Tesla Will Eventually License Its Self Driving Technology — Here’s How That Could Look
Autonomous Driving is one of the most exciting applications of AI and robotics that is available to the general public today. It will alter our perceptions of cities, highways, parking, commuting, leisure, and ownership, as well as create new types of jobs as the business models develop. When it comes to business models, infrastructure, technology direction, and development, there are many unanswered questions. As Tesla explores options to license their self driving capabilities, there are a number of ways we could see this technology offered to the automobile and other industries.
Value Chain Supported by an Operating System
Operating systems are the lifeblood of any ecosystem, yet they’ve been monetized in diverse ways by technology companies. Apple (iOS) charges a premium for hardware, Google (Android) makes more money from advertising, and Microsoft (Windows) makes money directly. Control of the operating system is critical to the autonomous driving value chain because it influences consumer experiences and relationships.
Both Detroit and Silicon Valley are competing to develop and dominate this operating system. While Tesla is emulating the iOS model, Baidu is considering open-source technology to commercialize ancillary products/services, and Uber is establishing a partner ecosystem to achieve scale. Multiple models will most certainly arise, compete, and effect capitalization, size, profitability, marketing, sales, and future investments.
Given the hardware focus and producing an excellent design, the Tesla business model should be aesthetically appealing, but the Baidu model will likely rely on a cheaper design while driving up accessories, services, and support. The proper package and amount of sensors will be determined by the OS approach chosen, which will have an impact on the value of ancillary players. Open versus closed OS strategies (e.g., iOS vs. Android) will have ramifications such as safety legislation, competitive dynamics, and infrastructure standardization.
TaaS (Transportation As A Service)
Many vehicle original equipment manufacturers (OEMs) anticipate that transportation will be fundamentally disrupted in the future and will look significantly different. Today, some OEMs, like as BMW, sell directly to consumers, but they’re also experimenting with ridesharing, fleet sales, and time splitting, all of which are part of the transportation as a service (TaaS) business model. In the transportation industry, on-demand business models will change profit pool arrangements, with value shifting away from dealerships and hardware and toward technology, data, design, and platforms.
Logistics And Fleets
With the removal of current restraints on commercial fleets, the current approach to logistics would alter, as higher asset utilization, fewer downtime, faster transportation of goods, decreased congestion, and lower operating costs would result. Governments have already discussed the possibility of autonomous-only zones and a reduction in the requirement for parking lots.
Through new participants, new business specialities and possibilities will arise in sectors such as urban planning and fleet management/monitoring. Access frequency, use time, and kilometers consumed are all factors that fleets consider when developing revenue models. There are semi-autonomous or retrofit capabilities as a short-term revenue source, given the long depreciation cycles of existing fleets and the costs of creating AV fleets.
Value Chain Driven by Data
Autonomous vehicles will use and generate massive amounts of data. The data comes from cameras, radars, lidars, GPS, sensors, maps, and smart infrastructure, among other sources and components. All of this information must be gathered in real time and trained to navigate roadways, avoid obstructions, maintain safety, follow rules, and give tailored experiences. New infrastructure, software-defined processing capabilities, and new business models will be required to handle the volume, velocity, and input of this data. Many partnerships, for example, have evolved to cocreate new datasets or have access to them, such as HD maps.
The human-in-the-loop strategy requires people and machine learning models to train this data at first. While deep learning will take place in the cloud, machine learning will take place on the car. As several new firms grow solely around this data value chain, the ownership, consumption, and monetization of all this data will inform business models.
Value Chain Defined by Software
By commoditizing suppliers and monetizing customers through efficient channels, technology businesses build value. In the transportation industry, ridesharing companies such as Uber and Lyft have commoditized the supply side and aggregated demand through a channel (platform technology), making the type of vehicle less significant to riders. Both Uber and Lyft want to cement their positions as the transportation hub of the future by investing in autonomous driving technologies.
The value chain is crowded with competitors, including Google, legacy OEMs, newcomers like Tesla, and auto suppliers. The viability of new powertrains and the electrification of cars and fleets will have a direct impact on AVs’ fixed and operating costs; a new transportation and mobility ecosystem is predicted to be faster, cheaper, cleaner, safer, more customizable, and efficient in every way. The top end of the value chain will establish better foundations for this nascent but promising sector to accelerate.
V2X And The Internet Of Vehicles
Infotainment, traffic information, real-time mapping, telematics, and data analytics are examples of business models that use numerous modalities of C-V2X. There are also network-based data analytics, monitoring, and opportunities. While V2I may be delayed due to a lack of progress incentives from local governments and their budget constraints, they will have a big impact on business models (access to services, access to space, etc.).
Business Models Driven by Regulation
Regulators have had a difficult time dealing with Big Tech and vice versa, and keeping up with rapid technological advancements is difficult. Regulators are key participants in the autonomous driving revolution, influencing players to shift to cleaner air, less traffic, and more efficient commutes. They must also account for the time it takes to move from human-driven to autonomous driving, as well as a period of cohabitation.
As the space develops, regulators will be interested in the allocation of economic value. Staying closer to action centers, for example, will be incentivized if taxes are paid on miles traveled. If taxes are based on the number of hours spent parked or the amount of space utilized, business models will change to reduce idle time. Between industry directing regulation and allowing regulation to direct the growth of the sector, it will be a “chicken vs. egg” issue.
Conclusion: Technology, legislative, and societal adoption of autonomous driving will be influenced by business models and commercial objectives. This is an ecosystem game involving value created by industries other than traditional automobiles, such as technology, media, telecommunications, insurance, health care, energy, and government.
What do you think?
What are the not-so-obvious industries that could leverage this technology?
