AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap
2026-09-27 · 63 min · episode 12105 · 13 entities
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://quince.com/impactpod
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://youtube.com/@TomBilyeu
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AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://quince.com/impactpod
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://tiktok.com/@tombilyeu?lang=en
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://quince.com/impactpod
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Link in episode "AI Giants HATE Open Source, the World is on Fire, Greenland | Weekly Recap": https://youtube.com/@TomBilyeu
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The team breaks down a real and fast-moving shift in the AI industry: open-weight models, which were a rounding error (~7% of traffic) back in December, have exploded—accounting for 56% of tokens generated by August, with Vercel CEO Guillermo Rauch reporting a single-day record of 78.4% on September 19th, and spending on three Chinese labs (Moonshot, DeepSeek, z.ai) on that day topping spend on OpenAI. The host walks the money: open-weight share of dollars on the Vercel gateway went from under 4% in June to 8.6% in July to 14% in August, average price per token fell 23% in August alone, and developers are splitting the workload—hard, high-stakes work still goes to Anthropic (which has held 61%+ of gateway spend every month since December), everything else goes to whatever's cheapest. His read on what's breaking: not the frontier, but the low end of the big players—OpenAI's and Google's cheap "gateway" models are getting obliterated as people conclude "you're not as good as Anthropic, so I'll either pay for the best or use the cheap stuff." The real winners, he argues, are the chipmakers and cloud/inference providers (like Vercel), because value is shifting from owning the model to just providing access to the compute—the commoditization of intelligence. He frames the optimistic upside: companies will increasingly host their own open-weight models to keep their work proprietary and build a real edge, rather than everyone using the identical public tool. Ryan pushes the counter-thesis—that if OpenAI had stayed the 2015 nonprofit and gone all-in on open source (his analogy: how OBS open-sourced screen recording and dominated), the US might be further ahead of China. Tom steelmans it but argues the fatal flaw: someone has to build the "brains," the data centers are staggeringly expensive, and there's still no obs