For most of the modern age, companies have been designed around business processes. Managers mapped how work moved from one team to another, from request to approval, from customer problem to service resolution, from opportunity to invoice. The process was the operating logic of the firm: a way of coordinating people, systems, controls and time. But the arrival of AI agents is beginning to change that picture. Jensen Huang recently put the shift starkly, arguing that companies now built around business processes may, in the future, be built on “frameworks” — proprietary systems that represent a company’s workflows and make their components more autonomous, proactive and efficient.

Last weekend I found myself sitting in a hotel restaurant facing a surprisingly difficult decision. The breakfast buffet cost $34. Coffee was additional. Juice was extra. Alternatively, I could order à la carte. Eggs were $27. Add coffee, perhaps some fruit, maybe juice, and suddenly the economics became unclear. Was the buffet better value? How much breakfast was I actually planning to consume? At what point did the all inclusive offer become the rational choice? And perhaps most importantly, how much mental effort was I willing to spend answering these questions before I had even had my first coffee? The irony was hard to miss. In an age of AI, I was performing an optimization exercise that felt better suited to an algorithm than a human brain.

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.