Why Apple has to make inroads in the autonomous self-driving car space, and fast.

Autonomous vehicles; not wearables will be the new mobile platform.

Autonomous self-driving vehicles (AVs) will be the new mobile platform, on top of which a host of new products and experiences will be developed. Like Apple’s iOS and Google’s Android mobile operating systems, on top of which multi-billion dollar iTunes and mobile advertising businesses have been built, connected autonomous vehicles will enable a multitude of new products and experiences that the consumer will be purchase and/or consume during the ride.

Ride-monetization opportunities are valuable competitive advantages to a robot taxi platform.

As we have learned first-hand in the ride-hailing business, any ability a ride-hailing platform possesses to keep ride fare prices low is a strong competitive advantage. The reason for this is simple. Consumer demand for a given platform’s rides is highly sensitive to ride prices/fares it charges. That is, the platform that offers the lowest prices and wait-times will enjoy the most demand. The ability to generate revenues on AV rides — on top of the ride fare — provides extra margin and room for the robot taxi service provider to lower ride fare prices and still be profitable.

Waymo, Apple, Amazon and Uber possess the best ride-monetization opportunities.

Google’s Waymo is the AV team arguably best positioned to create in-car ride-monetization experiences. Nest, Google Express, Youtube, and of course the digital ad business provide a multitude of opportunities for Waymo and its parent. Perhaps the next best-positioned providers of ride-monetization experiences are Apple and Amazon.

Apple needs to move fast in the autonomous vehicle space.

The smartphone will continue being a key tool in our lives, but Apple must play a role in this future mobile platform otherwise it risks massive declines in its enterprise value. Apple may be late to AV game, but there is hope for Apple to win. With its suite of entertainment, content, and home-system products, Apple is one of the best positioned to create ride-monetization solutions. Seamless cross-product integrations and experiences are what Apple does best. The autonomous vehicle will be just another one of the products with which Apple products will need to integrate. If Apple doesn’t win a major stake in the AV future, Google’s Android — through its integrations into the autonomous vehicle ride experience — could create a better-integrated mobile OS and capture not only Apple’s share of that market but also share of adjacent markets in hardware and content.

As it stands now, Apple has made its AV strategy clear, and it will partner with car manufacturers to provide the autonomous hardware/software technology needed to make a car drive from point A to point B. The challenge will be for it to get the technology working fast enough and to set up the manufacturing partnerships before other teams beat them to it, and there aren’t many car manufacturers left who haven’t already partnered with an autonomous vehicle technology team.

Very strong network effects will be at the core of the autonomous vehicle revolution, which will hinge largely on robot taxi platforms instead of direct-to-consumer vehicle sales. The first teams to get a working product to market will have significant advantage over late bloomers, who will struggle to profitably create a product that offers value in excess of the significant switching costs that the would-be customers would incur to leave its existing AV tech provider.

 

Does it make sense for OEMs to be investing in AVs and urban mobility products?

TLDR: AVs and robot taxi services are not a near-term (3-7 year) threat to existing OEM business lines, but they are a longer-term (7+ year) threat to OEMs; and this is why they are investing so heavily in AV technology.


Investments being made by large car manufacturer (OEMs) in self-driving autonomous vehicles (AVs) and urban mobility solutions are receiving a lot of attention these days, and for good reason. AVs promise significant societal benefits and economic opportunity, but I want to contemplate and deconstruct why OEMs are making these investments.  What is the business rationalle.

The going sentiment or explanation seems to be A) that AVs and these urban mobility solutions threaten the OEMs existing business and/or B) that the OEMs’ ability to design, manufacture, and distribute vehicles makes them a natural necessity in the future value chain of AVs. While I agree with the latter explanation, I think there is room to elaborate  on the former.

 

Vehicle sales will not decline significantly over near-term (3-7 years) because of robot taxi services.

First, the majority of cars and trucks are sold to consumers and businesses located outside dense urban city centers. Second, as I’ve explained in a previous post, robot taxi fleets will be economically constrained–over the near-term–to being deployed within dense urban city centers. As a result, over the near-term, if anywhere, robot taxi services will reduce car ownership rates within dense urban centers, but car ownership rates in urban centers are already low, therefore little impact will be made on the number of vehicles sold by OEMs. Simply put, car ownership is already low in the areas/populations wherein AV-based robot taxi services will be launched over the next 3-7 years. It won’t be until robot taxi services slowly make their way out to lower density suburban areas that car ownership there starts to be impacted.

I would suggest that vehicle sales by volume are declining not because of ride-hailing services, but rather vehicle build quality is improving. Cars are being built better and are lasting longer, and hence need to be replaced less frequently. Again, for most people living in the suburban US, ride-hailing is not a substitute for car ownership. And for most people living in urban environment, car-sharing (eg. GetAround) and short-term rental (eg. ZipCar) services are more likely to be a substitute* for car ownership than ride-hailing service is. Sure, hailing a ride is better than renting a car for two hours in most situations (that’s why ZipCar’s business is declining significantly), so ride-hailing services might be substitute for some urban market segments but I would argue that most car owners in urban areas own their car because they have consistent weekly inter-city transportation needs. Most people decide to buy/lease a vehicle because they have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example.

The same holds true for suburban car owners. If you live outside dense urban areas and decide to buy/lease a vehicle, it is because you have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example. For most people, car ownership is a better alternative for these consistent trips than Uber and Lyft.

As a result, I would argue that car ownership has not been impacted significantly by ride-hailing services–in neither urban nor suburban population.

Ride-hailing platforms have become a complementary transportation solution; not a substitute. They have become a substitute only for traditional taxi services. For urban dwellers, it complements mass-transit utilization. For suburban dwellers, it complements car ownership, as a solution for infrequent trip needs such as getting to/from the airport or nights on the town to avoid drinking and driving.

