LaunchDarkly announced the private preview of Warehouse Native Experimentation, its Snowflake Native App, to offer Data Warehouse Native Experimentation.
DEVOPSdigest invited experts across the industry — consultants, analysts and vendors — to comment on how AI can support the software development life cycle (SDLC). In Part 15 of this series, experts offer predictions about how AI's support for code generation will evolve in 2025 and beyond.
AI ADOPTION SCALES RAPIDLY
AI in development is set for rapid maturity in 2025, especially in the space of code generation and security patching. 2024, we saw initial adoption but the lack of AI governance impeded widespread roll outs. We saw a greater willingness to adopt AI within the code space in Q3 and Q4, 2024. As governance improves, we expect the adoption to scale rapidly — organizations will have formally budgeted for this. Enterprises will formally adopt co-pilots and agents applied in the IDE and CI/CD ecosystems.
Chris Hatter
CISO, Qwiet AI(link is external)
20% OF ALL CODE GENERATED BY AI
By the end of 2025, 20% of all software code generated worldwide will come from AI agents/assistants. Software code is one of the most prominent vertical applications of AI agents. As the companies integrate more tools into those agentic workflows and improve validation techniques for the AI-generated code, there's rapid progress in the quality and capabilities of those coding agents. We will see an increase in the productivity of professional software engineers, which will translate into productivity improvements for the software's end users. We will also see a dramatic acceleration in the migration from older legacy code bases to new ones. Last but not least, my mom will write her first software program.
Andrew Filev
CEO and Founder, Zencoder(link is external)
AI-GENERATED CODE IMPROVES
Code generation will continue to improve, impacting all tiers of modern applications. Expect to be able to build a full-stack containerized app from a single prompt.
Jason Bloomberg
President, Intellyx(link is external)
On the more utilitarian side of AI/ML and its use for optimizing the work of developers, we will see code generation via AI tools become more reliable next year as we optimize models.
Paul Davis
Field CISO, JFrog(link is external)
AI CONCIERGE
I believe that AI will evolve into a concierge or assistant for our developers, similar to how we used to use encyclopedias and thesauruses to enhance our content and writing. These concierges will allow developers and other script and code writers to leverage and adopt best practices more easily and accurately. As a result, the output for everyone will have fewer bugs and inspire more confidence. Ultimately, these assistants may slow us down in terms of rolling out products, but with increased accuracy, confidence, and security, the final product will be much better, thereby saving us time.
Joanna Schloss
CMO/AI Evangelist, Parasoft(link is external)
AI WILL BECOME LIKE GOOGLING
Eventually using AI will be synonymous with Googling. If you have to look for code in a sample, you'll just prompt AI. Personalized AI assistants could become a key part of every developer's workflow, giving them tailored code suggestions, learning from coding styles, and automating routine tasks.
Udi Weinberg
Director of Product Management, Research and Development, OpenText(link is external)
THE HYBRID MODEL: AI + HUMAN
We envisage that the focus will shift toward developing collaborative AI tools that seamlessly work alongside human developers, especially as AI gains advanced abilities to learn from human feedback. An up-and-coming example of this type of collaboration is natural language interaction to refine code suggestions.
Dotan Nahum
Head of Developer-First Security, Check Point Software Technologies(link is external)
Hybrid models: Many AI tools will likely adopt a hybrid approach, combining human expertise with AI capabilities to achieve optimal results.
Kumar Chivukula
Co-Founder and CEO, Opsera(link is external)
I predict the focus will shift toward creating symbiotic relationships between humans and AI, fostering collaboration to achieve more ambitious development goals.
Ed Frederici
CTO, Appfire(link is external)
STANDARDIZED WORKFLOWS
In 2025, there will be an urgent and increasing need for better developer workflows for AI-assisted coding. Whether writing a script or creating a fully-fledged project, there is a need for some form of top-down oversight into how development environments are used and created, how developers and data scientists are interacting with the tooling, and how AI interacts with the system. One way is to create development environments that are standardized, with approved tools or APIs that the tools automatically connect to, in order to decrease the number of bugs and increase test coverage or quality. It becomes easier to control the environments by adding internal mechanisms to validate code quality and test for issues, without stripping developers of the ability to run AI. I expect more teams will build standardized workflows, with administrators controlling certain layers of the stack and adding scripts or tools to protect code quality.
