Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Michael Zuercher, CEO, Prismatic
AI is changing software development. Engineering workflows are shifting, jobs are evolving, and economic pressures related to the use of AI are becoming apparent. As we look to the coming year, software development will continue to shift toward automated, orchestrated environments, where engineers will guide high-level logic and AI tools will handle execution. Platforms that implement these new methods will take on a more prominent role in the software planning and shipping process.
AI Will Push Software to a New Layer of Abstraction
AI coding agents will push software development into its next phase of maturity. The industry will move beyond writing code line by line to working at a higher level of abstraction, much as it once shifted from assembly to modern programming languages. Engineers will spend less time on syntax and more time defining what the software should do rather than how to write it. This marks the beginning of a more mature era of engineering, where automation handles the details and people shape the logic and outcomes. As this shift takes hold, platforms that manage the infrastructure, scale, and operational load behind AI-built software will matter more than ever.
A New Skill Set Will Define Engineers
The best engineers won’t be defined by how fast they can code but by how well they can manage AI-driven processes. They’ll juggle multiple coding agents, coordinate context, and translate business intent into working systems. Product and engineering roles will overlap, and context switching – once seen as a distraction – will become a core skill. The job will look less like writing code and more like orchestrating intelligent tools toward a shared goal.
Developers Will Work in the Languages AI Handles Most Effectively
Engineering groups will base language decisions on where AI performs best. As AI systems generate a larger share of the code, teams will emphasize languages that models understand thoroughly. Developers will place greater weight on how easily they can manage, review, and ship code generated by AI systems. Their preferences for specific languages will shift toward producing reliable performance through collaboration with their AI tools.
AI Costs Will Reset Expectations
AI usage will enter a period where operating costs become clear. Current pricing often falls far short of the cost required to run large models. Vendors will eventually align pricing models with actual consumption, and organizations will gain clearer insight into what they use and what they pay for. Budgets will adjust, and smaller AI providers will face pressure to consolidate or join larger platforms with stronger cost structures.
Preparing for the Next Phase
In 2026, engineering teams that will be successful will include AI as a reliable part of their workflows. The goal is to provide AI tools with clear direction, backed by strong infrastructure, while building skills to empower individuals to maintain control over the direction and intent of their work. And teams that combine automation with thoughtful design and maintain consistent operational discipline will be rewarded in the coming year and beyond.
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