Six Shortcuts, Six Capacities at Risk
Every stage of inquiry now has its AI shortcut. Hypotheses arrive pre-formed. Meaning arrives pre-structured. Users arrive pre-simulated. Artifacts arrive pre-built. Judgments arrive pre-rendered. Decisions arrive pre-optimized. None of these shortcuts is the work of fools.
Each is the product of competent, well-intentioned practice — and each rests on presuppositions its practitioners rarely stop to examine. On the 27th, the working groups take six such practices seriously enough to question them, asking the same three questions of each: What is being uncritically presupposed? What human capacity is quietly being placed at risk? And what boundary would protect it? Together the six trace the full arc of knowledge-making — from generating a hypothesis to deciding what to do.
The Hypothesis That No One Abduced
When a model proposes the hypothesis, the researcher receives something hypothesis-shaped — but has performed no abduction at all. Human abduction works from a limited base: cognitive constraint forces selection, and what we attend to carries valence earned through lived experience — the anomaly that nags, the guess that once failed. The constraint is the engine, not a defect. Ask a machine to abduct and both features vanish: nothing constrains attention, nothing is at stake, and valence is assigned by repetition across a corpus. Salience without a life behind it — relevance simulated by frequency.
The Ontology That Closed the World
Knowledge graphs and enterprise ontologies promise machine-readable meaning: entities fixed, relations typed, semantics settled. The promise is real — and so are its buried commitments, including an unexamined circularity now that AI builds the ontologies that then constrain the AI.
The User Who Was Never There
Synthetic user research replaces interviews, observation, and ethnography with simulated personas — built from the very expectations the designer brought to them. A simulated user can never resist you, contradict you, or reconfigure your understanding of the problem.
The Artifact Its Maker Never Touched
Prompt-to-product workflows deliver functioning artifacts whose makers never engaged the material. Yet craft has always fed theory — geometry itself grew from the practices of masons, carpenters, and surveyors — and friction in making, including the friction of debugging, is where tacit understanding forms. Experts who already carry such models wield generative tools powerfully; novices who begin with the tools may never build the models at all.
The Judgment Nobody Exercised
Journals, funders, schools, and firms are adopting AI screening and review — treating quality judgment as the application of criteria. But connoisseurship is tacit: it lives in trained judgment that cannot be fully codified, and it is acquired only by being exercised.
The Future That Was Only a Forecast
Decision-intelligence systems treat the future as a distribution over the past and deciding as optimizing. Genuine anticipation is something else: running internal models of possible futures while remaining able to re-perceive the situation itself — to notice that the model’s ontology of the problem is not the problem.
We are not staging prosecutions. Each group is anchored by participants whose life’s work names what is at risk, joined by practitioners of the very approaches under examination — invited not as defendants but as co-explorers. Each group produces a short statement of its presuppositions, its capacity at risk, and its proposed boundary. These statements become raw material for that evening’s AI Dramaturg synthesis, for the overnight remote cohort, and for the Orthogonal Map drawn on the 29th.