In today's CTO Radar, we're looking at the investment boom in AI coding tools: what makes it exciting, and what makes it risky.
The exciting part is clear: AI coding tools are moving from experiment to budget line.
The market for AI code tools reached $7.37B in 2025 and is projected to reach $9.35B in 2026. Developer adoption is also becoming mainstream, with recent surveys showing that 84–91% of developers either use or plan to use AI coding tools.
The productivity case is also becoming harder to ignore. Research on tools like GitHub Copilot has shown developers completing 26% more pull requests.
AI can help developers move faster, reduce repetitive work, and create more leverage inside engineering teams. But here is the risky part: many organizations may measure the impact too narrowly.
More AI investment does not automatically mean better engineering performance. Higher adoption does not automatically prove business impact. And more pull requests do not automatically mean faster delivery.
AI can accelerate coding while moving the bottleneck somewhere else: review, testing, architecture, security, release readiness, or senior engineering capacity. That is why we think CTOs need to treat AI coding tools as a system-level change, not just an individual productivity tool.
The question is no longer only: "Are developers using AI?" It is: "Is AI improving delivery speed, quality, maintainability, and team capacity?"
Our view: the companies that get the most value from AI will not be the ones that only drive adoption.
The winners will be the teams that can prove AI is improving the engineering system, not just increasing activity around it.
