What's Actually Wrong with GPT-5? A Theory on its Cognitive Model
Z
Zack Saadioui
8/10/2025
What's Actually Wrong with GPT-5? A Theory on its Cognitive Model
The dust has settled on the GPT-5 launch, & honestly, the internet seems a little… underwhelmed. After months of hype that felt like the lead-up to a world-changing event, the release of OpenAI's latest flagship model has been met with a strange mix of polite applause & quiet disappointment. Some users are calling it a "letdown," while others, perhaps more attuned to the nuances of AI development, are pointing to its significant reduction in hallucinations as a massive step forward.
But the real story isn't about the features, the speed, or even the benchmarks. The real story is about a growing suspicion, a theory that's been bubbling under the surface for a while now: the idea that we're hitting an "AI Wall." It's the nagging feeling that simply making our large language models bigger isn't leading to the kind of breakthroughs we once saw. & if that's true, then the "problem" with GPT-5 isn't a flaw in the model itself, but a crack in the very foundation of its cognitive architecture.
The GPT-5 Paradox: Better, But Not a Revolution
On paper, GPT-5 is a beast. It’s a unified system, meaning you no longer have to pick between different versions for speed or smarts. It has a "real-time router" that intelligently decides how much "thinking" to dedicate to a problem, a feature that’s a significant leap in efficiency. It's truly multimodal at its core, able to reason across text, images, & sound in a way that feels more integrated than ever before. OpenAI even boasts that it can create "beautiful & responsive websites, apps, & games" from a single prompt.
These are all incredible engineering achievements. The 6x reduction in hallucinations alone is a game-changer for any business that relies on factual accuracy. For developers, the consolidated API is a godsend, simplifying the process of building complex AI-powered products. So why the lukewarm reception?
Because for all its improvements, GPT-5 still feels like a GPT model. It's a more refined, more reliable, & more capable version of what we already have, but it doesn't represent a fundamental shift in how AI thinks. & that's where the theory of a flawed cognitive model comes in.
The Gilded Cage of the Transformer Architecture
To understand what might be "wrong" with GPT-5, you have to look under the hood at its core architecture: the transformer. First introduced in a 2017 paper from Google, the transformer has been the engine of the generative AI revolution. Its key innovation, the "attention mechanism," allows the model to weigh the importance of different words in a sequence, giving it a powerful grasp of context.
But the very thing that makes transformers so powerful is also their biggest limitation. Here's a breakdown of the potential issues:
Quadratic Complexity & the Memory Hog: The attention mechanism has to compare every single word (or token) in a sequence to every other word. This means that as the context window gets bigger, the computational power required to process it grows exponentially. It's a memory-hungry process that makes scaling up to truly massive contexts incredibly expensive & difficult.
The Lack of True Reasoning: Transformers are masters of pattern recognition, but they don't reason in the way humans do. They can't, for example, easily compose functions or perform multi-step logical operations without being explicitly trained on them. This is why even the best LLMs can sometimes fail at seemingly simple tasks that require a bit of abstract thought. They're not building a mental model of the world; they're just predicting the next most likely word based on the patterns in their training data.
The Autoregressive Trap: LLMs like GPT-5 generate text one token at a time, in a process called autoregression. This means they can't plan ahead or structure an answer globally before they start writing. They're essentially writing a story without knowing how it's going to end, which can lead to rambling, incoherent, or logically inconsistent responses in complex tasks. They also have no way to "store their work" or backtrack if they make a mistake early on.
The Specter of Bias & Hallucination: Because transformers are trained on vast amounts of internet data, they inevitably inherit the biases present in that data. & while GPT-5 has made strides in reducing hallucinations, the fundamental architecture of the transformer means that the risk of the model "making stuff up" is always present. It's not lying; it's just generating statistically plausible text that doesn't happen to align with reality.
For businesses looking to leverage AI, these limitations are more than just academic concerns. They have real-world implications. If you're using an AI to generate legal documents, for example, the risk of a subtle hallucination could be catastrophic.
