Auren Hoffman

General Partner, Flex Capital · 1 appearance on the record.

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founderinvestorexecutivehost@auren ↗blog.summation.net ↗Wikipedia ↗

Auren Hoffman is an entrepreneur and investor who co-founded and served as CEO of LiveRamp and SafeGraph. Through Flex Capital and angel investments, he has backed companies including Coinbase, Vercel, and Flexport, and hosts the Summation podcast.

18statements → 11claims → 2claims resolved → 3.56/5average certainty → 2.22/5average debate potential →

1 supported 1 partly supported 0 contradicted 9 not checkable as stated how the 11 claims stand · each chip opens the sources

5 predictions · 6 assertions · 1 opinion · 6 insights · every statement was checked. The predictions and assertions are the 11 claims: statements the public record can support or contradict. 2 are resolved, and 9 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 Auren 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

Prediction Held up

Hoffman: Privacy-preserving data querying solutions will emerge by 2022

“But I expect within the next five years, we'll probably have answers to some of those problems.”

Auren Hoffman Nov 20, 2017 ▶ 18:44 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Expressed certainty vs assessment result

none yet certainty 1

none yet certainty 2

100% certainty 3

50% certainty 4

none yet 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? →

250 words/min while actually speaking · 24 um and uh per 1k words

No argument clarity score for Auren Hoffman: 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: 3,668 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 said18 Episodes1

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

Insight

Hoffman: Advanced AI models cannot overcome exclusive access to core data

“In that world, even the most advanced models, deep learning, and machine learning frameworks can't beat them because they have access to this kind of core underlying data.”

Auren Hoffman Nov 20, 2017 ▶ 8:46 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Insight

Hoffman: Great data usually beats great algorithms in machine learning

“Great data usually beats great algorithms.”

Auren Hoffman Nov 20, 2017 ▶ 0:46 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Prediction Not checkable as stated

Hoffman: A data breach will put a data company out of business

“And of course, keeping that data secure is, is a huge issue, and you're going to build a security team, run penetration tests, you know, or of course you're going to be out of business if you have some sort of breach.”

Auren Hoffman Nov 20, 2017 ▶ 4:48 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Not checkable as stated

Hoffman: Google has location data on 70% of US mobile phones

“Companies like Google have great access to this data because they have 70% of the phones in the U.S. That they have data on, and probably even a higher percentage of phones worldwide that they get to see all this great location data on.”

Auren Hoffman Nov 20, 2017 ▶ 12:51 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Insight

Hoffman: Most so-called data companies are actually application companies

“There are very few data companies out there. Most companies that are quote-unquote data companies are actually application companies.”

Auren Hoffman Nov 20, 2017 ▶ 13:36 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Insight

Hoffman: Future technological innovation will occur where data exists today

“If you really wanna know, like, where is innovation going to happen in the future, I think you can really look at innovation as to where we have data today.”

Auren Hoffman Nov 20, 2017 ▶ 15:18 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Prediction Not checkable as stated

Hoffman: Nutrition tech will see little progress over the next 20 years

“On the flip side, if you think about nutrition, likely, 20 years from now, we'll still have fad diets. Just be, it's just incredibly difficult to collect all the data of, you know, everything that goes into your body, you know, your eventual outcome, which cou…”

Auren Hoffman Nov 20, 2017 ▶ 15:55 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Prediction Not checkable as stated

Hoffman: China may lead in healthcare machine learning due to data regulations

“And so we could see, it's very possible we could see more machine learning innovations, or at least in certain areas, Like maybe in healthcare, for instance. We might see more machine learning innovations that happen in China than happen in, in other places.”

Auren Hoffman Nov 20, 2017 ▶ 21:25 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Not checkable as stated

Hoffman: Machine learning engineers spend up to 99% of time organizing data

“So now, you know, you have these great machine learning engineers, and they thought they were going to be spending You know, all their time predicting the future, but it turns out they're spending 95 to 99% of their time organizing the past.”

Auren Hoffman Nov 20, 2017 ▶ 4:07 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Not checkable as stated

Hoffman: Google ML engineers spend 95% of their time building models

“As a machine learning engineer, now you can spend 95% of your time predicting the future, which is what you want to do as a machine learning engineer.”

Auren Hoffman Nov 20, 2017 ▶ 5:24 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Insight

Hoffman: Data overhead is the core struggle for non-big-tech AI companies

“So this is the course, the core struggle for almost every single company, except for maybe Google, Facebook, Amazon, Tencent, that has to deal with all these types of issues.”

Auren Hoffman Nov 20, 2017 ▶ 5:32 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Opinion

Hoffman: TensorFlow is one of the top 10 innovations of the past decade

“In my opinion, it's one of the top 10 innovations in, in the last 10 years.”

Auren Hoffman Nov 20, 2017 ▶ 7:33 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Not checkable as stated

Hoffman: Compute power access prices have dropped every single month

“And the price, because of things like containers, et cetera, the price of access and compute power has gone down every single month.”

Auren Hoffman Nov 20, 2017 ▶ 9:57 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Not checkable as stated

Hoffman: Internal data represents under 0.01% of global data for most companies

“Most companies out there, their own data represents, like, point oh one percent of the world. It's, unless you're Google, Facebook, Amazon, Tencent, a couple of others, you have a very small sliver of what's happening in the world.”

Auren Hoffman Nov 20, 2017 ▶ 11:57 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Prediction Not checkable as stated

Hoffman: Oncology will see huge innovation in the next 20 years

“So the probably oncology is a place where we'll probably will see some innovation because there's a defined data set. There's, there are definitely some vendors today that have access to really good oncology data. So I expect that we'll see a lot of really gre…”

Auren Hoffman Nov 20, 2017 ▶ 15:31 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Prediction Held up

Hoffman: Privacy-preserving data querying solutions will emerge by 2022

“But I expect within the next five years, we'll probably have answers to some of those problems.”

Auren Hoffman Nov 20, 2017 ▶ 18:44 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Insight

Hoffman: Datasets are significantly more valuable when they are temporal

“The other thing about data is that it's more valuable if it's temporal.”

Auren Hoffman Nov 20, 2017 ▶ 14:35 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Assertion Partly supported

Hoffman: SafeGraph has 120 investors

“We've got we have a 120 investors in our company.”

Auren Hoffman Nov 20, 2017 ▶ 19:35 Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)

Appearances (1)

Episode	Date	Speaking time
Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)	Nov 20, 2017	17m

Auren Hoffman

The record, in short

Their most notable supported claim

Expressed certainty vs assessment result

How they sound: speaking style how? →

Appearances (1)

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