Robby Stein, VP of Product for Google Search, joins host Logan Kilpatrick to explore how Search is evolving into a frontier AI product. Their conversation covers the shift from simple keywords to complex, conversational queries, the rise of agentic capabilities that can take action on your behalf, and the vision to help billions of users truly "ask anything." Learn more about the technology behind AI Overviews, AI Mode, Deep Search, and the future of multimodal interaction.

Watch on YouTube: https://youtu.be/zUB5A_ezIOU

Chapters
01:07 Search as a Frontier AI Product
02:38 Reaching 1.5 Billion Users
03:37 What Is AI Mode?
04:17 Understanding Query Fan-Out
05:18 Balancing Latency and performance with Gemini 2.5 Pro
06:51 How Deep Search works
09:08 Fine-tuning models for product experience
11:24 Shifting user behaviors
14:07 The rise of visual search
16:52 Speech and conversational AI in Search
18:36 Comparing Gemini and Search
20:04 Real-time tool use in Search
22:52 Evolving the Search interface
26:03 Making Search more personal
29:15 The agentic future of Search
31:15 Agents beyond booking tickets
37:11 On-the-fly software creation
38:06 Google DeepMind and Search collaboration
40:08 What's next for Search

Ani Baddepudi, Gemini Model Behavior Product Lead, joins host Logan Kilpatrick for a deep dive into Gemini's multimodal capabilities. Their conversation explores why Gemini was built as a natively multimodal model from day one, the future of proactive AI assistants, and how we are moving towards a world where "everything is vision." Learn about the differences between video and image understanding and token representations, higher FPS video sampling, and more.

Chapters:

0:00 - Intro
1:12 - Why Gemini is natively multimodal
2:23 - The technology behind multimodal models
5:15 - Video understanding with Gemini 2.5
9:25 - Deciding what to build next
13:23 - Building new product experiences with multimodal AI
17:15 - The vision for proactive assistants
24:13 - Improving video usability with variable FPS and frame tokenization
27:35 - What’s next for Gemini’s multimodal development
31:47 - Deep dive on Gemini’s document understanding capabilities
37:56 - The teamwork and collaboration behind Gemini
40:56 - What’s next with model behavior

Watch on YouTube: https://www.youtube.com/watch?v=K4vXvaRV0dw

Connie Fan, Product Lead for Gemini's coding capabilities, and Danny Tarlow, Research Lead for Gemini's coding capabilities, join host Logan Kilpatrick for an in-depth discussion on how the team built one of the world's leading AI coding models. Learn more about the early goals that shaped Gemini's approach to code, the rise of 'vibe coding' and its impact on development, strategies for tackling large codebases with long context and agents, and the future of programming languages in the age of AI.

Watch on YouTube: ⁠https://www.youtube.com/watch?v=jwbG_m-X-gE⁠

Chapters:

0:00 - Intro
1:10 - Defining Early Coding Goals
6:23 - Ingredients of a Great Coding Model
9:28 - Adapting to Developer Workflows
11:40 - The Rise of Vibe Coding
14:43 - Code as a Reasoning Tool
17:20 - Code as a Universal Solver
20:47 - Evaluating Coding Models
24:30 - Leveraging Internal Googler Feedback
26:52 - Winning Over AI Skeptics
28:04 - Performance Across Programming Languages
33:05 - The Future of Programming Languages
36:16 - Strategies for Large Codebases
41:06 - Hill Climbing New Benchmarks
42:46 - Short-Term Improvements
44:42 - Model Style and Taste
47:43 - 2.5 Pro’s Breakthrough
51:06 - Early AI Coding Experiences
56:19 - Specialist vs. Generalist Models

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