Adam Benenson

Quantitative portfolio manager and AI-systems builder. Founder of Forethink Labs. Current work: portfolio intelligence (Prism), ambient AI for the browser (Forethink), a production digital twin, and machine-checked AI governance (legitimacy).

Writing

Alignment Is Downstream of Legitimacy
April 2026 · Medium
Not whether the system produced a good outcome, but whether it was entitled to decide. Why agentic AI alignment is downstream of legitimacy — and why the Constitution needs a compiler.
When the Forecast Becomes the Cause
March 2026 · Medium
Prediction markets work best when the future doesn't care what the market thinks. An incompleteness result: the more powerful they become, the larger the class of events they cannot price.
The Proof Is the Program
January 2026 · Medium
What changes when AI works inside a formal type system and hallucination is structurally rejected on contact.
The Compiler Is the Harness
January 2026 · Medium
Agentic coding is optimization, not authorship — and language choice determines the quality of the feedback signal. Why Rust's type system makes the compiler an unambiguous oracle.
The Cost of Cognitive Fragmentation
2024 · Medium
Every context switch leaves behind a shard of attention. Tabs multiply, threads blur. We spend more time reconstructing state than building. The founding argument for anticipatory AI.

All writing →

Machine-Checked AI Governance

Rust · Lean 4 · sorry-free
An AI agent's real risks aren't only in what it says — they're in the rules that decide what it may do: which tool runs, what gets escalated to a human, which of several competing requests gets the one scarce review slot. That rule layer is the agent's governance, and today it is mostly unverified scripts. Legitimacy asks whether the rule is even coherent — and proves that on exactly these surfaces some coherence is impossible, so a trustworthy rule has to declare what it gives up. A machine-checked Lean 4 kernel plus a Rust audit that runs on real agent-harness code; v1.0.0 is public. A chatbot can be behaviorally aligned. An institution has to be legitimate.

View the source on GitHub ↗

Read the six papers →

Quantitative Systems

Engines for systematic investing — understanding what a portfolio is really exposed to, constructing better hedges, and testing whether a claimed edge survives honest scrutiny before trusting it.

Prism
Python · Svelte · CPCV/DSR-gated
A general-purpose portfolio-intelligence engine. Bayesian view translation (entropy pooling over Black-Litterman), instrumented-PCA factor decomposition, RMT-cleaned covariance (Bouchaud RIE, Ledoit-Wolf NLS), sparse-cardinality mean-CVaR optimization, graphical-matching pair selection, and an immutable decision ledger. Every result must survive a deflation-aware validation battery — combinatorially-purged cross-validation, deflated Sharpe, probability of backtest overfitting — before it is allowed to claim anything. In production in a live long/short equity consulting engagement, driving book analytics, hedge construction, and optimization counterfactuals.
Snowball
Rust
A recursively self-learning alpha compounding engine for prediction markets. Hypotheses enter as replay-scored candidates, survive shadow and paper validation, earn size through bounded live review, and get clawed back when evidence degrades. Every fill updates a market factor graph; the graph reshapes candidate selection; better candidates generate more evidence.

Forethink Labs

An applied AI research laboratory building production AI with verified governance properties — a public consumer surface for knowledge workers, an enterprise platform for professional services firms, and the lab's internal AI orchestration substrate.

Rust · WASM · TypeScript · Svelte
Ambient AI layer for the browser that resurfaces relevant information before you search. The problem is structural: knowledge workers lose hours daily not to hard problems but to context reconstruction — finding the thing you already found, rebuilding the thread you already held. Most AI tools respond to prompts; Forethink anticipates context, proactively surfacing connections across your entire digital history. All processing happens on-device, with no data extraction or centralized indexing. Privacy isn't a policy — it's the substrate.
Workgraph
Rust · Python · TypeScript
A permissioned research-intelligence platform for professional services firms — domain ontologies spanning legal matters and investment research (firms, strategies, factors, themes). Captures and reasons over a firm's research, matter, and client history through Email/Drive change-data-capture into a temporal-hypergraph knowledge graph with tri-temporal indexing (event, effective, ingest time). Specialized agentic workflows for retrieval, drafting, compliance, and escalation sit on top. Every retrieval and agent action passes through scoped ACLs with information-barrier enforcement and a full audit plane.
Digital Twin
Python · Rust · JavaScript
A personal digital twin — built to reconstruct and extend my own causal reasoning (synthesizing Pearl's causal hierarchy with Rubin's potential outcomes), belief structure, and intellectual trajectory. Ingests arbitrary data: conversations, documents, reading history, behavioral traces, structured profiles. Extracts and consolidates claims across ten psychological dimensions; maintains a 24,000-episode store, a 50-node calibrated belief graph, and a queryable knowledge graph. Context engineering is formalized as a Markov Decision Process (DT-MDP) with a contrastive IRL retrieval policy. The system runs continuously, improving its own models between sessions. It also orchestrates structured agentic execution across the lab's other systems — augmenting my day-to-day work and accelerating Forethink's development end-to-end.

All Forethink Labs projects →

Background

Novus Partners
Managing Director, Head of Product · 2013–2020
Led the product, engineering, data, and design departments at an enterprise analytics company serving hedge funds, sovereign wealth funds, pensions, and family offices — 140+ institutional investors managing $3T+ in assets. Full org ownership from strategy through execution; member of the executive team.
Diamondback Capital
Quantitative Analyst → Portfolio Manager · 2008–2012
Analyst on a $2B+ systematic equity book — Sharpe above 2 sustained through the 2008–2009 regime shift. Promoted to PM and managed own $200M+ portfolio in parallel.
Board Governor · 2022–present
RustNYC
Speaker