Ah, got it! Apologies for the confusion earlier. Now, let's explore the risks associated with Qualcomm's vision of positioning processors for AI "on the edge" rather than relying on cloud-based infrastructure:

1. Limited computational power: Mobile devices, such as phones, have limited computing resources compared to large data centers. Running complex AI models on these devices may result in reduced performance and efficiency.

2. Heating and battery drain: Intensive AI computations can cause increased heat and battery usage on mobile devices, potentially leading to overheating or decreased battery life.

3. Privacy concerns: Storing and processing large language models locally on phones may raise privacy concerns since personal data could be at a higher risk of exposure or misuse if not adequately protected.

4. Compatibility issues: Developing AI applications that can run seamlessly across different mobile devices might pose challenges due to variations in hardware specifications and software ecosystems.

5. Lack of scalability: Edge-based AI solutions may struggle when it comes to scaling up to handle large-scale deployments or high-demand scenarios due to resource limitations.

6. Development complexity: Building sophisticated AI models specifically designed for edge deployment often requires specialized expertise, making development more complex compared to utilizing cloud-based resources.

7. Security vulnerabilities: Localized processing increases the surface area for potential security breaches as opposed to centralized cloud systems that benefit from dedicated security measures implemented by providers.

It's essential for Qualcomm and other companies venturing into this space to address these risks effectively while leveraging the advantages of running AI "on the