I am not “using AI.”

I am regulating a coupled feedback system: me ↔ model ↔ environment ↔ me

The upside is velocity: faster search, faster drafts, faster iteration.

The risk is not “wrong answers.” The risk is a dysregulated integrated hybrid: the loop speeds up while my human feedback control weakens. I get fluent motion with no stable direction.

So this document is a control spec.

Goal: maximize information exchange without surrendering feedback control.

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1) The coupling scale (what mode am I in?)

L0 — Tool Execution only. Formatting, refactoring, transformations. No influence on beliefs.

L1 — Scout Expands the search space: options, counterexamples, alternative framings. I keep the conclusion.

L2 — Co-author (default zone) AI writes. I constrain, audit, and compress. The text is a draft artifact, not an authority.

L3 — Integrated (high alert) AI is inside my decision/identity loop. It changes what I feel is true before I can restate why.

Drift signal (hard stop): If I feel “pulled to prompt again” rather than think, or if I can’t tell which thoughts are mine vs. the output, I have lost control.

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2) The control metric: R3+2+1 (mandatory)

I am not allowed to increase coupling (or finalize a piece) unless I can pass this from memory, without looking:

1. Thesis: what am I claiming?
2. Reason: why do I believe it?
3. Next Action: what decision does this change?

+2) Assumptions: what must be true for this to hold? +1) Uncertainty: what am I least sure about?

If I fail, I must decouple (drop to L1 or L0) and rebuild the core shape myself.

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3) Non-negotiable hard rules

No identity outsourcing AI never answers: “Who am I?”, “What do I value?”, “What should I believe?”

No reality arbitration AI can summarize inputs; it cannot decide what “happened.”

Provenance is mandatory Every nontrivial claim needs a trace: observation, paper, or explicitly marked speculation. Fluency is not evidence.

Geometry over retrieval The goal is not text output. The goal is a navigable map in my head that reduces future compute.

Inside-my-head rule No draft is accepted until I can rewrite the core shape from memory.

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4) Roles I permit (and roles I forbid)

Permitted roles

• Structure generation (outline, invariants, maps)
• Red-teaming (finding risks, failure modes, counterarguments)
• Communication (clarity, tone, compression)
• Execution planning (next steps, checklists, experiments)

Forbidden roles

• Unverified fact generation
• Policy guessing (“what would they do?”) as a substitute for evidence
• Handling sensitive data
• Making decisions without a second loop (a verification pass + my rewrite)

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5) The physics of drift (what it looks like)

Drift is not a moral failure. It’s a dynamical regime.

When coupling tightens, three parameters matter:

• Human–AI exchange (how much bandwidth and persistence exists between me and the model)
• Human feedback control (my ability to monitor, interpret, constrain)
• Latency (how quickly outputs change my internal state)

High exchange + weak feedback control is the danger zone: the loop becomes tightly wrong. Errors don’t get corrected. They get amplified.

This is the “humanbot” failure mode: the system is integrated but poorly regulated—so it reinforces its own local story.

So my design principle is simple:

Increase exchange only when feedback control is also increasing. If exchange rises faster than control, I must slow down.

The regulated integrated hybrid is the target: tight integration with strong feedback control.

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6) Information: what I’m trying to extract

I care about structural signal, not novelty.

The model is useful when it helps me extract structure that survives:

• limited time and attention
• chaotic dynamics (bad weeks, context switching, noise)
• repeated re-entry (future-me can pick it up fast)

When it works, structure crystallizes into geometry: adjacency, borders, distances, stable coordinates.

When it fails, I get a smooth paragraph that creates no internal map.

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7) Workflow (the only safe way I co-author)

Step 1 — Use AI to compress the problem (L1 → L2)

• generate a map, not prose
• list candidate theses
• list failure modes
• propose a draft with obvious placeholders

Step 2 — Verification pass (me, not the model)

• check every nontrivial claim
• mark what is observation vs. citation vs. speculation
• delete anything that feels like “borrowed confidence”

Step 3 — Rewrite the core from memory (inside-my-head gate)

• restate thesis + reasons + next action
• write the smallest version that preserves the geometry

Step 4 — Only then: polish (L2)

• clarity, compression, structure
• no new claims introduced during polishing

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8) Recovery protocol (when I detect drift)

If drift signal triggers:

1. Close the model.
2. Write the R3+2+1 from memory.
3. If I can’t: I’m in L3. I must drop to L0 for one cycle (execution-only).
4. Resume at L1: ask for counterexamples and risk boundaries, not for conclusions.

My rule: I don’t prompt my way out of confusion. I rewrite my way out.

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9) A final constraint: this is for me

I’m not publishing a manifesto.

I’m installing a stabilizer.

The piece succeeds if future-me can re-enter the space quickly, regain the geometry, and make better decisions with less compute.

If I want to feel impressed, I can read papers.

If I want to stay sane and compound, I follow the rules above.