I keep being accused of using AI. I’ve even been accused, more than once, of being AI-generated. So I owe you something better than irritation: an actual explanation of where I stand.
In this episode, I work through the real concerns: the scraping of artists’ work, the environmental cost, algorithmic bias, the fear of job displacement, the worry about deskilling, and argue that every one of them is a problem of how, not of whether. They are arguments for regulation, not for personal abstention. I talk about my own practice (yes, AI images sometimes; yes, Grammarly; no, not the writing or the thinking), about teaching at university in the middle of all this, and about why, as an anthropologist, I think this debate is really a debate about authorship and authenticity wearing a technological costume.
The question, in the end, was never if AI. It was always, only, how.
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REFERENCES
Cognitive offloading and deskilling
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Training data, consent, and copyright
Buick, A. (2025). Copyright and AI training data – transparency to the rescue? Journal of Intellectual Property Law & Practice, 20(3), 182–192. https://doi.org/10.1093/jiplp/jpae102
Dornis, T. W., & Stober, S. (2025). Generative AI training and copyright law. Transactions of the International Society for Music Information Retrieval, 8(1). https://doi.org/10.48550/arXiv.2502.15858
Algorithmic bias and discrimination
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31
Rosenthal-von der Pütten, A. M., & Sach, A. (2024). Michael is better than Mehmet: Exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring. Frontiers in Psychology, 15, 1416504. https://doi.org/10.3389/fpsyg.2024.1416504
Labour displacement and the reshaping of work
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Environmental cost of AI
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355
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00:00 My Stance on AI
01:05 Why I’m Addressing AI Accusations
01:39 AI, Authorship, Authenticity, and Moral Panic
02:12 “Used AI” Means Almost Nothing
03:09 AI as Research Aid vs AI as Plagiarism
03:59 Teaching Students How to Use AI Responsibly
05:20 Why I Use AI-Generated Images in Videos
08:03 Grammarly, Deep Research, and AI as Scaffolding
09:40 AI Art, Consent, and the Ethics of Training Data
10:43 Deskilling, Writing, and the Fear of Losing Thought
11:46 AI’s Environmental Cost: What Actually Matters
12:29 Algorithmic Bias, Auditing, and High-Stakes AI
13:42 Will AI Take Our Jobs? Displacement and Retraining
15:10 Why AI Ethics Needs Regulation, Not Purity
16:04 Responsible AI Use: Creativity, Consent, and Protection
17:10 The Real Question Is Not If AI, But How AI
17:54 Join the Conversation on Patreon
️ Copyright of Dr Angela Puca, in all of its parts ️
Music by Erose MusicBand. Check them out!
Published on June 14, 2026
