What's left when AI does the managing?
Will there be will?
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Happy Wednesday,
I’m spending a lot of time right now thinking about what knowledge, intelligence, and “humanity” mean. Reading books about how the brain works, what consciousness is, that sort of thing.
It feels relevant. When you work with AI every day, these questions stop being abstract philosophy and become practical. What does it mean to “know” something? What separates pattern matching from understanding? Is there even a difference?
And it’s making me think hard about leadership.
Because if we’re being honest, what is the point of a leader?
Let’s get into it.
Last week I asked whether AI could be a better boss than your current one.
But there’s a harder question underneath it: how many bosses do we actually need?
What is a boss actually for?
Not what do they do. We know what they do. Follow up. Check in. Give feedback. Translate strategy. Coach. Remember.
What are they for?
Because everything I just listed, AI does now.
(And if setup in the right way, they do it more consistently than most humans.)
What’s left?
If AI handles the operational stuff, the feedback, the follow-up, the coaching, the check-ins, what’s the manager for?
I think what’s left is will.
Not the doing. The wanting.
AI can optimize toward any goal you give it. But AI can’t choose the goal. It can’t wake up and think “I want to build something beautiful.”. It can’t care.
You’ve met managers with will. The one who killed a project everyone else approved because “it’s not good enough and I don’t care what the timeline says.” The one who stayed until midnight rewriting a presentation she could have let slide, because she couldn’t put her name on something mediocre. The one who kept blocking the easy hire because “we’re not lowering the bar, I don’t care how long the seat stays empty.”
The spreadsheet was indifferent. They weren’t.
So?
If the manager’s job shrinks to “setting direction and wanting things,” that’s a small fraction of what managers actually spend their time on today.
In my workshops, I ask managers to track their time for a week. The operational percentage is usually north of 70%. Following up. Checking in. Translating.
Stuff that AI can do more consistently than any human.
If AI handles that, we need far fewer managers. Not zero. But dramatically fewer.
This isn’t a prediction for 2040. The technology is largely here already (hey hey MoltBot).
What’s missing is trust, integration, and the organizational courage to try.
What this means?
If most of your job, or most of your managers’ jobs, is operational, that work is going to be automated. Maybe not this year. But soon enough that you should be thinking about it now.
The managers who survive will be the ones with genuine vision. The ones who want something for their domain. The ones who make choices that can’t be optimized.
What percentage of management work in your organization is operational?
If the number is 70%, you don’t have a management structure.
You have an expensive workaround for software that didn’t exist yet.
It exists now.
Loved this piece - especially the distinction between operational management and the harder, human work of vision and will.
Most AI in HR today is great at the follow‑ups and translation layer, which really does beg your question: if 70% of a manager’s time is operational, are they actually leading or just patching gaps in the system?
It's becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman's Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990's and 2000's. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I've encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there's lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar's lab at UC Irvine, possibly. Dr. Edelman's roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461, and here is a video of Jeff Krichmar talking about some of the Darwin automata, https://www.youtube.com/watch?v=J7Uh9phc1Ow