Flapping Airplanes, a year-old AI startup, announced on Wednesday that it has raised a $1 billion Series A round co-led by Index and Lux at a reported $6 billion valuation. The financing comes nine months after the company launched with a $180 million seed round from GV, Sequoia and Index that had valued the company at $1.5 billion.
Flapping Airplanes funding and investors
Other participants in the new round included earlier backers Sequoia, GV and Kleiner Perkins. Valor, Atreides, A* and Nvidia are also listed as investors. The seed round nine months earlier had valued the company at $1.5 billion, meaning the new valuation is roughly four times higher.
Founders and talent pipeline
Flapping Airplanes was founded by Aidan Smith and brothers Asher and Ben Spector. Smith is a former founder and Thiel fellow who worked at the brain‑implant company Neuralink during college. Asher Spector was a statistics PhD at Stanford, while Ben Spector was a computer science PhD student at Stanford and founded the student accelerator Prod while at MIT. Sequoia partner David Cahn, who wrote earlier this year about why he invested, described Prod as having “become one of the most powerful talent incubators in Silicon Valley,” and said the founders together “are building the ‘young person’s AGI lab.’”
Technology: a megakernel framework and efficiency claims
The founders describe their work as “the world’s most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.” (A kernel is a program that runs on a GPU, and a megakernel bundles many tasks into one program.)
They claim the framework “can overlap memory‑bound inference with compute‑bound training,” allowing tasks that wait for data to be fetched to run alongside tasks that spend most of their time doing math so the chip is rarely idle. The premise, the founders say, is that AI has barely scratched the surface of what GPUs can do and that more efficient use of chips could enable intelligent systems “without ingesting half the internet.”
The stated goal is systems that cost less to train and run. Asher Spector said shortly after the startup launched, “A model that’s a million times more data efficient is probably a million times easier to put into the economy.” The company now has a fresh $1 billion to hire and attempt to turn that goal into reality.
