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NotebookLM Adds Code Execution and Exportable Research Artifacts

Gemini 3.5, a secure cloud computer, more than 100 research skills, and office-file exports turn notebooks into deliverable workflows.

ENHE AI5 min4 views
NotebookLM Adds Code Execution and Exportable Research Artifacts

Key takeaways

Google published its NotebookLM research upgrade on June 8, 2026 and updated availability on July 16. The workflow combines Gemini 3.5 with Antigravity, can run code and browse from a secure cloud computer, uses more than one hundred curated research skills, and can produce PDF, DOCX, Markdown, text, PNG, SVG, CSV, JSON, XLSX, and PPTX files. Google lists access for Google AI Ultra and Workspace AI Expanded Access customers. Users still control which sources enter a notebook. Before sharing a deliverable, open every citation, verify dates and context, recalculate important numbers, inspect formulas and charts, check file permissions and sensitive data, and preserve a source snapshot with the final version.

The workflow can run code and browse.
More than 100 research skills are included.
Office, data, document, and image exports are supported.
Users remain responsible for sources and review.

Direct answer

NotebookLM can now turn source-grounded research into spreadsheets, presentations, and documents. It still requires source selection and separate validation of every delivered file.

Fact sources

Google published the update on June 8 and revised availability on July 16, 2026.

Google describes Gemini 3.5, Antigravity, secure cloud code and browsing, and more than 100 skills.

Supported outputs include document, data, image, spreadsheet, and presentation formats for listed premium plans.

Six steps for deliverable research

  1. Define the question, deadline, audience, and allowed sources.
  2. Add primary sources and exclude unverifiable or unclear material.
  3. Request an evidence table before document generation.
  4. Open every citation and verify date, context, numbers, and conclusion.
  5. Inspect formulas, cells, charts, permissions, and sensitive data.
  6. Save the source snapshot and final version for review.

Why it matters

Code execution and file generation reduce format handoffs while increasing the surface for propagated errors. Evidence and file security matter more, not less.

Impact for ordinary AI users

Users can produce a report, workbook, and deck from one source set. Time is saved only when evidence and each artifact are independently checked.

Related tools and tutorials

Start with one reversible task, verify version, permissions, cost, logs, and accepted output, then record the result in a team checklist.

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FAQ

Can every NotebookLM user access it?

Google currently lists AI Ultra and Workspace AI Expanded Access.

Does it automatically choose trustworthy sources?

No. Users still control and must verify the source set.

Can exported files go directly to clients?

Review data, formulas, citations, permissions, and sensitive information first.

Source links

  • Google: Better research in NotebookLM (published 2026-06-08; updated 2026-07-16)
  • Google: NotebookLM
  • Google Workspace: NotebookLM

What this means for everyday users

Record question, source list, access dates, citations, calculations, output formats, formula checks, sensitive data, permissions, reviewer, and final version.

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Summary

Use NotebookLM to connect sources to deliverables, but validate evidence, calculations, and final files as separate gates.

Sources

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