Is ChatGPT’s New Shopping Research Solving a Problem, or Creating One?

When OpenAI announced its new shopping search capabilities, I took the news with a grain of salt (perhaps the whole shaker).
For the past decade, we have watched the slow evolution of traditional search engines. What began as tools for pure information discovery gradually morphed into ecosystems dominated by SEO-optimized content and sponsored results. My initial fear with ChatGPT’s update was simple: Are we seeing the beginning of a similar shift? Is the purity of the “reasoning engine” being diluted by the necessity of commerce?
After testing the new shopping integration, the results suggest that we are at a pivotal moment in the user experience of Generative AI, one that requires an open discussion about what we actually want these tools to be.

The “Vacuum” Paradox

The defining characteristic of Large Language Models (LLMs) is their ability to handle nuance. When we interact with ChatGPT, we expect a Socratic dialogue. We expect the AI to ask clarifying questions to narrow down our intent.
To test this, I entered a simple prompt: “I want to buy a vacuum.”
I anticipated a conversation, questions about my home’s square footage, my floor type, or my budget. Instead, the conversational nuance was replaced by a display that felt familiar: a grid of product photos, names, prices, and direct links to retailers.

While efficient, this experience felt like a regression. It mirrored the “keyword search” experience of Web 2.0 rather than the “intent-based” promise of GenAI. It replied to my prompt, but it stripped away the intelligence.

When “Research” Becomes a Filter

Scrolling down, I engaged with the new feature in a call to action: “Research the best vacuums.”
This is where the user experience (UX) friction became most apparent. Rather than synthesizing data or comparing technical specifications in a chat format, the tool presented a polling interface designed to filter results.

The experience is oddly time-sensitive; pause too long to think or drink water, and the screens will skip forward, dumping you back into a list of product cards.
The interface presents products with a binary choice: “More like this” or “Not interested.” It offers brand names and price tags, but virtually no information to help the user actually make a choice.

For a user seeking genuine research, being presented with a list of brands and prices without deep comparative analysis feels like a missed opportunity.
It raises a question: If I wanted to filter products by price and brand, wouldn’t I use a traditional retailer? The value proposition of Gen AI should be synthesis, not just aggregation.

The Tension Between Reasoning and Revenue
This update highlights the inevitable tension facing major AI companies: the balance between user utility and business sustainability.
As OpenAI scales, the pressure to demonstrate revenue models to investors is natural. However, there is a risk in prioritizing transactional features before the core product, reasoning and logic, is fully matured. By introducing a shopping experience that feels closer to a “click-through” engine than a “knowledge” engine, the platform risks blurring its own identity.
Is ChatGPT a research partner that helps me think? Or is it a shopping assistant trying to speed me to checkout?

A Call for “Smart” Shopping

To be clear, I believe there is a place for shopping within AI. But the execution matters.
A truly Generative AI shopping experience shouldn’t just list products; it should understand the user. It should read between the lines of a prompt to understand that a user asking for a vacuum might actually be solving a problem about pet hair or allergies.

The current iteration feels like a beta test of a business model rather than an evolution of intelligence. As we move forward, the hope is that OpenAI will refine this tool to prioritize the “Chat” over the transaction. We don’t want it to be just another place to see ads. We need a better way to make decisions.

About the Author

Viviane Mendes is a growth strategist and innovation leader with more than 20 years of experience driving technology-enabled transformation across global markets. She has led initiatives integrating AI-driven strategies, digital transformation, and scalable business innovation for companies such as PSINet, MP3.com, Match.com, UOL and Best Buy Canada, and founded Vitrinepix, one of the first print-on-demand e-commerce platform, later acquired by Spreadshirt. Committed to lifelong learning, Viviane is now focusing on applying emerging technologies to foster digital literacy, responsible AI adoption, and positive human impact.