20VC: AI Chip Wars: How Cerebras Plans to Topple NVIDIA's Dominance | Why We Have Not Reached Scaling Laws in AI | What Happens to the Cost of Inference | How We Underestimate China and Shouldn't Sell To Them with Andrew Feldman | The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

This podcast features an interview with Andrew Feldman, co-founder and CEO of Cerebras, a company challenging Nvidia in the AI inference market. The conversation covers Cerebras's innovative wafer-scale architecture, its advantages over GPU-based systems in terms of speed and efficiency, and the future of AI, including the potential shift away from transformer models. Feldman highlights the inefficiency of current AI algorithms and the vast potential for improvement, emphasizing the importance of speed in interactive AI applications. A key takeaway is that the cost of inference will decrease due to more efficient algorithms and hardware, leading to the emergence of new AI applications.

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20VC: AI Chip Wars: How Cerebras Plans to Topple NVIDIA's Dominance | Why We Have Not Reached Scaling Laws in AI | What Happens to the Cost of Inference | How We Underestimate China and Shouldn't Sell To Them with Andrew Feldman

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

Introduction: Inefficiency of Current AI Algorithms and Cerebras' Potential

Cerebras' Founding and Vision in 2015

AI's Impact on Chip Requirements and Cerebras' Architectural Approach

Cost and Yield in Wafer-Scale Architecture

Prioritization of Speed, Efficiency, and Cost in AI Inference

The Equation for Inference and Future Resource Distribution

The Current State of Data Center Infrastructure for AI Inference

The Future Cost of Inference and Algorithmic Inefficiency

Reconciling Scaling Laws with Algorithmic Improvement Potential

The Future of Data in AI Model Training

The Impact of Advancements in Compute, Algorithms, and Data on AI Experience

The Future Beyond Transformers and the DeepSeek Model

Defensibility in the AI Model Market and the Role of Open Source

Value Distribution in AI Hardware and Compute and the CUDA Lock-in Myth

Future Market Structure in AI and NVIDIA's Position

Cerebras' Cash Flow Positive Model and the G42 Deal

Cerebras' IPO Strategy and the Value of Public vs. Private Markets

Export Controls, China's AI Capabilities, and Cerebras' Decision Not to Sell to China

Comparing US and Chinese AI Development Strategies and Lessons Learned

Quick Fire Round: Predictions, Mistakes, and Future Vision for Cerebras

20VC: AI Chip Wars: How Cerebras Plans to Topple NVIDIA's Dominance | Why We Have Not Reached Scaling Laws in AI | What Happens to the Cost of Inference | How We Underestimate China and Shouldn't Sell To Them with Andrew Feldman

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

00:00Introduction: Inefficiency of Current AI Algorithms and Cerebras' Potential

Introduction: Inefficiency of Current AI Algorithms and Cerebras' Potential

04:20Cerebras' Founding and Vision in 2015

Cerebras' Founding and Vision in 2015

05:57AI's Impact on Chip Requirements and Cerebras' Architectural Approach

AI's Impact on Chip Requirements and Cerebras' Architectural Approach

11:16Cost and Yield in Wafer-Scale Architecture

Cost and Yield in Wafer-Scale Architecture

15:07Prioritization of Speed, Efficiency, and Cost in AI Inference

Prioritization of Speed, Efficiency, and Cost in AI Inference

17:13The Equation for Inference and Future Resource Distribution

The Equation for Inference and Future Resource Distribution

20:33The Current State of Data Center Infrastructure for AI Inference

The Current State of Data Center Infrastructure for AI Inference

22:28The Future Cost of Inference and Algorithmic Inefficiency

The Future Cost of Inference and Algorithmic Inefficiency

24:06Reconciling Scaling Laws with Algorithmic Improvement Potential

Reconciling Scaling Laws with Algorithmic Improvement Potential

26:05The Future of Data in AI Model Training

The Future of Data in AI Model Training

28:13The Impact of Advancements in Compute, Algorithms, and Data on AI Experience

The Impact of Advancements in Compute, Algorithms, and Data on AI Experience

30:11The Future Beyond Transformers and the DeepSeek Model

The Future Beyond Transformers and the DeepSeek Model

31:16Defensibility in the AI Model Market and the Role of Open Source

Defensibility in the AI Model Market and the Role of Open Source

34:07Value Distribution in AI Hardware and Compute and the CUDA Lock-in Myth

Value Distribution in AI Hardware and Compute and the CUDA Lock-in Myth

37:38Future Market Structure in AI and NVIDIA's Position

Future Market Structure in AI and NVIDIA's Position

40:16Cerebras' Cash Flow Positive Model and the G42 Deal

Cerebras' Cash Flow Positive Model and the G42 Deal

42:07Cerebras' IPO Strategy and the Value of Public vs. Private Markets

Cerebras' IPO Strategy and the Value of Public vs. Private Markets

45:08Export Controls, China's AI Capabilities, and Cerebras' Decision Not to Sell to China

Export Controls, China's AI Capabilities, and Cerebras' Decision Not to Sell to China

47:51Comparing US and Chinese AI Development Strategies and Lessons Learned

Comparing US and Chinese AI Development Strategies and Lessons Learned

52:27Quick Fire Round: Predictions, Mistakes, and Future Vision for Cerebras

Quick Fire Round: Predictions, Mistakes, and Future Vision for Cerebras