Meet notebooks in the Gemini app. 📓 We’re connecting Gemini with NotebookLM, our AI-powered research partner, to create a home base for your projects that stays in sync across both tools. Think of a notebook like a dedicated project folder for your big ideas and specific tasks. You can even move past chats into notebooks and add files like documents and PDFs for it to reference. Because Gemini draws from your specific sources, its answers are uniquely tailored to help you with the task at hand. The best part? It all stays in sync. You can start a project in Gemini and jump into NotebookLM to use features like Video Overviews or Slide Decks to wrap things up — and vice versa. We're rolling out notebooks in Gemini this week, starting with Google AI Ultra, Pro and Plus subscribers on the web. In the coming weeks we'll expand access to mobile, more countries across Europe and to free users. Learn more → goo.gle/4eadmGA
Syncing projects across cloud tools solves one layer. The harder problem starts when runtime has to allocate work and orchestrate intelligence across cloud, edge, and device in real time. That is where efficiency decisions start to compound.
The future of work isn't about having more tools, but about having tools that actually stay in sync so you never lose your best ideas.
Love how Google is bridging Gemini and NotebookLM to simplify our digital workflows! This focus on 'seamless connection' is exactly why I’ve been thinking about the same logic for security. Imagine if Google account recovery was as tangible and synchronized as a mobile SIM card a 'Google Identity SIM' that bridges physical ownership with cloud access. Just as you are making projects easier to sync, we need a way to make digital identities easier to recover. Keep innovating 🚀
This is where AI starts to move from “assistant” to actual thinking infrastructure. The real shift here isn’t just integration it’s context continuity. Bringing Gemini and NotebookLM together creates a workflow where knowledge isn’t fragmented anymore, but structured, connected, and reusable. That’s what makes AI truly powerful: not just generating answers, but supporting deeper thinking, better decisions, and more consistent execution. Curious to see how far this can go, especially in complex project environments.
Nice UX improvement. The next feature I’d love to see? A meaningful, default-off data training policy for paid subscribers. That’s what would actually bring me back to keeping chat history enabled.
Long overdue but better late than never, Google.
La centralización de fuentes en un solo entorno no garantiza la calidad del output estratégico. Es simplemente una forma más rápida de organizar el ruido corporativo si no existe un criterio de gobernanza que filtre qué datos tienen peso real en el negocio. En implementaciones de Revenue Operations a gran escala, veo que el problema nunca es la falta de herramientas de notas. El fallo sistémico ocurre cuando el equipo confunde tener la información a mano con tener la capacidad de ejecutar decisiones bajo presión comercial.
Google Most people celebrating this are focusing on the wrong thing. This isn’t about “AI taking notes.” That’s the surface. The real shift? Meetings are no longer conversations. They’re now data assets. Every call becomes: - searchable - reusable - leverageable The companies that win won’t be the ones with more meetings… They’ll be the ones who compound insight from them. Most teams will still just “have meetings.” A few will build intelligence systems. That gap is about to get expensive
The biggest friction point in enterprise AI has always been the reset button. Every new chat session previously meant rebuilding the entire business context from scratch. Bridging tools to create a persistent workspace changes the fundamental nature of how we interact with these models. This mirrors the exact evolution we saw in early cloud computing. We moved from stateless applications to stateful architectures. Now our intelligent systems are making that exact same leap. We are shifting away from isolated text generation. The focus is now on compounding knowledge, retaining project history and enabling continuous workflows. The organizations that treat AI as a persistent digital asset will easily outpace those still treating it like a simple search engine.
It feels like equipping an AI with a "super workbench" could features like this become the new standard for enterprises and research teams?