Has AI Scaling Hit a Wall? New Research Challenges GPT-5 Expectations
OpenAI has officially released GPT-5, but the reception has been mixed. Many users found it underwhelming, sparking renewed debate about whether the long-hyped progress of artificial intelligence through scaling has finally reached its limits.
A recently published research paper sheds new light on this question. Interestingly, the answer appears to be both yes and no.
The first study, released on a preprint server, revisits the famous scaling laws that once generated massive excitement in the AI community. These same laws led researchers like Leopold Aschenbrenner to predict the possibility of an intelligence explosion by 2027. However, the new findings suggest that such rapid breakthroughs are unlikely.
Why? Because the original scaling models overlooked a crucial factor: the immense computational cost of reducing errors in large language models. According to the authors, eliminating errors in LLMs requires an exponentially increasing amount of compute power.
Their analysis shows that cutting error rates even slightly demands astronomical resources. In fact, reducing errors by just one order of magnitude could require as much as 10²⁰ times more compute power—a near-impossible threshold with today’s technology.
The Hidden Cost of Reliability in Large Language Models
Of course, reducing errors in large AI models might be feasible—if you had a galaxy-sized solar farm to power it. According to the researchers, “raising a model’s reliability to meet the standards of scientific inquiry is intractable by any reasonable measure.”
That statement may be an exaggeration of how strict scientific standards truly are, but if accurate, it helps explain a long-standing discrepancy: experts in the AI field insist that scaling works, yet everyday users still encounter frequent mistakes.
The reason lies in what’s known as the error tail. Humans naturally notice and amplify these errors, and as the study shows, eliminating them demands immense computational resources. In other words, while there isn’t a hard wall to scaling, the required compute power is so massive that, in practice, it might as well be a wall.
Adding to the concern, another recent paper analyzed small language models by testing how reasoning chains affect their ability to solve problems. Researchers tasked these models with solving complex logical puzzles that required out-of-distribution generalization. The outcome wasn’t encouraging—results pointed to further limitations in large language models when it comes to genuine reasoning skills.
Why “Chains of Thought” May Not Deliver True AI Reasoning
In simple terms, researchers argue that chains of thought in large language models (LLMs) fail to generalize beyond their training data—just as earlier studies suggested. According to their findings, the reasoning abilities of LLMs are “largely a brittle mirage.”
Similar to Anthropic’s study on Claude, the researchers discovered that the reasoning steps produced by models often don’t align with the final outcome. Sometimes the steps appear logically correct but lead to a wrong answer, and other times the reverse happens. This mismatch occurs because what we call “reasoning” in LLMs is, in reality, a simulation of reasoning, not true logical processing.
Their conclusion is clear: LLMs are not genuine reasoners. Instead, they are sophisticated text predictors that imitate reasoning patterns learned during training. They note:
- “LLMs are not principled reasoners but rather sophisticated simulators of reasoning-like text.”
- “Instead of demonstrating a true understanding of text, chain-of-thought reasoning under task transformations merely reflects a replication of patterns learned during training.”
The implication? Companies betting on LLMs as a path to AGI (Artificial General Intelligence) may face a harsh reality check. The pattern has been: first, scale up the models; then, scale up the data. Yet without breakthroughs in genuine reasoning, simply adding more size and data may not close the gap.
Why Scaling LLMs Isn’t Enough – and How You Can Help Train the Next Generation of AI
As companies continue scaling language models with bigger architectures and larger datasets, the frustration is growing: these models are not magically developing emergent reasoning or true logical understanding. If you’ve ever argued with a chatbot and felt annoyed that it didn’t actually learn from your feedback, you already know the problem.
But here’s the good news: the future of AI won’t rely only on scaling—it will rely on human expertise.
That’s why Alignerr is hiring people to help train the next generation of AI systems—and yes, they’ll pay you for your contribution. The concept is straightforward: AI has already absorbed all the text, images, and videos it could find on the internet. What it lacks is the judgment, expertise, and problem-solving skills that only real humans can provide.
