Tim Hwang

1 appearance on the record.

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

5statements → 5claims → 1claims resolved → 3.4/5average certainty → 2/5average debate potential →

0 supported 1 partly supported 0 contradicted 4 not checkable as stated how the 5 claims stand · each chip opens the sources

1 prediction · 4 assertions · every statement was checked. The prediction and assertions are the 5 claims: statements the public record can support or contradict. 1 is resolved, and 4 name 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 Tim argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

268 words/min while actually speaking · 61.9 um and uh per 1k words

No argument clarity score for Tim Hwang: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 2,700 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

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

Prediction Not checkable as stated
Tim Hwang predicts AI models will enable automated 'high-frequency litigation'
“In the future we might have something like, ah, high frequency litigation, and that's, I don't think that's too far from the truth in terms of being able to understand and, ah, predict these types of outcomes based off of, ah, past datasets.”
Tim Hwang Jan 16, 2014 ▶ 17:51 Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital)
Assertion Not checkable as stated
FiscalNote's prediction algorithms determine bill passage with 94% accuracy
“Currently, our prediction algorithms are about 94% accurate in being able to determine at first reader whether or not a bill is going to pass.”
Tim Hwang Jan 16, 2014 ▶ 4:22 Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital)
Assertion Not checkable as stated
Tim Hwang: 90% to 95% of government data is unstructured
“90 to 95% of government data is unstructured.”
Tim Hwang Jan 16, 2014 ▶ 1:09 Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital)
Assertion Not checkable as stated
Tim Hwang states state legislation impacts $4 trillion in US spending
“So, state legislation obviously impacts almost four trillion dollars in in, ah, in our economy spending today.”
Tim Hwang Jan 16, 2014 ▶ 5:59 Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital)
Assertion Partly supported
Tim Hwang says roughly 94% of New York state legislative bills fail
“In New York, I think something like 93 or 94% of bills end up failing you know, by the time they get to committee.”
Tim Hwang Jan 16, 2014 ▶ 22:06 Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital)

Appearances (1)

EpisodeDateSpeaking time
Tim Hwang, FiscalNote // Data Driven NYC 21 // Dec 2013 (Hosted by FirstMark Capital) Jan 16, 2014 12m
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