In this conversational podcast, Fabrizio Ferri Benedetti (Passo.uno) and I talk about the impact of AI on the technical writing profession. We tackle the anxiety, seen and felt almost everywhere, but especially on Reddit, within the community about job security and analyze the evolution of the technical writer's role into a more strategic context curator or content director. We also cover practical applications of AI, such as using agents markdown files to guide language models (with style overrides or API reference contexts), and the role documentation plays in improving AI's outputs (Fabri's phrase AI must RTFM).
This past year in the tech comm community, there's been a lot of angst about job security with AI. In this post, I argue that our roles are shifting from writers to content directors. In this new role, the skills we need for success are two-fold. I propose that we focus on developing (1) deep subject matter expertise and (2) tools expertise. I also share my optimistic view about why technical writers will remain essential in a future with ever-expanding technology. The tldr is that even as AI might remove some jobs, the exponential growth of tech will create more opportunities and needs for documentation. Additionally, AI tools need good docs for accuracy.
In my AI Book Club, we recently read Empire of AI: Dreams and nightmares in Sam Altman's OpenAI, by Karen Hao. In this post, I'll briefly share some of my reactions to the book. The main focus in my review is to analyze Hao's treatment of the mission-driven ideology around AGI that explains many of the motivations for the workers at OpenAI and similar AI companies.
This is a recording of the AI Book Club discussion about Karen Hao's Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. The discussion is an informal, casual discussion with about half a dozen people online through Google Meet. You can also read a transcript and other details about the book here.
In my previous post about achieving bug zero, I introduced the goal and some motivations for it, but I didn't fully articulate the whole connection to AI. I also didn't explain much of what a doc bug queue is in my context, or why it even matters. In this post, I'll define doc bugs in more depth and explore two major obstacles to accelerating documentation work: review time and context gathering.
This is a recording of our AI Book Club session discussing Kai-Fu Lee's AI Superpowers: China, Silicon Valley, and the New World Order. There are 4 people in this book club discussion, and our conversation focuses on the emerging AI duopoly between the US and China. We share our own US-centric blind spots and weigh the political and cultural implications of China potentially winning the AI race. We also talk about Kai-Fu Lee's prediction of mass job displacement and his proposed social investment stipend, questioning both its feasibility and its potential drawbacks. The discussion also explores how our own professional roles, particularly in tech comm, might evolve to manage and direct AI rather than be replaced by it, and how surprisingly relevant the book feels today despite being published in 2018.
In the pursuit of bug zero, it helps to identify different types of bugs and the right strategies for dealing with each of them. In this post, I explore four common types of complex bugs I've encountered: Russian-doll bugs, scope-creep bugs, non-actionable bugs, and wrong-owner bugs.
I recently appeared on the Coffee and Content episode, hosted by Scott Abel, with another guest, Fabrizio Ferri Benedetti, who writes a blog at Passo (passo.uno). The episode theme comes from a post Fabrizio wrote titled What's wrong with AI-generated docs, but the episode didn't focus exclusively on AI's problems and gotchas so much as AI strategies with documentation in general. This post provides a recording and transcript of the episode.
This post is my review of Parmy Olson's Supremacy: AI, ChatGPT, and the Race That Will Change the World. Among many possible AI topics, I focus on the sellout aspect of the book, comparing the AI entrepreneurs' sellouts to big tech to fund their massive compute needs to the sellout decisions that creative writers make to tech companies in exchange for liveable salaries.
The following is a guest post by David Kowalsky continuing his exploration of productivity and time management topics. This post covers his experiences with Cal Newport's Slow Productivity and Oliver Burkeman's Meditations for Mortals, along with practical experiments and advice for technical writers. Kowalsky considers how these philosophies can help technical writers find more sustainable, meaningful approaches to their work.
In this podcast, I chat with Fabrice Lacroix, founder of Fluid Topics, about the evolution of technical communication. Fabrice describes the industry's progression from (1) delivering static, monolithic PDFs to (2) using Content Delivery Platforms (CDPs) that provide dynamic, topic-based information directly to users to (3) developing content not just for human consumption, but for AI agents that will use this knowledge to automate complex tasks and workflows.
Technical writers' attitudes toward AI can be all over the map, from enthusiastic early adoption to cautiously optimistic to complete rejection. In this post, I try to unpack the reasons that lead some writers to believe what they do about AI. Using research from several articles, I look at AI's jagged frontier, the impact of domain expertise, and interaction modes as ways of understanding the influences that lead to different attitudes.
This is a recording of our AI Book Club session discussing Ray Kurzweil's The Singularity is Nearer podcast: When We Merge With AI. You can watch the recording on YouTube, listen to the audio file, read some summary notes, browse discussion questions, and even listen to a NotebookLM podcast (based on the summary). There are 5 people in this book club discussion, and we focus a lot on the topics of acceleration, especially as we see it happening in the workplace. We also weigh in on Kurzweil's techno utopianism and how persuaded we are by the arguments about AGI landing in 2029, the likelihood of machine and biology merging (through nanobots), and more.
To use AI for fact checking, AI tools might do better with a complete, self-contained set of documentation to check against a reference. Single-sourcing, with its conditional and fragmented content, complicates this model.
In this experiment, I try to implement a loop of recursive self improvement on an essay, but it fails. I gave Gemini a prompt to continuously improve a blog post through 10 iterations, hoping to see exponential quality gains. Instead, the AI's editorial judgment deteriorated over time, resulting in awkward, pretentious writing that was worse than the original.
