“Google, OpenAI and Amazon all are racing to create tools that would allow for seamless AI-powered shopping.” – Associated Press
When the Associated Press observes that “Google, OpenAI and Amazon all are racing to create tools that would allow for seamless AI-powered shopping”, it is capturing a pivotal moment in the evolution of retail and of the internet itself. The quote sits at the intersection of several long-running trends: the shift from search to conversation, from static websites to intelligent agents, and from one-size-fits-all retail to deeply personalised, data-driven commerce.
Behind this single sentence lies a complex story of technological breakthroughs, strategic rivalry between the worlds largest technology platforms, and a reimagining of how people discover, evaluate and buy what they need. It also reflects the culmination of decades of research in artificial intelligence, recommendation systems, human-computer interaction and digital economics.
The immediate context: AI agents meet the shopping basket
The Associated Press line comes against the backdrop of a wave of partnerships between AI platforms and major retailers. Google has been integrating its Gemini AI assistant with large retail partners such as Walmart and Sams Club, allowing users to move from a conversational query directly to tailored product recommendations and frictionless checkout.
Instead of typing a product name into a search bar, a shopper can describe a situation or a goal, such as planning a camping trip or furnishing a first flat. Gemini then uses natural language understanding and retailer catalogues to surface relevant items, combine them into coherent baskets and arrange rapid delivery, in some cases within hours.1,3 The experience is meant to feel less like using a website and more like speaking to a highly knowledgeable personal shopper.
Walmart leaders have described this shift as a move from traditional search-based ecommerce to what they call “agent-led commerce” – shopping journeys mediated not by menus and filters but by AI agents that understand intent, context and personal history.1,2,3 For Google, this integration is both a way to showcase the capabilities of its Gemini models and a strategic response to OpenAIs work with retailers like Walmart, Etsy and a wide range of Shopify merchants through tools such as Instant Checkout.2,3
OpenAI, in parallel, has enabled users to browse and buy directly within ChatGPT, turning the chatbot into a commercial surface as well as an information tool.2,3 Amazon, for its part, has been weaving generative AI into its core marketplace, logistics and voice assistant, using AI models to improve product discovery, summarise reviews, optimise pricing and automate seller operations. Each company is betting that the next era of retail will be shaped by AI agents that can orchestrate entire end-to-end journeys from inspiration to doorstep.
From web search to agentic commerce
The core idea behind “seamless AI-powered shopping” is the replacement of fragmented, multi-step customer journeys with coherent, adaptive experiences guided by AI agents. Historically, online shopping has been built around search boxes, category trees and static product pages. The burden has been on the consumer to know what they want, translate that into search terms, sift through results and manually assemble baskets.
Agentic commerce reverses this burden. The AI system becomes an active participant: interpreting vague goals, proposing options, remembering preferences, coordinating logistics and handling payments, often across multiple merchants. Google and OpenAI have both underpinned their efforts with new open protocols designed to let AI agents communicate with a wide ecosystem of retailers, payment providers and loyalty systems.3,5
Google refers to its initiative as a Universal Commerce Protocol and describes it as a new standard that allows agents and systems to talk to each other across each step of the shopping journey.3,5 OpenAI, in turn, introduced the Agentic Commerce Protocol in partnership with Stripe, enabling ChatGPT and other agents to complete purchases from Etsy and millions of Shopify merchants.3 The technical details differ, but the strategic goal is shared: create an infrastructure layer that allows any capable AI agent to act as a universal shopping front end.
In practice, this means that a single conversation might involve discovering a new product, joining a retailers loyalty scheme, receiving personalised offers, adding related items and completing payment – without ever visiting a conventional website or app. The Associated Press quote calls out the intensity of the competition between the major platforms to control this new terrain.
The Associated Press as observer and interpreter
The Associated Press (AP), the attributed source of the quote, has a distinctive role in this story. Founded in 1846, AP is one of the worlds oldest and most widely used news agencies. It operates as a non-profit cooperative, producing reporting that is syndicated globally and used as a baseline for coverage by broadcasters, newspapers and digital platforms.
AP has long been known for its emphasis on factual, neutral reporting, and over the past decade it has also become notable for its early adoption of AI in news production. It has experimented with automated generation of corporate earnings summaries, sports briefs and other data-heavy stories, while also engaging in partnerships with technology companies around synthetic media and content labelling.
By framing the competition between Google, OpenAI and Amazon as a “race” to build seamless AI shopping, AP is doing more than simply documenting product launches. It is drawing attention to the structural stakes: the question of who will mediate the everyday economic decisions of billions of people. APs wording underscores both the speed of innovation and the concentration of power in a handful of technology giants.
