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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

Explore Q1 2026 trends in developers’ AI usage and how organisations measure AI coding tool ROI, with practical signals for product, marketing, and DevRel teams. About More Coming up: Free live AI webinar | AI Developer tools usage & ROI on March 31st New data: AI usage and measuring AI ROI Sourced through an independent, global survey of 11,880+ software developers ✅ Tuesday, March 31, 2026 ✅ Live briefing + Q&A ✅ Recording + slides sent to all registrants ✅ First time showing data to be published in Q2 SAVE YOUR SEAT SAVE YOUR SEAT They trust us Register and save your spot Register now to get the full insights report as soon as it is available. SAVE YOUR SEAT AI developer tools adoption, satisfaction & what to expect in 2026 , based on data AI coding tools are now mainstream: 69% of professional developers who responded to our survey use them for most or nearly all of their coding work. At the same time: Product managers and AI coding tools PMMs lack clarity about what features drive developer engagement and performance Developer team leads struggle to choose a tool to adopt, which could make or break their team’s current productivity. In this session, we unpack clear signals and what these mean for developer-facing activities around AI coding tools. Register to access the live briefing + a Q&A session with Bleona Bicaj. SAVE YOUR SEAT Why you can't get this from an AI search In this webinar In a typical AI search Results from a structured study of 200+ questions tracked across 20 years Summaries of publicly available articles or opinions Comparisons across tools measured using the same survey design and methodology Fragmented information from different sources using different metrics Signals about how professionals measure ROI based on real use cases and validated responses Publicised internal telemetrics or calculated guesses Interpretation from the analysts who designed the research and analysed the dataset Synthesised summaries without context about how the data was generated Discussion of patterns emerging across the AI developer and coding tools usage Summaries based on tool provider announcements Opportunity to ask questions about what the findings mean for your product or strategy AI guesses based on popular breakdowns Why you can't get this from an AI search 1. AI usage How developers are interacting with AI technologies in Q1 2026 Trends on developers' interaction with AI technologies 2. AI ROI Do senior engineering leaders believe AI coding tools are worth the investment? Are organisations actually tracking whether AI tools deliver, or relying on instinct? Among measurers, how structured is that process – and does "we measure it" mean anything consistent across the market? How does measurement maturity differ between different company sizes? 3. Introducing: AI Developer Tools Benchmark We know that choosing the right AI developer tool, or making the case for one internally, is harder than it should be. There's a lot of noise, a lot of vendor claims, and not enough independent signal. That's exactly why we built the AI Developer Tools Benchmark. It is a rigorous, survey-based study that gives engineering leaders and product teams a clear, comparable view of how today's leading AI coding tools actually perform: across productivity, trust, quality, and value. Real data from real developers so that you can make decisions with confidence. Presenter Bleona Bicaj, Principal Research Consultant & Product Strategist 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. Methodology & data source Developer Nation 31st edition reached over 11,880 respondents around the world. As such, the Developer Nation series continues to be the most comprehensive independent research on mobile, desktop, Industrial IoT, consumer electronics, 3rd party app ecosystems, cloud, web, game, AR/VR and machine learning developers and data scientists combined ever conducted. All insights are based on the large-scale online developer survey designed, produced and carried out by SlashData over a period of six and a half weeks between December 2025 and January 2026. All SlashData surveys are monitored and cleaned to ensure the highest standards of retained responses. Our proprietary cleansing is designed to mitigate and remove opportunistic, fraudulent, and bot responses. Consisting of multiple criteria formulated around logic rules, speed, consistency, and response-taking behaviour, this holistic assessment is key to ensuring the highest degree of data quality. FAQs about the webinar If I can’t attend live, should I still register for the AI developer tools webinar? Yes. Registrants receive the recording and slides after the session, so you can still use the content internally but you will miss the ability to interact during the QnA section and the chance to connect with the speakers. What exactly do I get immediately after registering? A confirmation and access details via Luma, plus delivery of the free benchmark snapshot as part of the registration flow. Can I share the webinar materials internally? Of course. The recording and slides are designed to power decisions, so all insights are presented in a way that encourages discussion and internal circulation across engineering, product, procurement, and finance stakeholders. What data will be discussed publicly versus reserved for the full benchmark? This webinar covers the benchmark signals and how to interpret them for strategic decisions. Deeper data cuts, dashboards and tailored competitive benchmarkings are available exclusively through an analyst briefing. Contact us to organise a walkthrough of the available insights you can access. Make AI your competitive advantage, not a liability Contact us Subject * I'm contacting you for First name* Last name* Work Email* Company * Role* Message SUBMIT

