The New Gatekeeper

ChatGPT Shopping Research, which quietly went live in November 2025, is not just another update from OpenAI—it's a potential shift in how we discover new products from now on.

To understand it, you have to stop thinking like a software engineer and start thinking like a retail owner.

Imagine the best salesperson you have ever met in a physical store.

That is what Shopping Research is. It is a built-in, personalized shopping assistant that costs near-zero. Go to Google or Amazon right now and search for "quietest cordless stick vacuum for a small apartment under ₹30,000."

What happens? You get SEO spam. You get products that are paid to be at the top. You get listings that are ranking because they shoved "Quiet" and "Cordless" into the title tag, not because they are actually quiet.

Then begins the manual labor: You open ten tabs. You read five different review sites to see if "quiet" actually means quiet. You cross-reference prices. You suffer.

ChatGPT Shopping Research ends the suffering.

It scans the entirety of the internet's knowledge—reviews, technical specs, Reddit threads, complaints, and product descriptions—to curate a personalized answer.

If you run the same Vacuum search in "Shopping Research Mode" on ChatGPT, the interface changes.

It asks smart, clarifying questions:

• "What type of flooring do you have?"
• "Do you have pets that shed?"

Based on your answers, it performs real-time research. It triangulates the perfect product based on the nuance you provided. Then, it enters a Feedback Loop. It presents options as a temperature check. "Is this closer to what you were looking for?"

Once you confirm, it generates a Personalized Buyer's Guide, a custom document built just for you, explaining exactly why these three vacuums fit your small apartment and your shedding dog.

If you enable Memory, it uses all the context it has on you for even more personalized recommendations.

If you told it three weeks ago that you hate polyester or that you are trying to be more eco-friendly, it applies that filter to your current search automatically. You don't have to give it context again and again. It learns you.

End-To-End Execution by Chatbots?

Right now, the system guides you to a link where you go to the brand's website to buy. But let's be real. We know where this goes. OpenAI has already signaled the future. Perplexity's "Comet" feature is already doing it—allowing you to check out directly through the chat interface.

The friction is disappearing. The "visit a website" step is being erased.

We are moving from a world of "Search, Scroll, Filter, Buy" to a world of "Ask, Confirm, Done." And for anyone in retail, that changes everything.

We need to zoom out for a second. We aren't just telling you to watch OpenAI. That would be too easy.

Every major tech giant is rushing to own this interface, but they aren't building the same thing. They are building three very different approaches to address the same problem.

Think of this as the high-touch, unbiased consultant. It runs on brutal logic. It loves constraints. "Under $100, fits in a small apartment must be eco-friendly."

It doesn't have its own marketplace. It has to source from everywhere. That makes it the ultimate brand blind researcher. It doesn't care about your brand loyalty; it cares if you actually match the specs.

This is the "Empire Strikes Back." Google has something OpenAI doesn't: The Shopping Graph. 50 billion products. Real-time inventory. Live pricing.

The threat here isn't just intelligence; it's breadth. Google is deep in the ad game. This is where the commercial war will be fought. If OpenAI is the "Organic" play, Google is the "Pay to Play" warzone.

This is the wild card. The vibe here isn't "Research." The vibe is "Hands-on-Keyboard." It doesn't just want to find the product; it wants to manage the cart and place the order for you. It is aggressive. It is fast. It is already colliding with legal boundaries (just look at Amazon's recent lawsuit against them). But make no mistake—this represents the frictionless end state.

You might be wondering how we can so confidently explain what is happening under the hood of a feature that just launched.

In late 2023 and early 2024, our team at Ionio worked with a client to build this exact vision. We built the prototype. We saw the potential. And we hit the wall.

The vision was identical: A user says, "I need a lightweight stroller for city sidewalks," and the AI asks, "Do you have a budget? Does it need to fit in a trunk?"

We cobbled this together using the best tech available at the time—open-source LLMs, custom scrapers, without using any LLM APIs.

This triggers a hard reset on everything we thought we knew about discovery.

You have probably heard the term GEO (Generative Engine Optimization) floating around LinkedIn for the last six months. It was pitched as the "New SEO"—the art of getting your brand cited when someone asks ChatGPT a question.

