AI & Society
Exploring how artificial intelligence is reshaping society, culture, and human relationships.
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Bounded Rationality: Why More AI Makes Your Organisation Confidently Wrong, Not Smarter
In 2014, Amazon built an experimental hiring tool. Feed it a stack of resumes, and it would score each candidate from one to five stars. The model was trained on ten years of the company’s own recruitment data. It had more information, more processing power, and more historical depth than any human recruiter could bring…
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Feedback Loops: Why Your AI Investments Are Either Compounding or Collapsing
Every AI investment your organisation makes is on one of two paths. Either it is compounding, gathering data and capability in a self-reinforcing spiral that grows harder for competitors to match. Or it is collapsing, oscillating between overreaction and correction, burning capital without converging on anything useful. The difference between these outcomes is not the…
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The Explore-Exploit Dilemma: Why Your AI Strategy Can’t Decide Whether to Search or Settle
In 2000, Netflix offered to sell itself to Blockbuster for $50 million. Blockbuster declined. The decision looked reasonable at the time. Blockbuster had more than 9,000 stores, 84,000 employees, and a business model that worked. Netflix was a loss-making startup mailing DVDs. Why buy the problem child when you own the category? Blockbuster spent the…
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Information Cascades: Why Your AI Adoption Is Converging on Everyone Else’s
Two restaurants sit on the same street. One is packed. The other is empty. You walk past both and your own information is ambiguous. You haven’t eaten at either, and the online reviews are mixed. So you join the queue at the full one. Not because you know it’s better, but because the crowd’s choice…
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The Red Queen Effect: Why Your AI Strategy Is Running Faster but Going Nowhere
Pick any SaaS category in 2024. CRM platforms. Helpdesk tools. Analytics suites. Within a six-month window, Salesforce, HubSpot, Zendesk, Intercom, and Notion each shipped an AI copilot. Each one does the same thing: a chatbot that summarizes threads, drafts replies, and suggests next actions. The features are nearly indistinguishable. Nobody pulled ahead. Everyone spent a…
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Hysteresis: Why Your Organisation After AI Will Never Return to Normal
Senior leaders commonly treat AI adoption like a controlled experiment: if it doesn’t produce results, roll it back. This assumption shapes how organisations structure their governance, their vendor contracts, and their change management plans. It is also wrong, in a way that physics describes with unusual precision. When an external magnetic field is applied to…
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Emergence: Why AI Cannot Be Managed, Only Cultivated
The most consequential things AI does in your organisation were not planned. Nobody decided that the recommendation engine would lock your customers into a narrowing content corridor. Nobody scheduled the moment when three AI-optimised suppliers simultaneously de-prioritised your orders because they all read the same market signal. Nobody chose the emergent norm where your AI-assisted…
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Co-Evolution: Why Your AI Strategy Is Already Out of Date
Every organisation deploying AI is making a version of the same mistake. They assess what AI can do today, decide whether to adopt it, and build a strategy around that assessment. The mistake is not being wrong about current capabilities. The mistake is treating capability as a fixed property of the technology. It is not.…
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Requisite Variety: Why AI Monocultures Will Fail Your Organisation
In 1956, British cybernetician W. Ross Ashby articulated a principle so fundamental it should sit above every AI strategy deck: Only variety can destroy variety. Ashby’s Law of Requisite Variety states that a control system must possess at least as much variety — the number of distinct states it can occupy — as the system…
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The Edge of Chaos in AI-Augmented Teams
How AI moves teams to adaptive complexity—and when it tips into fragility. Understanding Kauffman’s edge of chaos in knowledge work.