According to XDA-Developers, a technology journalist recently experimented with using Google’s NotebookLM as an unconventional video editing tool, despite the AI research assistant not being designed for this purpose. The experiment involved using NotebookLM’s Video Overview feature, which normally creates narrated slides from research content, to generate animated illustrations for a fictional story project. The journalist customized the output by selecting the “Brief” format, choosing visual styles including Anime and Paper Craft, and providing specific thematic prompts like “focus on the themes of fear, folklore, and supernatural belief.” While the tool didn’t perfectly follow color palette requests and had limitations with text-heavy graphics, the generated illustrations successfully captured the story’s themes. The final video was then imported into CapCut Online for further refinement using NotebookLM’s editing advice. This creative experiment demonstrates how AI tools are increasingly being repurposed beyond their original design intentions.
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The Blurring Boundaries of AI Tools
What makes this experiment particularly significant is how it reflects a broader industry trend: the convergence of AI tool capabilities. We’re moving beyond specialized software toward multi-modal AI assistants that can handle diverse tasks from research to creative generation. NotebookLM was designed as a research companion, but its underlying language model capabilities make it surprisingly adaptable to creative tasks. This isn’t just about one tool – it’s about how foundation models are creating unexpected bridges between traditionally separate domains. The same AI that can analyze research papers can also understand narrative structure and visual storytelling, suggesting that future tools may not fit neatly into our current software categories.
The Rise of Hybrid Creative Workflows
The journalist’s approach of using NotebookLM for initial concept generation and then moving to a traditional editor like CapCut represents an emerging pattern in creative work. We’re seeing the development of what I call “AI-assisted creative pipelines” – workflows that leverage multiple AI tools in sequence rather than relying on a single solution. This approach acknowledges that while AI excels at certain tasks like concept generation and thematic consistency, traditional editors still provide superior control for final refinement. The most effective creative workflows of the future will likely involve this kind of tool-hopping, where each AI contributes its strengths to different stages of the creative process.
Where This Leads: The Next 18-24 Months
Looking ahead, I expect we’ll see three significant developments in this space. First, major creative software companies will likely integrate more research and analysis capabilities into their products, essentially bringing NotebookLM-like functionality into traditional editing environments. Second, we’ll see more specialized AI tools emerge that are specifically designed for these hybrid workflows, with better interoperability between research, planning, and execution phases. Third, the line between “prompting” and “editing” will continue to blur, with more creative control moving upstream into the instruction phase rather than the manual editing phase. The tools that succeed will be those that understand they’re not just replacing existing software but enabling entirely new ways of working.
The Inevitable Limitations and Opportunities
While this experiment shows impressive adaptability, it also highlights fundamental limitations that won’t disappear quickly. AI tools like NotebookLM struggle with consistent visual styling because they’re primarily language models with visual generation bolted on. The text-heavy output the journalist encountered reflects the educational origins of these tools – they’re optimized for clarity over aesthetics. However, this creates an opportunity for tools that can bridge this gap, offering both the thematic intelligence of research assistants and the visual sophistication of dedicated creative tools. The companies that can solve this integration challenge will define the next generation of creative software.
What This Means for Creators and Companies
For individual creators, this experiment suggests that learning to creatively repurpose AI tools may become as important as mastering specific software. The most valuable skill might be understanding how to chain different AI capabilities together to achieve results no single tool was designed for. For companies, it highlights the importance of building flexible, open systems rather than tightly constrained specialized tools. The most successful AI platforms will likely be those that encourage this kind of creative misuse while providing enough structure to be genuinely useful. As AI capabilities continue to expand, the most interesting applications may come from users who ignore the intended purpose and discover what these tools can really do.
