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Shopping agents are positioned to become invisible sales conversion engines. As OpenAI, Perplexity, and others race to capture this trillion-dollar opportunity, the future of how consumers search and buy hinges on how these platforms will make money, and how their algorithms will decide which products to show consumers (or buy on their behalf). The answers will determine whether these chatbots deliver on their promise of personalized commerce in their truest and most authentic sense — or become a more sophisticated version of today’s pay-to-play search and commerce platforms. Their strength as a conversational interface, capable of understanding complex requests, makes them well-suited to complete purchases without users ever visiting a physical or digital store or leaving the conversation. An emerging Agentic AI commerce ecosystem now stands at the ready to help advance their ambitions. The speed at which the GenAI chatbots have amassed an audience shows the potential for how these models could upend the retail and commerce status quo by changing where consumers start their searches and end by making a purchase without a lot of steps or friction in between. As AI agents increasingly handle the search and presentation of results (or completed sales), traditional retailers risk becoming invisible in the commerce ecosystem altogether. In this world, payment credentials might emerge as the real winner as embedded offers, financing, rewards and other data-driven incentives become an invisible part of the transaction. The venture capital pouring into these platforms signals expectations for massive adoption and ROI. GenAI represents a new form of commerce orchestration across marketplaces, social signals and retail inventory through a simple and single conversational interface. The unique nature of conversational AI suggests that different approaches might not just be possible but necessary to compete. LLM platforms can build on GenAI’s growing sense of user trust, its potential for creating a distinctive new shopping and buying utility and its ability to monetize these new forms of value. How these models present recommendations and act on behalf of users presents new challenges because of their current lack of clarity — and new opportunities because of what they could become for the entire commerce ecosystem. And that could reshape how retailers and the ecosystem adapt their products and platforms to drive sales. For retailers and brands, that now means competing for both customer and AI attention. Retailers will need to ensure their inventory, pricing and product information are optimized for AI crawling and decision-making algorithms. The winners in this new era may be those who recognize that when conversations drive commerce, trust itself becomes the product. And that monetizing trust comes wrapped around a different business model.

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AI visionary Sam Altman is leading a project to distinguish real people from software fakes on the internet using eye scans. The World identification project, which uses eye scans to distinguish people from machines, is entering the money transfer and financial services business. Users can send money to friends and family free of charge via the World app and will have an account number for interactions with the banking system. The project aims to make it increasingly difficult to distinguish people from software online. Users create a profile called “World ID” using an eye scan on World scanners called Orb. As an incentive, World is launching its own digital currency. The project is also targeting online dating markets, such as gaming specialist Razer and dating platform operator Match Group. With these new functions, World is moving closer to the vision of a super app that covers all possible areas of everyday life, similar to WeChat in Asia. World, a web3 project started by Altman and Alex Blania that was formerly known as Worldcoin, is based on the idea that it will eventually be impossible to distinguish humans from AI agents on the internet. To address this, World wants to create digital “proof of human” tools; these announcements are part of its effort to get millions of people to sign up. After scanning your eyeball with one of its silver metal Orbs — or now, one of its Orb Minis — World will give you a unique identifier on the blockchain to verify that you’re a human.

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IBM Corp. is leaning into compact, specialized models — such as its new Tiny Time Mixers — to tackle network automation challenges where traditional large language models fall short. The key lies in understanding time-series data, something most large language models simply weren’t built to handle, according to Andrew Coward, general manager of software networking at IBM. “There’s new models, and IBM’s built one called Tiny Time Mixer. Very small parameters, million parameters, and they understand time. We can take network data, and then we can apply it to weather information or TV schedules. Then we can make predictions about what’s likely to happen. What we are seeing is the democratization of AI,” he said. “It’s almost free to put data in and run it against AI models, but if you need to train it, that’s the expensive bit. The training piece is coming down massively in costs.” Using small models, IBM helps address telco infrastructure problems, such as bandwidth congestion and poor network coverage. This explains why AI model accuracy takes center stage, Coward pointed out. 

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