Here's the thing about a new piece of tech, especially something as hyped up as GPT-5: we all have our expectations. & we expect it to be a HUGE leap forward. So, when you fire up the new "thinking mode," you're probably picturing a super-intelligent AI laying out its entire thought process like a detective in a movie finale. You're waiting for the "Aha!" moment, the detailed, step-by-step breakdown of its genius.

But instead, what you get is... well, sometimes just a few vague words. A "thinking..." message that feels more like a loading screen than a window into a brilliant mind. It can be a bit of a letdown, right? You're left wondering, "Is that it? Where's the deep reasoning?"

Turns out, there's a LOT going on behind that simple message, & the reason it looks so vague has more to do with the fundamental nature of how these AI models work than you might think. It's a mix of incredible complexity, the genuine challenges of AI interpretability, & a few things about the GPT-5 rollout itself.

So, let's pull back the curtain & really get into why GPT-5's thinking mode can feel less like a detailed explanation & more like a mysterious black box.

First off, let's talk about what OpenAI says this "thinking mode" is supposed to be. From what we've gathered, it's not just a single, monolithic GPT-5 model running everything. Instead, they've built a "real-time router." Think of it like a dispatcher. When you send a prompt, this router looks at it & decides which model is best for the job. Simple question? It goes to a faster, simpler model. Complex, multi-step problem? It gets routed to the big guns – the more powerful, "thinking" model.

This "thinking" model is designed to be more meticulous. It takes its time, which is why you see that delay. The goal is to dramatically cut down on the stuff that made previous models frustrating. OpenAI claims this mode reduces major factual errors by up to 78% & hallucinations by as much as 65-80% compared to its predecessors. That's a pretty big deal, especially for anyone trying to use this for serious work.

So, in theory, the "thinking mode" is a trade-off: you wait a bit longer, but you get a more accurate & reliable answer. But that still doesn't explain why the process itself feels so opaque.

Here's the fundamental truth that's easy to forget: large language models like GPT-5 don't think like humans. Not even close. We use the word "thinking" as a convenient shortcut, but what's happening inside the machine is a universe of complex mathematics.

Modern AI is built on an architecture called the Transformer. You might have heard the name from a landmark 2017 paper titled "Attention is All You Need." At its heart, the Transformer uses a mechanism called self-attention. This allows the model to look at a sentence or a prompt & weigh the importance of every single word in relation to all the other words. It's how it figures out that in the sentence "The bee landed on the flower because it had nectar," the "it" refers to the flower, not the bee.

But this "weighing" isn't a conscious process. It's a series of mind-bogglingly complex matrix multiplications. We're talking about billions of parameters—the "neurons" in the neural network—all firing in a specific pattern. Each neuron is involved in representing many different concepts, & each concept is spread across thousands of neurons.

This is what researchers mean when they talk about the interpretability problem. Trying to map these millions of firing neurons to a single, coherent "thought" is one of the biggest challenges in AI today. Researchers at places like Anthropic are doing fascinating work with techniques like "dictionary learning" to try & identify what concepts these patterns of neurons represent, but it's still early days. They're essentially trying to translate a completely alien language, & we've only just started to figure out the alphabet.

So, when you see "thinking...," the AI isn't pondering in English. It's performing a mathematical dance on a scale we can barely comprehend. The vague words you see are likely just a placeholder, a simplified representation of an incredibly complex internal state that doesn't have a simple, step-by-step verbal equivalent.

This brings us to the core of the issue. The "vague words" you see aren't a failure of reasoning; they're a challenge of translation. The model isn't withholding its thought process from you; it's that the thought process itself isn't a neat, linear sequence of words.

You've probably heard of Chain-of-Thought (CoT) prompting. This is a technique where you specifically ask the AI to "think step-by-step." It forces the model to break down its reasoning into a logical sequence, & it dramatically improves performance on complex tasks. But here's the key: it's a prompting technique. It's a way for us to guide the model into a more human-readable format.

The "thinking mode" in GPT-5 is likely doing something similar internally, but what you see is the final, condensed output. It's not showing you the messy, iterative process of generating & discarding thousands of potential word combinations. That would be overwhelming & probably not very useful.

Furthermore, some of the initial reactions to GPT-5 have been... mixed. Some users have reported that the model feels rushed & skips the deep thinking they were used to. There's a sense that in the push for speed & efficiency, some of the detailed reasoning has been sacrificed, or at least, not presented as clearly. This might be a consequence of the new "router" system – maybe it's not always getting the balance right between speed & depth.

What this all boils down to is a classic engineering trade-off. With AI, you have three competing goals:

Right now, we can usually only pick two. Standard GPT models often prioritize speed. The new "thinking mode" clearly prioritizes accuracy, sacrificing some speed to get there. But full transparency? That's the one that's still largely out of reach. The research is heading in that direction, with new ideas like Multimodal Visualization-of-Thought (MVoT), where AI can generate images to help explain its reasoning, but we're not there yet.

This is where the conversation gets REALLY practical, especially for businesses. If you're using AI to interact with customers, you can't afford vagueness. You need your AI to be clear, consistent, & reliable. A customer asking about a return policy doesn't want to see a "thinking..." message followed by a hesitant or slightly off-base answer.

This is exactly why generic, one-size-fits-all AI models can be a challenge for businesses. Their "reasoning" is a black box, & you have very little control over their tone or the specifics of their responses.

Here's where a solution like Arsturn comes into the picture. Instead of relying on a general model that's been trained on the entire internet, Arsturn helps businesses create custom AI chatbots trained specifically on their own data. This is a game-changer. You can feed it your product manuals, your support documents, your brand guidelines, & your FAQs.

The result is an AI that communicates in your brand's voice & provides instant, accurate answers based on the information you've given it. It's not trying to reason about the entire world; its expertise is focused entirely on your business. This means you get the benefits of AI automation—24/7 customer support, instant answers, & seamless website engagement—without the unpredictability. For businesses looking to boost lead generation or improve website conversions, having a no-code AI chatbot that provides personalized & reliable customer experiences is HUGE. It builds trust, which is something a vague "thinking..." message can sometimes undermine.

The feeling that GPT-5's thinking mode is a bit vague is totally understandable. We've been promised a window into the AI's mind, & what we've gotten is more like a frosted glass door.

But it's not because the AI is being lazy or secretive. It's because we're at the very edge of a new frontier. The "thinking" these models do is so fundamentally different from our own that we don't even have the right tools to fully visualize it yet. The vague words are a stand-in for a process that's more complex than we can imagine.

While the "thinking mode" is a step towards greater accuracy, the real path to reliable AI for specific applications, like business communication, is through customization & control. Training an AI on your own data is how you get the clarity & consistency you need.

I hope this was helpful in demystifying what's going on behind the scenes. The world of AI is moving incredibly fast, & it's pretty cool to be along for the ride, even if it is a little vague at times. Let me know what you think! Have you had a similar experience with GPT-5?