Among other issues, such as potential payback from deploying generative artificial intelligence, is the timing of the payback, and history suggests payback will take far longer than many expect. If AI does develop as a general-purpose technology, as were earlier GPTs including steam power and electricity, and even granting that many technological innovations--which are largely virtual--can propagate much faster than did earlier innovations.
The initial impact of steam power and electricity on productivity was not as immediate or dramatic as expected.
Consider steam power. Early adoption was slow. The first practical steam engine was invented by Thomas Newcomen in 1712. James Watt significantly improved the steam engine in 1765 and kicked off the process of commercialization. Still, by 1830, only 165,000 horsepower of steam was in use in Britain, for example.
Even in 1870, about two-thirds of steam power was concentrated in just three industries: coal mining, cotton textiles, and metal manufactures. So, while invented in the early 18th century, it took about 50 to 75 years for steam power to begin having truly widespread and transformative effects on industry and the economy.
The major productivity gains from electricity in the United States came in the 1920s, about 40 years after Thomas Edison first distributed electrical power in New York in 1882.
And there is ample prior evidence of actual productivity dips in the early days of new technology diffusion. The J Curve, for example, illustrates the pattern that there is an early period of disruption and actual productivity decline when a major new technology is introduced. Only later are the tangible benefits seen.
The J-curve effect in GPT adoption typically follows a few stages, from initial investment to realized productivity. AI clearly is in the early investment phase, which ought to imply significant costs without immediate financial returns.
Which ought to clue us in to the fact that investors are likely to be quite disappointed when most entities cannot show significant financial returns.
And though the J curve might not apply when innovations do not require value chain disruption and displacement, Verizon’s experience with fiber-to-home upgrades still show that even innovations that do not require business model change can take a while to reach maturity.
As significant as fiber-to-the-home was deemed to be by Verizon, one would be very hard pressed to show significant financial returns to Verizon for five years from mass deployment.
FTTH was not a GPT that required changes in consumer behavior or disruptions of Verizon’s supply and value chains.
The thing about GPTs (and if AI is a GPT the J curve should apply) is that disruption is required. Still, Verizon arguably reached scale in about four to five years of construction, with very-significant revenue contributions for new video entertainment services enabled by the FiOS network. In the second quarter of 2011, for example, Verizon had about 4.5 million broadband accounts, as well as3.8 million video accounts.
In the second quarter of 2011, FiOS generated 57 percent of consumer wireline revenues, up from 48 percent a year earlier, Verizon said that year.
By the third quarter of 2011, FiOS accounted for nearly 60 percent of consumer wireline revenues. In the last quarter of 2014, FiOS contributed 75 percent of consumer wireline revenues. Keep in mind that statistic also includes the diminution of Verizon’s landline voice business, plus the maturation and decline of its linear video entertainment business as well.
In other words, FiOS revenue became the driver of Verizon consumer fixed network revenue in part because the voice and video entertainment businesses declined.
The main take away is that productivity might actually dip in the near term as firms deploy AI technologies.
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