Piotr Zaniewski
Head of Engineering Enablement, Loft Labs(link is external)
MULTI-AGENT SYSTEMS
Multi-agent systems for code development are a promising area of research and can help enable AI to solve increasingly complex coding tasks end-to-end over the next 2-3 years though some degree of human oversight will still be required.
Shourabh Rawat
Senior Director, Machine Learning, SymphonyAI(link is external)
BUILDING APPLICATIONS REMAINS THE DOMAIN OF DEVELOPERS
AI will make huge strides in software development — but not where you think. Despite the hype around AI-generated coding, software development is inherently about building new things that previously did not exist, which AI simply cannot do. In 2025, I expect enterprises to identify new AI use cases for developers, but building applications will mostly remain in the hands of the creative professionals using AI to supplement their work. I think the biggest gains for software developers next year will come in the form of AI-powered search and providing suggestions for where improvements can be made. However, at the end of the day, the actual developers will have to engineer these changes and create the code to ensure a human touch is present throughout the process.
Tobie Morgan Hitchcock
CEO and Co-Founder, SurrealDB(link is external)
FAST AI CODE ENDS IN GRIDLOCK
Fast AI code today will end in system gridlock tomorrow. While AI makes writing code faster, engineering teams will be challenged in 2025 and beyond to take control of their software architecture as thousands of AI-generated components interact. Teams rushing AI development will spend more time untangling messy code than writing new features. Software fixes that once took days will stretch into weeks as developers wade through AI-generated functions with hidden dependencies. Bad architecture carries many costs: skyrocketing cloud bills, increased carbon emissions, engineering teams burnout, and more.
Ori Saporta
VP of Engineering and Co-Founder, vFunction(link is external)
CODE ACCOUNTABILITY
AI is already transforming the way developers work, streamlining processes and alleviating the repetitive nature of writing code … However, as adoption grows, a major challenge is emerging: code accountability. AI-generated code must undergo rigorous review to identify potential security vulnerabilities and quality issues early on — before they can lead to costly problems. Yet, the responsibility for ensuring this review often gets overlooked. In 2025, as AI tools become essential for developers, they'll need to take greater responsibility for code accountability. By integrating a "trust and verify" approach early in the software development life cycle, developers can save time and increase their capacity to tackle large-scale projects that drive business success. The same level of scrutiny applied to human-written code must be extended to AI-generated code. With human oversight embedded throughout the workflow, development teams can ensure that AI-driven code meets established quality and security standards.
Tariq Shaukat
CEO, Sonar(link is external)
AI-NATIVE CODE
At present, AI can generate code that is readable by humans and can be used in applications. This is a result of the training and development of AI models. I believe that the technology underlying large language models or generative AI could eventually lead to the creation of AI-native code. This would be code not explicitly trained by humans for human understanding, but instead, code written specifically to be read and understood by other large language models, and potentially edited by them. I anticipate that this transition will occur soon and will bring about substantial changes in software development practices. Austin Vance
Co-Founder and CEO, Focused Labs(link is external)
EVOLUTION FROM reactive tools to proactive collaborators
AI assistants are getting smarter, moving beyond reactive prompt-based interactions to proactive problem-solvers. As central hubs for code assistance, AI agents will anticipate developers' needs and offer real-time suggestions for optimizing application performance, security, and maintenance. This shift will streamline the entire SDLC, making it more accessible through a simple user interface. The role of developers will evolve alongside these advancements. AI will not replace developers but augment their capabilities, allowing them to focus on higher-level tasks and strategic decision-making. By automating routine tasks and providing expert guidance, AI assistants will empower developers to delve deeper into business problem-solving, become guardians of code quality, and explore new technologies and skills. This evolution will not only enhance developer productivity but foster a new era of innovation.
Emilio Salvador
VP of Strategy and Developer Relations, GitLab(link is external)
AI TAKES ON CREATIVE ASPECTS OF DEV
AI code creation will only become faster, more flexible, and able to handle a wider variety of coding tasks. We may even see AI systems take on more creative aspects of software development traditionally reserved for human developers.
