Here’s the thing about AI models: the next big thing isn't always the next better thing. At least, not in the way we expect.

We’re all waiting for GPT-5. The hype is massive. People are expecting it to be this earth-shattering leap, a true step towards AGI that will make GPT-4o look like a pocket calculator. But I'm going to tell you something that might sound crazy: when it finally drops, GPT-5 might actually feel like a downgrade.

And honestly, it wouldn't be the first time.

Before we get into the weeds, let's be crystal clear: GPT-5 isn't out yet. This is a thought experiment. But it's a thought experiment based on some very real patterns we've seen over & over again in the AI world. If you look closely at how these models evolve & how we, as users, react to them, a "disappointing" GPT-5 isn't just possible—it's pretty damn likely.

This isn't just baseless speculation. We have a perfect case study: the transition from GPT-3.5 to GPT-4.

Remember when GPT-4 first came out? It was supposed to be the undisputed champion. It scored off the charts on exams, it could reason better, it was a genius. But on user forums like Reddit & OpenAI's own community pages, a different story started to emerge.

Threads popped up with titles like "ChatGPT 4 is worse than 3.5" & "GPT 4 giving less that gpt 3.5 atm. What's going on?". Users complained that the new, "smarter" model was slower, "lazier," & often gave needlessly complex or unhelpful answers. For many everyday tasks, the zippy, eager-to-please GPT-3.5 felt better. It was faster, more direct, & less prone to errors or getting stuck.

People were genuinely frustrated. They were paying $20 a month for what they felt was a worse experience. OpenAI even had to publicly acknowledge the feedback that GPT-4 was getting "lazier" & promised to look into it.

This wasn't because GPT-4 was actually dumber. It was a more complex & powerful model. But that complexity came with trade-offs. It was slower, more expensive to run, & seemed to have different behavioral quirks that users, accustomed to the rhythm of GPT-3.5, found jarring.

Now, apply that exact same logic to the jump from GPT-4o to GPT-5. We're poised for a repeat performance, but this time, the stakes are even higher. Here’s a breakdown of why the next-gen AI could feel like a step back.

It's not just one thing. It's a combination of technical realities, psychological factors, & the business of AI itself. Let's dig in.

This is probably the BIGGEST reason. As AI models get more powerful, the potential for misuse & generating harmful content skyrockets. So, naturally, the companies building them have to install more & more safety guardrails. This process is called "alignment," & it comes at a cost.

It's called the "alignment tax" or "safety tax."

Think of it like putting a governor on a sports car. The engine is capable of incredible speed, but for safety reasons, you limit its top end. You're sacrificing raw performance for control & safety.

AI researchers have found that this is a very real trade-off. Studies show that making a model "safer" can significantly reduce its performance on complex reasoning & creative tasks—sometimes by as much as 15-40%. The very mechanisms that stop a model from giving you dangerous information also tend to inhibit its ability to explore unconventional ideas, think outside the box, or even answer legitimate questions that it mistakenly flags as problematic.

So, GPT-5 will almost certainly be the "safest" model OpenAI has ever released. It will be more risk-averse, more politically correct, & more likely to refuse a prompt if it even sniffs something controversial. For a user who just wants to brainstorm a fictional story with a morally grey character or ask a complex technical question, the model might just say "I can't help with that."

This is what some users in the speculative forums are already worried about, describing future models as potentially feeling "colder" or more like a "strictly professional... efficient, but cold assistant." You'll be interacting with a model that feels less like a freewheeling creative partner & more like a heavily-lobotomized corporate assistant. That's a downgrade in feel, even if the underlying engine is technically more powerful.

This is a huge problem for businesses that rely on consistent AI performance. You can't have your customer service bot suddenly refusing to answer questions about your product because a new safety filter was implemented poorly. It creates unpredictability. That’s why many businesses are opting out of the public model arms race for their core functions. Instead of relying on a general-purpose tool that could change overnight, they use platforms like Arsturn. It helps businesses create custom AI chatbots trained specifically on their own data. This means the AI provides instant, accurate customer support based on company documents, not the entire wild west of the internet. The result is a reliable, on-brand experience, free from the "safety tax" drama of massive public models.

GPT-4o was a game-changer, not just because of its intelligence, but because of its speed & cost-effectiveness. The "o" stands for "omni," but it might as well stand for "optimized." It delivered GPT-4 level intelligence at a much faster pace & a lower cost for OpenAI to run. This made it accessible & practical for real-time conversations.

But what if GPT-5 follows that trend? What if the primary focus is on making it even more efficient, even faster?

There's often a trade-off between speed & depth. A model optimized for quick responses might not perform as well on tasks that require deep, multi-step reasoning. Users noticed this with early versions of GPT-4 Turbo, feeling it was a step back from the original, slower GPT-4 in terms of reasoning. They felt the faster model was cutting corners.

