Discuss the challenges posed by generative AI to intellectual property rights and creative industries, with reference to recent global responses.

Generative AI can now compose, clone voices and produce near-studio-quality work, unsettling copyright law built on the premise of a human author. It threatens both the ownership of creative works and the livelihoods that depend on them, prompting a first wave of industry and statutory responses.

Challenges to intellectual property rights

• Authorship and ownership: the US Copyright Office held that AI outputs are protectable only where a human contributes sufficient expressive elements; mere prompting does not qualify [2]. India's Copyright Act, 1957 similarly presumes a human author, leaving purely machine outputs in a protection vacuum.
• Unauthorised training: the IFPI has flagged AI models trained on artists' music without rights-holder authorisation, raising consent and remuneration questions [1].
• Enforcement gap: voice cloning and style imitation evade traditional "substantial copying" tests, while absent provenance trails make infringement hard to prove.

Challenges to creative industries

• Displacement of earnings: an AI-assisted cover of Madonna's Like a Prayer by producer Josh Fawaz became Australia's most-played radio song and reached No. 4 on two ARIA charts, with its AI-generated vocals and drums disclosed only afterwards [1].
• Consumer deception and chart/stream manipulation distort discovery, royalties and reputational rewards.
• Devaluation of skilled creative labour discourages long-term investment in talent.

Recent global responses

• Industry self-regulation: ARIA barred wholly AI-generated tracks from its charts from 29 August 2026, requiring recordings to be "substantially human made" and free of manipulation concerns [1].
• Statutory transparency: the EU AI Act's Article 50 requires synthetic audio, image, video and text to be marked in a machine-readable form, applicable from August 2026 [3].
• India: the IT Amendment Rules, 2026 mandate prominent labelling and traceable metadata for synthetically generated information [4].

These responses converge on disclosure rather than prohibition — treating transparency as the bridge between innovation and creators' rights. A durable settlement needs licensed, remunerated training datasets, statutory clarity on AI-assisted authorship, and interoperable provenance standards, so that technology augments human creativity rather than displacing it.