Background

Questions

1. A lot has happened in generative AI in 2022 with DALL-E 2, Stable Diffusion, Midjourney, and ChatGPT coming out. Can you walk us through how you see the story of the development of generative AI over the last year?
2. Have you seen your core customer profile evolve in the last one year, with the new developments in the generative AI space? Have you seen any new customer segments that have started using Runway?
3. Can you compare at a high-level generating new videos via generative AI vs. generating new text and images? How are they different and how are they similar?
4. In the world of text-based generative AI, people have this understanding that there’s a GPT-3 model, and then individual startups are adding a layer of fine-tuned models on top of it. Can you talk a bit about how Runway works?
5. At ~$0.05 per query with roughly 2M users doing 100 queries per month, ChatGPT’s monthly costs would be at around $10M. How does that level of per-query cost and usage roughly match up to video?
6. LLM/GPT latency would apparently have to come down and costs would have to come down about 10x before it would be feasible to integrate AI-generated results into search. How much of a bottleneck is pricing and latency for video generation today and how do you see it trending?
7. Video post-production from a veteran editor costs roughly $250 per hour. At 20 hours per month, that’s about $5K—similar usage of Runway costs $12. Does this roughly track to how you think about Runway’s value and cost-savings potential? How do you see Runway changing how video editing/post-production teams work?
8. Let’s take another creator tool — Figma — not only has it made creating vector graphics and UX UI cheaper, but it has also changed the way designers work by giving them something to collaborate on. Five years from now, how do you see Runway changing how post-production teams and video teams work together?
9. With the costs of training models going down, what do you think prevents more startups from training their own foundational models, and owning their full stack?
10. Andrew Ng observed that 80% of an AI developer's time is spent on data preparation and labeling and data is the big bottleneck to building AI. How do you see self-supervised learning from large text and image models changing the role of data preparation in building AI models/applications?
11. Today, TikTok has a strong position in the consumer video market via their in-built distribution and wide selection of video filters that allow people to quickly make videos that would have taken hours to edit together years ago. How do you think about competing with tools like TikTok, or do you see them as more complementary to Runway’s more B2B focus? Is there a world where Runway tools would show up in TikTok’s list of video filters?
12. What’s your take on talent availability for AI, given that it's a relatively new field and training the models, or even owning the full stack, in your case, requires certain specialized skill sets? What trends do you see emerging from this scarcity of AI in the market?
13. Is there anything else you’d like to share with us about Runway or generative AI for videos we haven’t discussed?