Sources: Meta considers using Google's Gemini and Gemma AI models to improve its ad targeting; Meta says it regularly evaluates 3rd-party tools for benchmarking
Erin Woo / The Information :
Context & Ripple Effects
Meta's reported evaluation for advertising follows discussions inside its Superintelligence Labs about using Google or OpenAI models in Meta AI and social products, indicating that outside-model testing was not confined to consumer-facing features. It also precedes Meta's later opening of third-party AI connectors for advertisers, which gives marketers more flexibility in the campaign workflow.
The significance is less a confirmed model switch than a potential expansion of AI procurement into Meta's core monetization system. Meta's stated benchmarking rationale leaves open whether any Google model would be deployed.
First-order effects
- Meta can compare Gemini and Gemma against its own systems for ad-targeting quality, creating a formal evaluation path for an external supplier in a core advertising function.
- Google gains a potential high-value enterprise use case for its models, while Meta retains leverage to select, combine, or reject third-party tools after testing.
Second-order effects
- A deployment decision could make model capacity and commercial terms material constraints; later reporting that Google could not supply all the Gemini capacity Meta sought illustrates the operational dependency such a choice can create.
- Other model providers and Meta's internal teams would face pressure to demonstrate targeting performance, cost, and reliability rather than compete solely on broad chatbot capabilities.
Third-order effects
- If large platforms increasingly source models selectively for revenue-critical workflows, AI competition may shift toward a procurement market where model quality, capacity, and integration terms matter alongside proprietary research.
- That pattern could concentrate bargaining power among suppliers able to serve hyperscale demand, although Meta's continued benchmarking and multi-model posture would limit any single vendor's lock-in.
The trend: This is one data point in the AI procurement phase, as major platforms test external foundation models for specific business functions while preserving multi-vendor optionality.