Artificial Intelligence Classification Matrix

All the problems discussed in the previous posts can create two major cross-sectional problems: the likely event to run out of money before hitting relevant milestones toward the next investment, as well as whether pursuing specific business applications to break even instead of focusing on product development. In terms instead of classifying different companies operating in the space, there might be several different ways to think around machine intelligence startups (e.g., the classification proposed by Bloomberg Beta investor Shivon Zilis in 2015 is very accurate and useful for this purpose). The solutions usually provided might either integrate with the clients' stack (through APIs or building specifically on top of customers' platform) or otherwise full-stacks solutions. Virtual agents and chatbots cover the low-cost side of the group, while physical world systems (e.g., self-driving cars, sensors, etc.), drones, and actual robots are the capital and talent-intensive side of the coin. The results of this categorization can be summarized into the following matrix, plotting the groups with respect to short-term monetization (STM) and business defensibility.