Matthew Zeiler

SVP, Research, Nebius · 1 appearance on the record.

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

founderexecutivescientist@MattZeiler ↗LinkedIn ↗nebius.com ↗

Zeiler founded Clarifai in 2013—a pioneering computer vision and deep learning platform—after placing in the top five spots of the 2013 ImageNet challenge during his Ph.D. at NYU. In May 2026, he joined AI cloud infrastructure provider Nebius as SVP of Research to lead frontier AI research.

9statements → 5claims → 2claims resolved → 4.44/5average certainty → 1.56/5average debate potential →

2 supported 0 partly supported 0 contradicted 1 not yet assessed 2 not checkable as stated how the 5 claims stand · each chip opens the sources

5 assertions · 2 opinions · 1 insight · 1 disclosure · every statement was checked. The predictions and assertions are the 5 claims: statements the public record can support or contradict. 2 are resolved, 1 is not yet assessed, 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 Matthew argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Clarifai won the seminal 2013 ImageNet computer vision competition
“Clarify happened to be this result at the bottom. We won the 2013 competition.”
Matthew Zeiler Nov 20, 2014 ▶ 6:53 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
100% certainty 4
100% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

232 words/min while actually speaking · 32.2 um and uh per 1k words

No argument clarity score for Matthew Zeiler: only 1 usable question→answer exchange 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: 2,854 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 Matthew Zeiler said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion
Clarifai does not view Google as a direct competitor
“So Google, we don't consider them a competitor. They have great research teams and really big research teams and great resources but they're working on their own problems. They have their own users, and they don't they don't really compete with us in terms of …”
Matthew Zeiler Nov 20, 2014 ▶ 19:07 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Opinion
Competitiveness in computer vision now strictly requires using neural networks
“And you can see now, to even be competitive, you have to use neural networks.”
Matthew Zeiler Nov 20, 2014 ▶ 6:48 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Assertion Supported
Clarifai won the seminal 2013 ImageNet computer vision competition
“Clarify happened to be this result at the bottom. We won the 2013 competition.”
Matthew Zeiler Nov 20, 2014 ▶ 6:53 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Assertion Not publicly verifiable
Clarifai's video recognition runs ten times faster than real time
“It can run about 10 times faster than real time,”
Matthew Zeiler Nov 20, 2014 ▶ 13:08 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Insight
Unsupervised neural networks will never match models trained on well-labeled data
“These models work really well with labeled data. There are approaches where you can train without any labels but the performance is never as good as if you have well-labeled data.”
Matthew Zeiler Nov 20, 2014 ▶ 15:48 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Assertion Supported
Siri and Android voice recognition already rely entirely on neural networks
“Speech recognition, basically all the Siri processing or Android voice recognition is done with neural networks these days.”
Matthew Zeiler Nov 20, 2014 ▶ 5:07 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Assertion Not checkable as stated
Clarifai automatically tagged 1.3 million stock images in a few minutes
“And again, this is done automatically on, this is 1.3 million images, it takes a matter of minutes to do this, so you can scale this up to billions of images, no problem.”
Matthew Zeiler Nov 20, 2014 ▶ 10:46 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Disclosure
Google, Qualcomm, and Nvidia are strategic investors in Clarifai
“We got some great investors like Google, Qualcomm, and NVIDIA who help power this technology for us.”
Matthew Zeiler Nov 20, 2014 ▶ 12:23 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)
Assertion Not checkable as stated
Stock photography platforms still rely entirely on manual labeling in 2014
“Same story with stock photography. They are all manually labeled at this point.”
Matthew Zeiler Nov 20, 2014 ▶ 8:19 Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital)

Appearances (1)

EpisodeDateSpeaking time
Matthew Zeiler, Clarifai // Data Driven #31 // Nov 2014 (Hosted by FirstMark Capital) Nov 20, 2014 14m
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