Why GPT-5 Could Feel Like a College Graduate Who's Totally Lost Their Common Sense

Alright, let's talk about GPT-5. The hype is REAL. Every time a new version of these models comes out, the world kind of holds its breath. & what we're hearing about GPT-5 is pretty wild. We're talking about massively improved reasoning, the ability to process not just text but also video & audio, superior coding skills, & a context window that might be able to swallow a whole library of books. It's being designed as a system of specialized models, with a "thinking" component built for deep, complex problems. Honestly, it sounds like it's going to be brilliant. A genius, even.

But here's the thing that keeps nagging at me, & I've been in this AI game for a while. Despite all this raw intellectual power, there's a very good chance GPT-5 will still feel like that brilliant college grad you hire who has a 4.0 GPA but can't figure out how to work the coffee machine. All book smarts, no street smarts.

It’s this weird paradox where an AI can write a flawless sonnet about quantum physics but then give you an answer so bewilderingly stupid you have to read it twice. It's the "common sense" gap, & it's one of the most stubborn & fascinating problems in all of AI.

Think about it. Humans learn in two ways. We go to school, read books, & memorize facts. That's our "training data." But we ALSO live in the world. We trip, we fall, we burn the toast, we learn that you don't interrupt two people who are having a hushed, intense conversation. This is called embodied experience, & AI has none of it.

LLMs like GPT-5 are trained on a truly mind-boggling amount of text & data from the internet. They are masters of patterns. They can recognize, replicate, & remix human language & ideas with incredible sophistication. This is where the "book smarts" come from. They've read the entire library.

But common sense isn't something you typically write down. It's the unspoken rulebook of reality. Things like: "if you push a table, the cat sitting on it will probably jump off," or "a glass is a good container for milk, a shoe is not." Because this stuff is so obvious to us, it rarely gets explicitly stated in the books & articles the AI learns from. As Yann LeCun, one of the godfathers of AI, has pointed out, much of this foundational, non-verbal understanding is something even babies acquire, & it's just not present in text.

This is why even the most advanced AIs can make what we'd call "silly mistakes." They don't understand the world in a physical or social sense; they just understand the statistical relationships between words.

This isn't just an AI problem, by the way. We see it in people, too. I'm sure you know someone who is an absolute genius in their specific field but makes questionable life decisions. Psychologists even have a term for it: the "clever silly."

Studies have explored why highly intelligent people can sometimes be shockingly deficient in common sense. One theory is that they have a tendency to over-use their raw intelligence for every problem. Instead of relying on instinct or simple, evolved behaviors that work perfectly well for everyday social situations, they try to abstractly analyze everything. This can lead them to come up with novel but completely maladaptive ideas for how to behave.

For example, a highly intelligent person might miss obvious social cues because they're busy analyzing the semantic content of a conversation instead of just vibing with the emotional tone. They might struggle to adapt to change because it disrupts the complex systems they've built in their minds.

Sound familiar? This is almost a perfect analogy for how an LLM operates. It's a system of pure, abstract analysis, detached from the messy, intuitive, & often illogical reality of the physical & social world. It doesn't have the evolved "domain-specific" behaviors for social interaction that we take for granted. So, when faced with a problem that requires simple, practical wisdom, it might come up with a solution that is technically logical but practically absurd.

It’s not always obvious, but these common-sense failures pop up in subtle & not-so-subtle ways.

1. Misunderstanding Physicality & Causality: An AI might tell you to "use a jacket" if you're cold & don't have a blanket, which is a decent inference. But it doesn't know what a jacket or a blanket is. It doesn't understand the concept of warmth, fabric, or wrapping something around you. It's just repeating a pattern it saw in the text. Ask a more complex physical question, & it can fall apart. For example, a classic test is asking an AI to reason about a simple setup of objects in boxes & how to move them. For a long time, models would fail at this simple planning task. It’s because they can't "see" the consequence of an action like a human can. Professor Peter Gärdenfors at Lund University notes that even a two-year-old is better at causal thinking than current AI.

2. Context Blindness: This is a HUGE one. AI systems often struggle with context. A chatbot might see the word "pizza" & give you a list of toppings instead of restaurant recommendations because it missed the contextual clue in your request. A famous example involved an AI that was asked about a surgeon who couldn't operate on their own son. When told the surgeon was the boy's mother, some AIs still got confused because their training data had a stronger statistical association between "surgeon" & "male." They default to the most common pattern, even when the immediate context contradicts it.

This is a massive challenge for businesses. If you’re using an AI for customer service, you NEED it to understand context. A customer might be frustrated, joking, or asking a multi-part question. Here's the thing, a standard chatbot might just latch onto keywords & give a canned, unhelpful response. This is where having more sophisticated AI becomes critical. For instance, a platform like Arsturn helps businesses build no-code AI chatbots that are trained specifically on their own data. This helps immensely because the AI isn't just pulling from the entire internet; it's steeped in the specific context of that business's products, services, & customer interactions, which helps it provide much more personalized & relevant experiences.

3. The Inability to Handle the Unexpected: AI is trained on past data. It's great at handling situations that look like things it's seen before. But the real world is messy & unpredictable. A self-driving car might flawlessly navigate a highway but get completely flummoxed by an unexpected construction zone with a human waving a flag. The AI doesn't have the common-sense knowledge to understand that a human waving a flag is a temporary traffic rule that overrides the painted lines on the road.

This is where that "college grad" analogy really hits home. They can solve the textbook problem perfectly. But throw them a real-world curveball that isn't in the book, & they freeze up.

Here’s the million-dollar question. OpenAI is VERY aware of these limitations. The new "thinking" model within GPT-5 is specifically designed to improve reasoning & tackle more complex, multi-step problems. They are also claiming a significant reduction in "hallucinations" or factual errors. This is all great news.

However, the fundamental problem remains: GPT-5 will, in all likelihood, still be a text-in, text-out system. It won't have a body. It won't live in our world. Its knowledge is second-hand, learned from our descriptions of the world, not from direct experience. The core architectural limitations of transformers—things like their struggle with true causality & their reliance on learned patterns—aren't just going to disappear, even with more data & bigger models.

What we'll likely see is a reduction in the most obvious common-sense errors. GPT-5 will probably be much better at not making itself look silly. But the underlying gap in understanding will still be there. It will just manifest in more subtle, harder-to-spot ways. It might ace a medical exam but then fail at providing ethically sound advice in a nuanced patient scenario because it's matching patterns, not truly empathizing or weighing consequences.

For businesses, this means that while these powerful models are incredible tools, they aren't a replacement for human oversight, especially in high-stakes situations. Using them for brainstorming, drafting emails, or analyzing data is a no-brainer. But when it comes to direct, unmonitored customer interaction, you have to be careful. This is why specialized solutions are so important. When a customer interacts with a business, they expect a certain level of understanding. This is where Arsturn comes in, helping businesses create custom AI chatbots that can provide instant, 24/7 support. By training the AI on the business's own knowledge base, it can answer specific questions accurately & engage with visitors in a way that feels helpful & natural, bridging that common-sense gap in a controlled, effective way.

So, as we get ready for GPT-5, it's okay to be excited. It's going to be a technological marvel. But let's also keep our expectations grounded in reality. We're about to get an AI that's more brilliant than ever, a true genius in its own right. But it's a genius that has spent its entire life in a library, never once setting foot outside.

It will be able to tell you the history of the coffee bean in ten different languages, but it might still struggle to understand why you shouldn't pour the coffee on the floor. And that's the beautiful, frustrating, & endlessly fascinating challenge of building true artificial intelligence.

Hope this was helpful & gives you a different way to think about what's coming. Let me know what you think