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.

Something you hear a lot in Silicon Valley is how hard it is for the little guy to compete against the Googles, the Facebooks, and the Amazons of the world.  They have a platform.  And, it’s hard to compete against a platform.

A lot of the value of platforms is driven by data and how that data can be used to optimize business decisions.  The Economist magazine claims that “The world’s most valuable resource is no longer oil, but data”.  No surprise, then, that substantial investment focus by Amazon and Google is in artificial intelligence (see CBInsights’s very detailed analyses on these two companies).  For a good understanding of the incumbent value of platforms, however, Facebook’s relatively clean business model is the easiest to examine.

Facebook revenue increased almost 6 times from $5B in 2012 to $28B in 2016.  Certainly, a secular increase in internet advertising spend explains part of the trend.  More fundamental in this growth, though, is the increase in the number of daily users from 0.6B to 1.2B, and the time each user spends on Facebook properties.  More time, individually and as a group, means more opportunity to serve up advertising.  And, very importantly, optimizing what ads to serve up and how (hello AI).  In that same period, average revenue per user grew from $5 to $16.

In essence, the platform and the value that it provides is a simple function: # users x time per user.  A startup has to do a great job maximizing the amount of time users spend on its properties.  But, to achieve a compelling financial model, it will inevitably need a large number of users.  Snap ended 2015 with 107M daily users, and averaged $0.6 per user.  By 2016, number of users had risen to 158M, and average revenue per user to $2.7

Facebook’s strategy is focused on pushing these two numbers up, and the bulk of the $25B it has spent since 2012 to acquire companies was directed at Instagram and Whatsapp.  It also made an unsuccessful run at Snap.  That failure led to Facebook mimicking Snap by offering camera-related features on its platforms. Instagram Stories alone has reached 200 million active users.  Which exceeds Snap’s total active users, showcasing the power of platforms.  Facebook now has 1.2B daily users that spend an hour a day on its combined properties.  A digital river of information that Facebook gleefully monetizes.

Not every startup can be Snap, and achieving meaningful volume may require leveraging existing platforms.  For which the platforms will get a meaningful cut.  The other lever that startups have, is to maximize the amount of time users spend on them.  Some of the most popular categories of applications are:

• Social media and browsing: Facebook – an hour a day for each user
• Video: Netflix – 2 hours; YouTube – 1 hour
• Gaming: as a category, 2 hours a day
• Music: Pandora – 1 hour

The killer app would be one that combines all, or a significant subset of these applications into one unified platform.

Sources: “The new face of Facebook” The Economist; “Google Strategy Teardown” CBInsights; “Amazon Strategy Teardown” CBInsights