February 2019

After reading LM Sacasas’ recent piece on moral communities, I couldn’t help but wonder if the piece was written in the esoteric mode.

Let me explain by some meandering.

Now, I am surely going to butcher his argument, so take a read of it yourself, but there is a bit of an interesting call and response structure to the piece. He begins with commentary on “frequent deployment of the rhetorical we,” in discussions over the morality of technology. Then, channeling Langdon Winner, he notes approvingly that “What matters here is that this lovely ‘we’ suggests the presence of a moral community that may not, in fact, exist at all, at least not in any coherent, self-conscious form.” Continue reading →

Every week, it seems, there is a news story about another air taxi startup or test flight. Another signal of the industry’s development is that at a House Transportation and Infrastructure hearing last week, Eric Fanning, the President and CEO of the Aerospace Industries Association, devoted most of his testimony to urging lawmaker action on air taxi (also called vertical takeoff and landing aircraft and, colloquially, flying cars) policy and infrastructure.

The technology is exciting but federal officials are interested in whether the air taxi industry will be a drain on taxpayers. Using government estimates of the air taxi industry and current tax rates for infrastructure-based industries like wireless and oil extraction, I estimate that the air taxi industry could deposit tens of billions of dollars into the US Treasury annually. Hopefully the hundreds of air taxi “vertiports” required are privately funded as well.

Air Taxi Market Size

In November, I published a Wall Street Journal piece about the rapid development and promise of the air taxi industry. Some people inquired as to the potential size of the air taxi market and government revenue. I wasn’t aware of any estimates at the time. Nevertheless, I estimated that the US market could one day reach $200 billion in revenue annually–about the size of the current US aviation market and the US wireless broadband market.

Other analyst and government estimates are now coming out, turns out, my estimates were on the conservative side. For instance, a NASA-funded study (.pdf) estimated that, at the upper limit, the US market could approach $500 billion annually, which is nearly the size of the US auto market. That would require tens of thousands of air taxis serving over 10 million passengers per day.

Experts at McKinsey, NASA, and JP Morgan Chase estimate that the global air taxi market could be anywhere from $615 billion to $3 trillion annually by 2040. Given the potential for this industry, other countries are moving quickly to commercialize air taxis. A German consultancy, Roland Berger, predicts there will be 3,000 commercial air taxis by 2025. The drone expert at the World Economic Forum believes Chinese companies are far ahead when it comes to autonomous air taxi service. That said, the operator of the Frankfurt airport announced a partnership with an eVTOL company recently, and the powerful Japanese trade and industry ministry has convened a 25-member private-public council to develop air taxis. Japanese regulators intend to make Japan the birthplace of urban air taxi service.

Private or Public Funding of Vertiports?

A key decision for US lawmakers is whether the hundreds of vertiports in the US will be privately funded and operated or will, like today’s airports, receive subsidies and public operation. A NASA study estimates that each major US city could support on average about 200 “vertiports.” That would be a major drain on taxpayers if publicly funded.

My working paper on the subject of air taxi traffic management contemplates entirely private funding of urban vertiports and infrastructure. It also proposes that the government auction aerial corridors to air taxi operators. Private infrastructure and the auction of exclusive aerial corridors, in my view, is the safest and most fiscally responsible way to develop the American air taxi market.

However, the FAA and NASA’s plans are unclear on whether air taxi infrastructure will be funded by taxpayers or funded privately. There’s a good chance the FAA and NASA will import the norms and regulations for traditional aviation–open access airspace and public funding of shared airports–into the urban air mobility market. I think that would create an anticompetitive market and be an unnecessary drain on taxpayers.

Government Revenue From the Air Taxi Industry

How much government revenue could be generated by the air taxi industry? We can look to other assets that are auctioned by government for analogues: spectrum and offshore oil sites. There is no “spectrum tax,” but wireless taxes and fees resemble a de facto tax on cellular spectrum. The Tax Foundation puts government (federal, state, and local) wireless taxes and fees at around 9% of annual wireless revenues. For oil leases on federal property, there is a government royalty amounting to about 12.5% of oil revenue.

With these figures in mind, let’s assume that government taxes and fees will one day amount to about 10% of air taxi revenues. Supposing that the US air taxi market will one day fall between my conservative estimate, $200 billion annually, and NASA’s best-case estimate, $500 billion annually, the air taxi industry could one day generate about $20 billion to $50 billion in tax revenue annually. That doesn’t include the auction revenues of aerial corridors, if implemented. If spectrum auctions and offshore oil leases are the best comparison, the auction of aerial corridors could return another $100 billion to the US Treasury.

These are tentative estimates. Market size estimates vary widely, and much depends on whether a workable regulatory framework develops. In any case, like aviation 100 years ago, it’s an exciting area to watch.

-Coauthored with Mercatus MA Fellow Walter Stover

Imagine visiting Amazon’s website to buy a Kindle. The product description shows a price of $120. You purchase it, only for a co-worker to tell you he bought the same device for just $100. What happened? Amazon’s algorithm predicted that you would be more willing to pay for the same device. Amazon and other companies before it, such as Orbitz, have experimented with dynamic pricing models that feed personal data collected on users to machine learning algorithms to try and predict how much different individuals are willing to pay. Instead of a fixed price point, now users could see different prices according to the profile that the company has built up of them. This has led the U.S. Federal Trade Commission, among other researchers, to explore fears that AI, in combination with big datasets, will harm consumer welfare through company manipulation of consumers to increase their profits.

The promise of personalized shopping and the threat of consumer exploitation, however, first supposes that AI will be able to predict our future preferences. By gathering data on our past purchases, our almost-purchases, our search histories, and more, some fear that advanced AI will build a detailed profile that it can then use to estimate our future preference for a certain good under particular circumstances. This will escalate until companies are able to anticipate our preferences, and pressure us at exactly the right moments to ‘persuade’ us into buying something we ordinarily would not.

Such a scenario cannot come to pass. No matter how much data companies can gather from individuals, and no matter how sophisticated AI becomes, the data to predict our future choices do not exist in a complete or capturable way. Treating consumer preferences as discoverable through enough sophisticated search technology ignores a critical distinction between information and knowledge. Information is objective, searchable, and gatherable. When we talk about ‘data’, we are usually referring to information: particular observations of specific actions, conditions or choices that we can see in the world. An individual’s salary, geographic location, and purchases are data with an objective, concrete existence that a company can gather and include in their algorithms.

Continue reading →