by John Markoff
The first AI Winter had actually come a decade earlier in Europe. Sir Michael James Lighthill, a British applied mathematician, led a study in 1973 that excoriated the field for not delivering on the promises and predictions, such as the early SAIL prediction of a working artificial intelligence in a decade. Although it had little impact in the United States, the Lighthill report, “Artificial Intelligence: A General Survey,” led to the curtailment of funding in England and a dispersal of British researchers from the field. In a footnote of the report the BBC arranged a televised debate on the future of AI where the targets of Lighthill’s criticism were given a forum to respond. John McCarthy was flown in for the event but was unable to offer a convincing defense of his field.
A decade later a second AI Winter would descend in the United States, beginning in 1984, when Breiner managed to push Syntelligence sales to $10 million before departing. There had been warnings of “irrational exuberance” for several years when Roger Schank and Marvin Minsky raised the issue early on at a technical conference, claiming that emerging commercial expert systems contained no significant technical advances from work that had begun two decades earlier.39 The year 1984 was also when Doug Engelbart’s and Alan Kay’s augmentation ideas dramatically came within the reach of every office worker. Needing a marketing analogy to frame the value of the personal computer with the launch of the Macintosh, Steve Jobs hit on the perfect metaphor for the PC. It was a “bicycle for our minds.”
Pushed out of the company he had founded, Breiner went on to his next venture, a start-up company designing software for Apple’s Macintosh. From the 1970s through the 1980s it was a path followed by many of Silicon Valley’s best and brightest.
Beginning in the 1960s, the work that had been conducted quietly at the MIT and Stanford artificial intelligence laboratories and at the Stanford Research Institute began to trickle out into the rest of the world. The popular worldview of robotics and artificial intelligence had originally been given form by literary works—the mythology of the Prague Golem, Mary Shelley’s Frankenstein, and Karel Čapek’s pathbreaking R. U. R. (Rossum’s Universal Robots)—all posing fundamental questions about the impact of robotics on humans life. However, as America prepared to send humans to the moon, a wave of technology-rich and generally optimistic science fiction appeared from writers like Isaac Asimov, Robert Heinlein, and Arthur C. Clarke. HAL, the run-amok sentient computer in Clarke’s 2001: A Space Odyssey, not only had a deep impact on popular culture, it changed people’s lives. Even before he began as a graduate student in computer science at the University of Pennsylvania, Jerry Kaplan knew what he planned to do. The film version of 2001 was released in the spring of 1968, and over the summer Kaplan watched it six times. With two of his friends he went back again and again and again. One of his friends said, “I’m going to make movies.” And he did—he became a Hollywood director. The other friend became a dentist, and Kaplan went into AI.
“I’m going to build that,” he told his friends, referring to HAL. Like Breiner, he would become instrumental as part of the first generation to attempt to commercialize AI, and also like Breiner, when that effort ran aground in the AI Winter, he would turn to technologies that augmented humans instead.
As a graduate student Kaplan had read Terry Winograd’s SHRDLU tour de force on interacting with computers via natural language. It gave him a hint about what was possible in the world of AI as well as a path toward making it happen. Like many aspiring computer scientists at the time, he would focus on understanding natural language. A math whiz, he was one of a new breed of computer nerds who weren’t just pocket-protector-clad geeks, but who had a much broader sense of the world.
After he graduated with a degree in the philosophy of science from the University of Chicago, he followed a girlfriend to Philadelphia. An uncle hired him to work in the warehouse of his wholesale pharmaceuticals business while grooming him to one day take over the enterprise. Dismayed by the claustrophobic family business, he soon desperately needed to do something different and he remembered both a programming class he had taken at Chicago and his obsession with A Space Odyssey. He enrolled as a graduate student in computer science at the University of Pennsylvania. Once there he studied with Aravind Krishna Joshi, an early specialist in computational linguistics. Even though he had come in with a liberal arts background he quickly became a star. He went through the program in five years, getting perfect scores in all of his classes and writing his graduate thesis on the subject of building natural language front ends to databases.
