As many of the most innovative companies in the world race to bring autonomous vehicle solutions to market, a fierce debate has emerged in the industry about the best way to build those solutions. The debate centers on the proper role for deep learning in vehicle automation.
Past transportation revolutions, like those precipitated by the steam engine and the automobile, dramatically changed where and how cities were built. The impending revolution in vehicle automation will do the same. The effects of autonomous vehicles (AVs) on real estate can be broken down into impacts to land (where we build) and product (what we build). Both land and product will be affected through direct and secondary means:
- Direct: changes in the interaction between real estate and cars; e.g. lower parking requirements
- Secondary: changes in real estate preferences driven by AV-triggered alteration of behavior; e.g. decreased commute pain leads to increased demand for rural single family homes
Despite not yet being profitable on a global basis, Uber’s position as a leader in today’s U.S. ride hailing market is clear. It has the largest network of drivers, making its service the fastest and most convenient. But, how can Uber be able to maintain its lead in a future where its supply of drivers is no longer an advantage, because swarms of driverless vehicles (Highly Automated Vehicles, or “HAVs”) roam the streets?
Our hypothesis is that Uber will be most successful if it can convert its drivers from a network of laborers into a network of capital asset owners/providers, thereby allowing Uber to remain asset light and minimize supplier power, two characteristics of its success to date. However, in order to navigate the decade-or-longer period before HAVs are available for consumer purchase at a reasonable price, Uber must carefully partner with OEMs to advance the development of HAVs and prove their effectiveness in initial markets.
Uber and Lyft may say otherwise, but ridehailing is inching closer to personalized pricing: the ability to charge the maximum price -- known in economics as the reservation price -- but enable that to be different for every customer, for an identical good or service. Considered one of the most elusive targets of consumer economics, personalized pricing is difficult to achieve. Even airlines, which have become savvy at price discrimination (using cookies and customer history) can’t compete with ridehailers. Here’s why: Uber and Lyft have far more data points (people take many more rides than flights) and do not have price aggregators (Google Flights, Expedia, etc.) that allow price comparisons.
Connected, autonomous vehicles are around the corner. Many of the most innovative and deep-pocketed companies in the world are racing to bring them to market — and for good reason: the economic and social gains they will generate will be tremendous.
But any transformative technology creates new challenges and risks in addition to benefits. This is no exception.
One of the biggest threats that society will face as transportation transforms in the coming years is vehicle cybersecurity. It is a topic about which much is still unknown, even among those working at the cutting edge of the industry; vehicle connectivity is a new phenomenon and the technology continues to evolve rapidly.
As companies race to bring autonomous vehicles (AVs) to market, investment activity in the space is heating up.
General Motors made headlines in March when it paid over $1 billion for Cruise Automation. A few weeks later leading venture capital firm Andreessen Horowitz entered the space, announcing investments in two early-stage autonomous startups,Comma.ai and Dispatch.
Most recently, secretive AV startup Zoox raised a massive $250 million funding round, making it Silicon Valley’s newest unicorn. These and other recent deals point to a growing investment frenzy as AVs get closer to mainstream commercialization.
The AV investment landscape is complex. It includes both hardware and software players and features competitors ranging from early-stage startups to large publicly traded corporations. This article will provide a primer for those interested in the rapidly evolving AV space.
Concern surrounding the negative economic effects associated with technological progress is hardly confined to contemporary discourse. There is evidence dating back to the writings of Aristotle of trepidation over technological progress and the concurrent loss of labor opportunities. Aristotle warned that, “[i]f every instrument could accomplish its own work, obeying or anticipating the will of others…chief workmen would not want [human laborers].” Going forward, the First Industrial Revolution featured the rise of the “Luddites,” a group of manual laborers staunchly opposed to the proliferation of the electrically powered machines that would replace them in the workplace. The Luddites focused on the destruction of these machines while society, writ large, dismissed their platform as socially and economically regressive. Thus, their prominence was short-lived.
There is a growing consensus that autonomous vehicles (AVs) will soon be a reality. The debate today centers not on whether, but how soon, AVs will be commonplace on our roads. But for all the buzz surrounding AVs, many details about what a driverless future will look like remain unclear.
Which business models will work best for the commercialization of AVs? Which AV usage models will be most appealing for consumers? Which companies are best positioned to win in this new market?
These are big questions, and no certain answers can be given at this stage. Nonetheless, it is valuable to reflect, in a concrete way, on how this transformative technology might develop. This article will present some conjectures.