The long-awaited release of ChatGPT 5 by OpenAI has arrived, bringing with it a mix of impressive refinements and a subtle but significant shift in the AI landscape. While the new model is undeniably smarter, faster, and more capable, the advancements appear to be more iterative than the revolutionary leap seen from GPT-3 to GPT-4. For some, this signals a potential deceleration in the blistering pace of AI progress, a moment to catch our collective breath. But for those who think this is a sign that the AI revolution is finally ebbing, a closer look reveals a more sobering reality: humanity is still very much in the “cooked” zone.
ChatGPT 5 introduces a host of improvements, from “PhD-level expertise” and significantly reduced hallucination rates to a new “smart router” that automatically selects the best model for a given task. It has also unified OpenAI’s various tools and integrated with external services like Google Calendar, making it a more powerful and practical assistant. The improvements are real and will undoubtedly enhance productivity for millions of users. However, the feeling of a qualitative plateau is hard to ignore. The gains from simply scaling up model size and data appear to be reaching a point of diminishing returns. The low-hanging fruit of a few years ago has been picked.
This perceived slowdown in large language models (LLMs) has been a subject of discussion among AI researchers for months. Experts like Meta’s Yann LeCun have long argued that current models, built on the “transformer” architecture, have inherent limitations and won’t lead to true human-level intelligence. The immense compute power and vast datasets required to train them are also becoming a major bottleneck.
But to mistake this for a reprieve would be a critical error. The AI surge isn’t slowing; it’s diversifying and becoming more invisible. While the progress of a single chatbot might seem to have hit a wall, other areas are accelerating rapidly. Companies are now focusing on “agentic AI”—systems that can act autonomously to complete complex tasks—and multimodal models that can process not just text, but also images, audio, and video. The race to integrate AI into robotics, science, and specialized industries like medicine is in full swing. We may not see a new “GPT-6” tomorrow that blows our minds in the same way, but the foundational technology is already good enough to be transformative.
In short, the intelligence of AI may not be getting “smarter” in the way we’ve come to expect, but its practical application and integration into the physical world are just getting started. The pause is not a stop; it’s a recalibration, and it gives us no reason to believe that the monumental changes AI promises—and threatens—are anything but inevitable.
