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.
OpenAI's initial ad offering is intentionally basic (CPM-based, low targeting) to gather data and advertiser feedback. This MVP approach is necessary to build the foundation for a more sophisticated, conversion-optimized platform like Meta's, even if it seems underdeveloped at first.
The Instant Checkout feature is a strategic tool designed to collect valuable first-party conversion data. This data is essential for building and tuning a future performance-based ad platform. The feature's primary purpose is data acquisition, not direct e-commerce revenue.
The 4% fee on ChatGPT's checkout isn't comparable to ad spend because it doesn't grant merchants a long-term customer relationship. With restrictions on remarketing, it's a simple transaction cost that erodes margins, not an investment in acquiring a customer with future lifetime value (LTV).
Sam Altman's evolving stance on ads, from a "failure state" to an opportunity, suggests a shift driven by investors to commercialize ChatGPT. This pivot, marked by key hires like Fiji Simo, was likely necessary to overcome internal resistance from the company's research-focused origins.
The power of Meta's AI-driven ad improvements lies in their compounding effect. Small quarterly boosts in ROAS (return on ad spend) are not one-off wins; performance marketers immediately reinvest these returns, creating an accelerating growth flywheel that fuels Meta's re-accelerated revenue growth.
Unlike Netflix, which struggles with attribution using clean rooms and IP matching, ChatGPT's ad platform can leverage direct clicks. This allows for high-fidelity measurement similar to Meta's CAPI and pixel, providing advertisers with much clearer, less probabilistic attribution for their ad spend.
By licensing Google's Gemini model, Apple avoids the messy and potentially brand-damaging process of training large AI models on vast datasets. This "privacy washing" allows them to deliver competitive AI features while outsourcing the associated privacy risks and controversies to Google, preserving their carefully crafted image.
The increasing power of iPhones presents a challenge for Apple. Since core apps like Instagram don't demand more hardware resources, users have less incentive to upgrade. This lengthens the device replacement cycle, pressuring Apple to introduce compute-heavy features like on-device AI to compel consumers to buy new hardware.