Search Results

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. Welcome to your new journey as a Developer Advocate at XYZ company! This guide is here to help you learn the key things to remember, making a great impression on your manager, directors, and team. Note: This guide is for when you've decided to join the company and have signed the contract. Before Day 01 Whether coming from a different company, shifting from another role like software engineering, or stepping into your first job, taking a week off before day 01 is beneficial for your health. Take this time to relax with family, go on a trip, or enjoy activities that refresh your mind. This break will help you start your new job with fresh energy and enthusiasm. Getting Familiar You're stepping into a new world with a new position, new responsibilities, a new team, and a new environment. It's natural to need some time to adjust and let your brain get used to the new setting and role. The first few weeks are about learning and getting comfortable in your new environment. Progress might feel slow, but that's a part of the onboarding journey. Things will pick up speed as you start understanding more. Now is the time to learn and absorb, not to rush into planning strategies or defining roadmaps. Meet with your team members, especially your manager or the person you'll report to. Understand the expectations and learn what the company hopes to achieve with your role. Based on these expectations - you will be planning your strategies. For example, if you’re the first one in the DevRel team and you’re reporting directly to the CEO, the following are the expectations of the stakeholders: Actively engage with the online and offline developer community to build a positive brand image. Create regular content on Social media (i.e., Twitter and LinkedIn) Host annual XConf in collaboration with other tech companies Gather and share informative content such as tutorials, blogs, and videos to educate and assist developers Gather feedback from the developer community to internal teams to help improve production and services. Once you understand the expectations, you can use them later to measure success by comparing your progress with those expectations; we will discuss that later. Planning your strategy Now that you have a grasp on the company’s expectations and you’re learning the new skills you need to acquire, it’s time to plan your strategy. Begin by setting short-term and long-term goals aligned with your company’s objectives. This will help you narrow down. Short team goals: Establish a regular schedule for engaging with the developer community both online and offline. Start creating and sharing content on social media to build a presence. Long-term goals: Organize the annual XConf and other collaborative events to foster partnerships. Develop a feedback loop to continuously improve the company’s products and services based on community feedback. Feedback loop creating At this point, you’re still new to the company, and you have to learn new things. One thing that is quite helpful is establishing a mechanism for collecting and acting on feedback internally and externally. Internally Start with asking for feedback on social posts and video content from the team. Ask for improvements and suggestions. You will be able to get feedback on this, like the content tone, which can be improved before publishing content. Ensure to include your manager or the person you directly report to. This will ensure both of you are aligned in terms of work. Externally A second feedback can be taken from people outside the company - this is like a beta access. You’re sharing content outside the company but with a few people. Please think of this as you’re writing a book, and you need people to review it, so you reach out to some friends and industry experts and ask them to give you a read to your book before you make it public and available to everyone. This type of feedback doesn’t tell you about the content tone and alignment with the company, but it can give you an idea of how an outsider will digest the content. Creating Systems As you start taking responsibility, start documenting it to make it easy for others to understand what’s happening, especially your manager. Moreover, begin creating processes or systems to help you repeat the tasks. For example, for posting content on social platforms, there should be a platform where everyone can view the upcoming posts and a specific time for every post if anyone wants to provide feedback. Everything should be planned and go through a process before you make it public. Creating that process is a time-consuming task, and you have to experiment with something, but once you have a basic system, you will see things speed up. Moreover, start with a fundamental process and go from there; don’t overcomplicate things. Note: You may not always need to create a system. There might be a process that’s being followed already. If that’s the case - look for improvements. Otherwise, create one from scratch. Measuring success At this point - you have to start looking at the success to see if the work you’re putting in is creating an impact and you’re getting closer to the goals. One of the standard practices companies follow is using Key Performance Indicators (KPIs) to measure success. The KPs are measurable values demonstrating how effectively a company or individual achieves vital business objectives. You can define the KPIs when you start working on the content. If your company doesn’t follow the KPIs practice, you can write your quarterly KPIs and try to achieve this. Example KPIs are: Publish at least ten insightful blog articles to educate the developer community. Grow the Twitter community to 10,000 followers through engaging and relevant content. Produce at least ten in-depth video tutorials and a minimum of 25 bite-sized video snippets to assist and educate developers. At the end of the quarter, you can review this list and compare it with your work to measure the success. It’s possible and expected that by the end of the quarter, you will achieve 2 out of 3 KPIs; if that’s the case, you can move the unfinished KPIs to the next quarter. When writing your KPIs, balance realistic goals and going too easy. Write KPIs you think you can achieve, but ensure they are not too simple or easy. For example, publishing one blog article is a too easy task for a quarter. Conclusion As you delve deeper into your role, remember the essence of a Developer Advocate lies in fostering a symbiotic relationship between the developer community and the company. Your continuous efforts in engaging with the district, enhancing your skills, and measuring your success against well-defined KPIs will propel you forward and contribute significantly to XYZ company's vision.

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. It ’s impossible to ignore the hype around AI right now, and all of us working in Developer Relations have been grappling with the right ways to leverage it. This year’s DevRelX Summit was largely focused on AI, and one of the panels I found most interesting was the one on How DevRel Teams Can Use AI . Moderated by Ash Ryan Arnwine from Nylas , the panel featured insights from Kerri Shotts of Adobe , Jon Gottfried of Major League Hacking , and Joyce Lin of Postman . In this post, I want to dive into their thought-provoking conversation and explore some of the ways DevRel teams are currently using AI to enhance their work and products. Whether you're a developer, a member of a DevRel team, or simply intrigued by the intersection of AI and software development, this discussion offers valuable perspectives on harnessing AI's potential today and envisioning its role in the future of DevRel. How DevRel Team Are Using AI Today The first topic the panelists dug into was how DevRel teams are most impacted by AI today. Because all the panelists are building products and extensions specifically for developers (i.e., developer tools), all of them have been exploring using AI to enhance their products. From direct integrations that enable auto-complete to custom-trained models that help developers explore documentation, AI is becoming part of almost every developer tools’ product portfolio. On the other hand, the panel also discussed ways that they’re using AI to help their developer relations teams do their jobs more efficiently. While there are limitations to large language models (something I’ve noted before ), they can supplement the work that DevRel teams are doing in some interesting ways. Let’s dive into some of the discussion on both of these AI use cases for DevRel teams. Question 1: How are you seeing DevRel teams use AI already? The first point brought up by Jon Gottfried at Major League Hacking is that AI is changing the way developers write and debug their code: “One of the things we’ve seen is AI replacing some aspects of pair programming and one-on-one support. We see a lot of developers posing questions to Chat GPT, which is interesting.” One of the biggest limitations to this use case is that AI models might not be correct. Often, the training data is out of date or because there is little information available on the topic, the model may just make up answers rather than admit that it’s not certain. While inaccurate answers might be okay for more experienced developers who know to question the model and interweave their own knowledge, giving less experienced developers incorrect but confident answers to their questions is downright dangerous at times. Kerri Shotts of Adobe pointed out that if the AI can’t give accurate answers, users have to be familiar enough with the API docs that they know how to parse information for correctness. Obviously, this is frustrating. With this use case in mind, DevRel teams are tuning or training models on their docs to ensure that the answers users get from AI chatbots are as accurate and up-to-date as possible. This helps overcome the correctness problem, but it still doesn’t make AI a perfect tool. Another important factor Kerri pointed out is that the context matters. A person typing a question into Chat GPT may expect different answers from a developer writing code into their IDE or terminal. By building AI into specific product experiences, you can give the model more context than by relying on purely general models. Finally, Ash pointed out that AI is also being used as a discovery tool by developers, and that DevRels (especially those with KPIs around growth) should be aware of this. He noted that a developer came to Nylas through a Chat GPT recommendation, and that while this is good, it’s also not a channel they have much control over yet. Question 2: How are you seeing developers use AI in conjunction with your API? With his unique experience running hackathons, Jon noted that he sees a lot of developers using AI to figure out how they can use a particular tool or leverage its full capabilities. This really resonated with me. I always hated spending hours reading through a tool’s entire documentation site just to figure out what it was really capable of, but a well-trained model could act as instant support for developers in that situation. This frees up DevRels from having to answer all these questions manually and it means users can get real-time support without expensive full-time staff on call at all hours. Ash pointed out that building custom models used to be the only way to accomplish this, but now GPT-based tools can be built using OpenAI’s API, making the cost to develop and maintain custom models much lower. Joyce’s perspective was also interesting as Postman is one of the most widely used API management tools out there. While they have AI-based documentation tools, she pointed out that in general, the AI tools available to developers are more mature than those made for DevRel use cases: “I would love for it to be able to help me write a conference talk faster, but the human aspect is still not quite there.” This mirrors my experience too. Last year, I tried out several of the top AI writing tools , and found that none of them could really replace a few years of personal experience when it comes to technical depth. This response served as a nice segue into the next question… Question 3: How are you using AI models to specifically help DevRels do their jobs? Right off the bat, everyone agreed that AI isn’t coming to replace DevRels anytime soon. Ash and Kerri pointed out that you have to use your own experience and judgement to filter out the information LLMs generate, and you should still double check everything it gives you back. Joyce added that cutting edge or niche topics are especially problematic for off-the-shelf AI models because LLMs can only draw from existing content. Because keeping documentation and blog posts up-to-date is a huge challenge for DevRel teams with older products, this makes the current state of generative AI minimally useful for many. That said, it might help you find things you might be missing or get started on something new. The “blank slate” problem was brought up a couple times in this discussion, and that’s where I’ve found generative AI to be most helpful too. I wrote the first draft of the introductory paragraph for this blog post using ChatGPT, and then revised it heavily to make it sound more like my writing. Despite writing thousands of blog posts in my career, I still leverage AI sometimes to help me get a new document started. Question 4: How are you leveraging AI for onboarding? As use cases were discussed, Ash brought up one especially tricky problem that they’re working on at Nylas: new user onboarding. In theory, AI could build bespoke onboarding flows based on the developer’s experience level, use case, company size, stack, etc. In practice, giving AI enough of this information to be useful makes this hard to do. Kerri pointed out that skill level matters a lot in onboarding and in giving answers to common questions. A newer developer might get an incorrect code sample from AI and have no idea how to start debugging it, while a senior developer might not need as much boiler plate code to work from. The worst case scenario is that AI slows down onboarding or frustrates new users. You could try to gather this information through a form, but you still have to worry about getting the wrong information or burdening users too much early on. Retaining context seems to be part of the solution though. Joyce pointed out that GitHub’s Copilot X retains its knowledge about the user and their interactions with the IDE to help it give more personalized responses. Ash noted that a transferable configuration (like a JSLint file) for any AI coding app would be an interesting solution too. Question 5: What will AI integrations and tools look like in the future? Ash pointed out that throughout this topic, we’ve been talking about AI in the form of chatbots and text autocomplete, but these aren’t the only forms the technology could take. For example, what would it look like to have CLI-based AI tools that returned non-deterministic results based on their understanding of context or the user’s network configuration? Jon added that we’re starting to see it used less transparently in things like dynamically generated content on pages. For example, a tool’s homepage could generate different text based on who’s viewing it or how they’ve interacted with the site before. He also pointed out that more programmatic use cases will help alleviate some of the back-and-forth currently necessary. He mentioned a use case where he’s building a tool that will look at a code sample and error message, recommend a fix, and then create a GitHub issue with the fix suggestion. Currently, this workflow would be pretty manual, but it could be automated or built into other tools in the future. Kerri pointed to two ways that developer tools will use AI in the future. First, they’ll help guide users to the right solution or point out mistakes proactively. This could be very helpful if you’re new to a tool or working in a new stack. Second, AI could be used to suggest “happy paths” in onboarding, helping new users see what else they can do with the tool or analyze any risky patterns they employ. Conclusion While the AI hype cycle has probably peaked , we’re still in the early days for generative AI technologies. Joyce pointed out that similar to buzzwords like “low code,” chatbots have promised a lot, but we haven’t seen the end of what they can offer. AI has the potential to both open up coding to non-developers as well as help users who “don’t know what to ask” because AI can decipher fuzzy contexts much better than traditional structured queries. Finally, DevRel will undoubtedly find new ways to leverage generative AI as the technology matures. Whether it’s helping us update or generate documentation, create technical tutorials in new programming languages, or respond to users faster and with more correct information, we’re just scratching the surface of AI and developer relations in 2023. About Karl Hughes Karl is a former software engineer and CTO. He’s currently the Founder and CEO of Draft.dev , where his team has helped 150+ developer tools companies create compelling, technical content at scale. You can reach out to him on Twitter/X or via email .

