Elizabeth Warren is calling for aggressive enforcement of the antitrust laws. The House Judiciary is opening hearings on big tech, and the Justice Department and FTC have allocated amongst themselves responsibility for particular Big Tech companies. Pundits are saying it’s time for Big Tech to be broken up. So, should Big Tech be worried? Are we going to see an antitrust renaissance? Is Gilded Age 2.0 coming to an end? The short answer is, I doubt it.

The economic and legal theories that have enabled Big Tech to grow into the colossuses they are today are entrenched in the jurisprudence, and world views, of the courts and enforcers, and represent significant barriers to any meaningful enforcement and ultimately change. Antitrust enforcement underwent a major contraction, beginning in the 1970s as fiscal conservatives grafted onto the jurisprudence with increasing success “the Chicago school” notion that antitrust should promote economic efficiency. One of the most troubling of their successes, particularly for proponents of diluting Big Tech’s social and economic power, was Brooke Group. Brooke Group held that predation is only actionable if the plaintiff can show that the defendant has monopolized a market and can recoup the losses associated with its predation through supra-competitive pricing, in the monopolized market. Big Data platforms price significantly below cost in high-value data feeder markets, specifically for purposes of becoming the de facto provider of the underlying good. The purpose is not to extract rents in the underlying market, but to observe as much of the activities of the price insensitive purchasers, within that underlying market, as possible. Doing so allows them to develop a comprehensive understanding of those customers’ demands. Big Data then takes that individualized understanding and correlates it to others, to build a product-by-product, consumer-by-consumer demand database that can move product more quickly and more efficiently, at maximal prices. Losses in the data input markets are then recovered in the data analytics markets. Consumer ignorance of the value of their data—which is intractable because they can never know how their data augments the value of the other data within the set—enables continued predation in the underlying markets.

So long as the narrative espoused by the Agencies, Congress and the politicians remains consistent with Chicago school notions of economic efficiency, you will see no meaningful change, or outcome, in how Big Tech will be treated. This is the Grinnell narrative–that monopoly gained by virtue of superior product, business acumen or historical accident, will immunize Big Data’s behavior. If you see Google described as a “search company,” Facebook as “social media” and Amazon as an “ecommerce marketplace,” you will not be able to recognize the predation or remedy it. Google mastered search, Facebook social media and Amazon ecommerce. Their monopolies are the archetypal Grinnell monopolies. They give away valuable products in exchange for a “few bits of information” about their participants. “Zero cost” markets must benefit consumers because they are “costless.” But search, social media and ecommerce are not where the company’s value or power is nor are these “costless markets” costless. These companies provide advanced, granular data analytics about their users to the highest bidders. And they compete for data inputs. Their output is consumer data analytics that enable sellers (purchasers of the data analytics) to increase sales, and, beyond commerce, increase the penetration and effectiveness of their social and political messages. This data has value, and consumers exchange their data for their costless search, social media and ecommerce baubles, which may be nothing more than bags of beans. The companies compete for dominance in data input markets (like purchases of New York Times Best Sellers) so that they can control, and observe, as much behavior as possible by the profitable price insensitive.

A far more radical, and effective, enforcement initiative would be to add another antitrust statute. Rather than simply try to reframe existing, and entrenched, caselaw around concepts that render behaviors that have historically been illegal but now find themselves excused under rubrics of “economic efficiency,” make them illegal under statutes that contemplate more than just economic efficiency. These statutes would view substantial lessening of innovation and the marginal loss of competition (something less than a “substantial lessening of competition”) as harmful and illegal.

Absent a serious discussion about expanding the common law of antitrust enforcement and changing the narrative around Big Data beyond the current Chicago school orthodoxy, you won’t see any meaningful change in enforcement. Big Data is safe.


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