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 Tulsee Doshi, Sr Director and Head of Product for Gemini models.

The occasion for this conversation is Google's annual I/O 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 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 2024 to 2025, Google grew annual revenue by $50B dollars, as much as Anthropic is pulling in today – and they still have 25% of global compute, the deepest pool of research talent, and the most comprehensive AI portfolio of any company, with top-tier positions not just in language models, but also self-driving cars, medical & life sciences, and robotics.

So, after discussing the headline launches they're announcing this week, which include a new video generation model called Omni which they hope will create a nano banana moment for video, a new & improved and more agent-focused Antigravity, and a product called Gemini Spark which will bring more agentic functionality to the Gemini app, I really wanted to dig in on Google's overall AI strategy and philosophy.  

We discuss their decision to lead with the Flash model and generally emphasize the cost-adjusted pareto frontier, while Anthropic and OpenAI are more focused on competing to have the most capable models in absolute terms.

We talk about how DeepMind is no longer shipping models and leaving it to product teams to figure out how to use them, but instead now providing a robust agent harness that 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 models' knowledge cutoff is now more than a year ago, and what happened to the Diffusion model line of work.  

And perhaps most importantly, we discuss how the team at Google relates to the AIs they're creating, how they think 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 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 sustained the company's historic run far beyond the point that many analysts wrote them off.  

With that, I hope you enjoy my first-ever in-person conversation, with Logan Kilpatrick and Tulsee Doshi, of Google DeepMind. 

Watch now!

Thank you for being part of The Cognitive Revolution,
Nathan Labenz