RELATED TERMS: Promptography; Hallucination and Confabulation; World; World-Building
In recent years, Large Language Models (LLMs), such as ChatGPT, have become synonymous with the idea of artificial intelligence (AI). In order to train these chatbots, as Jack Apollo George (2024) notes, technology companies are employing human annotators or ‘data quality specialists’. The companies and the LLMs need examples of the kind of writing that the model will then emulate.
George points to two levels of irony in the situation as it currently stands. At the more immediate socio-economic level the irony is that the LLMs were developed in order to automate the task of writing. The better such models become at writing, the more rapidly the careers of human writers will decline and perish. Working as a digital annotator to improve the capacities of LLMs may be viewed as an exercise in self-destruction.
At the axiological level, LLMs as a socio-technical phenomena rewards and places a high value on language and writing. The irony here is that it simultaneously devalues writing as a human endeavour by viewing it as inferior and in need of improvement through ‘automating’ it.
In this latter case, it may be said to engage the logic of the supplement, as raised by Derrida (1976, 1981) and discussed by Nancy (2013) and Stiegler (2020). As Nancy explains, Derrida inscribes a twofold value into the logic of the supplement: as a potential, AI ‘supplements’ (human) writing by ‘adding to’ the practice of (human) writing but at the same time ‘supplants’ (human) writing by claiming a greater or superior access to the written archive as literary ‘memory’. Whether or not any one particular person ‘uses’ AI ‘to write’, writing as a ‘whole’, or as a body of texts, remains irrevocably changed.
Nancy and Stiegler talk of technology rather than writing, but here we are displacing the notions of writing and technology onto that of ‘design’.
Thus, displacing Derrida, Nancy and Stiegler, the twofold value in question is that design ‘adds to’ and supplants ‘nature’, here understood as that which is given, the existing, accepted, assumed-to-be-fundamental state of affairs. Firstly, design comes to supplant or take the place of the given wherever what is given as the state of affairs does not provide certain ends, for example, they do not provide a house or a bed. Secondly, design comes to supplement the given when it adds itself onto the ends and means of what is given, for example, enhancements of housing as shelter and dwelling or aids to sleeping through ergonomic means, the social practices (dwelling, sleeping) by means of which artefacts, such as houses and beds, are framed or contextualised.
Nancy contends that the supplement and its twofold movement, of displacing and adding to, falls under the category of technology, artifice, or art but to confirm what was said above, it is argued here that the supplement falls under the category of design.
Nancy further elaborates that two conditions are necessary for this supplementing to take place: the given state of affairs must evince some characteristic lacks, for example, while (undesigned) shelter may be offered the affordances of fully designed house are not; and it must be possible for designs to be grafted or collaged (‘engineered’) onto the given, using the prevailing (‘natural’) materials and the forces.
For Nancy, what is at stake in these processes is sense. He argues that whereas we were in the habit of relating sense to an ultimate purpose or final end, whether that be one of history, wisdom, or salvation, in a world pervaded by designs we are discovering that ends are proliferating at the same time as they are constantly transforming themselves into means. Paraphrasing Nancy, it can be argued that the ‘lesson’ of design is that through design nature itself, from which design is descended, shows that nature is by itself devoid of an end, without a ‘why’.
Language and Image
The emphasis on language is also evident in AI image generators or generative image AI platforms, such as, for example MidJourney. In this case, it is the relationship between word and image that is at stake. In ekphrasis, a vivid description or poem is written in response to a visual work of art. In reverse ekphrasis, in which AI image generators engage, visual representations are created in response to the written word, as discussed in the post Promptography.
Large Language Models and (Superhuman) Intelligence
Yann LeCun, Turing Award winner and one of the pioneers of artificial intelligence, argues that LLMs are useful but crucially limited and constrained by language. Achieving human-level intelligence requires an understanding of how the physical world works in addition to the capabilities of language. To address this challenge, LeCun has developed an architecture called V-JEPA, a world model that aims to understand the physical world by learning from videos and spatial data, in addition to language. LeCun labels this kind of intelligence Advanced Machine Intelligence (AMI), a kind of intelligence that is able to plan, reason and have persistent memory, enabling it to rely on past experience and evaluations to guide its decisions, characterised as a kind of ’emotional’ response (Heikkila, 2026).
Given this stance, LeCun concludes that LLMs, if taken by themselves, are dead ends when it comes to developing ‘superintelligence’ or ‘superhuman’ intelligence.
