The Rise and Fall of Thinking Machines

As a result, there still wasn't much of a market for Connection Machines. But thanks to the support of DARPA, which continued to broker deals, Thinking Machines didn't have to seriously contemplate building a machine that had a natural market. "Our charter," says Tucker, "wasn't to look at a machine and figure out the commercial profit. Our charter was to build an interesting machine." But the definition of interesting would soon change.

In the late 1980s, DARPA and the Bush administration, having accepted the fact that the end of the cold war had reduced the urgency for military supercomputing, came up with a new challenge for parallel computing. They began to talk about solving what D. Allan Bromley, the president's science adviser, dubbed "grand challenge" scientific problems: modeling the global climate, analyzing the folding of proteins, mapping the human genome, predicting earthquakes, revealing the nuances of quantum mechanics. The problems didn't require artificial intelligence, just enormous computing power.

The official name of the new project was the High Performance Computing and Communication (HPCC) program, and DARPA was the lead agency, with a projected budget of several billion dollars through 1996 to accomplish its goals. At the top of the list: building a computer capable of a teraflop -- a trillion floating-point operations per second.

Not surprisingly, Thinking Machines had an inside track on getting a chunk of the projected budget. While other computer companies were out wooing customers, Handler had been cultivating a friendship with Bromley. As soon as Thinking Machines promised it would have a scaled-down version of a teraflop machine ready by 1992, the agency awarded the company an initial contract of $12 million.

In the meantime, several computer companies were exploring a new technology -- a compromise between the comfort of sequential computing and the performance of massively parallel machines. A sort of "moderately parallel" design, the technology entailed stringing together a smaller number of the powerful, cheap, off-the-shelf microprocessors used in PCs and workstations -- rather than the thousands of highly customized but less powerful processors used in the Connection Machines -- into a single supercomputer that would work with existing software.

The cost advantages of using off-the-shelf chips, as well as the functional advantage of running existing software, seemed overwhelming -- especially considering the fact that few customers outside the tiny AI community had much interest in Thinking Machines' massively parallel design. Even Hillis eventually came around and chose the moderately parallel design for the company's next generation of machine. Unfortunately, the old dream died hard: the decision came only after 18 months of internal bickering. Once again, the company was off to a late start.

What's more, there were signs that the company was still chasing the wrong market. Industry analysts in 1992 were projecting that the growth in supercomputers was not in science but in business applications -- in particular in what's known as "database mining," an area that could well become, as IBM parallel-computing expert Art Williams put it, "the killer application" for parallel computers. With the country in a recession, businesses needed every competitive advantage they could get, which meant knowing their customers' preferences and buying habits in intimate detail. They had begun to collect all conceivable data and were feeding them into their mainframes, looking for any insight that would help them maximize profits. But it sometimes took mainframes hours, even days, to churn out the answer to a single question. So large companies were beginning to check out parallel computers.

In fact, Thinking Machines had sold two Connection Machines to American Express. That got management at Thinking Machines talking about starting a business supercomputer group, an idea that appears at first to be a no-brainer. But at Thinking Machines the idea got stuck in endless discussions. Hillis and Handler already were bitter about having to target general scientific computing rather than artificial intelligence; they weren't about to jump on the idea of servicing mere merchants. Hillis later complained about the injustice of a world where "the real money is in handling Wal-Mart's inventory rather than searching for the origins of the universe."

Nonetheless, thanks to DARPA, Thinking Machines went into the black for the first time. In 1989 the company reported a profit of $700,000 on revenues of $45 million. Handler promptly signed a 10-year lease with the Carter Ink Building for a whopping $6 million a year -- about $37 a square foot. (Lotus Development Corp., which was virtually across the street from Thinking Machines, was paying $8 a square foot.) Thinking Machines also hired another 120 employees, bringing the total to over 400. Meanwhile, the company had developed an image as one of the leading high-tech companies in the country. It was, says Stephen Wolfram, who founded the highly successful software company Mathematica, "the place that foreign trade delegations would come to visit to see where American business was at these days."

Yet competition was looming. Cray Research launched a crash program in 1990 to get a massively parallel machine on the market within two years. IBM was doing the same. Even Fujitsu Limited, one of Japan's major supercomputer manufacturers, was in the process of opening a parallel-computing lab, looking toward marketing a 1,000-processor machine.

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