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. Development communities like GitHub and StackOverflow are the bedrock of progress for all kinds of developers and their projects. Furthermore, many organisations curate their own communities to enhance developers’ experience with their products. In this post, taken out of SlashData’s public report “ The State of the Developer Nation ”, we’ll learn what developers look for when joining a technology-centric community and which factors encourage them to participate and engage more. What factors do developers consider when joining a technology-centric community? Developers primarily engage with technology-centric communities to learn. More than half (52%) consider the quality of available information to be one of the five most important factors to consider when joining a community. That’s considerably more than the next most popular factor, the availability of courses/training (38%), which, incidentally, also facilitates learning and knowledge-sharing. Developers primarily engage with technology-centric communities to learn – the quality of available information is by far the most important factor Interestingly, we see changes in importance among developers who didn’t select either of these two learning-related factors. In-person events (+6%), member-only benefits (+5%), links to industry (+4%), and online events (+4%) all increased in importance. This suggests that when developers aren’t there to learn, they are more likely to join a community in order to network. However, these factors are still among the least important for this group. A positive culture enables developers to get the most from their community experience Community culture is also important. 35% of developers consider the friendliness of a community when deciding to join, making this the third most important factor. Clearly, curating a welcoming and friendly experience should be a priority for community managers. In fact, it seems that a positive culture is an enabling factor in allowing information-seekers to get the most from their community experience: developers who prioritise the quality of information and having access to expert community members are more likely than average to also think that a positive culture is important. Further down the list, the platform on which the community is based is actually not that important when developers decide to join a technology-centric community. Only 21% of developers selected this option, making it the seventh most important factor, just ahead of recency and frequency of activity. However, as we will see later, the right platform can ignite greater engagement and participation. For now, though, we’ll take a closer look at which factors developers in different generations consider before joining a technology-centric community. As developers age, they become even more focused on the aspects of a community which facilitate learning. The quality of information present rises in importance the most — increasing from 47% amongst the youngest developers to 68% of the oldest. We see a similar – though less intense – story for the availability of courses/training, a friendly and welcoming culture, and expert community members. Once again, a positive culture is an enabler of accessing learning materials, and, as we will see later, also an enabler of increased engagement and participation. 68% of developers aged 45 and up consider the quality of information available when joining a community On the other hand, younger developers have subtly different preferences. They are more likely than the oldest developers to consider mentorship programmes, links to industry/recruiters, and membership benefits as being important factors. These younger developers are focused on the employment and the networking benefits of community membership. Younger developers are more focused than their older peers on the employment and the networking benefits of community membership Importantly, the youngest developers consider mentorship programmes to be more than 2.5 times as important as older developers (26% vs 10%), and mentorship programmes are the fifth most important factor to these developers, likely due to their focus on building their careers. Now, this creates a point of tension – how to attract older and more experienced developers to a community, and then engage them in mentorship programmes to help younger developers? Firstly, ensuring that communities meet older developers’ expectations around learning will go a long way to attracting them in the first place. However, to raise engagement for this group, we see that older developers who value mentorship opportunities are more than twice as likely than those who don’t to say that inviting their peers and friends and the availability of in-person events, contribute to increasing their participation levels. In a nutshell, the type of older developer who values mentorship is already predisposed to the networking benefits of a community. Identifying these older developers should be a priority for any community where demand for mentorships outstrips supply. You can access the full report here. What encourages developers to actively participate in a community? From joining a community to actively participating in it, we see large differences in what makes developers tick. A well-designed community platform – which is far down the list of factors that developers consider when joining a community – sits at the top when we ask about what encourages greater community participation. The time and effort that goes into selecting the right platform might not have immediate returns for growing a community, but it is likely to help to keep developers engaged and active. A well-designed platform is what encourages developers the most to actively participate in a community Participation is a two-way street. 27% of developers say that getting regular updates inspires greater engagement. Here, developers want to see activity from the community managers and founders. Looking at this from another direction: there’s likely nothing less inspiring than joining a community where the leaders aren’t themselves engaged. Indeed, further down the list, 17% of developers say that direct interaction with community leaders helps with their engagement. Strong leadership has benefits beyond day-to-day participation. Having a well-defined purpose for the community encourages more active participation for 26% of developers, and an inclusive and welcoming culture is encouraging for 25%. Interestingly, active moderation is a much less popular driver of engagement – only 15% of developers selected this, but those who did are much more likely than average to engage more in communities with a positive culture. A positive community culture not only encourages people to join but also keeps them engaged. This said, sometimes a heavy moderating hand is necessary – whilst developers might not always appreciate it, the alternative of a negative, exclusive, or toxic community culture is likely worse. Developers who value strong leadership in a community are often less engaged by games, quizzes, and prizes It’s not only good vibes that encourage greater participation; prizes and rewards (26%) appear to be a powerful lever in encouraging greater community involvement, with fun activities close behind (22%). However, developers who are encouraged to participate through factors relating to strong leadership and expertise – positive culture, well-defined purpose, direct interaction with leaders, and having access to recognised experts – are often much less inspired to participate by prizes, activities, and polls and quizzes, whilst the inverse is also true. This points to there being two distinct groups of developers – those who engage ‘seriously’ and those who perhaps take a more whimsical approach to community engagement. For community organisers and contributors, it’s important to understand which mindset a community tends towards: a focus on fun and games in a more serious community may appear to lack authenticity and gravitas, whereas taking a more sober tone may exclude those with a lighter agenda. For developers of all backgrounds and skill levels, communities offer an opportunity not only to learn but also to connect. Different types of developers have different expectations for how they want to interact with their communities, but learning opportunities and a welcoming culture are consistently highly rated. This article is part of the developer insights offered in the State of the Developer Nation 25th Edition . You can access the full report which covers: 1. Language communities - An update 2. Creating A Sense Of Community - How Developers Interact And Engage With Their Peers 3. How Generative AI Will Affect Developers' Work 4. Web3 Unveiled - Exploring The Diverse Landscape Of Web3 Development Projects 5. From Code To Consumer Magic - The Software Developers Behind Our Everyday Electronic Devices 6. What Are People Building In AR/VR? Want to dive deeper into data on developer population, data and segmentation? SlashData has all the answers.

