Both my favorite AI tools, now unstoppable together.
As someone who has been closely following the AI race since the very beginning, one thing I wholeheartedly believe is that Google’s winning it. Sure, there was a time when Gemini was clearly behind, and OpenAI’s ChatGPT was the model everyone swore by. That gap doesn’t really exist anymore.
However, I had this realization far before the launch of Gemini 3.0. I realized Google was ahead when it announced Project Tailwind at Google I/O 2023, the tool we now all know as NotebookLM. It’s been over two years since that, and NotebookLM has grown massively. Google recently added Gemini integration to NotebookLM, and that single upgrade has completely transformed how I use both the tools.
What is NotebookLM’s Gemini integration?
It’s changed how Gemini works
In December 2025, users began spotting that Google was adding a NotebookLM integration for Gemini. Essentially, the integration is a new «Attachment» type inside the standard Gemini interface.
When you tap the plus (+) icon while in a Gemini thread, you’ll find a NotebookLM option, which appears alongside existing options like Upload files, Add from Drive, Photos, and Import code.
This functionality allows you to instantly pull an entire NotebookLM notebook into a Gemini thread and begin asking questions about it without having to re-upload anything or manually rebuild your sources.
This update has singlehandedly changed the way Gemini works. Instead of simply uploading a file (or multiple files) to a Gemini chat, this update lets you attach entire grounded knowledge bases — NotebookLM notebooks.
NotebookLM notebooks essentially become another source Gemini can cite from
Entire knowledge bases within a Gemini thread
Now, if you’re wondering, «Can’t I already chat with my sources in NotebookLM?» — yes, you can. But there’s a key difference in how you can interact with them now. NotebookLM’s biggest strength is that each notebook is effectively grounded in the sources you upload, allowing you to ask highly specific, context-aware questions and get answers directly tied to your materials.
