The era of explosive growth in large language models (LLMs) is facing a critical slowdown as providers hit a "data wall." Having largely exhausted the high-quality public data available online, companies like OpenAI are struggling to achieve fundamental improvements in their core models. To maintain a facade of rapid innovation, they are increasingly resorting to feature-based "gimmicks" that mask this underlying stagnation.

The recent release of OpenAI's ChatGPT Pulse is a prime example. While it adds a variety of new tools and capabilities, these features cater to niche use cases and distract from the central issue: the core intelligence of the LLM is not significantly advancing. Most users would gain more from a model that is more accurate, reliable, and less prone to errors than one with more peripheral functions.

This industry trend highlights a shift from genuine progress in AI reasoning to a superficial arms race over features. The real challenge—the desperate and costly search for new proprietary and synthetic data sources—continues behind the scenes. Until the data scarcity problem is solved, we can expect more flashy updates that offer the illusion of progress without delivering the foundational improvements users truly need.