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. Generative AI seems to have taken the world by storm, introducing new productivity tools and even passing medical and bar exams. While the tech industry seems to still work out what this will mean for the future, we are glimpsing at the people at the heart of the revolution, developers and Developer Relations practitioners. The data from the recent State of the Developer Nation report suggests that 63% of developers are engaged in some aspect of AI-assisted development , making it evident that this technology is rapidly maturing and transforming from a mere trend to a valuable tool. Which developers are working on Generative AI projects? We know developers are highly interested in AI technologies, with 57% of developers globally already working on or learning about Generative AI. But who are these developers? Let’s take a closer look! Recently we hosted a session where we looked at what developers have to say about working on Generative AI projects. You can re-watch this session on demand and discover answers to these questions How are developers involved in generative AI projects? How are they engaging with generative AI projects? Where are these developers located? What is their level of experience? Where did they learn how to code? What are their roles? How do developers involved in generative AI projects, get information about software development, and what types of content do they prefer? How are Developer Relations adopting AI? While developers are at the frontiers of emerging technologies, what about those of us engaging with developers daily and catering to their needs? Recently we asked our DevRelX Community members about the topics they are most interested in, and they, just like the developers themselves, are most captivated by the current state of Generative AI. We also asked about our members’ views on how AI technologies are affecting the Developer Relations field and developer communities overall. You can also join the discussion with them and share your thoughts and insights on this Slack thread . Here are some of the key highlights and resources shared by our members. ⭐ Ash Ryan Arnwine , Director of Developer Relations at Nylas At Nylas, our DevRel team is using a framework to help us explore where AI can be useful in key areas of our work. My hope is that our team can grapple with emerging AI capabilities while keeping a couple of guiding principles in mind. First, the current wave of generative AI is the newest "bicycle for the mind"; it doesn't need to solve every problem perfectly to be a valuable tool. It's on us to learn this tool and wield it constructively in the service of our developers. The second principle is related: we experiment aggressively in private; we ship to developers with care and consideration. We're using AI to aid in completing the meta task of inspiring developers on ways to enhance their Nylas integrations with AI. On the developer experience side, it feels like the possibilities are almost infinite! And the more we play, the better informed our product ideas and AI-enhanced DevRel work can become. Learn more about Ash’s team’s experiments with AI. ⭐ Karl Hughes , CEO at Draft.dev We've been starting to explore/use AI a bit mostly just for outlines and briefs for articles. The content it writes still needs a lot of hand-holding from knowledgeable engineers to ensure it's accurate. The inability to get very deep, especially in esoteric or nuanced topics still holds it back though. Learn more about Karl’s thoughts about the future of AI writing tools. ⭐ Kamran Ayub , Principal Consultant at Lovely DevEd AI isn't going to go away and will keep getting more sophisticated unless you believe competition will stop and capital will stop flowing to these startups and services. Hallucinations are a problem but that will likely be handled soon, we are already beginning to see services like Azure OpenAI offer ways to safely and securely deploy AI solutions that avoid hallucination, sandbox confidential data, and integrate across enterprise tools. Generative AI is "creative" but not as creative as humans -- it cannot generate original thought, it cannot think for you, which means it will be even more important for companies to create quality creative content since quantity will be easy. In other words, start thinking of gourmet content instead of fast food content creation. Someone I know uses the phrase, "AI-Generated, Human Curated" -- Human curation, human creativity, will be a differentiator in the sea of average-to-below-average bot content. AI will help assist folks with disabilities. If they cannot speak, you can now use AI voices to make technical videos that aren't terrible using Text-to-Speech. If you have trouble learning, you can now ask questions about code or what you're seeing with GitHub Copilot. If you have trouble articulating, AI can help you be clearer. If you don't speak the language, AI can help you translate accurately. AI-assisted accessibility will provide more ways for people to contribute and more ways to make your content more accessible. AI can help with dev tools in tons of ways. It can speed up rich content creation: visual essays, videos, music, etc. all of which could be hard to do before and took a lot of time will get easier and easier to create -- it'll feel more like "directing" than creating. We're already seeing some work being done with support bots or "Ask Docs" functionality. GitHub Copilot is evolving rapidly and it's already saving me time and solving real issues (see my blog post). This will change the way developers interact with docs and education. AI can help assist with workflows. Imagine being able to summarize community posts, Discord channels, and Slack channels -- I can almost guarantee we'll begin seeing Community Copilots that help coordinate between different channels and outlets. Microsoft is already doing a land grab for enterprise AI with their new services and offerings. It's their fastest-growing service. They have all the MLOps and DataOps infrastructure and they're creating a Copilot Platform across Microsoft 365, GitHub, Windows, ChatGPT, Bing, etc. -- that is going to be the new app frontier, accessible to billions of people. How will your dev tool help people in an everyday context outside your product UI? That'll be an interesting question to wrestle with. AI impacts data privacy and regulations. We'll start seeing crazy data breaches, jailbreaking, and other security-related issues. Learn more about Kamran’s experiments with AI tools. ⭐ Christie H. Kristensen , VP, Global DevRel at Mastercard We recently published a blog about how the Open Banking API team uses AI . Mastercard has a suite of Open Banking (US) APIs that are powered by AI and Machine Learning. Although AI is beginning to make headlines, we have been using AI for the better part of a decade and AI is embedded into a whole range of Mastercard’s products. Mastercard is committed to the responsible use of AI in our products and will remain dedicated to customer data privacy. Stay tuned for the next post which will cover the challenges with AI! ⭐ Eric Ciarla , Co-FounderCo-Founder Mendable by SideGuide Here are a few thoughts I have to share: Improved Efficiency and Accessibility: Generative AI products like Mendable.ai drastically streamline developer relations by making it easy for developers to access and navigate the knowledge base. They can find answers quickly, without needing human intervention, which speeds up their workflows and improves overall satisfaction. Greater Engagement and User Retention: With an AI-powered search and chat interface, developers can interact with the knowledge base in a more intuitive and engaging manner. This engagement can increase retention, as developers appreciate the readily available assistance and feel more connected to our product and brand. Scalable Support System: Our AI-powered solution can handle a vast number of queries simultaneously, without the need for scaling human resources. This allows us to provide consistent, reliable support to an ever-growing user base and ensures that every developer, regardless of their timezone or location, can get the help they need when they need it. ⭐ Brett Bush , Senior Software Engineer at LogicGate I’ve got a few notes too! APIs are centre stage: AI services and their APIs are available off-the-shelf, allowing for straightforward integrations to be built with existing platforms, as opposed to building AI functionality internally. However, in order to integrate, the value of APIs for non-AI platforms has also risen, as these APIs will benefit from being built out and streamlined in order to support enhanced functionality by integrating with AI APIs. Token-based API pricing: OpenAI’s API model is placing the token-based API pricing model in the spotlight, perhaps paving the way for more adoption of pay-as-you-go API pricing models as opposed to subscription or tiered models. OpenAPI x OpenAI opportunities: Given the text-based and semantic nature of OpenAPI Specifications, they provide a compatible format for describing an API to LLMs, allowing for possibilities ranging from code snippet creation, debugging, and helping with building complex solutions with a given API. Developer Portals: With the wiki and webpage formats of many Developer Portals, there is an opportunity to supplement Developer Portal documentation with AI-backed chatbots. This would allow developers to query specific questions from a single prompt, saving them time from parsing through pages of documentation or contacting a developer relations representative. And what are your views on how AI technologies are affecting the Developer Relations field and developer communities? Share your thoughts with us on Slack !

In a tech landscape obsessed with speed and innovation, software development remains one of the most complex and failure-prone processes in business. Poorly structured codebases, outdated documentation and brittle architecture may sound like engineering issues, but the ripple effects of failures in these areas can deeply impact product leaders, marketers, operations teams, and even customer-facing departments! SlashData’s latest State of the Developer Nation report, based on input from over 10,500 developers across 127 countries, takes a data-first look at the persistent obstacles software teams face. The findings detailed in the report go beyond the status quo to clarify which organisations are most at risk, and how these challenges scale with company size and age. This isn’t just a developer survey. It’s market research for tech decision-makers ready to prioritise long-term software sustainability. The Top Challenges: A Crisis in Code and Communication Nearly 9 out of 10 professional developers face at least one challenge in their day-to-day software development work. At the top of the list? Code that is difficult to read, maintain, or extend (31%) Insufficient or outdated documentation (31%) Insufficient test coverage or ignored test results (27%) It is vital to note that these are not new, exotic issues but rather foundational issues whose widespread nature point to systemic breakdowns in software quality assurance and team knowledge management. For organisations betting big on digital transformation, these cracks can silently sabotage scalability, increase time-to-market and erode developer morale. Key takeaway for Product and Program Managers If your team is regularly missing sprints, shipping bugs or onboarding new hires slowly, these aren’t growing pains! They are warning signals of deeper architectural and documentation debt that compound over time!  Company Size: More Developers, More Challenges? It’s a natural assumption that larger development teams should have more resources to handle complexity. SlashData’s findings show the opposite trend. Developers in organisations with over 1,000 employees involved in software projects face an average of 3 challenges, compared to just 2.4 challenges in small teams (1–10 developers). The biggest gaps emerge in documentation and design: 46% of developers in large teams report documentation issues, versus just 28% in small teams. 30% face outdated or flawed software designs, making scalability difficult. It's clear that size doesn’t actually guarantee maturity. Without strong engineering practices and a scalable knowledge-sharing culture, larger teams simply amplify dysfunction. Key takeaway for Engineering and DevOps leads: These findings support investing in scalable documentation systems, internal developer portals and automated linting or code hygiene tools that evolve with your team size. Company Age: The Paradox of Experience SlashData’s report also reveals a nuanced relationship between company age and software challenges. Newer organisations (under 5 years old) are more likely to report facing challenges overall (92%). That being said, older organisations (30+ years) are more likely to experience a higher number of distinct challenges per developer, especially around: Documentation (39%) Flawed software design (29%) This dual dynamic suggests that while younger companies face resourcing and maturity issues, older companies struggle with legacy systems and institutional inertia. Strategic implication for CTOs and innovation teams: Avoid the trap of “it still works” thinking. Legacy systems should be proactively modernised, especially if your organisation is expanding into AI, mobile, or cloud-native architectures that demand modularity and flexibility. What’s Fueling the Fire: Beyond Code In addition to documentation and architecture, other common challenges include: Software components not kept up to date (26%) Reliance on outdated or unsuitable third-party libraries/tools (24%) Improperly set up or maintained development or production environments (21%) These aren’t isolated issues. They are interconnected symptoms of insufficient planning, fragmented tooling strategies and a lack of shared ownership across teams. How market research helps: Segmented insights and data on across parameters like company size, age and performance (like that provided by SlashData) empower Product and Engineering leads to benchmark against global peers. This can then translate into prioritised roadmaps, smarter resourcing and better tooling choices.  A Checklist for Cross-Functional Teams To Consider:  No single role can fix systemic software challenges alone. But the solutions must begin with cross-functional alignment. Here’s how different teams can act on SlashData’s findings: For Product & Program Managers Allocate time in roadmaps for refactoring and documentation. Track developer satisfaction and onboarding metrics as health indicators. Choose tools that support structured code hygiene (e.g., internal style guides, IDE plug-ins). For Engineering Leads Implement pair programming or code review policies to reduce knowledge silos. Encourage modular design to make legacy code more maintainable. Use metrics (test coverage, commit quality) to track progress on technical debt. For Business Analysts & Executives View software challenges as risk vectors in product delivery and team productivity. Use SlashData’s benchmarks to evaluate where your organisation stands. Fund developer experience initiatives as part of broader digital strategy. For Marketing & Talent Leaders Position your company as an engineering-first culture with modern practices. Use insights from this report to inform recruitment messaging (e.g., “we invest in clean code, not just fast delivery”). Understand that developer satisfaction and output are tied to sustainable systems, not just perks or pay. Conclusion: Software Challenges Are a Business Challenge As the tech industry continues to chase AI, DevEx, and cloud innovation, the basics still matter. SlashData’s State of the Developer Nation Q1 2025 report makes clear that foundational software issues such as code quality, documentation, architecture remain unsolved at scale! For any organisation serious about growth, these insights should be a catalyst for change. This is why SlashData’s research isn’t just developer data. It’s strategic market intelligence. Whether you’re building the next big SaaS platform or scaling a legacy IT system, understanding the nuances of developer challenges can directly inform better decisions across engineering, product and business strategy. To explore more findings and see where your organisation stands, access the full report at: slashdata.co/free-resources

