Disembodying CEO leadership through AI-assisted speechwriting
Bibliographic record
Abstract
This paper examines the use of Artificial Intelligence (AI) to generate a speech for the CEO of a hypothetical publicly traded coal mining company, intended to be delivered at that company’s annual general meeting. Using ChatGPT , we prompt this Large Language Model (LLM) to generate the CEO’s speech. We then analyze the speech using close reading, highlighting important language features, critical ethical considerations, and broader implications of AI-driven speechwriting for corporate leadership. By exploring four language features of the speech (metaphor, semantic tone, pronoun use, and readability) we find that the language pattern of the ChatGPT speech closely mirrors that of a speech prepared by an authentic human leader, inasmuch as it was customized to the controversial topic, the audience and setting, and possessed many “human-like” linguistic features. We highlight ethical concerns about the role of AI in leadership discourse and demonstrate the potential of AI to disembody executive authority. If AI’s contribution to speechwriting is not acknowledged transparently, CEO communication risks becoming a leadership charade. Any decision to use AI as a speechwriter should be reached after weighing the efficiency and convenience of doing so, against the potential erosion of authenticity in leadership communication.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".