As a newly minted Ph.D., Kaplan gave job audition lectures at Stanford and MIT, visited SRI, and spent an entire week being interviewed at Bell Labs. Both the telecommunications and computer industry were hungry for computer science Ph.D.s and on his first visit to Bell Labs he was informed that the prestigious lab had a target of hiring 250 Ph.D.s, and had no intention of hiring below average. Kaplan couldn’t help pointing out that 250 was more than the entire number of Ph.D.s that the United States would produce that year. He picked Stanford, after Ed Feigenbaum had recruited him as a research associate in the Knowledge Engineering Laboratory. Stanford was not as intellectually rigorous as Penn, but it was a technological paradise. Silicon Valley had already been named, the semiconductor industry was under assault from Japan, and Apple Computer was the nation’s fastest-growing company.
There was free food at corporate and academic events every evening and no shortage of “womanizing” opportunities. He bought a home in Los Trancos Woods several miles from Stanford, near SAIL, which was just in the process of moving from the foothills down to a new home on the central Stanford campus.
When he arrived at Stanford in 1979 the first golden age of AI was in full swing—graduate students like Douglas Hofstadter, the author of Gödel, Escher, Bach: An Eternal Golden Braid; Rodney Brooks; and David Shaw, who would later take AI techniques and transform them into a multibillion-dollar hedge fund on Wall Street, were all still around. The commercial forces that would lead to the first wave of AI companies like Intellicorp, Syntelligence, and Teknowledge were now taking shape. While Penn had been like an ivory castle, the walls between academia and the commercial world were coming down at Stanford. There was wheeling and dealing and start-up fever everywhere. Kaplan’s officemate, Curt Widdoes, would soon take the software used to build the S1 supercomputer with him to cofound Valid Logic Systems, an early electronic design automation company. They used newly developed Stanford University Network (SUN) workstations. Graduate student Andy Bechtolsheim—sitting in the next room—had designed the original SUN hardware, and would soon cofound Sun Microsystems, thus commercializing the hardware he had developed as a graduate student.
Kaplan rapidly became a “biz-dev” guy. It was in the air. He had an evening consulting gig developing the software for what would become Synergy, the first all-digital music keyboard synthesizer. It was chock-full of features that have become standard on modern synthesizers, and was used to produce the soundtrack for the movie Tron. Like everyone at Stanford, he was making money on the side. They were all starting companies. There was a guy in the basement, Leonard Bosack, who was trying to figure out how to interconnect computers and would eventually found Cisco Systems with his wife, Sandy Lerner, to make the first network routers.
Kaplan had a research associate job at Stanford, which was great. It was equivalent to a non–tenure track teaching position, but without the pain of having to teach. There was, however, a downside. Research staff were second-class citizens to academic faculty. He was treated like the hired help, even though he could write code and do serious technical work. His role was like Scotty, the reliable engineer on the starship Enterprise in Star Trek. He was the person who made things work. Fueled in part by the Reagan-era Strategic Defense Initiative, vast new investments were being made in artificial intelligence. It was military-led spending, but it wasn’t entirely about military applications. Corporate America was toying with the idea of expert systems. Ultimately th
e boom would lead to forty start-up companies and U.S. sales of AI-related hardware and software of $425 million in 1986. As an academic, Kaplan lasted just two years at Stanford. He received two offers to join start-ups at the same time, both in the AI world. Ed Feigenbaum, who had decided that the Stanford computer scientists should get paid for what they were already doing academically, was assembling one of the start-ups, Teknowledge. The new company would rapidly become the Cadillac of expert system consulting, also developing custom products. The other start-up was called Symantec. Decades later it would become a giant computer security firm, but at the outset Symantec began with an AI database program that overlapped with Kaplan’s technical expertise.
