The latest twist in the AI job replacement debate is not that machines are coming for everyone’s work. It is that, in a growing number of cases, the machines may not be cheaper. That is an awkward development for some. For the past two years, many executives have been encouraged to imagine digital labor as a form of near-frictionless substitution: fewer people, lower costs, faster output. Replace the call-center agent. Replace the analyst. Replace the junior engineer. Replace the back office. But the economics are becoming more complicated.

What is the difference between a high-performing leader and someone who is simply good at performative leadership? That question is becoming more urgent in the age of AI. The latest game in the surreal, parallel universe of big organizations is tokenmaxxing: the attempt to appear highly AI-enabled by generating, consuming, or reporting large volumes of AI usage. This is not really a story about tokens. It is a story about incentives.

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.

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.

For the last year, the debate around AI at work has split into two unhelpful extremes. On one side, we have breathless talk of “AI coworkers,” complete with onboarding rituals, performance reviews, and soft-focus imagery of humans and machines collaborating happily at their desks. On the other, we have an anxious counter-reaction that insists this language is dangerous, misleading, and fundamentally wrong, because AI systems are not people and should never be spoken of as if they were. Both camps miss the point. The real question is not whether machines deserve human metaphors, but whether leaders understand what kind of economic force they are unleashing, and what kind of organization that force demands.