As users turn to AI models for recommendations, ad-tech faces new demands for transparency and value attribution. Franklin Rios says the industry must build infrastructure for LLM-driven discovery.
As more consumers rely on large language models (LLMs) to guide their purchasing decisions, the advertising industry faces mounting pressure to deliver transparency and measurable value in AI-generated answers. Franklin Rios, CEO of Next Net, argues that the current ad-tech infrastructure is not equipped to support this shift, leaving both advertisers and publishers exposed to opaque economics and unclear value exchange.
Independent ad-tech platforms once positioned themselves as the objective alternative to walled gardens, promising buyers smarter decisions without hidden conflicts or legacy incentives. However, as agencies and clients demand greater accountability for every dollar spent, the old model of complexity as a protective moat is losing credibility. Buyers now scrutinize fees, value creation, and the true beneficiaries of supply-path optimization, challenging the economics of even the most established platforms.
Rios notes that while independent platforms have helped professionalize programmatic advertising, market maturity means that scale and profitability now come with heightened scrutiny. The demand for transparency is intensifying just as consumer behavior is changing again: instead of browsing, users are prompting LLMs for advice on what to buy, where to go, and which brands to trust. In this environment, the most valuable asset is no longer the click or the landing page, but the citation within the AI's answer.
He suggests that marketers must adapt their strategies, moving from optimizing for links and audiences to focusing on inclusion within LLM-generated responses. The influence of brands will increasingly depend on their presence in the retrieval, citation, and recommendation layers of AI systems-areas that currently lack robust monetization and standardization.
According to Rios, the industry lacks a true supply-side platform (SSP) for LLM discovery. There is no widely accepted infrastructure to help publishers, retailers, data owners, and brands package content for citation, signal value, manage pricing, enforce quality, or create auction dynamics tailored to AI interfaces. Without such a layer, the risk is a repeat of past mistakes: hidden fees, black-box intermediaries, and a concentration of power among a few dominant players.
Advertisers, publishers, and agencies all stand to benefit from a transparent, interoperable system that supports commercial discovery in an answer-driven internet. Rios believes the opportunity lies in building market structures that foster trust, pricing logic, and transaction rails for AI-mediated attention, rather than introducing another opaque middle layer disguised as innovation.
These concerns echo issues raised in recent reporting on persistent revenue loss in programmatic advertising, where hidden fees and lack of transparency continue to challenge publishers and buyers alike.
Next Net, led by Franklin Rios, is developing infrastructure aimed at enabling trusted content discovery and digital value exchange in the AI era. The company collaborates with content owners, rights holders, and technology platforms to create more transparent and accountable ways for external knowledge to participate in AI-powered environments.
