Why Apple Picked Gemini for Siri Upgrade

Apple is basing Apple Foundation Models on Google Gemini in a multi‑year Siri deal. Key lessons for enterprise AI buyers on performance, privacy, lock‑in.

Apple’s multi-year plan to bring Google’s Gemini models into a rebuilt Siri is more than a routine Big Tech tie-up—it offers a clear look at how Apple assesses foundation models when the outcome will shape a core product experience. For enterprises choosing an LLM or broader model stack, the signals in Apple’s decision are hard to ignore because the same trade-offs show up in large-scale commercial deployments.

Apple had already been positioning OpenAI’s ChatGPT inside its devices since late 2024, giving OpenAI an unusually visible role within the Apple Intelligence experience. With Gemini now selected as the underlying layer, OpenAI shifts toward a “supporting” position—still available for more complex, user-triggered requests, but no longer serving as the default intelligence backbone.

Gemini becomes the default layer

Apple’s selection is framed explicitly as a capability call rather than a convenience-driven partnership. In the companies’ joint statement, Apple said that after a “careful evaluation,” Google’s AI technology was judged to be “the most capable foundation for Apple Foundation Models,” language that puts model strength at the center of the rationale.​

That wording matters for any organisation buying or building around foundation models because it implies Apple prioritized what the models can consistently deliver—at scale—over softer factors like brand alignment, ecosystem familiarity, or headline pricing. It also hints that Apple’s internal benchmarks likely focused on “real product” constraints: responsiveness, reliability, and quality under heavy usage, not just lab scores.

What Apple likely evaluated

For enterprise buyers, Apple’s decision maps cleanly onto the same criteria that usually decide whether an AI feature feels usable or frustrating in production. The considerations implied in the reporting include model performance at scale, inference latency, multimodal capability, and the flexibility to run workloads both on-device and in the cloud while still meeting strict privacy expectations.

Google also comes with consumer-scale deployment proof: Gemini already powers Samsung’s Galaxy AI across a large installed base, which demonstrates operational maturity beyond demos. Apple’s rollout raises the bar further because the integration is expected to work across Apple’s massive active-device footprint, under Apple’s performance expectations and privacy guardrails.

What changed after ChatGPT

The timing naturally invites questions because Apple introduced ChatGPT support a little over a year earlier, positioning Siri to hand off tougher requests to the chatbot. Apple has indicated there were no major changes to that ChatGPT integration at the moment the Gemini partnership was announced, yet the competitive balance between model providers appears to have shifted.

The report points to the pace of advancement as a risk enterprises often underestimate: foundation-model leaders can change quickly, and a provider that looks strongest today may not stay ahead throughout a multi-year contract. Apple’s choice to sign a multi-year agreement—rather than optimizing for maximum switching flexibility—signals confidence that Google’s improvement curve, infrastructure capacity, and sustained R&D will remain compelling over time.

Infrastructure, dependency, and market ripple effects

The partnership also amplifies a classic enterprise concern: concentration risk and dependency on a single vendor for critical AI capabilities. Elon Musk publicly criticized the deal as an “unreasonable concentration of power,” pointing to Google’s existing control points like Android and Chrome, and the broader worry is recognizable for any buyer wary of lock-in.

The practical reality is that Google is positioned to deliver AI features across both major mobile ecosystems—directly through Android and indirectly through iOS via this integration—raising the strategic stakes of relying on one provider. Apple, for its part, emphasized a hybrid architecture: Apple Intelligence will continue operating on-device and through Private Cloud Compute while aiming to preserve Apple’s “industry-leading privacy standards,” which doubles as a template for enterprises balancing governance with capability.​

Beyond product design, the news had immediate market impact: the report notes Alphabet’s valuation crossing the US$4 trillion mark on the day of the announcement, alongside commentary about rising investor confidence tied to AI momentum. The broader takeaway for enterprises is that foundation models increasingly connect to an entire ecosystem—cloud infrastructure, developer tools, and distribution—so vendor evaluation can’t stay narrowly focused on model output quality alone.

The article also flags the business history underneath the technical decision: Apple and Google already share a deep commercial relationship via Google’s payments to remain the default search engine on Apple devices, a precedent that can shape how AI partnerships get negotiated. For enterprise procurement teams, that’s a reminder that “model licensing” discussions often bundle into wider strategic agreements—sometimes helpful (integration trust and speed), sometimes limiting (reduced leverage or fewer true alternatives).

OpenAI, meanwhile, remains present on Apple devices, but now more as an optional add-on than the foundational layer—an awkward repositioning for a company widely seen as a category leader. The report’s implication for enterprise strategy is straightforward: keep optionality where possible (through abstraction layers, multi-model approaches, or portable architectures) because exclusive deals and fast-moving competition can reshape the market faster than typical IT refresh cycles.

Google has also said Gemini will support not only the revamped Siri expected later this year, but additional future Apple Intelligence capabilities, suggesting the dependency could deepen over time. With financial terms undisclosed, one key variable remains unknown—how pricing was structured for deployment at this scale—yet enterprise buyers will be watching closely because it may influence how model access is priced in other large contracts.​

Apple’s move does not automatically make Gemini the right answer for every enterprise deployment, but it does validate what one highly selective buyer appeared to prioritize under demanding requirements. The more practical question for enterprise teams is not “Which model did Apple pick?” but “Are the evaluation criteria rigorous enough to survive real-world scale, privacy constraints, and multi-year dependency?”