Anekha Sokhal
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Latent Space: The AI Engineer Podcast · The Recommendation Engine for Commodity Traders — Anekha Sokhal…
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How Should AI Fit Into a Commodity Trader's Workflow?
Anekha Sokhal says getting someone new to take a tool into their workflow is not the same problem as building the tool. A dashboard a person builds for themselves is that person's brain. What remains is a UI problem: how people interact with it, because users do not always use what they are given.
Spotify and Instagram are her examples of products that push the next piece of data, so a person hooks onto it without realizing they are still on the platform. Moshi has been working out how that interface relates to a recommendation system. She calls the pair a chicken-and-egg problem. The thesis is that solving one also solves the other. If everyone can build software, the hard part is software
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How Does an AI Recommendation Engine Help Commodity Traders?
Anekha Sokhal says Moshi's recommendation system is for a trader who is looking at one thing and still needs the second and third order derivatives they would not immediately know. She would not call the product a new Bloomberg. If Bloomberg, Claude, and Netflix had a baby, it would be Moshi, a different kind of software.
An oil trader is the example. An energy equities company can do something that would indirectly impact oil, and the trader needs to know that right now. A news stream does not show the link. A connected graph does, because the two pieces of information are coming out at the same time. A person can connect them, or a recommendation system can mimic that person.
She wished
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What Market Data Does Moshi Process for Traders?
Anekha Sokhal says Moshi processes the kinds of market data a trader looks at to decide what trade to put on and how to manage the risk. Structured data is the futures time series, the technical indicators, and the moving averages, and that set updates constantly. Fundamentals add demand and supply, especially in commodity markets. Unstructured data is the news, posts on X, and audio from podcasts. Private data is the user's own: the analysts they already follow, the subscriptions they pay for, and internal figures such as how many LNG ships are going through the Strait of Hormuz.
In the four seconds it takes her to say a sentence, Moshi has learned 300 new things. The company cleans those
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Why Is Moshi Hosting Its First Webinar?
Anekha Sokhal says Moshi is hosting its first webinar because inbound demand is too heavy to cover with one-to-one meetings alone. After an initial meeting, the team wants a clear picture of the problem and a plan, then a way to stream the rest of the demand.
She had meant to leave New York the same day. Meetings then landed on Thursday, Friday, and Monday, plus energy meetings in Houston, filling a full week after the conference. Her team kept flagging another signup. On the buy side, banks set the meetings up and brought people into small rooms for a round table. A CFO doing investor relations would meet five funds at once, and she was one of the five funds.
Shawn Wang has not got on-sit
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How Does Moshi's Confidence Score Work?
Anekha Sokhal gives a confidence score when a claim is shown to someone: one person is saying one thing, and the score also covers everything else happening around the conversation.
When so much data is available, the hard part is knowing what is important. In conversations about posts on X, and about any other kind of data, people follow trusted voices in industry, especially in commodities. Suppliers of commodities stay active on X. LNG facilities stay active on X too, because a post is the quickest way for them to give a status update. Users describe the individual they believe. The aim is to filter out as much noise as possible before the confidence score is shown.
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