Year: 2026
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Cognitive Surrender
Helen Edwards from the Artificiality Institute tells the story of the researcher who, before LLMs came along, read every transcript of every interview he had ever done. He handed that job to an AI and then mid-presentation he had to stop because he realised that he couldn’t speak to any of it. The analysis might have been…
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Decision-making under pressure
Early morning on the 16th October 1962 and US National Security advisor McGeorge Bundy has just handed a folder of photographs to President John F. Kennedy. Inside were pictures taken by a U-2 plane that had flown over Western Cuba two days earlier. Analysts who had studied the pictures believed that they showed launch sites…
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Thinking (and working) in loops
In early June Peter Steinberger, the developer behind OpenClaw, posted (on X, but I’m not linking to X) something which seems to have changed a lot of influential people’s approaches to working with AI. ‘Here’s your monthly reminder’, he said, ‘that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt…
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The Hidden Costs of AI
A couple of months ago the Oxford academic Carl Benedikt Frey wrote an opinion piece in the New York Times arguing that, as the application of AI becomes increasingly ubiquitous, more tasks that were once handled by professionals or businesses will shift onto consumers. This, noted Frey, is not a new phenomenon. The washing machine may have…
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Strategists, AI and the Edge Effect
In 1933 the American naturalist Aldo Leopold set out a principle which shaped decades of wildlife management. His ‘law of interspersion’ noted that wildlife species that required different types of food and cover thrived where distinct habitats intersected. Animals that required more than one thing to survive concentrated in the areas where those requirements were…
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The age of the expert generalist
In 1936 a 20 year-old Claude Shannon came to MIT from the University of Michigan to work with Vannevar Bush on his differential analyser, an early, room-sized mechanical computer. This analog computing machine could solve differential equations through a series of rotating discs and shafts whose operations were governed by a bank of around a…
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Why AI fluency doesn’t scale
By the early 1980s the General Motors assembly plant in Fremont California was widely regarded to be the worst performing car manufacturing site in the U.S. The company had a huge employee engagement problem with absenteeism regularly running at over 20%, and sometimes approaching 50% on a Monday. When managers had too few people turn…
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Why AI shifts bottlenecks rather than remove them
In 1793 Eli Whitney invented a machine that could separate cotton fibers from their sticky seeds. The ‘Cotton Gin’ (short for ‘Cotton Engine’) mechanised what was previously a hugely laborious and inefficient manual process and it transformed the textile industry almost overnight. Suddenly mass-scale cotton production became not only more possible, but vastly more profitable.…
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The Great Unbundling of Work
On 4 May 1948, the US Supreme Court ruled in a landmark antitrust case that would change the way that Hollywood studios worked forever. Until that point the studios had owned everything, from the production lots, through to the cinemas that the films played in. Actors, Directors and writers were tied in to long-term, exclusive…
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Technology, inflection points and cascading impacts
In the late 19th Century sharpshooter Annie Oakley was one of the most popular acts within Buffalo Bill’s Wild West Show. Annie would demonstrate her skills performing tricks like shooting the flames off candles and her grand finale, shooting the end off a lit cigarette held in the mouth of a brave volunteer from the…