#StartupsEverywhere: New York, N.Y.

#StartupsEverywhere: Chris Lucas, Founder & CEO, Secondsense

This profile is part of #StartupsEverywhere, an ongoing series highlighting startup leaders in ecosystems across the country. This interview has been edited for length, content, and clarity.

From pitch competition to social media sensation

After an idea conceived and prototyped in business school went viral, founder and CEO Chris Lucas raised a $2 million pre-seed round for his company Secondsense, a platform that aggregates data across resale listings to determine the fair market value of commercial goods on the secondhand market. We sat down with Chris to talk about his company, the perception of algorithms versus their actual use, and more.

Tell us about your background. What led you to Secondsense?

I started my career as a machine learning (ML) engineer writing content recommendation algorithms for Instagram. After a few years, I left Silicon Valley to get my Master of Business Administration at Harvard. The idea for Secondsense came from my own pain point and from seeing others experience similar circumstances. I tinkered around with a prototype in business school, and, during a pitch competition, Alix Earle posted my pitch to her TikTok for her 10 million followers. That attention catalyzed Secondsense. I raised a $2 million pre-seed round in three or four weeks. It was surreal. I had never gone through something like that. I’m thankful for Alix Earle; her attention to my pitch reversed the roles in my funding conversations. Investors were coming to me, and I didn’t have to send out a single message. I went from preparing to face persistent and consistent rejection to literally 90 investor meetings in the span of two weeks. After I graduated from business school, I dedicated myself full-time to building Secondsense.

What is the work you are doing at Secondsense? Who are your users/customers?

There are platforms for certain things we buy secondhand that give clear, informed data on its fair market value. Although we may not think of it like this, we buy our homes secondhand, and platforms like Zillow tell us what a particular house is worth based on prior sales. It’s similar to cars and Kelley Blue Book. I realized that we lacked that same level of market clarity when it comes to luxury consumer goods. Our goal is to determine and provide the fair market value of those consumer goods. Secondsense is taking advantage of recent advancements in AI to make sense of the messy, unstructured product, and transaction data that’s across the entire resale market. We work directly with over 30 secondhand online sellers like eBay, and the RealReal to aggregate the prices on consumer goods and then give our users links to those resale sites. We’ve started with luxury handbags, but the goal is to cover a much larger range of goods. 

How do you avoid pulling in information about and listings for counterfeit goods on Secondsense?

We are very careful about which sites—and businesses—we pull our data from and present to our customers as purchase options. We select who we work with based on their own authentication mechanisms. A lot of the best practices in the luxury resale market come from Japan because of the strict laws there that criminalize counterfeit production. Those laws have a trickle-down effect across the global industry. So we’re trying to get ahead of the mass adoption of those laws and build a system that’s ready to work in a landscape where the Japanese standards are the norm.

As a former ML engineer, what do you think more people should know and understand about algorithm-driven platforms? Are algorithms an effective way for customers to sift through what they are and are not interested in?

Algorithms drive the ultimate conversation. Imagine if we all opened up TikTok and we’re all served the exact same feed; no one would use that product. More people need to understand that it’s all math; it’s all based on user attention and action. Algorithms aren’t good or bad; they’re neutral, and their value depends on who is using them and for what purpose. When we think about platforms that use algorithms to serve users content, we need to find a happy middle ground where the user has sufficient tooling to be able to opt in, opt out, curate, and refine the content they’re served while not being required to sift through multiple thousands of pages of information.

How have the advancements in AI impacted your search for talent?

In a little under a year of building Secondsense, I’ve learned a lot about hiring and employment. Early on, I hired a new grad student and had to let them go after three weeks. My hypothesis is that, now, when students are learning to code in the era of AI, they can get around learning the fundamentals and a deeper understanding of code in general. Going forward, I’ve opted for hiring coders who learned to code pre-mass adoption of AI and have had several years of experience. 

Are there any local, state, or federal startup issues that you think should receive more attention from policymakers?

There needs to be a more straightforward and comprehensive checklist for starting a business. I missed a piece of paperwork when I was setting up Secondsense in New York—and with no warning—I was facing a $50,000 fine. This was an earnest, first time founder mistake. I’d never intentionally try to skirt around the law, but there’s no distinction between mistake and willful neglect in those kinds of determinations. I was able to get the fine down to something around $3000, which is definitely survivable, but these kinds of measures have the potential to kill so many businesses and ventures before they even start.

What are your goals for Secondsense moving forward?

I want to make Secondsense an up-and-coming formidable force in the resale sector. I’d like to see us with a seat at the table. There are opportunities to expand in the Business-to-Consumer and Business-to-Business spaces and expand our reach without a massive scale up of a team. My end goal is to make Secondsense a public company.

All of the information in this profile was accurate at the date and time of publication.

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