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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 .

Cloud computing continues to reshape how organisations manage infrastructure, data, and digital services. As adoption accelerates, data privacy, residency, and compliance have gained prominence alongside ongoing concerns around performance, cost, and security. The increasing complexity of regulatory requirements and the diversity of cloud deployment models underscore the need for organisations to balance innovation with risk management and operational efficiency. The 2025 Cloud Landscape in Europe and the US. We worked together with UpCloud to research European and US organisations and examine current cloud service providers’ (CSPs) adoption trends, key challenges, and future priorities across both regions. Together, we produced a report where we: Analyse the landscape of cloud provider usage, distinguishing between organisations that rely solely on US-based providers, European providers, or a combination of both. Explore where these organisations choose to store their data physically.  Examine the various cloud deployment models adopted by organisations of different sizes and the key factors they consider when choosing a cloud service provider. Look at the challenges organisations encounter with their current cloud environments.  Analyse the motivations behind adopting or avoiding European CSPs.  Look to the future, outline the main organisational priorities for cloud services and infrastructure and explore how organisations are integrating AI-related practices into their operations. This latter part is what we will present in this article, so keep reading. Access and deep dive into the full report here . You can find a short summary of our methodology at the end.  UpCloud and SlashData will also publish a webinar to discuss the findings. You can watch it here . Future cloud services and infrastructure priorities Exploring organisational priorities around cloud services and infrastructure reveals that AI integration will be the main focus in the next two years. One in three organisations identifies it as one of their main priorities.  The trend is far more pronounced in the US, with 40% calling AI integration a priority, compared to only 28% in Europe, highlighting the US’s stronger investments and more aggressive approach to AI integration. Additionally, those in management positions (such as CEOs, CTOs, or tech leads) are substantially more likely to identify AI integration as a key priority (36%) compared to others (27%), suggesting that leadership sees AI as a strategic lever for transformation, competitive advantage, and operational efficiency. Integrating AI into cloud infrastructure and processes is a key priority for one-third of cloud decision-makers Following closely are objectives to improve scalability (32%) and performance (30%). This highlights the need for infrastructure that can flexibly support growth and deliver consistently high performance, enabling organisations to remain agile and resilient in a context of rapid change. How organisations are supporting AI workloads To understand how organisations are supporting AI workloads and integrating AI into cloud infrastructure, we asked cloud decision-makers about the status of several key practices within their organisations. As it turns out, for each practice listed, around half of the organisations have already implemented these or are currently in the process of implementing them. Training developers on cloud-based AI tools and infrastructure (56%) and the adoption of AI platforms and services from cloud vendors (55%) are the most widely adopted activities, highlighting the need both for upskilling teams and leveraging specialised AI solutions to facilitate adoption. More than half (51%) have also incorporated or are in the process of incorporating AI-specific security and compliance measures, further emphasising the concerns surrounding data protection and regulatory obligations. One thing is clear: few organisations are opting out. For all these activities, only a small minority (11–14%) report having no plan to implement them, indicating strong industry momentum. This widespread engagement signals that AI integration is no longer limited to early adopters or specific sectors; it is becoming a mainstream priority as organisations face increasing pressure to innovate, improve efficiency, and remain competitive. Where does the data come from The findings of this report are based on data collected from an online survey designed, hosted, and fielded by SlashData in May 2025. The survey reached 300 professionals in Europe (55%) and the US (45%) who are involved in the selection and purchase of cloud services in organisations with at least five employees.  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.

This is the transcript of our latest live session “Artificial Intelligence in Tech: usage, adoption and challenges in 2025” which you can watch in the following video. Intro & welcome  Moschoula Hi everybody, welcome back to SlashData's webinar series for 2025. For those who aren't familiar with us and are joining for the first time, SlashData is a market research firm active in the technology community for nearly 20 years. We serve the technology community, helping companies make data-backed, high-impact decisions with confidence. We help you understand your customers, your users, and your decision-makers, and understand how to do everything from product design to marketing strategies with data. We will continue this series throughout the year, so stay tuned and join our newsletter to get invited to the next ones. For housekeeping, before I hand off to our featured speakers, we will be open for questions. The live chat that you should see to the right of your screen is available, and we will be reading through that at the end of the presentation. We have two senior analysts here with us today: Bleona Bicaj and Alvaro Ruiz. They will address the most topical subject we are all dealing with and learning more about each day—AI and tech usage, adoption, and challenges. Without further ado, I'll hand it over to Alvaro. Alvaro Ruiz Hello everyone, and welcome again. I'm Alvaro from the research team at SlashData. Today, in this first half of the webinar, we will explore how developers are working with AI and integrating it into their applications. Here’s a quick overview of what we’ll cover. First, we’ll look at how developers are actually working with ML and AI—whether that’s using AI tools in development workflows, adding AI functionality to applications, or building AI models. Next, focusing on the second group—those adding AI functionality to applications—we’ll explore the types of models they’re using and do a deep dive on open and open-source models to understand why developers choose to use them and the challenges they face. Finally, we’ll look at the type of AI functionality developers are adding to their apps—generative versus non-generative—and how the proportion of developers adding GenAI functionality varies based on experience, region, and company size. Developers using AI tools in their workflows According to our data from the 27th edition of our Developer Nation survey, fielded in Q3 2024, about two-thirds of developers are already using AI tools in their development workflows. The most common use case is AI chatbots for coding questions, with 46% of developers doing this, followed by 32% using AI-assisted development tools like GitHub Copilot. Another 21% use AI to generate creative assets for their projects, such as 3D models. When it comes to adding AI functionality directly to applications, 21% of developers are doing so—15% through fully managed AI services or APIs, and 10% using self-managed or local models. Finally, 15% of developers are involved in creating AI models themselves—customizing with their own data, building and training models, or fine-tuning hyperparameters. That leaves only about a quarter of developers not yet working with AI, highlighting just how integrated AI has become in software development. For the rest of this presentation, we’ll focus on those adding AI functionality to their applications. In the next presentation, Bleona will share insights on those using AI tools in development workflows. How developers bring Artificial Intelligence into their applications Now let’s take a closer look at how developers are bringing AI into their applications. Here, we see the types of AI models developers use. Of the 21% of developers adding AI functionality, 66% indicate they use open or open-source AI models, which equates to around 6.3 million developers. As these are the most popular types of AI models, we’re going to explore developers’ experiences using them. It’s worth noting that while 58% within this 66% rely exclusively on open and open-source models, a substantial portion—42%—also use in-house or proprietary models. Use cases of developers adding AI models to their applications Now, moving on to use cases—modern AI models are opening up a world of possibilities. We asked developers what kinds of AI features they’re building using these models.  