Dillon Erb

Co-founder & CEO, Autonomous Technologies Group · 1 appearance on the record.

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founderexecutiveinvestor@dlnrb ↗dillonerb.com ↗

Dillon Erb co-founded the GPU cloud and MLOps platform Paperspace in 2014, scaling the company through Y Combinator to an acquisition by DigitalOcean in 2023 for $111 million. He subsequently co-founded Autonomous Technologies Group (ATG), an applied AI research lab developing Autonomous, an AI-driven financial advisor.

14statements → 6claims → 1claims resolved → 3.71/5average certainty → 2.07/5average debate potential →

1 supported 0 partly supported 0 contradicted 5 not checkable as stated how the 6 claims stand · each chip opens the sources

3 predictions · 3 assertions · 7 insights · 1 disclosure · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 1 is resolved, and 5 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 Dillon 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

Erb: Billions have been invested in pre-product AI hardware unicorns

“A few billion dollars have been invested in new hardware startups. There are a few unicorns already for companies that don't have products fully out yet.”

Dillon Erb Feb 25, 2019 ▶ 4:14 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

How they sound: speaking style how? →

301 words/min while actually speaking · 43.8 um and uh per 1k words

No argument clarity score for Dillon Erb: 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: 3,952 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 said14 Episodes1

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

Insight

Erb: Existing CI/CD workflows do not fit machine learning pipelines

“Existing CI CD workflows, talking about how those don't really fit anymore. And then making the case that you know, these new pipelines are fundamentally different and require new tools, new workflows, and generally like new paradigms.”

Dillon Erb Feb 25, 2019 ▶ 1:50 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Assertion Supported

Erb: Billions have been invested in pre-product AI hardware unicorns

“A few billion dollars have been invested in new hardware startups. There are a few unicorns already for companies that don't have products fully out yet.”

Dillon Erb Feb 25, 2019 ▶ 4:14 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

Infrastructure, not algorithms, is the primary barrier to AI adoption

“I'm gonna make the case that the biggest barrier to adoption is an infrastructure and tooling problem, not necessarily an algorithmic problem.”

Dillon Erb Feb 25, 2019 ▶ 4:57 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

Erb: AI researchers and infrastructure teams view algorithmic primitives completely differently

“So if you're an AI or machine learning or deep learning developer or researcher, you think of your primitives as, you know, hidden layers and LSTMs and things like that. From an infrastructure perspective, it's actually much higher up”

Dillon Erb Feb 25, 2019 ▶ 2:22 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Assertion Not checkable as stated

Tech giants like Google and Uber rely on home-rolled ML infrastructure

“Most of the tooling is kind of home rolled. Best practices haven't emerged yet. We're seeing a lot of, especially large companies, roll their entire own stacks. So Facebook has FB Learner, Google has TFX. Uber has Michelangelo. Airbnb has Big Head.”

Dillon Erb Feb 25, 2019 ▶ 3:10 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

CI/CD principles address the black-box interpretability problem in deep learning

“You care about it because it adds reliability, reproducibility, determinism. You know, your systems are no longer inscrutable, which is a very big criticism, especially of deep learning architectures, which is that they're black box or they're, you know, they …”

Dillon Erb Feb 25, 2019 ▶ 6:43 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Prediction Not checkable as stated

Erb predicts ten new machine learning hardware accelerators will launch in 2019

“My guess is by the end of the year, there will be 10 more new devices that are coming out.”

Dillon Erb Feb 25, 2019 ▶ 8:23 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

Machine learning systems must trigger pipelines based on data or model drift

“Code drift there, which arguably is the number one, is the only trigger for most web apps. You know, your code drift, someone adds something, you rebuild your model, which is your web application. But actually what you care about in these new systems is data d…”

Dillon Erb Feb 25, 2019 ▶ 9:56 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Prediction Not checkable as stated

Erb: AI ops tools will expand into broad platforms within two years

“Tools in this space, this is an observation, ah, I'll check back in in a year or two, we'll see if I was totally wrong. Will go wide and deep to close this AI ops feedback loop.”

Dillon Erb Feb 25, 2019 ▶ 11:30 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Prediction Not checkable as stated

Software platforms will almost entirely abstract away underlying machine learning hardware

“Hardware will also be almost entirely abstracted away by these platforms, which is going to be an enormous mountain of work that will be done by lots of companies.”

Dillon Erb Feb 25, 2019 ▶ 12:04 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Assertion Not checkable as stated

Erb: The largest deep learning teams generally do not exceed 20 people

“The biggest teams generally don't go much larger than, say, 15 to 20 people, especially in the deep learning space.”

Dillon Erb Feb 25, 2019 ▶ 14:25 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

Lack of standard model repositories forces companies to hack solutions using Dropbox

“There's no good model zoo or model repo, and, you know, almost every company that I've worked with has their own version of that, and it can range from a Dropbox folder to, you know, actually repurposing Travis to actually do that.”

Dillon Erb Feb 25, 2019 ▶ 16:37 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Insight

Deep learning teams using standard code branches are light years ahead

“If you find, you know, companies that are doing deep learning that have, you know, staging development and master branches, that's already, like, light years ahead of, I would say, 99% of the companies that will be using this technology in five years.”

Dillon Erb Feb 25, 2019 ▶ 20:03 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Disclosure

Paperspace customers run machine learning training workloads lasting up to three weeks

“We run workloads for customers that can go up to like three weeks for a training task”

Dillon Erb Feb 25, 2019 ▶ 9:11 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

Appearances (1)

Episode	Date	Speaking time
CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)	Feb 25, 2019	16m

Dillon Erb

The record, in short

Their most notable supported claim

How they sound: speaking style how? →

Appearances (1)

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