Artificial intelligence is transforming how software is built, deployed, and maintained. Yet, questions persist: Are AI-assisted tools really improving software delivery performance? Do high-performing teams use them more? And what lessons can the rest of the tech industry learn from these trends? These and more insights are being uncovered as part of the Developer Nation Series, currently in its 29th edition - and counting! SlashData's latest research report, USAGE OF AI ASSISTANCE BETWEEN DORA PERFORMANCE GROUPS  tackles these questions by comparing the usage of generative AI tools across DORA performance groups (the industry-standard framework for measuring software delivery performance). From elite performers to lower-performing teams, the report investigates where AI helps, where it doesn’t, and how these insights can inform not just developers, but marketing, product, HR, and executive leadership alike Why This Matters for Everyone in Tech This isn’t just a developer story. Understanding the impact of AI tooling at a performance level informs: Marketing on how to position productivity-enhancing technologies HR and Talent Acquisition on what skill sets to recruit and where gaps may appear Product Leaders on prioritising AI capabilities in internal tools or user-facing platforms Executives on investment decisions and operational strategy In short, this is market research for tech  that drives value across the entire business ecosystem. AI Tools Alone Don’t Improve Lead Time Across all DORA performance groups, AI-assisted coding tools like GitHub Copilot showed minimal impact  on lead time for code changes. Even elite performers didn’t significantly outperform lower-performing teams in this metric due to AI adoption. Why? Because lead time is more influenced by internal processes, team coordination, and review cycles than by the speed of code writing alone. Implication:  Organisations investing in AI should balance tool deployment with process refinement. Without the latter, AI becomes a speed bump, not a shortcut. AI Boosts Deployment Frequency Among Elite Performers Elite teams that deploy code frequently are also the highest adopters of AI-assisted development tools. 47% of elite performers  use these tools versus only 29% of low performers . Why it matters: Teams that ship often may be more open to experimentation and continuous improvement. These environments are fertile ground for AI adoption because they allow quick iteration and feedback. Implication for product teams: If you’re building AI features into dev tools or platforms, target high-performance environments first. They’re more likely to adopt and validate your innovations. AI Chatbots Help Restore Service - But Come With Tradeoffs When it comes to time to restore service, AI chatbots like ChatGPT were more commonly used by elite performers (50%) than low performers (42%). These tools help developers recall information, identify fixes, and reduce downtime. However, an interesting contradiction emerges:  elite teams also have a high proportion of developers who don’t use any AI tools at all . This could reflect a reliance on well-documented, deterministic processes that work faster than AI in critical moments. Implication for operations and support: AI tools are helpful, but they must complement - not replace - structured incident response frameworks. Low AI Usage Correlates with Lower Failure Rates Among the most striking insights: elite performers in the “change failure rate” metric are  least likely to use AI-assisted development tools . Just 31% of elite performers use them, compared to 40%+ of other groups. Why? AI-generated code often lacks contextual awareness. If poorly understood or inadequately reviewed, it can increase the risk of service impairments and rollbacks. Implication for engineering and quality teams: A higher volume of AI usage doesn’t always mean better outcomes. Vetting, review, and knowledge-sharing processes remain vital. The Industry Factor: Why SaaS and Regulated Sectors Differ SlashData’s research also notes that SaaS companies  have higher adoption of AI tools - but also higher failure rates. This is likely due to a culture of rapid deployment and lower tolerance for delay. By contrast, financial services, energy, and government sectors  showed the lowest AI adoption  and the highest proportion of elite performers . These industries typically require rigorous testing, governance, and audit trails-all of which demand caution when using generative AI. Insight for leadership: Don’t blindly chase AI adoption. Tailor usage to your industry’s risk profile, regulatory environment, and tolerance for failure. Bringing It Together: AI in Context, Not in Isolation This report is a reminder that generative AI isn’t a silver bullet. Its success depends on the context in which it’s used: the team culture, the processes in place, and the performance goals driving development. For decision-makers across the tech ecosystem, these findings are a call to action: Use AI adoption data to refine your hiring strategy Align product development with proven high-performance behaviors Set realistic expectations for AI-driven outcomes Invest in complementary capabilities: documentation, QA, and developer education SlashData’s DORA-based analysis of AI adoption  helps demystify where AI actually moves the needle in software performance - and where it doesn’t. More importantly, it shows that market research for tech  is not just for engineers or analysts. It’s for anyone who wants to build smarter strategies, stronger teams, and more resilient products. To explore more AI adoption data and performance insights, visit [ https://www.slashdata.co/free-resources ] . You can also read more on the Developer Nation Series by access our blog library covering;  78% of developers feel the team camaraderie & 21% of startups generate revenue from subscriptions Sizing programming language communities The rise of AI chatbots for problem-solving Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Profiling of professionals working at startups

The tools developers choose say a lot about the future of software and the businesses that support it. Programming languages are more than syntax; they shape hiring, tooling, revenue models, and even operational risk. That’s why SlashData’s latest Q1 2025 Developer Nation report offers essential insights not just for engineers, but for business function leaders, operations teams, and strategists across the global tech ecosystem. Here’s why this matters for decision-makers; JavaScript Still Reigns Supreme But Growth is Slowing With 28 million active developers, JavaScript remains the world’s most-used programming language. It’s used across virtually every software domain, making it an indispensable skill in both front-end and full-stack roles. That being said, JavaScript’s growth is slowing. While it still added 2.8 million new developers in the past year, this is modest compared to expansion in previous periods. This may indicate a maturing market which is something teams should watch when forecasting tool adoption or developer availability. Why it matters:  JavaScript is essential but competitive. Teams hiring for JS-heavy roles should consider differentiating to attract top talent. Culture, projects, or compensation are only a few examples. Java Surges Ahead of Python For the first time in recent years, Java (23.2M developers) has surpassed Python (22.9M) to become the second-largest language community. Java’s strength lies in its consistent growth across multiple development domains from desktop to game development. This distributed growth suggests long-term resilience. Unlike trends driven by a single technology (like Python and AI/ML), Java’s adoption is diversified and stable. Why it matters:  Enterprise-ready, reliable, and multi-domain, Java is a safe bet for scaling teams especially those managing legacy systems or building for high-availability environments. Rust and Swift See Big Growth With AI’s Help Rust added 1.1M developers this year, now reaching 5.1M globally. It’s growing steadily, supported by its memory safety, performance, and appeal in secure system design. Swift also saw a surprise resurgence, jumping from 4.6M to 5.6M developers. This growth was likely fueled by the rise of AI coding assistants. 42% of Swift developers use tools like GitHub Copilot, compared to 32% of developers overall. Ruby followed a similar pattern, with a 1.6M increase year-over-year. AI tooling appears to be lowering the learning curve for languages previously considered niche or difficult. Why it matters:  When evaluating tools, onboarding time, or training needs, language-community trends shaped by AI support should be part of your planning. Midsize Languages with Specialised Appeal Languages like Kotlin (6.3M), C# (11.1M), and Go (5.0M) show strong presence in specific ecosystems such as Android development, enterprise platforms, and cloud-native tooling, respectively. These languages may not lead in total users but are critical in vertical markets. Teams focused on mobile, backend scalability, or DevOps should keep these ecosystems in scope. Why it matters:  Specialisation matters. A smaller language community can still represent a critical competency for strategic hires or partnerships. C and Rust Are Favoured by High Earners SlashData’s income analysis shows that high-earning developers are more likely to use C and Rust, languages associated with performance, reliability, and foundational system development. This suggests a connection between deep technical expertise and compensation, which is especially true in roles where performance and security matter. Why it matters: Upskilling in performance-focused languages can yield strong ROI for tech talent and the companies that employ them. It’s also a signal of where critical infrastructure investments may be headed. PHP and JavaScript Dominate the Lower-Income Brackets Interestingly, PHP and JavaScript see higher usage among lower-income developers. For PHP, the explanation may lie in its popularity among freelancers and micro-businesses. 35% of companies with fewer than five employees use PHP, compared to just 19% of enterprises. JavaScript’s ubiquity makes it widely used, but also highly competitive, which can suppress wage growth in some markets. Why it matters:  Language choice can signal market segment, pricing pressure, or training needs. Business teams working on pricing models or developer outreach should factor this in. New Developers Experiment & Then Specialize Developers with less than five years’ experience use more programming languages (avg. 4.2) than those with 16+ years (avg. 3.4). This reflects a natural progression: early-career developers explore, while experienced professionals focus on depth. This insight is useful for workforce planning, especially for training programs or internal mobility strategies. Why it matters:  New hires may require time to settle into a language ecosystem. Plan for ramp-up time and give room for experimentation. The Big Picture: 47 Million Developers, 17 Major Languages As of Q1 2025, there are 47 million active software developers globally. JavaScript, Java, and Python account for the lion’s share, followed by C++, PHP, C#, and a cluster of rising stars like Rust, Swift, Kotlin, and Go. For business and operations teams, these numbers aren't just academic. They help answer critical questions: Are we choosing languages our current and future teams can support? Do we understand where to find (and how to retain) top talent? How do language ecosystems affect vendor choice, tooling integration, and roadmap planning? Conclusion Programming language trends are a window into developer behavior, talent markets, and operational strategy. For product leads, hiring managers, operations teams, and executives, these insights are key to making smarter decisions in tech. SlashData’s research doesn’t just explain what developers are doing, it helps global tech companies understand what’s possible, next. To dive deeper into the data or access more developer ecosystem insights, visit: https://www.slashdata.co/free-resources You can also read more on the Developer Nation Series by access our blog library covering;  78% of developers feel the team camaraderie & 21% of startups generate revenue from subscriptions Sizing programming language communities The rise of AI chatbots for problem-solving Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Profiling of professionals working at startups

