Pradient
Pradient
Practical perspectives on implementing AI, managing data, and building smarter operations.
In February, OpenAI, Anthropic, Microsoft, and Google each shipped products designed not to assist with enterprise jobs but to fill them. The platforms they built aren't organized around software concepts like configuration and deployment. They're organized around employment concepts: onboarding, role assignment, performance review, permissions.
The four most powerful AI companies on earth have converged, independently and simultaneously, on the same conclusion. The product they are selling is labor.
Follow the money. Traditional software sells subscriptions: you pay per seat, per month, regardless of output. AI agent platforms are structured differently. You pay per token processed, per task completed, per agent-hour consumed. Anthropic bills by token throughput; its enterprise business already accounts for roughly 80 percent of revenue. The company is valued at $380 billion on the back of a single promise: replace billable human hours with billable agent hours.
That is not a software bet. That is a labor market bet.
And yet a deep confusion persists. People force AI agents into one of two categories: tool or replacement. Both framings are wrong.
What's emerging is a third category, one that demands an entirely different management philosophy.
Not so long ago, I had an amazing boss. We had bi-weekly one-on-ones where he listened to my incentives, drive, and career interests, and crafted a job where I was motivated. He offered mentorship, psychological safety, and the frequent free lunch. He wasn't just a nice guy, he was a strategic businessman who could understand people and how to get them to work hard. The entire discipline of management is built on the reality that human beings are emotional creatures who need reasons to care about their work. With all of that, I still quit.
An agent needs none of this. It doesn't get demoralized by monotony. It doesn't quietly quit or start job-searching after six months of mind-numbing process work. It simply does the thing, at scale, without complaint.
But it is also not software you install and forget. You have to scope its role, evaluate its output, decide what it should escalate, retrain it when conditions change, and monitor it for errors that follow patterns no human mind would produce. This is management, just not the kind anyone has been trained to do.
The supply side is being built at extraordinary speed. The demand side remains almost entirely unsolved.
I hear people scoff at the idea that "using AI well" is a meaningful skill. These people see an AI chatbox and see an email rewriter. They're using an iPhone to hammer a nail.
We're always happy to talk through problems, even before there's a project on the table.
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