Billionaire entrepreneur Mark Cuban is pushing back on the idea that artificial intelligence will automatically replace human workers, noting that the economics are far more complicated than headline-grabbing claims suggest.
In a new post on X, Cuban lays out a practical framework for comparing the real cost of AI agents to the fully loaded cost of a human employee.
According to Cuban, employers must take into account the aggregate token cost of AI agents as well as developer and maintenance costs to see whether the expenses justify the replacement of a human worker.
“This is the smartest counter I’ve seen to AI taking over jobs in the short term. Is the ((aggregate tokens cost to do what an employee does + plus fully encumbered developer and maintenance costs ) / (fully encumbered employee cost ) )<= productivity?”
He offers a numerical example to illustrate the breakeven point.
“If it takes eight Claude agents, at $300 for tokens per day, plus $200 per day in dev/maintenance, to do what an employee does per day, at a fully encumbered cost of $1200. That’s $2600/$1200. But then you need to factor in the productivity rate. Is it more than 2.16x productive?”
Cuban says the raw cost comparison is only part of the equation, noting that qualitative factors may also influence the decision.
“Are there qualitative issues like morale, morality, whatever, that can’t be quantified, that need to go into the decision? What is the going forward progression of burdened costs for the tokens?”
He adds that human workers still possess a contextual awareness that current AI systems lack.
“The one thing I will add on the plus side of the ledger for humans. Humans have a far greater capacity to know the outcomes of their actions. Agents and LLMs, as well, never do. Even an 18-month-old will learn what happens when they push the sippy cup off their high chair. Mom gets upset. Comes and has to clean up. They figure it out quickly.
Agents can tell you the sippy cup will fall. But they have no idea of the context and what will happen next.”
Cuban also questioned the reliability and consistency of autonomous agents.
“The other issue is that agents ‘Space Out’. (I’m sure there’s already a term for this ). There are no assurances, for many different reasons, that the agent will execute the same way every time. To make matters worse, they can’t tell you they have spaced out, why and when.”
He concludes by likening AI agents to inexperienced workers who may not recognize or own their mistakes.
“Agents are still like college interns that come in hungover, make mistakes and don’t take responsibility for them.”
Cuban suggests that for AI systems to replace employees purely on economic grounds, they must deliver significantly higher productivity than humans once token costs, development expenses, maintenance and execution risk are fully accounted for.
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