𝟴𝟴% 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘀𝗮𝘆 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗔𝗜 𝗥𝗢𝗜. 𝗢𝗻𝗹𝘆 𝟯𝟵% 𝗱𝗼 𝗶𝘁 𝗳𝗼𝗿𝗺𝗮𝗹𝗹𝘆. SlashData's latest State of the Developer Nation report looks at how engineering executives are measuring AI ROI today, where formal measurement is taking hold, and where many organisations still rely on manual or informal approaches. Download the full report here: research.slashdata.co/report… #AIROI #EngineeringLeadership #SoftwareEngineering #AIAdoptionStats #DeveloperTools #SlashData

AI agents are in the workflow, but they aren't running the show. While micromanagement is down, only 15% of developers trust agents to run end-to-end. This is the real AI adoption gap. Read the full analysis in the AI Developer Tools Benchmark Q1 2026: research.slashdata.co/report… #AIAdoption #AIAgents #DevTools #EngineeringLeadership #SlashData

The most anticipated numbers from our research are here! Which are the world's largest coding language communities? 🥇 JavaScript continues to hold the top spot with 27.3M developers worldwide. 🐍 Python (26.3M) has overtaken 🥉 Java (24M) for the second wave in a row, claiming a clear second place 📈 C# is becoming a strategic powerhouse, growing from 10.2M to 13.9M developers in two years, a growth backed by @unitygames dominance in mobile gaming and @Microsoft ’s push to make .NET a credible cross-platform environment. Full analysis is free to access: research.slashdata.co/report… 🧐 Why is this important? Programming languages sit at the heart of the software development ecosystem. These languages do more than provide syntax for logic; they define the boundaries of developer ecosystems, dictate the availability of talent, and determine the long-term viability of platform integrations. For product executives and engineering leaders, monitoring the ebb and flow of these communities is a strategic necessity. Selecting which languages to prioritise for SDKs, APIs, and documentation is a high-stakes decision that directly impacts a platform’s friction, reach, and eventual market share. #programming #coding #languages #python #javascript

What’s one of the most underappreciated shifts in cloud native right now? In this clip, Liam Bollmann-Dodd highlights a key insight from our research with CNCF: many highly capable developers are working in resource-constrained organizations that are adopting cloud-native technologies on a very different timeline from large, well-funded teams. For some, technologies like Kubernetes and observability are still new, and that gap matters for how vendors, enterprises, and the wider ecosystem plan for the future. Watch the clip and let us know: what trend do you think the industry is still underestimating? Read the full report here: research.slashdata.co/report… #cloud

The AI developer tools market is no longer just a race for awareness. In Wave 2 of our AI Developer Tools Benchmark, most major tools are now known to a large share of professional developers. The real gap appears after that: which tools convert awareness into active use, satisfaction, and trust. GitHub Copilot stands apart on conversion, turning 63% of aware developers into active users, while most other tools convert fewer than one in five. But usage is only part of the story. Satisfaction rankings look very different from adoption rankings, with several smaller-reach tools matching or exceeding the market leaders on CSAT. Wave 2 preview: lnkd.in/dWwZ2f3w The benchmark also shows where the market still falls short. Debugging is the second most important task developers perform with AI tools, yet it has the lowest satisfaction score. And developers assigned a tool by their organisation are less satisfied than those who chose it themselves. At the same time, the next wave is taking shape. Claude Code leads on editing existing code, architecture planning, and complex multi-step work, while AI agents are gaining autonomy, though only 15% of developers trust them to run end-to-end. The next competitive edge will come from conversion, task-level satisfaction, developer trust, and rollout quality. Get in touch to explore the full AI Developer Tools Benchmark Wave 2. #AItools #AIcoding #developertools #copilot #gemini #codex #claudecode

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. This is where the AI Developer Tools Benchmark comes in: research.slashdata.co/report…

AI in the Developer Workflow: High adoption, uneven trust, and the tasks current tools haven't solved 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. Full report available: research.slashdata.co/report… 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. #aiworkflow #aicoding

