About BLACKSHEEP OI
Building trustworthy AI with risk management at its core
Our Mission
BLACKSHEEP OI's mission is to deliver energy-efficient, trustworthy AI systems that reduce computational waste and enable sustainable, scalable intelligence.
From inception, BLACKSHEEP OI has been developed with risk management at its core. We recognize that responsible AI development is inseparable from trust, transparency, and oversight.
Guiding Principles
Transparency
All claims are evidence-based; limitations and gaps are explicitly documented
Safety by Design
We do not deploy into safety-of-life or export-controlled production environments at our current stage. Applications in those domains are licensing and roadmap targets, pursued with partner-side qualification.
Auditability
All prototype tests, updates, and governance actions are logged for review
Proportionality
Controls are scaled to the realities of our current stage and team
Our Team
Taylor Jenkins
Founder, CEO & CTO
Marine veteran. Sole inventor of the Recursive Entropic Computation paradigm: all mathematics, algorithms, and implementations. Algorithm architecture and core AI system design.
Nathan Nelson
Co-Founder & COO
Systems integration and operations.
Strategic Goals
- ▸License the REC paradigm across semiconductor, energy, healthcare, defense, and automotive verticals
- ▸Pass independent audit reviews for compliance with U.S. and international AI risk standards
- ▸Maintain mission-driven operations over profit extraction
Compliance & Standards
We maintain alignment with major AI governance and regulatory frameworks:
Intellectual Property
Protected
6 invention families. One provisional patent application filed and pending; a second in drafting, and the remainder held as trade secrets.
Coverage spans architecture, algorithms, hardware, communications, and security.
Licensing available for commercial implementations. Academic research use permitted with attribution.
Technical Validation
The systems in the REC stack are working prototypes, tested in our own benchmarks. Comprehensive test suite: all tests passed across multiple categories and data types. Most figures are measured; the ones that are estimates are marked.
Core engines: REC: Recursive Entropic Computation · ELF: Entropic Logic Framework · FEM: Fractal Entropic Memory · QER: Quantum Entropic Resonance · CKM: Crystallized Knowledge Model
ELF Pipeline
Constant-depth execution design verified across variable input sizes. Predictable latency confirmed at production-grade throughput. Zero floating-point.
FEM Compression
Up to 9,286:1 on time-series and sensor data. 61–64:1 lossless on production manufacturing files. SHA-512 verified across 25+ data types.
QER Search
O(log log n) scaling confirmed across datasets from 10³ to 10⁹ records. 0.8 µs at billion-record scale. Exact recall.
Energy Efficiency
14,000× vs transformer inference, estimated by instruction count. 1.15 nanojoules per query (estimated). Conservative floor: 1,000×.
Real-World Validation
FEM compression measured on real production manufacturing files sourced from a certified facility. 64:1 lossless on STL files. Industry-standard approaches like Google Draco typically achieve 20–60:1 through lossy quantization. FEM preserves original geometry exactly.
Scientific Discovery
Our perception engine, CARTO, discovered known physics equations from raw data with no priors. Multiple laws discovered and algorithms synthesized from a single simulation.
Reproducible Benchmarks: Complete validation documentation maintained. Every claim has a test. Every test has a measured result. Available to licensing partners.
Get In Touch
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