The Model Eats the Scaffolding: DeepMind's Logan Kilpatrick & Tulsee Doshi on 3.5 Flash, Omni & More

Hello, and welcome back to the Cognitive Revolution. Today, after some 340 episodes, I am very excited to share the first episode that I've ever recorded in person with fan favorite Logan Kilpatrick, member of technical staff at Google DeepMind, and Tulsi Doshi, senior director and head of product for Gemini models. The occasion for this conversation is Google's annual IO event, where they're launching the new Gemini 3.5 flash model, all sorts of agent infrastructure and AI product integrations, and plenty more. We've recorded on Friday, May 15, just a couple days before the event. And while many at Google, including my brother Craig, who's giving a keynote on Wednesday, were working overtime to polish their demos and presentations, the overall vibe, at least compared to the rest of the AI space, was one of relatively relaxed confidence. And why not? From twenty twenty four to twenty five, Google grew annual revenue by $50,000,000,000, as much as Anthropic is pulling in today. And they still have 25% of all global compute, the deepest pool of research talent anywhere, and the most comprehensive AI portfolio of any company with top tier positions not just in language models, but also self driving cars, medical and life sciences, and robotics. So after discussing the headline launches that they're announcing this week, which also include a new video generation model called Omni, which they hope will create a nano banana moment for video, a new and improved and more agent focused anti gravity, and a product called Spark, which will bring more agentic functionality to the main consumer Gemini app, I really wanted to take a step back and dig in on Google's overall AI strategy and philosophy. We discussed their decision to lead with the Flash model and more generally to emphasize the cost adjusted performance Pareto frontier, whereas Anthropic and OpenAI are clearly much more focused on competing to have the single most capable model in absolute terms. We talk about how DeepMind is no longer shipping models in isolation and leaving it up to product teams to figure out how to use them, but instead now providing a robust agent harness, which should help elevate and standardize AI experiences across Google's vast product surface. We get into the weeds on questions like why context windows seem to have mostly stopped growing, why Gemini model's knowledge cutoff is now more than a year ago, and whatever happened to that diffusion model line of work. Perhaps most importantly, we discuss how the team at Google relates to the AI's they're creating, how they're thinking about things like model psychology and welfare, and their views on recursive self improvement, which as you'll hear, is definitely a part of their plan, but not something that they seem to be so singularly focused on as other AI leaders. Overall, I think this is a great window into the thinking that underlies Google's AI research and product development, which has clearly sustained the company's historic run …