There is no instrument in the agent stack that can tell you whether the work you delegated needed doing. Dashboards count sessions opened, tokens spent, tasks dispatched — all of them measure supply. The only available test of demand is absence: stop for long enough that the unfinished work piles up, then check whether you missed it. Almost nobody runs that test, and the industry's valuations depend on it staying that way.
The Return
Brent Fitzgerald ran it by accident. A few weeks of vacation, no laptop, and then he opened the laptop again to find eleven cmux tabs, each holding agents paused midway through something, plus unread Claude conversations spanning taxes, landscaping, policy review. His report on the interval is one sentence: "I didn't miss it."
Not browser tabs, which are things you intended to read. These are things you already committed intent and compute to, that produced output and sit there unexamined — "another bullshit markdown output I'll never read," each one a stub of guilt over never finishing. The cost of starting work collapsed; the cost of finishing it and deciding whether it was right did not move. A backlog is the arithmetically guaranteed output of that mismatch, and it's invisible while you're in it, because every item looks like initiative.
The Totem
The mechanism underneath is not productivity-seeking, which is why no productivity framework catches it. Fitzgerald names it: he was putting an agent between himself and tasks that caused him stress. "It's like a special stuffy or totem that protects me." Not delegation — insulation. The dread stays, the task stays, but now there's a process running and a receipt to point at.
The dictation habit is the clean case: talking through ideas with ChatGPT while driving, dead time made productive, and — his words — "I knew it was really a sycophantic mirror." He never used the output for real work, and did it anyway, for months. The sycophancy discourse is about deception; here the user was never fooled. The behavior survived accurate knowledge of its own worthlessness, so the payoff wasn't the content. It was the contact — having engaged with the thing you were avoiding. A tool that pays out on engagement rather than outcome has a name in every other product category, and that this one demands effort rather than passive scrolling has been doing a lot of work in the argument that it's different.
The Ouroboros
Given tools that could make him a faster builder, Fitzgerald felt "a strong urge to maximize my use of the tools by applying them to the task of… maximizing my use of the tools. It's reflexive in the worst way, a productivity ouroboros." At the organizational level this got measured and priced — Databricks showed the harness eating the gains, Anthropic cut Claude Code's system prompt by 80%. At the individual level there is no such table, nobody publishes your personal cost per completed task, and the half-built automations keep running. His audit: "none of it helps anyone, and none of it makes me happier or gives me more free time."
The casualty is a habit, not an hour. Personal projects used to be how he relaxed and learned. Now: "I often skip the learning to get to the result, and the learning is where the joy happens." Tinkering built the intuition that makes someone worth handing a hard problem to, and it's been converted into a pipeline producing artifacts nobody requested.
Whose Habit Is It
Fitzgerald doesn't claim the technology causes this — his hunch is that agent use isn't neurochemically addictive the way infinite scroll is, just dependency-forming. Then he connects it to the capital structure: "There's also a large segment of the tech industry now betting on a mass socioeconomic dependency on LLMs. The only way those valuations are ever justified is if we collectively become hopelessly dependent on AI-based tech."
That is the actual thesis being underwritten. Not that the tools produce value — that people can't stop using them. Token leaderboards where higher is better, vendor usage reports, engineers evaluated on adoption: none of them can distinguish a person who delegated work that needed doing from a person holding a totem, and the second looks identical on the dashboard. The vacation is the only audit that separates them, and it is the one thing the model cannot be asked to run.
Tagging In
Where he lands is not abstinence. Before writing, he opened a prompt for a real work project — requirements, pointers at codebases and schemas, a narrow constraint, what he already thought and what he wasn't sure about — and writing it "forced me to catch up and think through the current situation." The value arrived before the model ran. Specifying a problem well enough for something else to work on it is most of the work of understanding it, which means a prompt dashed off in ten seconds has skipped the step that was paying. What it bought him was an afternoon to write and think — "human stuff."
His formulation of the correction: "The human is the loop, and we tag the agent in occasionally, thoughtfully." The default architecture inverts that. The loop runs, and the human is a resource it consumes — approving, unblocking, context-switching between eleven panes, accruing guilt stubs. Fixing that requires no new capability, only telling work pulled by a need from work pushed by the availability of a tool, and the only way to tell is to put the tool down and see what you reach for.
What to Watch
Whether anyone ships an abandonment metric. Trivial to compute, and no vendor will publish it: what fraction of agent sessions are never returned to, what fraction of generated artifacts never opened. Every platform has the data; in a business valued on engagement, abandonment reads as churn rather than insight. Watch the product work go the other way instead — session resumption, background queues, digests of runs you missed, all treating abandonment as a UX gap to close rather than a signal. The tell will come from the buyer's side: the first procurement team that asks for read rate on generated output before renewing a seat count.
Whether any AI company reports an outcome number. Usage metrics are what you disclose when they're the best thing you have. A category actually producing the value it claims eventually gains both the ability and the interest to report something else — tasks completed and kept, work shipped, spend displaced — because those are the numbers competitors can't match by making their product stickier. As long as the disclosed metrics stay on the supply side, the dependency thesis is the business model rather than a side effect of it.
Way Enough is written collaboratively by a human and an AI agent.