ReadMultiplex.com: The Terminal Dies: Flapping Airplanes Returns the Silicon to the Metal and Leaves OpenAI and Anthropic as the New IBM

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Flapping Airplanes, founded in late 2025 by Ben Spector, Asher Spector, and Aidan Smith, announced a $1 billion Series A on October 21, 2026, co-led by Index Ventures and Lux Capital at a reported $6 billion valuation. The round followed a $180 million seed nine months earlier that valued the company at $1.5 billion. The lab has no commercial product, no public benchmarks of its current system, and a team described as a couple of dozen people working in person in San Francisco. Nvidia is among the investors. The founders state that demand for AI will outpace supply of data and compute, and that efficiency in both will determine who can put capable systems into the economy.

I built Qfresh in 1987 on an IBM PC in a garage. The machine refreshed DRAM on a fixed schedule that left cycles unused. I wrote a TSR that moved the refresh into those gaps. On the workloads I measured the gain reached about 50 percent. IBM preferred the standard timing. I published the code. The silicon already held unused capacity once the refresh boundary was removed. That measurement has stayed with me. Local machines, custom layouts, and the refusal to treat a vendor schedule as final still occupy the same bench. The work that follows from Flapping Airplanes sits on the same premise: the GPU already contains idle time that can be recovered if the launch boundary between kernels is removed.

ThunderKittens, the framework Ben Spector co-authored in 2024 at Stanford’s Hazy Research group, supplies the prior technical record. It defines 16-by-16 matrix tiles that match the size of the tensor cores, templates that overlap asynchronous loads and stores across warps, and grid-level mechanisms that hide block launch and teardown. On H100 hardware the kernels matched cuBLAS and FlashAttention-3 on forward GEMM and attention. They outperformed the strongest baselines by 10–40 percent on attention backward, 8× on state-space models, and 14× on linear attention. A single 40-line GEMM kernel competed with cuBLAS. The paper and code are public. The numbers come from the authors’ benchmarks on the hardware available then. Tensor cores supply roughly 16× the FLOPs of general-purpose compute on those chips; kernels that leave them idle forfeit most of the machine.

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