Testing Grok 4.5 AI Brain Drain: How to Use the "Pelican Riding a Bicycle" Test?

# Testing Grok 4.5 AI Brain Drain: How to Use the "Pelican Riding a Bicycle" Test? In daily software development and production workflows, AI acts as an indispensable team member. However, it sometimes "slacks off" or experiences a temporary drop in performance. In this video, tech creator DP addresses the widely discussed phenomenon of **"AI brain drain" (intelligence degradation)** and introduces a quick, intuitive inspection method: the **Pelican Riding a Bicycle Test**. ## What is the "Pelican Test"? The core idea of the Pelican Test is to provide a simple prompt requiring the AI to render an image (usually via SVG code) of "a pelican riding a bicycle." By evaluating the logic and structural accuracy of the final image (e.g., proper alignment, complete body/mechanical components), users can visually judge the AI's current cognitive capability and compute allocation. If the output is an incoherent mess, it highly indicates the model is currently running in a severely degraded or resource-restricted state. ## Strict Testing Setup To ensure objective results, the experiment followed these strict configurations: - **Environment**: Cursor IDE. - **Workspace**: An entirely empty directory with **zero dependencies or hidden prompt injections**, allowing 100% free-range generation from the model. - **Target Model**: Grok 4.5 (with thinking mode set to 'High'). - **Variables**: Three different officially connected channels were tested. The recording wasn't sped up, presenting real-time "thinking" and code-generation durations for accurate comparison. ## The Three Blind Tests and Analysis The video features three independent tests, with the workspace thoroughly cleaned out before each round: 1. **First Test (The Baseline Pass)**: Moderate thinking and generation speed. The final image showed slight misalignment between the head and legs, but overall captured the clear concept of a "pelican" and the motion of "riding." This indicates an **acceptable and usable state**. 2. **Second Test (The Best Performance)**: Took the longest time to think. Highly structured code and properly scaled image components with no noticeable misalignment. This proves that longer thinking time generally yields higher intelligence. This is a **high-quality usable state**. 3. **Third Test (Severe Brain Drain)**: Fast output with minimal reasoning visibility. Output file was sloppily named `index` instead of specific keywords. The resulting SVG was utterly broken and illogical. This is a classic example of **severe performance degradation**. ## Practical Takeaways for Developers - **Unpredictability in Dynamic Compute Allocation**: Even with identical basic parameters and IP environments, AI models can still suddenly degrade (as seen in Test 3). Cloud providers likely scale down resource allocation dynamically when servers are under heavy load. - **Defining "Usability"**: In a real-world production environment, you don't always need absolute perfection like in Test 2. As long as the core logic meets fundamental requirements (like the acceptable flaws in Test 1), it is safe to assign coding tasks to your "AI employee." - **Establishing Cross-Model Baselines**: The Pelican Test helps quickly gauge the current baseline for a single model (like Grok 4.5). When evaluating multiple AI models from different providers, users should run similar iterative tests to establish new thresholds for performance accuracy. > 💡 **Resources**: For related prompts, codes, and follow-up articles regarding this test, please visit `dpit.lib00.com` and search for the keyword **Grok**.