Here’s what we found: Text generation leads with 37% of developers using open or open-source AI for this. Right behind are conversational interfaces such as chatbots at 36% and text summarisation at 34%. This is no surprise, as natural language processing powers many of today’s most useful AI features—from creating content to streamlining customer support. But the story doesn’t end there. Developers are also using AI for predictive analytics (30%) and personalisation or recommendation systems (29%). Image generation is equally popular at 29%, reflecting demand for creative, visual AI tools. Many other functionalities also show substantial adoption, highlighting how AI is shaping the next generation of smart applications. Why developers use open source models Now let’s explore why developers use open or open-source models. Top reasons include ease of integration, customisation, flexibility, and belief in the open-source model—cited by 34% of developers. This shows that developers want models that fit into their workflows and can be adapted to their needs. Community support is another major factor, cited by 33%, tied closely to the open-source philosophy—developers can share knowledge, get help quickly, and contribute improvements. No licensing costs (26%) and transparency (25%) are also key. Developers gain visibility into how models work, which is critical for trust, compliance, and addressing ethical concerns. Other reasons, each cited by fewer than 25%, include algorithm suitability, alignment with organisational values, and avoiding vendor lock-in. However, using these models comes with challenges. According to our data, 86% of developers using open or open-source models face at least one challenge. Top among these is security and privacy, cited by 25%. Developers must ensure that AI models don’t compromise user privacy or create vulnerabilities. Finding the right model is another major issue (23%), especially for those adding conversational interfaces, where this rises to 29%. These use cases generate added complexity, as models may not meet the nuanced needs of conversational AI. Other challenges include ensuring accuracy (21%), lack of specialised support (19%), and difficulties with fine-tuning or customisation (19%). Many developers also cite limited training resources, knowledge gaps, or compatibility issues (18%). To complement this, we asked developers why they avoid open or open-source models. The top reasons closely match the challenges discussed earlier. However, 19% say they opt for managed services simply because they’re more convenient. And 25% avoid open or open-source models due to security and privacy concerns. While this doesn’t mean open-source models are inherently insecure, they may lack the guarantees offered by proprietary solutions. Now, for the last part of the presentation, let’s see what types of AI functionality developers are adding and profile those using GenAI. Types of AI functionality developers are adding and who the developers using GenAI are According to new Q1 2025 data, 25% of developers are now adding AI functionality to applications, up from 21% in Q3 2024. This shows rapid growth. Breaking this down, 20% of developers are adding generative AI, while 11% are adding non-generative AI for tasks like analysis, prediction, or classification. Looking at experience levels, developers with less than a year of experience are least likely to build GenAI-powered apps—only about 1 in 10 have done so. This makes sense, as newcomers are often focused on learning the basics. Developers with 6 to 10 years of experience lead the way at 26% In contrast, developers with 6 to 10 years of experience lead the way at 26%, followed by those with 3 to 5 years at 23%. These mid-career professionals have built enough expertise to handle complex projects and are often tasked with experimenting with new technologies. Interestingly, adoption drops among developers with over 10 years of experience—only 17% are adding GenAI features. Many senior developers focus more on oversight, refining workflows, or mentoring. Regionally, North America leads with 27% of developers integrating GenAI, thanks to its strong tech ecosystem and funding environment. Eastern Europe and South America have the lowest rates, at 11% and 12%, respectively. Contributing factors include weaker infrastructure and economic barriers. Looking only at professional developers, company size also plays a role. Freelancers and those at very small companies are least likely to integrate GenAI—13% and 16%, respectively, likely due to limited resources. Mid-sized companies show the highest adoption at 29%, striking the right balance of resources and agility. At large enterprises, adoption drops to 24%, likely due to legacy systems, regulatory concerns, or segmented team responsibilities. So that’s all for today. We’ll take your questions during the Q&A session at the end of the webinar. And now I’ll hand it over to Bleona to cover how developers—and non-developers—are using AI in their daily work. The users of Artificial Intelligence in 2025  Bleona Bicaj Thank you, Alvaro. I'm Bleona, and I’m also part of the research team here at SlashData. Now that Alvaro has walked us through the builder side of AI-enabled apps, let’s switch to the people who use them. I’ll open with a snapshot of how developers are working with AI-assisted coding tools. These figures are not from our most recent data set, so think of them as a baseline—we’ll be collecting fresh numbers soon. According to our data, 32% of developers are already using tools like GitHub Copilot, DeepCode, or Source3. Looking at experience, those new to software development are least likely to use these tools—only 22% of those with under a year of experience. That’s not surprising, since beginners tend to be cautious about suggestions they can’t yet debug. But usage rises quickly. By the six-year mark, it reaches 37% as productivity starts to matter more than practice. It levels off and even dips for developers with 16+ years of experience—28%. These veterans may be more selective or focused on tasks like architecture or mentoring, which don’t benefit as much from code generation. One use case clearly dominates: code generation, reported by 55% of AI tool users. The more seasoned the developer, the stronger the uptake—75% of developers with 16+ years rely on AI to generate code, compared to 37% of those with less than a year. When asked for their top three reasons to adopt AI tools, 51% mentioned increased productivity. That priority grows with seniority, as senior developers handle larger projects and more responsibility. Related reasons—like automating repetitive or time-consuming tasks—follow the same pattern, resonating most with experienced professionals. As I said, this is just a snapshot. We’re collecting new data and will share updates soon. How decision makers use Generative Artificial Intelligence Now, shifting from engineers to decision-makers—earlier this year, in January, we interviewed 10 leaders in large tech companies (five in the U.S. and five in Europe), all heading marketing or sales teams. We focused on these functions to explore how GenAI is reshaping non-technical work. For marketing and sales, GenAI’s promise is clear: it can amplify human effort and streamline operations. Over the past few years, these teams have used GenAI for content creation, customer support, and lead generation. But they’re also learning its limitations. In our interviews, we asked:  Why did you introduce GenAI? What tasks does it handle today? What benefits or risks are you seeing?  Their responses gave us a well-rounded picture of GenAI’s impact. Interest in GenAI spiked as soon as the wider industry started talking about its potential. Early adopters launched pilot projects 2–3 years ago to streamline workflows, deepen engagement, and extract better insights from data. As one sales strategy manager put it:  “GenAI became a topic in our company ever since OpenAI came into existence and the world started talking about it.” However, other firms moved more recently, potentially encouraged by a new generation of easier and more capable tools. Whatever the timing, we can see a very clear pattern. What began as a small-scale experiment has now shifted to the strategic core of many organisations. And for most of these organisations, Gen AI is no longer just an optional R&D project—it is a priority for staying competitive in this rapidly evolving digital market. Across every interview, three motives came up again and again for using Gen AI: greater efficiency, relief from repetitive work, and sharper decision-making. Sales teams turned to AI for lead qualification, customer segmentation, and personalised outreach—tasks that once took hours but now convert faster with far less manual effort. How marketing teams use generative AI  Marketing teams use Gen AI to generate blogs, social posts, and email campaigns at scale, while also keeping tone and quality consistent, which is very important for marketing firms in particular. Most organisations began with small, low-risk pilots, trialing tools like ChatGPT, Gemini, or Copilot before committing at an enterprise level. As one sales enablement manager told us: “One or two years ago, we started playing around with co-pilots to author materials both internally and externally within a very small group. Based on our input, we decided to implement a pilot throughout the company.” This start-small-and-then-scale approach was pretty common—launching Gen AI in one team, learning fast, and only then extending it across the organisation. When we asked where Gen AI shows up in day-to-day work, three main buckets emerged. We have certain use cases for sales, others for marketing, and some that span both. How sales teams use generative AI  Sales teams are using Gen AI to zero in on high-potential leads, automate initial outreach, and tailor follow-up messages. It also helps crunch historical data for sharper forecasting and takes care of routine tasks like drafting sales briefs or updating the CRM. That way, sales representatives can spend more time building relationships and closing deals. Marketing tells a similar story. Gen AI drafts blog posts, social copy, and even edits images in minutes while keeping brand tone intact. Marketers feed Gen AI campaign data to fine-tune and personalise their messages, and they rely on it for quick-turn visuals such as infographics or short videos. Some tasks, however, cut across both functions. AI tools record and summarise client calls, draft email replies, and generally serve as an idea sparring partner during planning sessions. By offloading these repetitive jobs, sales and marketing teams alike can redirect their time toward higher-value