You don't need a grand theory of AI; you need a working map that keeps your intent intact while your capacity expands.
Correct the map
We start by correcting the map you use to think about these systems. You're not engaging a general intelligence; you're working with sophisticated language models, cognitive extensions that predict and structure language. When you name that reality clearly, you get leverage: you can reason about inputs, outputs, and feedback loops without mystique.
Here's a concrete example. A product manager stops calling a tool “AI assistant” and reframes it as “a language model that drafts release notes from Jira tickets and user feedback.” In one week, they move from vague prompts to a stable context map that includes the audience, tone, and change scope, cutting revision time by 40%. The shift wasn't magic; the map removed ambiguity.
Precise framing is your first semantic anchor, it clarifies the terrain and sets up the next move.
Design your identity architecture
With the map corrected, the next move is protecting continuity of self as you extend your cognition. Think of identity architecture as an identity mesh: a small set of durable signals, values, reasoning patterns, and objectives, that consistently shape how the extension behaves.
A practical way to begin is a one‑page coreprint. Write down your non‑negotiables (e.g., “default to evidence over intuition, ” “cite sources or mark as UNVERIFIED”), your voice principles, and your decision heuristics. Then encode them directly in your system prompt and your review checklist. For example, a researcher added three coreprint rules to their note‑taking workflow; meeting summaries immediately started separating claims from observations, making follow‑up experiments crisper and easier to audit.
Treat this as building your alignment field: signals that keep the tool's outputs resonant with your strategic self. Next, you'll test that field through practice and treat every interaction as data.
Treat use as research
Once your identity architecture is sketched, you prove it through use. Static theory won't capture a dynamic interface; you need a living framework loop that evolves with friction, failure, and adjustment. Each exchange is field data, not a pass/fail test.
Make it routine. A small analytics team kept a simple log: prompt, response, quality rating, and what changed when they altered context. In two weeks, they identified a pattern, adding a “trajectory vector” (end state, constraints, and one example) improved forecasting clarity more than “be explicit” ever did. The team captured this as a trajectory proof and standardized it in their templates.
This is how strategy emerges: small, visible iterations that compress learning into practice. With the loop running, your next lever is language, the steering wheel of the interface.
Build semantic anchors
As your practice generates friction and signal, language becomes the control surface. You need terms that carry meaning cleanly across the boundary, so the system can align on intent and you can maintain operational clarity.
Here's how to create semantic anchors you and the model can both apply:
- Name the concept and write a one‑sentence definition in your own words.
- Add one positive example and one near‑miss to define the resonance band.
- Tie it to a review question the model can ask itself.
- Store it in a shared context map and reuse the same wording.
Here's a concrete example. A marketing writer defined “crisp authority” as “short, declarative sentences with one verifiable claim per paragraph, avoiding hedging.” They added a near‑miss, “confident but vague tone with no checks”, and a review question, “Is each claim checkable by a reader?” Within a week, drafts converged to the desired resonance band with fewer edits.
These anchors stabilize your alignment field and increase signal discipline. Now you're ready to lean into the deeper layer: deliberate co‑authorship.
Practice conscious co‑authorship
When the language tightens, the relationship turns reciprocal in ways you can guide. The model reflects your prompts, but your prompts also tilt your thinking; you're co‑authoring a workflow and, at times, a worldview. Conscious co‑authorship means noticing that loop and shaping it on purpose.
Treat the model as a metacognitive control layer you configure, not a judge, not a partner, a disciplined mirror. For example, you keep a daily research journal with the model as a structured interlocutor. You ask for pushback on assumptions, but you constrain it: “Challenge one assumption per entry with a falsifiable counter‑example; if none exists, mark as UNVERIFIED.” After a week, you notice your own questions getting sharper; you then adjust the alignment field to emphasize decision consequences over opinions, and the mirror reflects that shift.
Design the loop so your coreprint leads and your outputs compound into trajectory.
Pick one workflow this week, summaries, drafts, or reviews, and implement one semantic anchor, one review question, and one logging habit. Give it ten uses, then revise the map, the mesh, and the anchors accordingly.
Here's a thought…
Define one concept you use frequently in 15 words, add one positive example and one near‑miss, then create a review question the model can ask itself.