Explore how different developer profiles, experience levels, and company sizes are shaping the adoption of generative AI. A must-read for product, programme, and analyst teams. Generative AI is no longer a niche trend. It’s becoming foundational to modern software. However, while excitement surrounding AI is widespread, actual adoption among developers calls for further exploration. According to SlashData’s Q1 2025 Developer Nation report, only 20% of developers are actively adding generative AI functionality to their applications. What we want to unpack is;  why is that number not higher, and  what patterns can we detect in those who are leading the charge? This blog unpacks the report’s findings, highlighting insights that matter most for product managers, programme leads, and analyst teams who want to make smart, forward-looking decisions in a rapidly evolving tech landscape. Professional Developers Are Leading the AI Push While 20% of all surveyed developers are integrating generative AI, the adoption rate doubles among professional developers compared to hobbyists and students (22% vs. 11%). This disparity could stem from several factors: Access to infrastructure and tools, Organisational pressure to innovate, and Incentives to build feature-rich applications Professional developers often work in environments that demand functionality, performance, and competitive advantage (all areas where generative AI can shine). By contrast, non-professionals may face limited resources or fewer incentives to explore complex technologies. Takeaways for product and programme teams:  If your tools or platforms support generative AI capabilities, focus your enablement strategies on professional environments where the stakes (and budgets) are higher.  Experience Counts: Mid-Career Developers Are the Pioneers Adoption of generative AI functionality peaks among developers with 6–10 years of experience (26%), followed closely by those with 3–5 years (23%). This "mid-career" group sits in a unique sweet spot: They’ve moved past the basics and are trusted with advanced projects They’re still hands-on with code and innovation They’re often in roles where technology choices shape product direction By contrast, junior developers (<1 year experience) show the lowest adoption (11%), most likely due to skill gaps and simpler project scopes. Interestingly, developers with 11+ years experience also show a drop (17%), perhaps due to more managerial or oversight responsibilities. Takeaway for analyst teams:  When assessing AI readiness or predicting adoption trends, pay attention to team composition. Mid-career developers are often the changemakers and early adopters. North America Leads, but Regional Gaps Remain When it comes to geography, North America is significantly ahead with 27% of developers in the region integrating generative AI. Other notable leaders include Western Europe & Israel (22%) and Oceania (21%). At the other end of the spectrum, adoption is lowest in Eastern Europe (11%) and South America (12%). These gaps reflect broader economic, infrastructure, and market maturity differences that also affect access to cutting-edge technology. Insight for global strategy teams:   Plan regionally. Go-to-market, support, and partnership strategies must reflect where the demand and capability for AI integration actually exists. Company Size Shapes Adoption Developers working at midsize companies (101–1,000 employees) show the highest rates of generative AI adoption (29%). These organisations strike a balance between having the resources to invest in innovation and the agility to act quickly. Large enterprises (>1,000 employees) have slightly lower adoption (24%), possibly due to bureaucratic inertia or legacy systems. Freelancers and very small companies trail behind at 13–16%, constrained by limited infrastructure or time. Takeaway for product and growth teams:  Midsize companies are a prime segment for AI-related offerings. They’re big enough to invest and small enough to move. AI Isn’t for Everyone and There Is A Reason Why Despite the buzz, 80% of developers are not yet building generative AI features. This is a crucial reminder: adoption is still early-stage and requires careful targeting. Whether due to technical complexity, unclear use cases, or resource constraints, many developers remain cautious and that caution is rational. Generative AI comes with challenges in cost, implementation, ethics, and data privacy. Strategic implication:  Don’t treat AI as a one-size-fits-all solution. Tailor enablement, messaging, and product features based on real adoption patterns, not just hype. Conclusion: What This Means for You For product managers, this data helps clarify where and how to build generative AI into your roadmap. For programme leads, it informs training and capability-building efforts. For analyst teams, it sharpens forecasting and benchmarking. SlashData’s 2025 report is more than a snapshot, it’s a directional compass for the broader tech community. As generative AI matures, the teams who pay attention to who is adopting and why will be best positioned to lead. To explore more AI adoption data and performance insights, visit( https://www.slashdata.co/free-resources ) You can also read more on the Developer Nation Series by accessing our blog library covering;  78% of developers feel the team camaraderie & 21% of startups generate revenue from subscriptions Sizing programming language communities The rise of AI chatbots for problem-solving Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Profiling of professionals working at startups

Discover how technology professionals use social media in 2025 to stay informed, learn, and solve problems while uncovering key insights across experience level, role, and company type. Social media isn’t just a communication tool. It’s a strategic channel shaping how today’s technology professionals learn, connect, and make decisions. In one of our latest pieces of market research for tech, How Technology Practitioners Use Social Media,  we explore how more than 10,000 practitioners from 127 countries use social platforms in their daily workflows and what this means for leaders in marketing, HR, and product functions. Whether you’re building brand campaigns, sourcing future talent, or validating your next digital product, understanding how your audience engages online is critical. This report provides market research and social media insights that allow organisations to fine-tune content, messaging, and engagement strategies across multiple departments. The result? Increased relevance, better alignment with audience behaviors, and greater ROI from digital channels. Social Media's Strategic Role for Tech in 2025 Despite the explosion of new content formats (like AI-generated summaries, livestream events, and voice-led media like podcasts) social media remains a dominant force among technology professionals. According to the report, 77% of technology practitioners still use social media as part of their professional activity. In fact, social media ranks just behind long-form content and AI chatbot responses as one of the most consumed formats by tech professionals. Here’s a breakdown of how social media is used in professional contexts: Keeping up-to-date with industry news and trends (37%) Engaging with peers and communities (24%) Learning and upskilling (22%) Conducting research (16%) Solving technical problems (14%) For anyone conducting market research for marketing in an effort to improve audience reach or position products and services, these numbers tell a compelling story: social media continues to play a vital role not only in awareness and thought leadership but also in influencing how practitioners gather knowledge and make decisions. This makes it a high-value channel across the customer journey. Audience Segmentation: What Experience Level Tells Us A key dimension of this research is the segmentation by career stage, which reveals stark differences in how social media is used by beginners vs. mid-career and senior professionals. Beginners (0-5 years experience) This group is the most active on social media, with over 75% relying on it regularly. Their primary motivations are: Learning Staying informed Finding quick tips or community support They are still shaping their skills and knowledge base. For content creators or brands doing market research for talent acquisition, this is critical. It signals that social-first, skill-building content (like explainer threads, short how-to videos, or community Q&As) is highly effective for engaging early-career tech talent Mid-Career (6–10 years) and Senior (10+ years) This cohort continues to use social media (63%+ report regular use) but with a more selective approach. They turn to more authoritative formats like long-form video, research papers, and trusted newsletters. Still, they do use social media for: Keeping up-to-date on trends Networking with other professionals Discovery of new tools or thought leaders From a market research for marketing standpoint, this means your content must shift in tone and depth depending on audience maturity. For senior practitioners, bite-sized content needs to lead somewhere valuable like a research-driven blog, white paper, or webinar. Startups vs. Enterprises: A Tale of Two Strategies Social media usage also varies by company type, reflecting the realities of different work environments. Startup Practitioners Use social media primarily for problem-solving and peer collaboration. In fast-paced, resource-constrained environments, social media offers a lifeline to immediate solutions, tool recommendations, and best practices. Non-Startup Practitioners Use social platforms mainly to stay informed, reflecting a more strategic, structured role within larger, established organisations. For those engaged in market research for tech or go-to-market planning, this is a valuable signal: If you’re targeting startups, focus on utility, community, and responsiveness. If you’re engaging with enterprises, emphasise credibility, leadership, and insight. It also helps inform content tone and platform selection. For example: X (Twitter) and Reddit may work better for peer-led conversations and tools in startup environments. LinkedIn and long-form blogs might resonate more with enterprise teams looking for thought leadership. Professionals vs. Amateurs: Learning Curves and Content Design One of the most nuanced findings in the report is the difference in learning behavior between amateurs (students, hobbyists) and professionals. 83% of amateurs use social media for multiple, cross-function purposes 27% of amateurs use it for learning, compared to only 20% of professionals Amateurs tend to lean on platforms like YouTube, Reddit, and X for accessible, low-barrier content. Professionals, especially as they gain experience, gravitate toward more in-depth, verified sources. This has two clear implications: For employer branding and DEI strategies, educational content on social media can position your organisation as a mentor and supporter of the next generation. For market research for talent acquisition, this underscores the value of creating layered content ecosystems — where short, helpful social content leads to deep, career-enhancing resources. Practical Actions for Leaders in Marketing, HR, and Product Whether you’re driving brand awareness, candidate engagement, or product validation, this research offers actionable insights for every tech-facing team. Adapt Content by Career Stage Early-career: Prioritise quick wins, tutorials, career advice, and relatable storytelling. Senior-level: Focus on research-backed insights, expert opinions, and data-led forecasting. Define Each Platform’s Purpose LinkedIn → Credibility, leadership, and visibility Reddit / X → Community learning, unfiltered feedback YouTube / Blogs → Trust-building through depth Use Social as a Live Feedback Loop Engagement metrics (likes, comments, shares) are your real-time market research. Don’t just run static campaigns — treat content as iterative experiments. Think Cross-Functionally Social media data isn’t just for the marketing team. Share insights across: Talent acquisition (for employer branding and pipeline strategy) Product teams (for roadmap validation) Research/insight teams (for audience segmentation) By treating social data as a cross-functional asset, you unlock a feedback engine that powers smarter decisions across the business. Final Word: Social Media as a Strategic Mirror Social media isn’t noise! It’s a signal.  For leaders building the next wave of digital products, tech teams, or B2B communities, platforms like LinkedIn, Reddit, X, and YouTube serve as a mirror. They reflect how your audience learns, collaborates, and makes decisions. The best market research for tech doesn’t just study demographics or job titles. It digs into behaviour, motivations, and content preferences and social media is where much of that behavior plays out in real time. With SlashData’s latest report, How Technology Practitioners Use Social Media,  marketing, product, and talent leaders can align their strategies to meet their audience where they already are: online, informed, and ready to act.

Discover how generative AI boosts sales and marketing efficiency, personalisation, and decision-making — plus the challenges and future opportunities. For marketing leaders, product strategists, and research analysts navigating the ever-evolving world of technology, generative AI (GenAI) has become one of the most discussed (and transformative) innovations of the last two years. No longer a distant trend, GenAI is reshaping how organisations execute campaigns, manage customer relationships, and make data-driven decisions. But while the headlines often focus on breakthrough capabilities, the real picture, the one grounded in actual business practice, comes from careful research. SlashData’s latest market report, based on interviews with senior marketing and sales leaders at major technology firms in the U.S. and Europe, offers precisely that: a grounded, evidence-backed look at how GenAI is transforming commercial functions, where the opportunities are, and what challenges companies need to address. This article distills those insights specifically for marketing and analyst teams, helping you understand where GenAI fits into the bigger picture and how you can best prepare your strategies to capture its value. What Is Generative AI, and Why Does It Matter for Marketing and Sales? Generative AI refers to artificial intelligence models that can create new content (including text, images, audio, video, and code) by learning patterns from vast amounts of data. Popular tools like ChatGPT, Copilot, and Gemini are prime examples, now widely embedded into commercial workflows. In marketing and sales, GenAI enables a broad range of capabilities that just a few years ago would have been considered cutting-edge or even futuristic: Automatically generating blog posts, email campaigns, social media copy, and product descriptions, Personalising outreach at scale by tailoring messages to individual customer segments, Summarising sales calls, generating reports, and automating routine administrative tasks, and Supporting campaign design with creative suggestions, draft visuals, and ad copy variants For marketing analysts, these shifts are particularly important because they signal a rapid change in how campaigns are planned, executed, and measured. Understanding which tasks can be AI-powered and which depend on human oversight is still essential for designing future strategies. Research-Backed Benefits of Generative AI Adoption According to SlashData’s research, companies adopting GenAI across their marketing and sales teams are capturing four primary benefits: 1. Significant Time Savings Routine tasks that once absorbed hours of human time can now be completed in minutes. Reports that needed manual summarisation, creative drafts that took days, or emails that required hand-crafting are now generated at the click of a button. This efficiency allows teams to focus less on administrative work and more on high-value activities such as strategic planning, experimentation, and creative innovation. For analysts, this also opens up bandwidth to dive deeper into market trends and customer insights. 