APs technology and business correspondents, in covering this domain, typically triangulate between company announcements, analyst commentary and academic work on AI and markets. The quote reflects that blend: it is rooted in concrete developments such as the integration of Gemini with major retailers and the emergence of new commerce protocols, but it also hints at broader theoretical debates about platforms, data and consumer autonomy.
Intellectual roots: from recommendation engines to intelligent agents
The idea of seamless, AI-mediated shopping is the visible tip of an intellectual iceberg that stretches back decades. Several overlapping fields contribute to the current moment: information retrieval, recommender systems, multi-sided platforms, behavioural economics and conversational AI. The leading theorists in these areas laid the groundwork for the systems now shaping retail.
Search and information retrieval
Long before conversational agents, the central challenge of online commerce was helping people find relevant items within vast catalogues. Researchers in information retrieval, such as Gerard Salton in the 1960s and 1970s, developed foundational models for document ranking and term weighting that later underpinned web search.
In the context of commerce, the key innovation was the integration of relevance ranking with commercial signals such as click-through rates, purchase behaviour and sponsored listings. Googles original PageRank algorithm, associated with Larry Page and Sergey Brin, revolutionised how information was organised on the web and provided the basis for search advertising – itself a driver of modern retail. As search became the dominant gateway to online shopping, the line between information retrieval and marketing blurred.
The move to AI-powered shopping agents extends this lineage. Instead of ranking static pages, large language models interpret natural language queries, generate synthetic descriptions and orchestrate actions such as adding items to a basket. The theoretical challenge shifts from simply retrieving documents to modelling context, intent and dialogue.
Recommender systems and personalisation
Much of seamless AI-powered shopping depends on the ability to personalise offers and predict what a particular consumer is likely to want. This traces back to work on recommender systems in the 1990s and 2000s. Pioneers such as John Riedl and Joseph Konstan developed early collaborative filtering systems that analysed user ratings to make personalised suggestions.
The famous Netflix Prize in the mid-2000s catalysed work on matrix factorisation and latent factor models, with researchers like Yehuda Koren demonstrating how to predict preferences from sparse interaction data. Amazon itself became synonymous with recommender systems, popularising the idea that “customers who bought this also bought” could drive significant incremental revenue.
Over time, recommendation theory has expanded to consider not just accuracy but diversity, serendipity and fairness. Work by researchers such as Gediminas Adomavicius and Alexander Tuzhilin analysed trade-offs between competing objectives in recommender systems, while others explored issues of filter bubbles and echo chambers.
In AI-powered shopping, these theoretical concerns are amplified. When a single conversational agent mediates choices across many domains, its recommendation logic effectively becomes a form of personalised market design. It can nudge users towards particular brands, balance commercial incentives with user welfare, and shape long-term consumption habits. The underlying theories of collaborative filtering, contextual bandits and reinforcement learning now operate in a more visible, consequential arena.
Multi-sided platforms and the economics of marketplaces
The race between Google, OpenAI and Amazon is also a contest between different platform models. Economists such as Jean-Charles Rochet and Jean Tirole provided the canonical analysis of multi-sided platforms – markets where intermediaries connect distinct groups of users, such as buyers and sellers, advertisers and viewers.
The theory of platform competition explains why network effects and data accumulation can produce powerful incumbents, and why controlling the interface through which users access multiple services confers strategic advantages. Amazon Marketplace, Google Shopping and ad networks, and now AI agents embedded in operating systems or browsers, can all be seen through this lens.
Further work by David Evans, Andrei Hagiu and others explored platform governance, pricing structures and the strategic choice between being a neutral intermediary or a competitor to ones own participants. These ideas are highly relevant when AI agents choose which merchants or products to recommend and on what terms.
Seamless AI shopping turns the agent itself into a platform. It connects consumers, retailers, payment services, logistics providers and loyalty schemes through a conversational interface. The Universal Commerce Protocol and the Agentic Commerce Protocol can be understood as attempts to standardise interactions within this multi-sided ecosystem.3,5 The underlying tensions – between openness and control, neutrality and self-preferencing – are illuminated by platform economics.
Behavioural economics, choice architecture and digital nudging
While traditional economics often assumes rational agents and transparent markets, the reality of digital commerce has always been shaped by design: the ordering of search results, the framing of options, the use of defaults, and the timing of prompts. Behavioural economists like Daniel Kahneman, Amos Tversky and Richard Thaler have demonstrated how real-world decision-making deviates from rational models and how “choice architecture” can influence outcomes.
In online retail, this has manifested as a rich literature on digital nudging: subtle interface choices that steer behaviour. Researchers in human-computer interaction and behavioural science have documented how factors such as social proof, scarcity cues and personalised messaging affect conversion.
