A multitudes study which followed 500 developers found an interesting soundbyte: “Engineers merged 27% more PRs with AI - but did 20% more out-of-hours commits”.

While I won’t comment on the situation at Google, there are many anecdotes online about folks online who raise concerns about increased work pressure. When a response to “I’m overloaded” becomes “use AI” - we’re heading for unsustainable workloads.

The problem is compounded by the fact that AI tools excel at prototyping - the type of work which makes other work happen. Now, your product manager can prototype an idea in a couple of hours, fill it with real (but often incorrect) data, sell the idea to stakeholders, and set goals to productionize it a week later.

“Look - the prototype works, and it even uses real data. If I could do this in a couple of hours, how hard could this be for an experienced engineer?” - while I haven’t heard these exact words, the sentiment is widespread (again, online).

In a world where AI provides a surface-level ability to contribute across almost any role, the path to avoiding global burnout is to focus on building empathy. Just because an LLM can churn out a document doesn’t mean it’s actually good writing, and we’re certainly not at the point where a handful of agents can replace a seasoned PM. However, because the output looks polished - especially to those without deep domain knowledge - it’s easy to fall into the trap of thinking you’ve done someone else’s job for them.

That gap between “looking done” and “being right” is exactly where the extra professional pressure begins to mount. This is really caused by the way we still measure knowledge worker productivity - by the sheer number of artifacts they produce, rather than the outcomes of the work.

The right way to leverage AI in workspace is as a license to work better and focus on the right things, not as a mandate to produce more things faster.