Universities’ Strategies to Address the Issue of Artificial Intelligence
One of the most interesting approaches by universities to AI strategy that John Naughton (2026) has come across to date has been developed by the University of Chicago’s law school. This strategy has three strands.
The first involves developing AI-resilient teaching and assessment. The aim here is for students to be able to build their own critical thinking and writing skills, rather than outsource that work work to AI tools. For this purpose, their core course forbids laptops, tablets and phones in classroom; sets exams that are in-person with no internet or device access; and emphasises the Socratic method of teaching, which has been de rigueur in law schools for a very long time.
The second strand is to prioritise the crucial human skills that students will need, such as oral advocacy, judgment and dealing with clients.
The third strand focuses on teaching students to use AI responsibly and ethically, on the grounds that the technology is now unavoidable.
This approach is underpinned by, firstly, an institutional acknowledgment that denialism is not a viable strategic approach to AI; and, secondly, that the arrival of the technology means change has become inevitable. In short, universities have to rethink their pedagogy to make it both AI-resilient, on the one hand; and to harness the capabilities of the technology to enhance learning, on the other hand.
For a discussion of AI and universities in a UK context, see the report edited by Carden and Freeman (2025).
Strategies for AI in Design Practices and Design Education
In the context of design education, to take the example of one particular institution, the three main uses of AI at L’École de design Nantes Atlantique, as outlined by Michel, Patoizeau and Gouret (2026), are:
- as a creative tool, integrated into the project process to support methodology, research and the development of ideas;
- as an educational tool, to explore how learning and teaching design take place in the age of smart technologies; and
- as an integral part of the products and services designed, enabling the development of new features and enhanced experiences.
The L’École aims to develop students’ skills in three dimensions:
- technical skills for understanding and using AI tools;
- methodological skills for integrating AI into a structured creative process; and
- critical and ethical skills to examine data usage, data transparency and intellectual property.
Reports
Carden, G. and Freeman, J. (eds) (2025) AI and the future of universities. Oxford, UK: Higher Education Policy Institute. Available at: https://www.hepi.ac.uk/wp-content/uploads/2025/10/AI-and-the-Future-of-Universities.pdf (Accessed: 30 July 2026).
Jørgensen, T. E. and Phelan, C. (2026) Adopting AI that serves the needs and values of universities. Final report of the EUA Task-and-Finish Group on Artificial Intelligence. Brussels, Belgium: European University Association.
OECD (2023) Artificial Intelligence in science: Challenges, opportunites and the futire of research. Edited by A. Nolan. Paris, France: OECD Publishing.
Weil, D. (2025) Three years in: Reflections and considerations for the next chapter of AI in higher education, EDUCAUSE Review, (26 November). Available at: https://er.educause.edu/articles/2025/11/three-years-in-reflections-and-considerations-for-the-next-chapter-of-ai-in-higher-education (Accessed: 27 July 2026).
References
Derrida, J. (1976). Of grammatology. Baltimore, MD: Johns Hopkins University Press.
Derrida, J. (1981) Dissemination. Translated by B. Johnson. London, UK: Athlone Press.
George, J. A. (2024), ‘If journalism is going up in smoke, I might as well get high off the fumes’: confessions of a chatbot helper. The Observer, 7 September. Available at https://www.theguardian.com/technology/article/2024/sep/07/if-journalism-is-going-up-in-smoke-i-might-as-well-get-high-off-the-fumes-confessions-of-a-chatbot-helper [Accessed 15 September 2024]
Heikkila, M. (2026). “Intelligence really is about learning”. Financial Times, 3 January, Life & Arts p.3
Michel, F., Patoizeau, M. and Gouret, S. (2026) The Impact of artificial intelligence on design, L’École de design Nantes Atlantique. Available at: https://lecolededesign.com/en/formations/impact-artificial-intelligence-design (Accessed: 27 July 2026).
Nancy, J.-L. (2013) Of Struction, Parrhesia. Translated by T. Holloway and F. Méchain, (17), pp. 1–10.
Naughton, J. (2026) Chicago University is laying down the law on their students’ use of AI. Observer, 26 July 2026, New Review, p.17 https://observer.co.uk/news/columnists/article/chicago-university-is-laying-down-the-law-on-ai
Stiegler, B. (2020) ‘Elements for a general organology’, Derrida Today, 13(1), pp. 72–94. doi: 10.3366/DRT.2020.0220.
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