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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. At DevRelX our mission is to empower the more Developer Relations practitioners to understand developers and make educated decisions based on data to create the best experience for their developer communities. To get there, we bring you an all-around view of the developer population, who they are and what are their needs. This time, we are taking a closer look at developer happiness ! ☀️ Does happiness lead to productivity? Join our community and share your thoughts with us! It might sound intuitive already, but we are obsessed with data. So, we looked into it. And by “we” I mean SlashData and Sentry joined forces to analyse the feedback taken from survey respondents who are professional developers who write software on a regular basis. To make our filtering even more accurate, it mainly involved experienced developers with at least 10 years of software development experience, as they were required to have a live application. This intentional filtering ensures that the average developer surveyed possesses extensive knowledge and can provide valuable insights into the software development process. Are happier developers more productive? Firstly we wanted to identify what makes developers happy and we found: 1. Company size and colleague count don’t significantly impact happiness levels. 2. Whether you’re an experienced coder or new to the field, everyone’s happiness is similar. 3. Delving into infrastructure tasks brings more joy! Devs spending 10 extra hours a week on these issues experience a 3% happiness boost. 4. Managers or those with ‘chief’ titles tend to be 6% happier than their peers . These insights shed light on what contributes to developer satisfaction in the workplace. Understanding these factors can help foster a more positive and productive environment for all developers. We developed a unique productivity metric by combining three crucial measurements, focusing on how quickly developers complete programming tasks and deploy code to production. Here’s what our productivity metric considers: Time from code committed to code in production. Time taken to recover from an unexpected outage. Frequency of code deployment to production. Interestingly, we observed that developers in larger companies tend to take slightly more time to complete tasks compared to their counterparts in smaller organizations. This information provides valuable insights into the dynamics of developer productivity across various company sizes. What hinders and boosts productivity? When it comes to barriers, larger companies might experience a slight dip in productivity, with every 500 additional employees contributing to a 1% drop. Internal processes and bureaucracy can be culprits, but fear not – we’ll share tips to optimize workflow! Communication is another key player; if it’s smooth sailing, devs thrive, but if not, productivity could plummet by a whopping 48% . However, only 10% of developers face this issue. By combining frequency and time metrics, we unveil a cool productivity score measured in hours, allowing us to understand the overall productivity landscape. The best part? Happy developers are productive developers! Being 10% happier means completing tasks 10% faster, and each year of experience in software development boosts productivity by 6%. Let’s take a closer look at developers’ workloads and what they wish for versus reality! The biggest difference lies in dealing with internal messaging, processes, and infrastructure issues. Developers express the desire to allocate 19% and 17% less of their time to these time-consuming tasks. It’s clear that efficient communication and workflow tools are essential for smooth business operations. We analyzed their productivity and found that developers spend the most time on software development, followed by project management. They spend about 31% and 16% of their week on these tasks. Interestingly, developers want to keep doing these tasks as they’re crucial components of their ideal week too. Oh, and here’s a nugget; the more time they spend coding, the happier they are! Software development Let’s dive into how developers spend their time on software development! Writing code is the most time-consuming activity for 29% of developers, with a whopping 69% spending a lot of their overall time on it. The conceptual design phase also takes up significant time, but it’s an enjoyable activity for 60% of developers . However, debugging or fixing code is another time-consuming task, with 67% of devs dedicating a lot of time to it. But here’s the catch – only 51% actually enjoy it. Debugging can be a real workflow challenge and hurt productivity. What do they feel about their tasks? Fixing bugs and improving software performance bring joy to 65% of developers. They take particular pride in improving software/app performance (21%) and debugging code (12%). Writing good code is a big source of pride for 27% of developers, and a total of 69% find pride in this task. What about the challenges? The top two challenges are cleaning up legacy code (33% of developers) and running into untested code (32%). Interestingly, cleaning up legacy code was more common in larger teams, where devs work with a 12% bigger team. But don’t worry, larger teams have more resources for testing, so running into untested code isn’t as big of a challenge for them. Now, onto the root causes of issues. A whopping 37% of devs say a rushed timeline is the biggest problem they face. Among programmers and software developers, 45% identify rushed timelines as a key challenge, 14 percentage points more than CEOs and managers (31%). Let’s explore the challenges faced by developers in different roles. We’ll break it down by the prominent positions, such as management/chiefs, programmers/software developers, architects, and IT workers. Surprisingly, shifting and unclear priorities are among the top three obstacles across all roles, but they’re especially prominent for programmers/software developers and managers/chiefs. Another common challenge for everyone, but particularly for architects, is too many meetings. Interestingly, many of the top challenges reported in all roles are process-related. This emphasizes the importance for companies to implement good policies and procedures to optimize workflow and boost developer productivity. Want to join peers on a mission to better understand and support developer and software creator communities? Check the latest developer trends , join the DevRelX Community and subscribe to our bi-weekly newsletter .

