For most of modern knowledge work, the defining anxiety has been simple and persistent: am I doing enough? Enough hours, enough output, enough visible effort to justify my role and my compensation. Performance was measured in activity, and productivity was largely a function of how effectively human effort could be applied to a problem. But what changes when effort is no longer the constraint? When intelligence itself becomes elastic, abundant, and on demand, the question shifts. The rise of the token economy is often treated as a technical or financial detail, but it is something more revealing. It is emerging as a new measure of productivity, not in terms of effort, but in terms of leverage.

The prevailing story about AI and jobs is seductively simple: break work into tasks, measure how many can be automated, and once enough of them are, the job disappears. That logic works well for routine work. But in high-stakes, human-facing roles, especially those performed by agents and advisors, it rests on a fragile assumption: that jobs are just workflows, collections of discrete steps that can be taken apart without changing where value is actually created—or whether it can be created at all.

With all the discussion about AI agents lately, you might be wondering: how exactly do you count them? If multiple agents collaborate to resolve a customer issue or approve a loan application, does that represent one digital worker or many? The question may sound trivial, but it will soon matter a great deal. Organizations will eventually track digital headcount the same way they track human employees today.

A dangerous new market narrative is spreading through boardrooms and earnings calls: artificial intelligence has made companies so productive that they can slash their workforce and barely notice the difference. Analysts applaud, the stock jumps, and executives describe a future where digital labor replaces the old human-heavy operating model. Unfortunately, the economy is rarely that tidy.

There is, perhaps, a small silver lining in the current wave of AI anxiety. Not long ago, the dominant fears revolved around killer robots, runaway superintelligence, and apocalyptic scenarios that ended with data centers being nuked from space. Today the panic is more grounded, and in many ways more sophisticated. We are no longer imagining machines conquering humanity; we are worrying about white-collar unemployment ticking above 10%, mortgage books wobbling in San Francisco, and private credit portfolios unraveling because software agents can write code faster than junior analysts. The monsters have moved from science fiction to the balance sheet.