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, and data architects […]

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 help of agentic AI leds to burnout; it’s fast, it’s fun, but keeping […]

Personal AI doesn’t have to run your life to change it. It just must see you clearly and feed your behavior back to you in a way you can’t dodge. Once you look at AI as feedback loops instead of little butlers, the whole “agent” conversation starts to feel upside down. We’ve overrotated on agents […]

Human-in-the-Loop becomes an operational bottleneck In my previous article, ”The Missing Layer in Agentic AI,” I argued that AI agents need a deterministic execution kernel—a privileged “Kernel Space” that validates every proposed action before it touches the real world. That article focused on what happens at the execution boundary: idempotency, JIT state verification, and DFID-correlated […]

As enterprise AI agent adoption scales, the absence of centralized, organization-level tool infrastructure is producing compounding costs. When adoption is built around optimizing for deployment speed, enterprises expose themselves to a combination of risks: duplicated engineering effort, security exposure, and operational opacity. Every enterprise needs its own shared tool registry, one that reflects its specific […]

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 analyst who built it left the company two […]

In “Don’t Automate Your Moat,” I argue that engineering organizations should match AI autonomy to two independent dimensions: business risk and competitive differentiation. I used AI Gateway cost controls as a worked example throughout the piece because a single feature touches all four quadrants depending on which piece you’re building. A piece making that argument […]

Everyone is adopting AI coding tools. Engineers are writing code faster than ever. But are organizations actually delivering value faster? That’s not obvious. I wrote Enabling Microservice Success with a big focus on engineering enablement, guardrails, automated testing, active ownership, and light touch governance. I didn’t know AI coding agents were coming, but it turns […]

The most significant tension in this issue is between two companies making different decisions about how to handle AI with frontier security capabilities. Anthropic restricted Claude Mythos to a small corporate cohort through Project Glasswing. OpenAI released GPT-5.5 to general availability, and some are calling it “Mythos-like hacking, open to all.” The AI Security Institute’s […]

Every day, millions of pieces of fake content are produced. Videos, audio clips, posts, articles, generated by artificial intelligence, distributed at industrial scale, aimed at shifting public opinion across entire countries. The people producing them are often outside the country being targeted. The people receiving them almost never know they’re fake. And they have no […]

The release of Gemma 4 has added energy to the discussion of local models and their importance. Models that you can download and run on hardware you own are becoming competitive with the “frontier models” hosted by large AI providers. These models have gotten good enough for production use, good enough for tasks that until […]

Cat Wu leads product for Claude Code and Cowork at Anthropic, so she’s well-versed in building reliable, interpretable, and steerable AI systems. And since 90% of Anthropic’s code is now written by Claude Code, she’s also deeply familiar with fitting them into routine day-to-day work. Last month, Cat joined Addy Osmani at AI Codecon for […]

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 of the neighborhood. […]

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 it works. […]

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 the system is considered healthy. Even in […]