Sam Altman: How to Make an Abundant Future
Invest Like the Best with Patrick O'Shaughnessy
← All recipes Recipe 01 · Podcast interview preparation
Build an interview brief from a guest's previous podcast appearances.
Find what your guest repeatedly discusses, what they have already answered, and where a better follow-up can move the conversation forward.
- For
- Podcast hosts, producers and guest researchers
- You provide
- A guest name and interview angle
- Audiogram finds
- Apple Podcasts appearances and available transcripts
- You receive
- A source-backed interview brief
How it works
Turn previous appearances into an interview brief.
The agent handles the research sequence. You choose the guest, the angle, and which evidence belongs in the final brief.
Input
[guest][interview angle]- 01
Find real guest appearances
Exclude episodes that only mention the name.
Search - 02
Retrieve relevant transcripts
Use the guest's own words as the source material.
Transcripts - 03
Compare the evidence
Separate repeated answers, changing positions and open gaps.
Analysis
Output
Source-backed interview brief themes · previous answers · follow-ups
Suggested prompt
Replace two variables and run the workflow.
[guest] identifies the person. [topic or interview angle] keeps discovery and comparison focused.
Prepare an interview brief for [guest] about [topic or interview angle]. Use Audiogram to: 1. Find Apple Podcasts episodes where [guest] is actually interviewed. 2. Select the most relevant appearances for [topic or interview angle]. 3. Retrieve the available transcripts. 4. Compare the transcripts and return: - recurring themes and examples; - positions that changed or became more specific; - questions the guest has already answered; - five follow-up questions based on gaps, tensions, or unsupported claims. For every finding, cite the podcast and episode. Separate direct transcript evidence from your own inference. If there are not enough relevant transcripts, say what is missing instead of filling the gap.
Example output
Sam Altman: will AI create abundance or concentrate power?
Built from two available transcripts retrieved with Audiogram. The sources and the useful gaps are shown below.
Sources retrieved
Sam Altman on God, Elon Musk and the Mysterious Death of His Former Employee
The Tucker Carlson Show
Both transcripts returned ready. The second contained speaker-label artifacts, so individual-line attribution requires care.
- Recurring themes
- AI power concentration, personal agents, privacy, work and governance.
- What not to repeat unchanged
- Broad questions about whether AI will be beneficial or how quickly capability will improve.
- Where to press further
- Measurable evidence of broadly shared benefit, privacy limits for personal agents, and independent review of model-behavior decisions.
- What measurable evidence would show that AI abundance is being shared broadly rather than concentrating power?
- What privacy threshold should an always-on personal AI agent never be allowed to cross?
- Who should independently review decisions about how a model behaves, and what should that review make public?
Before you run it
Coverage and quality checks.
Does this work for every guest?
No. It depends on the guest having relevant published Apple Podcasts appearances and transcripts Audiogram can retrieve.
Can it compare a single episode?
One episode can be analyzed, but claims about recurring themes or changing positions require multiple relevant transcripts.
Does this process an unpublished recording?
No. This recipe is for research across published podcast episodes, not raw audio editing or transcription.
What should be checked before recording?
Confirm the guest is actually speaking, review unclear speaker labels, and verify any quotation you intend to use publicly.
Use this recipe
Start with one guest and one focused angle.
Connect Audiogram to a supported agent, paste the prompt, and replace the two variables.