2. Cost Optimisation By automating processes that were historically outsourced (like media planning, ad placements, or creative production) companies can reduce their reliance on agencies or third-party vendors. This not only saves money but also shortens turnaround times; allowing for more agile campaign execution. Sales teams similarly benefit by automating lead generation and customer follow-ups, improving overall efficiency without needing to scale headcount linearly. 3. Enhanced Personalisation and Targeting In today’s digital landscape, relevance is everything. GenAI enables hyper-personalised marketing by analysing customer data and producing tailored content for different audience segments. Instead of mass messaging, teams can now deploy campaigns that speak directly to the interests, behaviors, or pain points of specific user groups seeing improvements across engagement, conversion rates, and long-term customer loyalty. 4. Smarter, Data-Driven Decisions Perhaps most critically, GenAI enhances decision-making by analysing vast data sets and providing actionable insights, quickly. From predicting sales trends to optimising campaign performance, AI delivers real-time guidance that helps teams make faster, more informed strategic choices. Marketing analysts, in particular, stand to benefit from this shift, as it expands the toolkit for understanding what’s working, what’s not, and where new opportunities might lie. Challenges across Marketing and Analyst Teams  Despite these benefits, the report makes clear that companies are not without concerns when it comes to GenAI integration. Marketing and analyst teams, in particular, should pay close attention to the following: Trust and Accuracy One of the most cited challenges is the risk of “hallucinations” — AI outputs that sound highly convincing but are factually incorrect. While GenAI can summarise data and generate content, human oversight remains essential to verify the accuracy of those outputs before they are used in public-facing materials or critical decisions. Security and Data Privacy With growing regulatory pressures and heightened consumer awareness, protecting sensitive or proprietary data is a top priority. Feeding customer or business data into external AI tools raises risks of data leakage or non-compliance with laws such as GDPR. Some companies are addressing this by developing in-house AI models or applying strict access and compliance controls. Uneven Adoption Across Teams Even within large organisations, the adoption of AI tools can be patchy. Some teams or individuals are early adopters, pushing for new efficiencies, while others remain skeptical or hesitant due to lack of training or familiarity. Without clear guidelines and structured change management, AI integration often results in fragmented, inconsistent use — limiting overall impact. Balancing Automation with Human Creativity While AI excels at speed, scalability, and pattern recognition, it lacks the emotional intelligence, intuition, and authentic creativity that human marketers bring. Over-relying on AI can lead to generic or uninspiring campaigns that fail to resonate on a deeper level with audiences. Companies must carefully balance automation with human-led innovation and relationship-building. Strategic Recommendations for Marketing and Analyst Teams To harness the full potential of GenAI, marketing and analyst teams should: Assess Internal Readiness:  Identify where your teams currently stand in terms of AI knowledge, confidence, and tool adoption. Address gaps with targeted training and upskilling. Define Governance Protocols:  Establish clear guidelines for when and how to use GenAI, who is responsible for oversight, and how to handle issues like output verification and compliance. Monitor Competitor Adoption:  Use market research to track how competitors are deploying GenAI, where they are gaining an edge, and where gaps remain. Invest in Human Skills:  Prioritise developing creativity, storytelling, strategic thinking, and emotional intelligence — areas where humans will continue to provide unique value, even as AI evolves. Frequently Asked Questions How is generative AI changing the way marketing teams work? It is automating repetitive tasks, accelerating creative production, enabling hyper-personalised outreach, and providing data-driven insights that inform campaign strategy. What should marketing analysts be tracking? Analysts should monitor adoption trends, evaluate the performance of AI-driven initiatives, and stay alert to the challenges and risks emerging from GenAI use in commercial functions. Will AI replace human marketing teams? No. While AI will increasingly augment and enhance human work, core aspects of marketing — such as creativity, relationship management, and brand stewardship — will continue to require human expertise. Conclusion: A Future Defined by Thoughtful Integration For marketing and analyst teams working in technology-driven industries, the rise of generative AI represents both an opportunity and a challenge. Companies that succeed will be those that integrate AI thoughtfully — using it to drive efficiency, insights, and innovation, while maintaining the human touch that makes brands compelling and relationships meaningful. SlashData’s market research shows that the future of AI in marketing and sales is not about replacement, but about augmentation. It’s about working smarter, not just faster, and about staying ahead of competitors by balancing cutting-edge tools with timeless human strengths. While you can deep dive the research in the full Slash Data report here , why not request a tailored consultation for your marketing and sales team at the click of a “ Button ”.

Staying true to our commitment to transform noise into actionable insights, SlashData is bringing you two webinars rich in data and insights and the developer trends you need to know in 2025. TL;DR Here's the quick preview. Keep reading for more details How many developers are there? Ending the debate with data | April 24, 2025 Watch here Artificial Intelligence in Tech: usage, adoption and challenges in 2025 | May 14, 2025 Both events are free to attend, but registration is required. Click on the webinar you want to register for to save your spot: Developer Trends 2025: Artificial Intelligence in Tech - Usage, Adoption, and Challenges in 2025 When 14 May 2025 - 9am PST/5pm UK Platform YouTube Presented by  Konstantinos Korakitis, Director of Research, SlashData & more TBA Hosted by  Moschoula Kramvousanou, CEO, SlashData​ How is AI used in Tech? This will be our main theme for this live session with SlashData's expert analysts. More specifically, we will look at Generative AI and ​How Tech professionals have incorporated it into their processes ​How sales and marketing professionals utilise it for performance ​What challenges are organisations facing with its adoption ​How are developers integrating it into their projects Concerns about its usage +Live Q&A with your questions You can watch the webinar here: Save your spot for the AI in tech webinar. See also our free reports on Artificial Intelligence: Generative AI for Business: Success, Challenges and the Future The rise of AI-chatbots for problem-solving How developers build AI-enabled applications Watch now: Global developer population trends: How Many Developers Are There? When 24 April 2025 - 9am PST/5pm UK Platform YouTube Presented by Konstantinos Korakitis, Director of Research, SlashData Hosted by Moschoula Kramvousanou, CEO, SlashData In this session, we will look at the global developer population. More specifically, Konstantinos will present the latest data and insights on:​ The total number of developers worldwide Distribution across various sectors and industries Developers' programming language adoption and community size Developers' geographical distribution +Live Q&A You can now watch this session anytime: All insights are drawn from SlashData's independent Developer Nation survey, which gathers over 9,000 responses per wave.​ Learn more about the Developer Nation survey , its community and our methodology . Developer Trends 2025: Artificial Intelligence in Tech - Usage, Adoption, and Challenges in 2025 When 14 May 2025 - 9am PST/5pm UK Platform YouTube Presented by  Konstantinos Korakitis, Director of Research, SlashData & more TBA Hosted by  Moschoula Kramvousanou, CEO, SlashData​ How is AI used in Tech? This will be our main theme for this live session with SlashData's expert analysts. More specifically, we will look at Generative AI and ​How Tech professionals have incorporated it into their processes ​How sales and marketing professionals utilise it for performance ​What challenges are organisations facing with its adoption ​How are developers integrating it into their projects Concerns about its usage +Live Q&A with your questions Save your spot for the AI in tech webinar. See also our free reports on Artificial Intelligence: Generative AI for Business: Success, Challenges and the Future The rise of AI-chatbots for problem-solving How developers build AI-enabled applications

This is the transcript of our latest live session “Global developer population trends 2025 - How many developers are there?” which you can watch in the following video. Join our newsletter  and keep up to date with new webinars . You can find all past sessions on our webinars page .  Getting started & quick developer population estimation methodology overview Moschoula Hi everyone. 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, your decision-makers, and guide everything from product design to marketing strategies. We will continue this series throughout the year, so stay tuned and join our newsletter to get invited to the next sessions. Without further ado, we have our Director of Research here today, Kostas Korakitis. He will end the long-winded debate on software developer populations. There has been a ton of discussion on the number of developers and whether that's been declining over the last couple of years—and even faster day by day. With the introduction of a massive wave of AI-assisted coding tools, this has become a big issue. So we want to talk about it as well and end that debate. I don't think we need anything else; I'll hand it over to Kostas. Thanks a lot, see you later. Kostas Korakitis Thank you, Moschoula, and hi everyone. Welcome to today's webinar on global developer population trends. As Moschoula said, I'm Kostas Korakitis, Director of Research at SlashData. Over the next half-hour or so, we are going to explore how the global developer population has evolved over the past three years: what's driving this growth, where it's happening, and how its composition, focus, and geography are changing. Let me first quickly walk you through what we will cover today. I'll start with a brief description of our developer population sizing methodology—essentially, how we derive the population estimates that I will present today. Then we'll look at how the developer population has grown over the past three years and how the balance between professionals and amateur developers has shifted. We'll then present several breakdowns of the global developer population by important dimensions, such as region, types of software development projects, programming language communities, industry verticals, and company size. First, on our population sizing methodology—how we arrive at our estimates. We calculate the number of active software developers globally using our own independent bottom-up methodology, firmly rooted in reliable measurement through our Global Developer Survey. We're not just using available third-party population estimates; we derive our own estimates independently. Our methodology is based on two main pillars. First, we make use of reliable sources of developer numbers or direct indicators of their activity. This includes the number of GitHub accounts, Stack Overflow accounts, and employment statistics from the USA and the European Union. Second, we rely on our Global Developer Survey data, where we directly measure developer activity. So far, we've run 29 waves of this large survey, and in each, we reach more than 10,000 developers globally. We combine these two main sources to derive our estimates. One important point is that we avoid making assumptions about similarities between geographies or other subsets of the developer population. For example, while we use employment statistics from the EU and USA, we do not extrapolate to other regions. Instead, we rely on measurements from our surveys about the geographic distributions of developers to estimate numbers by region. There are 47.2 million developers in the world in 2025 With that, let's begin with one of the most important questions: How many developers are there globally? According to our data, at the beginning of this year, we estimate the global developer population at just over 47 million. That's a striking increase of about 50% from Q1 2022, when the number was just over 31 million. Such growth is impressive in any sector, but particularly for developers—it shows how pervasive software development has become in shaping the global economy. How the developer economy and population is growing in 2025 However, while the three-year growth curve looks impressive, it's even more revealing to see how growth rates have changed over time. Between 2022 and 2023, we saw an increase of 15%. Then, from 2023 to 2024, there was a sharp spike—21% in just one year—likely fueled by post-pandemic investments, startup funding booms, and surging demand for digital services. However, in the last 12 months, the growth rate has decelerated to just 10%. This slowdown may mark the beginning of a new phase: a plateau. A cooling global economy, saturation in mature markets, or diminishing returns on digital investments could all be contributors. This doesn't necessarily mean the developer economy is in decline, but it does suggest that we should temper expectations for continued exponential growth. The immediate implication is that opportunities may shift from quantity to quality. That is, how companies train, retain, and support developers will become more important than simply how many are entering the field. This slowing of growth becomes even more meaningful when we dig into who is driving the