AI-powered shopping agents add another layer. Instead of static designs, the conversation itself becomes the choice architecture. The way an AI agent frames options, in what order it presents them, how it responds to hesitation and how it explains trade-offs, all shape consumer welfare. Theorists working at the intersection of AI and behavioural economics are now grappling with questions of transparency, autonomy and manipulation in agentic environments.
Conversational AI and human-computer interaction
The ability to shop by talking to an AI depends on advances in natural language processing, dialogue modelling and user-centred design. The early work of Joseph Weizenbaum (ELIZA) and the subsequent development of chatbots provided the conceptual foundations, but the major leap came with deep learning and large language models.
Researchers such as Yoshua Bengio, Geoffrey Hinton and Yann LeCun advanced the neural network architectures that underpin todays generative models. Within natural language processing, work by many teams on sequence-to-sequence learning, attention mechanisms and transformer architectures led to systems capable of understanding and generating human-like text.
OpenAI popularised the transformer-based large language model with the GPT series, while Google researchers contributed foundational work on transformers and later developed models like BERT and its successors. These advances turned language interfaces from novelties into robust tools capable of handling complex, multi-turn interactions.
Human-computer interaction specialists, meanwhile, studied how people form mental models of conversational agents, how trust is built or undermined, and how to design dialogues that feel helpful rather than intrusive. The combination of technical capability and design insight has made it plausible for people to rely on an AI agent to curate shopping choices.
Autonomous agents and “agentic” AI
The term “agentic commerce” used by Walmart and Google points to a broader intellectual shift: viewing AI systems not just as passive tools but as agents capable of planning and executing sequences of actions.1,5 In classical AI, agent theory has its roots in work on autonomous systems, reinforcement learning and decision-making under uncertainty.
Reinforcement learning theorists such as Richard Sutton and Andrew Barto formalised the idea of an agent learning to act in an environment to maximise reward. In ecommerce, this can translate into systems that learn how best to present options, when to offer discounts or how to balance immediate sales with long-term customer satisfaction.
Recent research on tool-using agents goes further, allowing language models to call external APIs, interact with databases and coordinate services. In commerce settings, that means an AI can check inventory, query shipping options, apply loyalty benefits and complete payments – all within a unified reasoning loop. Googles and OpenAIs protocols effectively define the “environment” in which such agents operate and the “tools” they can use.3,5
The theoretical questions now concern safety, alignment and control: how to ensure that commercially motivated agents act in ways that are consistent with user interests and regulatory frameworks, and how to audit their behaviour when their decision-making is both data-driven and opaque.
Corporate protagonists: Google, OpenAI and Amazon
The Associated Press quote names three central actors, each with a distinct history and strategic posture.
Google: from search to Gemini-powered commerce
Google built its business on organising the worlds information and selling targeted advertising against search queries. Its dominance in web search made it the default starting point for many online shopping journeys. As user behaviour has shifted towards conversational interfaces and specialised shopping experiences, Google has sought to extend its role from search engine to AI companion.
Gemini, Googles family of large language models and AI assistants, sits at the heart of this effort. By integrating Gemini into retail scenarios, Google is attempting to ensure that when people ask an AI for help – planning a project, solving a problem or buying a product – it is their agent, not a competitors, that orchestrates the journey.1,3,5
Partnerships with retailers such as Walmart, Target, Shopify, Wayfair and others, combined with the Universal Commerce Protocol, are strategic levers in this competition.1,3,4,5 They allow Google to showcase Gemini as a shopping concierge while making it easier for merchants to plug into the ecosystem without bespoke integrations for each AI platform.
OpenAI: from research lab to commerce gateway
OpenAI began as a research-focused organisation with a mission to ensure that artificial general intelligence benefits humanity. Over time, it has commercialised its work through APIs and flagship products such as ChatGPT, which rapidly became one of the fastest-growing consumer applications in history.
As users started to rely on ChatGPT not just for information but for planning and decision-making, the platform became an attractive entry point for commerce. OpenAIs Instant Checkout feature and the Agentic Commerce Protocol reflect an attempt to formalise this role. By enabling users to buy directly within ChatGPT from merchants on platforms like Shopify and Etsy, OpenAI is turning its assistant into a transactional hub.2,3
In this model, the AI agent can browse catalogues, compare options and present distilled choices, collapsing the distance between advice and action. The underlying theory draws on both conversational AI and platform economics: OpenAI positions itself as a neutral interface layer connecting consumers and merchants, while also shaping how information and offers are presented.
Amazon: marketplace, infrastructure and the invisible AI layer
While the provided context focuses more explicitly on Google and OpenAI, Amazon is an equally significant player in AI-powered shopping. Its marketplace already acts as a giant, data-rich environment where search, recommendation and advertising interact.
