While using Google Nano Banana for listing images provides creative advantages, it exposes sellers to serious intellectual property risks and potential customer distrust.
In the crowded Amazon marketplace, brands that fail to produce a continuous stream of fresh visual content risk appearing stale and losing customer attention.
However, the budget and time required to constantly refresh creatives are immense, leaving many sellers unable to keep up with competitors who seem to have an endless supply of new lifestyle images.
The release of Google’s Nano Banana offers a potential solution: limitless visual creation from a simple text prompt. With 49% of marketers worldwide already using AI daily for image generation, according to data from Canva and Morning Consult, could this tool level the playing field for sellers?
Breaking Down Google's New 'Nano Banana' Image Tool
Graham Barlow, TechRadar Senior Editor"If you want something that's very quick and gets the job done in the most realistic way possible, then for me, Gemini's Nano Banana is the clear winner."
In a significant update for digital marketers, Google launched Gemini 2.5 Flash Image or more fondly called Nano Banana. The new platform is a state-of-the-art model designed for both generating and editing high-quality images.
This version introduces several advanced features aimed at providing users with more powerful creative control. These new capabilities are particularly relevant for sellers needing diverse and specific visual assets.
Key features of the Nano Banana model include:
- Image Blending – It can merge multiple images into a single, cohesive picture.
- Character Consistency – The tool can maintain the look of a person or product across a series of images for consistent storytelling.
- Natural Language Editing – Users can make targeted transformations to any part of an image using simple written commands.
- Knowledge Integration – It uses Google’s world knowledge to generate and edit images with greater accuracy and context.
This release directly responds to user feedback on the previous version, Gemini 2.0 Flash. While users appreciated the speed and cost-effectiveness of the earlier tool, they requested higher-quality images and more precise editing functions.
The new model is available immediately for professional and business applications. Developers can access it through the Gemini API and Google AI Studio, while enterprise customers can use it via Vertex AI.
Google has also outlined a clear pricing model, charging $30.00 per one million output tokens. This structure makes each image generation highly affordable, costing approximately $0.039 based on the model’s usage of 1,290 tokens per image.
Real-World Application: From 3D Renders to Realistic Customer Photos
In his LinkedIn post, Jon Elder wrote that Google literally broke the internet last week with the launch of Nano Banana. The tool is not specifically designed for Amazon sellers, but millions of marketplace entrepreneurs are expected to adopt this game-changing technology.
Elder demonstrated the tool’s capabilities using supplement brand Birdman Health, which has over 33,000 Amazon reviews but needed fresh UGC content. Instead of hiring influencers or organizing expensive photo shoots, he transformed the product’s existing hero image into realistic user-generated content using specific AI prompts.
The AI converted a basic 3D product render into a photorealistic gym selfie featuring a muscular male user. The level of detail and realism significantly exceeded expectations, creating believable real-world scenarios from artificial product imagery.
When AI Images Backfire with Amazon Shoppers
A research published on Science Direct reveals that customers actively avoid services advertised with AI-generated images compared to real photographs. The rejection becomes more pronounced for high-involvement purchases and pleasure-seeking products, creating a significant challenge for Amazon sellers considering AI imagery.
Product Category Risk Assessment
The research distinguishes between utilitarian products (focused on functionality) and hedonic products (emphasizing pleasure and emotional satisfaction). Amazon sellers in beauty, supplements, luxury items, and experience-based categories face higher consumer skepticism with AI-generated images.
Categories focused on practical solutions experience less customer resistance to AI visuals. Sellers must weigh cost savings against potential conversion rate impacts from reduced consumer trust.
Strategic Implementation Guidelines
Academic findings suggest sellers should strategically choose between real and AI-generated images based on product characteristics and purchase involvement level. High-stakes purchases requiring significant research benefit from authentic photography that builds credibility.
Key Benefits:
• Automated content creation at scale with reduced production costs
• Enhanced targeting capabilities for specific consumer segments
Consumer Trust Risks:
• Perception of lack of authenticity leading to lower conversion rates
• Reduced consumer trust in AI-generated product imagery
• Potential negative impact on high-involvement purchasing decisions
AI Missteps Highlight the Risks of Image Generation
An ABC News article featured one of the potential dangers of using AI images and videos, showcasing a major error by an international retailer. Fast-fashion giant Shein faced intense criticism after it used the likeness of Luigi Mangione, a man accused in a high-profile murder case, to model a shirt.
The company promptly removed the image from its website and launched an investigation into its internal processes. Shein stated that a third-party vendor supplied the image and that it would take appropriate action against them.
While official analysis could not confirm if the image was AI-generated, an expert from the University of Maryland suggested it was likely a hybrid creation. It appeared to be a real photo of Mangione’s face superimposed onto an AI-generated body.
The key evidence pointing to AI was the depiction of the model’s hand, which was described as looking unnatural and malformed. Such distortions in complex features like hands and fingers are recognized as common flaws in many AI-generated images.
The Shein case is an extreme example of a broader trend that has seen other major brands face public scrutiny. Companies like J. Crew and Coca-Cola have also drawn criticism from customers regarding the authenticity of their AI-powered advertisements.
The Legal Risks Sellers Face When Using AI-Generated Listing Images
According to PatentPC, AI-generated content without meaningful human input may not qualify for U.S. copyright protection, leaving Amazon sellers vulnerable. The U.S. Copyright Office will not register fully machine-generated works, meaning competitors could potentially copy AI-created listing images without legal consequences.
Training Data and Infringement Risks
AI systems like Nano Banana train on massive datasets containing copyrighted materials that could inadvertently appear in generated images. Sellers may unknowingly publish product photos resembling protected content, creating infringement liability without realizing the connection.
Different AI platforms have varying ownership terms, and sellers must review Google’s licensing agreements before commercial use. Third-party vendors add complexity, as ownership transfer may not be clearly defined in contracts.
Protection Strategies
Key Safeguards:
- Save prompts and editing history to prove human creative input
- Review AI-generated content before publishing
- Document tools used and modification levels
- Create clear vendor contracts regarding AI content ownership
While using AI tools for generating e-commerce creatives appears innovative and cost-efficient, implementation must be executed flawlessly. Working with professionals like an Amazon agency that follows standard operating procedures on compliance and accountability can help prevent costly legal issues.
Amazon sellers should treat AI as a creative assistant requiring substantial human input rather than an autonomous content creator. This approach strengthens ownership claims while reducing the risk of producing unprotectable content that competitors could freely copy.
CXL"Research reveals that 67% of consumers consider clear, detailed product photos more important than product descriptions or customer ratings when making purchase decisions."