It was a time when Kaplan seemed to have an unlimited capacity to work. He wasn’t a big partier, he didn’t like being interrupted, and he viewed holidays as a time to get even more accomplished. Gary Hendrix, a respected natural language researcher at SRI, approached him to help with the programming of an early demo version of a program called Q&A, the first natural language database. The idea was that unskilled users would be able to retrieve information by posing queries in normal sentences. There was no money, only a promise of stock if the project took off.
Kaplan’s expertise was on natural language front ends that would allow typed questions to an expert system. What Hendrix needed, however, was a simple database back end for his demonstration. And so over a Christmas holiday at the end of 1980, Kaplan sat down and programmed one. The entire thing initially ran on an Apple II. He did it on a contingent basis and in fact he didn’t get rich. The first Symantec never went anywhere commercially and the venture capitalists did a “cram down,” a financial maneuver in which company founders often see their equity lose value in exchange for new investments. As a result, what little stock Kaplan owned was now worthless.
In the end he left Stanford and joined Teknowledge because he admired Lee Hecht, the University of Chicago physicist and business school professor who had been brought in to be CEO and provide adult supervision for the twenty Stanford AI refugees who were the Teknowledge shock troops. “Our founders have build [sic] more expert systems than anyone else,” Hecht told Popular Science in 1982.40 Teknowledge set up shop at the foot of University Ave., just off the Stanford campus, but soon moved to flashier quarters farther down the street in the one high-rise in downtown Palo Alto. In the early 1980s the office had a sleek modernist style that leaned heavily toward black.
The state-of-the-art office offered a clear indication that the new AI programs wouldn’t be cheap. Just one rule for one of the expert systems would require an interviewer to spend an hour with a human expert, and a working expert system would consist of five hundred rules or more. A complete system might cost as much as $4 million to build, but Hecht, like Breiner, believed that by bottling human expertise, corporations could reap vast savings over time. A complete system might save a manufacturer as much as $100 million annually, he told the magazine. An oil company expert system that they were prototyping might save as much as $1,000 per well per day, Hecht claimed. In the article Feigenbaum also asserted that the bottleneck would be broken when computers themselves began automatically interviewing experts.41 Hecht saw more than a hacker in Kaplan and made him a promise—if he came to Teknowledge he would teach him how to run a business. He jumped at the chance. His office was adjacent to Hecht’s and he set out to build a next-generation consulting firm whose mission was to replace the labor of human experts with software.
However, in the beginning Kaplan knew nothing about the art of selling high-technology services. He was put in charge of marketing and the first thing he did was prepare a brochure describing the firm’s services. From an entirely academic background he put together a trifold marketing flyer that was intended to attract corporate customers to a series of seminars on how to build an expert system featuring Feigenbaum as the star speaker. He sent out five thousand brochures. You were supposed to get a 2 percent response rate. Instead of a hundred responses, they got just three, and one was from a guy who thought they were teaching about artificial insemination. It was a rude shock for a group of AI researchers, confident that they were about to change the world overnight, that outside of the university nobody had heard of artificial intelligence. Eventually, they were able to pull together a small group of mostly large and defense-oriented corporations, making it possible for Hecht to say that there had “been inquiries from more than 50 major companies from all over the world,” and Teknowledge was able to do $1 million in business in two months at the beginning of 1982.42
It was indeed a Cadillac operation. They wrote the programs in Lisp on fancy $20,000 Xerox Star workstations. Worse, the whole operation was buttressed by just a handful of marketers led by Kaplan. The Teknowledge worldview was, “We’re smart, we’re great, people should just give us money.” It was completely backward, and besides, the technology didn’t really work. Despite the early stumbles, however, they eventually attracted attention. One day the king of Sweden even came to visit. True to protocol his arrival had all the trappings of a regal entourage. The Secret Service showed up first to inspect the office, including the bathroom. The assembled advance team appeared to be tracking the king in real time as they waited. Kaplan was standing breathlessly at the door when a small, nondescript gentleman in standard Silicon Valley attire—business casual—walked in unaccompanied and innocently said to the young Teknowledge executive, “Where should I sit?” Flustered, Kaplan responded, “Well, this is a really bad time because we’re waiting for the king of Sweden at the moment.” The king interrupted him. “I am the king of Sweden.” The king turned out to be perfectly tech savvy: he had a deep understanding of what they were trying to do, more so than most of their prospective customers—which, of course, was at the heart of the challenge that they faced.
