In 1865, William Stanley Jevons noticed something uncomfortable about steam engines: as they got more efficient at burning coal, total coal consumption went up. The cheaper something is to use, the more of it gets consumed. Efficiency doesn’t reduce demand. It unlocks it.
AI is doing this to cognitive work right now.
The productivity story is real. Drafts that took hours take minutes. Analysis that needed a team now needs a prompt. Execution costs are collapsing. But Jevons predicts what comes next: when doing becomes cheap, you don’t do less. You do dramatically more. When a document takes five minutes instead of fifty, you don’t produce one and reclaim the time. You produce ten. When analysis costs nothing, you run twenty.
Every new output creates a decision that didn’t exist before.
A Harvard Business Review study tracked an eight-month period at a 200-person tech company after they rolled out AI tools. Workers weren’t asked to do more. They just did more — because doing more felt possible. Product managers started writing code. Researchers took on engineering tasks. Individuals absorbed work that would have previously justified extra headcount.
One engineer summed it up: “You had thought that maybe, because you could be more productive with AI, you save some time, you work less. But then really, you don’t work less. You just work the same amount or even more.”
The workday doesn’t get shorter. It gets denser.
AI didn’t reduce cognitive load. It moved it from execution to evaluation, and from producing to deciding. The Harness-Uplevel study found developers using AI tools complete 21% more tasks, but code review times jump 91%. The work didn’t go away. It migrated to the approval layer. Senior engineers now spend more time vetting AI-generated code than they spent writing code before. And the natural pauses in the day — formatting a doc, waiting for a file to load — were also cognitive resets. When AI eliminates the pauses, what’s left is an unbroken sequence of decisions.
And here’s what compounds that: when more things run in parallel, coordination breaks down quietly. Agreements go unrestated. Ownership stays implicit. Context shifts between conversations and nobody marks the change. No one decides to create confusion, it just accumulates. I call this coordination entropy, and it exists in every organization. The difference is pace. When execution was slow, small ambiguities got caught before they spread. A vague decision affected one project. An unclear ownership assumption surfaced in a week. AI removes that buffer. Now the same ambiguity scales across twelve workstreams before anyone notices.
The part that frustrates me: none of this makes meetings less important. It makes them more important. Meetings are where coordination actually happens — where someone finally says “wait, I thought we decided X” and it either gets resolved or gets buried. That signal doesn’t live in Slack, in the task tracker, or in the doc. But most meetings aren’t built for deciding. They’re built for sharing. Status updates, decks, information transfers. As if getting everyone informed is still the hard part.It isn’t.
The hard part now is getting a group of people to evaluate what AI produced, decide what to do with it, and actually close on something. And we are, broadly speaking, terrible at that. Sensemaking as a group is hard. Holding a process while also participating in it is hard.
And the meetings industrial complex has made it worse — turning these moments where real value should get generated into performance. Another hour where everyone demonstrates their presence and nothing actually closes.
Work is going to get more complex, more uncertain, and more cognitively demanding. The HBR researchers call it a “self-reinforcing cycle” — AI accelerates tasks, which raises expectations for speed, which makes workers more reliant on AI, which widens the scope of what they attempt, which expands the quantity and density of work. Nobody mandated it. It just happened.
Structural complexity from AI is coming regardless. Coordination entropy isn’t. That part is a choice — but only for organizations that treat their coordination layer as something worth designing, not just scheduling.
The cost of doing is collapsing. The cost of deciding is just getting started.
This is the problem Growth Wise is built to instrument. Full piece at the link.
The shift from execution to decision-making is so important. Most people have never been trained how to make good decisions (especially under immense pressure) in the first place. And everyone's now experiencing more volume, more complexity and more speed than ever before, which makes the decision even harder to make. The most important thing we can do is learn how to make decisions from a clear, regulated place. And our ability to do that is just going to become more and more important as our world continues to technologically progress.