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. Back in May, we got together with the DevRelX Community to look into and discuss the latest Developer Program Leaders survey results. The session was joined by our research analysts and a panel of Developer Program Leaders Forum experts. Together we deep-dived into how Developer Relations practitioners prioritise their resources and activities, and justify the value of their developer program, offering everyone in the community an insight into how they compare against the industry’s practices, especially in times of uncertainty. Since not all audience questions were addressed during our recent Developer Program Leaders Forum session, we are back for a spotlight series with our Forum experts! In today’s spotlight is Yuri Santana, Developer Relations Advocate at Supabase, who answers your questions, and shares tips and takeaways from the 10th Developer Program Leaders survey. Continue reading below to hear more from Yuri's insights, watch the full session on-demand recording and get access to the results at devrelx.com/dpl-survey . The Biggest Challenges for Developer Relations One of the things that caught my eye was the current challenges for DevRel practitioners. We can see that the big 3 are growing the community, attracting new users and keeping current community members engaged in the community. Why is this? We can all agree that single communities are the fundamental building blocks of the tech community. It has a direct impact on the perception and interaction you have with individuals and can probably change the future of your product. The most effective and successful communities require making a self-sustainable environment where little supervision from the managers (or mods) is necessary. The point is not to bring members blindly into the community, but to make it easy for them to build relationships with each other and breathe life into the community with their interactions. By doing so, your community will not only grow in members who are looking to actively participate in a community but also in the amount of useful feedback, connections, ambassador programs and more that can come from it. How can we get over this hurdle? Make the team members engage with the community . You first need to lead by example. The members in your community will follow the lead of the team members who are interacting, organising events, answering questions and more. Foment that of what you want to see more of. By doing so, you will reward the behaviours you want to see in your community. A common example is someone who made GitHub issues or PRs helping your product, you might want to give them a shout-out on the general channel of the community or send some swag to show appreciation. To learn more about how to increase engagement within your community you can check out the article I made for DevRelX on the topic. What is your best advice in reaching devs and team leaders? Where are they and what do they want to see? The best advice is to find where they are. Make the exercise of finding out which developer personas your product is targeting and join them where they are, whether that is Reddit, Twitter, YouTube, Discord, Slack or any other social media account. Always keep in mind: Who are the decision-makers? Who will be implementing it? Developers are very hands-on when trying out a new product, find a way to ease the pain points your product is trying to solve, and explain the solutions and how they can use them to get there. This can be done with code examples or quick starter guides. Let them know there is available support if needed, why is it better than X, if it’s free to try out or if you can self-host. Is it easy to implement? How much does it cost? Is the community positive? How much money can this save us compared to X? Show the real evidence of the impact your product has and how it satisfies the needs in the market. In many orgs DevRel focuses on educating and enabling the masses - how do DevRel goals and tactics change when your target audience is 500 developers vs. 500k? We can understand why products with a 500 or less audience have as a priority the discovery, learning and building stages of the developer journey. There is a need to get the product out there and get users to spread the word. There’s a lot of emphasis on community engagement, organic growth and the implementation of the tool. With time, the focus will shift to scaling users or ways of conversion if necessary. There’s a lot of immediate interaction with the users, the feedback helps polish the documentation available for developers, ways to simplify the learning curve or adoption into an already set process, a blog with the latest releases and more. Products with more than 500k members, good discoverability and adoption from the community will focus more on ways to scale users, partner programs, sponsors, and more. It will also be more focused on things being built with the product. This will reinforce the idea that the product can solve the developer’s needs and it will give material to showcase to possible new users. In this phase, feedback is necessary on the learning stages to craft a better version of quick starter guides, code samples, tutorials, etc. Focus is also kept on the discovery portion of the journey with events, social media, blogs, newsletters and case studies. The focus will ultimately be determined by the current needs of the product and how it will help achieve the goals set by the company. How do you get started in DevRel? By accident, I thought, but looking back, it was the natural progression of the path I was taking. I was very publicly looking for an engineering job while I was creating content about my self-taught journey and