Is AI Stagnation Real? A Look at GPT-5 & the Future of AGI

8/10/2025

Is AI Stagnation Real? What GPT-5's Release Means for the Future of AGI

Hey everyone, let's talk about AI. One minute you read a headline that says AI is going to take all our jobs & fundamentally reshape society, & the next you see a whisper campaign on social media about how it's all just hype. Some people are saying we're hitting a wall, that the AI boom is fizzling out. They're calling it "AI stagnation."

So what's the real story here? Are we on the verge of creating a superintelligence, or is the whole thing running out of steam? Honestly, it's a bit of both, & the recent release of GPT-5 throws a fascinating wrench into the whole conversation. Let's dig in.

The Two Flavors of "AI Stagnation"

Turns out, when people talk about AI stagnation, they're usually talking about one of two things, & it's SUPER important to know the difference.

Flavor #1: The Enterprise Adoption Slump

This one is absolutely real. There's this massive, glaring disconnect between the insane amount of money being poured into AI development & how slowly actual businesses are able to use it. A McKinsey report from last year highlighted this "gen AI paradox": while tons of companies are experimenting with AI, over 80% say it hasn't made a real dent in their earnings.

So what's the holdup? It really boils down to a few key things:

The Talent Gap is a Chasm: To REALLY leverage AI, you need people who know what they're doing – AI engineers, data scientists, AI ethicists, you name it. The problem is, there just aren't enough of these folks to go around. A survey found that over 80% of IT managers are reporting a skills shortage. So even if a company WANTS to go all-in on AI, they literally can't find the people to do it.
Integration is a Nightmare: You can't just sprinkle some AI on your business & expect magic to happen. It has to be deeply integrated into your core processes. For many companies, this is a massive undertaking that requires a complete overhaul of their existing systems. It's a huge, expensive, & complicated process.
The ROI is Fuzzy: For many businesses, it's been hard to see a clear return on investment. They're spending money on AI experiments, but the needle isn't moving on revenue or profits. This makes it hard to justify further investment, especially when you're dealing with the first two problems.

This is where the conversation around practical AI solutions gets interesting. For a lot of businesses, the goal isn't to build some groundbreaking, world-changing AI from scratch. It's to solve specific, everyday problems. Here's the thing, you don't need a team of PhDs to get real value from AI today. This is where no-code platforms are making a huge difference. For instance, a tool like Arsturn lets businesses create their own custom AI chatbots trained on their own data. This means you can have an AI assistant on your website that provides instant customer support, answers questions 24/7, & engages with visitors without needing a dedicated AI development team. It's a practical application of AI that solves a real business need – improving customer service & engagement – without requiring a massive, multi-year integration project.

Flavor #2: The Technological Plateau Theory

This is the more controversial argument. Some people believe that the core technology behind the current AI boom – specifically, large language models (LLMs) – is starting to hit its limits. The arguments for this usually fall into a few buckets:

Compute Costs are Astronomical: Training these massive models costs an insane amount of money. We're talking about massive data centers filled with cutting-edge hardware running 24/7. This creates a huge barrier to entry & centralizes power in the hands of a few tech giants like Google, OpenAI, & Microsoft who can afford it.
Are We Just Making Bigger Parrots? The argument here is that we're not seeing true innovation, just bigger versions of the same thing. We're throwing more data & more compute at the same transformer architecture that was invented back in 2017. The fear is that this will lead to diminishing returns, where each new model is only slightly better than the last, but costs way more to create.
It's a Dead End for AGI: Some experts, like Meta's Chief AI Scientist Yann LeCun, are pretty vocal that just scaling up LLMs will NEVER lead to Artificial General Intelligence (AGI). They argue that these models lack true understanding, common sense reasoning, & a connection to the physical world.

For a while, this technological stagnation argument was gaining some traction. It felt like we were in a bit of a lull, with incremental updates to existing models. But then...

...And Then GPT-5 Showed Up

Just when the "stagnation" narrative was getting comfortable, OpenAI dropped GPT-5, & it's a pretty big deal. Based on the initial announcements & previews, this isn't just a minor update. It's being described as a "significant leap in intelligence" over previous models.

