Benedict Evans
Winner-takes all effects in autonomous cars
Rather, the place to look is not within the cars directly but still further up the stack – in the autonomous software that enables a car to move down a road without hitting anything, in the city-wide optimisation and routing that mean we might automate all cars as a system, not just each individual car, and in the on-demand fleets of ‘robo-taxis’ that will ride on all of this. The network effects in on-demand are self-evident, but will will get much more complex with autonomy (which will cut the cost of an on-demand ride by three quarters or more). On-demand robo-taxi fleets will dynamically pre-position their cars, and both these and quite possibly all other cars will co-ordinate their routes in real time for maximum efficiency, perhaps across fleets, to avoid, for example, all cars picking the same route at the same time. This in turn could be combined not just with surge pricing but with all sorts of differential road pricing – you might pay more to get to your destination faster in busy times, or pick an arrival time by price.
.. From a technological point of view, these three layers (driving, routing & optimisation, and on-demand) are largely independent – you could install the Lyft app in a GM autonomous car and let the pre-installed Waymo autonomy module drive people around, hypothetically. Clearly, some people hope there will be leverage across layers, or perhaps bundling – Tesla says that it plans to forbid people from using its autonomous cars with any on-demand service other than its own. This doesn’t work the other way – Uber won’t insist you use only its own autonomous systems. But though Microsoft cross-leveraged Office and Windows, both of these won in their own markets with their own network effects: a small OEM insisting you use its small robo-taxi service would be like Apple insisting you buy AppleWorks instead of Microsoft Office in 1995.
.. If you have sold 500,000 AVs and someone else has only sold 10,000, your maps will be updated more often and be more accurate, and so your cars will have less chance of encountering something totally new and unexpected and getting confused. The more cars you sell the better all of your cars are – the definition of a network effect.
.. It could be argued that Tesla has a lead in both maps and driving data: since late 2016, those of its new vehicles whose buyers bought the ‘Autopilot’ add-on have eight cameras giving a near-360 degree field of view, supplemented by a forward-facing radar
.. So, the network effects – the winner-takes-all effects – are in data: in driving data and in maps. This prompts two questions: who gets that data, and how much do you need?