strategies, creativity, and high-level conversations. Gen AI is proving to be a genuine workflow changer. Across all of our interviews, sales and marketing leaders highlighted four recurring payoffs: speed, personalisation, cost control, and sharper decisions. Generative AI: time-saving, personalising and cost-saving Starting with time saved—AI tools summarise reports, draft sales briefs, and generally clear away low-value admin work in minutes rather than hours. One sales manager put it this way: “Something that used to take two hours now takes 20 minutes.” Yes, there are accuracy issues, but for many tasks, AI dramatically improves efficiency. The result is more calendar space for strategy, creativity, and client conversations. Then there’s hyper-personalised content. By crunching customer data on the fly, Gen AI tailors ads, emails, and pitch decks to smaller segments—but at volume. A marketing manager said, “With more and more use, AI is starting to learn the tone of our brand and how we communicate to our audience. Now I barely need to tweak anything.” Sales teams see the same benefit: targeted messages that land better and convert faster. Next, we have cost savings. A major upside of Gen AI is straightforward savings. Marketing leaders told us they are trimming agency fees, especially around media planning and creative production, because AI now builds assets and places ads in real time. One head of marketing said, “We’ve cut down on agency costs significantly because AI allows us to automate creative production and ad placements in real time.” Sales teams see a similar impact—AI automates lead generation, keeps the CRM updated, and sharpens forecasts, reducing manual effort and freeing budget for higher-value activities. Better, faster decision-making—Gen AI not only automates but also improves the quality of choices. AI-driven analytics pull insights from live data rather than just last quarter’s reports, so strategy adjusts in real time. Automated transcripts and summary notes capture meetings, customer calls, and performance reviews verbatim. AI removes corporate amnesia “AI removes corporate amnesia,” one head of marketing told us. “It records exactly what was said, reducing confusion and ensuring clarity in decision-making.” By reducing human error and preserving a reliable record, Gen AI supports compliance and provides a solid data-driven foundation for next steps. Adoption is no longer the question—scaling is. Most companies we spoke with have Gen AI running in at least one part of the business. Yet rolling it out to new use cases is proving tricky, and the obstacles after the pilot stage tend to be the same. Trust sits at the top of that list. Gen AI still hallucinates—producing confident-sounding but incorrect answers. In data-driven roles where accuracy is key, employees hesitate to act on or even vet AI output. A director of sales operations said, “AI generates outputs that sound highly convincing but aren’t always factually correct. The challenge is that someone might trust this information without verifying it.” Security and privacy are also major concerns. Many firms handle sensitive or proprietary data, and sending that to external AI services raises the risk of leaks, third-party access, and compliance breaches. As a result, some limit AI use to low-risk tasks, while others are building in-house models or imposing strict governance frameworks. A sales enablement manager noted, “We handle a lot of sensitive data. We can’t afford to upload proprietary information into an external AI system without knowing how the data is secured.” A third roadblock is know-how. Some employees experiment readily with Gen AI, while others hesitate due to lack of trust or uncertainty about value. Without formal upskilling, adoption stalls—people don’t feel confident using AI in daily work. Companies that invested early in structured training saw faster uptake and a smoother transition. A marketing expert observed, “There is still a large portion not adopting or unwilling to adopt. It’s more about lack of education and confidence. Training needs to happen across the organization.” Even with skills in place, standardization is another hurdle. In many firms, one team races ahead with AI while another sticks to manual workflows. The lack of clear company-wide guidelines—when to use AI, how to validate output, who signs off—keeps adoption uneven and dilutes the impact. An advanced analytics manager said, “Some staff are pushing these tools, while others don’t care. It’s not affecting our roles much yet, but when AI catches up, we’ll need to rethink training.” There’s a clear pattern that more senior or experienced staff are usually more hesitant to adopt AI. The future of Generative AI for sales and marketing professionals Looking ahead, most leaders see Gen AI as an augmenter—not a replacement. Its value lies in speeding up routine work, lightening admin loads, and sharpening decisions, while humans supply context, judgment, and ethics. Adoption will expand across business functions, but human oversight will remain essential. Customer-facing roles, especially in sales, illustrate this well. AI can qualify leads and send automated follow-ups, but complex negotiations and relationship building still need human intuition. In other words, AI handles high-volume, low-value touchpoints—humans own the moments that matter. A global alliance lead from a sales team said, “I could see a world where maybe smaller deals have an AI rep selling to small clients, but for now, sales reps are still necessary.” According to them, we haven’t reached the point of replacement. Accuracy remains a top priority. Teams are refining models with better training data and validation loops. This “human-in-the-loop” approach builds trust and tamps down hallucinations. Beyond the tech, companies need to re-engineer workflows and invest in upskilling so people and AI can work side by side. Organizations that invest in training and thoughtful integration will capture the biggest gains. Those that don’t risk stalled adoption and employee pushback. In short, Gen AI’s future is as a productivity multiplier and strategic ally—if businesses strike the right balance between automation and human strengths like creativity, critical thinking, and relationship management. A head of marketing said, “AI isn’t going away—it’s only becoming more embedded in how we work. The key is using it responsibly and strategically.” Work alongside AI—not let it replace us. Key takeaways from professionals in sales and marketing using Generative AI Now let me wrap up with five key takeaways: From trial to strategy  – Two years ago, Gen AI was an experiment. Today, it’s on the board agenda. The shift from pilot to priority happened faster than any tech we’ve tracked. Sales and marketing see the fastest wins  – Leads qualified, campaigns drafted—often six times faster. Agency spend drops as creative moves in-house. Trust and security still matter  – Every AI output still needs a human eye. Many firms prefer private models to keep data locked down. Skills gap is the choke point  – Tech only scales as fast as people’s skills. Without structured training, adoption stalls. The future equals augmentation  – Gen AI takes the high-volume, low-judgment tasks, but humans stay accountable for complex decisions. These are the headline lessons from the report . Ready for your questions. Q&A from the audience Moschoula:   Thank you so much, Bleona. Thank you, Alvaro, as well. Here’s a question for Alvaro: Do you think that finding the right model for the job will remain a barrier to AI adoption, or will it decrease over time? Alvaro Ruiz: Good question—it could go either way. The open-source AI ecosystem is expanding rapidly. There are now hundreds of models, architectures, and fine-tuned variants, making it hard for developers—especially less experienced ones—to identify the best fit. If growth continues like this, it might get harder. But as the ecosystem matures, we may see more user-friendly platforms and automated model selection tools, making it easier. So, I think the proportion of developers citing this as a barrier will decrease, but it won't vanish, especially for niche domains or junior developers. Moschoula: Thank you, Alvaro. Now a question for Bleona. If AI boosts productivity by six times, doesn’t that reduce the need for more staff? Bleona Bicaj:   That’s a fair point, and it came up in interviews too. But the six-fold boost mainly applies to mechanical parts of a task—drafting boilerplate code, summarising meetings, first-draft copywriting. Instead of making roles redundant, it frees people to work on backlogs of higher-value tasks—shipping more features, localizing campaigns, strategic conversations. Since we must review AI output for accuracy and bias, the reclaimed time is repurposed, not cut. AI removes busywork, not brain work. Moschoula: Yes, I’ve seen that too. One more question for Alvaro: Are developers specialising in just one or two use cases, or are they integrating multiple functionalities? Alvaro Ruiz: According to our data, developers are using, on average, 3.8 out of 14 AI functionalities. So yes, most are working with multiple use cases. Moschoula: Last question for Bleona—what training formats are delivering the fastest results? Bleona Bicaj:   Companies use several formats, but the most effective is a blended approach: short self-paced modules for basics, followed by live workshops with real tasks, then reinforced through monthly micro-sessions and co-worker collaborations. The last part—peer support—proved especially helpful. Companies that relied only on written guidelines or video courses saw slower adoption. Moschoula: That's really helpful. Thank you both, Alvaro and Bleona. We look forward to the next session—stay tuned for announcements in our newsletter. And let us know if there are topics you want us to cover. Bleona Bicaj: Thank you. Bye. Alvaro Ruiz: Thank you. Bye.

