Latent Space Podcast: Ex-JPMorgan Trader Builds Moshi, a Recommendation Engine for Commodity Traders
Latent Space · rss · 2026-10-22
Latent Space interviews Anekha Sokhal, founder of Moshi and former energy derivatives trader at JPMorgan. Unable to keep up with market information as a trader, she won a Fulbright scholarship to the US, did ICLR-published research with Stability AI and worked with NASA, then founded Moshi.
Key points:
- Moshi is a recommendation engine for traders—"what if Bloomberg, Claude and Netflix had a baby"—processing market data at scale and surfacing what matters to a trader's book; by her estimate it learns 300 new things in the 4 seconds it takes to say a sentence.
- Discussion covers trusted sources, document extraction (mentions Reducto), and build-vs-buy model decisions.
- Observation from AI Engineer New York: AI engineers face "you are the team" pressure, doing work that used to take 10 people.
- She filled a full week of customer meetings right after the conference and argues for talking to users before hiring.
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