sharing what I was learning with the community when a company reached out because they liked the work I was doing, the content I was putting out and the way I interacted with the community. I’ve always been very active in the Tech Twitter community, so that’s how they found me. I ultimately helped them grow their community, work on documentation and manage their social media channels (Twitter, Discord, YouTube, LinkedIn). This helped me be a bit of an all-round DevRel, gaining lots of experience on the different branches and making me realise that DevRel is the perfect combination of content creation and programming. I’m forever grateful for the risk they took by offering me my first tech role. What success metrics apply in today's climate? The success metrics will vary from company to company. They will be determined by the current needs of the company and how it will help achieve the goals set by it. Some common success metrics we see are KPIs (Key Performance Indicators), those that can be traced back to a certain number of new users or sales and that can be replicated with the same if not better results. It’s important that before tracking any metrics there’s an alignment between what the DevRel team thinks and what the management team thinks. This will make it easier to deliver the results they want and for DevRels to move towards that clear previously set goal. Usually, our roles are not clearly defined, how to have clarity if the management refuses to define the role? Try identifying some pain points they’re hiring you to help them with, they’re hiring you to work on the product. To increase awareness? To manage the community? From there, identify which branch of DevRel your responsibilities fall into; DevEd, DevEx, Developer Marketing, Developer Success or if they are a mix of all of the above. This will also help you know with which departments you’ll be interacting and help you be on the same page as them with your DevRel efforts. It’s crucial to have a clear line of communication with management, so after you have understood your responsibilities in this role and where it falls under the DevRel umbrella, reach out with your findings and also help them understand the different variants of DevRels since it’s still fairly new. Kudos to Yuri for such insightful answers! You can rewatch the full session and get access to the latest Developer Program Leader survey results at devrelx.com/dpl-survey . To be part of peer discussions like this one, get notified about the next survey and access more industry data, join the DevRelX Community !

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. CFP is now closed, and we have already confirmed the first round of speakers and their sessions! Here is all you need to know about joining DevRelX Summit 2023 ! 📅 Date & Place: Oct 25-26 | Online 🎟️ Admission: Register | Free 🤖 Theme: Driving Impact in the Age of AI DevRelX 2023 Lineup We will get together to explore how AI is shaping the future of developer ecosystems and the ways in which DevRel professionals can harness its potential to create meaningful and impactful experiences. Here are some of the key sessions we will have in store for you! Panel Discussions ⭐ “What it means to be a developer in the Everything GPT era?” Hosted by Katherine Miller , Marketing Leader and Consultant and joined by: Sean Falconer , Head of Marketing & DevRel at Skyflow Leandro Margulis , VP of Product at Prove Rizel Scarlett , Staff Developer Advocate at TBD. ⭐ “One developer program, multiple audiences: How to think about content curation that matters to them?” Hosted by Ray Stephenson , Sr. Director, Developer Relations at Cisco and joined by: Tessa Kriesel , Head of Platform DevRel, Snap Inc. Kevin Blanco , Senior DevRel Advocate at Appsmith Esther Agbaje , Developer Advocate at Directus ⭐ “2.0 of your DevRel team - How DevRel teams can use AI today?” Hosted by Ash Ryan Arnwine , Director of Developer Relations at Nylas and joined by: Jon Gottfried , Co-Founder, Major League Hacking Joyce Lin , Director of Developer Relations, Postman Kerri Shots , Principal API PM, Adobe Lightning Talks Let’s unleash human intelligence to make the most out of AI and push the known boundaries. Join us as we share insights, and meet industry leaders who shape the future of DevRel in the era of AI! "Building learning communities that scale with developers" 🎙️ Lisa Tagliaferri , Senior Director, Developer Education at Chainguard “What I learned by building an AI-powered chatbot for our documentation site” 🎙️ Todd Kerpelman , Developer Plaidvocate at Plaid "Conferences and Communities: How to take the most out the them?" 🎙️ Jonathan Vila , Developer Advocate at Sonar "Fear of being replaced? Key to a winning partnership with Generative AI" 🎙️ Meredith Hassett , Developer Advocate at Canva "Future-Proofing Your Career: The Low-Code and AI Way" 🎙️ Paulo Tavares , Director, Developer Relations at OutSystems Stay tuned as the full agenda will be announced soon! Want to join us as an Event Partner? Get in touch ! If you’re looking to elevate your brand, network with industry leaders, and support the growth of the DevRel communities, join us as an event partner! This is a unique opportunity for organizations to showcase their products, services, and thought leadership to a highly influential audience. Are you looking for: Networking Thought leadership opportunities Recruiting talent Showcasing what makes you stand out Or engaging with the DevRel community? ​​ Then don't miss this chance to elevate your brand, network with industry leaders, and support the growth of the DevRel communities. We look forward to partnering with you to make the DevRelX Summit 2023 a success!

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