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.

One problem with the way we talk about AI and professional work is that we focus too much on what autonomous systems can achieve, and not enough on what we are willing to trust them with. Just because an AI model can generate a plausible answer, or an agent can complete a workflow, does not mean we have resolved the harder question of responsibility. Who decides when the output is good enough? Who understands the trade-offs? Who is accountable when the system fails? Engineering brings that tension into sharp relief. AI may generate the design, but a human still has to sign.

The most important users of enterprise software are no longer human. For decades, the design and economics of enterprise technology have been built around a simple idea: a person logs in, navigates an interface, and performs a task. Revenue scales with the number of users, the time they spend inside the system, and the workflows they complete. That model has produced some of the most valuable companies in the world, from Salesforce to Workday to ServiceNow, and it has shaped how we think about productivity itself. That assumption is now under pressure.

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.