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.
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.
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.
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?