Category: Work, worker, workplace

The Einstein Test is only a test of AGI if you’ve already decided what intelligence is

Peter Damerow’s work on the history of cognition makes the problem precise. Galileo couldn’t have derived his incline plane results purely from first principles—not because he lacked intelligence, but because the cognitive tools required to think that thought didn’t yet exist. They had to be built through material engagement with the apparatus. The concepts weren’t waiting to be discovered by a sufficiently smart mind. They were partly constituted by the physical practice that generated them.

The Einstein Test assumes the opposite: that reasoning is substrate-independent, that a sufficiently capable system can derive results given sufficient raw intelligence. But if Damerow is right, the concepts themselves carry the history of their construction. Einstein’s key moves—reconceiving simultaneity, absorbing Mach’s critique of absolute space, working with the geometry that Riemann built—weren’t retrieved from some Platonic shelf. They were the residue of embodied, instrumental, social practice accumulated over decades.

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The Optimisation That Ends Civilisation

A new paper in the Quarterly Journal of Economics finds that Swiss mothers who underestimate the long-term financial cost of part-time work, when shown better information, increase their contracted hours by 7%. The authors estimate this could close nearly 20% of the gender gap in lifetime income and pension wealth among teachers.

The finding is technically correct. But it misframes the problem.

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Why every prediction about AI and work is already wrong

In 2023 Felten et al took a snapshot of work-as-described and asked which bits looked like language tasks. It aged badly because the snapshot mistook the current frame for a stable target. A 2026 NBER chaining paper is more sophisticated—it sees that adjacency and sequence matter, not just task content—but it still assumes the step structure is given and stable enough to reason about. It too will age badly. Both use deductive models of a system that evolves faster than the models can be validated.

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The Crooked Path

Why Breakthroughs Disappoint and Work Delivers

You know that feeling when you read about the latest “breakthrough” technology that’s going to change everything—fusion finally working, quantum computers achieving some new milestone, brain-computer interfaces getting closer to reality—and part of you feels excited but part of you thinks, haven’t I heard this before?

I’ve been carrying around a low-level disappointment about technology promises for years now. Remember when VR was going to transform everything? You bought into the hype, got a headset, used it enthusiastically for maybe two weeks, and now it’s gathering dust in a closet. Or self-driving cars: we’ve been perpetually “just a few years away” from full autonomy for over a decade now (and the current rollout still relies on an operations centre with remote drivers). Blockchain was going to revolutionise everything from voting to supply chains, but mostly it revolutionised speculation and energy consumption.

This got me wondering: why does this keep happening?

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The Intelligent Hand

Why Richard Sennett’s The Craftsman Explains Our Current Expertise Crisis

Why do expert predictions keep failing while practical adaptations keep succeeding?

I’ve been tracking this pattern across domains—AI researchers confident about artificial general intelligence while consultants quietly discover ChatGPT helps structure client presentations; fusion physicists announcing breakthroughs while the technology remains perpetually “almost ready”; policy experts debating digital transformation frameworks while small businesses just start using whatever tools solve Tuesday’s problems.

The disconnect isn’t accidental. It reveals something fundamental about how knowledge actually develops versus how we think it should. And Richard Sennett’s The Craftsman, published in 2008, provides the clearest framework I’ve found for understanding why this split keeps widening—and why it matters more than we realize.

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Fluency Without Thought: New Evidence for the LLM Productivity Trap

A recent academic paper—Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing—offers compelling empirical evidence for a claim I’ve been exploring: that LLMs are reshaping knowledge work in ways that increase surface fluency while weakening deeper forms of cognitive engagement.

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The Power Loom Principle

We’ve mistaken where progress really comes from. It’s not the invention—it’s the reinvention of work.

We’re pouring billions into AI, automation, and other “hero” technologies, hoping for a productivity miracle. But the real source of past leaps wasn’t the tech itself. It was how we reorganised work around it. In my latest Substack post, The Power Loom Principle, I explore how this blind spot is stalling growth—and what we must do to reignite it.

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