Big Auto certainly still maintains a gigantic present advantage over Silicon Valley car companies in the marketplace, as do fossil fuels over electric and human-operated over driverless. But things change. Over the next few decades, these competitions are going to alter the industry in a grand way, and it’ll be interesting to see where the edge ultimately rests. One point to keep in mind: 3D printers could play a big role in the race, possibly making it so that software startups in garages could become automobile startups there, which would be very fitting.

From “The Auto Industry Won’t Create the Future,” by David Pakman of Backchannel:

Perhaps the most significant shifting of the automotive tectonic plates is the move to software. The future of the automobile will largely be built by software developers. Yes, existing combustion engine cars have embedded systems with lots of code in them to handle everything from HVAC to automatic transmissions. In fact, the complexity in integrating these many layers of software together is causing lots of consternation at the traditional car companies, given this is not their main areas of expertise. In addition to this, future cars will utilize software in profoundly different ways.

Of course we know that Tesla (currently) and Apple (future) are trying to re-imagine the interface between the driver and car, and their dashboards are (likely to be) gorgeous and vastly improved over the mostly superfluous dials and gauges car manufacturers think we need to see (when was the last time you had to check your RPMs or engine temperature?). Good hardware, software and UX designers will be behind all of that. But future vehicles equipped with ADAS systems and eventually autonomous capabilities will need to make trillions of driving decisions based on lots of sensory data. Vision, LiDAR, sonor and other sensors will combine with real-time streams from the internet, from other vehicles and even from municipal environmental data sources (our portfolio company INRIX is one such data supplier). These inputs are analyzed in real-time, likely with a combination of local on-board and cloud-based compute resources to make driving decisions. Such complex AI systems will be adaptable machine learning systems which continuously refine their decision-making models.

Understanding this makes it less of a surprise that Google leads the way in autonomous vehicle development today. Google’s search engine is an at-scale example of just such a system and much of Google’s core development expertise is in cloud-based predictive systems.•