SaaS Interviews with CEOs, Startups, Founders
He shut down his $50m quant fund to launch AI agency. I bet big SaaS is next.
23 Aug 2022
Chapter 1: What is the main topic discussed in this episode?
So we had somewhere between 50 and 100 million, but that's not sufficient. At a minimum, you need 500 million to a billion to make it a long-term business, sustainable business.
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Chapter 2: What led the guest to transition from a hedge fund to an AI agency?
Hey folks, my guest today is Robert Corwin. He's got a wide array of data science experience in many industries. Today's the co-founder of Austin Artificial Intelligence, a data science, machine learning, and AI joint venture service client, which services clients in the technology, financial, and industrial sectors.
Before this, he was co-founder of EVA Capital, a quant hedge fund, which traded long short factor strategies and US equities. All right, Rob, you ready to take us to the top? Yes, sir. All right, man. How'd you get out of the hedge fund space? Just sounds so cool and fun. Now you're sort of more in traditional SaaS, huh? But what made you walk away from the hedge fund world?
Well, to be perfectly honest, I started my own hedge fund and we didn't raise enough capital to make it work.
Chapter 3: What is the significance of raising capital for hedge funds?
So I could spin it, but that's the honest answer. But what I realized at the time was that... The same type of work I have been doing in finance for so long is now very prevalent outside the finance industry. I would say finance and defense have been doing this kind of work for 20, 30 years. They're ahead of the game, those two industries.
Chapter 4: How does Austin AI differentiate itself in the data science market?
The rest of the world is now finally catching on to data science, to quantitative analysis. It's not to say they didn't do it before, but they're taking it a lot more seriously now. I think the younger generation is also taking it a lot more seriously. So I think there's a huge opportunity here. You know, I think we're at the inflection point of this type of work.
Computer power is definitely no longer a bottleneck, right?
Chapter 5: What common misconceptions exist about AI frameworks?
I mean, in Amazon Web Services, you can open a computer that with a click of a button that 20 years ago, you know, would have cost you a lot of money.
Yeah. What, just there's people listening that are maybe thinking about opening their own hedge funds, right? What do you mean when you say you didn't raise enough money? How much do you have to raise to have a hedge fund?
So we had somewhere between 50 and 100 million, but that's not sufficient in a lot, you know, At the minimum, you need $500 million to a billion to make it a long-term business, a sustainable business. And so that's hard. It's kind of like the music industry or tech startups. For every huge success you hear of, there are many that don't make it for various reasons.
Chapter 6: How does Austin AI structure its data science teams?
And it was also easier 15 years ago. Anyone with a Bloomberg terminal could start a hedge fund 15 years ago. But it's a competitive place. It's a brutally competitive place. And markets are tougher. They're also no longer going straight up right now. So yeah, I say go for it.
When you say $500 million plus, what you mean really is for you to have the peace of mind to play a long-term game and make it through the dips and then celebrate the peaks, you've got to have that much capital behind you.
I mean that you need that much to pay your own bills. So that's not your money.
Chapter 7: What are the operational efficiencies that Austin AI offers?
That's a client's money. They're giving you $500 million to manage. From that, you will take a negotiated fee, right?
What did you take, like 1% or 2%?
I won't disclose that, but it was typical hedge fund fees, you know, typical hedge fund.
So what you're arguing is that the fee on $100 million is not big enough for you to cover just basic hedge fund expenses. You really need like 2% on $500 million to have enough admin expense, right?
Yeah. I mean, if you think about it, you can do the math. A percent of a hundred million is a million bucks.
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Chapter 8: How does Austin AI handle client relationships and project management?
And so, but you're paying for a lot of databases and computers and employees are expensive in that industry. So yeah, you need a substantial amount of AUM, asset center management.
Yeah. All right. So enter Austin AI. So what's Austin AI do?
Yeah, so Austin AI is a data science AI and ML services firm, and we have a real focus on practicality and getting real business results for our customers. So as you know, there's no plethora of AI frameworks and products that purport to do X or Y, and I have nothing against those things, but
Oftentimes, these things are sold as panaceas where you just install this in your company, hit a button, and then it comes up with all these wonderful predictions. That's rarely the case because at the end of the day, the devil is really in the details of this stuff. There's no substitute for good experimental design.
There's no substitute for good data engineering and good pipelines and good coding and all this kind of stuff. A lot of companies we've talked to have installed these seven figures or multiple seven figures on these frameworks, and they do unify things and they add a layer of abstraction.
So the whole company is kind of centralized, but they don't solve your data science problems at the end of the day by themselves. You still need data scientists. You still need very smart people running your analyses. And this is where we step in. We fill that gap. I like to say that we come in with a SWOT team, a data science pod, if you will, of a mix of seniorities of people.
And we're very empathetic to your business. What are your business goals? Where are you going to save time? Where are you going to save money? How can we help you do that? And we come in on a very efficient manner and just solve problems.
What does that mean when you say efficient? So if I'm going to get a data science pod from Austin AI, that's maybe four or five people strong. What am I going to pay you per month for that on average?
So contact me for the exact pricing, but let me put it this way. What we tell clients is think of your data science problem. Think of how many data scientists you're going to need to go hire to do that. Go look up the market rate. OK, now add taxes and now add benefits and you'll get you'll come to a number. Right.
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