expansion. What we see is a clear divergence: the professional segment is expanding, while the amateur segment has begun to contract. From early 2022 to early 2025, the number of professional developers grew significantly—by 70%—from 21.8 million to 36.5 million. By contrast, the population of amateur developers only grew moderately and actually declined by over 1 million in the last year. This is a telling sign: professional developers are staying longer and growing in numbers, while the traditional feeder population—amateurs—is shrinking. There are many forces at play. In the past decade, software development has become a stable career path. More people are attending university or boot camps with the express goal of becoming software developers, building long-term careers around it. Is the developer economy reaching maturity levels? At the same time, there's been a cultural shift. Coding is less about exploration and more associated with work, startups, and monetisation. Younger audiences now have many more ways to express their creativity—through game mods or design tools—than simply by coding. This shift could pose a risk to the long-term pipeline. If fewer people explore programming today, fewer professionals may enter the field tomorrow. While the professional population is growing now, declining engagement at the entry level is a red flag. Unless companies invest in developer education and make entry points more accessible, we may face stagnation or even decline in future years. This brings us to another related trend: the ageing of the developer population. At the beginning of 2022, developers aged 18–24 made up close to a third of the global population. By early 2025, they represent only 23%—a drop of eight percentage points in just three years. Meanwhile, the share of developers aged 35–44 has steadily climbed from 22% to 26%. The overall picture is clear: fewer young people are entering development, and more experienced developers are staying. The population is gradually aging, growing more seasoned, and becoming more professionalised. There are benefits—deeper experience, more institutional knowledge, more stable career paths. These developers are leading teams, driving architectural decisions, and mentoring others. However, the concern again is about sustainability. If fewer younger developers replace retirements and transitions, we may face long-term shortages. This also impacts companies offering developer tools and platforms. As developers grow older and more experienced, their learning and tooling preferences shift. There's less focus on quick experimentation and more on robust, reliable tooling and long-term support, including high-quality documentation. Companies that want to stay close to this evolving community need to account for these shifts in priorities. Geographical distribution of developers When we look at how developers are distributed geographically, we see a rich and evolving map of the global developer population. Western Europe and North America remain the largest communities, with about 9.5 million developers each. These markets have long been centres of software innovation and continue to be deeply influential. Western Europe and North America remain the largest communities, with about 9.5 million developers each. However, growth is happening in other regions too. South Asia, for example, has nearly doubled in size—from 4 million developers in 2022 to 7.5 million today—largely driven by India’s massive and increasingly sophisticated tech workforce. The region combines a young, dynamic workforce, strong STEM education, and a growing ecosystem of startups and tech giants. Greater China has seen explosive growth as well, nearly tripling its developer population since 2022—from 2.4 million to 5.8 million developers. This reflects China's investment in developer education, homegrown platforms, and government-backed initiatives. South America has also grown steadily, from 1.7 million developers to 3.4 million over three years. Countries like Brazil, Argentina, and Colombia are emerging as tech outsourcing hubs, with strong local demand in industries like FinTech and mobile solutions. The takeaway is clear: while Western markets remain dominant in size, the fastest growth and most dynamic momentum are coming from Asia and Latin America. What developers are working on in 2025 Now let's turn our attention and focus on what developers are actually working on—the types of projects they're involved in. First of all, it's perhaps unsurprising that the most popular application area is web. Over 23 million developers are working on front-end and back-end applications. Over 23 million developers are working on front-end and back-end applications. Right behind them are backend services and data science and ML/AI applications. However, what's perhaps a bit more interesting is that the top sectors—the most popular sectors—are the ones that are rising fast and declining. So let's talk about the fast risers first. Here we see that the development of applications and extensions for third-party ecosystems has seen steady growth since 2022. This sector includes things like extensions for commerce platforms like Shopify, extensions for IDEs, browser add-ons, and so on. Developers are really finding value in building on top of existing platforms where user bases are already established. This is a model that offers distribution, monetization, and low go-to-market friction. Another fast-growing area is embedded software, where the population involved in these types of projects has more than doubled since 2022. This growth reflects the rise of connected devices, automotive systems, and custom hardware. From consumer electronics to industrial sensors, embedded development is moving into the mainstream. Now the flip side: some projects have seen a decline. For example, mobile app development has slipped slightly in the last year. In previous years the growth was modest, but recently we've seen a downturn. This might be due to market saturation, app store consolidation, or even the rising costs of user acquisition. Similarly, desktop apps have seen a decline in the number of developers involved. This reflects the long-term trend away from native desktop software applications and toward web-based or cross-platform solutions. The message here is that developer interest, although still strong in traditional application areas, is slowly shifting toward other paradigms—especially those offering integration, automation, and ecosystem leverage. The most used programming languages Now, another question we often get is to size specific language communities. JavaScript continues to hold the top spot in terms of number of users, with 20 to 28 million users and healthy growth over the last three years. Java and Python have been in the top three for a while now. Both show steady and healthy growth since 2022, and each now has around 23 million users. These languages have wide applicability, strong communities, and very mature ecosystems. If we focus on growth rates, we find some interesting insights. Rust stands out as the fastest-growing of the major languages—those with over 5 million users—more than doubling in size since 2022. That growth is driven by Rust’s focus on safety, performance, and concurrency. It’s becoming the go-to choice for systems programming, embedded development, and blockchain infrastructure. C++ is another fast-growing language. Although often seen as a legacy language, it remains relevant. It has grown from 9.4 million developers in 2022 to 16.3 million in 2025. This reflects its continued importance in high-performance applications, gaming, and modern embedded systems where performance and efficiency are critical. Developer preferences are diversifying. It's not just about picking the most popular language—it's about choosing the right tool for the right job. So the key takeaway is that developer preferences are diversifying. It's not just about picking the most popular language—it's about choosing the right tool for the right job. Developers often use multiple languages at once, depending on the projects they work on. The most popular industries that attract developers Moving our attention to the industry verticals developers are active in, we see that software products and services is by far the largest vertical, with nearly 14 million developers. That’s expected, as it's the core of the tech economy. Software products and services is by far the largest vertical, with nearly 14 million developers. But beyond that, we see important growth in other verticals too. For example, manufacturing has nearly doubled its developer population in just three years—from just over 2 million developers to nearly 4 million in 2025. This is largely driven by Industry 4.0, where connected factories, automation, and robotics make software a central pillar of production. Telecommunication and networks have also seen strong growth. As telcos embrace 5G, edge computing, and software-defined infrastructure, they’re hiring developers to manage increasingly complex systems. Data analytics and BI is another fast riser. The number of developers in this sector has grown from 4 million to 5.8 million in three years. As every company becomes a data company, demand for people who can extract insights continues to rise. In short, software is no longer confined to the tech sector. Every industry is becoming digital, and every digital strategy needs developers to bring it to life. The size of companies developers work for Finally, let’s look at where developers are working. Medium-sized businesses—those with 51 to 1,000 employees—are the fastest-growing employers of developers. This group has expanded to 14.5 million developers at the beginning of 2025. Large enterprises also continue to grow, employing 7.5 million developers today. Together, these two segments account for over 60% of all professional developers globally. Small businesses and freelancers have remained relatively stable. This may point to consolidation in the industry, higher costs for independent developers, or more structured employment paths. This tells us that innovation is no longer just the domain of startups or tech giants. Mid-sized businesses are becoming innovation hubs. They’re growing fast, hiring aggressively, and often have the flexibility to explore new technologies without the red tape of large enterprise environments. For companies building software tools and platforms, this middle tier is a sweet spot. They’re large enough to have impact but nimble enough to adopt and scale quickly. Key takeaways and summary for software developer trends in 2025 This brings me to the end of the presentation. We've covered a lot. We started with the explosive growth of the developer population, then looked at signs of potential slowdown and stagnation. We saw that this growth is primarily driven by professionals, while the amateur segment is shrinking, and the developer community is gradually becoming older. We also looked at regional shifts—where the strongest growth is happening. We explored how developers are expanding into new types of software development, how language preferences are evolving, which industries are growing fast, and how medium-sized businesses are becoming centres of innovation. One of the main takeaways is that the developer economy isn’t just growing—it’s transforming. The era of rapid expansion is giving way to maturity, specialisation, and deep integration with all facets of industry and society. So understanding developers—what drives them, where they work, what projects they're involved in, and their technology choices—is more critical than ever. Thank you. I’d love to open the floor for questions now. Q&A with the expert Moschoula Thank you so much, Kostas, for that presentation. Indeed, it’s extremely insightful, especially at this time. I want to revisit something as well. You gave a clear view that while there is still growth, it’s declining. But not necessarily in population yet—we’re not there. Is there any forecast for when growth is estimated to stagnate? Our view is different from some others who are claiming the developer population is decreasing by the millions. We don’t see that in the data. From what we’ve seen and what your team has found, there's a slowdown, but what do you see for the next few years? Kostas Korakitis Yes, that’s a good point. This is something people talk about a lot—that the developer population is shrinking. But this is not what we see in the numbers. A slowdown is evident—we've seen it over the past year. It’s always hard to predict exactly what will happen, but if the slowdown continues at the same rate, then in a year or two, we may reach the point where the population is no longer growing. That doesn’t necessarily mean it will decline, but stagnation is a likely scenario. Moschoula Thank you for that. I also want to go back to the topic of programming languages. Understanding the size of programming language communities is probably the most popular data point that we—and our community—look at regularly. You mentioned Rust being the fastest growing, and touched on what’s next. But what about visual tools and C++—how do you justify their growth? Kostas Korakitis Yes, one thing I didn’t cover in detail is visual tools, which have seen really fast growth over the last three years. In 2022, they had 5 million users, and now we’re close to 9 million. That’s very impressive growth. This is proof that more people are using no-code or low-code platforms. More than anything, it expands the definition of who a developer is. In our survey, we use “developer” in a broad sense—anyone involved in software development projects in any capacity. The number of people using visual tools, who may not be traditional coders, is increasing. They're using no-code platforms to build business apps, automate tasks, and contribute to digital workflows. This is a real shift. We’ve been hearing about the rise of no-code tools, and now we’re seeing the data to back it up. Moschoula Really interesting and impactful. This is extremely useful information and very helpful in validating some of those numbers for us. Okay, I don’t see any more questions today, so we can close the webinar. We hope you all got a lot out of it. This will be available as a recording after the live session. Join our newsletter  and keep up to date with new webinars —we’ve got a few exciting ones coming up with our partners. You'll hear more soon. Thank you all, and have a great day. Bye for now. About the expert Kostas Korakitis, Director of Research at SlashData Konstantinos heads the Research Product team at SlashData and is responsible for all syndicated research products and custom research projects. With more than 10 years of experience as an engineer, consultant and manager, he oversees research planning, survey design, data analysis, insights generation and research operations.

