- Developers
- Learn about Google's latest models like Gemini 3.5 Flash and Omni, and how they are shipping agent harnesses.
- Business
- Understand Google's strategy of prioritizing cost-efficient AI and how it impacts practical AI deployment.
This interview features key researchers from Google DeepMind discussing major product announcements and strategic direction.
The Model Eats the Scaffolding: DeepMind's Logan Kilpatrick & Tulsee Doshi on 3.5 Flash, Omni & More
Audio streams from Erik Torenberg, Nathan Labenz. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Nathan Labenz hosts Logan Kilpatrick and Tulsee Doshi from Google DeepMind to discuss recent launches including Gemini 3.5 Flash, Omni video generation, and the Gemini Spark agent. The conversation covers Google's focus on cost-efficient AI, shipping agent harnesses, context window limits, and AI welfare. They also touch on recursive self-improvement and model psychology.
Google's AI team discusses their latest creations like Gemini 3.5 Flash and Omni. They explain how they are making AI more affordable and useful, not just more powerful, by building complete AI tools rather than just basic models.
This episode offers insight into Google DeepMind's current AI strategy, emphasizing efficiency and agent development over raw power. It's for AI practitioners and strategists interested in how a major lab is evolving its productization approach. The discussion on agent harnesses and cost-adjusted efficiency directly relates to the current industry push for practical, deployable AI solutions, moving beyond pure capability benchmarks.
- HostNathan Labenz
- GuestLogan KilpatrickGoogle DeepMind
- GuestTulsee DoshiGoogle DeepMind
- Episode Title: The Model Eats the Scaffolding: DeepMind's Logan Kilpatrick & Tulsee Doshi on 3.5 Flash, Omni & More
- Show: The Cognitive Revolution
- Length: 59 minutes
- New Products Discussed: Gemini 3.5 Flash, Omni video generation model, Gemini Spark agentic product
- Strategic Focus: Cost-adjusted efficiency over raw capability
Logan Kilpatrick and Tulsee Doshi of Google DeepMind join for a first-ever in-person episode recorded just days before Google I/O, covering headline launches like Gemini 3.5 Flash, the Omni video generation model, and the new Gemini Spark agentic product. The conversation digs into Google's strategic decision to lead with cost-adjusted efficiency over raw capability, how DeepMind now ships a full agent harness rather than bare models, and technical questions around context window limits and knowledge cutoffs. They also explore how the team thinks about model psychology, AI welfare, and recursive self-improvement.
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- agent harness
- Logan Kilpatrick & Tulsee Doshi
- a framework or set of tools that helps AI agents perform tasks
- recursive self-improvement
- Logan Kilpatrick & Tulsee Doshi
- an AI's ability to improve its own intelligence or capabilities over time
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Questions people ask
- What new Google AI models were discussed?
- The episode covers Gemini 3.5 Flash, the Omni video generation model, and the Gemini Spark agentic product.
- What is Google DeepMind's current AI strategy?
- They are prioritizing cost-adjusted efficiency and shipping full agent harnesses instead of just bare models.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Erik Torenberg, Nathan Labenz.







