Uber – 13 miles per driver intervention

https://www.nytimes.com/2018/03/23/technology/uber-self-driving-cars-arizona.html

A pedestrian was killed in Tempe, Arizona by an Uber autonomous car.  In 2015, Governor Doug Ducey enticed the self-driving car industry to Arizona by executive order clearing the way for testing in the state.  Last month, he updated this order touting Arizona’s “business friendly and low regulatory environment”.  Following the crash, Uber has stopped all real-word testing of its autonomous cars, which were happening in San Francisco, Phoenix, Pittsburg and Toronto.  The accident is now in the crosshairs of both the U.S. National Highway Traffic Safety Administration and the National Transportation Safety Board.

The recent Cambridge Analytica revelations on Facebook data to help Donald Trump’s campaign is ill-timed for autonomous car companies.  And is forcing regulators to increase scrutiny on the level of self-policing that has so far been granted to tech companies generally. The fatality and recent privacy breach revelations will almost certainly adversely impact the pace of autonomous car technology advancement in the U.S.

There are at least two broad black box areas regulators will want to examine and ultimately address.  One is conceptually straightforward, while being technically bedeviling.  Autonomous cars are trained using AI methods such as deep learning on massive amounts of data to interpret and react to driving conditions.  However, unlike traditional statistical predictive methods such as regression analysis, deep learning does not easily lend itself to transparency of decision making, which leaves it with an air of magic about it.  This reality will make it difficult for regulators to communicate with an increasingly skeptical public.

The other issue is philosophically much more challenging.  Inevitably, autonomous cars are going to be in situations requiring them to make an instantaneous choice between a set of bad outcomes.  For example, the decision when an autonomous car is faced with a choice of modest damage to itself versus more material damage to its surroundings.  Even more fundamentally, what happens when lives are at stake?  How will the car measure tradeoffs and react to them?  At some level the processes for making these ethical decisions must be programmed into the car.  The public at large is unlikely to accept a Google, BMW, Ford, or an Uber unilaterally making such decisions.  Recent headlines on Cambridge Analytica that erode public trust in tech companies, and, now, a self-driving car fatality will force a bright spotlight at the core of autonomous vehicle systems.

The rule of law and consumer protection is a strength of the U.S.  At the same time, these strengths could prove to be an impediment in the race for global leadership in the development of autonomous cars, and AI more broadly.

When Uber released its financials, the reasons were political. But the results breathtaking, nonetheless. Uber was founded in March 2009 and in a mere eight years had revenues of $6.5B (gross bookings were $20B).

For context, this is comparable to where Google was eight years after inception, and significantly higher than Amazon and Facebook. Without arguing about the nature of revenue, this is still a staggering achievement.

In essence, Uber has taken a tedious, hard to scale, capital-intensive industry and converted it to a sweet, scalable software play. Adding another vehicle to its fleet does not require someone purchasing or leasing a vehicle.  Just going through Uber’s sign-up process.  Success is dependent on very solid software and operational excellence.  That is the genius of the shared economy.  Well, along with some cavernous pockets.

There are questions. Uber is losing a lot of cash, with losses running at about a third of revenue in the last quarter of 2016.  There is no detail available on what’s driving losses. But, Uber’s rapid expansion to more than 50 countries is a large factor.

Also puzzling is the focus on some new businesses. Driverless cars will have a meaningful impact only in the (very) long-term, since there isn’t much of an economy of driverless cars to share in. The monetization potential from its app is another area with limited near-term upside potential as usage will be nowhere near that of industry leaders such as Facebook, whose 1 billion daily users spend about an hour a day on Facebook properties.

The sheer size of Uber’s footprint, however, points to substantial harvestable value. As Aswath Damodaran, finance professor at NYU said – “Uber is a one-of-a-kind company, in good ways and in bad ways. It’s going to be a case study.”

P.S. In fairness, Uber did not invent the concept of the shared economy. AirBnB provides an earlier example of success. There is just more information publicly available on Uber