A few years ago I undertook a new speech understanding research
project, aiming to explore innovative techniques rather than pursue
short-term results.  My method was to build on the classic 1970s AI
approaches to speech, as an alternative to the current mainstream
speech understanding research methods.  This led to a system that was
competitive, both in elegance and performance, with other recent
AI-inspired speech understanding systems.  However, evaluation of
results and prospects led to the realization that the system had no
future.  This paper analyzes the roots of this failure as a case study
in AI methodology gone awry.  In particular, it explains why my
original, classicly AI goals --- namely, be optimal in principle, be
well integrated, iteratively refine the interpretation, deal directly
with noisy inputs, be linguistically interesting, be tunable by hand,
work with clear hypotheses, be architecturally innovative, and relate
to general issues in AI --- are less important than they seemed.