A novel way to measure ad effectiveness in LLMs is "attention shift"—analyzing how much an ad pivots the conversation's topic toward the brand. This metric, derived from vector analysis of messages before and after an ad, captures influence beyond traditional clicks or impressions, reflecting deeper engagement.

OpenAI is testing ads on ChatGPT's free tier, mirroring the early monetization paths of Google and Facebook. This move signals the inevitable rise of generative AI platforms as a major advertising channel that marketers will need to understand and master.

To introduce ads into ChatGPT, OpenAI plans a technical 'firewall' ensuring the LLM generating answers is unaware of advertisers. This separation, akin to the editorial/sales divide in media, is a critical product decision designed to maintain user trust by preventing ads from influencing the AI's core responses.

OpenAI plans to personalize ads not just on immediate queries but by analyzing a user's entire chat history. This creates a powerful hybrid of Google's intent-based advertising and Meta's interest-based profiling, going beyond simple sponsored links to offer deeply contextual promotions.

As competitors like Google's Gemini close the quality gap with ChatGPT, OpenAI loses its unique product advantage. This commoditization will force them to adopt advertising sooner than planned to sustain their massive operational costs and offer a competitive free product, despite claims of pausing such efforts.

Instead of traditional cost-per-click models, ChatGPT could pioneer a "verified outcome" system where advertisers pay only upon a completed transaction and user satisfaction. This would inherently favor advertisers with superior products that lead to actual conversions, improving ad quality and relevance for all users.

Analyst Eric Sufert predicts OpenAI's ad model will not be anchored to the content of a user's query, which could compromise trust in the answer's objectivity. Instead, it will function like Instagram's feed, where ads are targeted based on a user's broader conversion history, independent of the immediate conversational context.

Meta's core moat is its ability to solve the classic advertiser's dilemma: knowing which half of their ad spend works. By providing granular data on impressions, conversions, and ROI, it created what Pat Dorsey called the perfect advertising platform.

AI's growth is hampered by a measurement problem, much like early digital advertising. The industry's acceleration won't come from better AI models alone, but from building a 'boring' infrastructure, like Comscore did for ads, to prove the tools actually work.