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
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.
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insights INSIGHT
Compute vs. Data
Scaling AI models with more compute leads to better performance, but it's inefficient.
Data and algorithmic improvements offer more effective ways to enhance models.
insights INSIGHT
Model Value
The current AI model landscape demands both specialized, efficient models and general-purpose models.
Users prefer prototyping with broad models before refining to specialized ones.
volunteer_activism ADVICE
Beyond Scaling
Focus on data and model innovations instead of just scaling.
Explore better data scraping, synthetic data, and new RL algorithms.
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Aidan Gomez is the Co-founder & CEO at Cohere, the leading AI platform for enterprise, having raised over $1BN from some of the best with their last round pricing the company at a whopping $5.5BN. Prior to Cohere, Aidan co-authored the paper “Attention is All You Need,” which introduced the groundbreaking Transformer architecture. He also collaborated with a number of AI luminaries, including Geoffrey Hinton and Jeff Dean, during his time at Google Brain, where the team focused their efforts on large-scale machine learning.
In Today's Episode with Aidan Gomez We Discuss:
1. Compute vs Data: What is the Bottleneck:
Does Aidan believe that more compute will result in an equal increase in performance?
How much longer do we have before it becomes a case of diminishing returns?
What does Aidan mean when he says "he has changed his mind massively on the role of data"? What did he believe? How has it changed?
2. The Value of the Model:
Given the demand for chips, the consumer need for applications, how does Aidan think about the inherent value of models today? Will any value accrue at the model layer?
How does Aidan analyze the price dumping that OpenAI are doing? Is it a race to the bottom on price?
Why does Aidan believe that "there is no value in last year's model"?
Given all of this, is it possible to be an independent model provider without being owned by an incumbent who has a cloud business that acts as a cash cow for the model business?
3. Enterprise AI: It is Changing So Fast:
What are the biggest concerns for the world's largest enterprises on adopting AI?
Are we still in the experimental budget phase for enterprises? What is causing them to move from experimental budget to core budget today?
Are we going to see a mass transition back from Cloud to On Prem with the largest enterprises not willing to let independent companies train with their data in the cloud?
What does AI not do today that will be a gamechanger for the enterprise in 3-5 years?
4. The Wider World: Remote Work, Downfall of Europe and Relationships:
Given humans spending more and more time talking to models, how does Aidan reflect on the idea of his children spending more time with models than people? Does he want that world?
Why does Aidan believe that Europe is challenged immensely? How does the UK differ to Europe?
Why does Aidan believe that remote work is just not nearly as productive as in person?
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