A massive, untapped market opportunity ($200-$300B) exists in financing stock options for rank-and-file employees who are systematically ignored by traditional financial institutions.
Proprietary data and machine learning models are superior to traditional methods for selecting top-performing private companies and accurately pricing their illiquid common stock.
Key employee flow events, such as a first CFO hire or a quiet layoff, are powerful and differentiated data signals that can predict a private company's future trajectory.
A highly diversified, index-like approach to venture investing, focused on the algorithmically-selected top quintile of companies, can generate superior risk-adjusted returns.
The greatest long-term competitive threat to Vested's model is not from existing niche players but from the eventual entry of large, well-funded asset management firms.
Pre-Vested
Developed an illiquid asset pricing model at Skilling Games. A subsequent version of this model was rebuilt and used by a major bank for algorithmic trading, though separate deals with pricing companies IDC and JJ Kenney were cancelled due to M&A by Intercontinental Exchange.
2020-2021
During the venture funding boom, Vested adopted a cautious investment stance, avoiding late-stage companies with high valuations from investors like SoftBank and Tiger Global, citing risks from large liquidation preference stacks.
Present Day
Vested actively employs an automated deal sourcing strategy using LinkedIn to contact departing employees from desirable companies. The firm uses LLMs to process communication data and has achieved a 100% success rate on share delivery after more than 60 liquidity events.
Near Future
Thornton announces the launch of a new tool, the 'Vestimate,' to estimate the fair market value of private company stock. The firm also plans to significantly scale its funds to hold nearly 1,000 positions across 600-700 companies.
Long-Term
Thornton articulates a long-term, speculative vision for Vested to leverage its growing proprietary data set to bring greater efficiency and liquidity to the historically anemic secondary markets for private shares.
▶Proprietary AI and Data as a Competitive MoatApr 2026
Thornton posits that Vested's core defensibility lies in its proprietary technology. This includes a machine learning model for pricing private company stock, a selection model that interprets employee flow as signals, and the use of LLMs to extract data from thousands of LinkedIn conversations.
This positions Vested less as a traditional financial services firm and more as a quantitative, data-driven asset manager applying algorithmic strategies to the inefficient venture capital asset class.
▶Unlocking the 'Long Tail' of Employee EquityApr 2026
A central theme is the focus on a massive, underserved market: rank-and-file startup employees needing relatively small amounts of capital (e.g., $50,000) to exercise options. Thornton contrasts this with large banks that cater only to senior executives from late-stage companies needing millions.
Targeting this high-volume, low-ticket-size market necessitates the automation and scalability provided by Vested's technology, as traditional, high-touch financial models would be unprofitable.
▶Systematizing Venture Capital via DiversificationApr 2026
Thornton advocates for a risk-managed approach to venture investing that diverges from concentrated bets. Vested's strategy involves building highly diversified portfolios, with funds aiming for up to 1,000 positions across 600-700 companies, all selected by its quantitative model.
This strategy attempts to capture the returns of the top quintile of the venture asset class while mitigating idiosyncratic risk, effectively creating a systematic, index-like product for private markets.
▶The Inefficiency of Private Secondary MarketsApr 2026
Thornton frames Vested's entire existence as a solution to the anemic and inefficient secondary markets for private shares. He points to the $200-$300 billion in abandoned options as the primary symptom of this market failure, and his long-term vision is to use Vested's data to help solve it.
The ultimate goal appears to be building a unique data asset on private company performance and valuation that could become more valuable than the financing business itself, potentially enabling new market infrastructure or index products.