想想泡沫出现的预警信号。风险投资家在赞助人工智能上已经超越了热心的范畴。研究公司PitchBook表示，这些人去年给1,028家人工智能相关的初创公司提供了资金，比起2013年的291家大幅提升。其中有26家公司的名字中包含“人工智能”，而五年前仅有一家。此外，还有大量会议打算向愚昧的管理者解释人工智能。在今年的瑞士达沃斯世界经济论坛（World Economic Forum）年度会议的议程中，有不少于11场涉及人工智能的专题讨论，题目都类似于“设计你的人工智能战略”、“为人工智能竞赛设立规则”。（《财富》杂志也赶了一次时髦，2018年在中国广州举办的《财富》全球科技论坛中也满是关于人工智能的讨论。）
Like bees to honey, tech trends generate hype. Merely appending the word “dotcom” to a company’s name drove up stock prices in the Internet’s salad days. Cloud computing, big data, and cryptocurrencies each have taken their turn in the hype cycle in recent years. Every trend brings genuinely promising technological developments, befuddling buzzwords, enthusiastic investors, and reassuring consultants offering enlightenment—for a fee, naturally.
Now the catchall phrase of artificial intelligence is shaping up as the defining technological trend of the moment. And yet, because the claims of what it will achieve are so grand, businesses risk raising their hopes for A.I. too high—and wasting money by trying to apply the technology to problems it can’t solve.
Consider the bubbly warning signs. Venture capitalists are beyond eager to fund A.I. They staked 1,028 A.I.-related startups last year, up from 291 in 2013, says researcher PitchBook. Twenty-six of those companies had “A.I.” in their names, compared with one five years earlier. Then there’s the profusion of conferences promising to explain A.I. to the benighted manager. At the annual meeting of the World Economic Forum in Davos, Switzerland, the agenda this year included no fewer than 11 panels that reference A.I., with names like “Designing Your A.I. Strategy” and “Setting Rules for the A.I. Race.” (Fortune has gotten into this act too: Its 2018 Global Tech Forum in Guangzhou, China, was dominated by A.I. discussions.)
The result is a serious subject running the risk of jumping the shark. “If advocates are not careful, they will have successfully Bitcoinized A.I.,” says Michael Schrage, a researcher at MIT’s Initiative on the Digital Economy.
Make no mistake—artificial intelligence is more than a fad. It represents a whole new way of doing business by turbocharging the existing trends of automation, sensor-based industrial monitoring, and algorithmic analysis of business processes. Computer science was already helping machines perform routine tasks more quickly than humans. The new techniques of A.I.—combined with ever faster computing power and the accumulation of years of digitized data—mean that for the first time computers learn the tasks humans require of them rather than merely doing as they’re told.
卡耐基梅隆大学（Carnegie Mellon University）的机器学习教授汤姆·米切尔表示，它引发的结果不亚于“影响未来十年社会和生活方式的主要推动力量之一”。对商业而言也是如此：研究公司IDC预测，未来三年内在人工智能上的投入将接近800亿美元。咨询公司埃森哲（Accenture）的首席技术和创新官保罗·多尔蒂推测这个数字还可能偏低，因为“它没有计算各公司围绕人工智能进行转型的投资”。
The result, says Tom Mitchell, a machine-learning professor at Carnegie Mellon University, is nothing less than “one of the major forces for society and lifestyle of the next decade.” And commerce too: Researcher IDC predicts spending on A.I. will near $80 billion in three years. Paul Daugherty, chief technology and innovation officer of consultant Accenture, reckons that figure will prove low because “it doesn’t account for the investment companies are making in transformation around A.I.”
Yet, as is the case with any exciting technology, there are limits to what A.I. can accomplish. Self-driving cars are the perfect example. We already have the technology for them to operate under ideal circumstances, but even John Krafcik—CEO of Alphabet’s self-driving car subsidiary Waymo—admits they’ll never be able to drive in all weather conditions without some human input. What’s more, computers are very good at learning clearly defined tasks, like identifying people in photographs or accurately transcribing speech. But understanding human motivations or drawing nuanced conclusions from text—insights at which humans excel—remains beyond the machines. Says CMU’s Mitchell, “We’re still in the very early stages of trying to productize this.”
What A.I. can’t yet do ought to be of some comfort to CEOs. Susan Athey, a professor of the economics of technology at Stanford University, reassures managers in her executive education courses of their worth—and also the limitations of the A.I. scientists they hire. “New Ph.D.s are all bought in, but they don’t have the experience of what doesn’t work, which projects not to do,” she says. A.I., says Athey, justifiably “feels magical.” But it is best at analyzing situations its designers have prepared it to interpret, as opposed to making decisions on subjects it hasn’t seen before. “It’s just not right that your A.I. will manage for you,” says Athey.
A.I., in other words, is no silver bullet. Jean-Fran?ois Gagné, CEO of the Montreal software startup Element AI, reminds clients that A.I. solutions are only as good as the accumulated data being fed into them. “The opportunity every organization is looking at is the ability to have adaptive systems,” he says. “It is a journey. It is not something you can buy and suddenly flip a switch. By the very definition of A.I., it takes time to learn.”
Gagné analogizes the process of building a useful A.I. to the difference between “teaching your children the right thing versus getting the right behavior in adulthood.” It will take at least as long to know if businesses were able to properly grasp this A.I. moment—or if it was another extremely expensive and elusive money pit.
A version of this article appears in the February 2019 issue of Fortune with the headline “Cast a Critical Eye Over the A.I. Hype Merchants.”