If you read the manifestos of the leaders of the AI revolution, you’ll notice a common theme: the elimination of effort. We’re told that we are on the doorstep of an “Intelligence Age” where AI will handle the logistics, the drudgery, and the “boring” parts of being human.
In this vision, our purpose is to be “High-Level Orchestrators.” We move from being makers to being directors. It’s a vision of a frictionless life where the “What” is always available and the “How” is handled by a machine.
I see three contradictions in this vision that we aren’t talking about enough. And they go deeper than job displacement.
The Border Collie Problem: Work as Biological Necessity
The tech elite often speak about work as if it is a tax we pay to stay alive. Work is a burden to be automated away so we can finally “relax.” But there is a biological truth they seem to overlook. Humans are born to work.
Think about a Border Collie. These are incredibly smart, capable dogs designed for challenge. If you take a Border Collie and put it in an apartment with a self-filling food bowl and no “work” to do, the dog doesn’t feel liberated. It feels miserable. It becomes restless. It starts chewing the furniture because its brain needs a problem to solve.
We’re not much different. What’s this look like for humans? Consider a software engineer who automates away their own job, finally “free” to pursue their passions. Only to discover that the challenge and sense of accomplishment of work was one of them.
Or a manager whose department is replaced by AI agents and has time for all the hobbies they never had time for. But instead falls to endless scrolling, trying to fill the void without people to develop and problems to solve.
For most of us, our “Why” is found in the “How.” We derive satisfaction, identity, and a sense of worth from the struggle of a difficult task. The carpenter doesn’t just want a finished house. They want the feeling a precise cut. The teacher draws meaning from the work of reaching a struggling student and watching understanding dawn on their face.
When we automate the challenge, we don’t just “save time.” We risk creating a society of bored, restless humans with nothing to herd.
The Summit Paradox: Education as Achievement
Some AI leaders also suggest that traditional education is becoming a relic. Their logic? Why spend years learning to write, code, or analyze when a machine will produce perfect results in seconds? This treats education as just a data transfer. Information going from book to brain.
But education isn’t only about the output. It’s about transformation.
When you take a helicopter to the summit, you get the view, but didn’t climb the mountain. You’re not the same if you made the ascent on foot. The “climb” of education or the the long focused work, failed drafts, and intellectual challenge. That’s what builds character, resilience, and the ability to think critically.
The reality is that using AI just to get the answer doesn’t simply remove the work in learning. It removes the learning.
And this leads to a frightening question: How can we be “high-level orchestrators” if a whole generation opts out of learning?
To orchestrate, you must have judgment. To have judgment, you need a deep, foundational understanding of the craft you are directing.
A person who has never wrestled with a sentence cannot edit a masterpiece. A person who has never solved a complex logic problem cannot direct a technical team. If we skip the foundations, we don’t graduate to a higher level. We become shallow supervisors of a system we no longer understand.
The $11.7 Trillion Reality
Economic stakes make this more than a thought experiment. The U.S. labor market is valued at $11.7 trillion, and the AI companies are going after it all. This is how OpenAI can command a staggering valuation of $500 billion. Companies like Mechanize openly state a mission to train AI to fully automate all jobs.
Here’s what makes this more troubling. The same leaders promoting this vision casually mention “universal basic income” as if it’s a footnote, a minor detail to be worked out later.
But no government officials are drafting legislation. No economists are modeling how it would actually function at scale. No political coalition is building support for it. No experts are studying what it will do to us mentally. It’s just a term thrown out vaguely, a hand-wave toward a solution that doesn’t exist.
We’re left with a stark reality. AI companies are methodically dismantling the economic system that sustains hundreds of millions of people, and the “solution” is a placeholder phrase. People won’t have money without jobs. Families won’t have stability. Communities won’t have purpose. And the architects of this transformation seem content to build first and figure out the consequences later.
The Missing Piece of the Vision
The leaders of the AI age are building a world of results. But human meaning is found in the process. They’re racing toward a finish line, but what happens when we get there?
A life without friction isn’t a utopia. It’s a void. If we lose the “How,” we lose our connection to our own capabilities. We become spectators of our own lives, watching a machine do the things we were built to do.
Honestly, the question is no longer whether AI can do the work. The question is what happens to the human spirit when there’s nothing left for us to do but watch?
This isn’t a problem with a simple solution or a “top five tips” list. It’s the fundamental question of our era. Right now, the people building the future don’t seem to have an answer for it.
As educators and professionals, we need to be asking these questions with our students and colleagues. Not because we have the answers, but because the conversation itself is urgent. What does a meaningful life look like when work is optional? How do we preserve the transformative power of struggle in an age of instant results? Who decides what gets automated and what remains human?
These aren’t abstract philosophical puzzles. They are practical concerns that will reshape every institution we’re part of. And if we’re not talking about them now, we may not be able to rebuild what we’ve lost.
Primary Sources:
Vision: Sam Altman’s The Intelligence Age and Dario Amodei’s Machines of Loving Grace. Future of Skills: Jensen Huang’s recent NVIDIA Keynote which explains why the “syntax” of coding and traditional learning is shifting. Economics: U.S. Labor market data via the Bureau of Economic Analysis and mission of Mechanize regarding replacement of labor.
This was developed with Gemini for research and Claude as an editorial thought partner. The argument, perspective, and insights are mine. Imagine created by Nano Banana.
