Ben Thompson argues AI apps should adopt a Meta-style advertising model based on deep user understanding, rather than Google-style contextual ads tied to prompts. This avoids conflicts of interest and surfaces products users didn't know they needed, creating more value for both users and advertisers.
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Advertising within LLMs like ChatGPT can be a win-win. For discovery queries (e.g., "what's the best tool for X?"), a relevant ad acts as an additional, valuable suggestion rather than an interruption. This improves the user's discovery process while creating a high-intent channel for advertisers.
Previously, marketers told Meta who to target. With the new AI algorithm, marketers provide diverse creative, and the AI uses that creative to find the right audience. Targeting control has shifted from human to machine, fundamentally changing how ads are built and optimized.
Unlike competitors who would struggle to introduce ads into AI chat, Meta's user base is already accustomed to ads in their feeds. This gives Meta a unique advantage to monetize a proactive consumer AI agent that can surface sponsored suggestions for shopping or travel without creating user friction.
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.
AI conversations capture high-intent moments, allowing ads to target active decision-making rather than passive attention-grabbing like social media. This fundamental difference could lead to significantly higher average revenue per user (ARPU), making social media's ad performance a floor, not a ceiling for AI platforms.
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.
The goal for advertising in AI shouldn't just be to avoid disruption. The aim is to create ads so valuable and helpful that users would prefer the experience *with* the ads. This shifts the focus from simple relevance to actively enhancing the user's task or solving their immediate problem.
As users delegate tasks to AI agents, a new targeting framework emerges. Instead of targeting based on keywords or past behavior, brands can target users based on the specific task they are trying to accomplish (e.g., "write a report," "plan a trip"). This allows for hyper-relevant, solution-oriented advertising.
An 11-year Meta veteran explains that Facebook's ad value shifted from demographics to interest targeting, and now to a sophisticated AI. Today, the best strategy is often to remove granular targeting and let the system's machine learning find the right audience automatically.
Meta's ad recommendations excel because Apple's privacy changes created a do-or-die situation. This necessity forced them to pioneer GPU-based AI for ad targeting, a move competitors without the same pressure failed to make, despite having similar data and talent.
