PEG – Trying to understand the intersection between business and technology

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.

The Crooked Path

by peg2025-09-042025-09-02 No Comments

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?

Technology and its malcontents

World Models and the Anchoring Problem

by peg2025-09-032025-09-03 No Comments

The AI research community is having another moment with world models. Quanta Magazine’s recent piece traces the concept back to Kenneth Craik’s 1943 insight about organisms carrying “small-scale models” of reality in their heads. Now, with LLMs showing unexpected capabilities, researchers are betting that better world models might be the key to AGI.

But there’s something revealing happening in this revival. The more we try to find coherent world models in current AI systems, the more we discover what researchers call “bags of heuristics”—disconnected rules that work in aggregate but don’t form unified understanding. When MIT researchers tested an LLM that could navigate Manhattan perfectly, it collapsed when just 1% of streets were barricaded at random. No coherent map, just an elaborate patchwork of corner-by-corner shortcuts.

This raises the grounding question: what keeps intelligence honest?

The AI Productivity Paradox

by peg2025-08-282025-08-27 No Comments

The recent decline in entry-level employment is not a consequence of AI’s revolutionary power. Instead, it is a symptom of a maturing industry entering a low-growth phase. AI is serving as a tool for cost optimisation rather than a driver of new value creation.

If generative AI were truly a general-purpose technology on par with electricity or the micro-processor, the macro data should already be unmistakable. Instead, the silence is deafening.

The Tyranny of the Ideal

by peg2025-08-272025-08-26 No Comments

There’s a persistent belief that policy failures happen because politicians ignore expert advice. Healthcare reform gets watered down by special interests. Climate action stalls because fossil fuel lobbies block rational carbon pricing. Immigration reform collapses because extremists prevent sensible comprehensive solutions.

The real story is more unsettling: these policies are failing precisely because politicians are following expert advice. The most sophisticated policy frameworks, implemented exactly as designed, produce the most predictable disasters. Our smartest people aren’t being ignored—they’re being followed faithfully toward systemic failure.

The Intelligent Hand

by peg2025-08-212025-08-21 No Comments

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.

The Collapse of Narrative Attractors

by peg2025-08-192025-08-19 3 comments

Watching the cathedral of certainty crumble while the rest of us quietly bolt the next floor on

You’ve felt this. The same people who promised social media would democratise information now warn it’s destroying democracy. The same voices who said smartphones would liberate us from our desks now fret about screen addiction. The experts who assured us globalisation would lift all boats are now explaining why supply chains are fragile and manufacturing should come home.

It’s not just that they were wrong—it’s the whiplash. The confident certainty followed by equally confident reversals, as if the previous position never existed. As if the complexity was always obvious to anyone paying attention.

But here’s what’s actually happening: you’re watching narrative attractors collapse in real time.