As AI agents and copilots accelerate code creation from numerous sources, the primary challenge for engineering teams becomes validating this massive influx of pull requests. This makes the code review process the new critical choke point in the software development lifecycle.

As AI coding agents generate vast amounts of code, the most tedious part of a developer's job shifts from writing code to reviewing it. This creates a new product opportunity: building tools that help developers validate and build confidence in AI-written code, making the review process less of a chore.

AI tools are automating code generation, reducing the time developers spend writing it. Consequently, the primary skill shifts to carefully reviewing and verifying the AI-generated code for correctness and security. This means a developer's time is now spent more on review and architecture than on implementation.

With AI generating 1,300 pull requests weekly at Stripe, the critical path is shifting. When coding becomes a commodity, the bottleneck moves to human review and validation. Engineering teams must refocus from pure creation to oversight and quality assurance at scale.

Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.

AI agents can generate and merge code at a rate that far outstrips human review. While this offers unprecedented velocity, it creates a critical challenge: ensuring quality, security, and correctness. Developing trust and automated validation for this new paradigm is the industry's next major hurdle.

Just as observability tools (e.g., Datadog) arose to manage complex cloud environments, a new software category of "explainability" will emerge to manage complex AI agent workflows. These tools will analyze agent reasoning traces to build trust in their output, whether it's code, content, or data analysis.

As AI generates more code, the developer tool market will shift from code editors to platforms for evaluating AI output. New tools will focus on automated testing, security analysis, and compliance checks to ensure AI-generated code is production-ready.

AI excels at generating code, making that task a commodity. The new high-value work for engineers is "verification”—ensuring the AI's output is not just bug-free, but also valuable to customers, aligned with business goals, and strategically sound.