Debt risk and AI security
The explanation of bond coupons is sound. The biggest errors concern bankruptcy protection, distillation defenses and pandemic history. Ben Horowitz joins the panel in Friedberg’s place.
Read the 16-claim reviewLatest host scores
Rubric v2 · /1003 claims · Balance 6/10
4 claims · Balance 4/10
Absent · 0 claims
5 claims · Balance 4/10
Guest: Ben Horowitz · 50 / D · 4 claims
Scores assess the selected arguments in this episode.
All-In host scores over time
8 episodes · 114 claims
Select a point to open its review.
Scores /100. Spacing follows episode order. Missing hosts leave gaps; guests are listed in their episode reviews.
View scores and sample sizes
| Episode | Jason | Sacks | Friedberg | Chamath |
|---|---|---|---|---|
| #286 Aug 21, 2026 | 553 claims | 554 claims | 704 claims | 503 claims |
| #287 Aug 28, 2026 | 603 claims | 754 claims | 504 claims | 653 claims |
| #288 Sep 4, 2026 | 703 claims | 454 claims | 504 claims | 453 claims |
| #289 Sep 11, 2026 | 603 claims | 604 claims | 454 claims | 503 claims |
| #290 Sep 25, 2026 | 553 claims | 504 claims | 604 claims | 503 claims |
| #291 Oct 2, 2026 | 603 claims | 554 claims | 504 claims | 503 claims |
| #292 Oct 10, 2026 | 703 claims | 504 claims | 454 claims | 553 claims |
| #293 Oct 17, 2026 | 703 claims | 554 claims | Not assessedAbsent · 0 claims | 505 claims |
Pivot comparison
The merger facts and public AI concerns hold up. The biggest deductions are for treating total debt as new borrowing, overlooking gains in leisure and dropping context from the California remarks.
Read the 16-claim review5 claims · Balance 6/10
6 claims · Balance 4/10
5 claims · Balance 4/10
Compare dimensions and claim samples
| Speaker / episode | Score | Accuracy | Coherence | Support | Calibration | Balance | Claims | Fact / opinion / forecast |
|---|---|---|---|---|---|---|---|---|
| Kara Swisher Pivot · 2026-10-09 | 70 / B | 8 | 7 | 7 | 6 | 6 | 5 | 2 / 2 / 1 |
| Scott Galloway Pivot · 2026-10-09 | 50 / D | 5 | 6 | 5 | 6 | 4 | 6 | 3 / 1 / 2 |
| Jessica Tarlov Pivot · 2026-10-09 | 60 / C | 7 | 6 | 6 | 6 | 4 | 5 | 2 / 2 / 1 |
| Jason Calacanis All-In · 2026-10-10 | 70 / B | 7 | 7 | 6 | 7 | 7 | 3 | 2 / 0 / 1 |
| David Sacks All-In · 2026-10-10 | 50 / D | 5 | 6 | 5 | 4 | 3 | 4 | 1 / 3 / 0 |
| David Friedberg All-In · 2026-10-10 | 45 / F | 5 | 6 | 4 | 4 | 4 | 4 | 0 / 3 / 1 |
| Chamath Palihapitiya All-In · 2026-10-10 | 55 / D | 6 | 6 | 5 | 5 | 5 | 3 | 0 / 2 / 1 |
The rubric is shared; the topics, roles and claim samples differ. This comparison does not establish a ranking of the podcasts. Jessica Tarlov is a guest. Read the scoring limits.
Episode archive
Aug 21, 2026 – Oct 17, 2026
- #293
- #292
- #291
- #290
- #289
- #288
- #287
- #286
Dario Defends Himself, Datacenter Panic, AI Doomer Trap, Senate Toss-Up
14 claims reviewed
How the scores work
Five dimensions, one weighted score
Each dimension is scored from 0–10. The weighted total is multiplied by ten and rounded to the nearest five, with halves rounded up.
Grades use that rounded score: A ≥80, B ≥70, C ≥60, D ≥50, F <50.
Accuracy tests factual correctness; coherence tests whether the conclusion follows; evidence support tests the strength of the sources. Calibration asks whether the speaker’s certainty matches the evidence.
Evidence balance
Balance assesses cherry-picking, omitted counterevidence, inconsistent standards and distorted alternatives. Higher scores mean fairer treatment of evidence. Each deduction names the affected claim, the missing evidence and why it changes the conclusion.
Political disagreement and presumed intent do not count. A false claim or weak source alone does not establish cherry-picking.
- 9–10Actively tests strong contrary evidence and represents it fairly.
- 7–8Generally fair selection and relevant qualifications; no material distortion identified.
- 5–6Mixed: fair treatment in some claims, material omissions in others.
- 3–4Materially selective samples, comparisons or treatment of contrary evidence.
- 0–2Repeated, severe distortion or dismissal of directly relevant counterevidence.
What these scores can tell you
Scores assess selected arguments. They do not measure a speaker’s overall reliability or percentage of correct statements. Confidence ratings are subjective; no statistical intervals are calculated. Automated-transcript attribution remains provisional. Small differences are not statistically meaningful, and no independent second rater has checked the scores.
New reviews select up to 20 significant claims, without speaker quotas. The sample is editorial, not random or exhaustive. All-In #286–292 retain their original 14-claim samples. An absent or unassessable speaker has no score and leaves a gap in the chart.
Primary sources support the assessments. Current product and methodology pages are labeled when used retrospectively. Unresolved forecasts are assessed for their support. All-In video links use chapter starts; Pivot links use transcript timestamps, which can differ from ad-supported audio.
Rubric v2.0. Earlier scores and correction records remain in the review data.