What role are artificial intelligence and network APIs playing in shaping the tumulus digital landscape? How much traction have AI chatbots gained in the last six months?  What does the adoption of network APIs look like among developers?  You can watch the webinar anytime: What You’ll Learn This exclusive webinar will offer deep insights into key technology trends. Together we will look at the most recent data, provided by real developers. Dive into the world of AI chatbots and Network APIs. Our expert speakers will shed light on key findings, including: The Rise of AI Chatbots for Problem-Solving Which roles and industries are experiencing the fastest growth in AI chatbot usage? ​How has the overall adoption of AI chatbots changed over the past six months across different user groups? ​How does AI chatbot usage differ between professionals, hobbyists, and students? ​What regional trends are emerging in AI chatbot adoption? ​What specific challenges or needs are prompting beginners and non-technical users to increasingly turn to AI chatbots for problem-solving? Network APIs: The New Oil in the 5G Economy ​What percentage of developers use network APIs? ​Which regions have higher adoption rates of network APIs? ​In what types of projects are developers using network APIs involved? ​What functionalities of network APIs do developers use?  Which roles and industries are experiencing the fastest growth in AI chatbot usage? Join the Conversation Don't miss this opportunity to stay ahead of emerging technology trends. Gain insights into AI chatbot growth, network API adoption, and more—straight from industry experts. Register now and be part of the discussion! Live Q&A: Ask our analysts your questions.  Exclusive Reports Each topic addressed in this webinar is backed by a free-to-access, in-depth report: The Rise of AI Chatbots for Problem-Solving Network APIs: The New Oil in the 5G Economy Meet the Experts Our presenters bring a wealth of experience in market research and technology analysis: Álvaro Ruiz, Research Manager 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. Bleona Bicaj, Senior Market Research Analyst Bleona is a behavioral specialist, enthusiastic about data and behavioral science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 6 years of professional experience as an analyst in the data analysis and market research industry. Hosted by Moschoula Kramvousanou, SlashData CEO Where the data come from  The insights presented in this webinar come from the global, independent State of Developer Nation  survey and its 27th wave which reached over 9,000 respondents worldwide. As one of the most comprehensive independent studies on developers across mobile, desktop, Industrial IoT, consumer electronics, cloud, game development, AR/VR, and machine learning, this survey, and the expert analysis after it, provides essential perspectives on the evolving tech landscape. Reducing bias ​To eliminate the effect of regional sampling biases, we weighted the regional distribution across nine regions by a factor that was determined by the regional distribution and growth trends identified in our Developer Nation research. To minimise other important sampling biases across our outreach channels, we weighted the responses to derive a representative distribution for technologies used, and developer segments. Using ensemble modelling methods, we derived a weighted distribution based on data from independent, representative channels, excluding the channels of our research partners to eliminate sampling bias due to respondents recruited via these channels. Each of the separate branches: Industrial IoT, consumer electronics, 3rd party app ecosystems, cloud, embedded, augmented and virtual reality were weighted independently and then combined.

In the competitive landscape of technology vendors, developer programs are often seen as essential for building robust ecosystems. Our Developer Program Benchmarking research consistently reveals a puzzling phenomenon: NVIDIA. Its developer program scores lower than average across all vendors we benchmark in terms of engagement and satisfaction for two consecutive years. Yet, the company maintains strong leadership in market capitalisation, having recently hit a record high in shares .  This paradox highlights a broader industry insight; dominance doesn't always stem from developer program polish. Instead, it can come from holistic ecosystem strategy. In this blog, we explore what has worked for NVIDIA and what other vendors, particularly silicon-focused players such as AMD, Intel, and Qualcomm, can learn from their model. The CUDA ecosystem NVIDIA’s most significant developer engagement lever is not its formal program, but the CUDA (Compute Unified Device Architecture) ecosystem. Launched in 2006, CUDA has become the gold standard for GPU programming in AI, HPC, and scientific computing. It’s a comprehensive ecosystem of libraries, including cuDNN for deep learning and cuBLAS for linear algebra, along with deep integrations with frameworks such as PyTorch and TensorFlow . This makes CUDA not only powerful but also incredibly sticky. Developers and researchers who build with it rarely look elsewhere, because switching means losing access to the world’s most mature and optimised GPU platform. What really sets CUDA apart is its network effect. It’s taught in universities, required in job postings, and baked into the workflows of thousands of startups and research labs. According to NVIDIA, over 4.5 million developers now use CUDA, up from 1.8 million in 2020 . That’s a 150% increase in just a few years. That growth is self-reinforcing: more users mean better community support, more shared code, and more third-party tools, an ecosystem momentum few competitors have matched. However, this community-driven approach can also present strategic vulnerabilities. NVIDIA has limited control over the developer experience, support quality, or messaging within this ecosystem. Much of this knowledge transfer is happening through informal channels or community groups, rather than optimised pathways. Silicon vendors like AMD and Intel, by comparison, have struggled to build similarly mature software ecosystems around their hardware offerings. University partnerships and training NVIDIA has strategically invested in academic partnerships that create a continuous pipeline of developers already familiar with their technology. Its partnership with the University of Florida is a prime example: a $70 million initiative that resulted in the HiPerGator 3 supercomputer, powered by NVIDIA DGX SuperPOD systems. Beyond infrastructure, this collaboration includes curriculum development and access to the latest GPU tools, embedding NVIDIA’s technology directly into teaching and research pipelines. This effort is mirrored in the NVIDIA Deep Learning Institute (DLI) University Ambassador Program . The program equips faculty with cloud-based GPU labs and ready-made teaching kits to deliver hands-on training in CUDA and AI. Rather than relying on documentation or forums, NVIDIA meets students where they are, inside classrooms, with real tools and real use cases.  This early career intervention is one of NVIDIA’s most successful developer strategies, and one that bypasses traditional program metrics entirely. For other vendors, especially those with strong hardware portfolios but weaker developer engagement, replicating this academic integration could yield significant returns in loyalty and talent development. A key advantage for other vendors is the ability to combine this intervention strategy with a superior formal developer program which accelerates developers' success and advocacy once they enter the workforce. NVIDIA’s full-stack integration strategy Beyond chips and training, NVIDIA’s edge lies in owning the full AI stack, from hardware to software to networking. Unlike competitors who sell only silicon, NVIDIA delivers integrated systems, such as the DGX SuperPOD and AI Factory reference architectures, which combine GPUs, NVLink switches, SDKs like TensorRT, and orchestration tools like NVIDIA Run:AI . These aren’t just hardware bundles; they’re turnkey solutions that enterprises can drop into production environments with minimal configuration . This vertical integration creates seamless workflows and performance optimisations that generic silicon providers can’t easily match. Competitors like AMD and Intel largely remain focused on component-level sales, often relying on third-party or open-source tooling to complete the developer stack. The result is a fragmented experience that can frustrate developers and delay deployments. NVIDIA’s approach, by contrast, offers plug-and-play performance for production AI environments, which shortens time-to-value and raises switching costs. While the technology integration is seamless, the developer experience of learning, troubleshooting, and optimising can heavily rely on informal, community support if they are not actively involved in a partner university program. Competitors exploring full-stack integrations can consider leveraging their more comprehensive developer program to support effective documentation, responsive support networks, and clear migration guides.   Strategic implications for technology vendors NVIDIA’s success despite low developer program satisfaction scores highlights a fundamental industry lesson: true developer loyalty stems not from polished portals or responsive forums, but from building a cohesive and indispensable ecosystem. This includes proprietary SDKs, full-stack integration, academic partnerships, and hands-on training, all of which create long-term reliance and lower the barrier to entry for developers. Developer programs support developers and encourage long-term engagement, but ecosystems draw people in. For silicon vendors and technology leaders seeking to expand their developer base, this means rethinking developer engagement as a long-term ecosystem investment rather than a series of touchpoints. A well-supported, fully integrated platform, even if it's not the most performant, can win developer mindshare by helping teams ship faster and with more confidence. For shareholders, the implication is clear: ecosystem depth is not just a differentiator, but a strategic advantage. Those who build ecosystems, not just programs, will define the next era of technological leadership. Do you know which are the drivers that make your company successful and your audience happy? Let's explore them together. Schedule a call with our experts . About the author Bleona Bicaj, Senior Market Research Analyst Bleona Bicaj is a behavioural specialist, enthusiastic about data and behavioural science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 7 years of professional experience as an analyst in the data analysis and market research industry.

