Podcasts

The best podcast prediction track records (2026-w38)

Who actually called it. Shows ranked by how their past, dated predictions held up against reality.

By Daniel Singer, Editor, StartupHub.ai

2 min read

The best podcast prediction track records (2026-w38)

In brief

  1. 1The Gradient Podcast: 3/3 calls aged well (100%).
  2. 2The AI Daily Brief: 8/11 calls aged well (73%).
  3. 3Y Combinator: 4/5 calls aged well (70%).
  4. 4BG2 Pod: 5/9 calls aged well (56%).
  5. 5Invest Like the Best: 1/3 calls aged well (33%).

Anyone can sound confident. We track the dated, falsifiable predictions made on each show and resolve them against what actually happened. Here is who has earned their confidence.

Who actually called it

  1. The Gradient Podcast: 7.6K views. density 16, specificity 68, attribution 71%, track record 100% (3/3 calls). Listen
  2. The AI Daily Brief: 145.7K views. density 43, specificity 71, attribution 81%, track record 73% (8/11 calls). Listen
  3. Y Combinator: 92.2K views. density 33, specificity 75, attribution 70%, track record 70% (4/5 calls). Listen
  4. BG2 Pod: 1.5M views. density 40, specificity 73, attribution 70%, track record 56% (5/9 calls). Listen
  5. Invest Like the Best: 24.3K views. density 40, specificity 75, attribution 76%, track record 33% (1/3 calls). Listen
  6. Doom Debates: 399.5K views. density 34, specificity 57, attribution 47%, track record 33% (1/3 calls). Listen
  7. All-In Podcast: 4.5M views. density 60, specificity 62, attribution 36%, track record 30% (5/15 calls). Listen

How we score: every show is graded from its episode transcripts on information density (named companies, people, figures, and mechanisms per minute), specificity (concrete, checkable claims versus vague punditry), source attribution (how often a claim credits where it came from), and a prediction track record we resolve against what actually happened. The full, continuously updated board lives at the podcast rankings.

Sources

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Topicspodcastspodcast rankingsAI podcastsoriginalitymedia

Daniel Singer

Written by

Daniel Singer

Editor, StartupHub.ai

Daniel Singer is the editor of StartupHub.ai, a technology expert and thought leader on AI and its applications across sectors, from fintech and healthcare to developer tooling and consumer software. He writes and tests the tools covered here thoroughly and regularly, and built StartupHub.ai to give founders, operators and buyers a clearer read on what they are actually being sold.

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