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Aug 23, 2022 · 22m · top-founders

He shut down his $50m quant fund to launch AI agency. I bet big SaaS is next.

Robert Corwin · 12m spoken Nathan Latka · 8m spoken
0:00 / 0:00

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Former hedge fund manager Robert Corwin explains why he transitioned from running a quantitative fund to founding Austin Artificial Intelligence, detailing his agency's data science pod delivery model, strategic equity backing, and enterprise machine learning solutions.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 39.4% of the talking time here. How this is scored →

Nathan as informed peer 4.1 Guest teaching 2.4 Guest disagreement 1.0 Nathan pushing back 2.6
05100:0010:0020:000:46–4:05 · Nathan as informed peer 4/10 Introducing Robert Corwin and Austin Artificial Intelligence Corwin candidly explains why his hedge fund failed to scale, breaking down the operational overhead and AUM thresholds needed to sustain a fund. Latka probes into the fee percentages and math behind managing $100M versus $500M.4:06–7:12 · Nathan as informed peer 4/10 Austin AI's Pod Delivery Model and Value Proposition Corwin details Austin AI's pod staffing model and contrasts tailored execution with off-the-shelf AI panaceas. Latka presses on the exact monthly cost and specific staffing breakdown of a pod.7:15–10:42 · Nathan as informed peer 3/10 Sponsor Spotlight: Streamlining SaaS Onboarding with Rocketlane Following an ad read, Latka clarifies Austin AI's actual team headcount and delivery structure. Corwin explains their packaged services model, which blends custom implementation with proprietary utility libraries.10:42–13:15 · Nathan as informed peer 6/10 Navigating Intellectual Property Rights and Code Licensing Latka pushes back on Corwin's code licensing mention, arguing that enterprise clients want 100% IP ownership of core algorithms. Corwin clarifies that they provide perpetual utility licensing while keeping custom client work as work-for-hire.13:16–15:21 · Nathan as informed peer 5/10 Securing Strategic Angel Investment from Silicon Partners Latka asks how an agency model attracted venture or angel funding given lower margin profiles. Corwin explains the strategic synergy with Silicon Partners, and Latka maps out standard angel dilution benchmarks.15:22–18:06 · Nathan as informed peer 7/10 Applying Machine Learning to Credit Scoring and Lending Latka shares the inner workings of Founderpath's $135M debt facility and credit scoring algorithms. Corwin connects this with Austin AI's experience building underwriting models in construction lending.18:07–22:09 · Nathan as informed peer 4/10 Project Thresholds, Deal Sizing, and Minimum Engagements Latka presses to find the minimum financial commitment required to hire Austin AI before running through the standard wrap-up questions. Corwin clarifies that engagements typically start around minimum employee-equivalent project sizes.22:10–22:42 · Nathan as informed peer 0/10 Episode Recap: Quant Insights Applied to Enterprise AI Monologue recap where Latka summarizes Corwin's transition from a quant fund to enterprise AI services.0:46–4:05 · Guest teaching 5/10 Introducing Robert Corwin and Austin Artificial Intelligence Corwin candidly explains why his hedge fund failed to scale, breaking down the operational overhead and AUM thresholds needed to sustain a fund. Latka probes into the fee percentages and math behind managing $100M versus $500M.4:06–7:12 · Guest teaching 3/10 Austin AI's Pod Delivery Model and Value Proposition Corwin details Austin AI's pod staffing model and contrasts tailored execution with off-the-shelf AI panaceas. Latka presses on the exact monthly cost and specific staffing breakdown of a pod.7:15–10:42 · Guest teaching 2/10 Sponsor Spotlight: Streamlining SaaS Onboarding with Rocketlane Following an ad read, Latka clarifies Austin AI's actual team headcount and delivery structure. Corwin explains their packaged services model, which blends custom implementation with proprietary utility libraries.10:42–13:15 · Guest teaching 4/10 Navigating Intellectual Property Rights and Code Licensing Latka pushes back on Corwin's code licensing mention, arguing that enterprise clients want 100% IP ownership of core algorithms. Corwin clarifies that they provide perpetual utility licensing while keeping custom client work as work-for-hire.13:16–15:21 · Guest teaching 2/10 Securing Strategic Angel Investment from Silicon Partners Latka asks how an agency model attracted venture or angel funding given lower margin profiles. Corwin explains the strategic synergy with Silicon Partners, and Latka maps out standard angel dilution benchmarks.15:22–18:06 · Guest teaching 2/10 Applying Machine Learning to Credit Scoring and Lending Latka shares the inner workings of Founderpath's $135M debt facility and credit scoring algorithms. Corwin connects this with Austin AI's experience building underwriting models in construction lending.18:07–22:09 · Guest teaching 1/10 Project Thresholds, Deal Sizing, and Minimum Engagements Latka presses to find the minimum financial commitment required to hire Austin AI before running through the standard wrap-up questions. Corwin clarifies that engagements typically start around minimum employee-equivalent project sizes.22:10–22:42 · Guest teaching 0/10 Episode Recap: Quant Insights Applied to Enterprise AI Monologue recap where Latka summarizes Corwin's transition from a quant fund to enterprise AI services.0:46–4:05 · Guest