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---
date: '2023-01-22T10:57:34'
hypothesis-meta:
created: '2023-01-22T10:57:34.532045+00:00'
document:
title:
- Who Owns the Generative AI Platform? | Andreessen Horowitz
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- ai
- generative ai
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- exact: "Vertical integration (\u201Cmodel + app\u201D). Consuming AI models\
\ as a service allows app developers to iterate quickly with a small team\
\ and swap model providers as technology advances. On the flip side, some\
\ devs argue that the product is the model, and that training from scratch\
\ is the only way to create defensibility \u2014 i.e. by continually re-training\
\ on proprietary product data. But it comes at the cost of much higher capital\
\ requirements and a less nimble product team."
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'
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Building features vs. apps. Gen'
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source: https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/
text: There's definitely a middle ground of taking an open source model that is
suitably mature and fine-tuning it for a specific use case. You could start without
a moat and build one over time through collecting use data (similar to network
effect)
updated: '2023-01-22T10:57:34.532045+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:
- ai
- generative ai
- hypothesis
type: annotation
url: /annotations/2023/01/22/1674385054
---
<blockquote>Vertical integration (“model + app”). Consuming AI models as a service allows app developers to iterate quickly with a small team and swap model providers as technology advances. On the flip side, some devs argue that the product is the model, and that training from scratch is the only way to create defensibility — i.e. by continually re-training on proprietary product data. But it comes at the cost of much higher capital requirements and a less nimble product team.</blockquote>There's definitely a middle ground of taking an open source model that is suitably mature and fine-tuning it for a specific use case. You could start without a moat and build one over time through collecting use data (similar to network effect)