Needle in a needle stack: How AI causes semiotic inflation which causes experiential devaluation

Abstract

While most discussions of generative AI center on issues such as algorithmic bias and disinformation, we should also consider the quantitative sea change brought about by these technologies. Large language models can generate contents at a rate uncoupled from the human datasets they were trained on. Forecasts about such artificial outputs are difficult to make, but it seems clear that the word count and image/video bank of the internet will grow far beyond what humans can actually produce. Although this growth may seem benign, I worry that it results in a semiotic inflation capable of devaluing many human experiences. What connects quantitative you discover a diamond, you become rich. When you discover two diamonds, you become twice as rich. However, when you discover a stash of diamonds so large that diamonds outnumber gravel, you become poor and instantly make every diamond owner poorer too. Similarly, AI’s vast output risks devaluing experiences that are vital to human flourishing. Adopting a wide evolutionary vantage that gives weight to proven cultural adaptations, I suggest that some situations have natural sign-to-object ratios. Hence, being flooded with too many signs can go against the long-term interests of users. Critics of AI typically cling to features which computers allegedly cannot mimic. Semiotic inflation, however, enables us to accept the possibility of perfect AI counterfeits and still detect them en masse, via their negative experiential effects. Hardcoded features like Bitcoin’s 21 million token ceiling show that “[i]n contrast to the linguistic sign, the money sign cannot be reproduced in arbitrary quantity” (Bankov 2023: 117). Prompted by the rise of generative AI, we are about to realize that non-monetary signs also cannot be reproduced in arbitrary quantity. I thus draw parallels between healthy semiotic systems and the constraints governing viable monetary systems.