Axis Brings AI Trading Systems to the Commodities Desks That Still Run on Spreadsheets

The Macro: Commodities Trading Is a $5 Trillion Market Running on Outdated Tools

Commodities make up a third of global goods exports. Physical commodities traders move metals, agricultural products, and energy across the globe. They trade in public markets and bilateral agreements, managing billions in exposure daily. And many of them are still making decisions using Excel spreadsheets and phone calls.

Equities and fixed income trading desks have been automated for decades. Sophisticated execution algorithms, real-time risk management, and AI-driven analytics are standard. But commodities trading has lagged behind because the market structure is different. Physical commodity trades involve logistics, storage, counterparty credit risk, and delivery terms that pure financial trading does not.

This complexity has kept commodities trading desks reliant on human judgment and manual processes. A physical oil trader is not just managing price risk. They are coordinating tanker bookings, storage contracts, quality specifications, and delivery schedules across multiple counterparties. The analytical tools available to equities traders do not handle these dimensions.

Axis, backed by Y Combinator, is building an AI trading system specifically for commodities. The platform lets traders deploy models that monitor markets, analyze strategies, and support decision-making across the unique complexities of physical commodities.

The Micro: Yale and UChicago Building for Commodity Desks

Ian Wang (Yale ‘25) and Eric Zhu (Math at UChicago, former quantitative trader) founded Axis with a focus on commodities as their first market. The quant trading background is relevant because building AI for trading requires understanding both the models and the market microstructure.

The platform enables traders to deploy models that continuously monitor relevant markets and strategies. This shifts the workflow from reactive (checking prices and positions manually) to proactive (AI identifies opportunities and risks in real time).

For physical commodities traders, the value is in connecting market analysis with the logistical and operational dimensions that make commodities unique. A model that identifies a pricing opportunity but does not account for shipping costs, storage availability, or counterparty risk is useless in physical markets.

Competitors include established commodity trading and risk management (CTRM) systems from Openlink, ION, and Triple Point Technology, plus newer entrants like Eka Software. Most of these are traditional enterprise platforms that handle trade capture and risk reporting but lack AI-driven analytics.

The institutional focus suggests Axis is targeting the large trading houses, commodity merchants, and asset managers that have the volume and sophistication to benefit from AI-driven trading systems.

The Verdict

Axis is targeting a massive market that is genuinely underserved by modern technology. Commodities trading desks need better analytical tools, and AI is the natural evolution.

At 30 days: are institutional trading desks piloting Axis for live market monitoring?

At 60 days: does the AI analysis meaningfully improve trading decisions compared to traditional analysis?

At 90 days: is Axis handling the full complexity of physical commodity trades (logistics, quality, delivery) alongside market analysis?

I think Axis is entering the right market with the right founding team. Commodities trading is one of the last major financial markets to adopt AI-driven tools, and the opportunity is enormous. The challenge is building trust with institutional traders who are risk-averse about new technology. If Axis can demonstrate consistent analytical edge, the adoption should follow.