Podbit · This Week in Tech (Audio)
China's open-weight models aren't just catching up — they're on track to make billion-dollar American AI labs economically unviable. Once DeepSeek-style models become free to run anywhere in the world, the pricing power of Anthropic and OpenAI collapses, and with it the incentive to fund AGI.
Where this was said
At 46:10 · chapter starts 39:50
The central topic of the episode arrives in full force: a wave of Chinese frontier AI models that are closing the gap with the best American closed models. Leo describes Kimi K3 as 'almost — not quite, but almost — Fable quality,' built entirely on 100,000 Huawei chips with no NVIDIA hardware. Kimi's subscription sold out immediately — a move Harper Reed compares to the classic T-shirt marketing trick of doing a small run to generate hype. Alex Wilhelm pushes back slightly, noting that Kimi K3 is not actually cheap to run, and that at current pricing it looks similar to a mid-tier American model. Harper uses GLM as his point of comparison: he was using Claude Code in early 2025 when it was still rough, and today's GLM is at least as capable as that early version — effectively making it a 6-month-behind Claude for free. The Toyota Corolla analogy emerges: it's not whether the model is the best, it's whether it's good enough and the price is rational.
When the US government and Anthropic pulled Fable access, it didn't just inconvenience users — it broke trust and sent developers experimenting with alternatives. OpenAI made it worse by publishing a guide to using Sol inside Claude Code. Once users discovered the models were interchangeable, Anthropic's lock-in evaporated.
Claude Fable costs ~$10/M input tokens and $50/M output tokens — a premium that Chinese open-weight models threaten to make untenable.
Anthropic is buying compute from xAI at ~$1B/month, illustrating how demand for frontier AI inference is outpacing what any single company can build alone.
OpenAI's single $122 billion raise is the largest in history, insulating it from cash concerns even as operating costs for training frontier models remain enormous.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
More connectivity doesn't produce more independent thinking — it produces more homogeneity. Kierkegaard called this 'leveling': the internet's utopian promise of everyone thinking for themselves is backwards. The more interconnected people are, the stronger the phantom of public opinion, and the more of a liability it becomes to stand out.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
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