Needle in a needle stack: How AI causes semiotic inflation which causes experiential devaluation
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".