AI - losing steam?

A recent article in the Economist said, " Since peaking last month the share prices of Western firms driving the AI revolution have dropped by 15%. A growing number of observers now question the limitations of large language models, which power services such as Chatgpt. Big tech firms have spent tens of billions of dollars on AI models, with even more extravagant promises of future outlays. Yet according to the latest data from the Census Bureau, only 4.8% of American companies use AI to produce goods and services, down from a high of 5.4% early this year. Roughly the same share intend to do so within the next year".

What is going on? We have seen some technologies go through the hype cycle, followed by disillusionment followed by a gradual slope of real adoption. Gartner came up with this cycle years ago. After an initial period of irrational euphoria and overinvestment, hot new technologies enter the “trough of disillusionment”, where sentiment sours. Everyone starts to worry that adoption of the technology is proceeding too slowly, while profits are hard to come by. However, as night follows day, the tech makes a comeback. Investment that had accompanied the wave of euphoria enables a huge build-out of infrastructure, in turn pushing the technology towards mainstream adoption.

Let us look at a two industries that have gone through this cycle. The Railway industry in the 19th. century England saw a huge buildup of expectation. There was a bubble followed by a crash in stock prices. Then with railway tracks as infrastructure, the industry picked up speed. How about the Internet in the 1990s? Euphoria was high and everyone predicted that within a couple of years online shopping will be commonplace. Then came the dotcom bust in 2000 when 135 big dotcoms went bust including garden.com and pets.com. However, the telecom industry laid out fiber-optic cables in anticipation which became the infrastructure for today's Internet.

Cloud Computing did not go through such a hype cycle. It followed a straight line path from start to maturity. Similar is the case with Solar Power. Some sectors went from euphoria to panic very quickly. For example, with Web3 everyone thought homes will have 3D printers and carbon nanotubes. In social media, Twitter (now X) had a halting journey. Facebook took a few years for its buildup from college campuses to mainstream use.

For starters, versions of AI itself have for decades experienced periods of hype and despair, with an accompanying waxing and waning of academic engagement and investment, but without moving to the final stage of the hype cycle. There was lots of excitement over AI in the 1960s, including over ELIZA, an early chatbot. This was followed by AI winters in the 1970s and 1990s. As late as 2020 research interest in AI was declining, before zooming up again once generative AI came along with ChatGPT.

The Economist article concluded by saying, "AI could still revolutionize the world. One of the big tech firms might make a breakthrough. Businesses could wake up to the benefits that the tech offers them. But for now the challenge for big tech is to prove that AI has something to offer the real economy. There is no guarantee of success. If you must turn to the history of technology for a sense of AI's future, the hype cycle is an imperfect guide. A better one is “easy come, easy go”.