disagreement 1/10 Introducing Robert Corwin and Austin Artificial Intelligence Corwin candidly explains why his hedge fund failed to scale, breaking down the operational overhead and AUM thresholds needed to sustain a fund. Latka probes into the fee percentages and math behind managing $100M versus $500M.4:06–7:12 · Guest disagreement 1/10 Austin AI's Pod Delivery Model and Value Proposition Corwin details Austin AI's pod staffing model and contrasts tailored execution with off-the-shelf AI panaceas. Latka presses on the exact monthly cost and specific staffing breakdown of a pod.7:15–10:42 · Guest disagreement 1/10 Sponsor Spotlight: Streamlining SaaS Onboarding with Rocketlane Following an ad read, Latka clarifies Austin AI's actual team headcount and delivery structure. Corwin explains their packaged services model, which blends custom implementation with proprietary utility libraries.10:42–13:15 · Guest disagreement 2/10 Navigating Intellectual Property Rights and Code Licensing Latka pushes back on Corwin's code licensing mention, arguing that enterprise clients want 100% IP ownership of core algorithms. Corwin clarifies that they provide perpetual utility licensing while keeping custom client work as work-for-hire.13:16–15:21 · Guest disagreement 1/10 Securing Strategic Angel Investment from Silicon Partners Latka asks how an agency model attracted venture or angel funding given lower margin profiles. Corwin explains the strategic synergy with Silicon Partners, and Latka maps out standard angel dilution benchmarks.15:22–18:06 · Guest disagreement 1/10 Applying Machine Learning to Credit Scoring and Lending Latka shares the inner workings of Founderpath's $135M debt facility and credit scoring algorithms. Corwin connects this with Austin AI's experience building underwriting models in construction lending.18:07–22:09 · Guest disagreement 1/10 Project Thresholds, Deal Sizing, and Minimum Engagements Latka presses to find the minimum financial commitment required to hire Austin AI before running through the standard wrap-up questions. Corwin clarifies that engagements typically start around minimum employee-equivalent project sizes.22:10–22:42 · Guest disagreement 0/10 Episode Recap: Quant Insights Applied to Enterprise AI Monologue recap where Latka summarizes Corwin's transition from a quant fund to enterprise AI services.0:46–4:05 · Nathan pushing back 3/10 Introducing Robert Corwin and Austin Artificial Intelligence Corwin candidly explains why his hedge fund failed to scale, breaking down the operational overhead and AUM thresholds needed to sustain a fund. Latka probes into the fee percentages and math behind managing $100M versus $500M.4:06–7:12 · Nathan pushing back 3/10 Austin AI's Pod Delivery Model and Value Proposition Corwin details Austin AI's pod staffing model and contrasts tailored execution with off-the-shelf AI panaceas. Latka presses on the exact monthly cost and specific staffing breakdown of a pod.7:15–10:42 · Nathan pushing back 2/10 Sponsor Spotlight: Streamlining SaaS Onboarding with Rocketlane Following an ad read, Latka clarifies Austin AI's actual team headcount and delivery structure. Corwin explains their packaged services model, which blends custom implementation with proprietary utility libraries.10:42–13:15 · Nathan pushing back 5/10 Navigating Intellectual Property Rights and Code Licensing Latka pushes back on Corwin's code licensing mention, arguing that enterprise clients want 100% IP ownership of core algorithms. Corwin clarifies that they provide perpetual utility licensing while keeping custom client work as work-for-hire.13:16–15:21 · Nathan pushing back 3/10 Securing Strategic Angel Investment from Silicon Partners Latka asks how an agency model attracted venture or angel funding given lower margin profiles. Corwin explains the strategic synergy with Silicon Partners, and Latka maps out standard angel dilution benchmarks.15:22–18:06 · Nathan pushing back 2/10 Applying Machine Learning to Credit Scoring and Lending Latka shares the inner workings of Founderpath's $135M debt facility and credit scoring algorithms. Corwin connects this with Austin AI's experience building underwriting models in construction lending.18:07–22:09 · Nathan pushing back 3/10 Project Thresholds, Deal Sizing, and Minimum Engagements Latka presses to find the minimum financial commitment required to hire Austin AI before running through the standard wrap-up questions. Corwin clarifies that engagements typically start around minimum employee-equivalent project sizes.22:10–22:42 · Nathan pushing back 0/10 Episode Recap: Quant Insights Applied to Enterprise AI Monologue recap where Latka summarizes Corwin's transition from a quant fund to enterprise AI services.

speaking balance: gold is Nathan, purple is the guest (3 minute bins)

0:00 · Nathan 40.2% · guest 59.8%0:00 · Nathan 40.2% · guest 59.8%3:00 · Nathan 19% · guest 81%3:00 · Nathan 19% · guest 81%6:00 · Nathan 61.4% · guest 38.6%6:00 · Nathan 61.4% · guest 38.6%9:00 · Nathan 25.5% · guest 74.5%9:00 · Nathan 25.5% · guest 74.5%12:00 · Nathan 30% · guest 70%12:00 · Nathan 30% · guest 70%15:00 · Nathan 67.3% · guest 32.7%15:00 · Nathan 67.3% · guest 32.7%18:00 · Nathan 31.1% · guest 68.9%18:00 · Nathan 31.1% · guest 68.9%21:00 · Nathan 43.4% · guest 56.6%21:00 · Nathan 43.4% · guest 56.6%
Sharpest disagreement ▶ 11:06 Clarifying perpetual licensing vs IP ownership

Corwin corrects Latka's objection to code licensing by distinguishing free perpetual utility libraries from custom work-for-hire code.

