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Watching the Generative AI Hype Bubble Deflate

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arxiv 2408.08778 v1 pith:UESAIBBF submitted 2024-08-16 cs.CY

Watching the Generative AI Hype Bubble Deflate

David Gray Widder , Mar Hicks This is my paper
classification cs.CY
keywords generativebubblebusinesscompanieshypeaddedamplifiedbecame
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Only a few short months ago, Generative AI was sold to us as inevitable by the leadership of AI companies, those who partnered with them, and venture capitalists. As certain elements of the media promoted and amplified these claims, public discourse online buzzed with what each new beta release could be made to do with a few simple prompts. As AI became a viral sensation, every business tried to become an AI business. Some businesses added "AI" to their names to juice their stock prices, and companies talking about "AI" on their earnings calls saw similar increases. While the Generative AI hype bubble is now slowly deflating, its harmful effects will last.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power

    cs.CY 2026-04 unverdicted novelty 5.0

    AI discourse employs strategically polysemous terms that blend technical precision with anthropomorphic implications, enabling glosslighting that sustains hype and deflects scrutiny.

  2. "If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace

    cs.HC 2026-02 accept novelty 5.0

    Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.

  3. Segment Transformer: AI-Generated Music Detection via Music Structural Analysis

    cs.SD 2025-09 conditional novelty 4.0

    A two-stage transformer framework classifies AI-generated music from short clips and beat-segmented full tracks, reporting 99.9% accuracy on SONICS without releasing code or ablations.

  4. Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead

    cs.LG 2025-07 unverdicted novelty 4.0

    Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.