For more than twenty-five years, I’ve watched technology cycles move from the "magic" phase to the "utility" phase, and finally, to what Cory Doctorow famously termed "enshittification." We saw it …
From Pilot to Product: How Product Leaders Turn AI Experiments into Durable Services
Too many AI pilots end up as well-intentioned slides or dusty prototypes. The technology gets praised, the metrics glow in a lab environment, and then the organisation asks the familiar question: why …
How to Run Fast, Safe AI Experiments — A Playbook for Product Leaders
Many product teams rush to build with AI because it is available, not because they understand what problem it actually solves. The result is neat demos, noisy Slack channels, and — sometimes — …
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How to Build Responsible, Scalable AI Tutors: A Practical Playbook for Product Leaders
Can AI tutors scale across millions of learners without eroding trust or quality? That’s the question keeping product leaders awake. AI in education promises personalised learning at unprecedented …
It’s debt, all the way down: the AI build‑out financed on borrowed time
There is a pattern emerging beneath the headlines: the rush to build AI capacity is not just a tech story, it is a financing story. When the biggest deals are underpinned by borrowed money, you stop …
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Multiply Learning: How AI Can Make Teacher Feedback Far More Effective
Making feedback count: process, not magic When was the last time feedback on a school assignment changed what a pupil did next? For most teachers the answer is: too infrequently. Feedback remains one …
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