(Opinion) The market’s high expectations for chatbots and generative AI to revolutionize industries and reduce costs may be greatly exaggerated.
Despite the soaring market valuations of companies like Microsoft and Nvidia following the launch of ChatGPT and other generative AI models, the actual revenue potential of these technologies remains questionable.
Market research firms predict the generative AI market to reach US$126.5 billion by 2031, but this figure seems far too low to justify the current sky-high valuations.
Even if chatbots could replace every help desk employee in the US and a significant portion of programmers, the estimated savings would only amount to a few billion dollars, not anywhere close to the projected market value.
Furthermore, the promises of generative AI in fields like medical diagnostics have often fallen short.
The use of AI for tasks like reading X-rays has not delivered the anticipated benefits and has even led to reduced accuracy in some cases.
This isn’t the first time the AI hype has inflated stock valuations.
Autonomous vehicles (AVs) and virtual reality (VR) are two examples where the market excitement didn’t align with reality.
AVs require low-latency data transmission, which current networks like America’s 5G can’t provide, while VR headsets have struggled to find widespread adoption due to their limitations.
Generative AI models like ChatGPT also face significant limitations in understanding human thinking and communication.
They lack the ability to make nuanced connections and engage in self-referential humor.
Their interactions with humans are often unsatisfactory, making it unlikely that they can replace help desk representatives or radiologists in the near future.
However, AI does excel in routine tasks such as quality control and operating autonomous machinery.
For example, AI-powered systems can swiftly identify and handle defective items on a conveyor belt or efficiently manage cranes and trucks at a port.
The true economic value of AI lies in its ability to free humans from repetitive and mechanical tasks rather than attempting to replicate human capabilities.
The previous AI bubbles serve as a cautionary reminder that overinflated expectations can lead to disappointment when the promised breakthroughs fail to materialize.