More so, Germanidis also said that working with robotics and self-driving car companies was not something that they had envisioned for Runway initially, when they launched the company back in 2018. It wasn’t until robotics and other companies in other industries reached out that the company realized their models had much broader use cases than they originally thought, he said.
Robotics companies are also using Runway’s tech for training simulations, as Germanidis said. He also added that just training robots and self-driving cars in real-world scenarios also comes at a cost for companies, takes a long time, and is hard to scale.
While Runway knows it isn’t going to replace real-world training by any means, Germanidis also said companies take a long time, and it is hard to scale.
Unlike in a real-world training, using these models makes it easier to test for specific variables and situations without changing anything else in the scenario, as he added.
“You can take a step back and then simulate the effect of different actions,” he said. “If the car took this turn over this, or performed this action, what will be the outcome of that? Creating those rollouts from the same context is a really difficult thing to do in the physical world, to basically keep all the other aspects of the environment the same and only test the effect of the specific action you want to take.”
Runway is, however, not the only company that is looking to tackle this. For instance, Nvidia released the latest version of its Cosmos world models, in addition to other robot training infrastructure, earlier this month.
The company does not anticipate releasing a “completely separate line of models” for its robotics and self-driving care customers, as reported by TechCrunch. Instead, Runway will fine-tune its existing models to better serve these industries.
Germanidis added that while these industries were not in the company’s initial pitches to investors, they are on board with this expansion. Runway has also raised more than $500 million from investors such as Nvidia, Google, and General Atlantic at a $3 billion valuation.
“The way we think of the company is really built on a principle, rather than being on the market,” Germanidis said. “That principle is this idea of simulation, of being able to build a better and better representation of the world. Once you have those really powerful models, then you can use them for a wide variety of different markets, a variety of different industries. [The] industries we expect are there already, and they’re going to change even more as a result of the power of generative models.”
