Apple Built Siri AI on a Model It Doesn’t Own. Your AI Product Probably Has the Same Problem.
Summary
Apple's recent Siri overhaul, powered by Google's Gemini models running on Nvidia chips within Google's cloud, exemplifies a critical challenge for any product built on third-party foundation models. This issue, distinct from Apple's specific drama, centers on the lack of control over a product's core intelligence when it's rented. While teams control the user experience, orchestration (prompts, context engineering), and partial adaptation (fine-tuning), the foundational intelligence remains with the model owner. Fine-tuning improves behavior but doesn't add fundamental reasoning capability, and it introduces further dependency risks, as providers can change terms or withdraw services. This dependency is exacerbated when the model provider is also a competitor, giving them strategic advantages and visibility into usage. Despite these risks, renting models is often the pragmatic choice due to the immense cost and complexity of training proprietary frontier models.
Key takeaway
For AI Product Managers or Directors of AI/ML building on third-party foundation models, you must acknowledge that your product's core intelligence ceiling is rented, not owned. Design your architecture to be model-agnostic, allowing for easy provider switching, and focus your team's innovation on user experience, unique workflows, and proprietary data. This strategy minimizes lock-in risk and ensures your product's future isn't solely dictated by a supplier's pricing, terms, or competing feature releases.
Key insights
Renting foundation models provides intelligence but cedes control over core capabilities and introduces significant supplier and competitor risks.
Principles
- Product intelligence ceiling is set by the rented foundation model.
- Fine-tuning adjusts behavior, not fundamental reasoning ability.
- Model providers can become direct competitors or reclaim features.
Method
To mitigate risks when building on rented models, design a model-agnostic architecture, concentrate differentiation in owned layers, anticipate provider feature expansion, and monitor open-weight alternatives.
In practice
- Abstract model calls behind an internal interface.
- Focus differentiation on UX, workflow, and proprietary data.
- Evaluate open-weight models for self-hosting options.
Topics
- Foundation Models
- Model Dependency
- AI Product Strategy
- Fine-tuning
- Vendor Lock-in
- Open-weight Models
Best for: CTO, VP of Engineering/Data, Executive, AI Product Manager, Director of AI/ML, Entrepreneur
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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.