The AI landscape is shifting from 'data' to 'knowledge.' Here's why that matters

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.