PEG – Trying to understand the intersection between business and technology

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.

Work, worker, workplace

Writing is not thinking

by peg2026-03-202026-03-20 No Comments

Writing is the externalisation of thought into symbols and manipulation of those symbols. That’s not thinking. It’s just one cognitive loop among many.

The Optimisation That Ends Civilisation

by peg2026-03-102026-03-10 No Comments

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.

What Jacobin Missed About AI and Work

by peg2026-03-022026-03-01 No Comments

AI and work presents a fork: redistribution within the workplace, or redefinition of the workplace. A socialist analysis should distinguish between the two. Vivek Chibber’s recent piece in Jacobin doesn’t.

The Texture of Progress

by peg2026-02-252026-02-19 No Comments

In a recent exchange about the current state of AI—the strange tension between “slop” output and “human-level” reasoning—the conversation inevitably turned to the Industrial Revolution. It usually does. When we are faced with a technology that feels like it might be a “clean break” from the past, we go looking for historical precedents to help us build a narrative for the future.

The problem is that we often go looking for clean mechanisms in the past to justify our predictions for the present.

Why every prediction about AI and work is already wrong

by peg2026-02-242026-02-24 No Comments

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.

Technology and its malcontents

AI and the Filter 1 Problem

by peg2026-02-192026-02-19 No Comments

A recent review by Alex Imas and Madhav Shukla provides a useful roundup of who is using AI and how. But while the analysis inside the frame is thorough, the frame itself deserves scrutiny.

The authors implicitly assume that AI has passed what I call a Filter 1 test: demonstrating clear, firm-level financial gains at market prices, independent of subsidized pilots or venture-backed “free tiers.”

If we look at the history of transformative technologies, the winners didn’t just diffuse—they pulled themselves into existence by solving a problem so clearly that firms couldn’t afford not to adopt them.

Tool-to-Work vs Work-to-Tool

by peg2025-11-102025-11-10 No Comments

There’s a fundamental tension between the top-down tool-to-work model foundational in economics and the bottom-up work-to-tool model we see across other disciplines—a tension that Mokyr’s recent Nobel highlights. The […]

Stop Comparing AI to Railroads. It’s More Like the Crypto Boom.

by peg2025-09-242025-09-24 No Comments

Another day, another article telling us the AI boom is just like the railroad buildout of the 1800s. “Don’t worry about the bubble—infrastructure always finds its users eventually!”

This is dangerously wrong. Here’s why.

Technology and its malcontents

AI and the Art of the Mundane Breakthrough

by peg2025-09-09 No Comments

The Economist piece on “What if artificial intelligence is just a “normal” technology?“, got me thinking about historical analogies and how we construct them.

Narayanan and Kapoor use factory electrification—a 30-year process requiring total rethinks of floor layouts and organizational structures. But this example has always felt like classic post-hoc sense-making: we see a transformation, find the “disruptive technology” preceding it, and assume causation.

A better analogy might be the PC. There wasn’t a 30-year lag waiting for “adoption.” What we saw was a broader wave of business process reengineering that crystallized around regulatory requirements like ERP. PCs participated in this transformation but didn’t cause it.

The difference matters for how we think about AI deployment. If it’s like electrification, we’re waiting for organizations to slowly restructure around the technology. If it’s like PCs, we’re watching AI get absorbed into larger systemic changes already underway—remote work acceleration, regulatory digitization, skill verification crises.