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20VC: Chips, Models or Applications; Where is the Value in AI | Is Compute the Answer to All Model Performance Questions | Why Open AI Shelved AGI & Is There Any Value in Models with OpenAI Price Dumping with Aidan, Gomez, Co-Founder @ Cohere
Aug 19, 2024
Aidan Gomez, Co-founder and CEO of Cohere, shares insights on the evolving landscape of AI. He discusses the critical balance between compute and data, arguing that more compute may not always equate to better performance. Aidan critiques the current value assigned to AI models, suggesting a race to the bottom in pricing led by OpenAI. He emphasizes the significance of quality data and the challenges of training AI with limited human-like reasoning. Additionally, he explores the future of the synthetic data market and the importance of privacy in enterprise AI adoption.
58:35
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Quick takeaways
- The podcast highlights that increasing AI model size can enhance performance, but innovative approaches may yield better efficiency without excessive compute costs.
- Aidan Gomez discusses the shift towards prioritizing high-quality data over sheer scale, emphasizing its critical role in improving model performance and competitive advantage.
Deep dives
The Value of Scaling AI Models
Increasing the size of AI models typically results in improved performance, making it a common strategy for those with ample financial resources. While this scaling approach is effective, it is seen as inefficient and overly simplistic, with a strong belief that more innovative methods could yield better outcomes. Recently, models with fewer parameters have been observed to outperform massively scaled counterparts, illustrating that efficiency can be achieved without excessive compute costs. This ongoing tension between scaling budgets and strategic innovation will likely shape the future of AI development.
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