AI promised less work. I've never worked more

The pitch was that AI would give us our time back. Write less boilerplate, delegate the boring parts, go home earlier. I am not complaining about the tools. I use agents for all of my engineering work now, and I wrote about how that setup evolved and why I still read the code. I can investigate and ship things that would not have been worth starting two years ago. But somewhere this year I noticed that I am working more than I ever have, and that it feels heavier than it used to. Not because any single task is harder. Because there are so many more of them, all at once. This post is me trying to understand that, with some data from my own GitHub history. The old load and the new load As a full-stack engineer, a normal week for me meant two or three serious tasks. A feature, a tricky bug, maybe a migration. Each one needed real focus, but I could hold one of them in my head at a time, finish it, and move to the next. Today my week looks completely different. I have several agents running in parallel: one implementing a change, one investigating a production question, one working through a review, another doing research I will need later. None of them needs me all the time. Every one of them needs me sometimes. That “sometimes” is the problem. Each agent eventually comes back with a question, a plan to approve, a diff to read or a result to make sense of. Responding means switching contexts: reloading a different problem, a different repository and a different set of decisions. Two or three serious tasks a week was tiring in a familiar way. Working with many agents is tiring in a new way. My attention is spread across more problems, and I find moving between them much more mentally draining. What my GitHub history shows I pulled my GitHub contribution calendar for every month from January 2019 to today and marked my roles at Bold.org, which I joined in January 2020: Full Stack Engineer until August 2022, Principal Engineer until August 2025, and Vice President of Technology since then. These are GitHub’s contribution counts, including private work, so they mix commits, pull requests, reviews and issues. It is a rough signal of activity, not a measure of value. 2019 is an incomplete baseline: my job before Bold.org used a self-hosted GitLab, so GitHub only shows what I did outside it. But the shape is hard to ignore. The first part of the curve is what I expected: My busiest coding years at Bold.org were the early ones. 2020 was my highest year, with 2,711 …