#96: Revisiting AI + Shopping (Part II)
Discovery commerce is a data problem, not a model problem
Author’s Note: Check out last week’s article for an analysis on the current landscape of agentic commerce and why OpenAI will face an uphill battle when building the AI layer for next-gen shopping. This week’s article will focus on how OpenAI can tackle discovery commerce.
Integrating AI into shopping is hard. Just ask Andy Jassy, CEO of Amazon:
“I think that [AI] has the chance to make it easier for customers to find what they want. If you know what you want it’s pretty hard to find a better experience than popping onto Amazon and searching and finding it. But the one place still where physical retail has some advantages in my opinion is the ability to go in, not know what you want, ask questions, refine those questions, have somebody point you to different things. And I think agents are going to help customers with that type of discovery” – Andy Jassy, being interviewed at the World Economic Forum in January, 2026. Original post from Juozas Kaziukenas here.
Five months ago may as well be an eternity in AI. But there hasn’t been a breakthrough moment in agentic commerce compared to what we’ve seen in areas like agentic coding. It’s not as though OpenAI’s GPT-5.5 has materially improved agentic commerce (in fact, OpenAI sunset their ChatGPT shopping module, Instant Checkout, back in March).
Let’s revisit Jassy’s quote. Particularly this line:
“But the one place still where physical retail has some advantages in my opinion is the ability to go in, not know what you want, ask questions, refine those questions, have somebody point you to different things.”
I believe what Jassy is referring to, and what is so difficult to replicate digitally, is the concept of discovery commerce. He’s not referring to the $7 latte you pick up at your local coffee shop (however, the prices of coffee in NYC are getting to the point where it may as well be a high ticket purchase). He’s referring to purchases where preferences are latent, evolving, or difficult for consumers to articulate. Typically, emotional or higher ticket transactions fall into this bucket, like buying a handbag or a car. But buying clothes can also fall under discovery commerce if the consumer is unsure of what they want to buy.
If Jassy says Amazon hasn’t cracked discovery commerce, then that means OpenAI may have a chance to catch up in the agentic commerce race. If I were advising OpenAI, I’d tell them to zone in one thing to accelerate their agentic commerce ambitions: the collection of offline data.
You’ve likely picked a theme over the past few months in Relentlessly Curious, and that’s the importance of physical, real-world data to support AI development.
When it comes to commerce, it’s no different. See, ~80% of commerce still happens offline, meaning in-person. For AI to revolutionize the shopping experience, it will have to offer a step-function improvement on driving to the mall, walking around and poking your head into a few different stores, trying a couple items on, and then making a purchase. As we chatted about last week, subscription offerings (whether they’re on Amazon or DTC) serve as a reasonable facsimile for low-stakes purchases. If consumers have already automated these purchases through subscriptions, it’s unclear how much additional value AI can create.
My anecdote last week covered my Banana Republic outlet mall success story. To make curated clothing suggestions, AI needs to understand my clothing style based on actual data rather than self-description. Because remember, I don’t even know what my style is, nor did I know what type of long-sleeved button-down shirt I wanted. If I could take a few pictures of my closet so AI has a sense of what I already own, it may be at a better starting point than the gibberish I came up with. It’s a classic GIGO (garbage in, garbage out) problem. For highly customizable, emotional purchases like clothing, more data will likely lead to better recommendations. In one line, preferences are best revealed through behavior, not language.
The elephant in the room is that shopping in person can be more productive than checking out online. It’s not a pleasurable experience to buy a few shirts online and then have to figure out how to return them when you realize the brand’s style doesn’t fit your body type.
Also, there are people who really enjoy the experience of shopping. Ever hear of the term “retail therapy”? Sure, this concept can apply online, but shopping in person and touching and feeling clothes or test driving a car can serve an emotional need.
What OpenAI will need to do is bring the real world to the AI experience.
I recommend OpenAI tackle categories one at a time, instead of tackling every shopping sub-industry. Start with fashion and apparel and build technology to capture and ingest someone’s wardrobe based on a few pictures. Have them take pictures and videos of their living space, to help with interior design for virtual furniture buying.
Investments in AR and VR will be critical to convert user-taken images into high-intent suggestions. Productize the process for one (large) industry and then move on to the next one. AR and VR are really data collection mechanisms, allowing your customers to create a structured data set in their own world.
They’re consumer preference extraction tools; bridging the gap between what you think your style is (in the case of fashion) and what it actually is. If you remember from You Are What You Buy (Part I), what you purchase says a lot about you. I’ll call back to the article for this helpful quote:
“In a world of nearly infinite choice, what we buy says a lot about us. Choice is not only a convenience, but also a signal. It’s an extension of our identity.”
AR and VR can help communicate who you are to AI. Sounds a bit ominous, but if you’re willing to allocate your resources to buying an item, that item becomes another data point in a preference graph that AI can use to understand what other products you are interested in.
With that said, I don’t believe OpenAI should look to create a pair of Google Glasses or Meta RayBans. AI wearables have proven to be a flop from an aesthetic perspective so far.
An acquisition idea: buy Snap for its camera and AR technology, assuming Evan Spiegel would give up voting control. Its stock has been in a constant free fall over the past few years and could be a bargain for what Snap has already normalized when it comes to the collection of highly personal visual data.
Another facet of the offline data collection conversation is understanding how people interact when they shop in person. What insights can be gained from understanding which clothes people try on but don’t buy? Is it size related? Pattern related? Color related? Or something else? What about how long it takes someone to find just the right dress? How many dresses do they try on?
OpenAI should partner with major retailers (think Walmart or Zara) to gain anonymized data on how shoppers behave in physical retail. Large retailers already collect plenty of data on in-store behavior through cameras, sensors, and transaction systems.
By understanding how people behave while shopping, OpenAI can build more curated recommendation algorithms, and guide the user through a more efficient questionnaire that fits how they shop in person, whether they realize their behavior or not. Maybe the insight is when someone shops for a shirt color like what they have in their closet, they are more likely to convert than taking a risk on another color, unless specified by the shopper. Having this data can create a meaningful advantage for OpenAI throughout the ambiguity of discovery commerce.
What will be critical to the collection of offline data is trust. OpenAI doesn’t have the best public image right now (to say the least), and for people to feel comfortable taking a picture of their belongings or their home, they’ll need to believe OpenAI is a trustworthy steward. Snap has already normalized sharing highly personal visual data with consumers, thus an acquisition could help the cause.
The winner of discovery commerce may be whoever builds the largest dataset of real-world human preferences, not whoever builds the best model.