The developer landscape is shifting faster than ever. AI agents are moving from buzzword to reality. Cloud deployment has become universal. Security threats are multiplying alongside AI's expanding attack surface. At SlashData, we don't just track these changes; we decode what they actually mean for the companies building developer tools and platforms. For our 30th wave of Developer Nation, we're expanding our focus areas, refining our intelligence gathering, and improving our methodology to ensure our research remains the most relevant and actionable in the industry. Professionals across the full breadth of the development sector — CTOs, product and program managers, engineers and DevOps teams, and more — rely on that intelligence.  Moschoula Kramvousanou, CEO talks about our research focus for 2025 Why now? Because as the technology landscape evolves, vendors are finding it increasingly challenging to understand what their developers truly need and how they truly work. We're introducing new research areas and enhanced methodologies based on direct feedback from leading technology companies who depend on our insights to make critical product and strategy decisions. The result is our most comprehensive survey yet, designed to give you the intelligence that actually drives business outcomes. Why This Intelligence Matters Now The technology sector is experiencing its most significant shift since the mobile revolution. AI capabilities are reshaping every development workflow. Cloud infrastructure has become invisible but critical. Security requirements are evolving faster than security practices. Companies making decisions based on outdated intelligence – or worse: vendor marketing – are missing the real opportunities and risks. Our H2 2025 research cuts through the noise to deliver the insights that actually drive successful developer products and strategies. Whether you're deciding where to invest AI development resources, how to position cloud tools, or which developer segments to prioritise, this intelligence gives you the foundation for decisions that matter. Core Intelligence: AI Agents and Security Reality Check AI Agents: Cutting Through the Hype Everyone's talking about AI agents, but who's using them? We're asking over 10,000 developers worldwide about their real experience with agentic architectures. Not the marketing promises, but the ground truth. Which platforms are gaining genuine traction? What specific tasks are developers successfully automating? How well do developers understand core concepts like Model Context Protocol compared to how well vendors think they do? We're asking over 10,000 developers worldwide about their real experience with agentic architectures. Not the marketing promises, but the ground truth. This intelligence emerged from conversations with major AI platform providers who need to understand whether their educational and marketing efforts are translating into adoption and where the genuine market opportunities lie. We're also investigating developer familiarity across the full spectrum of agentic AI platforms and frameworks, providing comprehensive competitive intelligence for this rapidly evolving space. DevSecOps: The Security Gap Analysis With AI agents expanding attack surfaces and security breaches dominating headlines, we're conducting a renewed investigation into DevSecOps. We're mapping the critical gap between developers' expressed security concerns and their actual implementation practices: examining the frequency of security checks, CI/CD pipeline ownership, and the implementation of security guardrails. Previous SlashData research revealed security as a top developer concern, but practice consistently lagged behind stated urgency. Now we'll provide definitive insight on whether the industry is finally catching up, and what real-world implementations actually look like. Cloud Intelligence Trends: Beyond Backend Specialisation Universal Cloud Reality Starting H2 2025, we're surveying all cloud users: not just backend specialists. We're breaking down the artificial barriers in cloud research that have limited the breadth of understanding we can provide. For years, we limited detailed cloud questions to backend developers, but with over 90% of developers now using cloud services, that approach missed the full picture and critical insights. Starting H2 2025, we're surveying all cloud users: not just backend specialists. This expansion gives you comprehensive insight from both the developers configuring cloud tools and infrastructure and those using cloud services in their daily development work. The difference in perspectives between these groups often reveals critical gaps in product positioning, user experience design, and feature prioritisation that can make or break adoption strategies. Cloud-Native Deep Dive We've significantly expanded our cloud-native development tracking to capture the real adoption patterns and preferences across emerging technologies and methodologies. With our survey scale spanning thousands of backend developers, you'll get unprecedented visibility into which cloud-native approaches are actually driving adoption versus which ones are generating buzz without substance.  Data Residency and CSP Intelligence We're also introducing comprehensive tracking of data residency compliance requirements and how they influence cloud deployment decisions. Combined with our enhanced cloud service provider preference analysis and expanded cloud-native research, these three focus areas reflect the growing complexity of cloud decision-making. Our CSP clients have shown particular interest in understanding how developers navigate these interconnected challenges, as regulatory requirements increasingly shape technical architecture choices. Developer Program Benchmarking: AI Integration and Sample Code SlashData's developer programme benchmarking has become a cornerstone for leading technology companies understanding where they excel and where they need to improve. For years, our benchmarking has enabled clients to directly compare their developer programmes against competitors, providing critical insights into how they fit within developers' core workflows and decision-making processes. This established expertise allows us to speak with authority about industry-wide trends and pain points. AI Assistant Integration Building on our benchmarking foundation, we're now tracking the integration of AI assistants and tools as a core programme feature. The largest companies in our network are aggressively pursuing these capabilities, but questions remain: How important is this to developers? Which developer segments care most? Our expanded benchmarking will give you clarity on where to invest your AI support efforts and how to position these against competitor offerings. Sample Code: The Industry's Biggest Failure Point Our extensive benchmarking work has consistently revealed that vendor-supplied sample code is where the entire industry falls short of developer expectations. This wave, we're conducting our most comprehensive sample code analysis yet. We’re examining what features developers actually value, their biggest frustrations, and the specific scenarios where they turn to sample code versus other resources. We're also having developers rate familiar vendors across multiple sample code dimensions: relevance, discoverability, production applicability, and currency. Combined with our curated understanding of developer preferences and pain points, this will provide a complete picture of how sample code impacts developer adoption and satisfaction. If you're not measuring up, you won’t know