A hypothetical GPT-5 that's twice as fast as GPT-4o but slightly less coherent or thorough in its reasoning would absolutely feel like a downgrade to power users who rely on it for complex tasks like coding, legal analysis, or writing detailed reports. It might be better for 90% of casual users, but that remaining 10% will feel the difference keenly.

It's the difference between a quick-witted friend who's great at conversation & a deep-thinking professor who takes their time to give you a perfectly constructed answer. We loved GPT-4o because it felt like a bit of both. If GPT-5 leans too far into the "quick-witted" camp to save on costs, it'll lose some of its magic.

This is a big one that people in the AI industry are talking about a lot more now. The "scale is all you need" era might be coming to an end.

For years, the formula was simple: more data + more computing power = a smarter model. The leap from GPT-2 to GPT-3 was ASTONISHING. The leap from GPT-3 to GPT-4 was still incredibly significant. But we're starting to hit a wall.

Reports from within AI labs suggest that simply scaling up isn't producing the same massive gains anymore. Databricks' CTO, Matei Zaharia, put it bluntly: "Whenever you double your training costs, you increase quality by only around 1% or something like that."

Let that sink in. Double the cost for a 1% gain.

The low-hanging fruit has been picked. All the text on the public internet has been scraped. Now, the improvements are getting harder & more expensive to achieve. Ilya Sutskever, a co-founder of OpenAI, even said that the results from scaling have "plateaued."

So what does this mean for GPT-5? It means that even if it's technically the best model ever created, the perceived jump from GPT-4o might be tiny. When users have been conditioned to expect exponential progress, a merely linear improvement feels like a failure. It won’t have the same "wow" factor. It will feel iterative, not revolutionary. & for many, that will be a huge disappointment.

Let's be honest, the expectations for GPT-5 are completely out of control. It's the AI equivalent of a rock band's follow-up to a legendary, chart-topping album. It's almost impossible to live up to the hype.

Gartner has a famous model for this called the "Hype Cycle." It describes how technologies go through predictable phases:

Generative AI is currently sliding from the Peak of Inflated Expectations right into the Trough of Disillusionment. GPT-5 is set to be released right at the bottom of that trough.

The public expects magic. They expect a model that understands them perfectly, never makes mistakes, has perfect memory, & can reason like a human genius. The reality is that GPT-5 will still be a language model. It will still "hallucinate" (make stuff up), it will still misunderstand context, & it will still have weird quirks.

When reality collides with those inflated expectations, the result is disillusionment. It doesn't matter if GPT-5 is 10% better than GPT-4o on every benchmark; if it's not 1000% better in the way users dreamed it would be, it will be labeled a disappointment.

Finally, there's a deeply human reason a new model can feel like a downgrade: it's different.

Users spend hundreds, even thousands of hours interacting with these models. They learn their specific quirks, their "personalities," the best way to prompt them to get the desired output. They build a workflow & a mental model around how the AI behaves.

A new model, even a better one, changes the rules. It has a different "vibe." Maybe it's less conversational. Maybe its creative writing style is more sterile. Maybe it formats code snippets differently. These subtle changes can be incredibly disruptive.

We saw this in the speculative user reviews comparing GPT-4o to a hypothetical GPT-5. People worried the new model would lose the "personable" nature of GPT-4o, becoming more formal & less creative. They were mourning the loss of a tool they had grown to love & understand, even if the new tool was technically superior in some ways. It’s like getting a new version of your favorite software, & they’ve moved all the buttons. It’s frustrating!

This is another area where a specialized business solution shines. For things like lead generation or customer engagement, you need a consistent tone & personality. You can't have your friendly, helpful website chatbot suddenly become a cold, robotic assistant after an update. That's why building a no-code AI chatbot with a platform like Arsturn is so powerful for businesses. It allows you to define & maintain a consistent AI persona trained on your data. You build a meaningful, predictable connection with your audience, boosting conversions without worrying that a random update will alienate your customers. It puts the business in control of the AI's personality, not the other way around.

Look, GPT-5 will almost certainly be a technical marvel. It will process more data, have a more sophisticated architecture, & will likely outperform GPT-4o on dozens of academic benchmarks.

But "better on a benchmark" does not always mean "feels better to use."

The combination of a heavier safety tax making it feel restricted, a potential trade-off for efficiency over depth, the diminishing returns of scaling, the impossible hype, & the simple shock of it being different all point to a rocky reception.

We, as users, have been spoiled by incredible leaps in a very short time. Our expectations are now running ahead of what is technically feasible or even desirable when you factor in safety & cost. The next big step forward in AI might not be a single, giant, general-purpose model like GPT-5, but a diversification into more specialized, reliable, & purpose-built tools that do one thing exceedingly well.

So when GPT-5 finally arrives, try to manage your expectations. It will be an incredible piece of technology, for sure. But don't be surprised if, for your day-to-day use, you find yourself missing the good old days of GPT-4o.

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