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Even the smartest experts have a hard time predicting the future of technology. Consider the example of Bob Metcalfe, the inventor of Ethernet who, in 1995, boldly predicted that the Internet would experience a catastrophic collapse – or “gigalapse” – the following year.
But when he was wrong, Metcalfe literally ate his own words. To chants of “Eat, baby, eat!” at a technology industry event, Metcalfe tore up a copy of his InfoWorld column on the futureHe put it in a blender and consumed the resulting pulp.
Metcalfe’s unhappy experience accepted with good grace and humility – is one of dozens of examples of erroneous predictions contained in the illuminating online resource that is the pessimists archive. Spanning the invention of the camera, electricity, airplanes, television and the computer, the archive records the many fanciful ways in which successive generations of technological experts have gone completely wrong.
The archive is worth exploring when considering the torrent of predictions about the wondrous technology of our age: artificial intelligence.
The only certain prediction is that the vast majority of these predictions will be exaggerated. Those optimists who predict that AI will imminently usher in a glorious new era of radical abundance will likely be disappointed. But those pessimists who predict that AI will soon lead to human extinction are no less likely to be wrong. On the other hand, no one will be around to congratulate them if they are right.
With AI, it may be easier to set the direction of travel than the speed of travel. Just as the industrial revolution magnified physical strength, the cognitive revolution magnified the brain. AI is best seen as the latest general-purpose technology that can be applied to an infinite number of uses, says Arkady Volozh, founder of Amsterdam-based startup Nebius, which builds and runs AI models for clients across the world. variety of industries. .
“AI is like electricity, computers or the Internet,” he says. “It’s like a magic dust that makes everything better. More and more functions will be automated in a more efficient way. Just as an excavator is more powerful than a person with a shovel, routine operations can be automated with AI.”
However, with earlier mainstream technologies such as railways and electricity, it has often been the case that it may take decades before they boost productivity. New infrastructure must be built. It is necessary to adopt new ways of working. New products and services must be launched.
Meanwhile, the adoption of new technologies can actually suppress productivity for a time as companies and their employees adapt to new ways of working. In fact, new technologies can even produce an increase in unproductive work: How many useless emails have you read today?
Some economists have described this phenomenon as J Curve— as productivity first falls, before later rising.
“General-purpose technologies such as AI enable and require significant complementary investments, including the co-invention of new processes, products, business models, and human capital,” say economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson. they argue in an article from the National Bureau of Economic Research. These complementary investments are often not well reflected in official economic statistics and can take a long time to be reflected in higher productivity growth.
Zooming out even further, it may be misleading to talk about AI as a separate revolution and not a continuation of the information technology revolution that began in the 1970s. According to an essay this year by economic historian Carlota Pérez: “A revolutionary technology is not the same as a technological revolution.”
In his 2002 book Technological revolutions and financial capitalPérez identified five major technological transformations, beginning with a wave of creative destruction followed by a massive diffusion of innovation and a golden era of economic growth. This pattern has been repeated periodically: beginning with the Industrial Revolution in the 1770s; followed by the steam and railroad era of the 1830s; the electrical and engineering era of the 1870s; the mass production era of the 1910s; and our current information revolution.
All of these technological revolutions have been accompanied by transformations of government and society, resulting in the creation of new institutions, such as unions, regulatory agencies, and welfare states, to help manage tumultuous change.
Now, in Perez’s view, we are just beginning to imagine the institutions needed to address our current IT revolution and counter economic inequality, autocratic populism, and climate-related disasters. “Changing this broader political-economic context has become the most urgent task of our time,” he argued earlier this year.
Designing appropriate new institutions will be a serious challenge, even with the help of AI.