Meta to Start Using Chatbot Conversations to Target Advertising (www.bloomberg.com)

Meta announced it will begin using users’ chatbot conversations as signals to personalize and target advertising across its services. Instead of relying solely on clicks, likes and search queries, the company plans to surface inferred interests and intents drawn from chat transcripts (likely processed server-side by its large language models) to inform ad selection and delivery. Meta frames this as improving ad relevance and monetization of its AI products, but it represents a material expansion of how conversational data is monetized. For AI/ML practitioners the move matters both technically and ethically. Technically, using conversational context enables richer, real‑time intent modeling and could boost ad performance through sequence-aware encoders, embedding representations, fine‑tuned ranking models and continual learning from live chat signals. It also raises engineering trade-offs around on‑device vs. server inference, latency, differential privacy, anonymization, and robustness to prompt injection or sensitive-data leakage. Ethically and legally, the funneling of private chat content into ad training pipelines intensifies regulatory scrutiny (GDPR, CCPA) and user trust risks; practitioners should expect demands for transparency, opt‑out mechanisms, stronger privacy-preserving training (DP, federated learning) and tighter audit trails if advertisers rely on conversational LLM outputs.