Cost of Predictivity
The Cost of Predictivity is the exponential increase in compute and latency required to make probabilistic AI models highly deterministic.
“The Cost of Predictivity states that squeezing 99.9% deterministic reliability out of a probabilistic AI model requires an exponential increase in architectural complexity and token spend.”
Enterprises require deterministic systems for compliance and security. However, forcing LLMs to act deterministically requires extensive guardrails, validation loops, and retries, which exponentially inflate the cost and latency per query.
Predictivity Cost Curve
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Cost of Predictivity
The Cost of Predictivity is the exponential increase in compute and latency required to make probabilistic AI models highly deterministic.
Direct Relationships (5)
Transitive Neighbors (Connected via Hop 1)
Extended Causal Ripple Effects
Reverse Citations: Implemented & Audited Across Platform
Richard Ewing’s Research Thesis
Do not use LLMs for tasks that require absolute precision if traditional code can do the job. The architectural cost of forcing an LLM to be deterministic always eclipses the cost of writing standard software logic.
Why This Specification Exists
AI applications fail in enterprise settings because engineering teams drastically underestimate the cost of achieving enterprise grade reliability.
Adding more complex prompts or relying on self-correction loops.
No framework quantifying the non-linear relationship between required reliability and inference costs.
Defined the Cost of Predictivity to guide AI architectural decisions.
What Changes If You Believe This?
Use deterministic code (like regex or standard APIs) for validation, rather than asking the LLM to self-verify.
Budget for the compounding token costs of multi-stage validation pipelines.
Set realistic expectations on system latency and reliability SLAs.
Rely on deterministic governance rather than probabilistic guardrails.
Specification Maturity & Ecosystem Spread
Recommended Action by Role
Design systems that use LLMs only for fuzzy reasoning, handing off to standard code for strict validation.
AI Reliability Cost Estimator
Estimate latency and cost multipliers for validation pipelines.
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Frequently Asked Questions
Q:What is the Cost of Predictivity?
The exponential rise in latency and cost when adding validation layers to ensure AI reliability.
Canonical Specification Origin
Do not use LLMs for tasks that require absolute precision if traditional code can do the job. The architectural cost of forcing an LLM to be deterministic always eclipses the cost of writing standard software logic.
Corpus Interconnections
Richard Ewing artifacts developed around this canonical framework, including publications, execution tools, and diagnostic models.
External Adoption & Peer Citations
Documented instances where independent researchers, engineering teams, and publications have cited, implemented, or referenced this concept outside Richard Ewing’s ecosystem.
External Evidence: No independently verified references recorded yet.
This concept is part of Richard Ewing’s original baseline canon. External citations and implementations are added only upon rigorous empirical verification.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
Recommended Citation
Ewing, R. (2026). "Cost of Predictivity." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/cost-of-predictivity
@article{ewing_cost_of_predictivity,
author = {Ewing, Richard},
title = {Cost of Predictivity},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/cost-of-predictivity}
}