Capital Allocators Podcast – EP.469

Guest: Dave Thornton – CEO & Co-founder, Vested
Host: Ted Seides
Date: November 6, 2025

Episode Overview

In this episode, Ted Seides interviews Dave Thornton, CEO and co-founder of Vested, a venture secondaries platform focused on unlocking liquidity for startup employees through their stock options. They discuss Dave’s unique journey blending entrepreneurship, finance, and technology; the overlooked complexities of startup equity for rank-and-file employees; Vested’s evolution from an educational platform to an investment fund; the intricacies of deal sourcing and selection; risk management; portfolio construction; data moats; and the future of indexing private markets. The episode offers a candid exploration of the challenges and opportunities in democratizing access to venture capital—and features memorable stories about poker, NBA data analytics, and the realities faced by startup employees.

Dave Thornton’s Background & Formative Experiences

Early Hedge Fund and Technology Experience

• Dave describes being the technical linchpin at Citigroup’s internal hedge fund, building risk models and automating trading:

  "I was the nexus between technology and trading… it was a good financial services operator background for me." (01:44)

• Created an illiquid asset pricing model for municipal bonds:

  "We built an illiquid asset pricing model that priced munis in between trades…A subsequent version…is currently ALGO Trading the $400 million book." (02:49)

Entrepreneurial Journey

• First startup: bespoke machine learning for sports analytics, poker “luck vs skill” models, and fantasy sports. NBA “expected point value” concept work with multiple teams.

  "We worked for the Rockets, the Bucks and the Knicks, we got paid. We built out a proof of concept…The idea was that you could put the number of points you expect the offense to score at any given moment in a game." (06:48)

• Early lessons about luck, timing, and legal environment (poker/gambling regulation shut down opportunities).
• Second startup in healthcare analytics was acquired; learned through first-hand experience how even with expertise, equity decisions are fraught:

  "If somebody with my background at this point in the game is screwing up startup equity, I'm sure your average startup employee is also probably screwing up their startup equity." (04:33)

The Startup Equity Problem

Lack of Employee Understanding

• Most startup employees do not understand their options or the related tax/employment deadlines.
• The critical use case:

  "I just left my job... I now have 90 days within which I have to exercise whatever stock options had vested... or else I lose them…do you have 50 grand? I need to not lose my equity." (09:32)

• The original idea of Vested was purely educational, building trust and an inbound audience.

  "The original version of Vested was just a startup equity education platform… tools that would help you calculate the alternative minimum tax associated with your incentive stock option exercise." (09:32)

Evolution of Vested’s Business Model

Transition from Education to Liquidity Provider

• Market research revealed a crowded market serving executives from late-stage companies, but essentially NO player serving the "long tail"—rank-and-file employees with smaller ticket sizes:

  "They were just the people that needed 50 grand instead of $12 million... because of the ticket size." (11:49)

• Vested focused on providing value to this overlooked segment.

Innovative Model for Access and Selection

• Offers cash to employees to exercise options, typically at a discounted price based on “fair market value” (FMV):

  "We can get exposure to the common stock…usually at the independently produced, company board-approved fair market value of that company's common stock, which tends to incorporate a discount for lack of marketability…The first concept was a VC index at a discount." (13:29)

• Recognized their differentiated access to private company data as a source of alpha.
• Built a proprietary model to identify the top 20% of companies to focus on.

Data, Signals, and Modeling

Three Data Streams Feeding the Model (16:24)

1. Table Stakes Data:
   • Financing trajectory, deal terms, quality and consistency of investors, investor behaviors.
2. Differentiated (Non-Proprietary) Data:
   • Financial performance estimates based on tax and labor filings, employee flows (e.g., hiring C-suite, layoffs).
   • "If a company just hired its first CFO, that's an incredible signal. If a company just fired 50% of its people quietly, it's going to zero."
3. Proprietary Employee Data:
   • Signals from how employees interact with Vested (e.g., whether they sell minimum or all shares, their willingness or reluctance, the details of deal negotiation).

Hypothesis Testing and AI

• The challenge: Venture is slow-feedback; liquidity events are rare, so they rely heavily on robust out-of-sample backtesting.

  "It's hard because real hypothesis testing requires that you live long enough to see your investments do well or poorly." (20:18)

• AI applicability most relevant in unstructured data analysis (e.g., parsing thousands of LinkedIn conversations for signal):

  "LLMs would be excellent for that…currently got an activity internally going on that is doing just that." (27:42)

Sourcing Deals

• Initially inbound via the educational site; shifted to proactively tracking employees leaving targeted firms via LinkedIn/job site data:

  "As soon as we see they update their bio in some relevant way...we will proactively and automatically reach out...on LinkedIn." (20:51)

• Planning to pivot back to education for earlier engagement, creating a full lifecycle relationship.

Structure of Vested Deals and Portfolio

Mechanics of a Deal

• Most critical variable: How much does the employee need to fully exercise?
  • Exercise cost
  • Taxes imposed at exercise (AMT or ordinary income)
  • Taxes from transacting with Vested (capital gain)
• Deal Example (23:00):

  "If you have 100,000 options at a $1 strike price... current board approved fair market value is $3 a share. We will typically be buying at $3/share until you have 100 grand... you'll owe us delivery of the 33k that we bought when the transfer restrictions lapse."

