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In this conversation, we chat withEva Shang - co-founder and CEO of Legalist. She is a Harvard drop-out, a Thiel Fellow, and was awarded Forbes 30 under 30 in 2018. After founding Legalist at the age of 20, Eva led the company through Y-Combinator’s S16 accelerator program.
Legalist raised its inaugural fund of $10.25 million in 2017 and its second fund of $100 million in 2019.Eva has been featured in the New Yorker, the Wall Street Journal, the Boston Globe, and other news outlets for her work on Legalist and access to justice. Along with her sister Melissa, Eva is the co-author of Mia Lee is Wheeling Through Middle School, a middle-grade novel starring a girl with a disability.
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1’23”: Overview of Legalist's Asset Management Strategies
2’31”: Litigation Finance: Core Strategy and Operation
6’20”: Integration of Data Analysis and Technology in Litigation Finance
9’37”: Early Development of Legalist’s Technology at Y-Combinator
14’59”: Transition from Legal Analytics to Litigation Finance
18’22”: Investment Process and Decision Making in Legalist
24’33”: Challenges and Innovations in Data-Driven Litigation Funding
27’24”: Growth and Expansion of Legalist Since 2019
31’51”: The Future of Alternative Investments and Legalist's Role
35’09”: The channels used to connect with Eva & learn more about Legalist
Sneak Peek:
Eva Shang: …investors ask us about this all the time. They want to know what the difference is between generative AI like they're seeing today and the AI that we used back in the day. And fundamentally, what AI is is a loop whereby the machine improves itself. And back in the day, what we were using AI for was really to understand the data that we were looking at. So, if you look at a court docket for instance, the access to the data that we're using is not revolutionary. LexisNexis has this information; Westlaw has this information. There are plenty of legal analytic startups that also scrape state and federal trial court records and assemble that information. But classifying and knowing what you're looking for, that's really where a lot of the AI that we built back then was based around. So, things that are pretty simple for determining whether you want to invest in a case or not, for instance, what type of case it is or what the party types are, are actually surprisingly complicated if you want to do them with any great accuracy or efficiency.
So, for instance, classifying whether the party that you're reaching out to is a business or an individual and whether their defendant is a business or an individual. I'll give you an example of why we don't want cases where a business is suing an individual. It's usually not collectible. So, if you have a business suing an individual, it might be a collections lawsuit, it might be a theft of trade secrets lawsuit, but the person on the other end usually doesn't have ability to pay, and that makes it really difficult for us to invest. And I can talk about this later in the underwriting process, but the three components that any litigation funding investor is looking for are liability, which means will the case be successful, collectability, which means will you be able to collect from the other party, and damages, which means if you are successful and you're able to collect, how much would you actually make at the end of the day? And collectability is a really important and often forgotten portion of that. So, a lot of the AI we built back then was just doing really simple things like that and with the end goal of being to identify the cases that we want to invest in.
Now today, what generative AI is really good at is…
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