There seems to be a fundamental misunderstanding here that many LLM advocates fall prey to. No one claims (anymore) that LLMs are completely useless. We agree that these tools can boost productivity. But there is a fundamental difference between human creativity and the statistical processing of large amounts of data in an artificial neural network. The latter can yield new insights for the person operating the LLM. But if the insight was more or less contained 1-to-1 in the training data, this confirms the observation that the original contribution of LLMs consists mainly of unearthing relevant facts, but is otherwise very limited when it comes to synthetic judgments (in the Kantian sense).

If the claims made by LLM manufacturers regarding these systems' own capabilities repeatedly turn out to be little more than a hoax, critics of this practice shouldn't simply be dismissed as Luddites. Instead, we should acknowledge that, despite their remarkable capabilities, these systems ultimately deliver far, far, far less than what the snake-oil salesmen—cruising through the valley in their Koenigseggs—repeatedly promise to the public and investors. Anyone who still hasn't grasped this, even though it's so obvious, should urgently focus more on what is actually the case and less on what is being sold to us as the future. Anyone who thinks that technological leaps can simply be extrapolated linearly like this is just naive!