As with any new area of technology, business understanding of AI lags the work being done by AI practitioners.  With the massive amounts of investments being made on AI, it is important, especially for executive decision makers, to look behind the curtain and get a better understanding of the constraints and applicability of AI applications to their business.

Three applications that have got a lot of press in the last year are AlphaGo Zero, Amazon Go, and autonomous cars. This post looks at the relative complexity, constraints, and cost to implement each solution, as well as their potential for disruption. It’s no surprise that AlphaGo Zero, which has shown the sharpest of results and is the least expensive to implement, has the narrowest applicability of the three. On the other end of the spectrum, autonomous cars have the potential to be fundamentally disruptive. They also represent the most complexity (what is the definition of “safe” in bits and bytes?) and are the most expensive of the three applications. While the technology is already having an impact on our lives, fully autonomous cars are still many years away.

A deeper examination of each, nonetheless, can provide insights on how to evaluate the constraints by which AI operates, and how it can have an impact on achieving business goals.

50 – number of driverless car companies in CA

0 – number of applications

No driverless cars in California? Why April 2 won’t make history

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