𝙵𝚘𝚛 𝚜𝚘𝚖𝚎, 𝚝𝚑𝚊𝚝 𝚒𝚜 𝚙𝚛𝚘𝚘𝚏 𝚝𝚑𝚎 𝚝𝚎𝚌𝚑 𝚠𝚒𝚕𝚕 𝚒𝚗 𝚝𝚒𝚖𝚎 𝚜𝚞𝚌𝚌𝚎𝚎𝚍． 𝙰𝚛𝚎 𝚝𝚑𝚎𝚢 𝚛𝚒𝚐𝚑𝚝？
Silicon Valley’s tech bros are having a difficult few weeks. A growing number of investors worry that artificial intelligence (AI) will not deliver the vast profits they seek. 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.
Gently raise these issues with a technologist and they will look at you with a mixture of disappointment and pity. Haven’t you heard of the “hype cycle”? This is a term popularised by Gartner, a research firm—and one that is common knowledge in the Valley. After an initial period of irrational euphoria and overinvestment, hot new technologies enter the “trough of disillusionment”, the argument goes, 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. Is the hype cycle a useful guide to the world’s ai future?
It is certainly helpful in explaining the evolution of some older technologies. Trains are a classic example. Railway fever gripped 19th-century Britain. Hoping for healthy returns, everyone from Charles Darwin to John Stuart Mill ploughed money into railway stocks, creating a stockmarket bubble. A crash followed. Then the railway companies, using the capital they had raised during the mania, built the track out, connecting Britain from top to bottom and transforming the economy. The hype cycle was complete. More recently, the internet followed a similar evolution. There was euphoria over the technology in the 1990s, with futurologists predicting that within a couple of years everyone would do all their shopping online. In 2000 the market crashed, prompting the failure of 135 big dotcom companies, from garden.com to pets.com. The more important outcome, though, was that by then telecoms firms had invested billions in fibre-optic cables, which would go on to became the infrastructure for today’s internet.
Although ai has not experienced a bust on anywhere near the same scale as the railways or dotcom, the current anxiety is, according to some, nevertheless evidence of its coming global domination.
“The future of ai is just going to be like every other technology. There’ll be a giant expensive build-out of infrastructure, followed by a huge bust when people realise they don’t really know how to use AI productively, followed by a slow revival as they figure it out,” says Noah Smith, an economics commentator.
Is this right? Perhaps not. 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.
It is also easy to think of many other influential technologies that have bucked the hype cycle. Cloud computing went from zero to hero in a pretty straight line, with no euphoria and no bust. Solar power seems to be behaving in the same way. Social media, too. Individual companies, such as Myspace, fell by the wayside, and there were concerns early on about whether it would make money, but consumer adoption increased monotonically. On the flip side, there are plenty of technologies for which the vibes went from euphoria to panic, but which have not (or at least not yet) come back in any meaningful sense. Remember Web3? For a time, people speculated that everyone would have a 3d printer at home. Carbon nanotubes were also a big deal.
Anecdotes only get you so far. Unfortunately, it is not easy to test whether a hype cycle is an empirical regularity.
“Since it is vibe-based data, it is hard to say much about it definitively,” notes Ethan Mollick of the University of Pennsylvania.
But we have had a go at saying something definitive, extending work by Michael Mullany, an investor, that he conducted in 2016. The Economist collected data from Gartner, which for decades has placed dozens of hot technologies where it believes they belong on the hype cycle. We then supplemented it with our own number-crunching.
Over the hill
We find, in short, that the cycle is a rarity. Tracing breakthrough technologies over time, only a small share—perhaps a fifth—move from innovation to excitement to despondency to widespread adoption. Lots of tech becomes widely used without such a rollercoaster ride. Others go from boom to bust, but do not come back. We estimate that of all the forms of tech which fall into the trough of disillusionment, six in ten do not rise again.
Our conclusions are similar to those of Mr Mullany: “An alarming number of technology trends are flashes in the pan.”
Ai could still revolutionise 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”.
