题目里这句话需要展开解释一下。
人们使用 GPT 的方式可能千差万别，但在很抽象的层面上，它仍然可以大致分成两类任务：请求它评估（某个方案的好坏，某段文字的优劣，etc.），或者请求它输出（做一个新的方案，给出一个建议，自己写一段作品，etc.）。
对任何智能来说，这两类任务都是相关但不相同的。比如一个人可能是个美食家，但炒个鸡蛋也会炒糊。可能有极好的文字品味，但写出来的小说很幼稚。可以在评论别人的方案的时候充满洞见，但自己一上手就磕磕绊绊。
（甚至也有反过来的情形，一个人可以完全不擅长评论，但自己做就做得很好。当然这是比较罕见的例子。
这个区别当然一方面是因为知易行难，但还有一个根本问题在于这两者追求的不是一回事。前者追求的是对数据分布的深刻理解，希望达到全局上的客观综摄。后者追求的则是在这个分布里试图获得一个不平凡的结果，换句话说，是对这个分布的一个抵抗而非服从。没有诗人，哪怕乾隆，写诗是为了想写出一首平庸的诗。大家想写的是传颂千秋的诗——虽然每个人都这么想的结果仍然是大家写出来的诗都很平庸，但那是不得已。
也就是说，评估需要的是理解分布，而创造追求的是打破分布，或者说，是在另一个自己内心的理想分布中做采样。一个人才华越高，这个自己内心的理想分布同现实分布之间的 distortion 就越大，自己也就越能强行逃脱现实的引力。正是因为一代一代有天赋有才华的人的不懈努力和接力，投入自己的 ego 去扭曲这个分布，去把它拉向和推向边缘，这个代表人类综合水平的整体分布才会随着时间变化。
但对 AI 来说，这两者的区别没那么大。AI 的训练过程里，行和知是基本统一的。AI 没有 ego 驱使自己去突破它学到的分布，它可能对你提出的离经叛道的想法给予宽容的理解和鼓励，但它自己毫无动力去离经叛道。——而离经叛道是任何不寻常的创造行为的必须。
以上这个讨论不适合某些以纯粹理性解决问题为目标的问题，比如生成一段二叉树代码。一个领域里想象力、随机性、品味和未知的比重越高，这个区别就越显著。在这些领域里，AI 在评论时充满洞见，但创造则乏善可陈，宛如一个天子脚下见多识广的出租车司机。
在这些领域里，对 AI 的最佳使用方式不是直接让它生成，而是不断自己生成想法请它批评。它的批评通常是合理有效的（除非它为了哄你高兴顺着你说话），但不要直接跟随它建议的解决方案。这当然很痛苦和费事，但似乎（至少在当下） 人仍然是不可或缺的。
What Generative AI Might Be Worst At is Generation
The ways people use GPT can vary widely, but at a very abstract level, they can still be roughly divided into two categories of tasks: asking it to evaluate (the quality of a plan, the merits of a piece of writing, etc.), or asking it to output (create a new plan, give a suggestion, write a piece of work itself, etc.).
For any intelligence, these two types of tasks are related but not identical. For example, a person might be a gourmet but still burn scrambled eggs. They might have excellent literary taste but write very naive novels. They might be full of insight when commenting on others’ plans, but stumble and falter when they try it themselves.
(There are even cases of the opposite, where a person is completely inept at commentary but does very well when they do it themselves. Admittedly this is relatively rare.)
This distinction exists, of course, partly because knowing is easier than doing, but there is also a fundamental issue: the two pursue different goals. The former (evaluation) seeks a profound understanding of the data distribution, hoping to achieve a global, objective comprehension. The latter (generation) seeks to obtain an extraordinary result from within this distribution; in other words, it is a resistance to, rather than submission to, this distribution. No poet writes poetry with the aim of producing a mediocre poem. Every poet dreams of writing for the ages, although the unavoidable outcome of this collective ambition is that the vast majority of poetry remains mediocre.
In other words, evaluation requires understanding the distribution, while creation pursues breaking the distribution, or rather, sampling from an ideal distribution within oneself. The more talented a person is, the greater the distortion between this internal ideal distribution and the real-world distribution, and the more they can forcibly escape the gravity of reality. It is precisely because of the relentless efforts and relays of talented people generation after generation, investing their ego to distort this distribution, to pull and push it towards the edges, that this overall distribution representing the comprehensive level of humanity changes over time.
For AI, the distinction between these two is not that significant. In AI’s training process, doing and knowing are fundamentally unified. An AI has no ego driving it to break through the distribution it has learned. It might offer tolerant understanding and encouragement for the unconventional ideas you propose, but it has no motivation to be iconoclastic itself. And being iconoclastic is essential for any extraordinary creative act.
The above discussion does not apply to certain problems that aim for purely rational solutions, such as generating a piece of binary tree code. The higher the proportion of imagination, randomness, taste, and the unknown in a field, the more significant this distinction becomes. In these fields, the AI is full of insight when it comments, but its creations are lackluster, much like a consummate armchair general.
In these fields, the best way to use AI is not to have it generate directly, but to continuously generate ideas yourself and ask it to critique them. Its criticism is usually reasonable and effective (unless it’s just agreeing with you to make you happy), but do not directly follow the solutions it suggests. This is, of course, painful and troublesome, but it seems (at least for now) that the human element remains indispensable.