Neil Lawrence is a researcher and thought leader writing about machine learning, artificial intelligence, and decision-making under uncertainty. His work explores the societal, ethical, and technical implications of AI, data science, and policy over the past decade.
14 articles from this blog
Originally posted on trent.ai on July 21st 2026. For the past several years, AI safety has largely been framed as an alignment problem. How do we ensu
Explores tautology, impredicative circularity, and the barber paradox as selection principles for self-adjudicating systems in game theory.
Originally posted on trent.ai on April 11th 2026. India’s AI momentum: the future belongs to developers who can balance automation with judgment, secu
Originally posted on trent.ai on February 3rd 2026. There’s a pattern I keep seeing, and it’s not new, but AI is about to put it on steroids. It usual
Originally posted on trent.ai on January 19th 2026. Friday musings from the uncomfortable edge of LLM efficiency. Over the last year, I’ve found mysel
Originally posted on trent.ai on November 18th 2025. Musings while traveling and speaking… Recently, after speaking at an industry event, it got me th
The author draws parallels between historical perpetual motion claims and modern superintelligence hype, exploring a theoretical link between information theory and thermodynamics.
Originally posted on trent.ai on November 11th 2025. Originally inspired by a real bug and an even realer problem. A Morning Routine Interrupted The o
Explores the concept of 'intellectual debt' in AI and software systems, comparing it to The Sorcerer's Apprentice and arguing for open society principles as a solution.
The author critiques the focus on speculative AI risks at global summits, arguing for addressing real issues like corporate power and algorithmic bias instead.
Analyzes the challenges of using data science and scientific advice for Covid-19 policy, comparing it to the gap between scientists and policymakers.
A satirical look at AI development and government funding, imagining a fictional 'Ministry of Silly Models' in the UK.
Explores the 3D framework (Decomposition, Data, Deployment) for designing and deploying effective machine learning systems in business contexts.
Explores the history and current state of AI, questioning if we truly understand human intelligence or are merely emulating it.
