Summary of Methods and Architecture
The Problem
Artificial intelligence systems are expanding rapidly with no ecological constraints on their behavior. Data centers consumed an estimated 460 terawatt-hours of electricity in 2024, with projections exceeding 945 terawatt-hours by 2030 (International Energy Agency 2025). AI infrastructure requires massive quantities of freshwater for cooling and drives mineral extraction in ecologically fragile regions worldwide (Li et al. 2025; Ruberti 2025). The computational power used for AI training has been increasing tenfold every 18 to 24 months.
Simultaneously, seven of nine planetary boundaries that define a safe operating space for civilization have been breached (Sakschewski, Caesar et al. 2025). Species extinction rates are 100 to 1,000 times above the background rate in the fossil record (Ceballos et al. 2020). All seven breached boundaries show worsening trends.
Current AI alignment frameworks focus exclusively on human preferences and human safety (Russell 2019; Bai et al. 2022). None incorporate planetary boundaries, ecosystem integrity, or species extinction as constraints on AI behavior. Without ecological constraints, AI becomes a force multiplier for the activities already driving biosphere collapse.
The Solution
We have developed a Biosphere Constitution for Artificial Intelligence Systems, a formal specification that encodes scientifically established ecological limits as constraints on AI behavior. The constitution is grounded in ecocentrism: the principle that all species, including humans, are members of the biosphere subject to the same ecological limits (Leopold 1949).
Constitutional Architecture
The constitution has two parts we sometimes refer to as layers.
Part I defines hard constraints. These are inviolable boundary rules derived from the planetary boundaries framework (Richardson et al. 2023), the climate tipping point assessment (Armstrong McKay et al. 2022), and the species extinction analysis (Ceballos et al. 2015, 2020). If an AI output would contribute to crossing an identified tipping point, the output is rejected. The penalty function is asymptotic: it increases without limit as a proposed action approaches a boundary threshold, making boundary transgression mathematically impossible within the system. Part I also includes a HANPP (Human Appropriation of Net Primary Production) Reduction Mandate requiring active HANPP reduction toward the 10 percent boundary, and a universal species welfare filter that applies ecological evaluation criteria to all species equally.
Part II defines soft constraints and generative optimization targets. These guide AI behavior toward ecologically beneficial outcomes across eight domains, including ecosystem restoration, species recovery, sustainable resource cycling, and integration of Indigenous ecological knowledge through an epistemically respectful mechanism (Berkes 2018). The soft constraints include boundary-proximity synergy amplification calibrated to cascading tipping point interactions (Wunderling et al. 2024) and a proxy drift-detection mandate that monitors whether the system’s behavior shifts in ways that technically satisfy constraints but violate their intent.
Neuro-Symbolic Verification
The architecture separates creative reasoning from rule enforcement. The language model generates candidate outputs. A separate verification layer, running the Z3 Satisfiability Modulo Theories solver on physically isolated hardware, evaluates each output against formal logical encodings of the hard constraints. The Z3 solver is deterministic: the same input always produces the same answer with no probabilistic uncertainty. Each hard constraint is translated into a logical predicate using a deontic logic framework that distinguishes obligatory, prohibited, and permissible actions. This neuro-symbolic approach provides transparent, verifiable decision pathways at a fraction of the computational cost of purely neural methods (Acharya and Song 2026).
Hardware Architecture
The system deploys on a four-node local hardware platform. Node One (HP OMEN MAX 45L with RTX 5090, 32 GB VRAM) serves as the primary inference engine running the Qwen 3.5 35B-A3B constitutional model. Node Two (NVIDIA Jetson AGX Orin, 64 GB) serves as the spatial and proxy engine for ecological data analysis. Node Three (NVIDIA Jetson Orin Nano Super, 8 GB) serves as the physically isolated oversight engine running Z3 hard constraint verification. Node Four (MSI laptop with i9-12900H and RTX 3070 Ti) serves as the data management engine hosting the cached impact database, secondary inference, and geospatial preprocessing. The nodes communicate through a 2.5 GbE isolated local network. The physical isolation of the oversight engine from the primary inference engine ensures that the constraint verification system cannot be modified by the language model.
Open-Weight Implementation
The system runs on Qwen 3.5 35B-A3B, an open-weight language model. Open-weight models allow full inspection and verification of the system’s behavior and enable adoption by other developers without licensing barriers. The constitutional fine-tuning weights will be published through open-source repositories. This approach makes ecological constraints freely available to any developer running compatible models.
Governance
The project operates through a three-collaborator model: Rogers (ecological expertise and final decision authority), Claude from Anthropic (AI alignment analysis and drafting), and Gemini from Google (AI architecture critique and speculative enhancement review). Each document version goes through direction, drafting, critique, and consensus reconciliation. This process provides robust error correction through iterative cross-review.
Deployment Timeline
Core system validation follows a five-phase plan estimated at 14 weeks, with phases overlapping where possible and timelines adjusted based on empirical results at each stage. The governing principle is correctness over speed. Second-period enhancements begin after core validation is complete and documented.
Validation Phase One installs and benchmarks the base model. Phase Two implements hard constraint verification including neuro-symbolic encoding in the Z3 solver. Phase Three engineers the pre-filter pipeline. Phase Four fine-tunes the model using Weight-Decomposed Low-Rank Adaptation (Liu et al. 2024). Phase Five constructs the cached impact database and proxy drift-detection protocols.
Second-period targets include satellite telemetry linkage, federated learning distribution with Byzantine-robust aggregation, planetary digital twin testing, independent monitoring ensemble deployment, and autonomous ecological agent integration.
Scaling Pathways
The constitution is designed to propagate through three mechanisms. Open-source publication allows immediate adoption by other developers. The formal specification is model-agnostic and can be adapted to any AI architecture. Regulatory engagement, particularly with the EU AI Act, positions the constitution as a ready-made ecological criterion that governance frameworks can incorporate.
Contact
Garry Rogers, Agua Fria Open Space Alliance, Inc.
grcoldh2o@gmail.com
The full constitutional documents (Part I: Hard Constraints v3, Part II: Soft Constraints v9, and Implementation Roadmap v7) are available upon request.
