We keep hearing that AI is going to replace employees in their jobs and that this will mean an apocalypse in the labor market. While it is true that a paradigm shift is coming, even the most ominous voices they have toned down their speech by slowing down the real impact about employment.
Four decades ago something similar happened with the personal computer. The offices were filled with computers that did the work of various accountants, but the companies’ balance sheets they didn’t notice its benefits nothing for years. Just like collect Fortunea Goldman Sachs economist has just reviewed that history with a magnifying glass and her conclusion is that AI could take even longer than computers to begin to work. demonstrate its benefits.
The J-curve of PCs. Elsie Peng, economist at Goldman Sachs, has analyzed What happened after the arrival of the PC in 1981. The data suggests that in the first four years of the implementation of computers, productivity fell a little. Then, that productivity stagnated another four. Only in the eighth year did the first signs of growth in productivity attributable to the use of these new technologies begin to be noticed. Its peak came twelve years after its release.
The trend that the implementation of computers in companies registered is known as J curve and, before computers, it was already experienced by other disruptive technologies in the labor market, such as the steam engine and the arrival of electricity. If AI is “the new PC”, that curve would place the first signs of impact around 2030, and its peak performance would not arrive until 2034.
It’s not AI, it’s teleworking. From Goldman Sachs They admit that much of the impact of current AI is still not noticeable in official figures. This happens despite the huge investment figures that companies are making in AI systems. In the eighties with the PC it happened exactly the same: Chips and hardware were expensive and applications that brought real value, like the Internet, took years to have a large enough critical mass of users to have an impact on real productivity.
In fact, according to the investigations conducted by Stanford expert Nick Bloom, the recent increase in productivity that has been recorded would not respond to the arrival of AI, but to the massive implementation of teleworking after the Covid-19 pandemic.
You don’t invest in technology, you invest in processes. According to the data it collects Fortune According to the Goldman Satch report, for every dollar spent on hardware by companies in the 1990s, another dollar and seventy cents was needed for something that is not always taken into account: redesigning how people worked. In other words, it was not enough to fill office tables with computers, but all processes had to be redesigned and staff trained to make the technology have a visible impact.
This expense in reorganizing did not take off until a decade after the arrival of the PC to the offices. The companies that changed their way of working won, not those that bought the hardware first. Investment in data centers for AI is growing faster than then. But the expense of reorganizing work with AI it goes slower than in the nineties with PCs. A survey by the Federal Reserve Bank of Atlanta estimated at about 280 billion dollars in intangible spending linked to AI in 2026.
When the employee says no. However, the Goldman Satch analyst highlights a big difference between the implementation scenario of computers and that of AI: computers did not have workers against them. This scenario may still delay that peak productivity expected for 2034.
Work reorganization is not done on its own, but depends on the employees. If a large part of the workforce resists, the implementation becomes complicated. A study of Writer and Workplace Intelligence to 2,400 workers found that 29% of employees sabotage in some way your company’s AI strategy. Among young people, the figure rises to 44%. Other survey of WalkMe points in the same direction. 54% of workers consciously avoided using AI tools at least once in the last 30 days. They preferred to do the work themselves.
Harvard researchers they have named it. They call it self-disruptive technology. Employees do not reject AI because it fails, but because they feel that threatens your job. The study revealed that at least 30% of generative AI projects will end up abandoned due to this employee rejection.
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