A few days ago I attended the strategy meeting of a portfolio company. Like all of our Synapse Partners portfolio companies, this one provides AI solutions to enterprise customers. Their initial success is in the medical devices industry. While reviewing the company’s sales pipeline and the progress of delivering the company’s solution to signed customers, several of the enterprise’s struggles with generative AI became evident. These struggles can be attributed to a lack of people with the right background and the state of the enterprise data.
AI solution providers and infrastructure providers committed to spend $1T on AI-related capex over the next few years, in anticipation of high demand for and heavy use of generative AI enterprise solutions. A few years ago, similar scale investments were committed by the automotive industry toward the development and deployment of battery electric (BEV) and other new energy vehicles (NEV) anticipating strong consumer demand. However, today as a result of slower-than-expected demand, automakers are reducing their investment commitments and run the risk of ending with unused manufacturing capacity. What return on investment (ROI) does an enterprise need from each generative AI solution it deploys so that it can cross the generative AI chasm and for the huge investments in software, data, and infrastructure that have been committed to be justified?
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’.




