For all the legitimate worries expressed about firm-level artificial intelligence revenue magnitude, compared to capital investments, firm-level accounting and business model issues never capture the full value of the innovations, any more than such statistics can capture the full economic surplus created by earlier innovations such as the personal computer and the internet.
It might in fact be the case that general-purpose technologies such as electricity create industry revenue that does not capture the value of electricity to society.
Or consider computing. The economic value of computation has exploded while the price of a unit of computation has collapsed. If computing enables a service used to cost $100 to fall to $1, measured consumer spending falls from $100 to $1.
So our accounting metrics might suggest a falling output, even though consumers are $99 better off.
That migration is one reason technological revolutions can produce enormous consumer welfare without creating a comparably enormous technology industry's share of GDP.
Consider Google Search. Users do not pay directly, but Google captures advertising revenue. But economic value is far greater.
Researchers might spend only five minutes finding information that would previously have required two hours. AI chatbots vastly create more value than that, functionally allowing people to tap the expertise of many other experts inside and outside of a given domain.
The point is that “surplus” or “value” are far greater than reported firm revenues and profits, across an entire economy. And that is an issue with nearly all digital goods, especially those available to users "for free.”
if AI eventually makes any product essentially free, conventional gross domestic product could actually show less spending, even while society is receiving vastly more value from it.
That earlier analogies are search, Wikipedia, online maps, free email, open-source software and other digital goods people can use without additional charge.
We are familiar with the "productivity paradox," where measurable output gains lag initial investment in information technology by years.
The issue for AI is not necessarily “productivity” metrics but impact on economic output. In a sense, productivity metrics are about inputs, while economic welfare or value is about outcomes. It will be easier to measure the former than the latter, if neither will be straightforward or easy.
Economic value and market prices might not correspond very well. But that also is “typical” of many computing innovations. During the early phases, producers may capture a large fraction of the generated surplus, and we can measure that in revenue and profits.
Later, an increasing fraction of value arguably shifts toward customers in the forms of lower prices, higher output, better quality or new products.
That is why high social value and low producer margins can coexist. Or, to put it another way, an extraordinarily successful technology can become a much-less extraordinary business.
As in the case of tax burdens, incidence (who ultimately pays)matters. In the case of the AI value chain, value will not be entirely captured by producers.
Some of the value will be gained by all firms and entities able to use AI. Consumers might see lower prices and the ability to consume more. Some workers might see higher wages. New businesses, products or industries could emerge.
The analogy might be the way the Internet changed and then created huge new types of products and activities. Search, instant messaging, video and audio streaming, online news sources, social media, location-based services and mapping as well as all online forms of media provide examples.
Much of that output was previously either expensive or did not exist. And in many cases, usage is at “no additional charge” or at lower costs than prior products required.
People also should gain more leisure time. All of that surplus will be created, but not captured at the firm level by producers.
Technological abundance tends to do that.
