People thought the internet would fail. So could we be underestimating gen AI’s long-term potential as we focus on short-term challenges?
Is it possible that the generative AI revolution will never mature beyond its current state? That seems to be the suggestion from deep learning skeptic Gary Marcus in his recent blog post in which he pronounced the generative AI “bubble has begun to burst.” Gen AI refers to systems that can create new content — such as text, images, code or audio — based on patterns learned from vast amounts of existing data. Certainly, several recent news stories and analyst reports have questioned the immediate utility and economic value of gen AI, especially bots based on large language models (LLMs).
We’ve seen such skepticism before about new technologies. Newsweek famously published an article in 1995 that claimed the Internet would fail, arguing that the web was overhyped and impractical. Today, as we navigate a world transformed by the internet, it’s worth considering whether current skepticism about gen AI might be equally shortsighted. Could we be underestimating AI’s long-term potential while focusing on its short-term challenges?
For example, Goldman Sachs recently cast shade in a report titled: “Gen AI: Too much spend, too little benefit?” And, a new survey from freelance marketplace company Upwork revealed that “nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.”
A year ago, industry analyst firm Gartner listed gen AI at the “peak of inflated expectations.” However, the firm more recently said the technology was slipping into the “trough of disillusionment.” Gartner defines this as the point when interest wanes as experiments and implementations fail to deliver.
While Gartner’s recent assessment points to a phase of disappointment with early gen AI, this cyclical pattern of technology adoption is not new. The buildup of expectations — commonly referred to as hype — is a natural component of human behavior. We are attracted to the shiny new thing and the potential it appears to offer. Unfortunately, the early narratives that emerge around new technologies are often wrong. Translating that potential into real world benefits and value is hard work — and rarely goes as smoothly as expected.
Analyst Benedict Evans recently discussed “what happens when the utopian dreams of AI maximalism meet the messy reality of consumer behavior and enterprise IT budgets: It takes longer than you think, and it’s complicated.
