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

There is a fundamental trade-off in AI design between guaranteed accuracy and broad capability: Systems engineered for provable correctness (like traditional symbolic AI) must operate within narrow, pre-structured domains, while systems that handle complex, high-dimensional data (like modern generative AI) cannot guarantee error-free performance—this trade-off appears to be unavoidable rather than a temporary limitation.

We should stop expecting AI systems to be both highly capable and perfectly reliable: The paper formalizes what has been an implicit assumption in AI development, suggesting that demanding both broad data-mapping capacity and zero-error guarantees from a single system may be fundamentally impossible, which should reshape how we set expectations for AI deployment.

This trade-off has major implications for how we govern and evaluate AI: If the conjecture is correct, evaluation standards and governance frameworks need to be redesigned around accepting irreducible error risks in capable systems rather than pursuing impossible certainty, and this should inform decisions about where different types of AI are appropriate to use.

Hybrid approaches combining symbolic and generative AI may be necessary: Rather than choosing one approach, the paper suggests that combining narrow, verifiable symbolic systems with broader generative models could be a practical path forward for responsible AI development.

We need new frameworks for moral responsibility in AI: Since capable AI systems cannot guarantee error-free performance, the paper argues we must develop better concepts of "prudent epistemic risk" and responsibility that acknowledge and manage unavoidable uncertainties rather than pretending they don't exist.