- - So as a philosopher turned engineer, I have, for better or for worse, been obsessed with two questions my whole life. What is a thing and why does it exist? So we're here talking about AI in the Fortune 500, and the future of AI commerce. So let's ask a hopefully straightforward question.

What is a store? A hundred years ago, a store looked like this, something we don't really recognize today. Inventory was in the back of a shop. You had to talk to a clerk, tell them what you wanted. They went and then fetched it and brought it to you. It wasn't until the 1950s and '60s with information systems that we were able to actually scale the concept of a store.

And you saw big box retailers like Walmart and Costco emerge. The inventory moved from the back to the front, and you now had this concept of browsing. With the internet, we took that store and then we put it online. And so we took the scale of merchandise and matched it with the scale of distribution.

You can now browse anywhere in the world 24 hours a day. A lot of people think that websites are dead. But a shocking number of people still shop online. Walmart had almost 500 million shoppers last month. Home Depot had 170 million shoppers in its online store last month. But the thing that we sacrificed was we got a sea of sameness.

Can you really tell the difference here between Adidas, Reebok, Brooks? And so maybe we can start piecing this together to answer this question: What is a store? A store is a location and a protocol that facilitates transactions. You have merchants that want to sell something, buyers that want to buy something, and then a system to facilitate that interaction.

So great, now we can start asking the question: What happens to the store with AI? If e-commerce digitized the merchandise and the distribution, AI digitizes the participants and their interactions. We go from static websites to merchant agents. We go from consumers browsing to consumer agents. And we go from low-level payment infrastructure to higher-level intent infrastructure.

But we still have the same goal. That's not changing. We're still trying to get to a transaction. And so what is this actually going to look and feel like, you know, qualitatively? That's something a new generation that we spend a lot of time obsessing over. How will a new generation of consumer, both human and agentic, interact with a new generation of interface that is dynamic and real-time and generative, built on a new generation of infrastructure that needs to serve those new needs?

So we see two possible futures. You'll have either AI agents that go to websites, or you'll have agents that have programmatic access. Option one, when they go to websites, will look something like this. Maybe you're in ChatGPT, you want a new TV. Maybe you're into gaming, so you say, "Hey, I want a new TV." Your agent goes to a new type of website optimized for that agent that can richly express your intent.

It dynamically cuts the product catalog and the brand style guidelines and returns that content directly into the chat interface that you're operating in. When you want to buy, that agent then can go back to the website and go through the entire checkout process for you. It gets interesting when you think about programmatic access in this world.

So maybe you have the same starting place, you're in Chat, but instead of actually going to a website, you have an MCP server or an API that's programmatically accessing every merchant on the internet. That API endpoint can then reason over the API call and return back a rich set of UI elements generated on the fly.

Similarly, when you're looking to buy, instead of going to a website, it's just going to hit an API endpoint. And so if that's the future, how do we get from today, this sort of static world of consumers browsing and websites, to this future world of agentic interactions between buyers and users?

So let's start with the goal. The goal is a transaction. We want high-quality conversion with users that hopefully are happy, hopefully they don't return the item that they get. And so in code, a payment is represented with a payment intent when a user clicks a buy button. That intent then goes through a set of transformations through the checkout process.

results in success and money movement. Our first challenge in this agentic world is, what if software is the one that's clicking this button? So if you use operator today, it'll mostly error out on these e-commerce websites. And so there's two solutions that people have come up with. One is the solution that exists today.

This is kind of the Stripe SDK solution. So instead of checking out with a merchant, you check out with ChatGPT or the software provider. That software provider then spins up a virtual card and buys the item on behalf of you using that new card. We think the more elegant solution is what one of our partners Visa is working on, which is delegated authentication.

The agent is able to use your actual credit card and go through the checkout flow for you. So that solves transactions, but it doesn't get us very far. What if we can move up a level of abstraction? What if we can actually go to the buying and selling intent and the preferences?

