Reply to "ChatGPT is NOT Bullshit"

I asked ChatGPT-4o to come up with a Reply to its own Rebuttal of the Hicks et al paper "ChatGPT is bullshit."

This is what it came up with.

It did quite well.

Again.

Reply to the Rebuttal "ChatGPT is NOT Bullshit"

The rebuttal “ChatGPT is NOT Bullshit” raises important points regarding the capabilities of ChatGPT, but it overlooks some key aspects of what makes calling it “bullshit” particularly appropriate in philosophical terms.

Firstly, the argument that ChatGPT can produce accurate information is acknowledged. However, this does not contradict the claim that it bullshits. As Frankfurt (2005) explains, the essence of bullshit is indifference to truth. ChatGPT, by design, does not know whether what it produces is true or false—it generates content based on patterns from its training data, without any concern for the veracity of its output [1]. This is precisely what differentiates it from intentional lying: it is not attempting to deceive, but it is indifferent to whether its statements are factual.

Secondly, the rebuttal draws a comparison between ChatGPT and tools like calculators or search engines, implying that all technologies are similarly indifferent to truth. This analogy falls short. Calculators are designed to perform precise mathematical operations, and search engines, while not perfect, use algorithms to prioritize relevance and factuality to a degree. ChatGPT, on the other hand, is focused on generating human-like text that appears coherent rather than on providing factual or verifiable information [2]. Therefore, calling its inaccuracies “bullshit” does not imply intentional deception but rather highlights its underlying design—a focus on fluent language generation without any built-in truth filter.

Furthermore, the concern about anthropomorphizing AI by describing its output as “bullshit” misunderstands the critique. Describing it this way doesn’t ascribe human-like intent to ChatGPT, but instead addresses its functional characteristics. It produces content that often mirrors human conversation, and while it may sometimes be accurate, this accuracy is incidental rather than purposeful. The term "bullshit" emphasizes the model’s indifference to truth, aligning with the concept that, even when correct, its outputs are generated through mechanisms indifferent to factual accuracy [3].

In conclusion, while the rebuttal rightly acknowledges the sophistication of ChatGPT and its utility in many applications, it misses the core philosophical critique. The label “bullshit” is not an attack on its usefulness but an honest description of its relationship to truth. ChatGPT is designed to generate text that seems plausible, whether it is true or not, and this is why calling its inaccuracies “bullshit” is both appropriate and necessary for a clear understanding of its capabilities.

References:

1.??????? Frankfurt, H. (2005). On Bullshit. Princeton University Press.

2.??????? Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. DOI: 10.1145/3442188.3445922.

3.??????? Marcus, G., & Davis, E. (2020). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon Books.