From Capture to Intelligence: How ZOOP Is Rebuilding the Creator Economy for the AI Era
The technology industry has spent the past decade perfecting the capture of consumer attention.
RJ Phillips is trying to build something different.
As founder and CEO of ZOOP, Phillips sees the company’s opportunity extending beyond social media itself. Rather than competing to build another feed, he is positioning ZOOP as an AI-powered platform that combines creator monetization, audience participation, and behavioral intelligence into a single ecosystem.
That philosophy extends beyond ZOOP’s revenue model. According to Phillips, the platform is designed so that social activity produces something more valuable than engagement alone: proprietary intelligence. Every interaction, from posts and comments to purchases and rewards, feeds into AI systems designed to measure audience behavior, creator influence, community sentiment, and commercial intent using verified platform activity.
Phillips came into technology through an unconventional route.
Early in his career, he gained firsthand experience helping scale a rapidly growing creator platform. That experience gave him a practical education in how digital platforms scale, how money flows through them, and how quickly incentives can become misaligned.
Watching that growth convinced him that the biggest weakness in today’s creator economy isn’t a lack of content—it’s how value is distributed. Creators supply the content. Fans supply the attention. Brands pay to reach both. And platforms capture most of the value.
That structure has shaped the dominant social platforms of the past decade. It has also created many of the challenges now facing the industry: creator income volatility, declining platform trust, opaque algorithms, bot activity, weak attribution, and increasing regulatory pressure.
“Most social platforms obsess over features, algorithms, and growth hacks,” Phillips says. “My intuition was that none of that matters if the underlying value structure is broken.”
ZOOP was created to address those structural issues rather than simply introduce another set of social features. Its model is designed to redirect a substantial share of advertising revenue—80%—to creators and engaged users, making revenue sharing a core part of the platform rather than a mere marketing feature. It is the foundation for a different kind of platform economy.
When people participate, they generate value. ZOOP’s thesis is that they should share in it.
From engagement to intelligence
While revenue sharing distinguishes ZOOP from many existing platforms, Phillips sees the company’s AI layer as its primary long-term differentiator.
Every creator post, fan reaction, share, comment, follow, reward, purchase, and brand interaction creates a behavioral datapoint. On traditional platforms, much of that information is absorbed into closed advertising systems that creators and users rarely access directly.
ZOOP is designing a different architecture. The platform aims to capture verified, consent-led engagement and convert it into actionable intelligence through the ZOOP Sentiment Index. Rather than emphasizing surface-level metrics like follower counts, the system aims to measure audience trust, engagement quality, purchasing behavior, community health, and commercial intent.
The system is intended to answer questions traditional social metrics rarely can: Which creators generate genuine trust? Which communities matter most to brands? Which audiences are most likely to convert? And where is cultural momentum forming before it becomes obvious?
Those insights have practical value across the platform. For creators, it gives better visibility into audience quality, monetization potential, and brand fit. For brands, it helps evaluate communities using behavioral signals rather than vanity metrics. For fans, it creates more relevant experiences and rewards participation. And for ZOOP itself, the data becomes a proprietary AI asset built on verified platform activity rather than scraped social content.
In Phillips’s view, the social platform is ultimately the mechanism that produces the data, not the end product itself. The intelligence piece is where he believes the company’s long-term value lies.
Although ZOOP operates in the social media space, Phillips’s background is rooted primarily in finance and capital markets. Before launching ZOOP, he spent more than a decade as a Partner and Portfolio Manager at Atom Capital LLP managing institutional fixed income and macro strategies across global markets before moving into creator platforms, an experience that continues to shape how he evaluates digital businesses: not as products first, but as economic systems.
Rather than asking how to maximize engagement, Phillips starts with different questions: Who creates value? Who captures it? And how can a platform align sustainable incentives as it grows? That systems-first approach underpins much of ZOOP’s design.
A social platform built for the AI era
ZOOP’s product model combines several elements that are usually separated across different platforms. Creators can build channels, monetize through subscriptions, commerce, and other formats, while using platform data to better understand their audiences. Fans can engage, earn platform rewards, and participate more directly in the value created by their activity. Brands can access creator-led communities with better visibility into audience sentiment and campaign performance, while partners can build dedicated channels that keep audiences engaged year-round rather than only during a single event, trip, campaign, or transaction.
Phillips believes those capabilities become particularly valuable as AI’s role increasingly evolves. As AI becomes the interface for marketing, media buying, customer engagement, and community management, platforms will need to be less like mere content destinations and more like intelligence systems.
That vision extends beyond human users. Phillips expects AI agents to increasingly assist with creator discovery, campaign planning, and budget allocation, seeing a future where approved AI agents query ZOOP’s systems directly to identify creator fit, campaign opportunities, and budget allocation, all while drawing on permissioned access to proprietary platform data rather than scraped information. In that model, ZOOP’s competitive advantage comes not from selling data, but from generating verified insights within its own ecosystem.
That strategy is closely tied to the platform’s revenue-sharing model. Phillips contends that rewarding creators and users for meaningful participation encourages higher-quality engagement, producing stronger behavioral data that can improve creator-brand matching, audience insights, and AI recommendations. In this way, the economics and the intelligence layer reinforce one another.
Phillips’s view is that a platform cannot build durable intelligence on top of broken incentives: if users distrust the system, the data degrades. If creators feel exploited, the ecosystem weakens. If brands cannot measure outcomes, spend remains limited. ZOOP’s model attempts to align those groups from the start.
That connection also makes trust a business issue, not simply a moderation issue. If users distrust the platform, creators feel exploited, or brands cannot measure outcomes, the quality of the underlying data deteriorates. Bot activity, fake accounts, and low-quality engagement therefore become commercial problems as much as safety concerns.
To address those challenges, ZOOP emphasizes verified user systems, community-led channel governance, and extensive user controls around AI-generated content. Phillips argues that as AI-generated media floods the internet, authenticity and verified identity will become increasingly valuable—not just for users, but for the quality of the platform’s intelligence.
Phillips’s confidence in that approach is informed by previous experience helping scale one of the world’s largest creator-platform businesses in OnlyFans. That experience provided firsthand insight into creator monetization, platform incentives, payments, and the operational challenges that accompany rapid growth. Rather than recreating that model, though, he says ZOOP intends on building on its lessons by combining creator monetization, audience intelligence, rewards, and AI within a single platform.
The wider vision
That broader vision extends beyond one social app. The platforms that shaped the last decade were built around attention. ZOOP is being built around a different idea: participation should create value, and that value should flow through the ecosystem more fairly.
Phillips believes the companies that win this next decade will be those that not only capture attention, but understand it and translate it into meaningful intelligence. For ZOOP, that means building a platform where creators can monetize more effectively, brands can better understand communities, and AI systems can operate on verified engagement rather than generic social data.
Whether that vision succeeds will ultimately depend on execution. ZOOP must demonstrate that its revenue-sharing model can attract sustained creator participation, that its user base can generate differentiated behavioral data at scale, and that brands see enough value in its intelligence layer to make it commercially meaningful. If it does, ZOOP will compete less as another social network than as a radical new infrastructure for the next generation of the creator economy.