Google renamed NotebookLM to Gemini Notebook and, underneath the rebrand, added a genuinely new capability: a secure cloud computer that lets users run code directly inside a notebook for data analysis, rolling out first to Pro users. Notebooks now sync across the Gemini app and Google Search, with integration into Search's AI Mode planned. NotebookLM had built a distinct following as a source-grounded research tool, most visibly through its viral podcast-style audio summaries, and served more than 30 million users and 600,000-plus organizations before the rename. The code-execution addition is the part that matters for builders: it turns a document-grounded question-answering tool into something closer to a lightweight data workbench, where you upload sources such as spreadsheets, PDFs, or research papers, ask questions grounded in them, and now also run analysis code against that same data without leaving the notebook or wiring up your own retrieval-plus-sandboxed-execution stack. That is a meaningfully lower-friction path to exploratory data analysis over a specific set of documents than assembling a custom RAG pipeline with a separate code interpreter, and it is worth testing against existing notebook tools like Jupyter-based AI assistants or code-interpreter plugins for teams doing document-grounded analysis. It also introduces a security question worth testing before trusting it with sensitive material: any tool that executes code against arbitrary uploaded sources needs to be evaluated for prompt-injection and sandbox-escape risk, since a malicious or malformed document is now not just untrusted text but a potential trigger for code the tool runs on your behalf.