Learn New Things Fast with Deep Research & NotebookLM

A fast workflow for turning “post-AGI economics and society” research into a readable document—and even an on-the-go podcast—centers on chaining multiple AI tools: voice-to-prompt, deep research, and NotebookLM audio generation. The practical payoff is speed: a detailed, source-rich write-up is produced in about 12 minutes, then converted into a podcast in a few more minutes. That matters because it compresses weeks of literature review into a repeatable pipeline for learning complex, speculative topics.

The process starts with advanced voice mode on a phone to draft a high-level instruction. The prompt asks for a roughly 100-page “book” on post-AGI and post-labor economics, explicitly demanding many perspectives on how society and the economy change once AGI or ASI arrives. It also requests a “plethora of books and rich resources” that tackle the issue from multiple angles, building on prior reading such as Nick Bostrom’s Deep Utopia, Kwi Fully Automated Luxury Communism, and related work.

That voice-generated prompt is then refined for machine use. The user takes the initial prompt and feeds it into an o1 Pro chat, instructing it to rewrite and “redefine and elaborate” the prompt so it’s optimized for ChatGPT Deep Research and ultimately for producing a PDF-length learning artifact. A key refinement is controlling output style: the material should include in-depth summaries of major ideas, concise descriptions of less important ones, and explanations that remain technical but still understandable—using analogies and citing sources in APA format. The user also specifies “text only,” partly to support later podcast creation from transcripts.

With those constraints set, Deep Research runs and compiles a long, source-backed document. Although the target was 100 pages, the first pass yields about 34 pages—still described as a complete guide that spans themes like technological singularity and paths toward superintelligence, while incorporating multiple viewpoints (including named perspectives such as Russell’s and Aaron’s views). The workflow then shifts from reading to reuse: the generated text is copied into a file, exported as a .txt, and uploaded to NotebookLM.

NotebookLM is used to generate an audio podcast directly from the document. The result is a roughly 16-minute episode summarizing the compiled research, with the narration leaning somewhat American in tone. The user treats this as a learning multiplier: the same material can be read as a document or listened to as a podcast, and the audio can be exported for later use.

Overall, the central insight is not a new theory of post-AGI economics—it’s a method for rapidly producing structured, multi-perspective learning materials and repackaging them into different formats without losing the underlying citations and breadth of sources.