The most anticipated numbers from our research are here! Which are the world's largest coding language communities? 🥇 JavaScript continues to hold the top spot with 27.3M developers worldwide. 🐍 Python (26.3M) has overtaken 🥉 Java (24M) for the second wave in a row, claiming a clear second place 📈 C# is becoming a strategic powerhouse, growing from 10.2M to 13.9M developers in two years, a growth backed by @unitygames dominance in mobile gaming and @Microsoft ’s push to make .NET a credible cross-platform environment. Full analysis is free to access: research.slashdata.co/report… 🧐 Why is this important? Programming languages sit at the heart of the software development ecosystem. These languages do more than provide syntax for logic; they define the boundaries of developer ecosystems, dictate the availability of talent, and determine the long-term viability of platform integrations. For product executives and engineering leaders, monitoring the ebb and flow of these communities is a strategic necessity. Selecting which languages to prioritise for SDKs, APIs, and documentation is a high-stakes decision that directly impacts a platform’s friction, reach, and eventual market share. #programming #coding #languages #python #javascript

The AI ROI Measurement Gap Why most engineering teams can’t prove their AI investment pans out 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. This report examines how developers in leadership roles 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. This deep dive is free to access: research.slashdata.co/report… #AI #ROI #AIROI #AItools

“The kind of safety with AI is making things better and worse at the same time,” Liam Bollman-Dodd tells The New Stack. “One of the approaches you can take is if you can take the kind of developer platform or other internal tooling, where you can prevent people from being dangerous to themselves, you can control everything at your end. All security is handled by someone who actually understands how it works. All the pipelines are built by people who actually know how pipelines work.” Listen to the full episode " Fresh data has us asking, does AI demand Kubernetes?" on @thenewstack podcast: thenewstack.simplecast.com/e…

Cloud native is no longer just the domain of traditional back-end and infrastructure teams. The community is getting bigger, the use cases are getting broader, and more developers are working with cloud-native technologies, even if they are not directly configuring the cloud or managing Kubernetes themselves. It’s a sign of a bigger shift: cloud native is becoming embedded across the developer ecosystem, not confined to one role or one team. Full report: research.slashdata.co/report… Interview from KubeCon CloudNativeCon EU 2026: youtube.com/watch?v=HQYi814u… "Are these new workers, are these people who've just picked up AI skills, is this an entirely different kind of developer that we're talking about here? So I think the first thing is that between now, between six months ago, we're kind of looking at the same kind of people. So we're not just seeing a growth in adoption. Before that, saw that our old number used to be around seven million cloud-native editor, and it jumped to 15. And that jump was basically, we looked at our data and we talked to CNCF and we're like. We don't think back end people are the only people we should be asking about cloud native technologies. We really should be asking anyone who does anything on cloud should be asked. And we did this and suddenly boom, we have like 15 million developers. Like, there's a lot of people doing cloud native who were not in back end services, are not in back end APIs, APIs, who don't actually configure clouds themselves, but they're suddenly using all these tools. I mean, that was a big thing. I just the shift with Kubernetes Kubernetes dropping, but the community expanding was like, could we change how we look at the audience? And it's like that larger community all consumes those cloud native developers, even though they're not interacting with the classic cloud native tools like Kubernetes Directive. They're not the traditional back end kind of infrastructure. I look at it and I think the rise of platform engineering and finally I think we're starting to see organizations adopt that model more #CloudNative #PlatformEngineering #Kubernetes #Developers #DevOps #CloudComputing #AI #CNCF

Just in: The AI ROI Measurement Gap: Why most engineering teams can’t prove their AI investment pans out A new report, free to access, that answers: - 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? Dig in: research.slashdata.co/report… #airoi #roi #ai #leadership #aiassistedtools

