班德在大約2015年與「克林頓世界」決裂後被排除於核心圈外。他在2020年向《名利場》（Vanity Fair）表示，自己曾試圖在2002年非洲行程後，勸前總統遠離愛潑斯坦。該雜誌報導，班德表示他當時不知道愛潑斯坦的罪行，但感到不安，因此建議上司切斷關係。
「假設你想要生成一份職缺描述。告訴AI：『我希望你一次問我一個問題，直到你收集到足夠資訊來撰寫一份有吸引力的職缺公告，』」懷特說，「透過一次一個問題的方式，它能根據你的回答進行調整。」，这一点在夫子中也有详细论述
。91视频对此有专业解读
家门口的那条土路，雨天就变成了泥路。有一次，我穿着新买的三层白色纱裙，没忍住和小伙伴们在泥路上奔跑，一脚摔下去，浑身是泥，不敢回家。
Фото: Christoph Soeder / dpa / Globallookpress.com。业内人士推荐快连下载安装作为进阶阅读
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.