AI as the New Middleman: Cheaper, Faster, and In Control
AI disrupting business models means intelligent systems are stepping in as low-cost intermediaries, replacing traditional agents in tasks like selling homes and guiding shopping choices, by automating coordination, search, and recommendations while keeping consumers in direct control of key decisions and transactions. The economic story here is simple: wherever an industry depends on a high-commission middle layer to translate complexity for consumers, AI is now stripping out that margin and turning expertise into software. In real estate, home selling has long been an arduous process filled with pricing puzzles, promotional work, legal documents, and opaque negotiations that can overwhelm ordinary homeowners. AI platforms argue they can handle that complexity with less friction. In retail, AI shopping recommendations from chatbots are quietly becoming the first stop for product discovery, shifting power away from search engines and brand sites toward conversational agents that decide which products appear first.
Ridley and the Automation of Real Estate Commissions
Real estate commission automation is no longer a thought experiment; platforms like Ridley are already coordinating home sales without traditional agents, answering the question "can AI sell your home?" with a resounding yes. Ridley’s pitch is blunt: most of what agents do—tracking deadlines, moving documents, scheduling inspections, and following up with lenders and title companies—can disappear into software over the next five years. Instead of paying for manual transaction coordination, homeowners get an intelligent system that understands their specific property, contracts, disclosures, inspection findings, and communications, and keeps a persistent view of what has happened and what needs to happen next. The homeowner remains in control; the AI makes the process far more manageable. This is a direct assault on the traditional 6% commission logic: if coordination is automated, the premium for being a gatekeeper collapses, and the only defensible human value is strategic advice and emotional reassurance.
Chatbot Retail Discovery: Winning Attention, Losing Data
In retail, the battleground is chatbot retail discovery. As shoppers increasingly turn to ChatGPT and Google Gemini for AI shopping recommendations, retailers are racing to ensure their products surface in conversational answers rather than classic search results. Growing online traffic from AI platforms has pushed major chains to update product pages so they rank highly when a chatbot responds to detailed questions, forcing brands to rethink how items are described and how customers find them. According to Adobe Analytics, 41% of consumers used generative AI for online shopping in June, and visitors referred by AI services generated 41% higher revenue per visit than shoppers arriving through traditional channels. That uplift explains the scramble: appearing in chatbot results is becoming as important as search engine optimization once was. But it also hands power to AI intermediaries that decide which brands get visibility and which are quietly filtered out of the conversation.
The Data Dilemma: Retailers Need AI Traffic but Fear Dependency
Retailers now face a stark dilemma around customer data in AI. They want the high-intent traffic that AI agents deliver, but they do not want to cede the browsing histories, basket sizes, and past purchase data that underpin future sales and loyalty programs. Many are fighting to keep transactions on their own sites so they maintain a direct relationship with each customer rather than hand it to general-purpose AI tools. Ulta, for example, is seeing double the conversion and intent from shoppers finding its products through Gemini and ChatGPT, and is working with Google to tie carts and its rewards program into AI-powered shopping experiences. Yet its leadership still prefers that purchases be completed on its own domain. Cloud providers are advising brands to treat AI platforms as marketing channels while making their own websites “the best place to buy,” reinforcing the idea that whoever owns the transaction data owns the long-term economics.
Winners, Losers, and the Next Five Years of Retail Economics
The pattern is clear: AI-driven intermediaries are extracting value from traditional gatekeepers by offering faster, cheaper alternatives to established service models. In real estate, transaction coordination—the part of the agent role least tied to human judgment—is poised to vanish into software over the next five years. In retail, conversational agents are becoming the first gate for product discovery, pushing brands to compete for algorithmic favor while they try to defend their data moats. The winners are AI platforms that sit between consumers and legacy providers, and cost-conscious buyers who gain more control over complex tasks like selling a home or choosing products. The losers are commission-dependent agents and data-dependent retailers that do not adapt their pricing and information strategies. The conclusion is uncomfortable but unavoidable: if your business model is built on being the expensive translator of complexity, AI is coming for your margin. The only safe ground left is human value that software cannot yet imitate.
