Anaconda CEO David DeSanto explores what it takes to make AI-native development practical, secure and scalable for modern engineering teams. DeSanto reflects on his journey from leading product at GitLab to stepping into the CEO role at Anaconda, and why the next wave of software delivery will be shaped by the intersection of open source, DevOps practices and AI governance.

Software is increasingly assembled rather than written. With dependencies pulled from countless repositories, the software supply chain has become one of the largest sources of risk—especially as attackers target packages and organizations push more workloads into production faster. DeSanto explains how trust, curation and reproducibility matter when teams are depending on thousands of third-party components to build and ship applications.

From there, they turn to the evolving needs of AI development workflows. Beyond packages, teams now have to account for models, agents and new “AI supply chain” artifacts that also require security controls, policy enforcement and governance. DeSanto outlines how organizations are trying to move from experimentation to production with guardrails in place—tracking lineage, performance characteristics and vulnerabilities, and creating an “AI bill of materials” that extends the same discipline DevOps teams expect in traditional pipelines.

A standout theme is developer enablement. As AI raises the cost and complexity of building and running models, the ability to test and iterate locally—then carry those changes forward into production—becomes a practical advantage. DeSanto emphasizes a hands-on approach: staying close to builders, listening to customers, and making it easier for teams to adopt new capabilities without sacrificing control.

As AI becomes part of everyday software delivery, the winning platforms will be the ones that reduce friction for developers while strengthening security and governance across the full lifecycle.