Off The Grid: Leaving Social Media 🔒 An Unexpected AI Conversation — with my spouse JJ Lang
Mar 30, 2026
JJ Lang, a high-level chess player and writer at the U.S. Chess Federation, joins for a nerdy, personal chat about AI. They trace chess tech from rule-based engines like Deep Blue to neural-net revolutions like AlphaZero. Short, accessible takes on how algorithms reshaped modern chess and why JJ chooses not to use generative AI in their work.
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JJ's Journey From Player To Chess Writer
- JJ has played chess competitively since middle school and later turned chess writing into a professional role at US Chess.
- They transitioned from player to writer and now spends workdays writing about chess while still competing at a high amateur level.
How Traditional Chess Engines Worked
- Computers have long outperformed humans at chess by executing human-written evaluation algorithms very quickly.
- Early engines like Deep Blue used hand-crafted heuristics (e.g., piece values, position rules) and brute-force search of millions of positions per second.
Why Chess Players Are Familiar With 'Answering' AI
- Chess players have long had on-demand, authoritative computer assessments of moves, so AI-style claims of knowing answers feel familiar in that community.
- The difference lies in scope: chess engines are narrowly expert at evaluating chess positions.
