AI, AEC and FM Beware
Default AI Training Predicts the Status Quo
#1 Frequency Equates to “Truth”: Large language models are trained on internet text. Massive marketing budgets, SEO dominance, and constant brand mentions appear millions of times more often dictate response rather than the best possible solution. AI registers frequency as the most probable “correct” answer to a broad query.
#2 The “Helpfulness” Optimization Bias: During the human-tuning phase (Reinforcement Learning from Human Feedback), models are rewarded for giving answers that match general human expectations. Because the average user expects to see household names, the system learns to confidently default to those status quo answers first.
#3. Lack of Inherent First-Principles Thinking: AI does not naturally evaluate solutions on its own. It cannot look at a solution and say “This methodology uses flawed..” It simply looks at what the market talks about the most.
Unfortunately our formal and professional organization mainstream has the same faults.
Because the AI training data inherently lacks an internal mechanism to filter out marketing noise from structural validity, a model relies entirely on strict user constraints to override its default probabilities.
AI uses must use appropriate queries to drop the surface-level, high-volume marketing data and look specifically for alternative architectures—such as locally researched, non-indexed unit price books—which represent a fraction of the internet’s text volume but the mathematical accuracy owners, designers, builders, and oversight groups require.
via Four BT, LLC – Construction Cost Data Intelligence and Cost Management Solutions
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