Dumi Erhan, co-lead of the Veo project at Google DeepMind, joins host Logan Kilpatrick for a deep dive into the evolution of generative video models. They discuss the journey from early research in 2018 to the launch of state-of-the-art Veo 3 model with native audio generation. Learn about the technical hurdles in evaluating and scaling video models, the challenges of long-duration video coherence and how user feedback is shaping the future of AI-powered video creation.

Chapter:
0:00 - Intro
0:47 - Veo project's beginnings
3:02 - Veo's origins in Google Brain
5:07 - Video prediction and robotics applications
7:45 - Early progress and evaluation challenges
10:30 - Physics-based evaluations and their limitations
12:18 - The launch of the original Veo model
14:06 - Scaling challenges for video models
16:02 - The leap from Veo1 to Veo2
19:40 - Veo 3’s viral audio moment
21:17 - User trends shaping Veo's roadmap
23:49 - Image-to-video vs. text-to-video complexity
26:00 - New prompting methods and user control
27:55 - Coherence in long video generation
31:03 - Genie 3 and world models
35:54 - The steerability challenge
41:59 - Capability transfer and image data's role
47:25 - Closing

Pushmeet Kohli, Head of Science and Strategic Initiatives at Google DeepMind, joins host Logan Kilpatrick to explore the intersection of AI and scientific discovery. Learn how the team's unique problem-solving framework led to innovations like AlphaFold and AlphaEvolve, and how new tools like AI Co-scientist aim to democratize these types of breakthroughs for everyone.

Watch on YouTube: https://www.youtube.com/watch?v=o7mdsL6BHsk

Chapters:
0:00 - Intro
1:04 - Recent Alpha launches
02:15 - Framework for selecting research domains
06:21 - Scientific, commercial and social impact
15:00 - Wielding AGI for breakthroughs
16:48 - Tech transfer and team collaboration
19:46 - IMO Gold Medal
21:42 - Evaluating math proofs
22:55 - From specialized models to Deep Think
24:22 - Do math skills generalize?
25:53 - Generalizing the IMO model
27:43 - Democratizing AI science tools
30:09 - AI Co-scientist
35:17 - An API for science?

Join host Logan Kilpatrick in discussion with some of the minds behind Google's new state-of-the-art image model, Gemini 2.5 Flash. Product and research leads from the Gemini team break down the technology behind its key capabilities, including interleaved generation for complex edits and new approaches to achieving character consistency and pixel-perfect control. With Nicole Brichtova, Kaushik Shivakumar, Mostafa Dehghani and Robert Riachi.

Watch on YouTube:

Chapters:
0:37 - New model introduction
1:21 -Demo - Image Editing
3:44 - Text rendering capabilities
4:44 Beyond human preference evals
6:44 - Text rendering as a proxy for quality
8:38 - Positive transfer between modalities
11:25 - Demo - Multi-turn, context aware image generation
13:54 - Pixel-perfect editing and character consistency
15:51 - Interleaved image generation
17:59 - Specialized vs. native models
19:52 - Understanding nuanced prompts
20:59 - User feedback shaping model development
22:37 - Improvements in character consistency
24:17 - More natural looking images from team collaboration
26:41 - What’s next for image generation models

Demis Hassabis, CEO of Google DeepMind, sits down with host Logan Kilpatrick. In this episode, learn about the evolution from game-playing AI to today's thinking models, how projects like Genie 3 are building world models to help AI understand reality and why new testing grounds like Kaggle’s Game Arena are needed to evaluate progress on the path to AGI.

Watch on YouTube: https://www.youtube.com/watch?v=njDochQ2zHs

Chapters:
00:00 - Intro
01:16 - Recent GDM momentum
02:07 - Deep Think and agent systems
04:11 - Jagged intelligence
07:02 - Genie 3 and world models
10:21 - Future applications of Genie 3
13:01 - The need for better benchmarks and Kaggle Game Arena
19:03 - Evals beyond games
21:47 - Tool use for expanding AI capabilities
24:52 - Shift from models to systems
27:38 - Roadmap for Genie 3 and the omni model
29:25 - The quadrillion token club

Shrestha Basu Mallick, one of the product leads for the Gemini API, joins host Logan Kilpatrick for a deep dive of Gemini Live API, Google’s real-time, multimodal interface for developers. Learn about how native audio alongside new capabilities like proactive audio and async function calling unlocks the unique power of audio as an interface.

Watch on YouTube: https://www.youtube.com/watch?v=4xlwlU6h-wM

0:00 - Intro
1:18 - Live API Overview
3:36 - Why audio is a special modality
5:07 - Speed vs. precision in audio
6:17 - Controllable and promptable TTS
8:31 - What developers are building with the Live API
11:14 - URL context and async calling features
15:02 - Proactive audio and affective dialog
16:55 - Addressing developer feedback
21:54 - Live API roadmap
23:49 - The role of long context
24:57 - What’s next for the Live API
26:41 - State of the AI audio market
30:10 - Advice for developers getting started with the Live API
31:16 - Live API demo
38:10 - Demo wrap up and closing

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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