A whistleblower complaint alleges John Solly claimed to have stored highly sensitive Social Security data on a thumb drive. Solly and Leidos, his current employer, strongly deny the allegations. John Solly, a software engineer and former member of the so-called Department of Government Efficiency (DOGE), is the DOGE operative reportedly accused in a whistleblower complaint of telling colleagues that he stored sensitive Social Security Administration (SSA) data on a thumb drive and wanted to share the information with his new employer, multiple sources tell WIRED. Since October, according to a copy of his résumé, Solly has worked as the chief technology officer for the health IT division of a government contractor called Leidos, which has already received millions in SSA contracts and could receive up to $1.5 billion in contracts with SSA based on a five-year deal it signed in 2023. Solly's personal website and LinkedIn have been taken offline as of this week.

AI researchers have long focused on poker-like games as a testbed for environments characterized by multi-player dynamics, imperfect information, and reasoning under uncertainty. While recent breakthroughs have matched elite human play at no-limit Texas hold'em, the multi-player dynamics are subdued: most hands converge quickly with only two players engaged through multiple rounds of bidding. In this paper, we present Solly, the first AI agent to achieve elite human play in reduced-format Liar's Poker, a game characterized by extensive multi-player engagement. We trained Solly using self-play with a model-free, actor-critic, deep reinforcement learning algorithm. Solly played at an elite human level as measured by win rate (won over 50% of hands) and equity (money won) in heads-up and multi-player Liar's Poker. Solly also outperformed large language models (LLMs), including those with reasoning abilities, on the same metrics. Solly developed novel bidding strategies, randomized play effectively, and was not easily exploitable by world-class human players.