• Delivery risk obsessively checked; track record so far is 100%, partly thanks to small ticket sizes and alignment of incentives at liquidity events.

  "We have had thus far a 100% delivery rate on enough liquidity events that the sample size is… you can trust the number." (25:07)

Portfolio Construction - Diversification is Key

• Natural diversification by stage, with most deals from series B–E companies. Avoids too-early (too few leavers) and too-late (secondary markets already exist).
• Sector diversity broadly matches overall VC market exposure.
• Avoids winner-picking hubris in favor of identifying a broad “top 20%” using the model:

  "Winner picking is very hard… you really do want to be diversified because it's so hard." (28:38)

VC Backer Quality

• Rather than relying on “brand name” VCs, sorts investors by empirical cash-on-cash performance and allows the algorithm to determine natural cutoffs:

  "First you have to actually agree on who top quartile VCs are, which is not that obvious...Instead what we did was we split the baby…let the algorithm figure out where the cutoff is." (30:58)

Moat & Competition

• Long-term competitive advantage (or "moat") is the proprietary data set generated by the breadth of employee interactions:

  "It's a data moat, there's no question about it… the more employees we serve, the more data exhaust we collect… the more comfortable we're going to be moving up on price 10% to win deals." (33:05)

• Risks: As secondary markets and IPO windows reopen, competition will intensify; Vested must stay ahead by leveraging their unique data.

Indexing Venture & The Future of Private Markets

• Sees Vested’s approach as analogous to the early creation of index funds in public markets:

  "We're playing a meaningful role in [indexing private markets], I hope. I would love Michael Lewis to write some book on that and I'd love us to have one chapter reserved." (47:40)

• Envisions using proprietary data eventually to power private company indices and better-functioning secondary markets:

  "Using our data to help characterize those startups...to the buy side of the secondary markets should be a profitable thing to do and should provide a lot of value in the world." (42:38)

Memorable Quotes & Moments

• On the equity knowledge gap:

  "If somebody with my background at this point in the game is screwing up startup equity, I'm sure your average startup employee is also probably screwing up their startup equity." – Dave Thornton (00:00, 04:33)

• On what data matters:

  "If a company just hired its first non-founding sales team, that means they found product market fit…If a company just fired 50% of its people quietly, it's going to zero." – Dave Thornton (16:24)

• On winner picking:

  "You really do want to be diversified because it's so hard.” (28:38)

• On delivery risk:

  "We have had thus far a 100% delivery rate on enough liquidity events that the sample size is—you can trust the number." (25:07)

• On scaling:

  "At the moment, around 70% of employees abandon their stock options…this market goes on for days." (38:29)

• On long-term moat:

  "It's a data moat, there's no question about it." (33:05)

• On personal motivation:

  "I really want to put a moat around it because it’s so interesting and it gives us a chance to stretch our brains almost daily." (42:38)

• On life advice:

  "Don’t let your highs be too high and don’t let your lows be too low." – Thornton’s father (47:17)

Notable Anecdotes

• Underground Poker in NYC:

  "I sat there totally card dead, waiting for my turn and never having had it come...afterwards...everybody but me knew that he was part of the mafia." (05:36)

• NBA Data Analytics:

  "We built out a proof of concept…The idea was you could put the number of points you expect the offense to score at any given moment in a game." (06:48)

• First "paid" job:

  "I was 16…called jodriver.com…And I was like, oh, I love the idea of stock options. Needless to say, I got paid $3 an hour that summer." (44:26)

Timestamps for Important Segments

• Dave’s Background & Hedge Fund Days: 01:44–04:33
• Early Entrepreneurial Ventures & Anecdotes: 04:55–09:24
• The Employee Stock Options Problem: 09:24–11:47
• Market Gap & Vested’s Unique Focus: 11:47–13:25
• Investment Model and Proprietary Data: 13:25–16:09
• Data Streams Feeding Selection Algorithm: 16:24–20:08
• AI and Data Processing at Vested: 27:34–28:33
• Deal Structure and Delivery Risk: 23:00–27:34
• Portfolio Construction (Stage, Sector, VC Backer): 28:38–32:04
• Moat, Competition, and Data Advantage: 33:05–34:38
• Indexing VC and Future Vision: 47:40–48:31
• Scaling Challenges & Companies’ Response: 38:17–42:04
• Audience Q&A and Personal Section: 44:00–48:31

Tone and Takeaways

The episode is candid, technical but pragmatic; Dave offers clear, direct insights into both the strategy and the realities for startup employees. The conversation is laced with humor, relatable stories, and a spirit of innovation rooted in Dave’s wide-ranging experiences.

Key Takeaways:

• The traditional VC market leaves out millions of employees with valuable but illiquid equity.
• Data and models can unlock diversified, attractively-priced venture exposure—if you can solve for access and selection.
• Vested’s moat is its proprietary dataset collected through unique employee interactions.
• The future of venture access could increasingly resemble indexing and data-driven selection—in many ways, echoing the history of public market innovation.

For more on Dave Thornton and Vested: [vested.co]
For more from the podcast: [capitalallocators.com]