Larry Page, one of the co-founders of Google, is reportedly setting up a new startup Dynatomics, currently in stealth mode, focused on manufacturing with AI-backed design and production to build a wide range of products.
Page, along with a small group of engineers, is exploring ways to use large language models to create highly optimised designs for a wide variety of objects and then have a factory build them, The Information reported.
“Working on something new,” said the stealth startup’s website.
Leading the charge at the new startup is Chris Anderson, the former Chief Technology Officer of KittyHawk – the electric aviation startup backed by Page that aimed to develop small electric airplanes. The startup, however, was shut down in 2022.
Further details about Dynatomics couldn’t be ascertained.
Page, an American computer scientist and entrepreneur, co-founded Google with Sergey Brin in 1998 to revolutionise access to information and data through the internet.
In 2015, Google underwent a corporate restructuring leading to the formation of the parent company Alphabet, where Page assumed the role of its CEO while Sundar Pichai became Google’s CEO. However, in 2019, Page and Brin announced stepping back from active roles in the organisation and remained board members with significant voting power.
Beyond Google, Page has backed multiple innovative projects such as Opener other than Kitty Hawk, focusing aerial vehicles for travel.
With AI transforming production across sectors and verticals, an overhaul in manufacturing would also bring efficiency, precision, and adaptability. One notable application of AI here is predictive maintenance, where AI forecasts failures of equipment by analysing sensor data to effectively cut downtime and reduce maintenance costs.
AI is also optimizing supply chain management by predicting demand fluctuations and optimizing inventory levels, ensuring timely delivery and cost savings. Moreover, in terms of generative design, the technology is transforming product development by creating different design iterations based on specified parameters to produce efficient product designs.