AI and cloud native are no longer separate conversations. If you’re building ML pipelines or running AI inference, cloud-native technologies are becoming the foundation by default, not by choice. The tools built to handle networking, data, communication, and scale for “normal” software problems are now powering the next wave of AI innovation too. Cloud native isn’t just part of the stack anymore, it’s becoming the requirement. Liam Bollman-Dodd says: "The cloud-native technologies are kind of like the pipelines. They're just the tools that you have to use to do AI inference, to do ML pipelines. You just end up incidentally having to use a lot of cloud-native stuff, not only because you're dumping it all to the cloud most of time because you need the compute and the power, but because they're just designed to allow this happen. It wasn't like they were built for this only. They were built to handle all of the networking and all the data and all the modeling and the communication which is all designed around normal problems. You stick in AI problems. These things happen to just be really good solutions, because they were designed to be moved to these sort of problems. And now, it's almost like you don't have to choose to be cloud native. It's just almost a requirement to do these things, which I think is really changing how we think about what cloud native means as a community and as a developer space." Full interview from KubeCon CloudNativeCon EU 2026 available on YouTube. #AI #CloudNative #MachineLearning #MLOps #Kubernetes #DevOps #Inference #TechLeadership

🤔 Where do software developers go for information? ⚡️ Highlights: ▶️ YouTube is the go-to social media platform for learning, highlighting a preference for long-form, structured video content. 👤 When it comes to learning, preference for LinkedIn is notably higher among those in technical leadership roles, likely serving as a source of strategic insights. 📲 X is particularly preferred for keeping up to date in East Asia, the Middle East & Africa, and South America. In Japan, 76% of developers prefer X as their platform to stay informed. Access all findings (free) in the full report: research.slashdata.co/report… 🧐 The report: Social media is undeniably a central infrastructure layer of daily life, and the developer ecosystem is no exception. It shapes how knowledge spreads, communities form, and professional identities are built. Yet, not all platforms serve the same purpose. In this report, we map where developers go across five distinct use cases: learning, problem-solving, keeping up to date, engaging with community and peers, and research. Drawing on responses from around 3,000 developers who use social media to find information about software development and stay current, the findings reveal a fragmented but structured landscape. It is one in which platform choices are strongly shaped by the nature of the task at hand as well as developer characteristics such as experience, role, and region. This report is designed to be actionable for a broad range of teams, including DevRel, marketing, and content, offering a practical foundation for channel strategy: knowing not just where developers spend time but what they are trying to accomplish when they get there.

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. Of course, on the surface, that's reassuring because an overwhelming majority is actively tracking AI value. And it seems that only 12% are sort of flying blind, making decisions about expensive tools based on trial and error. But when you look past this binary of measuring versus not measuring, a much more complicated picture emerges. One that especially looks different depending on where your organisation sits relative to the benchmark. So this is the story that I we want to discuss a bit more. 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. So this is really the gold standard. So your metrics are being collected continuously. They're being tracked systematically. And when you need to answer a question about AI value, the data is already ready and waiting for you. Source: AI developer tools usage and measuring ROI, based on professional software developers (Q1 2026 data) youtube.com/live/c1o_76MnfZI

In the CNCF Technology Radar Q1 2026 report, produced in partnership with the Cloud Native Computing Foundation, we evaluate developer perceptions of platform engineering tools across: - Workflow automation, - Application delivery, and - Security/compliance management. Based on responses from 400 professional developers, the report classifies technologies into 'adopt,' 'trial,' and 'assess' categories using weighted scores for maturity, usefulness, and recommendation likelihood. See the full report: slashdata.co/free-industry-r… #CNCF #technologyradar #workflowautomation

🌍Huge news! The #CloudNative community hit 19.9M devs (39% of all devs globally). #AI is the engine: 7.3M devs use #OpenSource to scale. As #Kubernetes complexity grows, #Hossted de-risks adoption by upgrading community software to enterprise standards. 🔗 Link in comments.

75% of professional developers are using AI-assisted tools: Insights on Developer Tools Usage and Measuring AI Tools ROI Read or watch the full session on software development AI tools usage and ROI in Q1 2026: slashdata.co/post/75-of-prof… #AI #ROI #AIusage #developmenttools

Cloud native adoption continues expanding, reaching 19.9 million developers (39% of all developers), though backend-specific adoption shows quarterly variation (52% in Q1 2026 vs. 58% in Q3 2025) while maintaining year-over-year growth. Platform engineering has reached critical mass, with 88% of backend developers now working with standardised infrastructure—a shift that increasingly abstracts underlying technologies like Kubernetes and containers from direct developer interaction. Access the full report in collaboration with @CNCF: slashdata.co/free-industry-r… #Cloudnative #industryinsights