exactly where to focus your improvements. Specialised Sector Intelligence Gaming Industry Transformation The gaming sector is navigating unprecedented change, but one thing remains constant: the quality of games continues to reach new heights. While the creative output has never been stronger, the developers and studios behind these exceptional experiences are facing significant challenges. Our upcoming State of Game Development report (coming November 2025) will provide comprehensive profiling of game developers to understand their current landscape, technology choices, and perspectives, with an additional focus on how AI tools are helping or hindering their work, adding crucial 2025 context to this analysis. We've enhanced our profiling capabilities to distinguish between technical roles, creative roles, and non-technical support staff. These are critical segmentations for companies targeting different aspects of the gaming development pipeline. This granular approach allows engine providers, tool creators, and platform companies to understand exactly which roles are most receptive to their solutions and what specific challenges each segment faces in their daily work. XR Reality Check Despite major moves like the Apple Vision Pro, XR hasn't achieved mainstream presence. Our XR Landscape report will provide updated intelligence on who's actually working in this space, plus 10-year predictions from developers across all sectors. Are XR developers more optimistic than the broader market? Do younger developers see more potential? The answers will likely shape XR investment strategies. AI at the Edge Edge computing and AI/ML are converging rapidly, creating new opportunities and challenges for developers and the companies serving them. This has become a growing topic of interest that's increasingly important to many vendors with the proliferation of small advanced models that can run effectively at the edge. We're conducting comprehensive research into which edge AI/ML projects are gaining real traction beyond proof-of-concept stages, what specific frameworks and development approaches developers prefer, and where the current pain points create the biggest opportunities for better tooling and platforms. This research covers the technical requirements and the developer experience challenges that determine success in this emerging market. IIoT Developer Journey We're also investigating how Industrial IoT developers first entered the field, focusing on their initial development board experiences and early learning journey. This research helps companies in the IIoT space understand how they stack up against competitors in terms of developer onboarding and initial experience; critical factors for long-term ecosystem adoption. That’s not all In addition to these comprehensive research areas, we offer client-exclusive questions designed around your specific intelligence requirements. This bespoke research capability ensures you're not just getting industry-wide insights, but the targeted data that directly informs your unique strategic challenges and opportunities. The 30th wave of Developer Nation is currently collecting responses from around the world. If you want to help shape these insights, have your say .  You can be the first to know when these insights become available by subscribing to our newsletter . Or you can get in touch  and we will make sure to get you all the intelligence you need.  About the author Liam Bollman-Dodd, Senior Market Reseach Analyst 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.

Or what skills are you focusing on improving? This is a post from DevRelX. DevRelX was a community-driven platform by SlashData dedicated to advancing Developer Relations (DevRel), Developer Marketing, and Developer Experience professionals. It provided research, insights, and resources through initiatives like the Future Developer Summit & the DevRelX Summit, the DevRelX Podcast, and a book. DevRelX created a vibrant space for learning, connection, and discussion within the global DevRel community, until the community was sunset in 2023 as SlashData shifted its focs to wide Technology Research. The DevRelX Community Voice column is one of a few ways we invite our community members to share ideas and solve challenges around various topics like DevRel strategy, metrics, career growth, and just DevRel’s day-to-day. Want to add your voice to this and more key DevRel topics? Join our community and participate in the next Community Voice prompts where you can ask your questions! Opemipo Disu, Developer Advocate at Fermyon I think communication is a skill an aspiring DevRel should have DevRel acts as a 'middle-man' between the company and the users. In a DevRel role, it's essential you get across to users' feedback and in this case, I think communication is pretty much important in this case. Pj Metz, Developer Community Manager #OpenToWork Empathy and authenticity. Developers don’t like to be marketed to, so making sure that you’re being yourself and understanding what it is that they need from a DevRel is very important. Developers have problems to solve, and DevRel has a possible solution. That’s the goal is to show them the problem being solved, not making promises about speed, agility, etc. Show the problem getting solved. Tabatha DiDomenico, OSS Developer Relations & Security Advocate Listening is a skill that deserves attention or, really, listening with intention —engaging with a goal in mind more often yields actionable, helpful ideas. Of course, the casual conversation has its place, but it may take a while to discover valuable insight for both parties. Even taking a few seconds at the start of a conversation to anchor to a context can help make the most of each connection. Sean Falconer, Head of Marketing & Developer Relations at Skyflow I think it's a bit context-dependent. You need to do a bit of self-analysis and figure out where your weak points are and proactively try to work on those things. If someone is highly technical but has less experience presenting and/or writing, then you should try to build up those skills. If presenting and engaging with people comes naturally but you have the less technical skill, then focusing on deepening your technical skills makes sense. Working in developer relations, especially advocacy, requires a wide breadth of skills. To be really good at it, you need to constantly be working on skill stacking: listening, writing, speaking, programming, and explaining technical concepts to different types of audiences are all skills that you need to be building. So my long-winded advice is to try to shore up your weaknesses. This is also great for storytelling in an interview, it shows you care about self-improvement, you have passion for what you do, and even if you don't perfectly line up with the job requirements, you're someone who's going to walk through walls to level up so you can do the job. Michael Arguin, Senior Manager, Marketplace & Developer Experience Curiosity and a passion for learning . You won't know everything so you will often have to find answers. Jason St-Cyr, Developer Relations leader at Sitecore A key piece for me is the ability to learn and then teach . In many situations, you need to be encountering something new before others, learn that to keep ahead of the need, and then be able to create something that helps others learn what you just had to. Katie Miller, Director, Developer Marketing Slack Cross-functional relationship building , which encompasses flexibility, curiosity, empathy, and how to reframe communication style and methods to meet folks where they're at!

