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How can Runway's AI-powered video editing technology compete with larger players like TikTok or Google that have access to more data sets?

Cristóbal Valenzuela

Co-founder & CEO at Runway

I think data is just a part of the challenge. Building great products powered by machine learning models involves a lot of research and investment in the algorithms and techniques, a rich dataset, and the deployment of those models at scale. Figuring out the right approaches to solving a problem in a research-only environment is very different from actually trying to solve those problems in production with real customers. Data alone will not help you answer those questions. So it all depends on what you are measuring and what your definition of success is. It's always easier to think about what’s possible when you have unlimited research capacity and a few constraints. Building a model to push the definition of what is state-of-the-art in a domain involves a different mindset than when you think about trying to solve a production problem for your customer. A company might have a model that performs better on a specific research metric, but that doesn’t necessarily correlate to product value. What’s most important is how a model helps solve a real problem. At Runway, our incentive is to make sure that any model we develop can be used in production as efficiently as possible for our customers.

Speed of execution is incredibly important as well, especially in a field that’s moving at light speed. I was speaking with a FAANG researcher the other day and they were telling me it took them two and a half years to get a video model to production. That’s insane. The moment you figure out a way of moving models to production faster, you are able to deliver value faster, and sometimes that’s really hard for big companies to do.

Find this answer in Cristóbal Valenzuela, CEO of Runway, on rethinking the primitives of video
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