Advances in artificial intelligence (AI), particularly large language models (LLMs) [1], have achieved remarkable success. This progress stems from "scaling laws" -- performance improves with greater computation, data, and model size [2]. They now excel at mathematics, medical, legal, and coding exams and competitions. Y et, this paradigm has a crucial vulnerability: scaling laws are effective only when data is abundant. Human reasoning, which relies on logical operations and abstractions rather than brute-force pattern matching on vast data, proves critical in tackling complex frontier domains, where usable data is often inherently scarce.

Artificial Intelligence (AI) can be quite a challenging topic to truly comprehend, especially for business managers, entrepreneurs and investors that lack a deep academic background in the field. They may instinctively sense the massive potential of AI -- all the science fiction movies and TV shows that Hollywood churns out probably plays a part in this -- but they are often left wondering, how should I think about AI? How does AI actually work? The follow article addresses this gap by presenting two broad and fairly dominant intuitions of AI -- cognitive and statistical. Despite the relative fragmentation of the field and varied backgrounds of AI practitioners, the cognitive and statistical intuitions seem to reflect the ways of approaching AI today. If you can grasp one or both of these intuitions, then you will be better positioned to meaningfully participate in discussions around AI as a business stakeholder, as well as build and invest in AI opportunities. Think about the last time you had to study for a test with multiple choice questions. Figure 1 shows a very simple example of such a question.