Options Trading: 3D Chess in Action

In this episode of The Build, Cameron Rohn and Tom Spencer dissect options trading as a form of 3D chess and unpack its implications for AI-driven developer tooling and startups. They begin by mapping the problem: how options strategies require layered decision-making and how agents can model those layers more effectively than simple chat interfaces, referencing concrete examples like long/short positions and multi-step hedging logic. The conversation then shifts to AI development and tools, with technical discussion of Langsmith for experiment tracking, MCP tools for multi-component pipelines, and deployment patterns on Vercel and Supabase. They detail developer workflows that combine vector stores, fine-tuned models, and observable pipelines to reduce latency and increase repeatability. They explore building in public strategies and entrepreneurship insights, advising founders to iterate publicly while instrumenting usage data, monetize via API and tooling tiers, and cultivate open-source communities to accelerate adoption. Technical architecture decisions surface throughout: modular agent design, state management, caching strategies, and trade-offs between serverless deployment on Vercel and managed databases like Supabase. The episode closes with practical takeaways for developers and entrepreneurs, urging a focus on composable architectures, measurable agent behaviors, and transparent public iterations as the fastest path to product-market fit and responsible AI-enabled trading tools.