---
date: '2023-01-22T11:02:54'
hypothesis-meta:
  created: '2023-01-22T11:02:54.339397+00:00'
  document:
    title:
    - Who Owns the Generative AI Platform? | Andreessen Horowitz
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  id: UhZ6LJpEEe2fsBs2mQHXSA
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  tags:
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  - AI
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    - exact: "Commoditization. There\u2019s a common belief that AI models will converge\
        \ in performance over time. Talking to app developers, it\u2019s clear that\
        \ hasn\u2019t happened yet, with strong leaders in both text and image models.\
        \ Their advantages are based not on unique model architectures, but on high\
        \ capital requirements, proprietary product interaction data, and scarce AI\
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    source: https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/
  text: All current generation models have more-or-less the same architecture and
    training regimes. Differentiation is in the training data and the number of hyper-parameters
    that the company can afford to scale to.
  updated: '2023-01-22T11:02:54.339397+00:00'
  uri: https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/
  user: acct:ravenscroftj@hypothes.is
  user_info:
    display_name: James Ravenscroft
in-reply-to: https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/
tags:
- generative ai
- AI
- hypothesis
type: annotation
url: /annotations/2023/01/22/1674385374

---



 <blockquote>Commoditization. There’s a common belief that AI models will converge in performance over time. Talking to app developers, it’s clear that hasn’t happened yet, with strong leaders in both text and image models. Their advantages are based not on unique model architectures, but on high capital requirements, proprietary product interaction data, and scarce AI talent. Will this serve as a durable advantage?</blockquote>All current generation models have more-or-less the same architecture and training regimes. Differentiation is in the training data and the number of hyper-parameters that the company can afford to scale to.