Every "AI is X times faster" claim hides a real question: faster at what? This post breaks down why document translation runs about 60x faster and training plan development close to 90x, mostly mechanical assembly work, while problem solving and root cause analysis lands closer to 15x, because that work is genuinely open-ended and the engineer's judgment stays the last step. Speed varies by how much of each task is standardizable assembly versus real judgment. That's by desig

Manufacturing doesn't trust a gage until it's proven repeatable and reproducible. This post introduces Reasoning R&R™, which applies that discipline to AI: does an evaluation match a known correct answer, does it repeat under the same conditions, and do different reviewers land in the same place. Running the framework on MAD-Ai's own 8D grading workflow surfaced 2,370 grading decisions, 93.2% repeatability, and 99% reproducibility between reviewers, plus twelve rubric criteri

In a 2019 study, three trained automotive inspectors evaluated the same 30 daytime running lights twice against a known standard, agreeing with each other only 36.67% of the time. This post explains why that's not a training failure but a measurement problem, and why manufacturing runs attribute agreement analysis on physical inspection but never on judgment work like grading an 8D. If unaided human judgment tops out near 37% agreement, that's the bar any AI grading quality w