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Y CombinatorY Combinator

How To Build Generative AI Models Like OpenAI's Sora

If you read articles about companies like OpenAI and Anthropic training foundation models, it would be natural to assume that if you don’t have a billion dollars or the resources of a large company, you can’t train your own foundational…

Harj TaggarhostDiana HuhostJared Friedmanhost
Mar 28, 202434mWatch on YouTube ↗

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Episode Details

EPISODE INFO

Released
March 28, 2024
Duration
34m
Channel
Y Combinator
Watch on YouTube
▶ Open ↗

EPISODE DESCRIPTION

If you read articles about companies like OpenAI and Anthropic training foundation models, it would be natural to assume that if you don’t have a billion dollars or the resources of a large company, you can’t train your own foundational models. But the opposite is true. In this episode of the Lightcone Podcast, we discuss the strategies to build a foundational model from scratch in less than 3 months with examples of YC companies doing just that. We also get an exclusive look at Open AI's Sora! Read more about the YC AI companies from this episode on our blog: https://www.ycombinator.com/blog/building-ai-models Chapters (Powered by https://bit.ly/chapterme-yc) - 00:00 - Coming Up 01:13 - Sora Videos 05:05 - How Sora works under the hood? 08:19 - How expensive is it to generate videos vs. texts? 10:01 - Infinity AI 11:23 - Sync Labs 13:41 - Sonauto 15:44 - Metalware 17:40 - Guide Labs 19:29 - Phind 24:21 - Diffuse Bio 25:36 - Piramidal 27:15 - K-Scale Labs 28:58 - DraftAid 30:38 - Playground 33:20 - Outro

SPEAKERS

  • Harj Taggar

    host
  • Diana Hu

    host
  • Jared Friedman

    host

EPISODE SUMMARY

In this episode of Y Combinator, featuring Harj Taggar and Diana Hu, How To Build Generative AI Models Like OpenAI's Sora explores Building Powerful Generative AI Without Billions: Lessons Beyond Sora The episode reviews OpenAI’s Sora video demos, highlighting breakthroughs in visual quality, physics simulation, and long-term temporal consistency while still noting subtle artifacts and errors.

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