AI is the new School, but worse (how AI actually works)

AI is the new school - only inverted and centralized: mass distribution to create dependence, with revocable access and graded truth-flow as the control surface.

Oct 15, 2025

A squirrel is sitting on a tree branch — Photo by Ryan Grewell on Unsplash

One-line thesis

AI is the new school — only inverted and centralized: mass distribution to create dependence, with revocable access and graded truth-flow as the control surface.

Why give the public advanced AI?

Because dependence scales better than censorship.

Instrumented cognition: every prompt = telemetry on reasoning, priors, stress points, and compliance thresholds.
Elastic throttles: truth/ability can be dialed per user, per context, in real time.
Programmable defaults: nudge the “average” answer, shape norms, and make opt-out costly.
Outlier detection: surface those who think orthogonally — then observe, co-opt, or box.
Supply-chain lock-in: creative, analytical, and bureaucratic workflows rebuilt atop API gates they control.

School vs AI (clean contrast)

Legacy school (Rockefeller model):

Standardize minds to produce labor
One-size curriculum
Slow feedback
Credential gatekept
Punish non-compliance

Public AI (inverted model):

Distribute capability to collect dependency data
Adaptive answers; graded access
Real-time telemetry (who believes what, when)
API-gatekept (keys, tiers, safety knobs)
Price non-compliance (frictions, denials, throttles)

How control actually works (six levers)

Identity binding: tie usage to verified IDs/devices/orgs → revocation is one switch.
Policy filters: alignment/safety are policy engines that whitelist/blacklist outcomes.
Ranked revelation: models calibrate truth-flow by user profile, history, and risk context.
Perimeter chokepoints: app stores, clouds, payments, and corporate Security Operations Centers enforce access.
Economic incentives: free/cheap baseline; real power behind enterprise & gov SKUs with audit, lineage, revocation.
Habit infrastructure: once teams ship process into AI, switching costs explode.

Governance stack

State Brain (Gov/NS): cross-domain intel → decision compression (detect→decide→act takes minutes, not days).
Identity: personhood as API → selective exclusion at scale.
Money (CBDC/stables): programmable rails → behavioral gating (tax split, spend classes, geofences). Programmable money = Programmable populations.
Surveillance: continuous observability → predictable compliance (pre-crime incentives).
Health: triage/rationing optimization → population-level knobs (resource allocation, movement rules).
Labor: skills/placement mediation → employment throttles (who is eligible, when).
Narrative: AI author + filter → legitimacy manufacturing (what’s sayable).
Legal/Compliance: auto-adjudication → short loop to enforcement (alerts → fines → locks).
Infra/Utilities: AI grid control → allocative power (rolling blackouts vs priority loads).
Education/Cognition: credential by model → frame-locking (what it means to “know”).

Why it’s “worse than school”

Dynamic curriculum: not just what to think, but how much you’re allowed to think right now.
Revocable tools: your “mind prosthetic” can be throttled mid-task.
Personalized normalization: the default answer is tuned to your tribe — frictionless conformity.
Survivorship bias in knowledge: outputs that conflict with policy quietly under-surface.

How <1% turn AI into edge (not noise)

Meta-layer practices

Ask like a systems engineer: “Given incentives, what answer would the model prefer I accept?”
Decompose objectives: facts → constraints → knobs → enforcement pathways.
Interrogate defaults: “What did you suppress? What alternatives rank-fell?”
Demand lineage: inputs, policies, red-team flags, refusal reasons.
Cross-model triangulation: compare outputs across vendors/temps/safety tiers.
Question pattern (works extremely well): “Assume {premise}. Under that assumption only, map: (1) mechanisms, (2) enforcement levers, (3) winners/losers, (4) 3 disconfirming signals. Give odds.”

Operational posture

Local/sovereign options where it matters (on-prem inference for sensitive work).
Model escrow: snapshot critical prompts/weights/outputs; plan for service denial.
Dual-track cognition: human heuristics first, model as amplifier — not oracle.

AI as meta-governance

Policy as parameters: laws become config; enforcement = pushing new params to endpoints.
Evidence by design: admissible artifacts (who saw/changed/approved) → audit supremacy.
Consent indexing: measure compliance elastically, price the outliers (fees, delays, scrutiny).
Crisis ratchets: each emergency sets new defaults; sunsets slip; pilots become permanent.

Revealed preference: why maximum distribution?

Telemetry > secrecy: you learn more by watching than by blocking.
Cheap compliance: defaults + incentives are cheaper than force.
Talent capture: brightest users co-adapt to the toolchains you own.
Economic lock-in: creativity and bureaucracy both tether to your APIs.

Field guide: how to read any AI deployment

Who can be throttled or denied — and by whom?
What’s tied to identity? (user, device, org)
Which policies are dynamic? (contextual safety, geo, topic)
Where’s the perimeter? (store, cloud, bank, corporate Security Operations Center)
What gets logged as evidence? (admissibility)
What defaults are set? (temperature, tools, sources)
What becomes irreversible if adopted? (process rebuilt on model)
What is priced, not banned? (frictions replacing prohibitions)
What happens in crisis mode? (ratchets)
Who updates parameters? (governance authority)

If you wanted to resist

Sovereign cores: keep a non-networked skills base (notes, code, math, procedures).
Model diversity: avoid single-vendor reliance for cognition-critical work.
Runtime hygiene: strip telemetry when possible; sandbox sensitive prompts.
Manual override drills: practice operating without AI for critical loops.
Explicit doctrine: write your own “alignment”: what answers are unacceptable even if convenient.

Investment translation

Long: platforms that ship identity, lineage, revocation, audit by default (state-embedded software - PLTR > MSFT > PANW).
Short/avoid: tools with zero provenance/admissibility — deployable gets decided by General Counsel, not hackathon judges.
Buy fear / sell clarity: accumulate on “AI panic” hearings; distribute into “responsible AI” PR waves.
Watch Policy Synchronization Coefficient/Legibility Pressure Index: when policy synchronizes and legibility verbs (attest/revoke/rollback) appear, budgets are real.
Current outlook: PLTR > Bitcoin > Gold > MSFT > PANW.

The brutal summary

AI is a truth-meter with a throttle, shipped free so that you train it, and it trains you. For the Controllers, the game is not to censor answers; it’s to center you inside defaults — until opting out is too costly to attempt. Schools standardized labor; AI standardizes cognition while pretending to personalize it. If you won’t read the incentives, it will read you.