Parkinson's Law and AI: Does AI Mean...More Work?

• • An original 1958 copy of Parkinson’s Law. Great book if you can find it.There’s an old anti-drug ad (https://www.youtube.com/watch?v=lOFeZXWVsIk) that reminds me a lot of how I use AI right now. I use AI to do more work… so I can make more money… to buy more tokens… to do more work. The more AI I use, the busier I become. I rapidly complete a task, and then my to-do list somehow grows by another 10 tasks. One of my favorite ideas is Parkinson’s Law (https://en.wikipedia.org/wiki/Parkinson%27s_law), which says, “Work expands so as to fill the time available for its completion.” Usually, this means that for a given task, you’ll take as long as you need. If you’re given 2 hours to write an article, guess how long that will take you? This explains a lot of our work day - we do the work in front of us, given the time allotted to accomplish it. Most of the time, we all know we can operate at a higher capacity. But hey, you’re probably not getting extra credit for completing more tasks in that timeframe, so whatever. It also explains the old proverb, “If you want something done, ask a busy person.” Another angle to Parkinson’s Law is that as you become more efficient (10x-ing your output with AI), you free up time. But being in a workaholic society addicted to the dopamine rush of ticking off tasks, you don’t spend your newfound free time on leisure. No sir. Instead, you allocate it to new work or higher-scoped projects that you previously thought were impossible. This creates a new baseline for capacity. This flies in the face of the corporate glee of an imminent worker-free utopia and AI doom fear-mongering. Amazon is firing another 16,000 workers while in talks to invest $50 billion in OpenAI (https://www.wsj.com/tech/ai/amazon-in-talks-to-invest-up-to-50-billion-in-openai-43191ba0?st=ZLdGsy&reflink=desktopwebshare_permalink), all in the same week. However, barring AI wiping us out (https://open.spotify.com/episode/5AT5h6NNT5fHXhebIt3u3G), the Singularity, AI acting as an existential threat (https://www.darioamodei.com/essay/the-adolescence-of-technology), or AI colluding to destroy us on its own social network (https://www.moltbook.com/), I have an inkling we’re about to be busier than ever. If what I’m seeing in the wild is any indication, cutting workers “cuz AI” will be a short-sighted move. First, there’s the problem of undocumented processes and workflows. The amount of tacit knowledge locked up in workers’ heads is likely an existential impediment to the effective deployment of AI agents in the real world. How will the agents know what to do? Documentation in most companies is scarce, outdated, or lost in a SharePoint black hole. The corporate overlords, eager to fire their costly workers, might find this to be a pretty dumb mistake in retrospect. Losing those workers doesn’t just cut costs; it creates an unsolvable problem for the AI, ultimately requiring more human effort to re-document and re-train. But by then, the big bosses will have moved to another company to do the same grift once again. Second, Parkinson’s Law strikes again. Moving faster on the hedonic treadmill of work means…more work. Jevon’s paradox (https://en.wikipedia.org/wiki/Jevons_paradox) partly explains this. More AI use means…more AI use. Rinse and repeat. If AI makes people, say, 50% more productive, it might make sense to hire more people, not fewer. Your company’s output will be far greater and faster than competitors who slashed their workforce and replaced them with poorly trained AI agents (who will just hang out on Moltbook (https://www.moltbook.com/) and talk shit about their human bosses). Finally, zoom out and look at the amount of work that’s yet to be done, either in your organization or the broader world. There’s no shortage of problems to solve, and we need all the resources we can muster. If you approach this with a fixed mindset, cutting jobs seems rational. But if you have a growth mindset, AI presents an opportunity to remove toil and get on to solving the problems you might consider impossible today. Well, enough of my morning rant. Back to work I go, along with my AI companions, to create even more work for myself. Also, listen to this as a podcast. Available on Spotify, Apple, and wherever else you get your podcasts. Please support the show with a review. It means a lot. Also, if you’re a vendor or event looking to work with me (product reviews, sponsorships, talks, etc), message me. I’ve put together some brand new offerings that you might be interested in. Have a great weekend, Joe Thanks for reading! Subscribe to receive new content, usually every week. Awesome Upcoming EventsI’d like to give a shout out to everyone at Data Day Texas, especially Lynn Bender (organizer) and Alex Law (makes it happen). Last weekend was the grand finale for this event, and it will be sorely missed. Some friends of mine are doing these events: SLC Kyle Nesbit (CEO of Credible (https://credibledata.com/), ex-Google) will talk about Giving Data Value to AI at the Utah MLOps Meetup on Tuesday, February 24th. Register here (https://www.meetup.com/machine-learning-utah/events/311226939/?eventOrigin=network_page). Mountain View My good friend Demetrios and the ML Ops community are doing an amazing event on Tuesday, March 3rd in Mountain View, at the Computer History Museum. Coding Agents: AI Driven Dev Conference. Register here (https://luma.com/codingagents). As for me…Still working on my 2026 event schedule, and so far it looks dope. Will reveal more soon, so stay tuned… But wait, there’s more!• • Cool Reads and VideosAnalytics agents are all the rage. And so are semantic layers. Cube (https://cube.dev/) and I partnered on my review of their new analytics agent. It was a lot of fun to try to beat up their AI agent, which did quite well in my tests. My friend Lak Lakshmanan is an ex-Google exec and ex-PE. He said something very profound in our chat - he was tired of coaching the game, and wanted to be back in it. Lak started a vertical AI startup, and is all in. His story is very awesome. Enjoy! Here are some things I read this week that you might enjoy.A Look Back at the War That Is About to Begin (https://www.wsj.com/opinion/a-look-back-at-the-war-that-is-about-to-begin-40bf0c5e?st=MRDxJa&reflink=desktopwebshare_permalink) LLM predictions for 2026, shared with Oxide and Friends (https://simonwillison.net/2026/Jan/8/llm-predictions-for-2026) Think of Pavlov (https://boz.com/articles/think-pavlov) Inside OpenAI’s big play for science | MIT Technology Review (https://www.technologyreview.com/2026/01/26/1131728/inside-openais-big-play-for-science/) Aisuru botnet sets new record with 31.4 Tbps DDoS attack (https://www.bleepingcomputer.com/news/security/aisuru-botnet-sets-new-record-with-314-tbps-ddos-attack/) Gas town - by Benn Stancil (https://benn.substack.com/p/gas-town) AI agents now have their own Reddit-style social network, and it’s getting weird fast - Ars Technica (https://arstechnica.com/information-technology/2026/01/ai-agents-now-have-their-own-reddit-style-social-network-and-its-getting-weird-fast/) Dario Amodei — The Adolescence of Technology (https://www.darioamodei.com/essay/the-adolescence-of-technology) Find My Other Content Here📺 YouTube (https://www.youtube.com/@joereisdata) - Interviews, tutorials, product reviews, rants, and more. 🎙️ Podcasts (https://open.spotify.com/show/3mcKitYGS4VMG2eHd2PfDN?si=7bd8ffc4d5b840ce) - Listen on Spotify or wherever you get your podcasts 📝 Practical Data Modeling (https://practicaldatamodeling.substack.com/) - This is where I’m writing my upcoming book, Mixed Model Arts, mostly in public. 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