How the Internet changed everything: a grand theory of AI, etc.

I have read many a think piece and critical take about AI, the Internet, and so on. I offer a new theory of What Happened, the best I can come up with based on my research and observations to date.

Consider this article, “The death of Don Draper”, as a story that represents the changes that occur more broadly. In this story, advertising was once a creative field that any company with capital could hire out to increase their chances of getting noticed and purchased, albeit in a noisy way. Because everything was very uncertain, those that could afford it blew a lot of money on it (“Half of advertising is useless; the problem is knowing which half”).

A similar story could be told about access to the news–dominated by big budgets that hid quality–and political candidates–whose activities were largely not exposed to scrutiny and could follow a similarly noisy pattern of hype and success.

Then along came the Internet and targeted advertising, which did a number of things:

  • It reduced search costs for people looking for particular products, because Google searches the web and Amazon indexes all the products (and because of lots of smaller versions of Google and Amazon).
  • It reduced the uncertainty of advertising effectiveness because it allowed for fine-grained measurement of conversion metrics. This reduced the search costs of producers to advertisers, and from advertisers to audiences.
  • It reduced the search costs of people finding alternative media and political interest groups, leading to a reorganization of culture. The media and cultural landscape could more precisely reflect the exogenous factors of social difference.
  • It reduced the cost of finding people based on their wealth, social influence, and so on, implicitly creating a kind of ‘social credit system’ distributed across various web services. (Gandy, 1993; Fourcade and Healy, 2016)

What happens when you reduce search costs in markets? Robert Jensen’s (2007) study of the introduction of mobile phones to fish markets in Kerala is illustrative here. Fish prices were very noisy due to bad communication until mobile phones were introduced. After that, the prices stabilized, owing to swifter communication between fisherman and markets. Suddenly able to preempt prices rather than subject to the vagaries to them, fisherman could then choose to go to the market that would give them the best price.

Reducing search costs makes markets more efficient and larger. In doing so, it increases inequality, because whereas a lot of lower quality goods and services can survive in a noisy economy, when consumers are more informed and more efficient at searching, they can cut out less useful services. They can then standardize on “the best” option available, which can be produced with economies of scale. So inefficient, noisy parts of the economy were squeezed out and the surplus amassed in the hands of a big few intermediaries, who we now see as Big Tech leveraging AI.

Is AI an appropriate term? I have always liked this definition of AI: “Anything that humans still do better than computers.” Most recently I’ve seen this restated in an interview with Andrew Moore, quoted by Zachary Lipton:

Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.

The use of technical platforms to dramatically reduce search costs. “Searching” for people, products, and information is something that used to require human intelligence. Now it is assisted by computers. And whether or not the average user knows that they are doing when they search (Mulligan and Griffin, 2018), as a commercial function, the panoply of search engines and recommendation systems and auctions that occupy the central places in the information economy outperform human intelligence largely by virtue of having access to more data–a broader perspective–than any individual human could ever accomplish.

The comparison between the Google search engine and a human’s intelligence is therefore ill-posed. The kinds of functions tech platforms are performing are things that have only every been solved by human organizations, especially bureaucratic ones. And while the digital user interfaces of these services hides the people “inside” the machines, we know that of course there’s an enormous amount of ongoing human labor involved in the creation and maintenance of any successful “AI” that’s in production.

In conclusion, the Internet changed everything for a mundane reason that could have been predicted from neoclassical economic theory. It reduced search costs, creating economic efficiency and inequality, by allowing for new kinds of organizations based on broad digital connectivity. “AI” is a distraction from these accomplishments, as is most “critical” reaction to these developments, which do not do justice to the facts of the matter because by taking up a humanistic lens, they tend not to address how decisions by individual humans and changes to their experience experience are due to large-scale aggregate processes and strategic behaviors by businesses.

References

Gandy Jr, Oscar H. The Panoptic Sort: A Political Economy of Personal Information. Critical Studies in Communication and in the Cultural Industries. Westview Press, Inc., 5500 Central Avenue, Boulder, CO 80301-2877 (paperback: ISBN-0-8133-1657-X, $18.95; hardcover: ISBN-0-8133-1656-1, $61.50)., 1993.

Fourcade, Marion, and Kieran Healy. “Seeing like a market.” Socio-Economic Review 15.1 (2016): 9-29.

Jensen, Robert. “The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector.” The quarterly journal of economics 122.3 (2007): 879-924.

Mulligan, Deirdre K. and Griffin, Daniel S. “Rescripting Search to Respect the Right to Truth.” 2 GEO. L. TECH. REV. 557 (2018)

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