From the Foothills to the Front Page: How Stanford Learned to Build With Computers

Every great technical culture has an origin myth, and Stanford’s is usually told as a story about garages. The more interesting story, at least where computing is concerned, begins in a building in the foothills, with a mathematician who had already given a field its name.

John McCarthy came to Stanford in 1962, having helped found the artificial intelligence project at MIT. He is widely credited with coining the term “artificial intelligence” in the mid-1950s. At Stanford he started a new AI project with funding from the Advanced Research Projects Agency that began in June 1963, with a staff of about six. When the university formed its Department of Computer Science in January 1965, the project had grown to around fifteen people.

The Laboratory in the Hills

By the summer of 1966, the project had outgrown its campus space and moved to the D.C. Power Laboratory in the foothills above the university. There, for roughly fifteen years, the Stanford Artificial Intelligence Laboratory, known as SAIL, became one of the leading centers of computing research in the world. McCarthy directed it from 1965 until 1980, when its activities were merged into the Computer Science Department and moved down to the main campus.

The department’s own memorial account of McCarthy notes that foundations for graphical interfaces, computer typesetting, speech recognition, expert systems, computer vision, robotics, and computer music all got their start at SAIL’s facilities. The list is striking less for its length than for its range. The lab drew faculty from far outside engineering: the geneticist Joshua Lederberg and the composers John Chowning and Leland Smith were among the affiliates in its early years.

That interdisciplinary mixing is the real inheritance. SAIL was reopened under that name in 2004 and continues today, but its deepest legacy may be a habit of mind: the assumption that computing problems are interesting precisely where they touch other fields.

The real inheritance of the laboratory in the hills is a habit of mind: the assumption that computing becomes interesting precisely where it touches other fields.

A Search Project Called BackRub

A generation later, that habit produced its most famous offspring. In 1996 two computer science graduate students, Larry Page and Sergey Brin, began a research project called BackRub that ranked web pages by analyzing the links pointing to them. The work grew out of Stanford’s Digital Libraries Project, part of a federally funded research initiative. They renamed the project Google in 1997 and incorporated the company in September 1998.

The story is often told as one of individual genius, and there was plenty of that. But it is also a story about an environment: a department that encouraged graduate students to pursue open-ended problems, public research funding that supported exploratory work, and a surrounding region that knew how to turn a research prototype into a company. Remove any one of those conditions and the outcome becomes far less likely.

Design as a Technical Discipline

The newest chapter in this story is less about machines than about method. In 2005 the Hasso Plattner Institute of Design, universally known as the d.school, was formally inaugurated, building on classes that had been running since 2003. It was founded by a group of faculty led by David Kelley, the founder of the design firm that became IDEO, and named for SAP co-founder Hasso Plattner, whose gift supported its creation.

The d.school institutionalized what came to be called design thinking: a practice of studying users closely, prototyping quickly, and working across disciplines. Whatever one makes of the phrase, which has been both celebrated and overused, the institution has steadily matured. Undergraduates can now earn a degree in design through the School of Engineering, and the first students received it in 2023.

The Culture Beneath the Code

Seen together, these three episodes suggest that Stanford’s technological influence rests on something more durable than any single invention. It rests on a culture that treats computing as a meeting ground, that tolerates long and uncertain projects, and that keeps the boundaries between research and application porous.

That culture has costs as well as virtues. The same porousness that lets ideas flow into companies can let commercial priorities flow back into the academy. The same speed that allows prototypes to become products can outrun careful thinking about consequences. These are tensions the university is still working through, particularly as artificial intelligence, the field McCarthy named, moves from the foothills into nearly every part of public life.

This section of The Stanford Chronicle will follow that work closely, with an eye not only on what is being built, but on the questions of judgment and responsibility that come with building it.