Until November, the playbook for GEO 1.0 was all about Influence Hacking.

We saw brands frantically trying to reverse-engineer the model's weights. They realized that LLMs trust "human" sources, so they started spamming Reddit and Quora. The goal was to trick the model into believing that everyone was talking about their product, so the AI would regurgitate a recommendation.

The problem here is that you were optimizing against a moving target.

Well, ChatGPT Shopping Research just killed that strategy as well (Saved your OPTIMIZATION time on Reddit tho).

Now we have officially moved from GEO 1.0 (Influence) to GEO 2.0 (Accuracy).

ChatGPT Shopping Research doesn't care how loud you are on Reddit. It cares about a metric we call the Product Accuracy Index. And as it becomes better other models will follow along.

Here is the shift: When a user asks for a "laptop under $1,500 with long battery life for coding," the model isn't looking for the most "popular" laptop. It is looking for the SKU that has the highest percentage match against those specific constraints. It scrolls through the infinite catalog of the internet and filters by data, not hype.

First, your product specs need to be Clean. If the data isn't clear and machine-readable, you don't even make the shortlist. You simply don't exist to the model.

Then there is the review trap. This is the part that has changed the most. In the old world, you could bury a few negative comments under a mountain of five-star ratings. That game is over. If a user specifically asks for "long battery life," and three verified reviews say your battery dies at noon, the model disqualifies you. It doesn't care about your average score. It cares about the specific truth.

It validates all of this against third-party editorial lists as well, not just for the link, but to verify you are legit. And finally, it checks if you can actually deliver it. If the price is wrong or you can't ship to the user's zip code, you become invisible. It is ruthless efficiency here.

This fundamentally changes the game for everyone reading this paper. The goal is no longer "How do I spam the model's favorite forum?"

In our first white paper, we coined a term for this: GXO (Generative Experience Optimization).

pictured, Agentic Commerce white paper #1

This is actually great news for the mid-sized players.

In the Google Ads era, the winner was usually the company with the deepest pockets who could outspend you on CPC. In the GXO era, you can beat a Fortune 500 competitor simply by having better, cleaner, and more honest data representation.

The platform that helps merchants win the "Product Accuracy" game is the platform that wins the market.

Before we dive into how ChatGPT Shopping Research rewires buying behavior, we need to take a quick detour into consumer psychology itself. 🤓…💀

Bear with me.

This might sound like theory, but it's the foundation for everything that follows. And here's a challenge while you read this: think about your own shopping habits. I'd bet 99% of every purchase you've ever made falls into one of these six categories.

I struggled to find anything that didn't.

For some purchases, there's no rhyme or reason. For others, the decision is made in your brain before the card ever comes out.

Each mode has different research intensity. And that research—historically—has been scattered across Google, YouTube, Reddit, WhatsApp, comparison sites. You've been the one stitching it all together across fifteen tabs.

AI agents enter as just another tab at first. But then they climb the ladder—from "one more opinion" to "the first place I check." Why? Because one conversation can synthesize what used to take ten tabs and three hours.

Here's how the shift plays out across each mode:

We have talked about technology. We have dug into the consumer psychology side of things. We are going to get to exactly what this means for your brand in a minute, but first, let's talk about the fallout.

If this shift is real (and the data says it is), it's going to break a few things. And it's going to make a few people very rich.

Here are the three massive impacts hitting the market right now.

Let's address this immediately.

In our previous drafts, we predicted this. And funnily enough, while we were writing this very sentence, OpenAI confirmed it. They are building an ad platform.

The math is simple. ChatGPT has 800 million users. Less than 5% pay for a subscription. They need to monetize the free tier.

But do not make the mistake of thinking these will be banner ads.

When you search on Google, you get ads based on keywords. They are targeted, sure. But they are often dumb. Now imagine an ad inside a conversation. The AI has context. It knows your constraints. It knows your history.

