By David DeSanto, Chief Product Officer, at GitLab
AI adoption within software development has grown exponentially over the last two years, and this trend will continue over the next year. GitLab research found that 78% of respondents use AI in their software development processes or plan to do so within the next two years. The industry will likely see a more refined and intentional application of AI to software development workflows. These transformations will fundamentally alter how developers work, how organizations approach AI implementation, and how the broader technology ecosystem functions.
Organizations have already begun to see strong returns from their AI investments, with increased efficiency, productivity, and faster cycle times, and 2025 will be an even more transformative year. We will witness significant shifts, from AI becoming an additional team member within software development teams to the open source community taking on the challenges of democratizing AI. Here are four AI trends that will shape software development in 2025.
AI will mature into a proactive solution, rather than a reactive tool.
As AI becomes a more proactive partner to software development teams, developers will spend more time on complex, strategic tasks than daily monotonous ones. These tasks can often consume a developer’s day. GitLab’s research found that developers spend less than a quarter of their time writing code, which opens up the opportunity to apply AI for more proactive use cases, such as mitigating security and privacy risks. For example, automated testing will shift from AI writing tests to AI managing test suites, including traditional application security testing. Treating AI as a partner will free developers to focus on creative problem-solving, driving innovation, and delivering strong business outcomes.
Technology powerhouses will continue to drive the AI infrastructure market.
AI infrastructure will likely remain dominated by a handful of tech giants and cloud hyperscalers. With their immense resources and expertise, these industry leaders have invested substantially in cutting-edge hardware, such as GPUs and TPUs, essential for training and deploying advanced AI models. This significant barrier to entry makes it challenging for smaller players to compete, as they may lack the necessary infrastructure and financial resources to keep pace and potentially limit the diversity of AI applications.
Open-source communities will democratize AI with improved model quality.
The democratization of AI will accelerate in 2025 as more organizations release high-quality, open-source, large-language models. While tech giants will continue championing their proprietary models, the increasing availability of open-source alternatives will lower the barrier to adopting AI and make it more accessible to individuals and organizations alike. Similar to the enduring coexistence of open-source and commercial software, both AI approaches will thrive, offering diverse functionalities and strengthening the industry.
ModelOps will evolve into a fundamental aspect of modern software development.
While many data scientists and engineers operate outside the traditional DevSecOps workflow, this disconnect will increasingly hinder their effectiveness. As AI becomes more deeply integrated into software development, ModelOps will emerge as a critical component of the software development lifecycle. By combining DataOps, which focuses on preparing and managing data, with MLOps, which handles the development, training, deployment, and versioning of AI models, ModelOps will provide a comprehensive framework for ensuring the successful integration of AI into the software development workflows.
This year will be a turning point as organizations capitalize on their AI investments and foundations. As AI matures, we expect more sophisticated applications and groundbreaking innovations, such as updates similar to the introduction of “reasoning” models. From personalized user experiences to autonomous systems, AI will reshape industries and redefine business models. Integrating AI into core business processes will become increasingly seamless, driving efficiency, productivity, and competitive advantage. Though there will be some friction as open-source communities work to democratize AI and distribute models beyond tech giants and hyperscalers, this work will ultimately contribute to a thriving, diverse tech ecosystem.
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ABOUT THE AUTHOR
David DeSanto is the Chief Product Officer at GitLab Inc., where he leads GitLab’s product division to define and execute GitLab’s product vision and roadmap. David is responsible for ensuring the company builds, ships, and supports the platform that reinforces GitLab’s leadership in the DevSecOps platform market.
David joined GitLab in 2019 to expand GitLab’s Ultimate tier and build security into the GitLab DevOps platform. He was promoted to chief product officer in 2022. Prior to GitLab, David has held product and engineering leadership roles at Spirent Communications, NSS Labs and ICSA Labs.
David holds an M.S. in Cybersecurity from New York University and a B.S. in Computer Science from Millersville University of Pennsylvania. He is a frequent speaker at major international conferences on topics including AI, DevSecOps, platform engineering, threat intelligence, and cloud security, in addition to being the co-author of Threat Forecasting.
