The AI-Jobs Paradox: A Tale of Human Relationships and Market Extent
In the age of artificial intelligence (AI), the narrative around jobs and automation has become a familiar one. We hear about the impending job losses, the rise of the machines, and the need for universal basic income to cushion the blow. But what if this narrative is missing a crucial piece of the puzzle? What if the real story is not about the tasks being automated, but about the human relationships that define our jobs?
The Oxford study from 2013, which predicted that 47% of American jobs were at high risk of automation, has been widely cited as a harbinger of doom. Yet, as the renowned VC investor Marc Andreessen pointed out, it's the tasks, not the jobs, that are being replaced. But why do the jobs survive once the tasks are gone? The answer lies in the heart of economics, in the words of Adam Smith himself.
Smith's famous anecdote about the pin factory is often read as a story about task decomposition. But what he was really describing was something more profound. The output of each worker depended not just on their own skill, but on the relationships between them. The wire drawer's output had to fit the straightener's technique, and the pointer's work had to be compatible with the header's. It was the web of human relationships that determined the job's shape and function.
This insight has profound implications for the future of work in the age of AI. The best granular data comes from the American Bureau of Labor Statistics, which projects that software developer jobs will increase by 18%, financial advisors by 17%, and lawyers by 5% by 2033. But why are these jobs surviving, even as AI takes over the tasks?
The answer lies in the nature of human relationships. Personal financial advisors, for example, are heavily exposed to AI, yet their profession is booming. This is because their real job is not rebalancing portfolios, but being the person a client trusts enough to ring. Similarly, software engineers are not writing code in isolation, but coordinating with product managers, designers, and other developers all day, every day. AI can take the tasks, but it can't take their position in the network.
In the knowledge economy, about 40% of what an organization does is automatable right now. Research synthesis, first-draft copy, scheduling, and data processing are just some of the tasks that AI is brilliant at. But the other 60% is relationships, judgment, and taste: knowing when the machine's output is brilliant and when it's rubbish. This is the part that scales at the speed of human trust.
The anthropologist Robin Dunbar showed that humans can maintain roughly 150 stable relationships. This limit is neurological and shows up across cultures throughout history. Hutterite farming colonies have split at that number for centuries, and Gore-Tex caps its factory units at 150 employees. The relational capital in any job builds slowly, can't be parallelized, and is destroyed in an instant when someone is displaced.
This is the real problem with retraining. Every program focuses on skills, but a displaced worker's actual deficit isn't skills. It's that they don't know who to ring or whose emails matter. Anyone who has started a new job knows the first six months aren't about learning the tasks; they're about learning the network. Policy that ignores this will keep failing.
The public's assumption of displacement is understandable, given the name we've given to AI: artificial intelligence. The word 'artificial' implies a machine coming for our livelihoods, while 'intelligence' suggests it's coming for our jobs. But what if we'd called it what it actually does? Not artificial intelligence, but augmenting intelligence. One name says the machine is coming for us; the other says it's working alongside us.
The evidence supports the second interpretation. But we've spent three years trapped by the first. The question was never whether the machine is coming for us; it's what we become alongside it. As we navigate this new world, we must remember that the future of work is not about the tasks being automated, but about the human relationships that define our jobs. And in that, there is both challenge and opportunity.