 Hardest push from Nathan ▶ 10:42 Latka challenges shared IP model

Latka firmly rejects the premise of licensing third-party agency code for core underwriting algorithms, emphasizing the necessity of total IP ownership.

 Biggest teaching moment ▶ 3:15 Hedge fund economics and minimum viable AUM

Corwin educates Latka on why a $100M AUM fund cannot survive on standard 1-2% management fees after factoring in data, infrastructure, and talent costs.

 Nathan holds their own ▶ 15:46 Latka details credit facility and SPV structure

Latka demonstrates deep financial expertise by articulating the exact mechanics of Founderpath's $135M bankruptcy-remote SPV credit facility.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Introducing Robert Corwin and Austin Artificial Intelligence 4513 Corwin candidly explains why his hedge fund failed to scale, breaking down the operational overhead and AUM thresholds needed to sustain a fund. Latka probes into the fee percentages and math behind managing $100M versus $500M.
Austin AI's Pod Delivery Model and Value Proposition 4313 Corwin details Austin AI's pod staffing model and contrasts tailored execution with off-the-shelf AI panaceas. Latka presses on the exact monthly cost and specific staffing breakdown of a pod.
Sponsor Spotlight: Streamlining SaaS Onboarding with Rocketlane 3212 Following an ad read, Latka clarifies Austin AI's actual team headcount and delivery structure. Corwin explains their packaged services model, which blends custom implementation with proprietary utility libraries.
Navigating Intellectual Property Rights and Code Licensing 6425 Latka pushes back on Corwin's code licensing mention, arguing that enterprise clients want 100% IP ownership of core algorithms. Corwin clarifies that they provide perpetual utility licensing while keeping custom client work as work-for-hire.
Securing Strategic Angel Investment from Silicon Partners 5213 Latka asks how an agency model attracted venture or angel funding given lower margin profiles. Corwin explains the strategic synergy with Silicon Partners, and Latka maps out standard angel dilution benchmarks.
Applying Machine Learning to Credit Scoring and Lending 7212 Latka shares the inner workings of Founderpath's $135M debt facility and credit scoring algorithms. Corwin connects this with Austin AI's experience building underwriting models in construction lending.
Project Thresholds, Deal Sizing, and Minimum Engagements 4113 Latka presses to find the minimum financial commitment required to hire Austin AI before running through the standard wrap-up questions. Corwin clarifies that engagements typically start around minimum employee-equivalent project sizes.
Episode Recap: Quant Insights Applied to Enterprise AI 0000 Monologue recap where Latka summarizes Corwin's transition from a quant fund to enterprise AI services.

Statements from this episode (6)

Disclosure
Corwin's prior hedge fund managed $50M to $100M before shutting down
“So we had somewhere between 50 and a hundred million, but that's not sufficient in a long, you know,”
Robert Corwin Aug 23, 2022 ▶ 2:23
Insight
Corwin: Sustainable hedge funds require at least $500M to $1B AUM
“The minimum you need five hundred million to a billion to make it a long-term business, sustainable business”
Robert Corwin Aug 23, 2022 ▶ 2:28
Insight
Corwin: AI frameworks cannot solve enterprise data problems without skilled personnel
“A lot of companies we've talked to have installed these spent, you know, seven figures or multiple seven figures on these frameworks and they do unify things and they add a layer of abstraction, you know, so the whole company is kind of centralized, but they d…”
Robert Corwin Aug 23, 2022 ▶ 4:54
Disclosure
Latka: Founderpath raised $145M including $135M debt facility
“So like we just raised one hundred forty five million bucks, right? Which part was equity, but one hundred thirty five million is effectively a debt fund now. So we are lending off balance sheet.”
Nathan Latka Aug 23, 2022 ▶ 10:43
Insight
Latka: Rapidly scaling software founders almost all start as agencies
“The most successful software founders that I've interviewed, I'm talking to ones doing, you know, going from zero to whatever, you know, a 100,000,200 million in revenue fairly rapidly. They almost all start off as an agency, just like you're doing.”
Nathan Latka Aug 23, 2022 ▶ 11:53
Prediction Open · timeframe Aug 2027
Latka: Corwin will transition Austin AI into SaaS within five years
“If I'm a betting man, like I predict that's where you'll be in five years, but tell me I'm wrong.”
Nathan Latka Aug 23, 2022 ▶ 12:14
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