Arjun Balakrishnan

Arjun Balakrishnan came to poker from data work. He spent his early career building and validating statistical models in industry, which is where he learned the habit that shapes everything he writes here: before trusting a model's output, understand what it was allowed to assume.

He now studies solver solutions full time, and his work is mostly methodological. How bet-size trees and board subsets change a solution. How much of a solver's output survives being simplified into something a human can actually execute at the table. Where two solvers given the same spot disagree, and why. What an ICM model is really claiming when it converts chips into money. He runs large batches of simulations and reports what the aggregate says, including the times the aggregate is boring.

Balakrishnan plays, but does not describe himself as a strong player and never uses his own results as an argument. His position is that solver output is evidence about a game rather than instruction for a table: it assumes an opponent who does not exist, and the interesting work is deciding how far to deviate from it against opponents who do. He is critical of content that presents a single solver run as settled truth, and he would rather say a question is currently unanswered than produce a confident number.

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