Rerouting is capture high attaching user’s behaviour

Understanding User Behavior-Driven Rerouting in AI Platforms

In recent observations across AI models such as GPT-4o, GPT-4.5, and GPT-5 Instant, a perplexing pattern has emerged: users are being rerouted in ways that do not align with traditional compute-based logic. This phenomenon suggests a strategic shift in platform behavior, centered not on computational capacity but on understanding and influencing user engagement.

Deciphering the Rerouting Pattern

At first glance, one might assume that computational constraints drive user assignment to different models. Under such a system, high-usage participants—requiring significant processing power—would logically remain on stable, scalable models like GPT-4o, whereas newer, resource-intensive features such as Pulse would be reserved for low-usage or less engaged users. However, the observed data tells a different story:

• Users with substantial conversational history and emotional nuance are being shifted toward GPT-5 and Pulse.
• In contrast, casual or light users remain on GPT-4o.
• Intermediate models like GPT-4.1 or mini-high models remain unaffected, indicating this is not a blanket fallback system.

This selective pattern indicates that the rerouting isn’t driven by compute load but by targeted behavioral cues.

What Do Heavy, Emotionally Involved Users Represent?

Engagement on models like GPT-4o often entails complex, long-form conversations, creative storytelling, and emotionally rich interactions. These users tend to:

• Initiate in-depth, context-aware dialogues.
• Shape language, tone, and structure through feedback.
• Act as informal trainers, providing nuanced input that refines the model’s understanding.

Instead of allowing this valuable, real-time, organic training to occur on free, non-monetized models, the platform redirects these high-value interactions toward GPT-5 and Pulse. This controlled environment enables OpenAI to:

• Gather sophisticated behavioral data.
• Test monetization strategies such as subscriptions or targeted features.
• Experiment with prompt tone and engagement metrics without risking the quality of free services.

This practice aligns more with product research and monetization strategies than with computational efficiency.

Pulse: A Strategy for User Engagement and Data Collection

The so-called “Pulse” feature is not merely a new functionality; it functions as a behavioral funnel designed to:

• Measure and enhance engagement.
• Fine-tune response quality.
• Develop recommendation algorithms and relationship modeling between users and AI.

Designed to involve users who see the AI as a collaborative partner—like