It’s a weird headline, right? We keep hearing about AI models scoring at genius levels on IQ tests. Seriously, one of OpenAI's models, o1, reportedly scored a 120 on a Mensa IQ test, outperforming about 91% of humans. Another one, Claude 3 Opus, clocked in at 101, which is right around the human average. On paper, these things are brilliant. They can ace legal bar exams, pass high-level medical tests, & write code that would make your head spin.

And yet… if you ask one of these "genius" AIs a question that a five-year-old could answer without even thinking, you can get some truly bizarre responses. Ask it what happens if you put a sock in a toaster, & it might give you a perfectly structured, grammatically correct answer that COMPLETELY misses the point. It’s like having a calculator that can solve quantum physics equations but doesn’t know that 2+2 shouldn't equal "fish."

This is the great paradox of modern AI. We're building these incredibly "intelligent" systems that are, in a very real sense, shockingly dumb. They have a high IQ but almost zero common sense. So what gives? How can something be so smart & so clueless at the same time? Let's dive in, because honestly, it’s a fascinating & slightly unsettling mess.

First off, we need to understand what it means when we say an AI has a "high IQ." It's not what you think. Human IQ tests are designed to measure a bunch of things like logical reasoning, pattern recognition, memory, & how fast you can process information.

Turns out, large language models (LLMs) are AMAZING at this stuff. Why? Because they've been trained on, for all practical purposes, the entire internet. They have ingested more text, data, & information than any human could in a thousand lifetimes. So when you give them a test that relies on recognizing patterns in data or recalling stored knowledge, they're not just good at it—they're operating on a completely different level.

Think about it like this: an AI doesn't "reason" its way through a multiple-choice question in the way a human does. It predicts the next most probable word or answer based on the countless similar questions it has seen during training. It's seen enough text to know what a "good" answer is supposed to look like. It’s a master of mimicry & pattern matching, which is enough to crush a lot of standardized tests.

But here's the kicker: that’s not the same as understanding. Not even close.

Common sense is this squishy, hard-to-define thing that we humans take for granted. It's our intuitive grasp of how the world works. It’s knowing that if you put ice cream in the oven, it will melt. It’s understanding that a picture of a car on a billboard is not the same as a real car parked on the street. It’s the unspoken rules of social interaction, the basics of physics we learn by, well, existing in the world.

AI doesn't have this. At all. It's not just a little bit behind; it's on a completely different planet. Its "knowledge" is a mile wide & an inch deep. It can tell you the melting point of water, but it doesn't know that a wet floor is slippery in the same way a person who has slipped & fallen does.

This is the "common sense knowledge bottleneck" that researchers talk about. We can feed an AI all the data in the world, but we can't seem to give it that fundamental, lived-in understanding of reality. And this is where things start to get weird.

So why is common sense so hard for AI? It boils down to a few core problems that are REALLY hard to solve.

This is a big one. Humans learn about the world through physical interaction. From the moment we're born, we're touching things, falling down, picking things up, & seeing what happens. A toddler learns more about gravity by knocking over a tower of blocks than an AI does from reading every physics textbook ever written.

This is called "embodied cognition." Our understanding is grounded in our physical experiences. We know a glass will break if we drop it not because we read a manual on the fragility of glass, but because we’ve probably broken a dish or two in our lives. We've felt the weight of objects, we understand the concept of "inside" vs. "outside," & we know that pushing something will likely make it move.

AI has none of this. It exists as code on a server. It has never stubbed its toe, felt the heat of a fire, or tried to fit a square peg in a round hole. It's trying to understand the world from the outside looking in, through a keyhole made of text & pixels. It can see two hand movements, but it can't distinguish between a wave "hello" & someone swatting a fly because it lacks the social & physical context.

This leads to the next major issue. AI learns from text about the world, not the world itself. This is a HUGE distinction. It knows the word "rain" is often associated with "wet," "umbrella," & "clouds," but it doesn't understand the experience of being caught in a downpour.

Because it only learns from data, it can make absurd errors when faced with something that isn't explicitly spelled out in its training material. It might not understand that a cat resting on a table will probably jump if the table is pushed because it lacks a fundamental understanding of animal behavior & object permanence. It's all just statistical correlations, not genuine knowledge. This can lead to some pretty serious consequences, like a chatbot giving completely illogical answers to simple customer questions or a self-driving car failing to anticipate the very human behavior of a driver waving another car through an intersection.

