GPT-5 May Be Proof That Scaling Alone Can’t Save AI

Bigger models may no longer mean better AI

OpenAI’s latest flagship model, GPT-5, was praised as a major breakthrough in reasoning and overall capability. However, it has already exposed the shortcomings of the “bigger is better” approach to AI. Shortly after its release, users noticed that GPT-5 could still fail dramatically on tasks it wasn’t explicitly trained to handle.

Given these limitations, it is important to shift the focus toward GPT-5’s scalability challenges, and what that means for the trajectory of AI, while also asking why simply piling on more data and parameters is producing diminishing returns.

The Scaling Era: How GPT-5 Reached Such Immense Size

In recent years, progress in AI language models has been driven primarily by scaling. This approach involves increasing the size of models by adding more parameters, training them on larger datasets, and using greater computational power.