AI Summary
5 min readThe AI Engineer conference in New York was a turning point for Anekha Sokhal, founder of Moshi. She arrived for her talk and ended up staying an extra week, filling her calendar with back-to-back meetings in New York and then Houston. The reason: she gave a talk about how her startup processes real-time data for commodity and energy traders, and the response was immediate. But the most surprising inbound did not come from traders alone. It came from AI engineers themselves, who told Sokhal they were feeling overwhelmed. Their managers had told them, “You are the team,” meaning they were expected to build systems single-handedly that would normally require ten people.
From Trading Floor to AI Startup
Sokhal’s path to founding Moshi began in quantitative finance. She studied mathematics, operational research, statistics, and economics at Warwick, then started her career at J.P. Morgan on the emerging markets desk before moving into energy derivatives. Trading commodities, she says, was always about breaking the market down into fundamentals, news, and physical events. She saw early that data was shifting and that something was about to happen. That conviction led her to leave J.P. Morgan, apply for a Fulbright scholarship to come to the U.S., and eventually found Moshi.
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What you'll learn
- 1 (00:00) **The "You Are The Team" Pressure** - Anekha explains that the biggest post-talk feedback from AI Engineers was the overwhelming pressure from management to build entire systems alone.
- 2 (01:00) **From Math Nerd to Trader: The Origin Story** - Anekha describes her background in statistics, math, and economics at Warwick, and how it led her to a career in energy derivatives trading at JP Morgan.
- 3 (03:55) **The Fulbright Path to Founding Mochi** - Anekha explains how a yoga class and a side hustle in computer vision for jewelry led her to pursue a Fulbright scholarship to come to the US to build a data company.
- 4 (05:46) **What is Mochi? The Netflix-Claude-Bloomberg Analogy** - Anekha defines Mochi as the program she wished she had while trading, providing real-time insights for commodity and macro traders.
- 5 (06:16) **The Data Stack: Structured, Unstructured, and Private** - A detailed breakdown of the three layers of data Mochi processes to build its n-dimensional graph.
- 6 (11:10) **The Critical Role of Recommendation Systems** - The conversation shifts to why recommendation systems (RecSys) are the most underappreciated yet critical part of AI, especially for user adoption.
- 7 (13:50) **The Buy-Side Perspective: Data Quality and Integration** - The host shares his experience as a former hedge fund manager, questioning how Mochi handles noisy data and integrates with proprietary systems.
+ Full timestamped outline available in the app
Show Notes
Anekha Sokhal, CEO of Moshi, joins swyx to discuss building a recommendation engine for commodity traders, turning market data into useful signals, and designing AI that fits users’ workflows.
Watch: YouTube · Moshi · Anekha
0:00 AI Engineer New York and “you are the team”
0:54 Meet Anekha
2:09 From mathematics to energy derivatives
3:46 Fulbright and becoming a founder
5:45 What Moshi does
6:10 Market data
9:03 Bloomberg and market workflows
10:03 Bloomberg, Claude and Netflix
12:03 From dashboards to workflows
15:03 Trusted sources and signal
17:13 Models and document extraction
18:20 AI Engineer New York
20:32 Customer meetings
23:54 Hiring and customers
25:55 swyx’s path into AI
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