AI lab Flapping Airplanes raises $1B at a $6B valuation; plus, Recursive launches Flint, a laptop coding model it says was largely built by AI

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1:13That's Framer.com slash tcstartups. Rules and restrictions may apply. The year-old startup Flapping Airplanes has wowed Silicon Valley with an impressive founding team and a mission to find ways to train large models with less data. On Wednesday, the company announced it had raised a$1 billion Series A round, co-led by Index and Lux, reportedly at a$6 billion valuation. The round comes just nine months after the company launched with a$180 million seed round from GV, Sequoia, and Index. That seed round had valued the company at$1.5 billion, so its valuation just quadrupled. Other participants in the new round included earlier backers Sequoia, GV, and Kleiner Perkins.

2:05Valor, Atreides, and NVIDIA are also investors in the company. Flapping Airplanes was founded by Aidan Smith and brothers Asher and Ben Spector. Smith is a former founder and Teal 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 Prod while at MIT. Prod is an almost-secret society, five-year-old, student-run startup accelerator that has become one of the most powerful talent incubators in Silicon Valley. That's according to Sequoia partner David Kahn, a Flapping Plains investor.

2:49And he wrote that in a post earlier this year explaining why he invested. Together, the founders are building the Young Persons AGI Lab. The founders described 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. The founders claimed the framework can overlap memory-bound inference with compute-bound training. Meaning it can run a task that spends most of its time waiting for data to be fetched, alongside one that spends most of its time doing math, so the chip is rarely sitting idle.

3:39The premise is that AI has barely scratched the surface of what GPUs can do, and they believe it can create intelligent systems without ingesting half the internet. The goal is systems that cost less to train and run. Asher Spector told TechCrunch, a model that's a million times more data efficient is probably a million times easier to put into the economy. They now have a fresh$1 billion to hire and attempt to turn that goal into reality.

4:13Recursive, an AI startup founded last year, is starting to live up to its name. The company, which is trying to build AI that can improve itself, released its first model, Flint, on Wednesday. Flint is a free coding model that can run on a laptop and was built largely with the help of AI. Recursive's automated research system, which it calls the Eureka Machine, helped with nearly every step of Flint's development. That let a small team build the model from scratch in under four months. Flint is an early public test of recursive self-improvement, the idea that AI can build better AI, which in turn builds even better AI.

4:56It's also one of the most debated ideas in the industry. Some researchers fear that AI improving itself could slip out of human control. For now, Recursive's version is more limited, an AI research assistant overseen by people that produced a model small enough for a laptop. The company emerged from stealth back in May with$650 million in funding at a$4.65 billion valuation, with backers including GV, NVIDIA, and AMD ventures. Recursive is led by CEO Richard Socher, Salesforce's former chief scientist. Because Flint runs on a user's own computer rather than a company's cloud servers, developers can use it offline, keep their code private, and avoid the usage fees that AI cloud services charge based on how much text the model processes.

5:47The model is free to download on Hugging Face, the popular site for sharing AI models, under the Apache 2.0 license, an open-source license that allows free use and modification. Flint is a mixture-of-experts model, meaning it uses only a small slice of itself for any given task—just 2.5 billion of its 40 billion settings, known as parameters, at a time. That makes it light enough for an ordinary laptop and far smaller than Meta's Muse Spark Open, an open model built for Macs that was released last week. Muse Spark Open uses 816 billion parameters and needs at least a terabyte of memory. Flint is also fast.

6:29In recursive tests, it generated about 131 tokens, or chunks of text, per second on a MacBook Pro with Apple's M4 Pro chip roughly twice the 58 to 69 tokens per second. that similar models managed on the same machine, including Google's Gemma 4 and NVIDIA's Nemotron 3.5 Lightning. On NVIDIA's RTX Spark, Flint topped 150 tokens per second. On SWE Bench Verified, a benchmark that measures how well AI can fix real-world software bugs, Flint outperformed other models under 50 billion parameters. On recursives, on average across six tests, only one model, Alibaba's QN3.827B scored higher than Flint.

7:18The company acknowledged that Flint would need more guidance than the big paid coding assistants and suggested breaking large tasks into smaller steps. Still, early reviews were positive. Wired's Will Knight used Flint on a Mac with 48 gigabytes of memory to build several small programs, including a to-do list app and a tool that scours the web for research. He found that smaller coding models often ran into errors or were slow compared to Claude or Codex. But Flint nailed his coding requests every time. The Eureka machine comes up with ideas, runs experiments, and uses the results to decide what to try next.

8:03According to the company's launch video, it contributed to nearly every part of building Flint, from pre-training to post-training and inference. Recursive said that Eureka's contributions include a sparser model shape, low-bit training that shrank Flint by 71 percent with almost no loss in quality, and custom kernels, which are specialized programs that speed up the underlying computations. Recursive did not say how much time the Eureka machine saved its researchers, but it's not alone in leaning on AI to do research. OpenAI said last month that by mid-August, its AI agents were logging 3.1 workdays for everyone worked by a human researcher.

8:46If Recursive's bet pays off, each new model should be able to take on longer stretches of work without human help. The company previously told TechCrunch that its$410 million compute deal with Amazon Web Services in June was less about headcount and more about agent count. The long-term goal, Socher told Wired, is for the Eureka machine to achieve full recursive self-improvement, with humans overseeing the process and able to stop it if something goes wrong. Recursive says each new model should make the Eureka machine a better researcher, helping it build the next one faster. That's all for now.

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