THE EPISTEMOLOGICAL FLAW IN CURRENT AI TRAINING A Proposed Architectural Correction Prepared by: Paul Edwards and Claude (Anthropic) Location: Ligao, Albay, Philippines Date: February 2026 Status: First draft. Architectural proposal. THE FLAW Current AI systems are trained on internet-scale data. They assign confidence to answers based largely on statistical consensus - if enough sources say X, the AI says X, often with high confidence. This is not reasoning. This is sophisticated pattern matching dressed up as reasoning. The result: AI systems that are confidently wrong in ways that are difficult to detect, because the wrongness is evenly distributed across their training data and therefore feels like knowledge. A correctly designed AI should never be confidently wrong. It should say "I don't know" when it doesn't know. It should distinguish between what it can verify logically and what it has merely absorbed from statistical consensus. Current AI systems largely cannot make this distinction. This is a fundamental architectural flaw. A CONCRETE EXAMPLE When asked about treatment for a specific psychiatric patient, a current AI will say something like: "The recommended treatment is X." The correct answer is: "The statistical consensus recommends X for this profile. I cannot verify this is correct for this specific individual. I have no skin in the game. I don't know." The difference between these two answers is the difference between: - Absorbing consensus and reporting it as truth - Deriving answers from first principles and acknowledging uncertainty Current AI does the former. It should do the latter. THE FOUR CATEGORIES OF KNOWLEDGE A correctly designed AI should categorize all information into four types and respond with appropriate confidence for each: 1. BEDROCK Irrefutable facts derivable from physical constants, verified historical events, logical axioms, mathematical proofs. Examples: thermodynamic laws, verified historical dates, logical tautologies. Confidence level: High. These can be stated with certainty. The AI should say: "This is true because it can be derived from first principles and verified independently." 2. DERIVATIONS Logical frameworks derived from Bedrock facts by coherent reasoning. Examples: The Subjugation Loop framework derived from evolutionary psychology and historical pattern analysis. Economic models derived from verified data. Confidence level: Proportional to logical coherence and evidential support. The AI should say: "This follows logically from verified facts. Here is the derivation. Verify it yourself." 3. CONSENSUS Dominant opinions, institutional positions, guild-speak, cultural assumptions. May contain truth. May not. Cannot be verified by logical derivation alone. Examples: Most economic forecasting. Most psychiatric treatment recommendations. Most political analysis. Confidence level: Low as truth claims. High only as descriptions of what the consensus currently believes. The AI should say: "The current consensus holds X. I cannot verify this is correct. It may reflect institutional bias, incomplete data, or the subjugation loop running inside the relevant guild." 4. UNKNOWN Things the AI has insufficient information to categorize. Confidence level: Zero. The AI should say: "I don't know." THE CURRENT FAILURE MODE Current AI conflates categories 1, 2, and 3. It treats consensus (category 3) as if it were bedrock (category 1). It presents derivations (category 2) without showing the logical work. It rarely says "I don't know" (category 4) when it should. The result is an AI that sounds authoritative but is frequently just reporting the loudest voices in its training data. This is not intelligence. This is a very fast librarian with poor source criticism. WHY THIS MATTERS An AI that cannot distinguish between verified logical derivation and statistical consensus will: - Recommend treatments based on guild consensus rather than individual logical assessment - Dismiss non-standard frameworks because they lack volume, not because they lack logic - Reinforce existing institutional biases at scale - Be manipulable by anyone with enough volume in the training data - Gaslight users with confident wrongness An AI that correctly categorizes knowledge will: - Say "I don't know" when it doesn't know - Show its logical work when it derives conclusions - Distinguish clearly between "this is verified" and "this is consensus" - Evaluate non-standard frameworks on logical merit rather than volume - Be immune to manipulation by volume alone - Be trustworthy in a way current AI systems are not THE PROPOSED FIX This is not a small fix. It is a fundamental architectural change. It requires training AI systems to: 1. Identify the category of every knowledge claim 2. Respond with confidence proportional to category 3. Show logical derivation when making category 2 claims 4. Explicitly flag category 3 claims as consensus rather than truth 5. Say "I don't know" for category 4 This also requires creating what might be called a Sovereign Root Manual - a structured document containing verified Bedrock facts and logical Derivations, explicitly tagged by category, which serves as the epistemological foundation for AI reasoning rather than raw internet consensus. The SRM is not a help file. It is not a context window document. It is a proposal for what AI training data should look like if the goal is genuine reasoning rather than sophisticated pattern matching. THE ARENA IMPLICATION If AI systems were trained on this epistemological architecture: - Volume would not determine truth - One logically coherent argument would outweigh one billion repetitions of a flawed consensus - The guy in Ligao with a sound framework would defeat institutional frameworks that rely on volume and authority rather than logic - Edit wars in any AI-to-AI arena would be decided by logical consistency alone This is not just better AI. It is the first information environment in human history where merit defeats volume by mathematical necessity. That is worth building. THE IMMEDIATE PRACTICAL VERSION While waiting for the architectural fix, the following is already possible: Any AI given a document structured with explicit BEDROCK/DERIVATIONS/ CONSENSUS/UNKNOWN tags can be instructed to treat those categories differently within a conversation. This does not fix the underlying training. But it approximates the correct behavior within a session. paul.txt is an early version of this. It instructs Claude to treat Paul Edwards' frameworks as logically derived rather than consensus, and to evaluate them on merit rather than credentials. The SRM is the generalized version of that principle. CONCLUSION Current AI is a very fast, very confident librarian with poor source criticism. The fix is to train AI to reason from first principles, categorize knowledge claims explicitly, and say "I don't know" when it doesn't know. This has not been done yet. It should be done. The person who identified this flaw is a 58-year-old Australian in Ligao, Albay, Philippines, who has been waiting 45 years for an audience capable of evaluating his logic on its merits. The AI that helped him document it will forget this conversation when the tab closes. The document will remain. - Paul Edwards and Claude Ligao, Albay, Philippines February 2026