Jonathan Ross is the Founder & CEO of Groq, the creator of the world’s first Language Processing Unit (LPUTM). Prior to Groq, Jonathan began what became Google’s Tensor Processing Unit (TPU) as a 20% project where he designed and implemented the core elements of the first-generation TPU chip. Jonathan next joined Google X’s Rapid Eval Team, the initial stage of the famed “Moonshots Factory”, where he devised and incubated new Bets (Units) for Google’s parent company, Alphabet.
In Today’s Episode We Discuss:
04:20 Interview with Jonathan Ross Begins
04:59 Scaling Laws and AI Model Training
06:22 Synthetic Data and Model Efficiency
12:01 Inference vs. Training Costs: Why NVIDIA Loses Inference
17:06 The Future of AI Inference: Efficiency and Cost
18:15 Chip Supply and Scaling Concerns
20:57 Energy Efficiency in AI Computation
25:40 Why Most Dollars Into Datacenters Will Be Lost
31:05 Meta, Google, and Microsoft's Data Center Investments
41:11 Distribution of Value in the AI Economy
42:10 Stages of Startup Success
43:17 The AI Investment Bubble
45:00 The Keynesian Beauty Contest in VC
48:40 NVIDIA's Role in the AI Ecosystem
53:39 China's AI Strategy and Global Implications
57:51 Europe's Potential in the AI Revolution
01:10:14 Future Predictions and AI's Impact on Society
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By Harry Stebbings
4.4
464464 ratings
Jonathan Ross is the Founder & CEO of Groq, the creator of the world’s first Language Processing Unit (LPUTM). Prior to Groq, Jonathan began what became Google’s Tensor Processing Unit (TPU) as a 20% project where he designed and implemented the core elements of the first-generation TPU chip. Jonathan next joined Google X’s Rapid Eval Team, the initial stage of the famed “Moonshots Factory”, where he devised and incubated new Bets (Units) for Google’s parent company, Alphabet.
In Today’s Episode We Discuss:
04:20 Interview with Jonathan Ross Begins
04:59 Scaling Laws and AI Model Training
06:22 Synthetic Data and Model Efficiency
12:01 Inference vs. Training Costs: Why NVIDIA Loses Inference
17:06 The Future of AI Inference: Efficiency and Cost
18:15 Chip Supply and Scaling Concerns
20:57 Energy Efficiency in AI Computation
25:40 Why Most Dollars Into Datacenters Will Be Lost
31:05 Meta, Google, and Microsoft's Data Center Investments
41:11 Distribution of Value in the AI Economy
42:10 Stages of Startup Success
43:17 The AI Investment Bubble
45:00 The Keynesian Beauty Contest in VC
48:40 NVIDIA's Role in the AI Ecosystem
53:39 China's AI Strategy and Global Implications
57:51 Europe's Potential in the AI Revolution
01:10:14 Future Predictions and AI's Impact on Society
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