https://danielzp.com/publications Each paper in Daniel Ziekenoppasser-Powell's research programme on the AI transition, narrated at its two-minute depth. From the civilisational diagnosis to how AI systems behave and what they can perceive. en-gb Daniel Ziekenoppasser-Powell false The framework’s central thesis, peer-facing: AI is a social-contract crisis, not a labour-market problem — and the answer is to align our human systems, not just the model. UBI is the floor, not the house. https://danielzp.com/papers/cultural-alignment https://danielzp.com/audio/cultural-alignment.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell Every information technology runs a five-phase settlement cycle, and the gap from arrival to institutional response has shrunk from 249 years for the printing press to about 20 for AI — putting the reckoning in 2037–2047. https://danielzp.com/papers/information-cycles https://danielzp.com/audio/information-cycles.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell For listed firms and public bodies, adopting AI isn’t a strategy choice but a fiduciary baseline the market enforces; firms sort into non-adopter, augmenter, and AI-native, and the “augmenter trap” leaves the middle permanently more expensive. https://danielzp.com/papers/adopt-or-be-undercut https://danielzp.com/audio/adopt-or-be-undercut.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell For two centuries displaced workers were re-absorbed because the new work was work only humans could do; AI removes that condition, so the returns accrue to compute, not to a new rung of human labour. https://danielzp.com/papers/abstraction-ladder https://danielzp.com/audio/abstraction-ladder.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell AI bias is not a fixable technical defect but a faithful reflection of the human cognitive patterns absorbed from training data — so governance must shift from pre-deployment certification to continuous monitoring of what the mirror reflects. https://danielzp.com/papers/mirror-problem https://danielzp.com/audio/mirror-problem.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell A cheap statistic computed from raw data in minutes — a signal’s “structure score” — predicts in advance how well a character-level AI will learn any symbolic stream, from whale song to tidal records (ρ ≈ −0.92 across ~30 domains): an a priori test for which non-language signals AI is ready to perceive. https://danielzp.com/papers/narrow-band-senses https://danielzp.com/audio/narrow-band-senses.mp3 Wed, 10 Jun 2026 17:00:00 GMT Daniel Ziekenoppasser-Powell