Google DeepMind on Local Models, Open Source & the Future of AI Competition

Notion’s Co-Founder on the Rise of AI Agent Workspaces

Description: In this exclusive Superintelligence interview, Kim Isenberg and Peter Thum sit down with Akshay Kothari, Co-Founder of Notion, to discuss how Notion is evolving from a notes and productivity app into an agent-first workspace. The conversation explores how humans, custom code, and AI agents could soon collaborate side by side inside the same operating layer for work. Akshay explains why Notion’s template ecosystem became such a powerful unlock, how AI agents can automate busy work without replacing human judgment, and why the future of work may be less about headcount and more about outcomes. We also discuss Notion Workers, internal AI agents like “Smilers,” self-improving knowledge bases, model optionality, and how specialized expertise could spread across entire organizations through shareable custom agents. A conversation about the next phase of software, the future of productivity, and what work looks like when AI becomes part of the team.

Google DeepMind on Local Models, Open Source & the Future of AI Competition

In this exclusive interview from Google I/O, I speak with Omar Sanseviero and Paige Bailey from Google DeepMind about the rapidly evolving AI landscape. We discuss the rise of local models, the growing importance of open source and open models, the role of developer communities, and how global competition — especially from China — is shaping the next phase of artificial intelligence. A conversation about where AI is heading next: from frontier labs to local inference, from closed systems to open ecosystems, and from model releases to real-world developer adoption.

LTX CEO Zeev Farbman on Open AI Video Models, Local Inference, and the Future of Creative AI

In this episode, Superintelligence Editor-in-Chief Kim Isenberg speaks with Zeev Farbman, CEO and co-founder of Lightricks/LTX, about the future of AI video, open foundation models, and local creative workflows. Farbman explains why LTX is betting on open weights, local inference, and efficient models optimized for Nvidia GPUs — and why closed API models may be a long-term problem for developers, studios, and enterprises. The conversation also covers Lightricks’ strategic restructuring, competition with Big Tech, the current AI hype cycle, upcoming LTX updates, and the vision that AI models could eventually replace traditional rendering engines. A conversation about open AI infrastructure, multimodality, creative production, and the next stage of generative video AI.

"Your Calendar Is Leaking Revenue" — How SkipUp's AI Agent Kills Scheduling Forever

Every company has a scheduling problem. Most just don't know how expensive it is. The coordination tax on mid-market companies runs up to $4,500 per employee per year, and up to 70% of inbound leads never even make it to a booked meeting. In this episode, Superintelligence Editor-in-Chief Kim sits down with SkipUp co-founders Dheer and Sasha to unpack why scheduling is still fundamentally broken in 2026 and how their AI agent is replacing the entire back-and-forth. SkipUp doesn't send a booking link and wait. It lives inside your email thread, reads context, proposes times across calendars and time zones, follows up autonomously, and books the meeting. No forms, no friction, no lost deals. We talk about why traditional scheduling tools like Calendly hit a ceiling, how a two-person team built an email-native AI agent in months, the hidden revenue impact of every meeting that doesn't happen, and what work looks like when the coordination layer is fully automated.

Beyond LLMs: How Large Quantitative Models Are Curing Diseases and Reinventing Materials

LLMs predict the next word. LQMs predict the physical world. In this episode, Kim sits down with Nadia Harhen, General Manager of AI Simulation at SandboxAQ — a company that spun out of Google's Moonshot Factory, raised over $950 million, and counts NVIDIA and Google among its investors. Nadia explains what Large Quantitative Models (LQMs) are, how they differ from the LLMs we all know, and why they could be the key to inventing new drugs, designing next-generation batteries, and tackling problems like rare genetic diseases and environmental waste. We talk about her journey from bench scientist at Johnson & Johnson to clearing cutting-edge AI medical devices to leading one of the most ambitious AI simulation teams in the world. We discuss SandboxAQ's work with Aramco on turning waste into valuable materials, why no AI-designed drug has passed Phase II clinical trials yet, and what breakthroughs she expects in the next five years. If you think AI is just about chatbots and text generation, this episode will change your mind. Topics covered: — What are Large Quantitative Models (LQMs) and how do they work? — LQMs vs. LLMs: Why language models can't invent new drugs — SandboxAQ's origin inside Google's Moonshot Factory — Drug discovery, battery chemistry, and catalysis breakthroughs — The case for rare genetic diseases — Why NVIDIA and Google are betting big on this technology Guest: Nadia Harhen — GM of AI Simulation, SandboxAQ Previously: Google, Johnson & Johnson | Harvard Medical School