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What We Learned Deploying AI within Bloomberg's Engineering Organization

AI Engineer via YouTube

Overview

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Learn from Bloomberg's real-world experience deploying AI tools across a 9,000+ engineer organization in this 18-minute conference talk. Discover the practical challenges and solutions that emerge when moving beyond initial AI demonstrations to enterprise-scale implementation. Explore how Bloomberg identified where genuine ROI lies beyond simple code generation, including the development of uplift agents for automating refactoring and maintenance tasks, and incident response agents that provide unbiased troubleshooting capabilities. Understand the infrastructure requirements for enabling AI at scale, including the creation of standardized "paved paths" for AI tool building using Model Control Protocol (MCP), platform components like gateways and discovery hubs, and deployment strategies. Examine the cultural shifts, training programs, and community-building efforts necessary for successful AI adoption across large engineering teams. Gain insights into leadership challenges and how AI is fundamentally changing the cost function of software engineering, moving beyond the initial hype to address the complex realities of integrating AI into mature engineering workflows.

Syllabus

Introduction to Bloomberg's Scale & Infrastructure
AI for Coding: Initial Adoption & The "Greenfield" Drop-off
Uplift Agents: Automating Refactoring & Maintenance
Incident Response Agents: Unbiased Troubleshooting & Speed
The "Paved Path": Standardizing AI Tool Building MCP
Platform Components: Gateway, Discovery Hub, and Deployment
Leveraging Training & Communities for Adoption
The Leadership Gap & The Changing Cost Function of Engineering

Taught by

AI Engineer

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