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The Confidence Asymmetry Principle - Why 80% of future AI compute will be AI Verification (The Correlated Blindness Penalty Theory)
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The Confidence Asymmetry Principle, AI Verification Economics, Correlated Blindness & When Code Generation Becomes Close to Zero - Confidence Becomes the Quality of the Product! For seventy years the scarce resource of software engineering was the production of correct code, and every methodology of the field was an attempt to manage that scarcity. Generative AI is dissolving it. The marginal cost of producing a plausible candidate program is collapsing toward a floor set only by the price of compute. AI is making software engineering radically cheaper to generate — but not necessarily cheaper to trust. In this episode, we explore The Confidence Asymmetry Principle, a working paper around Confidence Engineering Manifesto by Jason Arbon (2026), which argues that the real economic shift in AI-infused software is not the collapse in the cost of code, but the rising importance of the cost of confidence. As generative AI drives the marginal cost of producing software toward zero, a new constraint emerges: verifying that the generated artifact is correct, safe, reliable, and fit for production. The paper argues that generation and verification are governed by different cost laws — and those laws are now diverging. We unpack the core idea that in an AI software economy, code becomes cheap, but confidence becomes scarce. The conversation explores: * Why verification becomes more expensive as generation becomes cheaper * The three irreducible floors beneath verification cost: undecidability, execution, and interaction * How Rice’s theorem and the halting problem shape the future economics of software testing * Why “AI testing AI” can create correlated blind spots rather than genuine assurance * The Correlated-Blindness Penalty and why independent evidence matters * The Confidence Bound and the cost of reaching justified confidence * Why verification compute may become the dominant workload of future AI compute * How Confidence Engineering and AI Assurance emerge as practical responses to this shift The Confidence Asymmetry Principle the work of Jonathon Wright and Jason Arbon, in this episode reframes the AI engineering productivity revolution through a sharper lens: the bottleneck is no longer whether AI can generate software, but whether organizations can produce enough evidence to trust what it generates. The constructive consequence is not pessimism. It is that verification becomes the central discipline of software engineering, and that the discipline already has a name. Confidence Engineering — and its companion, AI Assurance — is the practice the Principle demands: justified confidence over performative coverage, decorrelated evidence over correlated convenience, continuous evaluation over static validation, a probability with an interval over a green check The next era of software will not be defined by who can generate the most code. By The Confidence Asymmetry Principle, generation is becoming free, and free things do not confer advantage. The era will be defined by who can justify confidence in what has been generated — at a known residual risk, on evidence whose independence can be exhibited and audited. When code becomes free, confidence becomes the product. The work, and the compute, and the value, follow the confidence, #TestTalks #ConfidenceEngineering #AIAssurance #AgenticAI #AITesting #QualityEngineering #TrustworthyAI #SoftwareTesting #LLM #AIConfidence #GenAI #AIVerification
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