Turning Prediction Markets Into Universal Risk Infrastructure

Castle Labs · Confidential

1. Our Thesis & Mission

Prediction markets are powerful information-aggregation mechanisms, capable of synthesizing dispersed beliefs about uncertain future events into continuously updated and accurate probabilities. Despite their promise, prediction markets have not yet matured into core financial infrastructure. Their failure to do so reflects not a theoretical limitation but a structural one.

Today, prediction markets are dominated by speculative and entertainment-driven activity. Liquidity is concentrated in sports, while economically consequential events such as inflation surprises, regulatory interventions, climate shocks, and geopolitical escalations remain fragmented and illiquid. As a result, prediction markets lack the stable, utility-driven order flow that underpins mature markets in financial products and derivatives.

Prediction markets will reach their full potential only when they are understood primarily as instruments of risk transfer rather than speculation.

Hence, we are constructing a Risk Translation Layer: an AI-driven platform that converts real-world financial exposure into automated, bundled hedges executed on regulated prediction markets. By abstracting away the notion of betting and instead delivering insurance-like protection, the platform introduces the benign, repeatable flow necessary to catalyze liquidity, attract institutional market makers, and embed prediction markets into global wealth management.

Once risk can be expressed, priced, and transferred in this way, the same framework extends beyond financial portfolios to real-world exposures. Event-based contracts can provide protection against climate shocks, supply-chain disruptions, or income volatility, delivering insurance-like outcomes through transparent, market-priced structures rather than opaque, intermediary-driven products.

Prediction markets will not reach broad adoption as gambling platforms; they will do so as the universal mechanism for transferring risk. That is the future we are building.

2. Our Team

Lucas Cavalieri: Lucas studied Mathematics at Stanford, where he focused on graduate level coursework in machine learning and optimization. He has experience as an engineer at Microsoft and a trader at top quant firms, including Susquehanna, Jane Street, and D.E. Shaw. He has also represented the U.S. national rugby team.

Bruno Felix Castillo: Bruno graduated from Stanford with degrees in Physics and Mathematics, focusing on probability and statistical mechanics. He previously worked as a quant trader at Flow Traders and participated in competitive programs at Jane Street. He also has experience at a startup spun out of Stanford's Plasma Physics Lab.

Alexander Michael: Alex is a Computer Science major at Stanford, where he focuses on reinforcement learning and financial applications. During gap years, he founded an alternative data company whose products are used by hedge funds and Apple supply-chain partners. He later spun out a hedge fund that trades on the same proprietary datasets, generating over $5M in revenue.

Arjun Pandey: Arjun studied Computer Science at Stanford, focusing on optimization, cryptography, and artificial intelligence, while conducting research on hardware accelerators. He has experience in venture capital and engineering at early-stage startups, and most recently, was a forward deployed engineer at Palantir's financial services arm. He also founded his own sports-tech company, ScoutMe.

3. The Market Problem

Currently, the liquidity problem in prediction markets follows a chicken-and-egg dynamic: low volume discourages market makers; wide spreads and shallow books discourage traders; and the resulting equilibrium is a long tail of ghost-town markets where meaningful size cannot be traded. In order to solve this, we can learn from traditional financial markets.

The efficiency of financial markets arises not from universal profit maximization but from heterogeneity in participant utility functions. Many economically significant trades are made not to exploit mispricing but to reduce variance and control tail risk.

Farmers hedge crop prices to stabilize income, airlines hedge fuel costs to manage operating risk, and portfolio managers hedge macroeconomic exposure in order to concentrate capital on idiosyncratic alpha. In each case, participants rationally accept negative expected value in exchange for higher overall utility.

Prediction markets lack this category of participants. Trading activity is dominated by actors who seek "lottery ticket" bets or information asymmetry, resulting in fragile liquidity, wide spreads, and adverse selection against market makers. This equilibrium persists not because prediction markets are unsuitable for hedging, but because they lack the interface required to translate exposure into hedgeable events.

