Shopify bets its future on AI-powered shopping

Shopify Inc. is giving its engineers broad freedom to spend on AI tokens as it builds infrastructure for shopping agents, betting that structured product data from its Catalog product will drive merchant discovery in AI systems like ChatGPT, Gemini, and Copilot. Chief Operating Officer Jess Hertz said agentic commerce is an additional growth path, with Shopify reporting five consecutive quarters of growth above 30% and nearly 90% of quarterly revenue from merchants on the platform for over a year. Shopify claims conversion rates are twice as high when agents use Catalog data versus scraped data, though no absolute figures were provided.

• 4 min read Shopify bets its future on AI-powered shopping Shopify is letting engineers spend freely on AI tokens as Catalog and Sidekick push products into ChatGPT, Gemini and other shopping agents. Image: Fast Company https://www.fastcompany.com/91600204/shopify-is-giving-its-engineers-free-rein-on-ai-heres-why?partner=rss&utm source=rss&utm medium=feed&utm campaign=rss+fastcompany&utm content=rss Shopify is giving its engineers broad freedom to spend on AI tokens as it builds infrastructure for shopping agents—a bet that product data, not just search rankings, will determine which merchants get discovered. The company’s core business is still doing most of the work. Shopify has posted five consecutive quarters of growth above 30%, and nearly 90% of quarterly revenue comes from merchants that have been on the platform for more than a year. But Chief Operating Officer Jess Hertz says agentic commerce is creating an additional growth path, even though purchases made through agents remain limited. “There is a lot of discussion around AI and agentic shopping, but how much business is it really?” Hertz said Shopify is treating the opportunity as two separate businesses: a durable merchant platform and a layer that helps AI systems understand, recommend and transact on products. “The way I think about it is really in two pieces. One is our core business, which is incredibly durable, and the second is that AI is expanding our upside.” Catalog turns product data into an agent interface Shopify’s Catalog is the central infrastructure product in that strategy. It provides structured product information that AI systems can use to understand what a merchant sells, discover relevant products and complete purchases. The company is integrating that layer with agentic storefronts connected to ChatGPT, Gemini and Copilot. The distinction matters because AI agents can use a merchant’s structured catalog data or fall back to information scraped from the web. Shopify says the conversion rate is twice as high when an agent uses Catalog data rather than scraped data. That is a relative claim—not a published conversion percentage—so the supplied reporting does not establish how many additional orders or dollars that difference represents. | Product-discovery path | Reported conversion result | |---|---| | Agent using Shopify Catalog data | 2× the scraped-data rate | | Agent using scraped data | Baseline | Hertz also said merchants participating in Catalog are beginning to make more money, while agentic gross merchandise volume is growing from a small base. Shopify did not provide an absolute GMV figure, merchant count or conversion-rate measurement in the available material. Catalog’s value depends on AI platforms using Shopify’s structured data consistently. The reporting does not establish whether ChatGPT, Gemini or Copilot will rely on Shopify’s infrastructure exclusively, or whether those companies will build competing commerce layers of their own. Sidekick is the merchant-facing counterpart Catalog handles how products are represented to outside AI systems. Sidekick is Shopify’s AI-powered commerce agent for merchants inside the platform. Hertz said merchants are bringing Sidekick more deeply into their businesses, and that early signs suggest it is helping them sell more. The available reporting does not specify Sidekick’s underlying model, supported workflows, token costs or the precise experiments Shopify used to measure the sales impact. It also does not give engineers a dollar budget for AI usage. The “free rein” strategy therefore describes Shopify’s tolerance for experimentation, not an announced spending limit or a quantified return on investment. That approach is unusual because AI inference costs are normally scrutinized against a measurable business outcome. Shopify is treating token spending as infrastructure investment while its engineers work on systems that connect merchants to AI-driven demand. The reporting describes Shopify as powering more than 14% of online shopping. The near-term evidence is narrower than the ambition. Core revenue growth is established, while agentic GMV remains small and the Catalog result is expressed only as a conversion-rate multiple. Shopify has shown a mechanism that could improve discovery and purchasing, but not yet the absolute sales volume needed to judge whether AI commerce is materially changing its business. The competitive question is just as important as the merchant results: Shopify is building the product layer for AI shopping while the largest AI platforms control the interfaces where shoppers and agents make decisions. If those platforms keep using Shopify’s catalog infrastructure, the company gains a distribution role. If they build their own commerce layers, Shopify’s engineers may be funding infrastructure that the platforms eventually control. Frequently asked questions What is Shopify Catalog?+ Catalog is Shopify’s product layer for agentic commerce. It provides data intended to make products understandable, discoverable and purchasable by AI systems. How much does Shopify spend on AI tokens?+ Shopify has not disclosed a dollar amount. The reporting says its engineers can spend freely on AI tokens, but provides no budget or usage limit. Is agentic commerce already a large part of Shopify’s business?+ No. Shopify says agentic GMV is still small, although it is growing. Marcus Vance /authors/marcus-vance/ Enterprise Editor Marcus follows the money. He covers enterprise software, cloud architecture, and the tectonic shifts in Big Tech strategy. He translates dense earnings calls and complex M&A activity into actionable insights about where the industry is actually heading. If a tech giant makes a silent pivot, Marcus is usually the first to notice.