of AI, developers are the drivers of the technology trends.  Today, developers adopt technology first, followed by builders (aka vibe coders), followed by the rest of enterprise users and the world. The exponential evolution and adoption of AI tech has created enormous uncertainty in the world. We are here to provide confidence and clarity.   Our next step for SlashData is to become the trusted analyst firm specialising in AI technology, helping tech companies make the right decisions when adopting AI technology.  We focus on helping clients navigate AI technology decisions with confidence & clarity, through analyst guidance validated by data. This is more than a change in branding or service lines. It’s a strategic shift, a recommitment with a sharpened focus. We are doubling down on delivering not only where AI tech is heading, but also on why, and how, always backed by rigorous, data-validated analysis. Andreas Konstantinou (SlashData founder) is returning to the role of CEO to lead us through this shift in strategy and work with a technology he is very passionate about. Why we are shifting our resources to serve AI tech right now AI is the fastest adopted technology on the planet, and also the most disruptive.  It is the most profound change the industry and the world has ever experienced. AI updates dominate news, media, your timeline, Slack messages, and hallway conversations.  Here are the core reasons we believe the world and businesses need us to take this step now: Explosion of AI options & fragmentation. New models, platforms, tools, frameworks, and deployment choices are multiplying rapidly. What works in one context doesn’t in another. Without an analyst lens, many organisations are overwhelmed by choices and risk making the wrong investments, losing credibility and opportunity in the race for AI adoption. Gap between hype and reality. Vendors, online communities, influencers, media, even some internal teams often overpromise on what AI can do. The real effects: performance, cost, security, ethics, maintainability, scalability can diverge wildly. Organisations need grounded, evidence-based guidance to separate signal from noise. High stakes in adoption. AI decisions are no longer just technical decisions. They affect strategy, operations, governance, risk, and customer trust. Poor choices can lead to compliance violations, security incidents, ethical lapses, or wasted budget. The “analyst + data” combination helps mitigate those risks: our analysts provide an expert outlook, backed by solid datapoints. What SlashData can do for your AI needs  Strategic AI technology roadmaps.  We’ll help you choose which models, platforms, infrastructure, and partners make sense for your goals. Vendor and product benchmarking.  Compare capabilities, performance, costs, and trade-offs in real-world conditions. Use-case validation.  Before investing heavily, validate which AI use cases are likely to deliver the ROI you are going for. Regular data-grounded trend & forecast reports.  Not just “what is” but “what is coming,” and what it means for you. Building on our strengths If you’ve worked with us, you know that we have been tracking developer trends for two decades. We know that developer trends are the early signal of which AI platforms and technologies will win. Success lies within the developers at the heart of AI.  Our proven track record of working with industry leaders is a true testament to that. We have worked with teams that push the world forward at Google, Microsoft, CD Foundation, Cisco, Dell, DigitalOcean, Intel, Linux, Meta, Okta, Qualcomm, SAP, Sony, Stripe, and many more.  Additionally, our insights are: Elevated and validated by data:  Developer population sizing, adoption curves, performance metrics, competitive benchmarking, and usage patterns all serve to prove our point. Accessible : clear language, insightful framing for both technical and non-technical stakeholders. Trusted : We have been tracking developer trends and the technology landscape for 20 years. We are prepared and know how to track AI in a way that brings value and reduces friction. Let’s see how we can work together. Talk to our analysts .