Will Google Become Our AI-Powered Central Planner?

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Agentic Commerce

Source: Matt Stoller; BIG by Matt Stoller (Substack) · 2026-01-16

Google may monopolize the market for AI consumer services. And now it is rolling out a product to help businesses set prices, based on what it knows about us. The failure of antitrust will be costly.

Earlier this week, Google made three important announcements. The first is that its AI product Gemini will be able to read your Gmail and access all the data that Google has about you on YouTube, Google Photos, and Search. While Google skeptics might see a Black Mirror style dystopia, the goal is to create a chatbot that knows you intimately. And the value of that is real and quite significant.

The second announcement is that Google has cut a deal with Apple to power that company’s Siri and foundational models with Gemini, extending its generative AI into the most important mobile ecosystem in the world.

And the third announcement is that Google will launch a new Gemini-powered ad service and open protocol to create personalized surveillance pricing for merchants across the economy. It is, as Google VP Vidhya Srinivasan said, a way to “offer custom deals to specific shoppers who are ready to buy, without having to extend the same thing to everybody.” Partners include Walmart, Visa, Mastercard, Shopify, Gap, Kroger, Macy’s, Stripe, Home Depot, Lowes, American Express, etc.

The answer, as it turns out, is that Google may be seeking to become our central planner and price setter. The third announcement is the key tell. CEO Sundar Pichai said the company will sell not only marketing, but price coordinating services. In the documentation for the universal commerce protocol, google lists “dynamic pricing” as a key tool for merchants. And Kroger, a partner of Google, already announced it will deploy Gemini, enriched with its own proprietary data, to do consumer pricing.

Google is also creating a pilot of something called “direct offers.” Rather than just buying advertising, businesses will pay to allow Google set prices when it makes recommendations to users through Gemini.

Here’s how Google presents it: “With Direct Offers, retailers set up relevant offers they want to feature in their campaign settings and Google will use AI to determine when an offer is relevant to display.” So for instance, as Gemini offers different tires to users based on its knowledge of their car and driving style, it could also offer different prices for those tires. Here’s a different example presented in Google’s marketing materials.

Here’s how it works: Imagine you search “I’m looking for a modern, stylish rug for a high-traffic dining room. I host a lot of dinner parties, so I want something that is easy to clean.” Google already elevates the most relevant products to meet your search criteria. But often, you are only ready to buy if you’re getting a great deal. Now relevant retailers have an opportunity to also feature a special discount. This helps you get better value and helps the retailer close the sale.

Marketers have allowed Google to automatically place ads under its own discretion for years, to pretty much run ad campaigns without that much input. But letting the search giant also run pricing strategies is new.

There are many unanswered questions about how this new system will work. Right now, both Nike and Reebok advertise on Google, and it’s a little weird, because it means that each of them teaches Google how to sell sneakers, then rival sneaker companies can also hire Google to also sell sneakers. That’s not illegal. But if both of them give Google the authority to do pricing, then all of a sudden Google is coordinating pricing for sneakers, which looks much more like an automated form of price-fixing.

There are several reasons to see what Google is doing as ominous. Pricing expert Lindsay Owens, who helped uncover the dynamic pricing scheme of Instacart, noted that this pricing engine could be a way of having Google help retailers analyze user data and then use it to “overcharge” consumers. Interestingly, Google responded by saying that its new service allows merchants “offer a lower priced deal or add extra services like free shipping — it cannot be used to raise prices.”

But of course, the idea that discounts mean lower price levels is foolish - just look at any health care bill from your insurance company, which often says something like the cost of an MRI is $8000 but the insurer got a $6000 discount and paid $1970, so your billed portion is $30. Obviously that $8000 number is fake, so is the discount, the only thing that matters is the cash changing hands. Pharmacy benefit managers get “discounts” off of ludicrously high list prices, but that’s fake. It’s like certain department stores who routinely mark everything as 80% off; we know that’s not a real discount. So I am worried when I see that Google is using the same rationale as PBMs, only for its new feature meant to change pricing strategies for the entire economy. More importantly, Google is explicitly saying it will use this tactic to increase revenue generated from consumers, to “help shoppers prioritize value over price alone.”

In 2000, Google began accepting advertising on its search results. Google rapidly gained share in the search market, until it became a monopoly in 2002. Then it went on an acquisition spree. In 2003, the company bought Applied Semantics, which allowed it to do contextual advertising across the web. It acquired Keyhole (Maps) in 2004, Android in 2005, and YouTube in 2006. Its capstone acquisition was DoubleClick in 2007, where it became clear that this company was a dominant force. But just how dominant wasn’t obvious at first. In order for Google to organize the world’s information, it had to stop others from doing so.

In 2006, Google encountered its first monopolization controversy, which involved a British comparison shopping site named Foundem. Foundem’s goal was to help consumers compare prices for different goods and services by creating a specialized search engine.

Lines of inquiry this paper opens 7

Research framings built by reading the notes related to this paper — the questions it feeds into.

How effectively can sellers exploit AI shopping agents through description changes? How can merchant-funded agents remain loyal to consumer interests in digital commerce? How do AI-generated summaries affect traffic and engagement with original sources? How do capability differences shape collusion in AI markets?