The five largest technology companies in America make up more than 20 percent of the S&P 500’s market capitalization, a collection of the largest 500 companies in the United States. Basically, more than one-fifth of the American economy is dominated by five companies. For comparison, the energy sector, once a dominant sector in the United States, makes up around two percent of the S&P 500. Information technology and communication companies make up about 38 percent of the index.
Amazon, Google, Microsoft, and Apple all have market caps over $1 trillion. The largest public company in America that is not in technology is Berkshire Hathaway with a market cap less than half of the size of the technology leaders at around $500 billion. And this is without mentioning Tesla’s unfathomable rise over the past year, which might not be sustainable.
As everyone says, follow the money. The money is in the big technology names right now because they have the most influence on American life and face little competition on the horizon due to a rapidly growing competitive moat built on data.
While Google does pose a threat to competition and innovation in the technology sector, antitrust is not a feasible course of action to take against Google, which has recently come under antitrust scrutiny due to its growing dominance.
Instead, legislators should focus on regulating and democratizing the use of data rather than crafting an antitrust argument that will be nearly impossible to win in court and could end up causing more harm to the consumer than good.
Google’s power grows at a faster and faster rate over time because of the network effect. The network effect has become even more robust as the coronavirus pandemic has moved most work online giving technology companies even greater influence over every aspect of peoples’ lives. The network effect is the idea that a product becomes better as the number of users increases creating a positive feedback loop (McIntosh, D., 2018).
Data is the enabler of the network effect and positive feedback loop that gives the largest technology companies in the world a competitive moat too large for smaller competitors to thwart. The innovation of a company today is more reliant on the acquisition of vast amounts of data and machine learning capabilities, which rely on data, rather than superior talent or software programs (Mayer-Schonberger, V., & Ramge, T., 2018).
Google is able to collect the most amount of data and then produce the best insights that acquire more users and data creating a feedback loop that makes it nearly impossible for smaller companies to break.
“Markets with a high reliance on data are experiencing positive feedback loops: the more data an enterprise has, the better the product. This leads to strong data-driven network effects. A search engine like Google is able to improve its search results by using the immense data its search database continually collects from its billions of users. This may include popular search queries that are easily answered, as well as the more obscure and rare searches that can only be well served with gigantic datasets,” (McIntosh, D. 2018).
Google’s chief scientist Peter Norvig even admitted in 2011 that the company doesn’t “have better algorithms than anyone else. We just have more data.”
“The nature of user data has several broad implications. First, if user data are commercially valuable, lack substitutes, and are not shared across platforms, then the existence of significant increasing returns in collecting and utilizing user data can limit the number of viable competitors and create a “data barrier to entry,” especially when the accumulation of the necessary data takes considerable time. The resulting levels of industry concentration raise the possibility that platforms will have substantial market power and that their conduct can raise antitrust concerns. Indeed, some people are concerned that big data will create unlimited advantages of scale and scope that will lead to the domination of a wide swath of the economy by a handful of firms,” (Katz, 2019).
Google has had the most popular search engine for decades supplying the company with more search data than any other entity in the world. With the most data, Google is able to create the best search platform. The great platform attracts more users and then acquires more data. With more data and users, the platform becomes better again creating a never-ending loop that can’t be circumvented by competitors.
University of Tennessee Law Professor Maurice Stucke and co-author of Big Data and Competition Policy Allen Grunes said size matters when it comes to data.
“Stucke and Grunes demonstrate that a poorly designed algorithm can find more valuable information and insights in high volumes of various data than a superior algorithm can when working with a cleaner, but smaller, dataset,” (McIntosh, D., 2018).
Google makes up more than 94 percent of all mobile searches on the internet (JP Morgan). The company has control over what pops up when people search for something, that is important as people seek answers and facts to many questions on Google. The search results that show up influence public opinion. The company also knows peoples’ thoughts and intentions by being able to see what a person searches and views on the web.
Considering its dominance in search, Google is a monopoly and has come under antitrust scrutiny lately around the globe. Google has faced legal challenges from the European Union resulting in a multi-billion dollar fine.
The United States’ Department of Justice recently filed an antitrust lawsuit against Google as well. The suit claims that the company “unlawfully maintains monopolies in general search services, search advertising, and general search text advertising in the US. The DOJ alleges that Google has exclusionary agreements with distributors of hardware, wireless carriers, and browsers that force consumers to use Google’s services and therefore limit competition. As such, the DOJ is asking the court to stop Google from maintaining its monopoly in Search and to restore competitive conditions,” (JP Morgan).
The current lawsuit lacks clarity on a possible financial settlement or remedy to the problem. Any antitrust lawsuit in general, with the current anti-competitive laws in place, will not yield positive results for effectively limiting Google’s anti-competitive data collection.
Three additional remedies for the antitrust situation that have come up in discussion have been to break up the technology companies, stop exclusionary agreements around search, or enforce a Glass-Steagall like policy on technology companies prohibiting them from competing on platforms they run. But all three ideas have major drawbacks.
Spinning Youtube off from its parent company Alphabet would damage consumers. What makes Youtube great is its ability to recommend videos that people want to watch. The more data the algorithm has the better it is able to recommend videos to consumers making the consumers happier.
But if there is a spinoff, YouTube would have less data and its product would suffer for consumers. A new Youtube with better data could emerge down the line creating the same problem that exists today in the future. And it is hard to argue that the company should be broken up, unless antitrust laws are amended, considering Google’s data monopoly makes its products better for the consumer over time.
The European Union has gone after Google for its exclusionary agreement to pre-install its products on Android phones resulting in the company paying a $5 billion fine. However, Google is likely to be refunded its $5 billion and be able to re-enter its exclusionary agreements as the company is expected to get the ruling overturned (Bloomberg Intelligence).