There was, however, one distinct upside for Kaplan. He was invited to an evening reception for the king held at the Bohemian Club in San Francisco. He arrived and fell into conversation with a beautiful Swedish woman. They spoke for almost an hour and Kaplan thought that maybe she was the queen. As it turned out, she was a stewardess who worked for the Swedish airline that flew the royal entourage to the United States. The joke cut both ways, because she thought he was Steve Jobs. There was a happy ending. They would date for the next eight years.
Teknowledge wasn’t so lucky. The company had a good dose of “The Smartest Guys in the Room” syndrome. With a who’s who of some of the best engineers in AI, they had captured the magic of the new field and for what might otherwise pass for exorbitant consulting fees they would impart their alchemy. However, artificial intelligence systems at the time were little more than accretions of if-then-else statements packaged in overpriced workstations and presented with what were then unusually large computer displays with alluring graphical interfaces. In truth, they were more smoke and mirrors than canned expertise.
It was Kaplan himself who would become something of a Trojan horse within the company. In 1981 the IBM PC had legitimized personal computers and dramatically reduced their cost while expanding the popular reach of computing. Doug Engelbart and Alan Kay’s intelligence augmentation—IA—meme was showing up everywhere. Computing could be used to extend or replace people, and the falling cost made it possible for software designers to take either path. Computing was now sneaking out from behind the carefully maintained glass wall of the corporate data center and showing up in the corporate office supplies budget.
Kaplan was quick to grasp the implications of the changes. Larry Tesler, a former SAIL researcher who would work for Steve Jobs in designing the Lisa and the Macintosh and help engineer the Newton for John Sculley, had the same early epiphany. He had tried to warn his coworkers at Xerox PARC that cheap PCs were going to change the world, but at the time—1975—no one was listening. Six years later, many people still didn’t comprehend the impact of the falling cost of the microprocessor. Teknowledge’s expert system software was then designed and deployed on an
overpriced workstation, which cost about $17,000, and a complete installation might run between $50,000 and $100,000. But Kaplan realized that PCs were already powerful enough to run the high-priced Teknowledge software handily. Of course, the business implication was that without their flashy workstation trappings, they would be seen for what they really were—software packages that should sell for PC software prices.
Nobody at Teknowledge wanted to hear this particular heresy. So Kaplan did what he had done a few years earlier when he had briefly helped found Symantec in his spare time at Stanford. It was Christmas, and everyone else was on vacation, so he holed up in his cottage in the hills behind Stanford and went to work rewriting the Teknowledge software to run on a PC. Kaplan used a copy of Turbo Pascal, a lightning-fast programming language that made his version of the expert system interpreter run faster than the original workstation product. He finished the program over the holidays and came in and demoed the Wine Advisor, the Teknowledge demonstration program, on his “toy” personal computer. It just killed the official software running on the Xerox Star workstation.
All hell broke loose. Not only did it break the Teknowledge business model because software for personal computers was comparatively dirt cheap, but it violated their very sense of their place in the universe! Everyone hated him. Nonetheless, Kaplan managed to persuade Lee Hecht to commit to putting out a product based on the PC technology. But it was crazy. It meant selling a product for $80 rather than $80,000. Kaplan had become the apostate and he knew he was heading for the door. Ann Winblad, who was then working as a Wall Street technology analyst and would later become a well-known Silicon Valley venture capitalist, came by and Kaplan pitched her on the changing state of the computing world.