How AI Is Making Employee Equity More Accessible For Startups And Their Teams – Forbes


For some people, a single event can be a missed opportunity to build wealth. That opportunity comes at the moment of exiting a company, and whether or not that individual exercises their stock options.

M&A events, IPOs or even investment rounds are typically disclosed in huge packets of printed material. One of the key points of disclosure was the schedule of exceptions. In many ways, these were full of warnings for buyers or investors, copies of canceled options agreements, etc. 

Businesses published these for many reasons, a small one being protection against employees who left and didn’t exercise. Not everything in disclosures is legally required. Most staff don’t read the full packet or understand what they could or should get if they leave.

Now, we’re in a new age of technology. AI and machine learning (ML) are changing the way businesses are run and changing the way business gets done. More importantly, they’re enabling systems of support to be alerted when an employee is leaving their job.

Employee Equity Compensation

Employee equity is a form of personal capital that can contribute to an individual’s long-term financial growth. The gap between that potential and realization has often been widened by insufficient communication, understanding and even liquid capital to cash out. Different business models face different levels of complexity and transparency. This can make it hard for an exiting employee to know if the company is profitable and healthy.

Option Grants

Employees who have option grants have a choice to acquire their shares at a set price. 

Two major issues have to be addressed before an employee will exercise options:

Capital: Coming up with the capital to exercise options can often mean cash equivalent to a year or more of pay. For many people, that is unduly burdensome and leads them to leave money on the table, simply because they can’t afford to do otherwise.

Risk: Taking on shares is a calculated risk that requires an understanding of the financial health of the company, and some revenue forecasting.

There are millions of privately-held companies in the world, some of which net billions of dollars a year. Artificial intelligence is being used by one platform to contact employees who leave a company, and share the cost burden of exercising options on their way out.

Resources for Employees

Scott Chou has been in venture capital since 1997 and watched colleagues in this exact predicament time and again: “the stack is huge because so many people don’t exercise – the number is staggingering. 55, 75, maybe 80 percent of all option grants don’t get exercised. Sometimes for a good reason, because the company isn’t doing well. Half of the companies, though, it’s not for good reasons.”

His first attempt to do something about it started with a colleague. This executive knew the company was doing well, but Scott noticed in the paperwork that his colleague wasn’t going to exercise his shares. Even if he exited at the current valuation, he would make money, so Scott questioned why he was abandoning the shares. His colleague explained that he was buying only what he could afford. Scott offered to pay and split the profits. The same scenario replayed several times over the course of a 12-15 year period.

He realized the opportunity was out there, but finding people at the right moment was an immensely difficult challenge. Scott explains the problem this way, “I analogize it to scattering a million dollars of quarters across the golf course. We’re so used to going to CEOs and trying to place money, but now we expand that to a million people out there working at all different companies and we don’t know who’s transitioning at what time.”

A Consumer Business

Over the course of the next 10 years, Scott’s individualized assistance of liquid capital and risk mitigation evolved into the company ESO Fund. The platform uses technology to interpret data about executive and corporate personnel transitions. Then, it deploys messaging to let those individuals know that assistance is available, if they want it to exercise options as they leave a company.

He explains, “It’s a consumer business. We had to build a consumer ecommerce business to execute this idea. For us to say yes and no to thousands of people per year, we have to have other mechanisms to stay on top of that. We have it organized in such a way that the information automatically spreads itself across all these companies. That way, when someone calls in from a company we’ve never heard of, everything we know about it is lined up, even if that was captured in the due diligence process for another company.” 

Machine learning makes offering this service at the right time possible. ESO fund leverages this technology to quickly assess the health and exit prospects of a particular company. By keeping timely records on every transaction and company, good or bad, they are constantly training the models to better identify opportunities. 

Every time there is a successful exit or failure they retrain the data to account for new dynamics, for example which VCs were lead investors in a funding round. This allows the model to identify companies likely headed for success, and those likely headed for failure without the need for traditional financial due diligence. This is especially helpful due to the fact that employees don’t have financial info, and neither do ESO fund consultants. AI allows them to act quickly, without that information as a requirement.

Scott further elaborates, “It maximizes our chance of saying yes or no very quickly. Our main goal is to be very convenient. At least two dozen times in our history, we’ve executed in 24 hours or less because someone is about to expire. That capability is special and it’s definitely a service. If they’re down to the last 24 hours, you can’t even get a credit card that fast. Especially if you need $100k, you at least need a home equity line of credit and that’s at least 30 days.”

New rules and better resources are the future for employees. ESO Fund is one example of how AI and machine learning took a good idea and made it possible in the real world. Leaders like Scott are saving people money and giving them opportunities they wouldn’t otherwise have. All of it is made possible by trained algorithms, machines, while still being fueled by human ingenuity.



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