This is where a tool like Arsturn becomes so important. Instead of relying on a general-purpose model that might have been trained on who-knows-what, Arsturn allows businesses to create custom AI chatbots trained on their own data. This means the chatbot's knowledge is confined to your company's documents, your product specifications, & your support articles. It's a way of building a more controlled, more reliable AI that can provide instant customer support, answer questions, & engage with website visitors 24/7, all without the risk of it going off on a tangent about something it learned from a random corner of the internet.
A Cognitive Model Stuck in Second Gear?
Cognitive scientists have been raising red flags about the limitations of LLMs for a while now. They argue that while these models are getting better at mimicking human language, they're not actually getting any closer to true human-like cognition.
One of the most compelling arguments is that LLMs lack any real-world grounding. They've learned language from text, not from interacting with the world. This means they can't truly understand the concepts behind the words they're using. They can tell you that the sky is blue, but they don't know what the sky is, what blue is, or what it feels like to look up on a sunny day.
This is a subtle but profound difference. It's the difference between a parrot that can mimic human speech & a human who can use language to express original thoughts & ideas. & it's a difference that has significant implications for the future of AI. If the current transformer-based approach is just getting better at being a parrot, then we might be a lot further from artificial general intelligence (AGI) than we think.
This is why the conversation around business automation needs to be nuanced. It's not just about plugging in the latest, greatest AI & hoping for the best. It's about finding the right tool for the right job. For tasks like lead generation & customer engagement, a conversational AI platform like Arsturn can be a game-changer. By building no-code AI chatbots trained on your own data, you can create personalized customer experiences that boost conversions & build meaningful connections with your audience. You're not trying to build a general-purpose brain; you're building a highly specialized tool that excels at a specific set of tasks.
Beyond the Transformer: Glimpses of a Different Future
The good news is that the "AI Wall" isn't an insurmountable barrier; it's just a sign that we might need to explore different paths. The research community is already hard at work on alternatives to the transformer architecture, & some of them are pretty exciting:
Mamba & State Space Models: One of the most promising alternatives is a new architecture called Mamba, which is based on state space models (SSMs). Unlike transformers, which have a quadratic complexity problem, SSMs can be trained in linear time, making them much more efficient for long sequences. They also have a built-in "memory" that allows them to be much better at recalling information from the past.
Hybrid Architectures: Some researchers believe that the future of AI lies in combining different types of models. We might see "heterogeneous architectures" that use transformers for their language prowess, but combine them with other models that are better at things like long-term memory or symbolic reasoning.
Brain-Inspired Models: There's a growing movement to design AI architectures that are more directly inspired by the human brain. One startup, Sapient Intelligence, has developed a "Hierarchical Reasoning Model" that uses two specialized modules for slow, strategic planning & fast, detailed computation, mirroring the way different parts of the human brain work. This approach has shown incredible results on complex reasoning tasks, outperforming massive LLMs with a tiny fraction of the parameters.
These are just a few of the exciting new directions in AI research. & while none of them are ready to dethrone the transformer just yet, they offer a glimpse of what a different, perhaps more powerful, cognitive model for AI could look like.
So, What's Really Wrong with GPT-5?
The problem with GPT-5 isn't that it's a bad model. It's an excellent model, a testament to the incredible engineering talent at OpenAI. The problem is that it might be the most polished & perfect version of a paradigm that's starting to show its age.
It's like we've spent the last few years building faster & faster steam engines, & GPT-5 is the most magnificent steam locomotive ever created. But in the distance, we can just start to see the faint outlines of the electric train & the airplane.
The "AI Wall" isn't a dead end; it's a turning point. It's a sign that the next great leap in AI won't come from simply scaling up what we're already doing. It will come from new ideas, new architectures, & new ways of thinking about what intelligence really is.
I hope this was helpful in reframing the conversation around GPT-5. It's not about being disappointed in what we have; it's about being excited for what's to come. Let me know what you think.