San Francisco-based artificial intelligence startup Flapping Airplanes has raised $1 billion in new funding at a $6 billion valuation, underscoring investor interest in research aimed at making AI systems more efficient.
The funding round was led by Index Ventures and Lux Capital, with participation from Sequoia Capital, Alphabet’s GV, Kleiner Perkins and other investors, according to Bloomberg, as reported by Quartz. Nvidia is also among the startup’s backers.
Founded in late 2025, Flapping Airplanes is part of a growing group of AI research startups, sometimes called “neolabs,” that prioritize developing new technologies before launching commercial products or generating revenue. The company’s valuation has quadrupled since its previous funding round in January, according to Quartz.
The latest investment comes as technology companies and investors continue to pour money into AI infrastructure, including advanced processors and data centers. Flapping Airplanes is pursuing a different part of that challenge by developing technologies designed to reduce the computing resources and data required to train AI models.
Startup focuses on AI efficiency
Flapping Airplanes is working to develop AI models that can learn from less data and require less computing power. It is also seeking to improve the efficiency of the chips used to train and run AI systems.
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Co-founder Asher Spector told Bloomberg that the company believes demand for AI will outpace the available supply of computing resources and data. He argued that companies capable of extracting greater performance from existing infrastructure could gain a significant economic advantage.
Spector said even a 10% improvement in the performance of every chip could carry substantial economic value, given the amount of money being invested in AI infrastructure. The company is also developing software to improve how graphics processing units, or GPUs, operate. GPUs are widely used to train and run modern AI models.
Co-founder Aidan Smith said the startup has been building its own version of the software that operates Nvidia’s GPUs. The technology is intended to improve chip efficiency and allow the processors to be deployed more flexibly, according to Quartz’s account of Bloomberg’s reporting.
Improving the efficiency of AI computing could help reduce the resources required to develop and operate advanced models. It could also make AI adoption more accessible to businesses that cannot afford the infrastructure costs associated with large-scale model development.
Investors bet on research-first AI startups
Flapping Airplanes’ funding reflects investor interest in AI companies that focus on foundational research rather than immediately building consumer-facing products.
The startup plans to commercialize its technology, Spector said, although its research-first approach places development ahead of an established commercial business.
Lux Capital partner Brandon Reeves said that getting more out of existing computing resources would become increasingly important as AI development advances.David Cahn, a partner at Sequoia Capital, also praised the founding team, saying the funding round reflected investors’ confidence in its potential.
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The latest round follows an earlier funding round in January, when the company raised $180 million, according to SiliconANGLE. The new investment brings the total capital raised to more than $1 billion, based on the reported figures.
The company was founded by brothers Ben Spector and Asher Spector, along with Aidan Smith. Its founders bring experience in computer science, AI research and software development.
Competition to make AI cheaper
The rapid expansion of AI has created demand for more computing capacity, prompting companies to invest heavily in specialized chips, cloud infrastructure and data centers. At the same time, the cost of training and running advanced models has become a major consideration for developers and businesses.
Flapping Airplanes is seeking to address that challenge by improving the efficiency of the underlying technology rather than relying solely on larger computing infrastructure.
If its research translates into commercially viable tools, the company could help businesses run AI workloads with fewer computing resources. However, its long-term commercial prospects will depend on the performance of its technology and its ability to turn research into products that customers are willing to adopt.
The latest funding gives the startup substantial financial backing as it continues its research and development efforts in an increasingly competitive AI market.