AI models flub these intelligence tests. Can you fare any better?

The latest findings from MIT suggest a crucial limitation in current AI models that presents both a challenge to overcome and a niche opportunity for human ingenuity. The piece highlights that despite their apparent sophistication, many AI models struggle with specific types of intelligence tests, particularly those involving pattern recognition, analogical reasoning, and abstract problem-solving that requires connecting seemingly disparate concepts. These are not about processing vast amounts of data quickly, but rather about inferring rules and applying them flexibly across novel situations, an area where human cognition still holds a distinct advantage. This insight profoundly affects anyone building with or relying on AI, especially given Redson Developers' recent entry into the market in 2022, signaling a need for more robust, nuanced AI. For an indie SaaS founder in Seattle developing a new analytics dashboard, this means current off-the-shelf AI might accurately summarize trends but fail to intuitively flag an emergent, non-obvious anomaly that only a human could connect to a broader market shift. A logistics startup in Dallas, aiming to optimize complex delivery routes, might find AI efficient for known variables but brittle when encountering unprecedented disruptions requiring creative, adaptive solutions beyond its training data. Similarly, an internal IT team at a mid-size manufacturing company in Pittsburgh, deploying AI for predictive maintenance, could discover that while the system excels at forecasting component failures based on historical data, it struggles to diagnose a novel, cascading issue triggered by an unexpected interplay of unrelated factors. Recognizing these gaps allows businesses to design human-in-the-loop systems, focus AI on its strengths, and dedicate human talent to the higher-order reasoning tasks where it truly excels. To capitalize, founders and operators should critically assess where their AI implementations are truly adding value versus where they might be masking or failing to address complex, non-obvious problems. Begin by auditing a process in your organization that uses AI for decision-making or pattern identification. Identify one instance where the AI's output feels "off" or where a human had to intervene with a creative solution. This week, try to articulate the specific type of reasoning the human applied that the AI missed. This exercise will help you delineate the current boundaries of AI intelligence in your context and identify specific, valuable human-centric workflows that need to be preserved or enhanced.