# A Conjecture on a Fundamental Trade-Off between Certainty and Scope in Symbolic and Generative AI

arXiv: https://arxiv.org/abs/2506.10130 · HTML: https://mlsanity.com/paper/2506.10130/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)

## Built on by (most cited first, 4 of 4)

- [The Semiotic Channel Principle: Measuring the Capacity for Meaning in LLM Communication](https://mlsanity.com/paper/2511.19550/) (2025)
- [Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of "Certainty-Scope" in AI](https://mlsanity.com/paper/2508.19304/) (2025)
- [No Universal Hyperbola: A Formal Disproof of the Epistemic Trade-Off Between Certainty and Scope in Symbolic and Generative AI](https://mlsanity.com/paper/2601.08845/) (2025)
- [Epistemic Compression: The Case for Deliberate Ignorance in High-Stakes AI](https://mlsanity.com/paper/2603.25033/) (2026)

## Abstract (arXiv)

This article introduces a conjecture that formalises a fundamental trade-off between provable correctness and broad data-mapping capacity in Artificial Intelligence (AI) systems. When an AI system is engineered for deductively watertight guarantees (demonstrable certainty about the error-free nature of its outputs) -- as in classical symbolic AI -- its operational domain must be narrowly circumscribed and pre-structured. Conversely, a system that can input high-dimensional data to produce rich information outputs -- as in contemporary generative models -- necessarily relinquishes the possibility of zero-error performance, incurring an irreducible risk of errors or misclassification. By making this previously implicit trade-off explicit and open to rigorous verification, the conjecture significantly reframes both engineering ambitions and philosophical expectations for AI. After reviewing the historical motivations for this tension, the article states the conjecture in information-theoretic form and contextualises it within broader debates in epistemology, formal verification, and the philosophy of technology. It then offers an analysis of its implications and consequences, drawing on notions of underdetermination, prudent epistemic risk, and moral responsibility. The discussion clarifies how, if correct, the conjecture would help reshape evaluation standards, governance frameworks, and hybrid system design. The conclusion underscores the importance of eventually proving or refuting the inequality for the future of trustworthy AI.