AI Creates New Class of Software Teams, Report Finds

SAN FRANCISCO, CA – February 18, 2026 – The proliferation of artificial intelligence in software development is not just accelerating the pace of coding; it is forging a deep and widening chasm between elite engineering teams and the rest of the pack. A new industry report reveals that the true measure of success in the AI era is no longer how fast code can be written, but how effectively it can be validated and deployed at scale, creating a new bottleneck that many organizations are unprepared to handle.

CircleCI, a leading software delivery platform, today unveiled its seventh annual 2026 State of Software Delivery report. Based on an analysis of over 28 million workflows from thousands of organizations, the report documents the largest surge in development activity since its first publication in 2019. However, the data paints a stark picture of a divided industry, where the benefits of AI-driven acceleration are not being distributed equally.

The Great Software Delivery Divide

While AI-powered code generation and agent-driven workflows have led to an average throughput increase of 59% across all projects on the platform, this figure masks a dramatic disparity. The report’s most striking finding is the performance gap between the top performers and the median teams.

The top 5% of engineering teams have nearly doubled their throughput in the last year, jumping from an average of 6.8 to 13.4 daily workflow runs. The top 10% and 25% of teams also saw significant gains of 47% and 25%, respectively. In stark contrast, the median team increased its throughput by a mere 4%, while the bottom quarter of teams saw no measurable improvement at all.

This data suggests that AI isn't creating a performance gap from scratch but is instead acting as a powerful amplifier, revealing and exacerbating pre-existing inefficiencies in how organizations build and ship software. Teams that were already efficient are leveraging AI to achieve unprecedented productivity, while those with cumbersome processes are finding themselves falling further behind.

"The industry is far past thinking about how to use AI to accelerate software delivery," said Rob Zuber, CTO of CircleCI, in the press release accompanying the report. "The teams that are realizing AI's benefits in software development are using autonomous validation to run laps around the competition that can't validate AI-generated code at scale."

Beyond Code Generation: The Validation Bottleneck

The core of the issue, according to the report, is a fundamental shift in the primary bottleneck of software development. For years, the bottleneck was the human developer's speed. Now, with AI assistants capable of generating code almost instantly, the pressure has moved downstream to the systems responsible for testing, integrating, and deploying that code.

Elite teams are now shipping changes at a velocity that would completely overwhelm traditional delivery pipelines. When a team is attempting to validate tens of thousands of code changes per day, success is no longer determined by developer speed but by the capacity and intelligence of the underlying validation systems. This is the new frontier of 'validation at scale.'

This involves more than just running tests faster. It encompasses a suite of advanced practices and tools, often referred to as 'autonomous validation.' These systems use AI to intelligently prioritize test cases based on code changes, predict potential deployment failures by analyzing historical data, and even automatically generate and repair test scripts. By automating these critical quality gates, top-performing organizations can translate the raw speed of AI code generation into deployable, high-quality features, while others see their pipelines clog with an unmanageable volume of unverified changes.

An Industry-Wide Arms Race for AI Integration

CircleCI's findings are not occurring in a vacuum. They reflect an industry-wide scramble to embed AI into every facet of the software development lifecycle (SDLC). Major technology players and industry analysts confirm that the focus is rapidly moving beyond simple code completion.

Competitors in the CI/CD space are making similar strategic moves. GitHub is pushing forward with 'Agentic Workflows,' allowing AI agents to automate repository tasks using natural language. GitLab has integrated its 'Duo Enterprise' AI features to assist with everything from vulnerability resolution to analyzing CI/CD bottlenecks. Even the open-source Jenkins platform is seeing a proliferation of AI-powered plugins for test automation and failure analysis.

This trend is backed by market analysis. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI coding assistants. The firm emphasizes that the most significant productivity gains will come not from coding alone, but from applying AI across the entire SDLC—from architectural design to complex testing and deployment. This industry-wide pivot validates the report's central thesis: the ability to manage AI-generated output is now more critical than the ability to generate it.

Navigating the New Landscape for Businesses and Developers

The emergence of this 'AI divide' presents both a strategic imperative and a significant challenge for businesses. For CTOs and engineering leaders, the report serves as a wake-up call. Simply providing developers with AI tools is not enough; success requires a holistic investment in modernizing the entire delivery pipeline and fostering a culture of adaptability.

This transformation demands a new set of skills. Proficiency in prompt engineering, a deep understanding of AI ethics, and the ability to critically evaluate AI-generated outputs are becoming essential for developers. For organizations, the challenge lies in upskilling their workforce and addressing the cultural and process-related hurdles that prevent the effective adoption of these new technologies.

Organizations must now re-evaluate their metrics for success. The focus must shift from simply measuring lines of code or the speed of individual tasks to optimizing for validated throughput and the rapid, reliable delivery of business value. As AI continues to reshape the technological landscape, the companies that thrive will be those that master the art and science of turning AI-driven speed into validated, customer-ready software. The gap between the haves and have-nots is already widening, and the ability to build, test, and ship confidently at AI speed will define the winners of the next decade.