The Artificial Intelligence (AI) revolution was ignited more than half a century ago. In the last decade, AI has grown from an academic scientific field to start being a practical part of our everyday lives. The most common AI business strategies we see are built around data. We believe that proprietary data is currently the most strategic moat for AI companies, but in the coming years, it will become less of a unique asset, making proprietary data differentiation less sustainable. Therefore, we expect a shift in focus, from data-based AI strategies, to knowledge-based AI strategies.
The big data advancement, facilitated by the deployment of numerous sensors, internet connectivity and hardware and software improvement in computational power, communication abilities and digital storage, have enabled AI to scale from small academic research projects to large enterprise production applications. Essentially, big data required sophisticated AI models to analyze and derive knowledge and insights, while the AI models needed the critical mass of big data for training and optimization. Hence, at present, data is often perceived as a sufficient strategic moat for AI startups. As venture capital investors, we see this phenomenon routinely. In recent years, we have seen many startups that place data acquisition at the heart of their business strategy. An increasing number of such companies emphasize the unique data sets they have acquired and their long-term strategy for acquiring additional proprietary data – as a sustainable barrier of entry. Moreover, as AI tools and AI-as-a-service platforms have commoditized the development of AI models, and public data has become ubiquitous, the perceived need to build and defend a data moat has become palpable.
In today’s technology ecosystem, the markets have increasingly rewarded companies with leading AI programs and control over proprietary data – as a substantial and sustainable competitive advantage. Companies such as Google and Netflix have developed and curated massive and authoritative datasets over a long period of time, while many other companies struggled in vain to match their success. An example is the massive disruption of rival media service providers and production companies, which were outmaneuvered by Netflix’ sophisticated data strategy.