Generative artificial intelligence does not merely introduce another instrument into the history of writing; it separates activities that modern culture has traditionally compressed into the single figure of the author. Conceiving an idea, selecting material, researching, composing sentences, structuring an argument, revising language, producing visual representations, judging alternatives and accepting responsibility can now be distributed between human and computational agents. This article proposes the concept of the Delegated Author to describe this emerging condition. Rather than approaching generative AI through the increasingly inadequate opposition between "human-written" and "machine-generated" texts, it examines authorship as a divisible process in which human intention may precede machine execution and human judgement may subsequently select, reject or transform the machine's output. The argument develops from the twentieth-century displacement of the sovereign Author through Roland Barthes's Scriptor and Michel Foucault's author-function towards the contemporary Prompter: a figure capable not merely of recombining inherited language but of delegating part of the linguistic labour of recombination itself. The article tests this theoretical transition through a controlled journalistic experiment derived from an already published human cultural product. The original publication remains unchanged as a control, while a generative Model is permitted to restructure, clarify, compress and visually reinterpret the material. This asymmetrical design deliberately distinguishes machine generation from AI-assisted transformation: the Model receives a problem, research, conceptual direction and textual architecture that already exist rather than beginning from an empty page. The comparison therefore asks not simply whether AI can produce competent language, but what happens when linguistic execution becomes partially detached from intellectual initiation. Particular attention is given to the Paradox of Improvement: the possibility that optimisation may increase clarity, coherence and accessibility while simultaneously reducing stylistic irregularity, ambiguity and recognisable authorial voice. The article argues that generative AI fragments rather than abolishes authorship. The resulting division of creative labour redistributes composition and editing while leaving intention, critical selection, refusal and responsibility unequally distributed. In this emerging configuration, the decisive human contribution may no longer consist in physically producing every sentence. Authorship may increasingly reside in determining why a text should exist, what may be delegated, which machine transformations should survive, where optimisation must stop, and who ultimately answers for what is published.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
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