Commentary

For most of the past decade, AI governance lived comfortably outside the systems it was meant to regulate. Policies were written. Reviews were conducted. Models were approved. Audits happened after the fact. As long as AI behaved like a tool—producing predictions or recommendations on demand—that separation mostly worked. That assumption is breaking down. As AI […]

Agentic AI has clearly moved beyond buzzword status. McKinsey’s November 2025 survey shows that 62% of organizations are already experimenting with AI agents, and the top performers are pushing them into core workflows in the name of efficiency, growth, and innovation. However, this is also where things can get uncomfortable. Everyone in the field knows

This post first appeared on Addy Osmani’s Elevate Substack newsletter and is being republished here with the author’s permission. TL;DR: Aim for a clear spec covering just enough nuance (this may include structure, style, testing, boundaries. . .) to guide the AI without overwhelming it. Break large tasks into smaller ones versus keeping everything in one large

This post first appeared on Addy Osmani’s Elevate Substack newsletter and is being republished here with the author’s permission. TL;DR: Aim for a clear spec covering just enough nuance (this may include structure, style, testing, boundaries. . .) to guide the AI without overwhelming it. Break large tasks into smaller ones versus keeping everything in one large

Think about what happens when you onboard a new employee. First, you provision them tools. Email access. Slack. CRM. Office software. Project management software. Development environment. Connecting a person to the system they’ll need to do their job. However, this is necessary but not sufficient. Nobody becomes effective just because they can log into Salesforce.

Addy Osmani is one of my favorite people to talk with about the state of software engineering with AI. He spent 14 years leading Chrome’s developer experience team at Google, and recently moved to Google Cloud AI to focus on Gemini and agent development. He’s also the author of numerous books for O’Reilly, including The

For most of the history of software engineering, we’ve built systems around a simple and comforting assumption: Given the same input, a program will produce the same output. When something went wrong, it was usually because of a bug, a misconfiguration, or a dependency that wasn’t behaving as advertised. Our tools, testing strategies, and even

Over the past six months, I’ve watched the same pattern repeat across enterprise AI teams. A2A and ACP light up the room during architecture reviews—the protocols are elegant, the demos impressive. Three weeks into production, someone asks: “Wait, which agent authorized that $50,000 vendor payment at 2 am?“ The excitement shifts to concern. Here’s the

This post first appeared on Addy Osmani’s Elevate Substack newsletter and is being republished here with the author’s permission. AI coding assistants have quickly moved from novelty to necessity, where up to 90% of software engineers use some kind of AI for coding. But a new paradigm is emerging in software development—one where engineers leverage

At a private dinner a few months ago, Jensen Huang apparently said what I’ve been thinking for some time. The US is significantly behind China in AI development. Here are some of the reasons. Huang starts with the ratio of AI developers in China (he estimates 1 million) to AI developers in the US (20,000).