The AI History That Explains Fears of a Bubble

Concerns among some investors are mounting that the AI sector, which has singlehandedly prevented the economy from sliding into recession, has become an unsustainable bubble. Nvidia, the main supplier of chips used in AI, became the first company worth $5 trillion dollars. Meanwhile, OpenAI, the developer of ChatGPT, has yet to make a profit and is burning through billions of investment dollars per year. Still, financiers and venture capitalists continue to pour money into OpenAI, Anthropic, and other AI startups. Their bet is that AI will transform every sector of the economy and, as happened to the typists and switchboard operators of yesteryear, replace jobs with technology.

Yet, there are reasons to be concerned that this bet may not pay off. For the past three decades, AI research has been organized around making improvements on narrowly-specified tasks like speech recognition. With the emergence of large language models (LLMs) like ChatGPT and Claude, however, AI agents are increasingly being asked to do tasks without clear methods for measuring improvement.

Take for example the seemingly mundane task of creating a PowerPoint presentation. What makes a good presentation? We may be able to point to best practices, but the “ideal” slideshow depends on creative processes, expert judgments, pacing, narrative sense, and subjective tastes that are all highly contextual. Annual review presentations differ from start-up pitches and project updates. You know a good presentation when you see it—and a bad one when it flops. But the standardized tests that the field currently uses to evaluate AI cannot capture the above qualities.

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This may seem like a minor problem, but crises of evaluation have contributed to historical AI busts. And without accurate measures of how good AI really is, it’s hard to know whether we’re headed towards another one now.

Read more: The Architects of AI Are........

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