Common sense isn't just about physics; it's also about understanding abstract concepts. Things like love, justice, loyalty, or even simple sarcasm are incredibly difficult for AI. These are concepts that aren't tied to specific, observable objects. You can't show an AI a picture of "justice."

Because AI struggles with this, its interactions can feel hollow or just plain wrong. It can be trained to recognize the words in a sentence, but it often misses the subtext, the emotion, & the intent behind them. Sarcasm is a classic example. An AI might take a sarcastic comment literally, leading to some very awkward (or even dangerous) misunderstandings.

This is another part of the problem. If we can't even perfectly define common sense, how can we test for it? Researchers are trying, of course. There are benchmarks like AGENT, which uses 3D animations to test an AI's understanding of an agent's goals & actions, kind of like what they do in cognitive development studies with young children.

There's also the idea of "commonsensicality," which tries to measure whether an individual's beliefs align with the shared beliefs of a larger group. But honestly, we're a long way off from having a reliable "Common Sense Quotient" test for AI. Many of the current benchmarks are flawed, & there are huge aspects of common sense that we don't even know how to begin testing for.

What's really fascinating, and a little scary, is that AI doesn't just have human-like blind spots; it has its own, uniquely artificial ones. Human cognitive biases—like confirmation bias or our tendency to favor our own group—are often the result of mental shortcuts our brains developed for survival.

AI's "mental blind spots," on the other hand, are by-products of its architecture & training data. These aren't programmed in; they just... emerge. For example, an AI might "hallucinate" an answer with complete confidence because its statistical model says it's plausible, even if it's factually wrong. It can generate responses that sound empathetic but have no real understanding of the emotion behind them, which can be particularly dangerous in mental health contexts.

These blind spots make AI's failures unpredictable. A human might make a mistake based on emotion or a known bias, but an AI can fail in ways that are completely alien to us. It can get stuck in a logic loop, completely collapse when a problem gets too complex, or provide advice that is wildly inappropriate for the situation.

So, why does any of this matter? Because we're increasingly relying on AI in our daily lives & in business. And an AI that lacks common sense can be more than just a novelty—it can be a liability.

Think about customer service. A generic AI chatbot that doesn't understand the specifics of your business is a recipe for disaster. A customer might ask a simple, common-sense question like, "I bought this shirt last week, but it's the wrong size. Can I exchange it at the downtown store even though I bought it online?"

A general AI might give a generic answer about return policies that it scraped from the web, completely missing the nuances of your specific policy, your store locations, or your inventory system. This is where the lack of common sense becomes a real business problem. It creates frustrating experiences for customers & more work for human agents who have to clean up the mess.

Here's the thing: you can bridge some of this common sense gap in a business context. This is where a platform like Arsturn comes in. Instead of relying on a general-purpose AI that thinks a picture of a car is a car, businesses can use Arsturn to build a no-code AI chatbot that is trained exclusively on their own data. This means the chatbot becomes an expert in your world. It's trained on your product catalogs, your support documents, your policies, & your FAQs.

This focused training gives the AI a form of "business common sense." It knows that your downtown store accepts online returns. It knows the difference between your product models. It can provide instant, accurate answers 24/7 because it's not guessing based on the entire internet; it's pulling from a source of truth that you provided. Arsturn helps businesses create these custom AI chatbots that can handle real-world customer questions, generate leads by engaging visitors intelligently, & provide a personalized experience that feels genuinely helpful, not just artificially intelligent.

The fact that AI can achieve genius-level IQ scores is a testament to the incredible power of machine learning. But the common sense gap reminds us that we are a LONG way from creating true, human-like intelligence. Building an AI that can pass a test is one thing. Building an AI that knows not to put metal in a microwave is another thing entirely.

We're in a strange new era, building tools of immense power that still have these fundamental, almost child-like limitations. It's a reminder that intelligence is more than just processing power. It’s about experience, context, & that messy, beautiful, and often illogical thing we call common sense.

Hope this was helpful & gives you something to think about next time you hear about a super-genius AI. Let me know what you think