This video demonstrates the capabilities of Google DeepMind’s Gemini AI model and NotebookLM, showcasing four distinct workflows for transforming data and ideas into engaging visual content.
[00:00:00.000] [Gemini 3 and NotebookLM interfaces]
The video begins by introducing Gemini 3, described as “Our most intelligent AI model that brings any idea to life.” On the right side of the screen, NotebookLM is presented as “Your research and thinking partner, grounded in the information that you trust, built with the latest Gemini models.”
[00:00:50.000] [NotebookLM interface showing “Understand anything”]
The initial interface displays options to “Try in Gemini,” “Try in Google AI Studio,” or “Read blog.” NotebookLM’s core functionality is highlighted: “Upload your sources. Upload PDFs, websites, YouTube videos, audio files, Google Docs, Google Slides, and more, and NotebookLM will summarize them and make interesting connections between topics, all powered by the latest version of Gemini’s multimodal understanding capabilities.”
[00:01:09.000] [NotebookLM interface showing “Listen and learn on the go”]
The video then transitions to showcase specific features. The “Listen and learn on the go” section highlights a new “Audio Overview” feature that can “turn your sources into engaging ‘deep dive’ discussions with one click.”
[00:01:13.000] [NotebookLM interface showing a player and “Audio Overview” button]
This feature allows users to listen to summaries and discussions derived from their uploaded content, offering a convenient way to consume information.
[00:01:16.000] [NotebookLM interface showing “How people are using NotebookLM”]
The video also briefly touches upon “How people are using NotebookLM,” mentioning “Power study” as one application, where users can upload lecture recordings, textbook chapters, and research papers for AI-powered explanations, real-world examples, and understanding reinforcement.
[00:01:18.000] [NotebookLM dashboard with featured and recent notebooks]
The demonstration then moves to the NotebookLM dashboard, showing a “Create new” button. The presenter intends to create a new notebook to explore an “AI adoption statistics by industry from 2010 to 2025” for an animated race bar chart.
[00:01:38.000] [NotebookLM search bar with AI adoption statistics query]
The search query entered is: “AI adoption statistics by industry from 2010 to 2025 including finance, healthcare, retail, logistics, manufacturing, and education.”
[00:01:56.000] [NotebookLM showing “Fast Research completed” with imported sources]
Upon completion of the “fast research,” the results are displayed, showing various reports related to AI adoption. The presenter clicks “Import” to bring these sources into the notebook.
[00:01:01:000] [NotebookLM chat window with AI adoption data in a table]
The data is then processed and presented in a table format, showing “AI Adoption Rates by Industry and Context (2017-2025).” The presenter then asks Gemini to create an “animated race bar chart showing AI adoption rates across major industries from 2010 to 2025,” with specific instructions for animation and labeling.
[00:01:47:000] [Gemini interface showing the prompt for an animated race bar chart]
The prompt to Gemini specifies: “Create an animated race bar chart showing AI adoption rates across major industries from 2010 to 2025. Include finance, healthcare, retail, logistics, manufacturing, and education. Make the bars move dynamically as the percentages change each year, with colors for each industry and a clear label for every year. Use the provided dataset to generate the animation. The sources provided offer specific data points on AI adoption rates, particularly in the later years (2021-2025), and focus heavily on the Education and Manufacturing sectors, as well as general business and generative AI adoption.”
[00:01:48:000] [Gemini generating the animated bar chart]
Gemini processes this request, and the video shows the resulting animated bar chart, demonstrating how AI adoption has evolved across different sectors from 2010 to 2025. The chart visually represents “who is leading, who is catching up, and who is falling behind.”
[00:02:17:000] [NotebookLM dashboard with featured notebooks]
The second workflow demonstrated involves creating an infographic from YouTube content. The presenter starts by creating a new notebook and then uses the “Discover sources” feature to find relevant YouTube videos on AI agents.
[00:02:41:000] [NotebookLM search bar with YouTube video topics]
The search query includes topics like “what an AI agent is,” “how agentic AI works,” “the internal process or loop,” “components or modules,” “real examples,” and “benefits and limitations.”
[00:02:53:000] [NotebookLM showing “Fast Research completed” with YouTube video results]
After the fast research completes, a list of relevant YouTube videos appears. The presenter selects several videos and imports them into the notebook.
[00:02:59:000] [Gemini interface with prompt to extract key explanations from videos]
The next step is to extract key information from these videos using a prompt: “Extract the key explanations from this video and structure them clearly. Focus on: What an AI agent is, How agentic AI works, The internal process or loop, Components or modules, Real examples, Benefits and limitations. Summarise everything in bullet points with no filler.”
[00:03:40:000] [Gemini generating an infographic from video explanations]
Gemini processes the request and generates an infographic that neatly organizes the information about AI agents, including their definition, workflow, strengths, limitations, and real-world examples.
[00:04:15:000] [NotebookLM dashboard with featured notebooks]
The third workflow involves creating a “structured dataset for a heatmap showing AI-related skills gaps from 2015 to 2025.” This process starts with creating a new notebook and using the “Discover sources” feature with the query: “Skills gap reports for 2015 to 2025 focusing on AI-related roles, including shortage intensity, demand peaks, and emerging new roles. Use sources such as WEF Future of Jobs, LinkedIn Workforce Insights, IBM Skills Survey, and McKinsey reports.”
[00:04:47:000] [NotebookLM showing “Fast Research completed” with skills gap reports]
After research, the relevant reports are imported, and NotebookLM creates a structured dataset. The presenter then asks Gemini to “Create a structured dataset for a heatmap showing AI-related skills gaps from 2015 to 2025. Use years as rows and skill categories as columns. Include shortage intensity levels, demand peaks, and indicators for emerging new roles. Format the result as a table.”
[00:05:12:000] [Gemini generating a structured data table for AI skills gaps]
Gemini transforms the data into a table that clearly displays the AI skills gap evolution, with a “Shortage Intensity Legend (0-10)” and “Key Insights” providing further context.
[00:06:06:000] [Gemini generating an animated heatmap of AI skills gaps]
The video then shows the resulting animated heatmap, which visually represents how demand for various AI skills has shifted over the decade, highlighting emerging roles and specialization.
[00:06:31:000] [NotebookLM dashboard with featured notebooks]
The fourth and final workflow involves creating an “interactive market trends strategy board for AI market trends from 2025 to 2030.” This starts with creating a new notebook and using the “Discover sources” feature with the prompt: “Latest AI market trend reports for 2025-2030. Include trend categories, key drivers, barriers, adoption rates, business impact, and recommended actions. Use sources like McKinsey, Gartner, WEF, IBM, OECD, Deloitte, and major industry surveys.”
[00:06:51:000] [NotebookLM showing “Fast Research completed” with AI market trend reports]
Once the sources are imported, Gemini is prompted to: “Create an interactive market trends strategy board for AI market trends from 2025 to 2030. Use the dataset below to populate all entries. Include filters for trend category, adoption rate, business impact, and recommended action. Add hover panels showing description, key drivers, and key barriers. Use a modern layout with soft color accents, and build it using HTML, CSS, and JavaScript. Use only the dataset provided.”
[00:08:13:000] [Gemini generating an interactive market trends strategy board]
The output is a visually appealing and interactive strategy board. The left sidebar allows users to filter trends by category, adoption rate, and business impact. Each trend card displays key information, and hovering over a card reveals further details about key drivers and barriers. This demonstrates how Gemini and NotebookLM can be used to create dynamic and informative data visualizations for strategic analysis.