The dominance of prediction markets in sports suggests that these exchanges can scale when the user experience is smooth and the purpose is intuitively valuable. The missing wedge is a product that makes economically meaningful contracts feel as legible and actionable as sports. We do this by tying them directly to something users already care about: the distribution of outcomes for their wealth.

Our mission is to institutionalize risk hedging through prediction markets by constructing this missing interface. By doing so, prediction markets can evolve from venues for gamblers into markets for insurance, structured risk transfer, and portfolio protection, thereby aligning them with the economic role played by derivatives and insurance in traditional finance.

4. The Risk Translation Layer

The proposed platform enables users to hedge by linking three domains: asset holdings, underlying sources of risk, and event-based contracts. Users begin by connecting their brokerage accounts, after which the system identifies the exogenous drivers of portfolio outcomes—macroeconomic, geopolitical, regulatory, technological, and environmental events.

These exposures are then mapped to live prediction market contracts whose outcomes exert measurable, causal effects on portfolio returns. In this way, portfolio risk is reframed not as an abstract statistical property, but as a set of concrete, tradable event exposures.

An optimization engine then constructs a bundled hedge across relevant contracts, calibrated to the user's exposure profile and constrained by liquidity, pricing, and cost considerations. Execution and monitoring are automated, allowing hedges to adjust dynamically as market conditions evolve, events resolve, and correlations shift.

The resulting market structure supports both risk-averse participants seeking protection and risk-seeking participants willing to sell that protection, thereby reproducing the two-sided structure that underlies efficient financial markets. That is, those seeking more upside potential at the expense of higher variance can simply short the hedging instrument.

This enables us to enter the prediction market space through two angles: risk-conscious investors and thrill-seeking gamblers. We enable both sides to be more informed about their risks, thus promoting better decision-making and, ultimately, more efficient markets.

5. From Wealth Management to Universal Insurance

Portfolio hedging represents an initial distribution wedge rather than the ultimate scope of the Risk Translation Layer. Once exposure can be expressed in terms of exogenous events and transferred through event-based contracts, the same architecture extends naturally beyond financial portfolios to real-world sources of uncertainty.

A homeowner in Florida can hedge hurricane risk through contracts that settle on measured wind speeds or storm intensity. A manufacturer can insure against supply-chain disruptions by protecting against port closures, shipping delays, or commodity price shocks. A household can hedge income risk through labor-market and macroeconomic indicators tied to layoffs, unemployment, or policy outcomes.

In each case, protection is delivered through transparent, market-priced contracts that settle on objective events—without underwriting, claims adjustment, or opaque intermediaries. What emerges is not a collection of niche hedges, but a general-purpose, parametric insurance layer built on prediction markets.

The same mechanisms that translate portfolios into hedges—risk identification, contract discovery, optimization, and execution—also translate real-world exposures into insurable risks. The underlying machinery remains unchanged; only the domain of risk to which it is applied expands, allowing users to insure against the specific uncertainties that shape their lives and livelihoods.

6. Liquidity and Market Structure

The proposed platform functions as a distribution engine rather than an exchange. By routing utility-driven hedging flow into prediction markets, it introduces benign order flow that reduces market maker risk—not because hedgers are altruists, but because their incentives are orthogonal to those dominating prediction markets today.

As liquidity deepens and spreads tighten, hedging becomes cheaper and more effective, reinforcing demand and completing a positive feedback loop. In this manner, the platform aligns the incentives of hedgers, speculators, market makers, and venues, establishing a reliable base of benign volume that can start the liquidity flywheel.

7. Positioning for Future Structures

While avoiding becoming an exchange, we maintain a catalytic role in their future: we collect data on which exposure categories generate demand and feed a contract roadmap back to venues. In effect, we reveal latent demand for new markets and insurance products, allowing exchanges to list these contracts and capture the resulting volume. We effectively become a demand oracle for contract creation.

8. Conclusion

Prediction markets will not reach their potential by competing with sportsbooks or novelty wagering. They will succeed by performing the function markets have historically served best: allocating risk to those most willing to bear it. By institutionalizing risk hedging through a dedicated translation layer, prediction markets can evolve from speculative curiosities into foundational infrastructure for global risk management.