Curt Doolittle

Glossary of Helpful Terms Part I – Single Slide for Presentation Part II – Gloss

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Curt Doolittle

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Glossary of Helpful Terms

  • Part I – Single Slide for Presentation
  • Part II – Glossary Outline: Narrative
  • Part III – Glossary Text
Content (clustered terms):
Foundations:
Causality • Computability • Operationalization • Commensurability • Reducibility • Constructive Logic • Dimensionality
Learning:
Evolutionary Computation • Acquisition • Demonstrated Interests • Constraint • Compression • Convergence • Equilibrium
Cooperation:
Truth/Testifiability • Reciprocity • Cooperation • Sovereignty • Incentives • Accountability
Decision:
Decidability • Parsimony • Judgment • Discretion vs. Automation
Strategy:
Audit Trail • Constraint Architecture • Alignment by Reciprocity • Correlation Trap • Scaling Law Inversion • Moat by Constraint
Closing Line at Bottom:
“We don’t make the model bigger — we make it decidable, computable, and warrantable. That’s the bridge over the correlation trap to AGI, and it’s the moat around the companies who adopt it.”
This way the slide works as a visual index. You control the pace in speech, and the audience sees that you have a complete system. The handout then fills in the definitions.
(Open with their pain, name the trap, introduce your frame)
  • Correlation Trap – Scaling correlation without causality; current LLMs plateau in accuracy, reliability, and interpretability.
  • Plausibility vs. Testifiability – Today’s outputs are plausible strings, not testifiable claims.
  • Scaling Law Inversion – Brute-force parameter growth produces diminishing returns; efficiency requires a new approach.
  • Liability – Enterprises can’t adopt hallucination-prone systems in regulated or mission-critical environments.
(Show the foundation that makes escape possible)
  • Causality (First Principles) – Move from patterns to cause–effect relations.
  • Computability – Every claim must reduce to a finite, executable procedure.
  • Operationalization – Expressing claims as actionable sequences.
  • Commensurability – All measures must be comparable on a common scale.
  • Reducibility – Collapse complexity into testable dependencies.
  • Constructive Logic – Logic by adversarial test, not subjective preference.
  • Dimensionality – All measures exist as relations in space; LLM embeddings are dimensions too.
(Connect to evolutionary computation — familiar and universal)
  • Evolutionary Computation – Variation + selection + retention = learning.
  • Acquisition – All behavior reduces to pursuit of acquisition.
  • Demonstrated Interests – Costly, observable signals of real value.
  • Constraint – Limit behavior to channel toward reciprocity and truth.
  • Compression – Minimal sufficient representations yield parsimony.
  • Convergence – Alignment toward stable causal relations.
  • Equilibrium – Stable cooperative equilibria, not unstable correlations.
(Shift from technical foundation to social/enterprise value)
  • Truth / Testifiability – Verifiable testimony across all dimensions.
  • Reciprocity – Only actions/statements others could return are permissible.
  • Cooperation – Reciprocal alignment produces outsized returns.
  • Sovereignty – Agents retain self-determination in demonstrated interests.
  • Incentives – The structure that drives cooperation and compliance.
  • Accountability – Outputs are warrantable, not just useful.
(Show how this produces usable outputs — not just words)
  • Decidability – Resolving claims without discretion; satisfying infallibility.
  • Parsimony – Minimal elements for reliable resolution.
  • Judgment – The transition from reasoning to action.
  • Discretion vs. Automation – Humans required today; computability removes that dependency.
(Land on the payoff: efficiency, moat, risk reduction)
  • Audit Trail – Every output carries its proof path.
  • Constraint Architecture – Middleware enforcing reciprocity, truth, decidability.
  • Alignment by Reciprocity – Preference alignment is fragile; reciprocity is universal.
  • Scaling Law Inversion – Smaller, constrained models outperform giants.
  • Moat by Constraint – Competitors can’t copy outputs without replicating the entire framework.
“We don’t make the model bigger — we make it decidable, computable, and warrantable. That’s the bridge over the correlation trap to AGI, and it’s the moat around the companies who adopt it.”
Causality (First Principles)
Definition: Modeling the cause–effect structure of phenomena rather than surface correlations.
Why it matters: Escapes the “correlation trap” that limits current LLMs, enabling reliable reasoning and judgment.
Computability
Definition: The property that every claim, rule, or decision can be expressed as a finite, executable procedure with a determinate outcome.
Why it matters: Ensures outputs are actionable, testable, and scalable into automated systems without human patching.
Operationalization
Definition: Expressing claims, rules, or hypotheses as executable sequences of actions.
Why it matters: Makes outputs testable and reproducible, turning vague text into computable logic.
Commensurability
Definition: Ensuring all measures and claims can be compared on a common scale.
Why it matters: Enables consistent evaluation of outputs, preventing hidden biases or incommensurable trade-offs.
Reducibility
