"Technology changes. Human nature does not."

Sahaj Garg, co-founder & CTO of Wispr, recently shared his thoughts on The Displacement of Cognitive Labor and What Comes After. They’re worth a read.

The crisis of meaning he talks about is something that worries me, especially at global scale. Then again, the tendency to associate identity with work is a 20th-century, North American thing. It’s a bubble I’m immersed in, and my creative background doesn’t help. Still, we’re going to see a lot of people with a lot of extra time on their hands. And chances are, I’ll end up among them.

What I do definitely falls into the broad category of “cognitive labour”, so I’ve been thinking: what part of that would AI replace? Cognitive labour can be measured in terms of volume (how many projects), or impact (how successful the projects are). Garg’s vision seems rooted in the idea that AI can help a company making 1,000 widgets per month do so with 10 people instead of 50. That’s likely true, but why would the company do that when it could use those 50 people to make 5,000 widgets instead? This is Jevon’s Paradox, and we don’t yet know how it will play out for cognitive labour.

That’s one of a few additional factors Garg didn’t get into that would influence the outcomes he’s considering. Here are three more:

• The last mile is the hardest. For as long as AI agents remain terrific at the first 90% of a task but require human interaction, ingenuity, or intuition to cross the finish line…the difficulty and importance of that final 10% will be underestimated and understaffed.
• Resource constraints are real. Some of these timelines are incompatible with the geopolitical reality of what it takes to establish and sustain the necessary infrastructure for this imagined future.
• Regulation moves slowly, but it does move. For the transitions in this piece to play out as quickly as Garg proposes, there would have to be no pesky interference from the likes of professional guilds, labour unions, and governments.

I don’t know how this will impact my career, but I do know that I’m not interested in optimizing for volume of output. Employers typically don’t care how many plates you’re spinning, they care how many of those initiatives deliver what I like to call “happy graphs”—up and to the right. They also care how much your efforts cost them, because only the difference matters. How efficient are you at translating company resources into company profits? At least in the tech industry, success

Agency, stewardship, mastery, and taste. We must find them and cherish them, or we risk becoming lotus eaters.

Here’s what I highlighted:

This is a remarkable change. I now view my intelligence as a commodity on tap. The primary skills I have left are my ability to synthesize different AI-generated perspectives into a combined whole that’s better than what the AI produces on its own, my ability to judge what I like and don’t like, and my ability to identify what I care about. Taste, direction, synthesis. Not raw cognitive horsepower. For someone whose whole identity was predicated on that horsepower, this is a significant transformation, and it’s one that millions of knowledge workers will go through in the next few years whether they’re ready for it or not.

It helps to think about human labor as falling into two broad categories. The first is cognitive labor: knowledge work and emotional work. Analysis, writing, coding, design, legal reasoning, financial modeling, medical diagnosis, therapy, coaching, project management. This is the category being automated right now. The second is physical labor: construction, manufacturing, agriculture, logistics, plumbing, electrical work, maintenance. This requires robotics and physical automation, which follows a different timeline.

If physical labor automation arrives on a ten-year timeline, it means we cannot treat physical labor as a permanent refuge for displaced knowledge workers. Unlike the Industrial Revolution, where one type of human labor (manual agricultural work) was replaced but another type (factory and later knowledge work) grew to absorb the displaced population, this transition has no obvious next category of labor for humans to move into. All three categories (knowledge work, emotional work, physical work) are converging toward automation within a single generation.

Economics doesn’t break down. But a good chunk of capitalism might. Capitalism operates on the basis of prices aggregating information to coordinate production and consumption. When the cost of producing many goods and services approaches zero, that price mechanism stops functioning for those goods. There’s no price signal to send when the marginal cost of one more unit of legal advice, or software, or medical diagnosis is effectively nothing. It’s an open question which parts of capitalism break down and which adapt.

Within three to five years, the majority of cognitive jobs will be substantially automated. This doesn’t mean every knowledge worker loses their job overnight. It means the number of humans needed to produce a given unit of cognitive output drops by an order of magnitude. One person with AI does what twenty did before. The math is simple: most knowledge workers become redundant.

This hits the upper-middle class hardest, which is counterintuitive. The truly wealthy have diversified assets and capital reserves. Working-class people doing physical labor still have scarce skills. It’s the $80,000-to-$400,000 household (the lawyer, the software engineer, the financial analyst, the radiologist) that gets caught. Their skills are the most directly substitutable by AI, and their lifestyle is built on continuous high income rather than accumulated capital. They lose their income, and the value of their primary asset (typically a home in an expensive knowledge-work city) declines as demand in those cities contracts.

A software engineer’s identity is as tied to their cognitive ability as a steelworker’s was to their trade. “I’m smart, I solve hard problems, I build things” is not just a job description. It’s a self-concept. When AI can solve harder problems and build things faster, that self-concept shatters.

The technology changes. Human nature does not. The drives that define us (status-seeking, hierarchy, competition, the desire to be valued relative to others) are not products of capitalism or knowledge work. They predate both. These dynamics existed before knowledge work was important to society, and they will continue after knowledge work is automated. What changes is the terrain on which these drives play out, and the social contracts that channel them.

What people actually seem to need is not work specifically but four things that work happens to provide: agency (the sense that you’re making choices that matter), contribution (the sense that you’re valued by others), mastery (the sense that you’re getting better at something), and connection (belonging to something larger than yourself). Work can provide all four, but it’s not the only thing that can. The challenge is building new structures that provide these when work no longer does.

In a world where AI can produce any material good, conspicuous consumption becomes meaningless as a status signal. So status migrates to whatever remains scarce: physical excellence, social and relational capital, creative originality, governance influence, spiritual attainment.