The Name Found Its Moment
In 1996, William Gibson published Idoru. In the novel, eigenheads are virtual personality constructs—digital identity models derived from mathematical analysis of human behavioral patterns. Gibson took a term from linear algebra and used it to describe something new: the essential qualities of human consciousness, captured in virtual space.
I read Idoru in San Francisco in 1999. The concept stuck. I registered eigenheads.com that year, not knowing what it was for. Just knowing it meant something.
The Mathematics Underneath
The scientific definition came later. Eigenheads descend from eigenfaces—a facial recognition technique introduced by Turk and Pentland in 1991. The method extracts essential features as eigenvectors, creating a reduced-dimensional space where identity becomes computable.
The "eigen" prefix runs through all of modern AI. Linear algebra—vectors, matrices, transformations—is the mathematical bedrock underneath every neural network, every large language model, every system that seems to understand what you're saying. When you talk to an AI, you're watching linear algebra operate at massive scale.
Gibson intuited something in 1996 that the mathematics would prove out decades later: identity, pattern, meaning—these things can be computed. Not perfectly. Not completely. But enough to change everything.
Twenty-Five Years Later
I started eigenheads as a consulting practice in 2014. The name finally had its purpose: a firm that balances analytical precision (eigen) with humanity (heads).
Then the AI acceleration happened.
In 2025, we're living in territory Gibson mapped in fiction. Large language models that hold conversations. Systems that reason, generate, and occasionally surprise even the people who built them. The Turing test, once a distant philosophical benchmark, now feels like a formality we're approaching fast.
The name I registered in 1999—before most AI consultancies existed, before most founders in this space had graduated high school—suddenly resonates more than ever.
What We've Learned
The more I work with AI, the more I recognize Gibson's world. Not the dystopia. The texture. The way human expertise and machine capability blur at the edges. The way specialized knowledge becomes more valuable, not less, when you can connect it to systems that scale.
We've run discovery workshops with reinsurance consultants and audit firms. We've built production applications for industries most AI companies have never touched. We've developed open standards for material identity data. Every engagement teaches us something about where the edge actually is.
The pattern holds: the humans who know their domain deeply are the ones who get the most from these systems. Our job is to make that connection—fast, live, without theatre.
The Mission
eigenheads exists to facilitate real collaboration between humans and artificial systems. Not demos. Not proofs of concept that never ship. Actual work: exploring concepts, developing solutions, building implementations that change how organizations operate.
Whether we're working with today's capable AI or whatever comes next, the focus stays the same: meaningful human-machine collaboration that advances what's possible.
The math caught up to the name. Now we help companies navigate the territory we've been mapping since 1999.