The work is broad but lands where humans come into AI: how firms and markets adopt it, how these systems model us, and what they change for society.
The papers are not a list; they hold each other up. Click any node.
The substrate — how the machine behaves, what it can sense
The diagnosis — what breaks, where, and when
The response — settlement by design
The public vision
How firms and markets take AI up: the structural pressure to adopt, what that does to work, and when institutions respond.
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.
In the programme · the firm mechanism: why adoption is compulsory and the middle position a trap
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.
In the programme · the labour break: why re-absorption may fail this time
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.
In the programme · the clock: dates the institutional response window
What these systems reflect of the humans who produced their training data: their biases, their capabilities, and what they can perceive.
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.
In the programme · the substrate claim: AI reflects the cognition in its training data
AI performance is jagged, superhuman on one task and failing an adjacent one. The paper argues the jaggedness is inherited: models mirror the silo structure of human knowledge, so the frontier of capability traces the seams in our own corpus.
In the programme · the mirror lens applied to capability: jaggedness tracks our knowledge silos
Generating candidate ideas is getting cheap; recognising which ones are valuable is not. The paper locates the durable human contribution in cross-domain recognition, the judgment that tells a promising synthesis from a merely plausible one.
In the programme · what stays human: recognising value across domains
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.
In the programme · the perception boundary: which signals AI can learn to sense
The umbrella essay: one lens, AI as a mirror of the humans who wrote its training data, applied across behaviour and capability, with the bias and jaggedness papers as worked instances, and the embodied far side marking where the reflection ends.
In the programme · the strand in one piece, for general readers
The civilisational response: what the transition demands of institutions, policy, and the social contract.
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.
In the programme · the capstone: what to do about all of it
AI's “political bias” isn’t a technical defect to debug but a social-contract question in disguise — the real divide is individualist versus collectivist, and the genuine problem is that AI’s value-choices were set commercially rather than ratified democratically.
In the programme · the governance companion: who has standing to set AI's values
Further papers are in development across all three strands; they appear here once a public version exists.