If Bloomberg, Claude, and Netflix Had a Baby, It Would Be Moshi
Anekha Sokhal explains why Moshi uses recommendation systems and connected graphs to surface hidden commodity risks.
The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi
With swyx, Anekha Sokhal · Sunday, October 25, 2026
Former commodities trader and Moshi founder Anekha Sokhal sits down with swyx at AI Engineer NYC to explore how AI-native systems are modernizing commodity trading. They discuss combining the strengths of Bloomberg, Claude, and Netflix-style recommendation engines, resolving conflicting market intelligence, and the mounting organizational pressures on AI engineers.
Key takeaways
- Former commodities trader Anekha Sokhal built Moshi by pairing Bloomberg market data depth with Claude text comprehension and a Netflix-style recommendation engine. Read more →
- Executive teams are cutting engineering allocations under the assumption that AI tools allow one developer to build systems that previously required ten people. Read more →
- Most software pipelines average conflicting data feeds into a single consensus number, which hides real market risk. Read more →
- Commodities trading desks make decisions by pairing public pricing feeds with strictly private operational data, such as tracking LNG tankers through the Strait of Hormuz. Read more →
4 articles from this episode
Moshi Founder on the Myth of the Solo AI Engineer
Anekha Sokhal explains why management is wrong to replace 10-person teams with one AI engineer, and why public hiring boards are broken.
Moshi Treats Disagreeing News Reports as Market Alpha
Anekha Sokhal explains why Moshi preserves conflicting reports from Bloomberg and Reuters instead of forcing naive AI consensus.
Moshi Ingests 300 Market Signals in Four Seconds
Anekha Sokhal explains how Moshi unifies public feeds and private trading desk intelligence using an n-dimensional graph.
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