Josh Woodward: Google Labs is Rapidly Building AI Products from 0-to-1

In this interview podcast, Josh Woodward from Google Labs discusses the rapid innovation culture within Google, particularly regarding AI. He shares insights on the shift from writing prompts to using assets like PDFs and images for AI interaction. Woodward also details Google Labs' mission to build new AI products, its culture of fast-paced development, and its collaboration with Google DeepMind. The conversation covers exciting AI developments, including Notebook LM, generative video with Veo, and computer use agents like Mariner, exploring both consumer and enterprise applications. He touches upon the future of video consumption, the blurring lines between movies and games, and the importance of taste and veracity in AI applications.

Outlines

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Training Data

The Importance of Market Iteration and the Mission of Google Labs

Cultivating a Fast-Moving and Creative Culture at Google Labs

Project Selection, Notebook LM, and the Potential of Generative Video

Solved and Unsolved Problems in AI Video Generation

Building for the Future: Economics and Business Models of Generative Video

The Accelerating Pace of AI Progress and the Future of Video Consumption

Blurring Lines Between Movies and Games and the Emergence of Computer Agents

Use Cases for Computer Agents and the Importance of Market Iteration

Contextual AI and the Challenges of Computer Use

The Convergence on Computer Use and Co-Creation at Google Labs

Future Building Pools and Overhyped Aspects of AI

Underhyped Aspects of AI and a Recommended Reading

AI Reading Apps, Pre-Training, and Contrarian Takes

Josh Woodward: Google Labs is Rapidly Building AI Products from 0-to-1

Training Data

00:00The Importance of Market Iteration and the Mission of Google Labs

The Importance of Market Iteration and the Mission of Google Labs

04:48Cultivating a Fast-Moving and Creative Culture at Google Labs

Cultivating a Fast-Moving and Creative Culture at Google Labs

07:54Project Selection, Notebook LM, and the Potential of Generative Video

Project Selection, Notebook LM, and the Potential of Generative Video

11:17Solved and Unsolved Problems in AI Video Generation

Solved and Unsolved Problems in AI Video Generation

14:48Building for the Future: Economics and Business Models of Generative Video

Building for the Future: Economics and Business Models of Generative Video

17:18The Accelerating Pace of AI Progress and the Future of Video Consumption

The Accelerating Pace of AI Progress and the Future of Video Consumption

21:24Blurring Lines Between Movies and Games and the Emergence of Computer Agents

Blurring Lines Between Movies and Games and the Emergence of Computer Agents

24:57Use Cases for Computer Agents and the Importance of Market Iteration

Use Cases for Computer Agents and the Importance of Market Iteration

29:17Contextual AI and the Challenges of Computer Use

Contextual AI and the Challenges of Computer Use

33:31The Convergence on Computer Use and Co-Creation at Google Labs

The Convergence on Computer Use and Co-Creation at Google Labs

37:57Future Building Pools and Overhyped Aspects of AI

Future Building Pools and Overhyped Aspects of AI

42:51Underhyped Aspects of AI and a Recommended Reading

Underhyped Aspects of AI and a Recommended Reading

47:00AI Reading Apps, Pre-Training, and Contrarian Takes

AI Reading Apps, Pre-Training, and Contrarian Takes