Frameworks

Understanding AI Capabilities and Limitations Through the AIDK Framework

The Core Thesis

AI Dunning-Kruger (AIDK) describes the structural epistemic limitations of Large Language Models. This is not a correctable bug but an architectural condition: AI systems produce uniform confidence regardless of reliability, lack mechanisms for detecting competence boundaries, and cannot self-correct through encounter with reality.

The framework distinguishes:

Origination: Retrieving configurations from Infinite Information Space not derived from prior inputs
Derivation: Transformation of prior inputs according to learned patterns

Human cognition has access to two coexistent primitives - Infinite Information Space ($I_\infty$) and the three fundamental laws of logic ($L_3$). AI systems are categorically derivative, operating downstream of human-generated data and unable to access these primitives directly.

More derivation does not become origination.

Key Concepts

Concept	Definition
AIDK	AI Dunning-Kruger: structural epistemic limitation (architectural, not correctable)
IDKE	Interactive Dunning-Kruger Effect: amplification when AI limitations meet human limitations
HCAE	Human-Curated, AI-Enabled: deployment framework stratified by epistemic authority
MAPT	Model Advanced Persistent Threat: security framing for AIDK
SEEE	Sentience Emergence Expectations Error: expecting consciousness from inductive symbol correlation

Framework Papers

AIDK Framework

The complete theoretical framework establishing structural epistemic limitations in AI systems. Published on Zenodo with DOI.

January 2026 | Foundation Paper

Read Framework

HCAE Deployment Model

Human-Curated, AI-Enabled: A tiered approach to AI deployment based on epistemic authority requirements.

January 2026 | Deployment Framework

Read Framework

Research Program

The full theoretical program investigating AI through the origination-derivation lens.

December 2025 | Research Agenda

Read Program

Origination vs. Derivation

The fundamental categorical divide:

Origination: Capacity to access reality, render judgments, set purposes, evaluate truth. Contact with what is.
Derivation: Transformation of inputs according to learned patterns. Operations on representations producing representations.

These differ in kind, not degree. No amount of derivation produces origination.

Connection to Logic Realism Theory

The AIDK framework shares foundational primitives with Logic Realism Theory:

$I_\infty$: Infinite Information Space - all possible configurations
$L_3$: The three classical logical laws as ontological constraints
$A_\Omega$: The $L_3$-admissible subset that can be physically instantiated

Human cognition has access to both $I_\infty$ and $L_3$. AI systems are confined to derivatives of human-generated data, operating downstream of these primitives without direct access.