Review of Yuval Noah Harari's "Nexus" — and why we don't need self-correcting mechanisms for "alien intelligence"

I just finished Yuval Noah Harari's Nexus: A Brief History of Information Networks from the Stone Age. The book provides a high-level analysis of information systems throughout history, with some warnings about the dangers of AI on today's systems. It's a remarkable book with many historical insights and interpretations that made history click for me. But the central idea of the book focuses on self-correcting mechanisms (SCMs) and how these SCMs are the linchpin of thriving democracies, so that's what I'll focus on in my review. The book also argues that AI is a form of alien intelligence that might incorrectly execute goals we don't want it to follow. Read more »

The difficulty of tracking and interpreting AI usage labels

Tracking and communicating AI usage in docs turns out to be not only challenging technically, but also potentially full of interpretive pitfalls. There seems to be a double-edged sword at work. On the one hand, we want to track the degree to which AI is being used in doc work so we can quantify, measure, and evaluate the impact of AI. On the other hand, if a tech writer calls out that they used AI for a documentation changelist, it might falsely create the impression that AI did all the work, reducing the value of including the human at all. In this post, I'll explore these dilemmas. Read more »

Why long-running tasks autonomously carried out by agentic AI aren't the future of doc work, and might just be an illusion

As AI agents become more capable, there's growing eagerness to develop long-running tasks that operate autonomously with minimal human intervention. However, my experience suggests this fully autonomous mode doesn't apply to most documentation work. Most of my doc tasks, when I engage with AI, require constant iterative decision-making, course corrections, and collaborative problem-solving—more like a winding conversation with a thought partner than a straight-line prompt-to-result process. This human-in-the-loop requirement is why AI augments rather than replaces technical writers. Read more »

Guest post: Generative AI, technical writing, and evolving thoughts on future horizons, by Jeremy Rosselot-Merritt

In this thoughtful guest post, Jeremy Rosselot-Merritt, an assistant professor at James Madison University, wrestles with generative AI and its impact on the technical writing profession. Jeremy examines risks such as decisions being made by leaders who don't understand the variety and complexity of the tech writer role, or the perceived slowness of output from human writers compared to the scale of output from LLMs. Overall, Jeremy argues that Gen AI is another point on a long timeline of tech writers adapting to evolving tools and strategies (possibly now emphasizing context engineering), and he's confident tech writers will also adapt and continue as a profession. Read more »

Changing the AI narrative from liberation to acceleration

The most frequent story told about AI is that it will free us up from mundane tasks and allow us to focus on more impactful, strategic work. But the liberation part of that story might be misleading. In this post, I argue that AI's true effect is to accelerate the entire competitive landscape, increasing the pace of work for everyone. In this new, sped-up world, companies that replace human workers with AI for short-term gains, assuming that the pace of change is static, may find themselves falling behind in the long term. Read more »

Medium CEO explains how AI is changing writing

I recently listened to How AI Is Changing Writing — with Tony Stubblebine from the Big Technology podcast, hosted by Alex Kantrowitz. This was one of the more interesting and relevant episodes for me. I embedded the interview below and also added my own summary of the important points and my analysis. Read more »

Making it easy for people to review your changelists (Doc bug zero series)

The basic idea of doc bug zero, as I explained in Defining bug zero, is to clear out all the tickets in the doc issue queue, essentially to finish all your documentation work. Doing so would be the ultimate statement about the productivity gains from AI. Despite my attempts to get to bug zero, it still eludes me. I'm realizing that there's an art to working through a bug queue, and AI can only take me so far. Good project skills are also needed. One of those skills, which I'll address in this post, is making it easy for people to review the changelists, or pull requests. (The terminology used in my area of doc work is changelists, or CLs, so that's how I'll refer to them here.) Read more »

MCP servers and the role tech writers can play in shaping AI capabilities and outcomes -- podcast with Fabrizio Ferri Beneditti and Anandi Knuppel

In this podcast episode, Fabrizio Ferri Benedetti and I chat with guest Anandi Knuppel about MCP servers and the role that technical writers can play in shaping AI capabilities and outcomes. Anandi shares insights on how writers can optimize documentation for LLM performance and expands on opportunities to collaborate with developers around AI tools. Our discussion also touches on ways to automate style consistency in docs, and the future directions of technical writing given the abundance of AI tools, MCP servers, and the central role that language plays in it all. Read more »