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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
Resources:
Watch more Release Notes → https://goo.gle/4njokfg
Subscribe to Google for Developers → https://goo.gle/developers
Speaker: Logan Kilpatrick, Anirudh Baddepudi
Products Mentioned: Google AI, Gemini Read More Google for Developers
