Let’s talk AI.

For a long time, the way companies scaled was almost embarrassingly simple. You raised money, hired leaders, built departments, and when things stalled, you hired more people.

The entire company ran on headcount math. More leads meant we needed BDRs. More customers meant more CSMs. It was the same play every time. We bought growth and called it scale.

It worked for a while because money was cheap. Then the market forced a reset.

Let’s get into it.

When things shifted profitability mattered more than growth. Companies responded the only way they knew how. They cut headcount. Fast. Broad. Aggressive.

Margins went up. Stocks rebounded. Then the market flipped again and said grow. That is where things broke.

You couldn’t rehire at scale, and you couldn’t keep squeezing more out of fewer people forever.

That’s when AI became the answer to everything. Everyone wanted an AI strategy, even if no one could clearly explain how it would actually deliver both growth and efficiency at the same time.

That’s where most companies went wrong.

They treated AI as a tool problem, not a labor problem. They made broken roles faster instead of questioning whether those roles should exist at all.

That is the wrong frame. The real shift is structural.

Humans set direction. Humans focus on setting direction, making tradeoffs, and owning outcomes, while autonomous systems handle the execution end to end.

Once you change the frame, scaling stops being about adding people and starts being about designing how the work gets done.

That’s why this moment feels so disorienting for so many leaders. Everyone can sense that growth through headcount is finished, but there isn’t a clear replacement yet.

So companies are stuck in the middle, cutting people on one side, talking about AI on the other, without actually changing how the organization works.

Getting this right doesn’t make you a more efficient version of the past. It creates a different kind of company, designed with smaller human teams, a larger layer of autonomous labor, and a clear split between strategy and execution.

If your AI strategy is still about making existing teams more productive, you are solving yesterday’s problem.

This is not a tooling shift.
It is a workforce reset.

I break down what it looks like in practice here.

That’s it for today. Connect with me on Linkedin if you actually want to understand what an Autonomous Organization looks like in the real world.