Jua raises $16M to build a foundational AI model for the natural world, starting with the weather

Large AI models — the big troves of language, vision and audio data that power generative artificial intelligence services — are shaping up to be as significant in the development of AI as operating systems have been in the development of smartphones: they are, in a way, looking like the platforms of the space (an idea others are noodling on, too). Now, a Swiss startup called Jua is using that paradigm with ambitions to build out a new frontier for how AI might be used in the physical world. It’s picked up $16 million to build what it is essentially a large “physics” model for the natural world.

The company is still very early stage. Its first application will be in modeling and predicting weather and climate patterns, initially in how they relate to players in the energy industry. This is due to launch in the coming weeks, the company said. Other industries that it plans to target with its model include agriculture, insurance, transportation and government.

468 Capital and the Green Generation Fund are co-leading this seed round for the Zurich-based startup, with Promus Ventures, Kadmos Capital, Flix Mobility founders, Session.vc, Virtus Resources Partners, Notion.vc and InnoSuisse also participating.

Andreas Brenner, Jua’s CEO who co-founded the company with CTO Marvin Gabler, says that the increasing “volatility” of climate change and geopolitics have led to a need among organizations that work in the physical world — whether in industrial areas like energy or agriculture or something else — to have more accurate modeling and forecasting. 2023 was a high watermark year for climate disasters, according to the U.S. National Centers for Environmental Information, resulting in tens of billions of dollars in damage: It’s this current state of affairs that is driving organizations to have been planning tools in place, not to mention better predictive tools for market analysts and others that use that data.

This is, in a way, not a new problem — nor even a problem that technologists have not already been addressing with AI.

Google’s DeepMind division has built GraphCast; Nvidia has FourCastNet; Huawei has Pangu, which last year saw launched a weather component that saw a flurry of interest. There are also projects underway building AI models out of weather data to hone in on other natural occurrences, as highlighted just last week in this report about a team trying to bring new understanding to bird migration patterns.

Jua’s response to that is twofold. First, it believes that its model is better than these others, in part because it is ingesting more information and is larger — by a multiple of 20x over GraphCast, it claims. Second, weather is just the starting point for considering a wider set of physical questions and answers, and challenges.

“Businesses must improve their capabilities to respond to all this [climate] volatility,” he said. “So in the short term, that is the problem we are solving. But looking into the future, we are building the first foundational model for the natural world… We’re essentially building a machine model that is learning physics… and that is one of the key pillars for achieving artificial general intelligence because just understanding language isn’t enough.”

The company has yet to launch its first products, but the leap of faith that investors are taking is not just couched in hype for all things AI.

Before Jua, Gabler headed up research at Q.met, a longtime player in weather forecasting; and he also worked on deep learning technology for the German government. Brenner has worked in the energy sector and previously founded a fleet management software startup. Taken together those experiences bridge not just technical awareness of the problems and potential solutions, but also firsthand understanding of how industry experiences this.

It’s also showing some early work to investors and would-be customers, getting their input on data, as it continues to develop the product.

One aim seems to be to take a new approach to the concept of what goes into the predictive models. When building a weather predicting model, for example, Brenner said that “using weather stations is pretty obvious.” But in addition to that, it’s ingesting what he describes as “much more noisy data” including recent satellite imagery and topography and other “more novel, recent data” to build its models. “The key difference is we are building this end-to-end system where all of the data that used to be used in different steps of the value chain is now all brought into the same pool,” he explained. The company said that it has around 5 petabytes (5,000 terabytes) of training data, versus some 45 terabytes for GPT3 and (reportedly) 1 petabyte for GPT4. (Understand that language data may well need less data than a physical world model, though.)

Another aim — not a small one — is that the company is trying to build something more efficient to bring down operational costs for itself and for customers. “Our system uses 10,000 times less compute