Ben Thompson reframes advertising not as a necessary evil but as a fundamental societal good. It enables small businesses to reach global markets and provides consumers with valuable product discovery and free access to high-quality services, creating immense consumer surplus.
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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.
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.
The narrative that users hate targeted ads is contradicted by their actions. When Meta offered an ad-free subscription in Europe, only 1% of users opted in. This demonstrates a strong revealed preference for free, ad-supported services, even if the ads are perceived as hyper-targeted.
A novel ad format would allow brands to sponsor access to premium features for free users. For example, McKinsey could underwrite deep research queries, or Nike could present a branded "training mode." This transforms advertising from an interruption into a value-additive, branded experience that enhances the core product.
Conversational ads offer an unprecedented one-on-one channel for brands to interact with customers at scale. The resulting data—customer questions, complaints, and feedback—is a goldmine for product development and other business functions, potentially exceeding the value of immediate customer acquisition.
In a competitive free market, corporate greed is a positive force. The desire for profit maximization compels companies to offer better products and services at lower prices than their rivals to win customers' money. This "greed" directly translates into improved value and a higher standard of living for consumers.
Countering criticism of ad-driven "slop," the podcast highlights that profits from Google and Meta's ad businesses fund their massive R&D in AI and AR/VR. This reframes advertising as the primary societal mechanism for bankrolling capital-intensive, frontier science like the pursuit of AGI.
For products valuable only when others use them (like credit cards or social apps), Super Bowl ads are uniquely effective. The value isn't just reaching many eyeballs, but ensuring those eyeballs know *other* eyeballs are also watching, solving the chicken-and-egg adoption problem.
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.
To earn consumer data, brands must offer a clear value exchange beyond vague promises of "better experiences." The most compelling benefits are tangible utilities like time savings and seamless cross-device continuity, which are often undervalued by marketers.
