About
The Origins of Kimpton
How we got here
Goldman Sachs
We built technology infrastructure for institutional finance and learned how consequential systems operate.
Level III Capital
At 21 and 22, we left to start a quantitative fund. We raised $10M and began running systematic strategies.
Built for ourselves
We built the entire loop: data and observability, signal research, backtesting, optimization, risk, and automated execution. The strategies ran autonomously.
Kimpton
We turned the system we had built for ourselves into Kimpton and applied it to the work investment teams actually do: agents read filings, transcripts, portfolios, and market data, then propose trades with the evidence attached. The team makes the decisions.
Koliseum
Proposing trades exposed the real problem: markets are a chaotic environment built on unstructured data, and no static benchmark could tell us whether a model's judgment would hold once conditions changed. At Y Combinator we built Koliseum to grade models against reality itself. We started in financial work, where we knew the cost of getting it wrong.
We learned to trust live results over historical fit. Kimpton brings that discipline to AI.
How the trading loop maps to AI systems
We used backtests to develop strategies. We used live markets to find out whether they worked. A backtest can reward contamination or historical fit. A live test forces a model to act on data it has never seen.
Kimpton applies that discipline to AI systems: models, agents, predictions, actions, and the results they produce.
Repeat from observe ↺
Quantitative tradingCapture market data, decisions, orders, risk, and outcomes.
AI systemsCapture inputs, outputs, tool calls, trajectories, and state changes.
Quantitative tradingReplay a strategy on point-in-time historical data.
AI systemsVerify the deliverable, resulting system state, and constraints.
Quantitative tradingChange signals, parameters, portfolio rules, or execution.
AI systemsChange the model, prompt, tools, policy, harness, or agent design.
Quantitative tradingFreeze the strategy and run it on market data it has never seen.
AI systemsFreeze the system and test it on tasks and data created after training.
Quantitative tradingCompare expected and realized performance, then repeat.
AI systemsTurn verified results and failures into the next improvement cycle.
Historical environments are the backtests. Arenas are the live test.
Koliseum makes financial work reproducible: the task, starting state, available data, tools, and verifier. Models can train and improve on historical episodes.
Arenas then capture new data in real time. After training, the system encounters work and data it could not have seen before. That is the AI equivalent of testing a strategy live, and where unique alpha can emerge.
Prediction Arenas
Record what a model believes before the outcome exists. Score it when reality arrives.
Action Arenas
Give a model new work and real tools. Verify what it accomplishes from the resulting state.
Data from the internet is priced in.Going forward, models will learn from environments.
If any of this resonates, come say hello
We would like to hear from anyone thinking about this too.