Podbit · The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
Fireworks processes more than 40 trillion tokens a day today. Lin Qiao projects that number could be 20x to 100x higher by end of next year. At those volumes, worries about a CapEx bubble look completely backwards.
Where this was said
At 36:50 · chapter starts 28:00
One of the most technically rich chapters in the episode. Lin explains that early AI tech adopters are hackers who want control, and Cursor — flush with frontier lab researchers — is the prime example. The problem they solved together is profound: hyperscalers run RL training on 100,000 interconnected chips. Cursor and Fireworks had no such cluster. Their solution was to decouple the trainer (which updates model weights) from the RL rollout (which deploys the model into a synthetic environment to collect rewards), distributing the system across five or six global data centre regions and syncing fresh model weights efficiently enough that the reward signal stays numerically sound. This distributed design enabled Cursor's recent model launches without a hyperscaler-level budget.
Fireworks CTO Dima embedded at Cursor for months to build a distributed reinforcement learning infrastructure that decouples the trainer from RL rollout across six global data centre regions. This let a capital-constrained startup run training jobs that previously required 100,000 interconnected chips at a hyperscaler.
Lin Qiao sees 2024 as the year of coding AI and 2025 as the year of co-work. Co-work is dramatically more diverse than coding — spanning legal, finance, healthcare, customer support, and consumer-facing AI — and Fireworks is already landing customers across all of those verticals.
Fireworks processes more than 40 trillion tokens per day, the majority from customised rather than off-the-shelf models.
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.
Frontier AI labs like OpenAI and Anthropic are priced as if they'll dominate every market while everyone else loses money. That's not how markets work, and cheap open-weight models are already proving it.
The best business model in history is offering 80% of the premium product at 50% of the price — Old Navy did it to Gap, Southwest did it to Delta, and now Chinese open-weight models are doing it to OpenAI. The value proposition is brutal.
China has every incentive to subsidize cheap AI to undercut US frontier labs. If OpenAI and Anthropic announce a growth slowdown, 40% of the S&P 500 gets torched — and that might be the point.
The price gap between open-weight and frontier AI models is staggering. Deepseek v4 Pro runs 30 cents per task. Claude Opus 4.5 runs $12.50. That's a 40x difference — and the UK's AI Security Institute found the performance gap is narrowing.
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