Jeff Chung

Investor, AME Cloud Ventures · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

1statements → 1claims → 0claims resolved → 4/5average certainty → 3/5average debate potential →

1 not checkable as stated how the 1 claim stands · each chip opens the sources

1 assertion · every statement was checked. The predictions and assertion are the 1 claim: statements the public record can support or contradict. 0 are resolved, and 1 names no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Jeff argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: not measured why? →

We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Jeff Chung on measured tape to publish a rate. This says nothing about how they speak.

Everything Jeff Chung said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Not checkable as stated
Chung: Tech giants offer seven-figure salaries to undergraduate AI recruits
“If you go straight to academia and try to pull them out of, even if it's, you know, undergrad, they're getting seven figure offers from Google, Facebook to go work on, whether it's core, Deep learning tech, AI, or better ad targeting.”
Jeff Chung Sep 30, 2016 ▶ 21:16 Venture Capital Investor Panel: Investing in Big Data (Data Driven NYC / FirstMark)

Appearances (1)

EpisodeDateSpeaking time
Venture Capital Investor Panel: Investing in Big Data (Data Driven NYC / FirstMark) Sep 30, 2016 5m
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.