Tag
environmental-intelligence
- EssayThe Missing Chapter of AI Safety
A verbatim term search of the five documents that govern frontier AI—OpenAI's Model Spec, Anthropic's Constitution, DeepMind's Frontier Safety Framework, Meta's Frontier AI Framework, and the EU AI Act—returns zero hits for watershed, aquifer, groundwater, wetland, habitat, emissions, conservation of mass, and conservation of energy. Argues that this is not an oversight but an unopened category, and sketches what an environmentally grounded system would commit to: disclosure rather than refusal, hard constraints drawn only from conservation laws, grounding in live measurement rather than recollection, and an auditable log.
- EssayThe Automation of Why
A first-principles case that the scarce resource in the information age is no longer the capacity to answer but the curiosity to ask—and that a question, measured in bits and joules, is the cheapest high-leverage act in physics. Argues for automating 'why' through Seeker, an environmental curiosity engine that generates, verifies, and compounds questions to close the epistemic gap in planetary stewardship.
- EssayEnvironmental Artificial Intelligence
A definitional anchor for environmental artificial intelligence: AI systems built specifically for environmental work, whose models, corpora, and success criteria answer to the state of physical environmental systems. Distinguishes environmental AI from generic AI that merely touches environmental data, and traces its maturation path toward environmental superintelligence.
- EssayEnvironmental Intelligence
A definitional anchor for environmental intelligence: the capacity to convert the state of the physical environment into understanding precise enough, and timely enough, to act on. Distinguishes intelligence from data collection, and places the term at the base of the conceptual ladder that runs through environmental artificial intelligence to environmental superintelligence.
- EssayThe Scaling Imperative: A First-Principles Comparison of Human-Cognitive and Integrated Computational Networks for Planetary-Scale Intelligence
Quantitative first-principles comparison of two architectures for planetary-scale environmental intelligence: the Human-Cognitive Network (HCN), defined by the brain's ~100-bit-per-second I/O bottleneck, and the Integrated Computational Network (ICN), with petabit-scale backbones. Frames the transition as a thermodynamic imperative driven by Whitehead's Law of Unthinking and details the architecture of the 'Inverted Stack'—a computer-native intelligence system.
- EssayInverting the Stack: A First-Principles Analysis of Computer-Native Environmental Intelligence and the Elevation of Human Cognition
The August 2025 first articulation of the Inverted Stack architecture, later developed in 'The Scaling Imperative.' Argues that the current Human-Cognitive Network is architecturally insufficient for 21st-century planetary stewardship and that a transition to an Integrated Computational Network is a thermodynamic imperative.