This is a post from DevRelX. DevRelX was a community-driven platform by SlashData dedicated to advancing Developer Relations (DevRel), Developer Marketing, and Developer Experience professionals. It provided research, insights, and resources through initiatives like the Future Developer Summit & the DevRelX Summit, the DevRelX Podcast, and a book. DevRelX created a vibrant space for learning, connection, and discussion within the global DevRel community, until the community was sunset in 2023 as SlashData shifted its focs to wide Technology Research. The start of 2023 marked a 3-year anniversary of DevRelX.com - thank you for being with us! Another Q1 highlight in our community is the 10th Developer Program Leaders survey , a bi-annual effort to understand your and your peers' challenges, and how DevRel professionals prioritise resources and justify the value of their developer program. We invite you to participate and gain access to insights into how you compare against peer practices and contribute to the industry’s knowledge share. Interested? You’ll need ±8 minutes. Take the survey For every complete response, we will donate $3 (and up to $500) on behalf of the DevRelX Community to support the people who have suffered from the devastating earthquake in Turkey and Syria . Now, let’s take a look at some of the key findings from our past surveys, complemented by expert insights from our members! Building a Developer Program Strategy Sean Falconer shared personal thoughts on Q4, 2021 survey insights. Below are some of the highlights and you can read more in this blog post . How do program leaders spend their time, budget and effort? Below are the results from the survey breaking down how developer program leaders spend the majority of their time. It’s encouraging to see that program leaders are spending the majority of their time on strategy. As Benjamin Franklin famously said, “ Failing to prepare, you are preparing to fail. “ When I joined Skyflow , I spent a lot of time immersing myself in the product, the problem space, meeting people and customers, and thinking deeply about strategy. I wanted to be able to put together a long-term vision. What does developer relations look like for Skyflow in 5 years? And, a short-term vision. Where will it be in 6 months? Starting with a strategy and creating a plan helped me focus on where I should put my attention and how to think about hiring. There are always a million things to do at a startup, but if you try to do everything at once, you’ll do nothing well. A strategy with clear objectives helps you tune out the distractions and focus on moving your key metrics in the right direction. Thinking about and creating a strategy should be the foundation of any developer program. You should outline your goals, and the things you’re going to measure, define a North Star metric, and plan the tactics that will get you there. However, just because you have a plan doesn’t mean it’s going to work. As Mike Tyson said, “ Everyone has a plan until they get punched in the mouth. “ You need to be able to adapt. Failure is to be expected. It’s a learning opportunity. Just don’t make the same mistake over and over again. Internal buy-in and funding The survey reports that 22% of a leader’s time is spent creating internal buy-in or securing funding. In a prior survey , this was 15%. I think part of the responsibility of any program lead, regardless of function, is to spend some time justifying their function’s existence and securing funding. It’s our responsibility to convey our program and team’s value. That being said, I think it’s unfortunately too common that developer program leaders spend A LOT of time on this and it’s a topic that continually comes up. At Google, I felt that several times a year, I had to give presentations explaining what my team did, the value we brought to the program, and the impact we’d had, but even then, I really had to fight for resourcing. Continually fighting this battle is counterproductive and exhausting. So although it’s part of a program lead’s responsibility, I believe there should be a limit. Ultimately, it’s critical that developer programs have executive buy-in and are seen as a strategically important investment. Without that, you’ll burn yourself out trying to make people understand something they are simply not ready or interested in accepting. Understanding where you are and where you need to go Ayan Pahwa has also highlighted the importance of internal buy-in when looking at the Q2, 2022 results in this blog post . Here's what he shared: Internal buy-ins and getting funding: Steadily bridging the gap Now, this is an interesting one. Seeing a drop in creating internal buy-ins and securing funding from 22% in the Q4-2021 survey to 15% this year, really proves that DevRel practitioners are now spending relatively less time in convincing and justifying the cost of their DevRel programs. This could be the result of developer advocacy programs becoming more mature and clearly linked to strategic goals. The impact of developer advocacy programs and their integration into company-wide strategy seems to be making it clearer for stakeholders to consciously invest both budget and resources in DevRel efforts. I’ve been on the spot, spending hours to justify the cost of attending a conference or organising an event or just buying a new service or platform subscription to support our Developer community, so it’s really good to see that time is being claimed back and spent doing other rather more impactful tasks such as : Direct developer engagement (rising from 11% to 18% ). This is one of my favourite activities as a developer advocate. The feedback you get during 1:1 or 1:many interactions is extremely useful and specific. This also gives you an opportunity to create relationships on a much more personal level with members of your developer community, which can later become the foundation of your ambassador program . Understanding of the product: Although not showing up in the results but of great importance based on personal experience, is time spent in understanding your own product better. This is especially true for developer-first organisations where the products are ever-evolving and getting complex.s a DevRel practitioner it’s important to stay up-to-date with your own product growth and for someone who just joined a new organisation as DevRel, quite a significant amount of time can go into learning about the product itself. Challenges calculating on-boarded developer value: The survey also asked how a Developer program budget is justified and the estimated lifetime value of an on-boarded developer. It’s clear that the majority of people (~45%) participating in the survey don’t have a solid methodology for calculating the value of an on-boarded developer and hence only a small portion (8%) has backed the budget allocated to the developer program by an exact FIAT value. Given the complex developer lifecycle and pricing plans of developer-facing - products, platforms and solutions, it’s fairly complicated to create a solid framework that can help calculate the exact dollar value associated with an on-boarded developer. Most products often have a free tier associated up to a certain usage and some also have free tiers for certain segments of developers such as student groups or non-profits. A developer can also evolve from being a part of a free tier to onto a paying payment plan over time and hence adding more complexity to put a dollar value on developer acquisition. It’s also difficult to know which acquisition strategy exactly works in onboarding a new developer, whether it’s that Youtube video you recently published or a past conference talk or demo during a local meet-up, It could very well be your SDK written in Go with good documentation. How companies and DevRel serve the communities developers join As we continue following Developer Relations and Marketing field, we notice how the community is becoming a more and more integral part of all strategic activities. Developer Relations is becoming (if not already) a community-led effort. There is a huge benefit to any vendor to maintain a community for all the reasons that data shows us. If we can enable developers get more out of a product, if we can enable them to be excited about the product, share their experience with their peers and also progress through the community member’s lifescycle from new joiner to expert, we are helping them progress in their career and we’re also getting them more invested in our product and ecosystem. If you keep those core needs in mind, that’s when vendor communities start to add value. - Jamie Langskov , Community and change management strategist. With that in mind, our most recent survey (Q4, 2022) , zoomed into the following: Where do communities fit in the perception of developers? Why are developers joining communities? How are developer-facing professionals address developers’ community needs? Where do communities fit in the perception of developers? Developers join communities to learn. According to the Q3 2022 Developer Nation study , which surveyed 23,790+ developers, 19% of developers rank community in the top 5 resources that companies should offer to support developers. This makes the community 7th most important resource overall, just ahead of answers in public forums and only slightly behind professional certifications. Are organisations paying attention to developers’ community needs? Yes, they are. And we will data-back this affirmation by looking at the data from the latest Developer Program Leaders survey, where we surveyed ~130 industry professionals in developer-facing roles. The data speaks for itself. Communities are now sharing the spotlight with other traditional popular methods of developer education. And developer-facing organisations are aware. According to their responses, when the professionals are setting their strategy on how to talk to developers and address their technical audience needs, 73% consider community as (at least) a key part of their strategy. More specifically 34% consider community as the most important part of their strategy 39% consider community as a key part of their strategy Only 6% do not include the community in their strategy. You can see all responses in this graph: Have you enjoyed these insights? Respond to the survey before March 31 to gain access to more insights like these and a chance to win exclusive DevRelX swag - Have your say !

