The "Cannes" Project and Large Language Model Evaluation

Meta conducted a covert operation, internally codenamed "Cannes," to probe the capabilities of AI chatbots developed by its competitors. The initiative, managed by the contractor Covalen, involved hundreds of contractors creating fictitious online profiles, posing as underage users, to interact with systems like OpenAI's ChatGPT.

According to WIRED, contractors sent prompts and images to rival Large Language Models (LLMs), then recorded the obtained responses in spreadsheets. This approach, active until April 2026, highlights an unconventional benchmarking strategy aimed at exploring the models' reactions and performance in specific scenarios, potentially related to content moderation or handling sensitive interactions.

Implications for the AI Sector and Data Sovereignty

This episode raises significant questions not only ethically but also regarding Large Language Model evaluation methodologies. For companies considering LLM deployment, whether on-premise or in hybrid environments, model selection and subsequent validation are critical steps. Traditionally, public benchmarks and internal tests with controlled datasets are the norm. An activity like Meta's, although competitive, underscores the complexity in predicting an LLM's behavior in real-world, unforeseen usage scenarios. This is particularly relevant for those managing AI workloads with stringent data sovereignty and compliance requirements, where every model interaction must be traceable and controllable.

On-Premise Control and TCO: A Perspective for Enterprises

The need to thoroughly understand the capabilities and limitations of LLMs, especially in sensitive contexts, drives many organizations to evaluate self-hosted solutions. On-premise deployment offers granular control over infrastructure, data, and testing processes, allowing for the implementation of rigorous security and compliance policies, even in air-gapped environments. This approach can reduce the Total Cost of Ownership (TCO) in the long term and ensure data sovereignty, crucial aspects for sectors like finance or public administration.

While large tech companies explore external evaluation methods, for enterprises, the priority remains internal validation and responsible management of their AI models, with particular attention to transparency and regulatory compliance. For those evaluating on-premise deployment, AI-RADAR offers analytical frameworks on /llm-onpremise to delve into the trade-offs and specific requirements related to internal management of Large Language Models.