07Research
From models to reality
Technical notes on context, provenance, and persistent identity: the infrastructure that turns first-person recordings into data enterprises can trust.
Harbor research
2026-08-01 · Harbor Research
From Models to Reality
Open-weight models, falling inference costs and synthetic data are commoditizing many model capabilities. 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.
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2026-08-01
From Models to Reality
Why Context, Provenance and Persistent Identity May Define the Next Generation of AI Infrastructure
2026-09-03
Continuous Generation, Inference Loops, and the Return of Real Grounding
A model that keeps generating still needs a real room to bump into. A stock set is not that room.
What we study
New research notes are published here. Practical field notes are on the blog.
Discuss this research with our team- Human feedback from chats that people choose to submit
- Real-world video and voice from the places models will actually be used
- Review criteria set before collection begins
- Provenance that stays attached to every record