Flapping Airplanes, a year-old artificial intelligence startup focused on training large AI models with less data, has raised $1 billion in a Series A funding round, reportedly valuing the company at $6 billion.
Announced on Wednesday, the funding round was co-led by Index Ventures and Lux Capital. Existing investors, including Sequoia, GV and Kleiner Perkins, also participated, alongside other backers such as Valor, Atreides, A* and Nvidia.
The latest investment marks a sharp rise in the company’s valuation. Flapping Airplanes launched in January 2026 with a $180 million seed round backed by GV, Sequoia and Index, at a valuation of $1.5 billion. Its reported valuation has now quadrupled in just 9 months.
The startup was founded by Aidan Smith and brothers Asher and Ben Spector. Smith previously founded a company and was a Thiel Fellow. He also worked at Neuralink during college. Asher Spector pursued a PhD in statistics at Stanford University, while Ben Spector studied computer science at Stanford and founded Prod while at MIT.
Prod is a student-run startup accelerator that has gained attention in Silicon Valley for developing entrepreneurial talent. Sequoia partner David Cahn, an investor in Flapping Airplanes, has described the founders’ venture as a potential “young person’s AGI lab”.
At the core of the startup’s work is a technology framework designed to improve how graphics processing units (GPUs) handle AI workloads. The founders describe it as a megakernel framework capable of running AI model training, inference and even video games within a single kernel.
The approach aims to run different computing tasks simultaneously, reducing idle time on GPUs. By combining memory-intensive inference with computation-intensive training, the system seeks to improve hardware efficiency and make AI development less dependent on massive datasets.
The company’s broader ambition is to explore whether more capable AI systems can be developed without relying on enormous volumes of internet data. Its latest funding highlights investor interest in research-driven approaches that could improve the efficiency and scalability of AI development.
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