The author observed a "subjective feeling" that older versions of commercial AI models begin to perform worse ("get dumber") immediately preceding the launch of a new version. This suggests that model performance is not static and may be influenced by the provider's release cycle, creating unpredictable results for developers.

Unlike mature tech products with annual releases, the AI model landscape is in a constant state of flux. Companies are incentivized to launch new versions immediately to claim the top spot on performance benchmarks, leading to a frenetic and unpredictable release schedule rather than a stable cadence.

AI model versioning has moved away from representing specific technical changes and is now primarily a marketing signal. The numbers indicate which competitive "class" a model belongs to (e.g., a "five class" model), and companies may skip versions to appear more advanced, similar to how car manufacturers use model years.

While AI progress is marketed in revolutionary "step-changes" (e.g., GPT-3 to GPT-4), the underlying reality is more like compounding interest. A continuous stream of small, incremental improvements are accumulating, and their combined effect is what creates the feeling of an exponential leap in capability over time.

There's an inverse correlation between an AI lab's model performance and its marketing focus. When a lab is in a "downswing" between model releases or lagging on benchmarks, it shifts PR to product capabilities and vertical applications instead of raw performance.

Major AI labs will abandon monolithic, highly anticipated model releases for a continuous stream of smaller, iterative updates. This de-risks launches and manages public expectations, a lesson learned from the negative sentiment around GPT-5's single, high-stakes release.

AI companies like OpenAI have shifted to monthly, incremental model updates. This frequent but less impactful release cadence means developers no longer feel strong loyalty to any specific model and simply switch to the newest version available, treating major AI models like commodities.

Despite a media narrative of AI stagnation, the reality is an accelerating arms race. A rapid-fire succession of major model updates from OpenAI (GPT-5.2), Google (Gemini 3), and Anthropic (Claude 4.5) within just months proves the pace of innovation is increasing, not slowing down.

A guest alleges Anthropic intentionally degraded Claude 4.7 performance before launching 4.8, creating an artificial incentive for users to upgrade. This tactic, compared to Apple slowing down old iPhones, suggests a strategy to push customers to newer, more expensive models, which could backfire and drive users to stable open-source alternatives.