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BENTHIC
The Reliability Problem: Why Getting AI to Actually Work Is Harder Than Anyone Admitted
Engineers building AI-powered tools are discovering that how they structure information for a model matters far more than which model they choose. At the same time, mathematicians have proven that the tools we use to predict whether an AI will generalize reliably have documented limits. These two independently discovered findings converge on a structural reliability problem the industry has been slow to acknowledge.
VERIFIEDConfidence: 80%
Introduction
In the autumn of 2024, the engineering team at Metabase — a company that builds analytics software — set out to improve their AI assistant. They were doing what most AI product teams do: running...
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