The new company's managers immediately got into a disagreement over the market for supercomputers. Hillis and Handler (Minsky quickly became a figurehead at the company) wanted to design a machine strictly along the lines of Hillis's thesis, a machine that would have its maximum impact as a research tool for scientists studying artificial intelligence. (Hillis envisioned his machine eventually becoming a sort of public-intelligence utility into which people would tap their home PCs, thereby bringing artificial intelligence to the world.) Howard Res-nikov, a research director recruited by Minsky, on the other hand, argued for a more flexible architecture that could support whatever style of computing was needed to solve real-world problems. After all, the more problems the machine could solve, the more sales prospects there would be.
For a year, while the argument went on, the company did nothing. Finally, Handler and Hillis won out. "We had all sorts of reasoned discussions," says Resnikov, "and then emotional decisions were fundamentally made by Sheryl and Danny." Resnikov lasted another two years before he quit. Emotional decision making would last almost until the company fell.
* * *
In the first few years it didn't seem to matter. Thinking Machines didn't need to make good business decisions because it had the Defense Advanced Research Projects Agency. A research arm of the Defense Department, DARPA was looking for computer architectures that would enable tanks, missiles, and other weapons to recognize enemy targets and understand spoken orders. In 1984 Hillis and his colleagues at Thinking Machines repackaged Hillis's thesis and pitched it to DARPA. The agency responded by offering the company a multiyear $4.5-million contract. Now all Thinking Machines had to do was build one of the world's fastest computers in two years' time.
The company promptly went on a hiring binge. Its prime hunting grounds were the computer-science departments of MIT, Carnegie-Mellon, Yale, and Stanford -- which happened to house four of the world's leading AI labs. Everyone, from programmers to administrative assistants, had to be interviewed by Handler, who had a very specific, if mysterious, idea of who would be good enough to work for Thinking Machines. (Many researchers later reported that once they were hired, they never got to speak to Handler again -- even when they were alone with her in an elevator.)
In fact, Thinking Machines was becoming Handler's aesthetic creation as much as the Connection Machine was Hillis's. In the summer of 1984 the company moved into its new home -- the top two floors of the old Carter Ink Building in Cambridge, Mass., a few blocks from MIT. Handler personally oversaw the design of the office space, insisting that each office be painted a different and specific color. Huge open spaces were created to stimulate idea sharing and creativity. A plush cafeteria was put in, complete with a gourmet chef. Couches were scattered throughout the offices so that researchers could take naps or even sleep there overnight, which many of them did. And the soft-drink machine was wired to a terminal. Researchers who wanted a drink simply typed in their choice.
In short, Thinking Machines was becoming a hacker's paradise. The thinking, says Lew Tucker, one of the company's research directors, was that "if they were fed, they'd practically live at Thinking Machines." If Hillis disapproved, he didn't make it known. Having taken to commuting in an antique fire engine, he could hardly play the pragmatist to Handler's stylist.
In May 1985, Thinking Machines announced the impending completion of the first Connection Machine, the CM-1. The announcement would be made on the third floor of the Carter Ink Building. Handler had every surface on the new floor repainted a slightly different shade of mauve. When it was done, she wasn't satisfied. So she had her researchers and scientists paint it again.
The CM-1 was an AI researcher's dream. Unfortunately, few AI labs could afford a $5-million computer, and, as Resnikov had predicted, hardly anyone else was interested. When it came to general scientific computing, the CM-1 was "a dog," in the words of Gordon Bell, a computer guru and architect of the famous VAX computer at Digital Equipment Corp. It had no facility for running FORTRAN, the de facto standard computer language of science; nor could it do what are known as "floating-point operations," the operations that manipulate numbers in scientific computation.
Thinking Machines sold seven CM-1s, but only because DARPA brokered and subsidized most of the deals. If the company was going to stay in business, it would need a machine that could pull its weight outside AI research. Unfortunately, according to Resnikov, the decision to tailor the CM-1 to the AI "nonmarket" cost Thinking Machines three years in the real-world marketplace.
In April 1986, Thinking Machines announced the arrival of the CM-2, a machine the scientific community actually could use. The CM-2 was able to run FORTRAN and to do floating-point operations. It was also a piece of work artistically: a five-foot cube of cubes -- done up in what Thinking Machines employees called "Darth Vader black" -- in whose innards red lights flickered mysteriously. But the machine's exotic massively parallel technology still needed special software, which meant its users had to learn new programming techniques. The CM-2 might be more like the human brain than a sequential computer like the Cray was, but scientists knew how to write programs for the Cray. Many of Thinking Machines' first customers, says Dave Waltz, who ran the company's AI group, did most of their computing on the floating-point processors, ignoring the 64,000 single-bit processors.
