2.28-29.24-Ethics-Apers_

FRANCIS HUANG: Good morning, everyone. My name is Francis Huang, and we are an MIT Sandbox team. Together with Harvard Real Estate, MIT Sloan, EECS, and Center for Real Estate, we are building an AI autonomous agent for institutional real estate asset, not like your single-family home but like property-- like Marriott Hotel data center that drives your TikTok video or [? LogicSpark ?] that helps your Amazon delivery in 24 hours.

So before we dive into what we're doing, we want to rewind a little bit. Several years ago, when I was doing my master's degree here in Cambridge, both Harvard and MIT, the tools we were using, like system dynamics tool from MIT Sloan, were all kinds of pricing model from both HBS, Harvard Econ, and even MIT Center for Real Estate.

That really gave us an impression that we are literally having the crystal ball in our hand. But in 2020, when I myself left Cambridge, the world is very different. This is a stark realization of a typical MIT Harvard student. So you did a partial differential equation in school, and you try to model the market dynamics. And then you realize you're doing a spreadsheet.

In my case, it was worse. I was in a private equity in real estate. And 90% of my time was transcribing the PDF to Excel and then do the Excel part, which is even worse. So what went wrong? As we might know, it's our billion-dollar decision, like this property. It's 2024. Institutional real estate investors still make nine-figure decisions using tools and mentalities from the '90s.

That might not surprise you when we realize there are tons of unstructured data, which is extremely difficult to analyze. Even the smartest students from MIT, they cannot read exponentially faster than anyone else. And just to give you a sense that we are talking about 50 to 100,000 pages in two days getting the data. It's a time-consuming process, and it's largely human-dependent.

Sometimes when a key person leaves your private equity firm, you lose your know-how from days to months. Let's see how the best players in the world solve that. Blackstone, which arguably the best, and Mr. Schwarzman generously donated the college, Schwarzman College of Computing. And their solution is pure brutal force, having 5,850 entry-level analysts, largely from Harvard College, do the manual work.

In a sense, that, to me, is not the best way to allocate the smartest brainpower in the world, transcribing data from PDF. And that's exactly where Apers is invented. And our team here in Cambridge, Massachusetts, we are building an AI autonomous agent. We combine the large-language model, either large or small one, combining with the frontier research in empirical asset pricing. We build the AI agent just like analysts.

We push the boundary of Turing test to the limit, just like your analysts, only fast and better and never quit and cheaper. We don't have to use the partial differential equation to know what we're doing. Our value proposition is simple.

AI automation plus frontier asset pricing models equals decision making, getting wrong less, getting right fast. And that is a nine-figure decision-making we're talking about. And that could be your pension fund decision, meaning you have more to spend when you retire. That could be sovereign wealth fund, meaning the welfare of the nation can be better.

100% automation, 10x faster, and we can reduce 50% of redundant personnel of any organization with just automate, analyze, and act, simple as that. And we're not alone. We are currently working with prospective early product designers in the United States, a private equity firm with diverse asset classes in retail, office building, and apartment and some exotic-- like data center. And we are looking for more partners to work with us to build the world a better place.

And that's exactly what I want to invite you. Let's reshape how the capital is allocated here, more optimally and more efficiently. So for anyone who's in the community of institutional investor, pension, endowment, sovereign wealth fund, or the asset owner/operator, like private equity, risk developer, data center logistics, or even just retail operators, we want to be a help, and we want to reshape the future of how capital is allocated here in Cambridge, in Kendall Square, at MIT. Thank you.