The process of insight generation is changing due to generative AI. While the foundations of insight generation I presented ten years ago remain relevant, the methods, tools, and implications have expanded dramatically. Generative AI is reshaping how insights are derived, validated, and applied across industries.
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.
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?