Adam Wenchel

Co-Founder and CEO, Arthur AI · 1 appearance on the record.

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

founderexecutiveengineer@apwenchel ↗LinkedIn ↗arthur.ai ↗

Adam Wenchel is the co-founder and CEO of Arthur AI, an enterprise platform for model monitoring, observability, and security. He previously founded Anax Security and served as Vice President of AI & Data Innovation at Capital One, where he established its Center for Machine Learning.

12statements → 4claims → 1claims resolved → 4/5average certainty → 2.08/5average debate potential →

1 supported 0 partly supported 0 contradicted 3 not checkable as stated how the 4 claims stand · each chip opens the sources

4 assertions · 7 insights · 1 disclosure · every statement was checked. The predictions and assertions are the 4 claims: statements the public record can support or contradict. 1 is resolved, and 3 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 Adam 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

Historical anti-bias regulations apply directly to AI models

“There's a lot of historical regulation around anti-discrimination and bias and things like that that, ah, certainly applies just as much to AI models as it does to humans and more simple analytical models.”

Adam Wenchel Jan 22, 2020 ▶ 2:35 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

How they sound: speaking style how? →

309 words/min while actually speaking · 51 um and uh per 1k words

No argument clarity score for Adam Wenchel: 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: 4,259 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 →

What they said12 Episodes1

Everything Adam Wenchel 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

Most companies using AI suffer from unpublicized model failures

“Every company that's doing anything substantive with AI probably suffers from any of these problems, it's just a few of them have actually, ah, made the headlines for it”

Adam Wenchel Jan 22, 2020 ▶ 3:27 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Assertion Not checkable as stated

Open-source explainable AI tools like LIME and SHAP fail enterprise scale

“If you look at the open source components they're not very scalable. They're not easy to deploy at scale. They're really, they're useful, like, if you're a data scientist, and you have your Jupyter notebook, and you're, you know, running an experiment locally,…”

Adam Wenchel Jan 22, 2020 ▶ 20:43 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Real-world AI deployments introduce distinct failure modes beyond lab environments

“And it's not only hard to develop in the lab, but once you develop it and put it in the real world, there's a whole new set of categories of ways it can go wrong.”

Adam Wenchel Jan 22, 2020 ▶ 1:49 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Assertion Supported

Historical anti-bias regulations apply directly to AI models

Adam Wenchel Jan 22, 2020 ▶ 2:35 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Disclosure

Lack of trust delays enterprise AI deployments for months

“We, you know, encounter this all the time, where in organizations, they have these big plans for AI but they're just, They're unsure about actually deploying them and turning them on, and things get held up for months and months and months because of that.”

Adam Wenchel Jan 22, 2020 ▶ 3:06 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Deployed AI models suffer immediate performance gaps and ongoing degradation

“The second you put it in the real world, models, ah, number one, there's a gap right from day one, and they get worse over time.”

Adam Wenchel Jan 22, 2020 ▶ 4:00 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Not collecting protected class data does not prevent algorithmic bias

“What's happened a lot in the past is people have kind of like taken the head in the sand approach where they've sort of said like, oh you know, we're not even collecting protected classes, so we can't possibly be biased as far as we know, and that's no longer …”

Adam Wenchel Jan 22, 2020 ▶ 6:56 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

A fractional drop in AI performance can cost hundreds of millions

“Even if your model encounters some sort of issue that drops at a couple 10th of a percent in performance, that can literally be hundreds of millions of dollars over time, and so the ROI on having that kind of monitoring in place is, is huge.”

Adam Wenchel Jan 22, 2020 ▶ 8:46 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Enterprise AI adoption fails without risk mitigation despite performance gains

“Especially large traditional enterprises there tend to be very consensus-driven cultures by nature, and so the, even if people, if a data scientist can demonstrate they have a model that, you know, generally, like, gets some huge five or 10% lift, which, you k…”

Adam Wenchel Jan 22, 2020 ▶ 9:00 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Visual AI explainability helps build trust in skeptical academic communities

“Ah, and then the other thing is, you can imagine this world of humanities research has not changed a whole lot in the, like, the last 200 years of study, and so bringing this sort of innovation, like, we can automate this and computers can find patterns that w…”

Adam Wenchel Jan 22, 2020 ▶ 12:18 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Insight

Early safety guardrails enable companies to pursue more aggressive AI strategies

“Like the more you can build these guardrails in from kind of day one of your AI projects, when you, it allows you to be more aggressive, right? Just like the safety systems on an F-one car allow you to lap faster. If you build this stuff in from the beginning …”

Adam Wenchel Jan 22, 2020 ▶ 14:23 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Assertion Not checkable as stated

Credit underwriting AI feedback loops take three to four years

“There's other ones like, ah, underwriting credit cards, where you might not know for three or four years whether you should have given that person a credit card, right?”

Adam Wenchel Jan 22, 2020 ▶ 17:44 Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC)

Appearances (1)

Episode	Date	Speaking time
Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data D	Jan 22, 2020	17m

Adam Wenchel

The record, in short

Their most notable supported claim

How they sound: speaking style how? →

Appearances (1)

Turn any episode into a week of clips.