Guide
How to get transcripts of the All-In Podcast videos
Ninety minutes a week of markets, tech and politics, and the one take you want to quote is somewhere in hour two. As text, every episode is searchable at once.
Get every available transcript in three steps
- Paste the podcast link. BulkTranscripts detects whether it is a video, playlist, or channel automatically.
- Press Get transcripts. Videos are processed in parallel with live progress, and anything already in your library is served instantly from cache.
- Export. Download a ZIP with one file per video, or a single combined document — TXT, Markdown, SRT, VTT, CSV, JSON, or AI-ready.
What people do with the All-In Podcast transcripts
The common use is to track what the besties predicted about a company or a policy and check it against what happened, with the episode and timestamp for every quote. Because the combined export carries each video's title, channel, date, and link in a header, an AI model can cite which video an answer came from instead of blending everything together.
About the the All-In Podcast archive
The All-In Podcast is a weekly roundtable with Chamath Palihapitiya, Jason Calacanis, David Sacks and David Friedberg covering markets, tech, venture and politics, plus long interviews from the All-In Summit. Episode titles are lists of topics, not summaries, so finding where a subject was discussed means searching the text. Most episodes carry YouTube's auto-generated captions; with four overlapping voices, the transcript records what was said but not who said it.
Questions the full transcript set can answer
Load the combined export into NotebookLM, ChatGPT or Claude and ask:
- What did the hosts predict about a company's stock, and in which episode?
- Build a timeline of everything said about AI regulation since 2023, with episode links.
- Which episodes discuss interest rates, and how did the take change over the year?
What the All-In Podcast looks like as text
Measured from the All-In Podcast videos our users have already transcribed, pulled from our production library on September 27, 2026. It is a sample of the archive, not the whole thing.
| Measure | Result | What it means |
|---|---|---|
| Videos already transcribed | 285 | Upload dates May 2020 to September 2026 |
| Hours of audio | 428 | Average 90 min, longest 179 min |
| Words per video | 17,147 on average | About 34 printed pages each |
| Speaking pace | 190 words a minute | Useful for estimating an episode you have not pulled yet |
| Caption source | 28 channel-uploaded, 257 auto-generated | Channel captions are preferred when both exist |
For sizing: 100 episodes at this length is roughly 1.7 million words and costs 100 credits, one per video. That is more than most AI chats will hold at once, so use the combined export in NotebookLM, or connect the MCP server and let the assistant fetch only the episodes a question needs.
Questions
Are the All-In Podcast transcripts auto-generated?
Mixed. Of the 285 videos from the All-In Podcast in our library, 28 carry captions uploaded by the channel and 257 use YouTube's auto-generated ones. When both exist, the channel's own captions are used.
Is there a limit on how many the All-In Podcast videos I can transcribe?
You can run a whole channel or playlist in one job. Large archives are processed in batches with live progress, and everything you have already pulled is cached so re-runs are instant.
Do I need to sign in?
No. Paste a link and go.
What if a video has no captions?
It is skipped and reported in the results, and the rest of the job continues. Videos with creator-written captions are preferred over auto-generated ones when both exist.
Can I get one file instead of hundreds?
Yes. Choose the combined export to get the entire channel or playlist as a single document, which is what you want for NotebookLM, ChatGPT, or Claude.