So a buyer intent today is expressed and it's inferred via keyword searches, click data, and site metrics. In the agentic future, we think that this is going to be explicitly captured rather than inferred. Conversation data is rich with user intent. And you can actually just ask a user agent what it's trying to do.

You don't have to statistically infer it. This gives us our second challenge. If we have fuzzy intent, often people are just searching for, hey, I want a pair of running shoes. How do we actually get that to the skew level item, which is the inventory representation of that product?

Today the main solution is to force it explicitly. So you actually have to provide a product detail page URL to an agent in order to buy. We think there's something very interesting that's emerging with the merchants that we're working with, which is users that come from an AI channel are much higher conversion, much higher dollar value, and much higher lifetime value.

That opens up a whole new set of possibilities for how to rethink the cost structure of fulfillment. a product detail page. Maybe it doesn't actually matter if the user gets the wrong thing if it's easy to return and they're high value. On the seller side, that selling intent, as I mentioned, is really represented with a product detail page.

We all know these. They're pretty static. There's just a buy button, a price. Maybe there's some discounts, maybe there's some bundles. In the future, this is going to be very dynamic. We think merchants will need to show real-time product availability, they'll have contextual pricing and discount that they can serve to a user in line, and the ability to infinitely bundle products across multiple merchants.

This starts to get complicated, so then we encounter our third challenge. How do we know if a specific item is actually available across all the thousands of stores that exist? So we have a user that wants to buy something, and now we actually have to go find the store.

There's two solutions today for this that we think are suboptimal. One is to use existing product feed infrastructure. So a lot of folks use this with Google. That requires chat products to individually work with every single merchant to get this data. The other alternative is you scrape product data from every website on the internet.

We've done that. We think it's suboptimal. It's both repetitive. You have to do it a bunch. And then you end up clogging websites with bot traffic. The more elegant long-term solution that we're working on is to actually create a unified API to access product data across every merchant. You can think of this like a plaid, but for product data.

So instead of aggregating over financial institutions, you can aggregate over merchants. And then the last piece at this layer is how do we represent buyer and seller preferences? Today, this is one-sided and very narrow. You have siloed user accounts, transaction data. You have limited LLM memory. And then businesses don't share really anything about their intent beyond quarterly and annual reporting.

In the future, we think this will be two-sided and very expansive. They'll have rich context on users, not just across the purchase, but across every aspect of their life. And businesses will be able to express their real-time strategic goals. What's low on inventory? What users do they care about?

What strategic change has happened with something like tariffs? The challenges here are very wide open. These are market design challenges. You have the challenges that preferences are very complex. They change over time. They often conflict between buyers and sellers. And there's a disincentive to honestly report your preferences. The current solution is you naively trust the information that you're given.

Other folks have talked about prompt injection or manipulation of LLMs. And so that's kind of the world that we live in. When I worked at Bridgewater on our trading system and market systems, the world of finance saw this with third-party institutions and market makers that manage those differences between buyers and sellers.

We think that's the world that this needs to move to. And so great. We've gotten a little bit further, but nothing is really agentic yet. You sort of need this last piece. This is the frontier, which is you need to add intelligence to the decision-making at each of those components.

And so consumers and merchants need not just intents and preferences, but they need intelligence that can reason over them and negotiate them. And then on the infrastructure side, you need to move from just market-making to actual coordination and reasoning over these participants. The logic for generating in real-time the interfaces that each of these people need.

Okay. So let's see how this is working in real life. I think a lot of people look at the Fortune 500 and they think, oh, these are big, old, slow-moving companies. But what people fail to realize is they had to survive the last 100 and 150 years of dramatic technological and societal shifts.

And so we find that they're actually quite forward-thinking in terms of this challenge. And so if you take an example of Samsung, they started 100, 150 years ago as a fish merchant in Korea with 20 or 30 employees. It wasn't until the '70s that they started selling televisions, and it isn't really until the '90s that they became what we know them today, this technological behemoth.