If you ask: "I need a laptop under $1,000 for a graphic design student," the AI doesn't just show you a banner for a Dell. It inserts a recommendation that fits the exact "graphic design" constraint. I feel the overall ROAS for ads would be way higher than any other platform simply because of the HYPER-TARGETING achieved by these chatbots.

If I were a Product Manager at Nosto, LimeSpot, or Criteo, I would be screaming at my product team right now. The race is on to build the integrations that let merchants plug their data directly into OpenAI's ad network.

The current filters—"Target San Francisco, Men, 25-34"—are dead. The new filters will be nuanced: "Target users asking about eco-friendly fabrics." The first marketing SaaS to unlock this "Hyper-Personalization" wins.

We are looking at this consumer shift from using search bars and manual research to completely conversation based product discovery.

Until now, online shopping was an act of manual labor.

You go to Google. You open Amazon. You open five different tabs. You check a blog. You watch a YouTube review. You check Reddit to see if the reviews are fake. This is Information Overload.

AI Chatbots kill this workflow. They compress five tabs into one answer. Consumers are already doing this. They are using Chatbots to narrow down the field before they ever click a link. But now, with features like "Shopping Research," the narrowing happens inside the discovery phase.

This is a nightmare scenario for Amazon.

Look at Perplexity's "Comet" feature. It allows users to buy directly through the chat. Amazon is already suing them. Why?

Because if the transaction happens in the chat, the user never sees the Amazon Ads. The user never gets distracted by "Sponsored Products." Amazon loses the eyeballs.

For brands, the stakes just got terrifyingly high. On Amazon, being the 14th result is okay. People scroll. On an AI Chatbot…umm.. there is no scroll. You get like 3-5 options max. You are either one of those three, or you are invisible. You are dead.

This is the new "Digital Shelf." It is smaller, tighter, and harder to get onto. This is both the biggest threat and the biggest opportunity for niche brands. You don't need to be the biggest; you just need to be the most accurate match.

The whole AI Shopping would just spoil consumers and increase expectations so high they won't be able to do it manually again (Just how we can no more write a simple email without GPT)

Comfort is a one-way street. Once you get used to a better way of doing things, the old way feels broken. As consumers get used to the personalized experience of ChatGPT, your static e-commerce website is going to feel ancient.

If a customer spends 30 minutes clicking filters on your site to find a product, they are going to leave. They will expect your site to talk back. They will expect your search bar to understand nuance.

If you don't offer an unbiased, conversational guide, they will just tab back to OpenAI to ask for one. Here is the good news for you: Decision speed is about to skyrocket.

The "Purchase Cycle" the time between "I want this" and "I bought this" is shrinking. This all happens as we start trusting the model more and more.

If a user asks the AI for a product, buys it, and it's actually good... their trust goes up. The next time, they ask again. It works again. Eventually, they stop checking.

They just buy. The "Research Phase" evaporates. We are moving toward a high-velocity commerce world where the AI acts as the verified stamp of approval.

There lies an important factor over here, Educating the consumer - The most underrated impact is that users will be smarter. When you use Shopping Research, the AI asks you questions. "You want a Wi-Fi router? Okay, what is the square footage of your home? Are the walls brick or drywall?"

Most users never thought about wall thickness. Now they do. The AI teaches the consumer what to look for. It highlights constraints the user didn't know they had. This leads to better decisions, fewer returns, and a consumer base that actually understands what they are buying.

Hang on, where did DoorDash come from??? lol. What has food delivery got to do with this? Well, "The DoorDash Problem" is a term coined by Nilay Patel, the editor-in-chief of The Verge, and now it's floating around the tech circles... So remember it! It is a term we will use to show the Existential Crisis for these legacy marketplace platforms. Let me explain...

Remember your local pizza place? You called them directly. You knew the owner. Then DoorDash inserted itself between you and the kitchen—and suddenly owned the customer, the data, and the intent. The pizza place became a ghost kitchen, sweating in the backend while the app captured all the value.

Right now, Amazon owns the interface. But when a user asks ChatGPT to "find the best headphones," the Agent takes control. It creates the shortlist. It picks the winner. It treats Amazon, Walmart, and Target as interchangeable backend utilities. It doesn't care which warehouse ships the box, as long as it arrives Tuesday.