Amazon has deployed AI across its operations: in demand forecasting, warehouse robotics, delivery routing, pricing optimisation and its Alexa voice assistant. It has also invested heavily in generative AI to enhance product search, summarise reviews and assist sellers with content creation.
From a theoretical standpoint, Amazon exemplifies the vertically integrated platform: it operates the marketplace, offers its own branded products, controls logistics and, increasingly, provides the AI services that mediate discovery. Its approach to AI shopping is therefore as much about improving internal efficiency and customer experience as about creating open protocols.
In the race described by AP, Amazons strengths lie in its end-to-end control of the commerce stack and its granular data on real-world purchasing behaviour. As conversational and agentic interfaces become more common, Amazon is well placed to embed them deeply into its existing shopping flows.
Retailers as co-architects of AI shopping
Although the quote highlights technology companies, retailers such as Walmart, Target and others are not passive recipients of AI tools. They are actively shaping how agentic commerce unfolds. Walmart, for example, has worked with both OpenAI and Google, enabling Instant Checkout in ChatGPT and integrating its catalogue and fulfilment options into Gemini.1,2,3
Walmart executives have spoken about “rewriting the retail playbook” and closing the gap between “I want it” and “I have it” using AI.2 The company has also launched its own AI assistant, Sparky, within its app, and has been candid about how AI will transform roles across its workforce.2
These moves reflect a broader theoretical insight from platform economics: large retailers must navigate their relationships with powerful technology platforms carefully, balancing the benefits of reach and innovation against the risk of ceding too much control over customer relationships. By participating in open protocols and engaging multiple AI partners, retailers seek to maintain some leverage and avoid lock-in.
Other retailers and adjacent companies are exploring similar paths. Home Depot, for instance, has adopted Gemini-based agents to provide project planning and aisle-level guidance in stores, while industrial partners like Honeywell are using AI to turn physical spaces into intelligent, sensor-rich environments.5 These developments blur the line between online and offline shopping, extending the idea of seamless AI-powered commerce into bricks-and-mortar settings.
The emerging theory of AI-mediated markets
As AI agents become more entwined with commerce, several theoretical threads are converging into what might be called the theory of AI-mediated markets:
- Information symmetry and asymmetry: AI agents can, in principle, reduce information overload and help consumers navigate complex choices. But they also create new asymmetries, as platform owners may know far more about aggregate behaviour than individual users.
- Algorithmic transparency and accountability: When an AI agent chooses which products to recommend, the criteria may include relevance, profit margins, sponsorship and long-term engagement. Understanding and governing these priorities is an active area of research and regulation.
- Competition and interoperability: The existence of multiple commerce protocols and agent ecosystems raises questions about interoperability, switching costs and the potential for AI-mediated markets to become more or less competitive than their predecessors.
- Personalisation versus autonomy: Enhanced personalisation can make shopping more efficient and enjoyable but may also narrow exposure to alternatives or gently steer behaviour in ways that users do not fully perceive.
- Labour and organisational change: As AI takes on more of the cognitive labour of retail – from customer service to merchandising – the roles of human workers evolve. The theoretical work on technology and labour markets gains a new frontier in AI-augmented retail operations.
Researchers from economics, computer science, law and sociology are increasingly studying these dynamics, building on the earlier theories of platforms, recommendations and behavioural biases but extending them into a world where the primary interface to the market is itself an intelligent agent.
Why this moment matters
The Associated Press quote distils a complex, multi-layered transformation into a single observation: the most powerful technology firms are in a race to define how we shop in an age of AI. The endpoint of that race is not just faster checkout or more targeted ads. It is a restructuring of the basic relationship between consumers, merchants and the digital intermediaries that connect them.
Search boxes and product grids are giving way to conversations. Static ecommerce sites are being replaced or overlaid by agents that can understand context, remember preferences and act on our behalf. The theories of information retrieval, recommendation, platforms and behavioural economics that once described separate facets of digital commerce are converging in these agents.
Understanding the backstory of this quote – the intellectual currents, corporate strategies and emerging protocols behind it – is essential for grasping the stakes of AI-powered shopping. It is not merely a technological upgrade; it is a shift in who designs, controls and benefits from the everyday journeys that connect intention to action in the digital economy.
References
1. https://pulse2.com/walmart-and-google-turn-ai-discovery-into-effortless-shopping-experiences/
3. https://www.businessinsider.com/gemini-chatgpt-openai-google-competition-walmart-deal-2026-1
5. https://cloud.google.com/transform/a-new-era-agentic-commerce-retail-ai
