Robotaxis and autonomoustrucks are the two autonomous vehicle use cases that receive the most attention. AI is a key enabling technology for both. To achieve the determinism required in mobility, #AV software platforms incorporated a combination of statistical and symbolic AI. A new crop of AV startups, led by Wayve and Waabi, received large financing rounds for their “end-to-end” deep neural network approaches similar to those used in generative AI. Could these approaches result in vehicles that are cheaper to produce and operate than those utilizing the approaches used to date, adhere to regulations, and be accepted by autonomous vehicle users?
Despite the noise from high-tech corporations, startups, venture investors, and analysts, we are still in a very early phase of generative AI. As a result, it is hard to assume, let alone declare, that generative AI will transform every business process driving operational efficiencies, create new revenue streams, result in massive productivity improvements, and curtail costs. However, the early signs are adequately encouraging for corporations to allocate budgets and start pilot projects.
The road to new mobility will not be a straight line. The twists and turns we encounter and will continue to encounter, will come as the result of economics, business models, industrial policy, politics, national security, but also culture. Mobility is impacted by at least three cultures: the automakers’, the dealers’, and the customers’.
The current AI spring is in full swing. Entrepreneurs remain extremely excited about generative AI, as manifested by the number of financing requests our firm and many other investors continue to receive but are starting to think more diligently about where the white space they can go after. Corporations are in testing and evaluation mode as they formulate, or reformulate, AI strategies and assess the impact that generative AI will have on their business. Venture investors remain upbeat about the sector but are also concerned about four issues.
Automakers are facing a complex dilemma relating to software-defined new energy vehicles. Should they proceed with aggressively investing and developing software-defined vehicles while facing a slowdown in demand for battery electric vehicles (BEVs), or continue developing Internal Combustion Engine (ICE) and hybrid vehicles (including plug-in hybrids) that are based on existing architectures and practices?




