Lei Zhang, head of technology infrastructure at Bloomberg, details how a 9,000-engineer organization deploys AI in a structured and effective way. With a massive, mission-critical codebase (financial markets), Bloomberg cannot afford chaotic adoption.
Zhang distinguishes AI for "coding" (writing new features) from AI for "Software Engineering" (maintenance, operations). Bloomberg emphasizes this second, often-overlooked aspect, which carries high ROI: 1. Uplift Agents: Agents dedicated to migrations and mass security patches, capable of proposing explained patches across the entire codebase. 2. Incident Response Agents: During an outage, AI is used to instantly scan logs, telemetry, and configurations. Its strength lies in being "fast and unbiased" (unlike humans, who carry preconceptions about the likely cause).
To manage this scale, Bloomberg applies the "Paved Path" principle: making the right method easy and the wrong one difficult. They built a centralized platform offering an AI gateway (Gateway), simplified PaaS deployment for internal tools, and above all an MCP Hub (Model Context Protocol) to share connectors and prevent every team from recreating the same tools.
On the human side, Zhang notes that adoption is stronger among individual contributors than among managers. To address this, Bloomberg has integrated AI into the training curriculum for new hires. These "juniors" then become vectors of change, challenging seniors with new methods.
Zhang concludes by noting that AI is changing engineering's "cost function": certain tasks that were once expensive (migrations, documentation) become cheap, prompting a rethink of software development's usual trade-offs.