Definition: Collapsing complexity into simpler, testable dependencies.
Why it matters: Drives interpretability and efficiency, lowering compute costs while improving reliability.
Constructive Logic
Definition: Logic built from adversarial resolution (tests of truth and reciprocity), not subjective preference.
Why it matters: Produces outputs that are decidable, auditable, and legally defensible.
Dimensionality
Definition: Every measure or representation exists in relational dimensions.
Why it matters: Connects directly to embeddings and vector spaces familiar to ML engineers.
Testifiability vs. Plausibility
Definition: Testifiability requires outputs to be verifiable by evidence; plausibility only requires surface-level coherence.
Why it matters: Sharp contrast with today’s LLMs, highlighting why your approach is enterprise-ready.
Evolutionary Computation
Definition: Learning as variation, selection, and retention—nature’s optimization process.
Why it matters: Provides a universal, scalable method of discovering solutions without brute force scaling.
Acquisition
Definition: All behavior is reducible to the pursuit of acquisition (resources, time, energy, information).
Why it matters: Provides a unified grammar for modeling human and machine decisions.
Demonstrated Interests
Definition: Costly, observable signals of value that reveal true preferences.
Why it matters: Grounds AI outputs in measurable reality, reducing hallucinations and false claims.
Compression
Definition: Reducing data or representations to minimal sufficient dimensions.
Why it matters: Produces parsimony, lowering model size and inference costs while retaining truth.
Convergence
Definition: Alignment of representations toward stable, causally true relations.
Why it matters: Prevents drift and ensures outputs get more accurate with use.
Constraint
Definition: Limits placed on behavior to channel search toward reciprocity/truth.
Why it matters: Engineers understand constraint satisfaction; investors see defensibility.
Equilibrium
Definition: Convergence to stable cooperative equilibria instead of unstable correlations.
Why it matters: Connects to game theory, markets, and strategy — resonates with both execs and VCs.
Truth / Testifiability
Definition: Satisfaction of the demand for verifiable testimony across dimensions of evidence.
Why it matters: Creates outputs that can be trusted, audited, and defended in enterprise/legal settings.
Reciprocity
Definition: Constraint that only actions/statements that others could do in return are permissible.
Why it matters: Prevents parasitic, biased, or exploitative outputs—critical for alignment.
Cooperation
Definition: Outsized returns from reciprocal alignment of interests.
Why it matters: Core to scalable human–AI collaboration and multi-agent systems.
Liability
Definition: Costs and consequences when errors, hallucinations, or deceit occur.
Why it matters: Reduces enterprise risk and regulatory exposure.
Sovereignty
Definition: The right of agents to self-determination in their demonstrated interests.
Why it matters: Explains alignment as preserving agency, not enforcing sameness.
Incentives
Definition: Structures that drive agents to comply with reciprocity and cooperation.
Why it matters: Investors think in incentives; this shows the mechanism is grounded.
Decidability
Definition: Resolving statements without discretion; satisfaction of demand for infallibility.
Why it matters: Moves models from “suggestions” to judgments, enabling automated decision pipelines.
Parsimony
Definition: Using the minimum necessary elements for reliable resolution.
Why it matters: Increases speed, lowers compute, and boosts generalization.
Judgment
Definition: Transition from reasoning to actionable decision.
Why it matters: Enables adoption in domains where outputs must directly inform action.
Discretion vs. Automation
Definition: Current models require human discretion; computable decidability reduces that burden.
Why it matters: Clarifies “will this replace humans or just assist?”
Accountability
Definition: Outputs aren’t just useful, they are warrantable.
Why it matters: Key for regulated industries — finance, law, healthcare.
Audit Trail
Definition: Every output carries a traceable chain of causal reasoning.
Why it matters: Creates interpretability, accountability, and compliance advantages.
Constraint Architecture
Definition: Middleware layer that enforces natural law (reciprocity, truth, decidability) on outputs.
Why it matters: Differentiates from competitors — turns LLMs from stochastic parrots into causal engines.
Alignment by Reciprocity
Definition: Aligning models by reciprocal constraints, not subjective preference tuning.
Why it matters: Scales alignment universally across cultures, domains, and industries.
Correlation Trap
Definition: The industry blind spot of scaling correlation without causality.
Why it matters: One phrase that crystallizes the problem you solve.
Scaling Law Inversion
Definition: Replacing brute-force scaling with constraint-guided convergence for efficiency.
Why it matters: Challenges the orthodoxy — smaller models can outperform giants.
Moat by Constraint
Definition: Competitive defensibility created by embedding universal constraints.
Why it matters: VCs see a technical moat that can’t be easily copied by rivals.

Source date (UTC): 2025-08-25 17:44:33 UTC

Original post: https://x.com/i/articles/1960035585239957928

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