This is a post from DevRelX. DevRelX was a community-driven platform by SlashData dedicated to advancing Developer Relations (DevRel), Developer Marketing, and Developer Experience professionals. It provided research, insights, and resources through initiatives like the Future Developer Summit & the DevRelX Summit, the DevRelX Podcast, and a book. DevRelX created a vibrant space for learning, connection, and discussion within the global DevRel community, until the community was sunset in 2023 as SlashData shifted its focs to wide Technology Research. As the demand for developers continues to grow, so does the need for great developer relations (DevRel) professionals. Companies are looking for individuals who have strong communication and interpersonal skills, as well as a deep understanding of various coding languages. Not only must these DevRel candidates possess technical aptitude, but they should also be problem-solvers with excellent time management abilities and even customer service experience. In this article, we will discuss what employers look for when hiring DevRel professionals and provide tips on how to gain the necessary experience. What Is DevRel And Why Is It Important? Before we dive into the skills, let's define DevRel as a career path. DevRel stands for Developer Relations and is often referred to as: Developer Advocacy Developer Evangelism Developer Marketing Technical Evangelism It’s a role within the software engineering industry that bridges the gap between developers and the company they work for. DevRel professionals act as liaisons, providing technical guidance while also advocating on behalf of their company by representing its products and services to the dev community. The goal of DevRel is to create a connection between developers and their employers, allowing companies to gain insights from their dev audience and providing devs with the resources they need to do their job more effectively. This helps companies increase customer loyalty, build better relationships with their dev communities, and improve brand perception overall. Qualities Employers Look For In DevRel Candidates In general, from what we have seen across our job board DevRel Careers , there is a specific set of skills that is important for prospective candidates to have and there seems to be a repeating pattern of the below skills: Technical Skills "Do I have to code?" is a common question I have heard from younger aspiring DevRel candidates and although not essential in every role, it draws down to how well you will be able to understand your developer community. Often times their problems are complex and it's almost impossible to understand without knowing at least the basics of a specific coding language. This is also the reason why many successful DevRel professionals were devs before. With a plethora of coding languages, you might be wondering which ones should be the first ones to learn, and this totally depends on the companies you are applying for. There are jobs like the recent DevRel Lead position at Fluence Labs , or the Developer Advocate role at Anvil , that are pretty technical and require proficiency in some coding languages like JavaScript, Typescript, Rust, or Python. Then, there are blockchain companies such as ConsenSys , Ava Labs , Aptos , and Kava Labs that require you to have knowledge of Ethereum, layer 1 blockchain networks and crypto in general. However, there are also less-code-heavy opportunities available, mostly within roles that are more inclined towards Community Management , Product Marketing or Developer Marketing , and require more interpersonal and marketing skills rather than coding. Within those roles, at least a minimal knowledge of coding is still beneficial though. The top coding languages that come up most frequently on our job board DevRel Careers are JavaScript, Python, Java, and TypeScript. Knowledge and experience in using APIs and SDKs are also popular requirements by employers. In conclusion, without any technical skills, it will be hard to climb the ladder of Developer Relations, and if this is the case, perhaps it's worth asking yourself if it's really what you want to pursue. After all, Developer Relations is all about being passionate about the technical solutions you are promoting and advocating for. Communication Communication, either written or verbal, is an essential skill for DevRel professionals. After all, their job is to build relationships and facilitate dialogue between developers and the company they represent. To do this effectively, DevRel professionals must be able to convey ideas in a clear and concise manner so that the dev community can understand them. For example, a recent Developer Relations open position at Netlifly states that This job requires not just understanding things but explaining them in a way that brings others along with you and inspires them to follow your lead. Public Speaking The ability to share technical knowledge and demonstrate product features in a confident and comfortable manner is essential for successful DevRel candidates. Public speaking engagements can take many forms, from presenting at conferences and webinars to giving product demonstrations or leading workshops. A recent DevRel Engineer position at LibLab requires candidates to be comfortable with public speaking and representing the company in developer communities while Discord requires applicants to have confidence presenting to different types of audiences, large and small, virtual and in-person. So, developing and practising your public speaking skills can be a great way to demonstrate your DevRel capabilities and make yourself more appealing to potential employers. Empathy Personally, I am very indecisive about whether empathy is a skill that can be learned or a natural talent. Being a mixture of patience and a deep understanding of the person you are communicating with, empathy is a quality that comes up in almost every developer relations job listing. And it's not surprising, because empathy helps DevRel professionals better understand their dev audience and anticipate their needs. In addition to having technical knowledge of the products and services they represent, DevRel professionals are expected to establish a human connection with developers by listening to their feedback and adapting to their needs. For example, a Head of Developer Relations position at Ably states that You will be successful if you empathize with the challenges developers face, and are excited by the prospect of reducing complexity for developers. Problem-Solving Problem-solving skills are key for DevRel professionals, as they need to quickly and effectively spot potential challenges and develop solutions. Problem-solving requires the ability to think critically in order to evaluate challenges from different angles and come up with creative ideas to solve them. Recruiters also look for DevRel professionals who are persistent and can work through problems independently. As stated in the DevRel and Marketing job listing at Packt , the ideal candidate should be "comfortable using a combination of intuition, experience, and expertise to identify potential problems and can take corrective action quickly”. This is where your technical experience can come in handy, as most developers are problem-solvers and have experience troubleshooting complex issues. Time Management Time management is also a highly sought-after skill for DevRel professionals. This role requires a certain level of organization and discipline to manage multiple tasks, projects, and deadlines. As DevRel professionals often have a large variety of responsibilities ranging from content creation to writing code to attending conferences, it's important to know how to fit everything into the limited amount of time. Customer Service Developers expect DevRel professionals to provide them with thoughtful and personalized support. From helping developers understand product features to troubleshooting technical issues, DevRel professionals must be able to work collaboratively with the dev community in order to ensure their success. For example, DevRel engineers at GitHub are expected to provide world-class customer service and support . A recent Senior Developer Relations opening at Audiomob states that the selected candidate will be guiding clients through the plugin integration process and championing their needs. Why Is Previous DevRel Experience Important There are companies like Matter Labs or Okta that already have a Developer Relations team and their work structure in place. However, many companies are only starting to put together a DevRel department and most are looking for their first DevRel hire . Someone who will be in charge of responsibilities like: Establishing operational procedures and standards for developer relations Building a community of advocates and champions for the company's software Creating an online content strategy Creating a strategy around events, webinars, and speaking opportunities Identifying opportunities for partnerships So, getting into a role with no actual experience is definitely risky for the employer which is why most DevRel open positions require you to have some. Having a background in DevRel shows recruiters that you understand the role and have proven yourself capable of performing it. Gaining Skills & Experience Gaining the necessary experience in order to apply for jobs in DevRel might look slightly different depending on whether your aim is to achieve a role in a more technical direction (developer documentation, developer support) or in a marketing-related role (developer marketing, community management). If you are someone with technical background , i.e a developer, and are thinking about switching your career to developer relations, then you might think about using some of the below ways to showcase your experience: Share your knowledge: Start writing, recording tutorials, or speaking about the software you use. Take on some DevRel responsibilities at your current position: There may be a need for DevRel activities at your company that you are not aware of. Talk to your employer and see whether, for example, creating content for the company could be needed. If you are someone without a technical background , i.e a marketing professional, and are thinking about transferring to a Developer Marketing role, consider gaining experience with the following: Research the differences between general marketing and marketing to developers: Create a report/presentation/video of your findings and share this with potential employers. Learn the basics of coding: There are lots of courses and bootcamps available online for acquiring some knowledge of the main coding languages. Final Thoughts To sum up, Developer Relations seems to be one of the most versatile career paths and it is an increasingly important role in the tech industry. Companies are looking for candidates with strong technical and interpersonal skills, one of the most important qualities being empathy. Whether you have a technical background or not there are ways to gain the necessary DevRel experience through writing content, taking on DevRel responsibilities at your current job or learning to code and sharing your knowledge along the way. However, most importantly, it is necessary to have passion and genuine interest in the role, because, as ConsenSys has beautifully put it: While we have a pretty good idea of what we need, we're ready for you to challenge our thinking on who needs to be in this role. Hope you found this article informative and helpful. If you wish to share ideas or discuss the subject more, you can message me in the DevRelX community . To find job opportunities in Developer Relations, feel free to check out DevRel Careers where we post new jobs daily.