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?
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.