Singaporean photographer Zhang Jingna has gone public with her campaign to stop AI companies from scraping artists' work for training data without permission or payment — a fight that goes directly to licensing revenue for working photographers.

Zhang, one of the most internationally visible photographers based in Singapore, is pressing the case that images pulled from the web into AI training sets represent uncompensated use of copyrighted work. For full-time shooters whose income depends on licensing, syndication and commissioned reuse, the underlying business question is blunt: if a model can reproduce a stylistic approximation of a photographer's portfolio, what happens to the market for the original?

The Straits Times reported her position under the headline "Singaporean photographer Zhang Jingna fights to keep art safe from AI scrapers," framing her as a leading voice among artists confronting generative-AI companies over training data harvested from online portfolios, social platforms and image archives.

Why does this matter to working photographers?

The scraping dispute sits at the intersection of copyright, licensing and client work. Photographers routinely publish images online to attract commissions. Those same published images are, according to artists' complaints in multiple jurisdictions, ingested into training corpora without consent, credit or royalties.

Unlike a stock license or a usage fee, training-set ingestion pays the creator nothing and offers no opt-out in most current systems. Zhang's stance signals that established professionals — not just hobbyists or illustrators — are now treating AI training data as a rights and revenue issue worth fighting publicly.

What is at stake for the licensing market?

The core economic risk is substitution. Generative models trained on a photographer's body of work can output images in a similar aesthetic at near-zero marginal cost. That undercuts the pricing power of the original creator across editorial, commercial and advertising assignments.

It also raises due-diligence questions for agencies and clients: was the model behind an AI-generated asset trained on licensed material, or on scraped work? That uncertainty is already reshaping how image buyers and platforms write their terms.

A broader artists' movement

Zhang is not alone. Her campaign runs parallel to lawsuits and public protests by illustrators, photographers and other visual artists worldwide, who argue that mass scraping for AI training is commercial use that should require consent and compensation. Regulators in several markets are still settling whether such scraping falls under existing copyright exceptions or demands a new licensing framework.

For photo businesses, the practical takeaway is immediate: audit where your portfolio lives online, review the terms of the platforms hosting it, and watch for opt-out mechanisms or licensing schemes as they emerge.

Zhang's public fight suggests the scraping debate will be settled not by sentiment but by creators willing to stake their names on enforcement — and by how courts and platforms respond to them in the coming licensing cycle.