Is A.I. Actually a Bubble?
Luckily for managers, building human capital takes a long time. Or, at least, it used to: artificial intelligence is, among other things, a technology that speeds up learning and increases capability. Millions of people now use large language models. They’re not all flirting with their chatbots; instead, they’ve discovered that, with the help of A.I., they can perform tasks they’ve never done before, and learn quickly about subjects they’ve previously found inaccessible. What happens when you suddenly increase the speed with which human capital can accrue? This is one of the challenges posed by A.I. to the business world, which is struggling to figure out what the technology is worth.
For a number of reasons, it feels odd to think of A.I. as a tool for increasing human capital. Doesn’t its usefulness lie in intellectual automation, which makes hard-earned human knowledge redundant? The leading A.I. firms talk about a future in which their systems have replaced workers en masse. The big companies that are currently integrating A.I. into their businesses are almost certainly thinking along similar lines. They have to, because A.I. is expensive. Microsoft’s charges on a per-user basis for its corporate chatbot, Copilot. If a big company—one with thousands of employees—wants to purchase Copilot “seats” for its staff, it’s looking at investing many millions of dollars each year.
Will that “spend” lead to a corresponding return? The simplest way for a company to answer that question is to think in terms of new products or staffing cuts, which could generate revenue or lower costs, respectively. (The two can be combined, of course.) In its new report on “enterprise” A.I., released this week, OpenAI offers a number of case studies focussed on products that replace human labor. A typical example is an A.I. voice agent, useful for customer-service calls; the company says one such agent is currently saving companies “hundreds of millions of dollars annually.”
All this makes it seem as though worker replacement is the logical endpoint of corporate A.I. But it’s important to note that, both conceptually and as a matter of internal accounting, big companies often have difficulty figuring out how to integrate new technologies. In the nineteen-eighties and nineties, when I.T. departments were new, it was sometimes unclear how they could be internally justified. An I.T. department might spend millions each year on new computers, networking hardware, or productivity software. Did all that spending produce a return? How could its value be judged? If a large corporation installed a mainframe, it might replace some accountants. If an I.T. manager wanted to explain to her boss why computers mattered, the simplest thing she could say might have been that they could replace the typing pool.
As time went on, however, it became clear that the costs and benefits of information technology far exceeded what could be accounted for in this way. Modern companies reorganized themselves around computers; in this new world, the point of I.T. departments wasn’t to replace computer-dependent workers but to enhance their effectiveness. Workers began demanding more from their I.T. departments. In a development known as “consumerization,” the tools used by tech-savvy employees at home—such as smartphones—became more advanced than the ones provided at work; employees, who wanted to do more, began demanding upgrades. The upshot is that, today, when I.T. “spend” is proposed, no one insists that those investments do anything so crude as replace workers. The important question is whether new investments help existing employees accomplish their agendas, and keep up with their competitors at other firms.
The idea that the best use of A.I.—perhaps the only profitable use—is the direct replacement of workers combines two strains of thought: one stemming from speculations about A.I.’s future, and the other from the short-term, balance-sheet thinking that’s probably unavoidable when companies explore new technology. It is, meanwhile, profoundly at odds with the experiences many of us have while actually using A.I. Vast numbers of individuals pay for accounts with OpenAI, Anthropic, and other companies because they find that A.I. makes them more capable and productive. It is, from their perspective, a multiplier of human capital. If you have a fine-grained sense of what you want to accomplish—write software, analyze research, diagnose an illness, repair something in your house—A.I. can help you do it faster and better. Companies today spend a lot of money to train their employees; even highly qualified white-collar workers are exposed to online seminars and sent to expensive retreats, in the hopes that they will return improved. Suppose that A.I. makes some employees five or ten per cent more knowledgeable and capable. How much should a company pay for that cognitive boost?
