Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [10]
A new chief executive, Louis V. Gerstner, arrived in 1993 and transformed IBM. He sold off or shuttered old manufacturing divisions and steered the company toward businesses based on information. IBM did not have to sell machinery to be a leader in technology, he said. It could focus on the intelligence to run the technology—the software—along with the know-how to put the systems to good use. That was services, including consulting, and it led IBM back to growth.
Technology, in the early ’90s, was convulsing entire industries and the new World Wide Web promised even more dramatic change. IBM’s customers, which included virtually every blue-chip company on the planet, were confused about how these new networks and services fit into their businesses. Did it make sense to shift design work to China or India and have teams work virtually? Should they remake customer service around the Web? They had loads of questions, and IBM decided it could sell the answers. It could even take over tech operations for some of its customers and charge for the service.
This push toward services and software continued under Gerstner’s successor, Samuel J. Palmisano. Two months after Charles Lickel came back from Poughkeepsie with the idea for a Jeopardy computer that could play Jeopardy, IBM sold its PC division to Lenovo Group of China. That year IBM Global Services registered $40 billion in sales, more than the $31 billion in hardware sales and a much larger share of profits. (By 2009, services would grow to $55 billion, nearly 60 percent of the company’s revenue. And the consultants working in the division sold lots of IBM software, which registered $21 billion in sales.) Naturally, a Jeopardy computer would run on IBM hardware. But the heart of the system, like IBM itself, would be the software created to answer difficult questions.
A Jeopardy machine would also respond to another change in technology: the move toward human language. For most of the first half-century of the computer age, machines specialized in orderly rows of numbers and words. If the buyers in a database were listed in one column, the products in another, and the prices in a third, everything was clear: Computers could run the numbers in a flash. But if one of the customers showed up as “Don” in one transaction and “Donny” in another, the computer viewed them as two people: The two names represented different strings of ones and zeros, and therefore Don ≠ Donny. Computers had no sense of language, much less nicknames. In that way, they were clueless. The world, and all of its complexity, had to be simplified, structured and spoon-fed to these machines.
But consider what hundreds of millions of ordinary people were using computers for by 2004. They were e-mailing and chatting. Some were signing up for new social networks. (Facebook launched in February of that year.) Online humanity was creating mountains of a messy type of digital data: human language. Billions of words were rocketing through networks and piling up in data centers. Those words expressed what millions of people were thinking, desiring, fearing, and scheming. The potential customers of IBM’s clients were out there spilling their lives. Entire industries grew by understanding what people were saying and predicting what they might want to do, where they might want to go, and what they were eager to buy. Google was already mining and indexing words on the Web, using them to build a media and advertising empire. Only months earlier, Google had debuted as a publicly traded company, and the new stock was sky-rocketing.
IBM wasn’t about to mix it up with Google in the commercial Web. But Big Blue needed state-of-the-art tools to provide its corporate customers with the fastest and most insightful read of the words cascading through their networks. To keep a grip on its gold-plated consulting business, IBM required the