Q&A with Getty Images' Craig Peters on the company's generative AI tool, copyright issues, attribution for rights holders, deepfakes, disinformation, and more
Getty's entire brand is built on authenticity. CEO Craig Peters sat down with us at Code to talk about how the company is dealing with AI and disinformation.
Context & Ripple Effects
Getty’s AI positioning follows its launch of a Nvidia-backed image generator built on licensed Getty photos, making provenance and rights-holder treatment central to how it differentiates the tool from unrestricted image generation.
The discussion sits at the intersection of Getty’s commercial image library and the trust problem posed by synthetic media. Its later allegations that Stability AI copied Getty images for training show why the company treats training rights and attribution as operating issues, not just product messaging.
First-order effects
- Getty must make its generative-AI offering credible to customers and contributors by tying image creation to copyright treatment, attribution practices, and a clear authenticity posture.
- Customers using Getty’s tool receive a licensed-library-based alternative, while rights holders become directly affected by how Getty defines credit and compensation around AI-generated outputs.
Second-order effects
- Rival image generators and stock-media platforms face pressure to explain their training-data permissions and contributor economics, rather than competing only on output quality or price.
- As deepfakes and disinformation become part of the product conversation, buyers of commercial imagery have stronger incentives to seek provenance and safeguards from their media suppliers.
Third-order effects
- The stock-image market may increasingly split between systems that commercialize licensed content and systems whose training-data rights remain contested; the durability of that split depends on enforceable attribution and licensing models.
- Synthetic-media trust is becoming a control-plane issue: rights, provenance, and misuse protections can become product infrastructure rather than optional policy commitments.
The trend: This is one data point in the commercialization of generative media through licensed datasets, contributor governance, and provenance-led trust controls.