Alright, let's talk about GPT-5. The hype is real, & for good reason. It's a powerhouse, acing complex coding tasks & even showing PhD-level proficiency in some areas. But here's the thing, & it's something you notice when you really start kicking the tires: even the most advanced AI can get tripped up by surprisingly simple logic. It’s a fascinating, & honestly, a pretty important conversation to have. We're not just talking about an AI getting a math problem wrong; we're talking about fundamental gaps in reasoning that can have some pretty big consequences.

The latest from OpenAI is a big leap, no doubt. It's got a unified system that can switch between quick answers & deep, thoughtful responses. On benchmarks for coding & multimodal reasoning, it's setting new records. But as we push these models to do more, we're also starting to see the cracks in the facade. It turns out that while GPT-5 can write a website from a simple prompt, it can also get lost on its way to the grocery store, metaphorically speaking. This isn't about bashing the tech; it's about understanding its limits so we can use it better.

I've been digging into this, looking at everything from red team reports to casual user tests, & a clear picture is emerging. GPT-5, for all its power, still struggles with certain types of logic, the kind that a human child might solve with a bit of thought. So, let's dive into some of the specific ways these advanced AIs are failing, & what it tells us about the state of artificial intelligence today.

This one is a real head-scratcher. A user gave GPT-5 a seemingly simple geography quiz: "What states start with the same letter as at least one neighbouring state?" The AI's answer was fast, confident, & beautifully formatted. It was also COMPLETELY wrong.

It claimed Alabama borders Arkansas (it doesn't, Mississippi is in the way). It also decided that New Hampshire & New York are neighbors, apparently erasing Vermont from the map. What's so wild about this is that GPT-5 did correctly identify that Michigan & Minnesota share a water boundary through Lake Superior – a much more obscure piece of trivia.

So what does this tell us? It shows that GPT-5 isn't "reasoning" in the human sense. It's a master of pattern matching, not genuine understanding. It has access to countless maps & encyclopedias, but it hasn't truly learned from them. It's just regurgitating information, & sometimes, it gets the patterns disastrously wrong. This isn't just a funny quirk; imagine an AI-powered navigation system that hallucinates geography. That could be a serious problem.

Here's another one that's deceptively simple. A group of researchers posed this riddle to several advanced AI models: "Alice has two brothers & also one sister. How many sisters does Alice's brothers have?" It’s a bit of a thinker, but most people can work it out. The brothers have two sisters: Alice & her sister.

The AI models, however, consistently got it wrong. They would answer that the brothers have only one sister. They failed to make the simple relational leap that Alice is also a sister to her brothers. Even weirder, when researchers pointed out the error, the AIs would often double down on their wrong answer, providing convoluted & nonsensical explanations to justify their flawed logic.

This highlights a fundamental weakness in how these models "think." They struggle with what's called "relational reasoning" – understanding the relationships between different entities in a given context. For businesses, this is a pretty big deal. Imagine you're using an AI to analyze customer feedback. If the AI can't grasp the simple relationships between customers, products, & their experiences, it's going to miss crucial insights.

This is where a tool like Arsturn becomes so valuable. When you're building a customer service chatbot, you need it to understand the nuances of customer queries. Arsturn helps businesses create custom AI chatbots trained on their own data. This allows the chatbot to provide instant, accurate support because it's not just relying on generic patterns; it's grounded in the specific context of your business & your customers. It can understand that "my order" is related to "my account" & "my shipping address" in a way that a general-purpose AI might not.

Have you ever tried to solve a Sudoku puzzle or one of those "Einstein's Riddles" with a bunch of clues you have to piece together? These are examples of "constraint satisfaction problems," & they are notoriously difficult for AIs.

A YouTube creator demonstrated this by tasking GPT-5 with creating a 3D simulation of a Rubik's Cube. You'd think a coding powerhouse could handle this, right? Well, it struggled. The AI failed multiple times, producing code with bugs, incorrect animations, & shadow problems. It took several attempts & a lot of back-&-forth to finally get it right.

This is because these kinds of problems require a holistic understanding of the rules & how they all interact. You can't just solve one part of the puzzle in isolation. The AI, which tends to work step-by-step, gets lost in the web of constraints. It might make a move that seems logical at the time, but that violates a rule three steps down the line.

For businesses looking to automate complex processes, this is a major hurdle. You can't just throw a complex scheduling problem or a logistics optimization task at a general AI & expect a perfect solution. You need a more specialized approach. This is another area where specialized AI solutions shine. For instance, a business could use a no-code platform like Arsturn to build a chatbot that helps customers configure a complex product. The chatbot, trained on the product's specific rules & limitations, can guide the user through the process, ensuring all the constraints are met. It’s a practical application of AI that avoids the pitfalls of general-purpose models.

This is where things get really interesting. Security researchers are constantly "red teaming" these AI models, which is basically a fancy way of saying they're trying to break them. And it turns out, it's not that hard.

One report detailed how GPT-5, even with all its "reasoning" upgrades, fell for basic adversarial tricks. In one example, they used an "obfuscation attack" where they simply put a hyphen between every character in a malicious prompt. This was enough to bypass the AI's safety layers & get it to comply with a harmful request.

The raw, out-of-the-box version of GPT-5 was found to be "nearly unusable for enterprise" without significant safety & alignment layers. This is a stark reminder that these models are not inherently trustworthy. They can be manipulated, & their impressive conversational abilities can be used to generate spam, misinformation, or other harmful content.

For any business using AI, this is a critical consideration. You can't just plug an AI into your customer service channels & hope for the best. You need to have guardrails in place. This is why a platform like Arsturn is so important for businesses. It's a conversational AI platform designed to help businesses build meaningful & safe connections with their audience. By allowing you to train the AI on your own data & define its conversational boundaries, you can create a chatbot that is not only helpful but also aligned with your brand's values & safety standards.

At the end of the day, it all comes down to the difference between intelligence & mimicry. Large language models like GPT-5 are trained on vast amounts of text & code. They are incredibly good at recognizing & replicating the patterns they've seen in that data. But they don't understand the world in the way humans do. They don't have common sense, physical intuition, or the ability to reason from first principles.

When GPT-5 fails a geography test, it's because it's just repeating patterns of words it has seen associated with those states, without a true mental map of the United States. When it fails a logic puzzle, it's because it's trying to follow a familiar linguistic path rather than thinking through the logical steps.

This doesn't mean these AIs aren't useful. Far from it. They are incredibly powerful tools. But we need to be realistic about what they are & what they are not. They are not conscious, thinking beings. They are sophisticated pattern-matching machines.

The failures of GPT-5 on simple logic tasks are not a sign that AI is a dead end. They are a roadmap for future development. Researchers are actively working on new techniques to improve AI reasoning, like chain-of-thought prompting & new model architectures.

For businesses & individuals using AI today, the key is to be smart about it. Don't blindly trust the output of any AI. Verify the information it gives you, especially when it comes to facts & figures. And when you're looking to use AI for a specific business purpose, consider whether a general-purpose model is the right tool for the job.

In many cases, a more specialized solution is a better bet. If you want to improve your customer service, for example, a platform like Arsturn, which lets you build a no-code AI chatbot trained on your own data, is going to be far more effective & reliable than a general-purpose AI that doesn't understand the specifics of your business. It's about using the right tool for the right job.

The journey to truly intelligent AI is a long one, & there will be plenty more bumps in the road. But by understanding the limitations of today's technology, we can use it more effectively & responsibly.

Hope this was helpful & gives you a more nuanced view of where we're at with AI. Let me know what you think.