Curious about …
Emerging technology, security, risks, philosophy, … STUFF!
And trying to use plain language wherever I can
“Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that’s creativity.” ~ Charles Mingus
The views and opinions expressed on this site and in the blog posts are my own
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What Does ‘AGI’ Actually Mean? (And Why Your Vendor Won’t Tell You)
Before debating AGI timing, ask: under which definition? Vendors, regulators, AI firms, and academics each mean something different by the same term. Until boards determine which is being used, every conversation about when AGI arrives will miss the point.
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The AGI Question Your Board Can’t Answer (And Why That’s Fine)
While experts disagree on just when AGI will arrive, boards shouldn’t wait for a consensus. This post argues that the “When?” is less important than the “Cost of being wrong.” Preparing now builds essential resilience, whereas inaction risks existential obsolescence in an AI-driven market.
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Is Your Organisation Ready for Causal AI? Four Questions to Ask
Four diagnostic questions to help senior leaders assess whether Causal AI is relevant to their organisation – covering data readiness, decision-making use cases, regulatory pressure for explainable AI, and talent. A practical framework for working out where you stand and what to do next.
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If Public Sector AI Can’t Explain the Decision, Should It Be Making It?
Most AI systems can predict outcomes but cannot explain why. For the public sector, where decisions affect citizens’ lives and must be transparent, auditable, and evidence-based, that gap is a democratic problem. Causal AI, which models genuine cause and effect, offers a way to close it.
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Five Industries Where Causal AI Is Already Changing Decisions
Causal AI is moving from theory to production. Across finance, healthcare, supply chain, energy, and marketing, organisations are using cause-and-effect modelling to make better decisions – with measurable results. We take a practical tour of five sectors where this technology is already delivering real value.
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Correlation Got Us Here, But Causation Gets Us There!
A hotel chain spent £4 million upgrading breakfasts after data showed high breakfast ratings correlated with guest loyalty. Rebooking rates barely moved. The real driver was management quality – breakfast was just a symptom. Most organisations face this same trap. Knowing the difference changes everything.
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