Lee Fan

Chief Technology and AI Officer, Circle · 1 appearance on the record.

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

executiveengineerscientistcircle.com ↗Wikipedia ↗

Li Fan oversees engineering and artificial intelligence innovation at Circle, the issuer of USDC. She previously served as Senior Vice President and Head of Engineering at Pinterest, CTO at Lime, and Vice President of Engineering at Baidu.

8statements → 2claims → 0claims resolved → 3.88/5average certainty → 2.12/5average debate potential →

2 not checkable as stated how the 2 claims stand · each chip opens the sources

2 assertions · 1 opinion · 4 insights · 1 disclosure · every statement was checked. The predictions and assertions are the 2 claims: statements the public record can support or contradict. 0 are resolved, and 2 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 Lee 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? →

252 words/min while actually speaking · 5.5 um and uh per 1k words

No argument clarity score for Lee Fan: 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 2,017 words across 1 episode, but every recording we have of Lee Fan is the aired feed, and an editor cleaned that audio before release. Some of the hesitation was cut before we ever heard it, so read these as floors: the true rates are at least this high. These are measurements of speaking style, not scores. How it's measured →

What they said8 Episodes1

Everything Lee Fan said on the a16z Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Not checkable as stated

Lee Fan: Individual engineer throughput can differ by 10x to 100x

“Meaning that engineer versus engineer, the throughput can be 10 X or even hundred X difference.”

Lee Fan Jan 2, 2019 ▶ 25:36 a16z Podcast | Engineering Intent

Disclosure

Lee Fan: Pinterest tests skin tone detection for personalized visual recommendations

“We have some experiment in-house. If you take a picture of yourself, and we learn your skin tone. But by the way, we can also learn from the pins you like, and we know there's certain style, certain shape of a model you like to see.”

Lee Fan Jan 2, 2019 ▶ 14:16 a16z Podcast | Engineering Intent

Opinion

Lee Fan: Engineering compensation should reflect individual productivity differences

“I also believe that we should reward according to it.”

Lee Fan Jan 2, 2019 ▶ 25:44 a16z Podcast | Engineering Intent

Insight

Lee Fan: Visual discovery on Pinterest differs from Google by lacking objective right answers

“In some way, this is different from Google. There's no right or wrong.”

Lee Fan Jan 2, 2019 ▶ 4:42 a16z Podcast | Engineering Intent

Insight

Lee Fan: Products should expand discovery before driving conversion based on intent

“What we could do as a product is expand your horizon and help you to discover new, interesting ideas. We don't have to push you into deep, say, purchase this or book this. But then later, we can tell users' behavior. They're going narrow and narrow. Now we, I …”

Lee Fan Jan 2, 2019 ▶ 9:26 a16z Podcast | Engineering Intent

Insight

Lee Fan: Computer vision progress in subjective domains is limited by data

“Right now, I will say a lot of domain is limited by the data. If you only have a limited data to teach, let's say fashion, how can we know this is a fashion that are high end and more for the runway instead of a daily? It's a lot of data because it is a subtle…”

Lee Fan Jan 2, 2019 ▶ 16:10 a16z Podcast | Engineering Intent

Assertion Not checkable as stated

Lee Fan: Pinterest trains AI to identify visual cues that make photos inspirational

“We are trained computer to learn why this image of the same living room, you take a picture of this way and that way, that looks so different. One just looks so inspirational. The other, like maybe just boring and the computer will start to learn those cues an…”

Lee Fan Jan 2, 2019 ▶ 13:30 a16z Podcast | Engineering Intent

Insight

Fan: Designing with non-technical founders reveals logical gaps in engineering

“When you talk to a designer, ask questions like, wow, I never thought about this. And the process of thinking about how to answer this and discover there's some hole in my logic or discover like maybe we were to, you know, go down this path and maybe we should…”

Lee Fan Jan 2, 2019 ▶ 27:38 a16z Podcast | Engineering Intent

Appearances (1)

Episode	Date	Speaking time
a16z Podcast \| Engineering Intent	Jan 2, 2019	9m

Lee Fan

The record, in short

How they sound: speaking style how? →

Appearances (1)

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