Make the most of your budget: Turn developers into high-conversion leads by investing on what they actually need. Don't waste budget on developer marketing activities that don't convert. Developer attention is expensive in the fast AI market. Let our analysts guide you on how to engage with developers. BOOK A CALL Trust SlashData for all your awareness, engagement and lead-gen needs 20+ years of surveying the developer space Our long experience in speaking to developers, ensures that you will get the promised results. We are paranoid about our methodology and have the largest data library from 20+ years of surveying developers. Analyst foresight Our team of analysts are subject-matter experts on AI and software development. They unlock trends in the data, foresee where the industry is heading and produce actionable insights you can immediately start applying. 24/7 Access to your data, the way you want it Access your insights in a within our Research Space. Make it work smart and hard for you by creating a personalised space with your favourite reports and dashboards, for you and your whole team. Have a look > Don't waste marketing budget. Target the right audience with a smaller budget. BOOK A CALL Make the most of your budget: Turn developers into high-conversion leads by investing on what they actually need. Here’s how independent developer research, analyst insight, and forward-looking signals help you engage the right developers. Then turn attention into qualified leads. 💡 Handy for: Professionals in Product, Marketing, DevRel trying to engage a software developer audience. Clear messaging, based on real developer priorities It's relevance that converts, not feature claims. Developer research reveals switching triggers, untapped needs, trust barriers, and must-have capabilities. In order to increase engagement and reduce friction, your campaigns must reflect what developers genuinely care about. Differentiate with credible, third-party insight In AI, everyone claims leadership. Also, everyone uses the saturated sources and tools. Original, independently validated data positions your brand as evidence-led, not self-promotional. This builds authority with both developers and internal stakeholders and improves brand awareness and lead quality. Allocate budget where it drives measurable impact Without clear insight, channel decisions become guesswork. Awareness tracking, engagement benchmarks, and competitive positioning data show where spend moves the needle. You redirect budget toward the activities that generate engagement, defensible ROI and industry-leading results you will be asked to present in conferences. Maximise your results. Talk to our team about your high-engagement needs. BOOK A CALL

All the frequently asked questions about our developer research, insights, how we work with our customers and why you should trust our data. Frequently asked questions (F.A.Q.) SlashData blog articles SlashData Developer Research & Insights Working with SlashData What kind of content does the SlashData Blog cover? Our blog shares analyst foresight based on data-driven insights on developer trends, AI engagement, adoption and AI tool benchmarks, cloud, DevOps, open source, tooling, and emerging technologies. We publish: • Research highlights from our global developer surveys • Analysis of developer adoption and sentiment trends • Commentary on AI, productivity, and tooling shifts • Strategic perspectives for product, marketing, DevRel, and engineering leaders Every post connects market signals to real business cases and decisions. Who writes the posts on the SlashData Blog? Our posts are mainly written by Alvaro, Bleona, Liam, Nikita, Jed our SlashData analysts, the de facto subject-matter experts. Our analysts behind the articles: • Design and run our independent, global developer research programmes • Analyse behavioural and sentiment data • Work directly with technology leaders across product, marketing, and engineering You’re reading insights from the same team trusted to inform high-stakes strategic decisions. Some posts are written by Andrea, Stathis, Petro, and Natasa, who help match the data to the business cases and problems we are solving. How is the blog different from your formal research reports? Our reports deliver deep datasets and structured analysis. The blog makes those insights timely and accessible, and does not go as deep. Our blog articles • Explain what the data means in practical terms • Connects trends to current industry shifts • Surfaces emerging signals earlier • Highlights implications for roadmap, positioning, and growth Think of it as applied research in motion. Who is the SlashData Blog for? The blog is for leaders and decision-makers who need evidence, not noise. Including: • Heads of Product • Product Marketing leaders • DevRel and Developer Marketing teams • CTOs and VP Engineering • AI strategy and tooling leaders If developer behaviour impacts your revenue, roadmap, or positioning, this content is for you. Are the insights based on real data or opinion? Our insights are grounded in large-scale, global developer research. We: • Run independent developer surveys across regions and technologies • Apply rigorous data cleaning and weighting • Analyse longitudinal trends • When we share a viewpoint, it is anchored in observed data, not speculation. Can I use insights from the blog in business cases or presentations? Absolutely. Just make sure you credit us as this adds more weight to your presentation. If you are unsure about whether you can use a piece of content, please contact us. Many readers use our blog to: • Support internal discussions • Frame executive conversations • Validate strategic assumptions • Identify emerging risks or opportunities For deeper validation, we also provide tailored data and advisory support. How often do you publish new blog content? We publish regularly as new trends emerge and new research waves are analysed. Our surveys run every 6 months, so as soon as the data is in, our analysts dive into it. Through our tailored surveys and collaborations with large organisations in Tech, we explore more focused topics and write about them. Topics evolve with the developer ecosystem, especially across AI, cloud, developer productivity, developer awarenes and adoption and market shifts. How can I stay updated on new insights? You can subscribe to our newsletter or follow SlashData on our social media pages. The newsletter is delivered to your inbox every 2 weeks and features an analyst deep dive.