Furthermore, JP Morgan expects that if the exclusionary agreements were deemed anti-competitive and not allowed, it would actually increase Google’s profit. The company would still see similar levels in search traffic as it is preferred by consumers without having to pay the estimated $15 billion or more in expenses per year for exclusionary agreements, most of which goes to Apple.
It is also difficult to prove monopoly power when Google pays more than $10 billion for its exclusionary agreements. A fair argument could be made stating that Google can’t have monopoly power since it had to pay a hefty price-tag for such exclusive agreements.
A Glass-Steagall policy would work more effectively for companies like Amazon who rely on selling products rather than advertisements. In Google’s case, the act would limit its ability to promote its own products hindering sales on searches and in its app store. But most of Alphabet’s revenue comes from selling advertisements, nearly 84 percent in 2019, not selling tangible products.
So a proposed Glass-Steagall policy wouldn’t stop Google’s ability to collect data from search adding to its data monopoly. A policy that would prohibit Google from acquiring competing companies with access to a lot of data, which could fall under a Glass-Steagall like policy, would limit some of Google’s power, however, it would not cut into Google’s data monopoly that currently exists.
For instance, Google is in the process of trying to acquire Fitbit for $2.1 billion to acquire data on people’s physical health (Google Dominates Search Ads More Than Ever. It’s Working Through That).
Google has been met with regulatory and political scrutiny putting the deal in jeopardy as people don’t want Google’s data monopoly to include data on physical fitness. A Glass-Steagall approach could outlaw a company like Google from acquiring a company like Fitbit for its data. But it might be difficult to determine moving forward if a company is buying another for its products, people, or data. In many cases, it could be all of the above. Situations in the future will not come in as black and white providing many difficulties for regulators. If the data is democratized, Google acquiring other companies who have a lot of data wouldn’t matter.
All three of the aforementioned antitrust arguments are missing the driving force behind Google’s dominance, its data. Google’s monopoly status is driven by its ability to gather data and keep it private. Any remedy to the technology company’s anti-competitive dominance should be centered around data. If regulations on data are not made, the current companies will continue their dominance unabated as the network effect will still run its course.
“While re-imagined competition and consumer regulations may work to prevent inflated prices and Draconian privacy policies, they will not address the more pressing problems of Big Tech monopolies on data,” (McIntosh, D., 2018).
A progressive data-sharing mandate would be the best course of action to regulate Google and the big technology companies. Companies would be forced to share anonymous slices of data that they have collected after a specified period of time.
The mandate would decentralize the data market and create innovation as every person and company would have a level playing field to extract the most valuable insights from data. The success of a company would then transition from being able to own the most data to be able to make the best insights from data. It would change the overall incentive from gathering the most data, which can be extremely intrusive to the individual, to being able to extract the greatest insights which then benefit everyone. A possible data-sharing system could look something like this.
“Every company above a certain size, say, those with more than a ten percent share of the market, that systematically collects and analyzes data would have to let other companies in the same market access a subset of its data. The larger a firm’s market share, the more of its data others would be allowed to see. Data would be stripped of personal identifiers, augmented with metadata to make clear what sort of information the data provided and where it came from, and selected randomly to prevent companies from gaming the system (by granting access only to largely useless data, for instance). Participants would have to agree to certain restrictions, including rules against sharing data with third parties. The role of regulators would be limited to assessing market share, an area in which they have already accumulated expertise. If necessary, regulators would also enforce access to data, but they would not actively organize or operate the sharing system,” (Mayer-Schonberger, V., & Ramge, T., 2018).
The progressive-data sharing mandate would make it so no one company would face an immediate reduction in revenue, consumers wouldn’t see an inferior product, and the anti-competitive worries would be wiped clean. Everyone would be on a level playing field. Google would be dominant, as it would still have a ton of data, capital and some of the world’s best talent, however, the democratization of data would guarantee that competition would be on the horizon forcing Google to innovate effectively. The marketplace would be competitive leading to the greatest innovation and best products for the consumer.
“If the universe of data were suddenly made available, it would unleash the creativity of problem-solvers to combine different data sets-public and private-to develop innovative solutions to innumerable challenges,” (McIntosh, D., 2018).
Bloomberg Intelligence: Alphabet Litigation Research
Google Dominates Search Ads More Than Ever. It’s Working Through That:
JP Morgan: Long-Awaited DOJ Action Focuses on Search Distribution, But Narrow in Scope & Lacks Clarity Around Remedy or Financials
Katz, M. L. (2019). Multisided platforms, big data, and a little antitrust policy. Review of Industrial Organization, 54(4), 695-716.
Mayer-Schonberger, V., & Ramge, T. (2018). A Big Choice for Big Tech: Share Data or Suffer the Consequences. Foreign Aff., 97, 48.
McIntosh, D. (2018). We Need to Talk about Data: How Digital Monopolies Arise and Why They Have Power and Influence. J. Tech. L. & Pol’y, 23, 185.
Rothstein, M. A., & Tovino, S. A. (2019). California Takes the Lead on Data Privacy Law. Hastings Center Report, 49(5), 4-5.
Venkat, A., Pichandy, C., Barclay, F. P., & Jayaseelan, R. (2014). Facebook privacy management: An empirical study of awareness, perception, and fears. Global Media Journal: Indian Edition, 5(1).
About Ryan Lipton
Ryan is a student at the University of North Carolina at Chapel Hill majoring in Business Journalism. He has written in the past for SB Nation’s Silver and Black Pride, USA Today Sports Media Group, North Carolina Business News Wire, the Daily Tar Heel, and has worked with Ice Cube’s BIG3 basketball league. Ryan is also a regular contributor to MeidasTouch.com