David DeSanto
Chief Product Officer, GitLab(link is external)
EVOLUTION FROM WRITING CODE TO EXPLAINING BUSINESS PROBLEM
Long-term, the world of coding will evolve from writing code to colloquially explaining a business problem to effectively an AI agent so that they can statically generate the right coding solution. And just like bringing up a child, checking the results of the code (homework) generated by the developing coding companion before releasing it to be integrated into the production environment will be important. This could mean that the next generation of software developers will have to combine the technical skills of building solutions, with the need to understand the overall business need and the soft skills of explaining what they want to an AI agent. This will be a tremendous step forward in bringing Dev(Sec)Ops teams out of their current siloes in their organizations and more into the day-to-day business decisions.
Paul Davis
Field CISO, JFrog(link is external)
BEYOND CODE COMPLETION
Developer tools are about to make a quantum leap beyond code completion. The next generation of AI-powered developer tools will transform from simple code assistants into comprehensive development partners. While current tools excel at code completion, documentation generation and test artifact creation, we're on the cusp of a dramatic evolution in developer productivity tools. Within the next year, expect these tools to become proactive development agents that can simultaneously validate code as it's written, run simulations for edge cases, check for security vulnerabilities and verify data privacy compliance — all before code reaches the main branch. This shift from reactive assistance to proactive validation will fundamentally change how developers work.
Just as the iPhone revolutionized mobile computing, making BlackBerry's approach almost instantly obsolete, these new AI-powered development tools will create a similar paradigm shift. Developers who experience these capabilities — having complex security checks, performance optimizations and compliance validations automated in real-time — won't be able to return to traditional development workflows.
This evolution is happening at warp speed. Features that would have taken a decade to develop are now being released in months. Organizations that fail to adopt these advanced development tools risk falling dramatically behind in both productivity and code quality. Success in the coming years will depend not just on having these tools, but on building development workflows that fully leverage their capabilities to create more reliable, secure and efficient software delivery pipelines.
Gopi Duddi
VP of Engineering, Couchbase(link is external)
Go to: Exploring the Power of AI in Software Development - Part 16: 2025 Predictions and Beyond
Industry News
SingleStore announced the launch of SingleStore Flow, a no-code solution designed to greatly simplify data migration and Change Data Capture (CDC).
ActiveState launched its Vulnerability Management as a Service (VMaas) offering to help organizations manage open source and accelerate secure software delivery.
Genkit for Node.js is now at version 1.0 and ready for production use.
JFrog signed a strategic collaboration agreement (SCA) with Amazon Web Services (AWS).
mabl launched of two new innovations, mabl Tools for Playwright and mabl GenAI Test Creation, expanding testing capabilities beyond the bounds of traditional QA teams.
Check Point® Software Technologies Ltd.(link is external) announced a strategic partnership with leading cloud security provider Wiz to address the growing challenges enterprises face securing hybrid cloud environments.
Jitterbit announced its latest AI-infused capabilities within the Harmony platform, advancing AI from low-code development to natural language processing (NLP).
Rancher Government Solutions (RGS) and Sequoia Holdings announced a strategic partnership to enhance software supply chain security, classified workload deployments, and Kubernetes management for the Department of Defense (DOD), Intelligence Community (IC), and federal civilian agencies.
Harness and Traceable have entered into a definitive merger agreement, creating an advanced AI-native DevSecOps platform.
Endor Labs announced a partnership with GitHub that makes it easier than ever for application security teams and developers to accurately identify and remediate the most serious security vulnerabilities—all without leaving GitHub.
Are you using OpenTelemetry? Are you planning to use it? Click here to take the OpenTelemetry survey(link is external).
GitHub announced a wave of new features and enhancements to GitHub Copilot to streamline coding tasks based on an organization’s specific ways of working.
Mirantis launched k0rdent, an open-source Distributed Container Management Environment (DCME) that provides a single control point for cloud native applications – on-premises, on public clouds, at the edge – on any infrastructure, anywhere.
Hitachi Vantara announced a new co-engineered solution with Cisco designed for Red Hat OpenShift, a hybrid cloud application platform powered by Kubernetes.