It was frustrating hearing those professors. I'd spent time in industry before going to Stanford for a MSCS, and I could tell that they were stuck and covering for it.
A few years later, Stanford moved computer science from Arts and Sciences to Engineering. This pushed the CS department into more practical directions. The School of Engineering had deans, supervision, and planning. CS under Arts and Sciences was sort of what each professor wanted to teach. There was no undergraduate CS at Stanford before that move; prospective undergrad students were told "get a liberal education".
That didn't end the "AI Winter" at Stanford, though. It took the DARPA Grand Challenge, in 2003-2005, to do that. The head of DARPA, Dr. Tony Tether, used the Grand Challenge to force the big-name AI schools to get some results. DARPA had been funding automatic driving work at Stanford and CMU since the 1960s, with disappointing results. The big-name schools were quietly told that if the private sector and amateurs outdid them in the Grand Challenge, DARPA was turning off their AI funding. Suddenly entire CS departments were devoted to the Grand Challenge. Stanford had to bring in machine learning people from CMU to compete. That's what turned the department around and finally pushed the logicians into the background.
This opened a window that allowed the statistical-based AI techniques that currently dominate the field to mature.