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The article profiles Moshi, a startup building an AI recommendation layer over market data for energy traders, and the founder's view that AI tools must fit existing trader workflows. For AI readers, the useful signal is the product design question: whether a domain-specific recommender can deliver value by connecting information to a trader's positions, rather than by adding a general-purpose chatbot. The transcript is short and promotional in places, so the claims about results and architecture remain unverified.
You are the Team — Anekha Sokhal, Moshi
How Moshi turns market data into actionable insights, and why AI tools have to fit the trader’s workflow.
Tickets and applications now live for AIE CODE - the last flagship AIE of the year. When Anekha Sokhal traded energy derivatives, staying on top of the market meant tracking more information than any person could read. Now, as CEO of Moshi, she is building the tool she wished she had: a recommendation engine that connects market information to what matters for a trader’s book. At AI Engineer New York, Anekha joins swyx to discuss her path from mathematics and JPMorgan to founding Moshi, and why useful AI needs to fit the way people actually work. She describes Moshi as what might happen if Bloomberg, Claude and Netflix had a baby: market data, an interface for working with it, and recommendations tailored to the trader. They also talk about trusted sources, document extraction, building versus buying models, and the pressure on AI engineers who are told, “you are the team.” Anekha shares what she learned from customer conversations at the conference and why meeting users comes before hiring. Automated transcript; wording may contain errors. Speaker labels are omitted. 0:00 So I’ve actually extended my stay in New York, because we’ve like now, I was meant to leave today. We’ve now…
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