Applied Operations
Live Market Operations
Seven years. Live capital. Real consequence. A validated proof of concept for the core ideas underlying AI safety at scale.
The central argument of this work, that complex adaptive systems produce emergent behavior that static evaluation frameworks cannot anticipate, is easy to state and difficult to prove. Most AI safety discourse operates at the level of theory, simulation, or controlled experiment. What is rare is a live, multi-year, financially consequential test of the same thesis.
From 2015 to 2022, that test was live.
The CASS agent-based simulation framework, developed over more than a decade for defense and national security applications, was extended to live US equity index derivatives markets. The specific target: the emergent cascading failure patterns produced when coupled autonomous execution systems and human traders interact under volatility stress. The nonlinear amplification dynamics that make these markets dangerous in dislocating conditions are structurally identical to the dynamics that make capable AI systems, increasingly autonomous themselves, dangerous at scale.
The system identified those dynamics in advance. It traded them.
Methodology
Simulation Before Capital
The operation began not in the markets but in a completely simulated environment. Roughly two years of simulation-only work, from 2013 to 2015, preceded any live capital deployment. During that period, the agent-based framework was used to model the interaction dynamics of human traders and automated execution systems under a range of market conditions, including high-volatility regimes, liquidity dislocations, and cascading price amplification events.
The simulation framework tested not just whether the system was profitable in historical conditions, but whether the underlying behavioral model, how market participants, human and automated, interact during stress, was structurally sound. Only when the simulation results were consistent, robust across regimes, and theoretically grounded in the complex systems literature did live capital deployment begin.
This sequence matters. It is precisely the pre-deployment validation discipline that AI safety governance demands and that most AI deployments skip.
Deployment Sequence
Step 1
Simulation (2013 to 2015)
Agent-based modeling of human-automated market dynamics. No live capital. Validation of behavioral model across regimes.
Step 2
Live Deployment (2015)
Capital deployed only after simulation results met consistency and robustness thresholds.
Step 3
Continuous Validation (2015 to 2022)
Live results continuously tested against simulation predictions. System refined iteratively.
Operating Environment
A Formally Structured Operation
The operation was run with the institutional discipline its complexity required. Exchange access was obtained at the institutional level, with multi-platform infrastructure across futures and options on ES (S&P 500) and NQ (Nasdaq 100) contracts, among the most liquid and competitive derivatives markets in the world. Legal structure was established at the outset with specialized counsel. Tax architecture was managed annually given the technical complexity of Section 1256 treatment, mixed straddle positions, and multi-platform reporting across instrument types.
Custom execution and forecasting systems were built in-house and coordinated with professional-grade institutional platforms. They combined market data with the behavioral-modeling approach at the core of the CASS framework. The specific signals and models are held privately and are not detailed here.
Operating Parameters
Markets traded
ES (S&P 500 futures) and NQ (Nasdaq 100 futures) and futures options
Active period
2013 to 2023
Simulation period preceding live deployment
Roughly 2 years (2013 to 2015)
Wind-down and transition to AI safety
2022 to 2023
Regulatory engagement
CFTC, SEC, CME, and CBOT sessions on cryptocurrency futures and derivatives framework development
Performance
Tested Where It Counts
The meaningful test of a systematic system is not its performance in favorable conditions. It is whether it holds in conditions designed to destroy it. The COVID crash of March 2020 was one of the fastest and most severe market dislocations in modern history, a roughly one-third decline in the S&P 500 in about a month. Many systematic strategies that had performed well in stable conditions failed or were badly damaged through it.
This system was built to be selective. It held no position the overwhelming majority of the time, entering only when the behavioral patterns the simulation framework identified were present and exiting when they resolved. That selectivity, not position sizing or stop-losses, was the risk control. It came through the 2020 dislocation intact, profitable long and short.
The live results are real and independently verifiable, confirmed across three independent institutional platforms and reflected in filed tax records. The specific performance figures are held privately and are not published here.
The full record is available under review
Vetted parties, prospective research partners, funders, and institutional collaborators, can review the complete verified performance record, the underlying methodology, and the full simulation-and-validation history under an appropriate confidentiality agreement. There is significantly more detail available than appears on this page. Start a confidential conversation to request access.
The Through-Line
The Same Framework, Applied to Two Domains
The connection between seven years of live derivatives trading and AI safety governance is not an analogy. It is a methodological continuity, and a direct one: the coupled-systems work led straight into the AI safety work, which overlapped its final years and then took its place.
The CASS framework was designed to model complex adaptive systems: environments where large numbers of heterogeneous agents interact, produce emergent collective behavior, and generate risks that no individual component was designed to create. It was built first for conflict dynamics and national security applications. Then it was extended to financial markets. The underlying structure of the problem is identical in both cases: human agents and automated systems interacting under stress, producing nonlinear amplification dynamics that static evaluation frameworks cannot anticipate.
AI systems at scale are the third domain. The dynamics are the same. A capable AI system interacting with human organizations, under deployment pressure, in messy institutional contexts, produces emergent behavior that was not present in controlled testing. The robustness gap between nominal safety and real-world resilience is the financial stability problem restated at the level of machine intelligence. Emergent misalignment is cascading failure with longer time horizons and more diffuse consequence.
What the derivatives work produced is not just evidence of profitable trading. It is a seven-year empirical record of governing a complex adaptive system under conditions where the feedback was immediate, the consequence was financial, and the theory had to be right before capital was risked. That discipline, simulation first, validation before deployment, continuous testing of the behavioral model against live results, is the discipline AI safety governance requires and that most AI deployments currently lack.
The system worked. The framework is the same. The application is larger.
Partnership
Ongoing Research and Partnership
This research program did not end in 2023. The theoretical and methodological work that produced the trading system continues to develop in the context of AI safety and sociotechnical systems governance. The agent-based modeling of human-automated interaction dynamics, the simulation-first validation methodology, and the empirical understanding of emergent failure in complex systems under stress are directly applicable to the governance challenges of capable AI deployed at scale.
The most consequential next step is extending this work into AI-relevant domains with the resources and institutional context to do it rigorously. That requires the right organizational partner.
What Partnership Could Look Like
Research collaboration applying the CASS methodology to AI deployment dynamics and emergent misalignment
Corporate-funded research program with joint publication rights
Embedded research leadership within an AI safety or governance function
Advisory engagement with access to deployment data and institutional context
The work speaks for itself. If it is relevant to what your organization is navigating, let's talk.
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