New technologies create excitement. The invention of the railroad led to the “Railway Mania” in 1840s UK, which saw investors pile into railway stocks. In the 1920s the radio similarly captured the imagination. And more recently, euphoria over internet adoption saw the Nasdaq rise fivefold between 1995 and 2000. The hysteria stems in part from high expectations over how far and how fast innovations can boost human wellbeing and productivity, in addition to the “fear of missing out”. But in each case, the initial bubble burst as reality caught up with expectations. The rise of generative AI — particularly large language models, like OpenAI’s ChatGPT — has sparked a similar frenzy. Since ChatGPT’s launch last November, glowing reports on the potential economic impact of the technology — which can automate tasks from writing essays to generating code — have been coming thick and fast. Goldman Sachs estimated that it could drive gains in productivity that could raise global GDP by 7 per cent over a ten-year period.
Estimating the benefits is largely a guessing game. Most experts agree it will take time to pay off. Indeed, the impact of prior technologies has often been conceptualised using the “J-curve effect”, where productivity may initially fall as it is adopted, before rising steeply. Will generative AI follow a similar path?
Electricity, railways, and computers all took decades before they generated productivity booms. In comparison, AI technology is far less capital-intensive and does not require the widescale development of new infrastructure. ChatGPT can be accessed at the click of a mouse; over 100mn people have already done so. Generative AI systems will, however, need vast computing power, which is not cheap. Companies will need to retrain staff and adjust their business models. This will take time, though user-friendliness will make adoption easier than prior technologies.