Harbor Research
From models to reality
Technical notes on context, provenance, and persistent identity—the infrastructure layer that turns egocentric signal into enterprise-ready training data.
Featured paper
August 1, 2026 · Harbor Research
From Models to Reality
Why Context, Provenance and Persistent Identity May Define the Next Generation of AI Infrastructure
Open-weight models, falling inference costs and synthetic data are making many model capabilities more widely available. At the same time, demand for real-world, domain-specific data continues to rise—especially for robotics, enterprise workflows and embodied systems. This short research note argues that competitive advantage is shifting toward data infrastructure that carries rich context, verifiable provenance and persistent contributor identity. We examine these properties as architectural primitives rather than product features, illustrate their impact with current egocentric dataset trends and performance gaps, and outline a provenance-first reference architecture realized in Harbor Passport and its surrounding capture–validate–deliver stack.
For AI labs, robotics teams, and infrastructure buyers
Papers
More Harbor Research notes will publish here. Meanwhile, see related field notes on the blog.
Discuss this research with Harbor
Partner on provenance-rich egocentric programs informed by this work.