AI’s allure is undeniable, and businesses invest heavily in its promise. Companies today invest significant amounts into generative AI initiatives. This pursuit is fueled by AI’s potential to improve productivity, cut costs, and unlock new opportunities that may create new revenue streams. However, many companies find the path to successful AI challenging because of fundamental mistakes in their approach.
Just before the holidays, I was asked to keynote an event sponsored by the German American Chamber of Commerce. This article is an updated version of that presentation. It is even more relevant today following CES 2025 and the news streaming out of Europe. The automotive industry, especially the European automotive industry, faces even greater challenges. These challenges are not from technology startups but from more formidable forces. China has become an international competitor, and its market is no longer an opportunity for incumbents. Vehicle sales, including sales of battery electric vehicles, are slowing, leading many companies to miss their financial targets and reconsider previously announced investments relating to electric vehicles. The regulatory environment is becoming more restrictive but also less reliable in terms of long-term goals and guidance for the industry. At the same time, Software-Defined Vehicles and AI require large capital investments at a time when the industry is cutting costs and continues to show an inability to deploy capital in the areas that will matter in the future. Labor is reacting to the automakers’ actions and introducing new work-life balance demands.
The biggest venture financing rounds during 2024 involved AI companies. Reviewing the characteristics of such financing rounds, combined with the performance of our portfolio startups, and our firm’s corporate advisory AI projects (completed and ongoing), I worry that venture investors expect corporate AI adoption will be fast and large-scale. In contrast, corporate AI spending, though growing, is more measured than VCs predict.
Most of you know our firm from its investments in early-stage AI software startups and as an AI advisor to corporations. Few know about the AI systems we have been developing and how we use them with our corporate customers or in new startups we spin out. Over the past several years we have been working on a class of AI-based mobility intelligence systems that are used for understanding a population’s mobility behavior within a region, such as a neighborhood, a city, or even an entire state. We found that neurosymbolic systems that incorporate generative AI components can be extremely effective in understanding such behaviors and providing their users with mobility intelligence.
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?