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
Summary
This episode focuses on Fireworks AI's thesis that the AI market will be shaped less by a single frontier AGI model and more by a large ecosystem of specialized models built for specific enterprise tasks. Lin Qiao argues that inference is the real battleground, not training, because most value will come from deploying, tuning, and routing models efficiently across workflows. A major theme is that open-source models will increasingly commoditize core model capabilities by giving enterprises control over weights, customization, and deployment. The conversation also explores how token costs could fall by 10x over time, which would trigger massive demand growth and make high-intelligence models far more widely used. Finally, the episode discusses infrastructure bottlenecks, the role of model routing/orchestration, and why custom chips may not be worthwhile until workloads become more stable.
Key Takeaways
- 1Token costs are expected to fall dramatically, and usage will likely explode as a result.
- 2The future of AI is likely to be a multi-model world, not a single dominant AGI.
- 3Open-source models are a strategic advantage for enterprises because they offer control and flexibility.
- 4The center of gravity in AI is shifting from model building to orchestration and routing.
- 5Fireworks is operating at enormous inference scale, which supports its thesis about accelerating demand.
- 6Custom chips may become attractive later, but today AI workloads are too dynamic to justify most bespoke hardware bets.
Notable Quotes
""I do think the cost of token will go down drastically. 10x cost reduction in the next three years.""
""We absolutely are not going to move into application layer. Very clear to us.""
""I really believe the future will be, it may be scary, but I think that's true. It will be millions of specialized model, one propagation per use case.""
""Today, we process more than 40 trillion tokens a day.""
""We still have a great system for very large model. I really believe the fundamental role of our infrastructure cars will go down.""
""For some in-tiles we should need less token. That will increase. So collectively, the cars were significant reduced.""
""We think we can't at least double. By the end of the year, wow.""
""Marketing is not about flows. It's more about education. It's a more about clarity and we're working on that.""
Episode questions
Why does Fireworks believe specialization is more valuable than a single general AGI model?
Because most enterprise value sits in private data, unique workflows, and company-specific judgment that a generic model cannot fully capture. Lin argues that future systems will need specialized models, routing, and tuning to match each use case.
How does Fireworks think about the role of open-source models in enterprise AI?
Fireworks sees open models as a way to give customers control over weights, customization, and cost. The company uses them internally and believes they already solve a large portion of enterprise tasks at far lower cost than frontier APIs.
What is the proposed future architecture for AI products?
A layered system: expensive models for the hardest decisions, smaller open models for sub-tasks, and an automatic routing layer to choose the right model. Lin sees this routing and orchestration layer as a major area of innovation.
Why does Fireworks focus on inference rather than training?
The company believes the market opportunity is in deploying, tuning, and optimizing models against real enterprise workloads rather than spending heavily on frontier training. Lin says the biggest gains come from lowering inference cost and improving task-specific quality.