This is an existential threat to Amazon specifically because of how they make money. Retail margins are razor-thin; their profit empire is built on ads—billions from brands paying to rank at the top of search results. But if I shop through ChatGPT, I never see the Amazon UI. No sponsored products. No upsells. Their highest-margin revenue stream evaporates.

Marketplaces now face a brutal bind: Block the agents, and users get annoyed—the Agent routes them to competitors who play nice. Embrace the agents, and they admit defeat as a commoditized backend.

This isn't theoretical. Amazon recently sued Perplexity over their "Comet" feature. They're not suing over copyright—they're watching a competitor try to DoorDash them in real-time.

Expect a war over the next twelve months: Amazon breaking bots, AI finding workarounds, and eventually a "partner or die" phase (Walmart's already there with OpenAI).

We have talked about technology. We have talked about psychology. We have talked about the "DoorDash" threat to Amazon. Now, we need to talk about you 🫵

If you run a D2C brand, or if you are a SaaS platform catering retail and ecom, or someone who manages retail strategy for a major player, the next three years are going to feel like a decade. We see this rollout happening in three very distinct waves, and you cannot afford to mistime any of them.

Let's be real about the timeline. The next six months are essentially a "hit and trial" period. It's going to be messy. Consumers are kicking the tires, testing these bots, and slowly climbing the ladder from "just curious" to "daily habit." But don't sleep on this, because that behavior hardens fast. Within a year, we are going to see a massive chunk of actual commerce volume happen entirely inside these chat windows—shifting from simple research to instant checkout. The agent stops being a tool you use before you buy, and becomes the place where you buy.

Fast forward three years, and the coup is complete. The AI owns the customer, and Amazon just owns the boxes. Agents like ChatGPT, Perplexity, and Gemini swallow the entire front-end of e-commerce, relegating giants like Amazon and Best Buy to backend fulfillment nodes—invisible utilities that just move packages. Unless these legacy platforms pull a miracle and fight unbiased agents with their biased ad platforms, they lose the interface war. And once you lose the interface, you lose the game.

Here is the insight that terrified our advisory board the most. We predict that Customer Acquisition Costs (CAC) are going to spike, but not for the reason you think.

Right now, you pay Facebook or Google to get a user to your site. They click the ad. They land on your page. They like the product. In the old world, they might convert.

In the new world, they pause. They open ChatGPT. They paste your product name and ask: "Is this brand actually good, or is it just good marketing?"

This is the "Second Opinion" Tax. Even if you win the ad auction, you have to win the interrogation that follows.

You paid for the click. You paid for the creative. You got them to the site. But you lost the sale because the neutral arbiter killed the deal at the one-yard line. It is infinitely easier for an AI to talk a user out of a purchase than it is to talk them into one. Rejection is an easy emotion. If your product has skeletons in the closet, your obsession with ROAS won't save you.

First, a warning. As this shift accelerates, a legion of agencies and consultants will descend upon you. They will sell you complexity because complexity bills by the hour. They will tell you to "train your own foundational model" or "build an in-house competitor to ChatGPT."

Do not do this. For 99% of brands, becoming an AI research lab is a distraction that will bleed your cash flow dry. Beware of false prophets selling you roadmaps for terrain they have never navigated. You do not need a GPU farm; you need leverage. The only "AI Strategy" that matters right now is making sure your existing stack—your PIM, your reviews, your feeds—is fluent in the language of agents. Don't build the Shopping Researcher; just make damn sure the Researcher can read you clearly.

This brings us to the "AI Slop" reality. The internet is about to get very messy. We are already seeing an explosion of AI-generated "best of" lists and fake reviews designed to game the models. But here is the counter-move: Radical Truth. The models are being trained to down-rank this sludge. They are hunting for high-integrity signals—structured schema, verified reviews, and consistent specs across the web. Your job isn't to out-spam the bots; it is to be the signal in the noise. While your competitors are trying to "hack" the algo with 10,000 AI-written blog posts, you win by having the cleanest, most machine-readable catalog on the market.

So, what is your actual move? It depends on your scale.