AI Agent Cost Versus Workload

When a landscaper considers purchasing more equipment, like a skid loader or excavator, they typically analyze the cost versus return.

And the return depends quite a bit on how many hours per week they can keep that equipment busy. They want to maximize the equipment’s workload. The more it sits around doing nothing, the less they’re making on it because its cost still hits. If they can’t keep that equipment running most of the week, they’ll rent or outsource that work.

We do the same with our people. We want them fully engaged throughout their entire workweek. If we don’t have enough work for maximizing their engagement, we turn to contractors (rentals) and outsourcing to get that work done.

For both equipment and people, whether we’re hiring, renting, or outsourcing, the costs are (mostly) fixed. We know how much per hour/week we need to spend.

So what about AI agents?

The cost model isn’t quite the same. When the agent sits around doing nothing, it costs us (almost) nothing. The more we give it to do, the more it costs us.

So we always have to ask the critical question: “Is this work worth giving to the agent?”

Today, with token costs on par with junior worker costs, that’s a relatively easy trade. But that won’t be true forever.

And that right there creates a critical distinction between building an organization around people versus agents.

Do we want our agents busy all the time?