Straight Answers
An LLM assessed 668 question → answer exchanges, the host's own answers included. Speaker names were hidden during assessment. Each exchange was marked answered, partly, redirected or not addressed, with a quote as its source. Every exchange below is timestamped and plays. How it works →
These readings are observational: the transcript shows whether the question was answered directly, whatever the reason. It is an AI reading of a public exchange, never a claim about intent, and the tape is one tap away on every row. full disclaimer →
Every one of the 330 people on the record is accounted for: 8 carry a full rate (8 or more assessed questions on raw tape), 154 more have their questions counted (21 of them with at least one not answered directly; too few raw questions for a fair percentage, edited feed included), and the remaining 168 never took a direct question in any tape we hold: compilations, panels and cameo appearances leave nothing to assess.
No needle for Peter Ludwig: fewer than 8 assessed questions on raw tape, and a percentage on a sample that small would mislead. Their counts are real: 2 not answered directly in 6 assessed questions.
Peter Ludwig: every exchange, playable
The question asked, then the AI assessment note from the answer. Tap to play the moment. Not addressed = the question was not engaged; redirected = acknowledged, then steered elsewhere.
“I should steer the car. So I don't, you probably want to remove that.”
answered: “We have a diversified bet strategy internally.”
“And then took seven years to actually get them on the street. Can you share about maybe like the last one percent that was really hard to, to get done technically?”
answered: “There's a concept called, uh, prize policy, which is so that there's, there's different ways”
All speakers
All 8 people with 8 or more assessed questions on raw tape. Click a name to see their exchanges. highest rate none identified small samples
| Person | Not answered directly (%) | Redirected + not addressed | Questions | Argument clarity /5 |
|---|---|---|---|---|
| Alessio Fanelli Partner & CTO, Decibel | 30% | 3 | 10 | 3.7 |
| Joon Sung Park Co-founder & CEO, Simile | 20% | 2 | 10 | 4.3 |
| Emily Glassberg Sands Head of Information, Stripe | 13% | 1 | 8 | 4.3 |
| Dharmesh Shah Co-Founder and CTO, HubSpot | 10% | 1 | 10 | 4.1 |
| Shawn Wang Cofounder & CEO, AI Engineer | 7% | 1 | 15 | 3.9 |
| Michelle Pokrass Post-Training Research Lead, OpenAI | 0% | 0 | 9 | 4.4 |
| Erik Schluntz Member of Technical Staff, Anthropic | 0% | 0 | 9 | 4.4 |
| Thomas Scialom Senior Staff Research Scientist, Meta AI | 0% | 0 | 12 | 4.2 |
Percentages come from raw unedited recordings only: produced podcast audio has tangents and stumbles cut in the edit, which moves the speaker up by about 12 percentile points. The small-sample counts include the produced feed. This is a reading of a transcript. It is not an accusation of dishonesty, and there are many good reasons not to answer a question directly (confidential numbers, unreleased products, someone else's news to break).