Flapping Airplanes closed a $1 billion financing that values the company at $6 billion, inclusive of the new capital. Index Ventures and Lux Capital are leading the round, which is slated for a Wednesday announcement, with Sequoia Capital, Alphabet's GV and Kleiner Perkins participating as well.
Founded in late 2025 and based in San Francisco, the company is a year old and part of a growing wave of so-called AI neolabs - outfits that start with research before turning on revenue.
Co-founder Asher Spector said the team is developing ways to train models using less data and less compute, while also improving how effectively the chips that build and operate those models perform. "The basic thesis is demand for AI is going to outpace chip and data supply," he said. "The company that's going to be most successful, and create the most value for the world, is the one that can squeeze the most out of the inputs." Even a 10 percent bump in performance per chip would be "tremendously economically valuable," he added.
Co-founder Aidan Smith said the company has been creating its own variant of the software that runs Nvidia's graphics processing units so it can use the chips more efficiently and in more flexible ways. Nvidia is an investor in Flapping Airplanes. Spector said the work aims to make AI more accessible for companies that want to use it, and that the startup plans to commercialize its technology over time.
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Brandon Reeves, a partner at Lux, said "getting more out of what we have" will be essential for "the next frontier" of AI, and called Flapping Airplanes "probably best positioned to do that." David Cahn, a partner at Sequoia, said he has known Spector for years, as well as his brother Ben, the third co-founder, and argued that the funding is a signal of how investors regard the team. "There are so many of these AI labs that you can get lost in the noise," Cahn said. "But this is not a one-of-many company; this is a one-of-one company."
The company's name nods to the ornithopter, an early aircraft concept, which Spector sees as a metaphor for blending the strengths of natural and artificial intelligence. More broadly, venture firms have been putting billions into research-centric bets from AI-driven scientific breakthroughs to self-improving systems.
This is a scale bet on efficiency: less data and compute to train models and more work squeezed out of each chip. If AI demand keeps racing ahead of data center and chip supply, the ability to wring even small percentage gains from existing hardware could matter a lot for margins up and down the stack.
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