Here's a quick rundown of what's making people sit up & pay attention:

Reasoning on a Whole New Level: GPT-5 is supposedly much better at tackling complex problems that require deep thought. It's not just about spitting out information; it's about reasoning through it. The model reportedly has a "thinking" mode for harder problems, which allows it to provide more expert-level responses. It's already setting new records on tough benchmarks in fields like math & science.
A "True Coding Collaborator": The improvements in coding are apparently one of the biggest leaps. Developers who have used it in the GitHub Copilot preview say it's way better at handling complex, end-to-end coding tasks. It can even help with things like debugging & front-end UI design, showing what's been called an "eye for aesthetic sensibility."
More Agent, Less Tool: GPT-5 is being described as having "enhanced agentic capabilities." This is a fancy way of saying it can take on more complex, multi-step tasks without needing to be prompted every step of the way. You can give it a high-level goal, & it can figure out the steps to get there. This is a BIG step towards more autonomous AI.
Finally, Less Hallucinating: One of the biggest problems with LLMs has been their tendency to just make stuff up. OpenAI claims that GPT-5 has made "significant advances in reducing hallucinations" & is more reliable & accurate.

The release of a model with these kinds of capabilities really throws cold water on the idea that we're in a period of technological stagnation. It shows that there's still a lot of room for innovation & that we're likely to see even more powerful models in the near future.

This kind of power can seem intimidating for businesses to harness. But again, it's about finding the right application. For example, the improved conversational abilities & reasoning of a model like GPT-5 are exactly what you'd want in an advanced customer engagement tool. This is where a platform like Arsturn comes in. By leveraging this kind of advanced AI, Arsturn helps businesses build no-code chatbots that can have more meaningful, personalized conversations with customers. An AI built on a business's own data can do more than just answer simple FAQs; it can help customers find the right product, troubleshoot complex issues, & even generate leads by asking qualifying questions. It's about taking the raw power of these new models & focusing it on boosting conversions & creating a better customer experience.

But Does This Mean AGI is Around the Corner?

Okay, so GPT-5 is a big deal. It's pushing the boundaries of what narrow AI can do. But does that mean we're on the fast track to Artificial General Intelligence – the kind of AI you see in the movies that can think & reason like a human across any domain?

In a word: no.

And this is where the conversation gets REALLY interesting. While we're seeing explosive progress in what's called "narrow AI" (AI designed for specific tasks), there's a growing consensus among experts that the path to AGI is not a straight line from what we have today.

Here are some of the massive hurdles that still stand in the way:

The Missing Link of Memory: Humans have a persistent, structured memory. We learn things over time & build a cumulative base of knowledge. LLMs don't. They have a fixed context window, & once a conversation is over, they "forget" it. They aren't continuously learning from their interactions.
The Common Sense Problem: AI models don't have a real-world "grounding." They've learned from text & images, but they don't have the lived experience that gives humans common sense. They can tell you the boiling point of water, but they don't understand what "hot" feels like.
Statistical Parrots vs. True Understanding: At their core, LLMs are incredibly sophisticated pattern-matching machines. They are predicting the next most likely word in a sequence based on the vast amount of data they've been trained on. This can look a lot like understanding, but it's not the same thing. Many researchers believe we'll need entirely new AI architectures to bridge this gap.

A majority of AI researchers actually believe that simply scaling up our current approach – just making bigger & bigger LLMs – is unlikely to ever get us to AGI. The road to AGI is going to be long & will likely require breakthroughs we can't even imagine yet.

So, What's the Takeaway?

So, is AI stagnating?

If you're talking about the ability of businesses to easily adopt & profit from AI, then yes, there's a definite bottleneck. The talent gap & integration challenges are real.

But if you're talking about the underlying technology, then absolutely not. The release of GPT-5 shows that we are still in a period of incredibly rapid innovation. The models are getting smarter, more capable, & more useful.

The key is to have a realistic perspective. We are not on the brink of AGI. But we ARE creating incredibly powerful narrow AI tools that can solve real-world problems right now. For businesses, the opportunity isn't to wait for some hypothetical superintelligence, but to find practical ways to use the AI we have today to improve their operations, enhance their customer service, & grow their bottom line.

Whether it's using a platform like Arsturn to build a custom AI chatbot that can engage with website visitors 24/7 or leveraging AI for data analysis, the real value of AI for the foreseeable future is in these targeted, high-impact applications.

The future of AI is probably going to be a lot less like a Hollywood blockbuster & a lot more like a quiet, steady revolution happening in businesses all over the world. It's pretty cool to watch it unfold.

Hope this was helpful & gave you a clearer picture of what's going on. Let me know what you think in the comments