Although I don’t think robot taxi fares will come down to the level of mass-transit fares, the already low car ownership rates in urban populations means that I see robot taxi trips being used by consumers as a substitute for an occasional mass-transit trip; not permanent car ownership.

It’s a long-term play for OEMs.

Robot taxi services are not a near-term threat to OEMs, but I do think they are a long-term threat (7+ years). For the reasons stated above and in this post, robot taxi ride fares over the near-term will remain prohibitively high for suburban populations, where car ownership is highest. Eventually, in the longer-term (7+ years), the technology and economic constraints will change such that serving more sparsely populated areas can be done profitably, and robot taxi platforms will start serving these areas.

This is why I think hundred-year-old OEMs are investing in urban mobility and AV tech. OEMs don’t and shouldn’t care about being relevant over the next decade; they’re aiming to be relevant over the next 100 years.

 

*According to Jeffrey Rifkin, “Some 800,000 individuals in the U.S. are now using car-sharing services. Each car-share vehicle eliminates 15 personally owned cars.”   

“Computers are already better than us at playing chess, but we are still better at recognizing a photo of our parents or children”

As I was leaving Tom Mitchell’s office, he says to me in the kind of hurried speech of a brilliant individual who has perhaps made the statement before:

“Computers are already better than us at playing chess, but we are still better at recognizing a photo of our parents or children.”

Many consider Tom Mitchell, chair of the machine learning department at Carnegie Mellon University, to be a leading pioneer and expert in the field; and I just met with him to get a better understanding of machine learning, its limits, and its future.

Some major takeaways include:

  • We should not be afraid of machine learning replacing humans, at least in the near term
  • Even the area of “unsupervised learning” still requires humans to tell the computer what relationships to tease out of data
  • The lack of understanding possessed by businesses and marketers about machine learning consistently causes them to come to machine learning experts, such as Dr. Mitchell, asking these computer scientists to make sense of these large datasets.  These are under specified and arguably therefore useless questions for which machine learning is of little use.  What is always needed is context, a hypothesis you wish to test, and an ability to gather/capture useful datapoints.

At the end of the day, humans are still needed to perform one very critical function: defining the dependent variable and the range of possible independent variables that explain/affect that dependent variable.

Will this technology replace 3D printing for design prototyping?

Maybe this technology could take a CADD file and quickly generate an interactive 3D model, which could be sent to colleagues over the internet!   …I think it offers a compelling sense for spatial aesthetics of a 3D product design, and then we wouldn’t have to wait for the 3D printer anymore.

Are Non-biodegradable plastics a thing of the past?!!!

Researchers have found a fungi in the Amazon rainforest that can degrade and utilize the common plastic polyurethane (PUR) for energy.   What a thought…that after all our destructive consumption of plastic has done to hurt mother nature, that she might hold the fix to this problem!!!?     Amazing!   The fungi can survive on polyurethane alone and is uniquely able to do so in an oxygen-free environment (I’m thinking landfills).

As someone who cringes at the thought of how much plastics are being put into landfills and our environment, this is fantastically exciting news, and I want to see it implemented if it works, so I wonder about two practical matters:

  1. Can we force these fungi to consume the PUR, or–given other options–will it choose to consume other materials?   That is, if we put them in the landfill, will they choose to eat paper instead of the PURs we want them to eat?
  2. What byproduct(s) if any are produced by the fungi when it degrades/consumes the PUR; and is it good/better for the environment?

I hope the scientists have come up with great answers for these questions!!

The Yale University team of researchers have published its findings in the article ‘Biodegradation of Polyester Polyurethane by Endophytic Fungi’ for the Applied and Environmental Microbiology journal.

One day, we will look forward to commercial breaks!

As data collection methods grow, advertisers will be able to better predict what information/ads would be relevant to each of us individual consumers.   And as more relevant ads are served to the consumer/individual, higher click-through rates will result, and advertisers will be able to afford an advertising model that has fewer numbers of ads shown to the user.  For example, today, say–on-average–click-through rates on ads are 10%, then this means that an advertiser–on average–has to show the consumer 10 ads before one click-through is generated; but in the future, say click-through rates on ads improve to 50%, now only two ads need to be shown to the consumer before a click-through is generated.  So this would lead us to believe consumers will have to sit through fewer ads; but in all likelihood, advertisers will not be satisfied with the same number of click-throughs per pair of eyes.

That is, say today, 10 commercials are shown to a pair eyes for a given 30-minute television episode, and one click-through is generated per pair of eyes.    Then, I am saying, that advertisers, in the future, when click-through rates improve to 50%, each pair of consumer eyes will likely be shown more than two ads per 30-minute episode.   Advertisers, content creators, and distribution channels will do this because they will know they can.  They will know that we consumers are happy to sit through 4 ads because 4 ads is still a 60% reduction in ads; not to mention these ads are more relevant to the individual.  So what will happen is total number of click-throughs generated per pair of eyes per 30-minute episode will increase from 1 to 2; and advertisers, content creators, and distribution channels will all be making more money off the same 30-minute long piece of content.

In this future state of the world of media and ad consumption, both advertisers and consumers are happier.

Data is what’s needed.

As I understand the state of the art, the artificial intelligence algorithums needed to make the accurate predictions not only already exist, but are considered quite basic now.   What is missing is the data needed by these algorithms to make predictions!

One day, an individual consumer watching TV any day of the week will be like watching the Super Bowl, when we look forward to the commercials; but it will actually be better.   Right now, many people hate ads; but ads actually serve a very useful function of informing us about things we should know about.   The problem with the current advertising system today is that ads annoy us more often then they inform us.   In the future, ads will do a better job of informing us, and will hence be less annoying.