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20VC: Are OpenAI and Anthropic Overvalued? The Open-Source AI Reality | How Token Costs Will Fall 10x And Usage Will Explode 100x | The Future Is Not One AGI; It's Millions of Specialised Models with Lin Qiao, Founder and CEO @ Fireworks
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Why Did Fireworks Bet on Inference When Everyone Else Was Chasing Training?
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The episode opens with a punchy teaser from Lin Qiao before Harry Stebbings delivers a rare investor endorsement: a $10 million check written after just a 15-minute meeting. Harry breaks down the five reasons — a world-class team, a fast-growing inference market, triple-digit ARR growth to $1 billion in four years, the ability to hire stars like ex-Salesforce President George Hu, and the sheer upside potential of a company he thinks could reach $500 billion. The framing sets the episode's bullish tone before three sponsor integrations (JPMorgan, Navan, Base44) round out the opening block.
Fireworks AI raised $1.5 billion at a $17 billion valuation, a remarkable outcome for a 200-person company.
Fireworks AI reached $1 billion in annual recurring revenue with only 200 employees, scaling in roughly 4 years.
Fireworks AI hit $1 billion in ARR with just 200 employees by betting on specialised inference when everyone else was chasing training. The company processes over 40 trillion tokens a day, mostly from customised rather than off-the-shelf models.
Navan claims the industry average for booking a business trip is 45 minutes, versus 7 minutes on their platform.
Lin Qiao founded Fireworks AI at 48 years old, after 7 years at Meta and earlier stints at LinkedIn and in academia.
Most of the world's valuable data sits locked inside enterprise applications, never touching a general model's training set. Fireworks AI was built on the conviction that activating this private data through specialised models is the real frontier of AI.
The AGI believers assume one model will solve everything. Lin Qiao thinks that's both technically wrong and philosophically depressing. The future is millions of specialised models — one per application, per use case, per company.
After Jensen Huang told Lin Qiao that every company must be special to justify its existence, Lin realised the implication was profound: all of a company's product design, data, and user relationships encode irreplaceable private intelligence that no external model can learn.