And you don’t need to be a Nobel Prize-winning scientist to contribute. Alignerr welcomes:
- World-class experts in science, coding, law, business, and more.
- Fast learners with strong reasoning and problem-solving skills.
- Self-taught enthusiasts with specialized knowledge.
This collaborative approach allows people from all walks of life to shape how AI learns, creating models that are more reliable, nuanced, and grounded in real-world expertise.
Earn Money by Training AI – Flexible Remote Work with Alignerr
One of the most exciting opportunities in the AI space right now comes from Alignerr, a platform that pays people to help train artificial intelligence systems. The work is flexible, remote, and paid weekly—with rates reaching up to $150 per hour, depending on the task’s complexity and the field of expertise.
In simple terms, your job is to guide AI by correcting its mistakes. That’s right—if you’ve ever enjoyed pointing out when a chatbot gets something wrong, now you can actually get paid for it. This innovative approach bridges the gap between human reasoning and machine learning, making AI smarter, more reliable, and better aligned with human judgment.
If you’re interested in joining, you can learn more by visiting Alignerr’s official website
or scanning their QR code in promotional materials.
The Bigger Picture: AGI and Large Language Models
In discussions about Artificial General Intelligence (AGI), opinions are sharply divided. Some believe AGI is inevitable, while others think it’s a far-off dream. The truth lies somewhere in between.
As many researchers—including cognitive scientist Gary Marcus—have argued, Large Language Models (LLMs) alone won’t get us to true AGI. While LLMs are powerful at simulating human-like text, they lack the depth of reasoning and real understanding required for intelligence. Future AI progress will likely come from new approaches that combine human expertise, cognitive science insights, and advanced reasoning frameworks.
From Physics to World Models: The Real Path Toward AGI
As someone with a background in physics, I firmly believe that true intelligence must begin with physics. Language, while powerful, is a limited tool for describing the fundamental workings of nature. There’s only so much knowledge that can be extracted from words alone.
Videos provide a better medium, but even they are not enough. To achieve genuine Artificial General Intelligence (AGI), an AI must have the ability to probe, interact, and test its environment. This is where the concept of world models becomes essential.
Imagine a system that not only learns from data but also experiments within a real or virtual world—constantly refining its understanding through interaction. That’s the clear pathway to AGI.
One promising step in this direction is DeepMind’s Genie 3, a cutting-edge release that demonstrates how interactive models could shape the future of AI. If AGI eventually arrives, one of its first questions might humorously be:
“Why did you train me on Reddit?”
Frequently Asked Questions (FAQ) About GPT-5 and AI Scaling1. Has GPT-5 reached the limits of AI scaling?
Some experts argue that GPT-5 shows diminishing returns compared to previous models, suggesting scaling alone may not be enough. However, others believe improvements in architecture and training data could extend progress.
2. What does “AI scaling wall” mean?
The term refers to the idea that simply making AI models larger (with more parameters and data) may no longer lead to significant performance improvements due to computational limits and error reduction challenges.
3. Will GPT-5 lead to Artificial General Intelligence (AGI)?
Most researchers agree that GPT-5 is not AGI. While it demonstrates advanced language understanding, true AGI requires reasoning, adaptability, and real-world interaction — not just text prediction.
4. Why does scaling require so much computational power?
Reducing error rates in large language models requires exponentially more compute power. Studies show that achieving even small improvements in accuracy can demand massive energy and infrastructure resources.
5. What alternatives exist to scaling for AI progress?
Instead of scaling endlessly, researchers are exploring world models, reasoning frameworks, and hybrid AI systems that combine symbolic reasoning with neural networks to push beyond current limitations.
6. How does GPT-5 compare to previous versions like GPT-4?
GPT-5 offers improvements in speed, accuracy, and contextual understanding. However, the leap is smaller than the jump from GPT-3 to GPT-4, fueling discussions about whether scaling is plateauing.