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Built-In AI Web APIs Will Enable A New Generation Of AI Startups

๐ŸŒˆ Abstract

The article discusses the trends and developments in frontier AI models, including the increasing cost and marginal improvements, the closing gap between closed-source and open-weight models, and the integration of AI capabilities into physical products like smartphones. It also explores the potential benefits and concerns around built-in AI models on devices, such as faster response times, offline functionality, and privacy implications.

๐Ÿ™‹ Q&A

[01] Trends in Frontier AI Models

1. What are the key trends observed in frontier AI models?

  • The latest and highest-scoring frontier models are almost indistinguishable from the user's perspective
  • The cost of training frontier AI models keeps rising while improvements are marginal
  • Cloning and approximating existing models is relatively cheap, as shown by the Stanford Alpaca model
  • The gap between closed-source and open-weight models is closing fast

2. How do the costs and capabilities of different frontier models compare?

  • Google's Gemini Ultra costs 2.5x more than OpenAI's GPT-4, but it is not 2.5x better in general ability, reasoning, or coding skills
  • Gemini Ultra has a significantly larger context (almost 8x) compared to GPT-4

3. What are the potential implications of these trends?

  • It raises questions about the commercial viability of continuing to train larger and larger models
  • The closing gap between closed-source and open-weight models may impact the competitive landscape

[02] Built-in AI in Physical Products

1. What are the potential benefits of built-in AI models in physical products like smartphones?

  • The cost of running a built-in model is virtually zero
  • Built-in AI models can provide faster response times, especially in areas with unstable internet connections
  • Built-in AI models can enable offline functionality and potentially offer more privacy-friendly interactions

2. What are the potential concerns or challenges around built-in AI models?

  • There are questions around which models different device manufacturers (e.g., Apple, Microsoft, Google) will choose to integrate and how that may impact competition and user choice
  • The privacy implications of built-in AI models may vary across device manufacturers and applications

3. How are tech companies addressing the integration of AI capabilities into physical products?

  • Microsoft offers the Phi-3 family of models, including the Phi-3 Mini with 3B parameters
  • Google is proposing AI Web APIs to integrate AI models, including large language models, directly into the browser

4. What are the potential implications of the integration of AI capabilities into physical products?

  • It opens up new possibilities and concerns, such as the potential for device manufacturers to lock users into their own AI models and the impact on developer choice and competition
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