And they're at the forefront now of thinking, how does the brand of Samsung evolve in the world of AI? And how are they going to bridge e-commerce to this agentic future? So the first step is we create an API and MCP server for any chat client to use. A lot of Fortune 500 companies have complex product systems.

Samsung, for example, has 10 different verticals, each with their own inventory representations of products, and so we do the work of attracting that into a consistent API with cohesive endpoints that will work across any merchant. The second step is then to connect that product data with other data sources at the company.

This is the first step in constructing that seller intent. The natural starting place with what we're doing is let's just connect the brand and design system that the company has to actually wrap the products in how they want to be represented. So we're moving away from these carousel and static representations of products.

That third step then feeds into a container for experimentation. And so we make an AI subdomain that allows for rapid experimentation of generative interfaces that can ingest both the product and brand data to serve customers. And so we're also experimenting with what does conversation look like when it's not just a bullet point and text list but actual images and products and content.

And then the last piece is handling agentic transactions. This is actually enabling the payment flow to work on this new surface for bot traffic, which is a real inversion of the typical posture of a .com website. And the reason that brands are excited about this is again because users from AI chat, while they might be small, are much higher intent, they're deeper in the funnel, and they convert much better.

And so we think every retail brand, every merchant needs to adopt this posture. And with any big change, we think the right approach is to start with the question of what and how. We don't think stores go away. We just think that they evolve. We think they return to the original form that they were in.

And we think that form is actually a conversation. Thank you. All right, we do have time for questions, if anybody wants to chat for a bit. And if not, yeah, go ahead. Can you use the microphone over here, please? Thank you. I'm not that tall. I don't know if you noticed that, but we are talking about machine customers, maybe, just like there is a book called "Where Machine Becomes." From customers.

Yeah. Yes, from the Gartner. So I work in a bank, and now it's very usable, what you said. But what is your projection in terms of how this is going to be, just like the break even, right? When it's going to start seeing this in our daily basis? Because today is only-- Yeah, it's happening fast.

I mean, even if you look at a product like ChatGPT, it is starting to bring shopping experiences into the product. The thing that's missing is you don't actually have the full journey within that application yet. And so you still link out to a website. And that's the thing that we're interested in exploring, which is, will you still link out to a website?

I don't think that goes away immediately. And then every brand that we talk to, there's a strong desire to own a Surface in this new world. So we think you can start with a web-like Surface that is built in a way that's transportable so that when these chat products want to bring shopping into the application, that you can actually just bring that data and components directly in.

You don't actually need to re-architect and rebuild. And so we're really excited about that. It's almost like this inversion where instead of going to the website, the website is going to go into a thousand different places. Yeah. Are credit cards the right payment mechanism for the agentic economy, or do we need something new?

I didn't want to open that can of worms in the talk. I think conceptually, there's a strong argument for stable coins and crypto to be the native payment rail for AI, mostly because the agents can actually live within the wallet. I think practically, consumers use credit cards. And so it's the most likely bridge to get to that world.

And then there's a third alternative, which is the agent itself just owns a perpetual credit card and you top it up, which is interesting. Thank you. There's an interesting parallel in places like China and Brazil where they have the form of super apps where everything happens in that one place.

Yeah. Do you see Claude and ChatGPT trying to become the super app and shopping just takes place entirely over that platform? I think that's their goal. And then it becomes this question of how do the rest of us, how do merchants have control in that environment? And I think the one thing that model providers are very open to and what's different than the internet is, is the goal is to get the user to the right outcome.

And so they have a, interestingly, they have a different incentive than tech companies in the past. And so we think there's actually kind of like a nice alignment between the goals of a merchant and the goals of these chat products. And do you foresee any form of revenue share happening there?

Definitely. I think we don't think it's going to be advertising. It'll probably look a little bit more like either affiliate revenue or, you know, if you provide high quality data and you can be attributed to a good answer, I think there'll be some portion of that that these model providers will give back to merchants.

Great. Adam, thank you so much. Yeah. That was a great talk. Thank you. Yeah. Thank you. Thank you. We'll see you next time.