I just sat in a room full of data engineers the other week who were worrying about AI automating them out of work the same way auto manufacturing in Detroit was upended half a century ago. All AI. All the time. That’s what technology professionals are talking about. Data scientists, data engineers, ...
Steve Yegge’s article about programmer burnout (“The AI Vampire”) along with Margaret Storey’s article about Cognitive Debt started an ongoing conversation about programmer fatigue and software quality—two topics that should be linked, but often aren’t. Steve argues that programming constantly with ...
The Best Risk Mitigation Strategy in Data? A Single Source of Truth
Every data leader has a version of this story. A regulatory audit surfaces a metric that doesn’t match across systems. A board member catches conflicting revenue numbers in two reports presented back-to-back. An AI tool generates a recommendation based on data that hasn’t been governed since the ana...
This is the fifth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, and part four here. I recently had a taste of humility with my AI-generated code. I live in Park Slope, Brooklyn, and recently I needed to get to the other side...
Don’t Automate Your Moat: Matching AI Autonomy to Risk and Competitive Stakes
I was talking to a senior engineer at a well-funded company not long ago. I asked him to walk me through a critical algorithm at the heart of their product, something that ran hundreds of times a second and directly affected customer outcomes. He paused and said, “Honestly, I’m not totally sure how ...
We tend to assume that if every part of a system behaves correctly, the system itself will behave correctly. That assumption is deeply embedded in how we design, test, and operate software. If a service returns valid responses, if dependencies are reachable, and if constraints are satisfied, then th...
Enterprise AI governance still authorizes agents as if they were stable software artifacts.They are not. An enterprise deploys a LangChain-based research agent to analyze market trends and draft internal briefs. During preproduction review, the system behaves within acceptable bounds: It routes quer...
The following article was originally published on Tim O’Brien’s Medium page and is being reposted here with the author’s permission. If you’ve spent any time around AI-assisted software work, you already know the moment when the Scope Creep Kraken first puts a tentacle on the boat. The project begin...
I sat down with Aaron Levie at the O’Reilly AI Codecon two weeks ago. Aaron cofounded Box in 2005, and 20 years later, his company manages content for about two-thirds of the Fortune 500. Aaron is one of the few CEOs of an incumbent enterprise software company thinking deeply in public about what AI...
“Conviction Collapse” and the End of Software as We Know It
In “An Ordinary Evening in New Haven,” the poet Wallace Stevens wrote, “It is not in the premise that reality is a solid.” That line came to mind during a fascinating conversation with Harper Reed, which amounted to something like “It is no longer in the premise that software is a product.” Harper i...
The following article originally appeared on Medium and is being reproduced here with the author’s permission. This 2,800-word essay (a 12-minute read) is about how to survive inside the AI revolution in software development, without succumbing to the fear that swirls around all of us. It explains s...
How to Build a General-Purpose AI Agent in 131 Lines of Python
The following article originally appeared on Hugo Bowne-Anderson’s newsletter, Vanishing Gradients, and is being republished here with the author’s permission. In this post, we’ll build two AI agents from scratch in Python. One will be a coding agent, the other a search agent. Why have I called this...
Not that long ago, we were resigned to the idea that humans would need to inspect every line of AI-generated code. We’d do it personally, code reviews would always be part of a serious software practice, and the ability to read and review code would become an even more important part of a developer’...
Steve Yegge Wants You to Stop Looking at Your Code
My “Live with Tim” conversation with Steve Yegge this week was one of those sessions where you could imagine the audience leaning forward in their chairs. And on more than one occasion, when Steve got particularly colorful, I imagined them recoiling. Steve has always been one of the most provocative...
Autonomous AI systems force architects into an uncomfortable question that cannot be avoided much longer: Does every decision need to be governed synchronously to be safe? At first glance, the answer appears obvious. If AI systems reason, retrieve information, and act autonomously, then surely every...
I’ve said in the past that AI will enable new kinds of applications—but I’ve never had the imagination to guess what those new applications would be. I don’t want a smart refrigerator, especially if it’s going to inflict ads on me. Or a smart TV. Or a smart doorbell. Most of these applications are s...
In a previous article, we outlined why GPUs have become the architectural control point for enterprise AI. When accelerator capacity becomes the governing constraint, the cloud’s most comforting assumption—that you can scale on demand without thinking too far ahead—stops being true. That shift has a...
Most multi-agent AI systems fail expensively before they fail quietly. The pattern is familiar to anyone who’s debugged one: Agent A completes a subtask and moves on. Agent B, with no visibility into A’s work, reexecutes the same operation with slightly different parameters. Agent C receives inconsi...
Control Planes for Autonomous AI: Why Governance Has to Move Inside the System
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 se...
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...
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 (2...
Reverse Engineering Your Software Architecture with Claude Code to Help Claude Code
This post first appeared on Nick Tune’s Medium page and is being republished here with the author’s permission. I have been using Claude Code for a variety of purposes, and one thing I’ve realized is that the more it understands about the functionality of the system (the domain, the use cases, the e...
The hard truth about AI scaling is that for most organizations, it isn’t happening. Despite billions in investment, a 2025 report from the MIT NANDA initiative reveals that 95% of enterprise generative AI pilots fail to deliver measurable business impact. This isn’t a technology problem; it’s an org...
The Five Skills I Actually Use Every Day as an AI PM (and How You Can Too)
This post first appeared on Aman Khan’s AI Product Playbook newsletter and is being republished here with the author’s permission. Let me start with some honesty. When people ask me “Should I become an AI PM?” I tell them they’re asking the wrong question. Here’s what I’ve learned: Becoming an AI PM...