AI is transforming industries at an incredible pace, but with its power comes significant security risks. From adversarial attacks to data breaches, companies must be prepared to protect the AI-powered applications they build. Yet, how do technology managers approach security and risk management in AI? Which practices are becoming standard, and who is leading the charge? Our recent research sheds light on how organisations secure their AI systems according to technology professionals in leadership positions, revealing some notable gaps. This blog post is based on a bigger report  that talks about trust, risk, and security management in AI overall. The blog narrows down the focus, diving deeper into data collected from 569 professionals in management positions within tech companies, namely tech/engineering team leads, CIOs / CTOs / IT managers, and CEO/management. They answered questions about trust, risk, and security management in AI in the 27th edition of our global Developer Nation Survey , which was fielded in Q3 2024. How are organisations protecting their AI-powered applications? AI security risks range from adversarial attacks and data breaches to model manipulation. To mitigate these threats, organisations deploy various protective measures. Companies are mainly investing in AI-specific security tools and technologies (33%) and encryption tailored for AI data (31%) to stay ahead of potential threats. Regular AI security audits (29%), staff training on AI security risks (29%), and data privacy management for AI (28%) are also common practices among organisations. However, not every organisation has made AI security a priority. While 82% of technology professionals report their company uses at least one mitigation strategy, 10% admit they have no AI-specific risk management in place, and another 8% simply don’t know what their company is doing to address security risks. Nearly one in five technology leaders either have no AI risk strategy or don’t know if their organisation has one Who is driving AI security efforts within organisations? Security is no longer just the responsibility of IT teams. CIOs, CTOs, IT managers, and senior executives (including CEOs) report equal adoption rates of AI-specific security practices, 86% and 85%, respectively. This suggests that AI security is recognised as both a technical challenge and a business priority at the leadership level. However, tech and engineering team leads lag behind, with only 72% reporting the implementation of AI security practices within their organisations. This gap indicates a disconnect between leadership’s security policies and awareness at the development level rather than differing priorities. Team leads may have less visibility into company-wide AI security strategies, which could explain the lower reported adoption. Among technology professionals, tech and engineering team leads report lower awareness of AI security practices within their organisations Company size matters: Are smaller firms falling behind in AI security? Company size plays a significant role in how AI security is handled. Managers in large enterprises (i.e., companies of more than 1,000 employees), with their expansive resources and dedicated security teams, report the highest adoption rate of AI security practices, with 90% implementing such measures. Managers in medium-sized businesses (51-1,000 employees) follow closely at 86%, but those in small businesses (up to 50 employees) lag far behind at just 64%. This gap isn’t just about awareness - it’s about priorities and resources. Large enterprises are far more likely than small businesses to conduct AI-specific penetration testing (32% vs. 10%), regular security audits (34% vs. 18%), and threat intelligence and risk assessments (28% vs. 14%). With tighter budgets and fewer specialised security personnel, smaller companies often struggle to allocate resources for AI-specific protections, relying instead on broader cybersecurity measures that may not fully address AI-related risks. However, medium-sized companies take the lead over both small and large companies when it comes to employing certain AI security practices. They lead in the adoption of AI-specific security tools, with 40% using them compared to 33% of large companies and just 20% of small businesses. Similarly, 35% of medium-sized businesses have data privacy management solutions tailored for AI, surpassing large enterprises at 27% and small businesses at 16%. This suggests that medium-sized companies, while not having the vast resources of large corporations, may be more agile in adopting emerging security technologies, striking a balance between strategy and execution. While large enterprises have the budgets and teams to prioritise AI-specific protections, small businesses struggle to keep up, leaving them more vulnerable to AI-related threats How does AI security vary across development types? AI security is far from uniform across industries. Each sector faces unique challenges shaped by the nature of its AI applications, the volume of data it processes, and the potential risks associated with AI-driven automation. While some industries have embraced AI security as a fundamental requirement, others are lagging, either due to a lack of awareness, lower perceived risks, or resource constraints. At the forefront of AI security adoption are managers involved in consumer electronics (96%), augmented reality (95%), and industrial IoT (95%) projects. Managers in these industries prioritise security not just because of regulatory pressures but also due to the inherent risks associated with their AI-driven operations. Consumer electronics and IoT devices, which process vast amounts of real-time personal and behavioural data, place a heavy focus on robust encryption and access control. In fact, 45% of managers in this sector report implementing these protective measures to prevent data breaches and adversarial attacks. Augmented reality (including mixed reality applications) goes even further, with 59% of managers reporting using encryption measures tailored specifically for AI data. This emphasis likely stems from the fact that AR systems often involve real-time spatial data processing, biometrics, and interactive user engagement, making them highly sensitive to security threats. However, not all sectors demonstrate the same level of urgency when it comes to AI security. Backend services fall significantly behind, with only 69% of managers working in backend reporting that their organisation has AI-specific security measures in place. Of the rest, 16% are unsure whether their company has any AI security practices at all, and a notable 15% confirm that their organisation has no such measures in place. This lack of adoption suggests that backend service providers may still be relying on traditional cybersecurity approaches, underestimating the distinct vulnerabilities that AI-powered applications introduce. Industries handling sensitive consumer data, like consumer electronics and IoT, lead in AI security adoption. However, backend services may be underestimating AI-specific risks by relying on traditional cybersecurity measures. How does experience impact AI security awareness? One of the more unexpected findings in AI security management is that less-experienced managers are more likely to implement AI security measures in their companies than their seasoned counterparts. Managers with less than two years of experience in software development report the highest adoption rate within their organisations, at 90%, while those with over a decade of experience drop to 74%.  This decline could indicate that organisations with more experienced managers rely more on traditional cybersecurity approaches rather than AI-specific frameworks. While awareness levels remain consistent across experience groups, companies led by seasoned professionals may be slower to adapt their security strategies to evolving AI threats. As AI risks become more sophisticated, ensuring that security measures keep pace will require continuous evaluation and adaptation at the organisational level. Organisations with more experienced managers may be slower to adopt AI-specific security frameworks, potentially relying more on traditional cybersecurity approaches Want to dig deeper? This post only scratches the surface of how tech leaders approach AI security and risk management. For a more comprehensive view, check out our full report on  Trust, Risk, and Security Management in AI . You'll find deeper insights into how organisations build trustworthy AI systems and where critical gaps still exist. You can also explore AI and related topics more on SlashData’s blog . Questions or feedback? We’d love to hear from you. Whether you're looking to collaborate, dig into our data, or simply want to chat, feel free to contact us. About the author Bleona Bicaj, Senior Market Research Analyst Bleona Bicaj 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.

Artificial intelligence (AI) is transforming the world of modern software development, with use cases that range from data processing to generating code. In this blog post, we explore the future of AI in software development from the perspective of professionals involved in software development who hold leadership positions [1] . We first consider how their opinions differ from their counterparts in non-leadership positions and then move on to breaking down their beliefs by company size and region. These insights provide a window into how the adoption of AI is evolving across the industry and where beliefs may diverge depending on organisational and geographical contexts. This blog post is based on data collected from over 4,500 technology professionals who answered questions about AI in the 28th edition of SlashData’s global Developer Nation Survey, which was fielded in Q4 2024. Looking for a broader business perspective? Discover how Sales & Marketing use Generative AI in our free report . Most important future use cases for AI in software development according to technology leaders When looking at the opinions of technology professionals in leadership roles and comparing them to those who work in non-leadership roles, we see a lot of broad similarities but also a selection of distinct differences. For instance, both groups show strong recognition of intelligent development assistants  (30% vs 29%) and data processing, analytics and visualisation  (26% vs 26%) amongst the most important future use cases of AI in software development. In terms of differences, we find that technology leaders are significantly more likely than their counterparts to emphasise the importance of AI in the future of cybersecurity  (25% vs 20%). While this is likely due to the differences between these two groups in terms of the scope of their responsibilities, the popularity of this particular use case points to AI playing a critical role in enhancing security against an ever-growing landscape of threats. Technology leaders are also more likely to believe in the future importance of areas such as AI for DevOps  (22% vs 18%) and predictive project management  (16% vs 13%), highlighting their focus on optimising workflows and managing their teams. However, they are less likely than those in non-leadership roles to consider code generation  (25% vs 29%) as important. This suggests that those who work closer to the code are more likely to see immediate benefits from automating coding tasks. Most important future uses cases of AI in software development by company size On looking closer at what technology leaders think, we find an interesting set of patterns when segmenting their beliefs by company size. While certain trends remain the same, our findings also show that the future importance levels of some use cases are perceived very differently across different company sizes. In terms of similarities, there is little variation in the perception of use cases, such as intelligent development assistants,  when we consider company size. This suggests that technology leaders expect future AI tools targeting such use cases to be just as useful for developers who work for small businesses as those who work for larger firms. This also points to a potential shift in the dynamics of the developer workforce as a whole, where developers can take on more strategic roles and focus on the bigger picture while leaving routine coding tasks to AI. We find that leaders who work for large companies are significantly more likely than average to place emphasis on code generation  (35%) when considering the future of AI in software development. This suggests that larger companies see greater potential in using AI-generated code in their applications. This may be because these companies often have extensive codebases that require a lot of developer resources. Large companies may be the most likely to use AI-generated code in the next three to five years Similarly, we see that the perceived importance of AI for cybersecurity  is strongly linked to company size. As businesses grow, they also increase the attack surface of their systems and require more complex security measures. This is reflected in our data, with technology professionals in leadership roles at large companies being the most likely to mention cybersecurity  (31%) in their beliefs of the most important use cases. This drops to 27% amongst technology leaders at midsize companies and further down to 20% at small companies. This suggests that smaller businesses may be less likely to prioritise advanced cybersecurity solutions when considering AI. Most important future uses cases of AI in software development by region Regional differences in culture, regulations and socio-economic circumstances often play important roles in technology. As such, it is no surprise that these differences extend to the opinions of technology leaders about which use cases for AI in software development will be most important in the next three to five years. As with the case of company sizes, some use cases receive similar favourability from technology leaders across Europe, North America and the Rest of the World. This suggests that certain use cases, like intelligent development assistants  and performance monitoring and optimisation , show universal promise of addressing challenges and opportunities in the landscape of modern software development. The benefits of intelligent development assistants are perceived to be not only company-size agnostic but also region-independent. Technology leaders working in Europe are the most likely to perceive cybersecurity  as one of the top use cases for AI in the near future of software development (30% vs 26% in North America and 21% in the Rest of the World). While this is partly due to Europe having an above-average concentration of large companies, it also highlights the greater emphasis placed on topics such as data protection in this region due to regulations. Despite this, these technology leaders also recognise the potential that AI has to bring to their future applications. In fact, technology professionals in leadership roles working in Europe are significantly more likely than their counterparts in other regions to believe that adding AI functionality to applications  will be amongst the most important future use cases of AI (25% vs 19%). Furthermore, we also see that they are disproportionately more likely to consider bug detection and fixing  as important than their counterparts in other regions (27% vs 20%). Key takeaways Technology leaders foresee AI playing a crucial role in software development, with strong recognition of intelligent development assistants and data processing. They also emphasise cybersecurity, AI for DevOps, and predictive project management more than those in non-leadership roles. Leaders in larger companies are more likely to believe in the growing importance of code generation and cybersecurity in the future. We also see some interesting regional differences, with European technology leaders placing a higher emphasis on cybersecurity, adding AI functionality to applications, and bug detection/fixing. These insights highlight the evolving adoption of AI in the industry and varying favour based on organisational and geographical contexts. What type of AI data are you looking for? Maybe we already have what you need. Get in touch with us. [1] We consider those who self-identify to be in at least one of the following roles as technology leaders: “tech/engineering team lead”, “CIO / CTO / IT manager”, “CEO/management”. About the author Nikita Solodkov, Market Research and Statistics Consultant Nikita is a multidisciplinary researcher with a particular interest in using data-driven insights to solve real-world problems. He holds a PhD in Physics and has over five years of experience in data analytics and research design.