This chapter explores the interplay between AIAI‘s representation in academic literature and its discursivediscursive construction by three AI platforms, namely ChatGPTChatGPT, GeminiGemini, and HuggingChatHuggingChat. These representations influence public perceptionspublic perceptions, expectations, and the development and regulation of AI. Employing the methodological frameworks of Critical Discourse AnalysisCritical Discourse Analysis and Corpus LinguisticsCorpus Linguistics, we analyse two datasets: a corpuscorpus of 25 academic publications and conference proceedings that explore the conceptualisation of AIAI in research and educationeducation, and 16 dialogic exchanges (each consisting of a question and its corresponding answer) conducted with the AI platforms previously mentioned. These questions were specifically crafted to elicit the platforms’platforms’ constructed self-perceptionsself-perceptions and discursivediscursive patternsdiscursive patterns. The academic corpus reveals a shift from framingframing AI as a hazard to exploring its potential for advanced inferential capabilities and reasoningreasoning. Conversely, the AI-generated corpus demonstrates improvements in logical-abstract reasoninglogical-abstract reasoning and increasingly natural interactions, with platforms exhibiting criticalcritical, balanced self-descriptions influenced by humanhuman interlocutorsinterlocutors‘ tone and phrasing. In conclusion, the discoursediscourse generated by academics has recently adopted a more technical and cautious tone, and AIAI is discursively framed as a moral and social agent requiring governance. These findings underscore the need for developers and institutions to controlcontrol and regulate the empowermentempowerment of AI systems before their generative capacities outperform those of their creators.