OUR MISSION
To build the intelligence behind modern fandoms
Thetafan is an American AI company developing technology to measure the relationships between people, content and communities. Its proprietary, patent-pending technology combines behavioral signals to understand how fandom, trust, loyalty, influence and dependency develop and change over time.
Founded in San Diego by AI scientist Dr. Gary B. Fogel and media and technology executive Philip Mordecai, Thetafan serves content creators and owners, communities, digital platforms and governance stakeholders. The same technology that helps organizations understand valuable fan relationships is also being developed to support digital wellbeing, protection of minors and measurable self-governance in regulated environments.
“Fans are the collective force shaping modern operational influence.”
— Dr. Gary B. Fogel, Co-founder
Meet the Team
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Dr. Gary B. Fogel Ph.D
CO-FOUNDER
Dr. Gary Fogel brings over 25 years of AI innovation and leadership, blending expertise in machine learning, computational biology and practical problem-solving. A Fellow of IEEE and AAIA with a Ph.D. in Biology from UCLA, he has authored 100+ publications, holds 13 patents, and leads Natural Selection, Inc as CEO.
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Philip Mordecai
CO-FOUNDER
Philip Mordecai is an award-winning leader in technology, media and data innovation. For more than 20 years, he has built and led joint ventures across Europe and high-growth digital businesses in the UK and the US.
ABOUT THETAFAN
It started with Fans & Fandoms
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Thetafan began with a simple question: what actually makes someone a fan?
A view, purchase or follow doesn't tell you. Traditional audience data records what happened, but says much less about the relationship behind it.
Thetafan approaches the problem differently. Its technology combines behavioral signals over time to infer the nature and strength of a relationship, creating a changing picture of how people relate to content, creators and communities.
That distinction sits at the heart of Thetafan's intellectual property.
From fandom to relationship intelligence
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We Fandom was the starting point because fans make the power of relationships easy to see. They don't simply consume; they return, recommend, participate, create, advocate and influence.
But the underlying technology raises a bigger question: If we can distinguish an audience from a fan, can we also distinguish attention from trust, engagement from loyalty, or healthy attachment from dependency?
Thetafan calls this broader field relationship intelligence.
AI makes that question much more important
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AI can increasingly create, personalise and optimise content around individual behavior. As people respond, systems learn and adapt what they experience next. That creates enormous potential, but engagement metrics only tell part of the story.
A platform may know that someone watched for longer or returned more often. It may know far less about whether that person is developing fandom, trust, influence or dependency. Thetafan is building technology to measure that difference.
The technology is evolving with the problem
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Thetafan's continuing patent work builds on the same underlying invention. The technology uses multiple behavioral signals to infer relationships, measure their intensity, identify communities and update that understanding as behavior changes. Thetafan's patent work extends this architecture to relationships shaped by human, algorithmic and AI-generated content.
The principle remains the same:
measure the relationship, not just the interaction.
Thetafan didn't start with fan analytics and then discover an unrelated AI safety problem. The measurement problem underneath both is the same. AI has simply made it more consequential.
When engagement becomes something else
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That matters particularly for digital wellbeing and children. Platforms often optimise for engagement, retention and time spent. Those metrics can show whether content is performing, but they are less useful for understanding when repeated, personalised engagement may be developing into a relationship that deserves attention. Thetafan is researching how relationship signals could help organizations understand changes in fandom, trust, influence and dependency, particularly where minors are involved.
The purpose isn't to label people or decide what they should watch. It is to give organizations another measurable signal for understanding what their systems may be creating.
From policies to measurable self-governance
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Platforms operating in regulated environments increasingly need to understand and evidence how they manage risk. Policies matter, but policies alone cannot show how relationships are changing. Thetafan is developing relationship intelligence that can sit alongside moderation, trust and safety, wellbeing and compliance systems, helping organizations identify changing relationships, assess interventions and provide evidence of how risks are being managed.
We call this measurable self-governance. Thetafan doesn't decide a platform's rules. It provides a measurement layer that can help organizations understand whether the relationships developing on their platforms are moving towards or away from the outcomes those rules are designed to achieve.
What happens when AI understands us better than our measurements do?
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AI is getting better at understanding what captures people's attention and generating more of it. The ability to do that needs to develop alongside the ability to understand its effects.
Content owners should know more than how many people watched. Creators should know more than how many followed. Platforms should know more than how long somebody stayed. And governance teams need measurable evidence alongside the policies designed to protect users, particularly children.
That is the problem Thetafan is working on.
Who Thetafan is for?
Thetafan is being built for organizations that create, own, manage or govern relationships between people and content.
Content creators can understand how audiences develop into fans and communities.
Content owners can understand relationships around brands, characters, franchises and intellectual property.
Communities can better understand participation, trust, loyalty and influence.
Platforms can add relationship intelligence to behavioral data across human, algorithmic and AI-generated content.
Governance stakeholders can use measurable relationship signals to support trust and safety, digital wellbeing, child protection and governance in regulated environments.
Different applications. One underlying question: What kind of relationship is developing?