Category: Technology and its malcontents

Rewired

Much of today’s business writing is reductionist, focused on clean cause-and-effect narratives. This isn’t a flaw; most of the time, what organisations need is tactical advice: if you have X, do Y. Rewired is a strong example of this genre, offering a practical guide to how contemporary organisations structure, run, and deliver technology.

But we’re not in a steady state. We’re at the end of an era shaped by firm-centric efficiency, and entering one defined by networked coordination, contested data, and shifting boundaries of control.

We’re living through a transition, away from the familiar paradigms of the last 30 years, and toward something still taking shape. In that context, advice grounded in what worked before may be increasingly ill-suited to what comes next. The book excels at guiding firms through internal change, but falters when the real challenge is how firms relate to everything outside them. Rewired is useful for what it is. But how useful that is, right now, is an open question.

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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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Why We Keep Misreading Disruption

We’re wired to spot disruption in the wrong places—chasing the latest AI feature or platform, expecting it to upend everything overnight. Google’s new ‘Shop with AI’ mode is already stirring such claims, but as my latest Substack essay Why We Keep Misreading Disruption explores the real question isn’t what these technologies do, but what deeper systemic shifts they reveal. This piece unpacks why our visions of the future often miss the mark, how globalisation’s story helps explain structural change, and what it means to see disruption as a ‘punctuation’ rather than just incremental progress.

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Prediction Without Disruption

The recent Stanford paper on Outcome-based Reinforcement Learning to Predict the Future1 (RLVR) could be seen as both a product of and a contributor to the cycle of misinterpreting disruption, as I discussed in Why We Keep Misreading Disruption.2 It’s advancing tools that improve prediction without necessarily addressing or understanding the foundational shifts that disruption entails.

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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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Eye of the Master

There’s no shortage of books about artificial intelligence right now. Most fall into one of two camps: breathless optimism or existential dread. The Eye of the Master is something else entirely—a rare and rigorous exploration of how we got here.

This is one of the most illuminating books I’ve read on the current wave of AI. Pasquinelli doesn’t waste time on hype or speculation. Instead, he takes us back to the intellectual roots of machine learning—tracing how ideas from neuroscience, cybernetics, psychometrics, and even art history quietly shaped the architecture of neural networks and the metaphors we use to describe them.

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