# Building Android at Scale: What Managing Camera & Media Taught Me About AI Integration

*By [Gianfranco Mileo](https://gianfranco-mileo.com/) · Published November 22, 2025 · updated September 6, 2026 · 2 min read · Category: Leadership · Tags: Program Management, Machine Learning, Leadership, Android · Canonical: https://gianfranco-mileo.com/blog/building-android-at-scale-what-managing-camera-and-media-taught-me-about-ai-integration*

> Working on Camera and Media in the Android ecosystem has given me a front-row seat to how AI is transforming mobile experiences — and how we manage that complexity.

## Where AI Meets Billions of Users

Working on Camera and Media in the Android ecosystem means working at an intersection that's both thrilling and humbling. Every feature we ship touches billions of devices. Every API we change affects thousands of app developers. And increasingly, AI is at the center of everything — from computational photography to video processing to accessibility features.

## Lessons from Managing AI-Powered Features

### Lesson 1: AI Features Have Non-Linear Risk Profiles

Traditional software features have predictable failure modes — they crash, they're slow, or they produce wrong results. AI features add a new dimension: they can produce *subtly* wrong results that look correct. This changes how we test, how we roll out, and how we monitor. As a TPM, I've had to build entirely new risk assessment frameworks for AI-powered features.

### Lesson 2: Cross-Team Coordination Gets Harder

AI features often span ML teams, platform teams, app teams, and hardware teams. The coordination complexity grows exponentially, not linearly. I've found that the best approach is to establish clear API contracts early and create shared dashboards that everyone can reference without scheduling another meeting.

### Lesson 3: Developer Experience is the Multiplier

In the Android ecosystem, our success is measured by how well app developers can build great experiences. When we ship a new camera API powered by AI, the real question isn't "how good is the model?" — it's "how easy is it for a developer to use this correctly?" I spend significant time on developer documentation, sample code, and feedback channels.

Related: see [Managed Agents Just Changed My TPM Workflow (And I Have Receipts)](https://gianfranco-mileo.com/blog/managed-agents-just-changed-my-tpm-workflow-and-i-have-receipts) and [MirrorCode and the Long-Horizon Problem: Why Week-Long Coding Tasks Are the Real AI Benchmark](https://gianfranco-mileo.com/blog/mirrorcode-and-the-long-horizon-problem-why-week-long-coding-tasks-are-the-real-ai-benchmark).

## The Program Manager's Role in AI

I've come to believe that program managers are uniquely positioned to bridge the gap between AI research and AI products. We speak both languages. We understand the technical constraints AND the user impact. We can ask "what does this model need to be useful?" and translate the answer into a roadmap that everyone can execute against.

That's not going away with more AI — if anything, as AI features get more complex, the need for someone to hold the big picture together only grows.

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