JCS-1:
The Jackson Coherence Standard
A revolutionary theoretical framework for thermodynamic integrity and coherent design in the age of advanced computing
The Philosophy of Coherence
All engineered systems exist in perpetual tension between two fundamental forces: dissipation and harmony.

Heat is not merely a byproduct of computation—it is matter's language of protest, a visceral signal that reveals the fundamental mismatch between the medium we've chosen and the mission we've assigned it.
Coherence represents the resolution of this protest: a state of profound alignment among materials, architectures, and electromagnetic fields where entropy production doesn't destroy order but sustains it.

This is not wishful thinking—it is the operationalization of thermodynamic law itself.
JCS-1 establishes a universal criterion for innovation in computing, energy systems, and artificial intelligence: coherence must precede claims.


Performance without coherence is merely endurance, not progress.

It is the difference between Mastery and Coping.
Core Metric:
The Jackson Coherence Index
Thermal Inefficiency
I_T = 100 × (Q_loss / E_in) represents the percentage of input energy converted to unwanted heat
Coherence Index
C = 1 - (Q_loss / E_in) measures true thermodynamic alignment, where C→1 indicates perfect coherence
Entropy Form
C = 1 - (σ / σ_c) alternative formulation using entropy production rate for advanced applications

The Coherence Index provides an unambiguous, quantitative measure of how effectively a system converts input energy into useful work versus protest heat.

Every percentage point of heat loss represents a percentage point of deviation from ideal coherence.

This is not a suggestion—it is thermodynamic reality speaking through mathematics.
The JCS Grading Scale
JCS-1 introduces a standardized grading system that translates abstract coherence measurements into actionable engineering guidance.

This scale provides immediate clarity on whether a system exhibits thermodynamic mastery or is fundamentally flawed in its physics.
1
A+ Grade: Physically Coherent
0-5% thermal inefficiency.

Near-perfect resonance between medium and mission.

Represents true thermodynamic mastery.
2
A Grade: Efficient Operation
5-10% thermal inefficiency.

Minimal incoherence with excellent operational characteristics.

Commercial viability with minor optimization potential.
3
B Grade: Acceptable Design
10-25% thermal inefficiency.

Minor material or architectural mismatch.

Redesign recommended but not critical.
4
C Grade: Significant Issues
25-50% thermal inefficiency.

Substantial incoherence indicating poor physics. Immediate redesign strongly indicated.
5
D-F Grades: Critical Failure
>50% thermal inefficiency.

Fundamental thermodynamic protest.

System represents bad science requiring complete reconceptualization.
Complementary Metrics
The Jackson Coherence Index forms the foundation of JCS-1, but complete system evaluation requires additional quantitative measures that capture economic viability, design quality, and operational autonomy.

Real Return on Energy (R)
R = (W_use / E_in) - (Q_loss / E_in)
This metric reveals a profound truth that market success often obscures: a device can generate profit while delivering negative physical return.

When heat loss exceeds useful work, the physics is extractive rather than progressive, regardless of the financial statements.
Design Fault Index (D)
D = k · ΔT / P_out

For systems with output power P_out and unintended temperature rise ΔT, the Design Fault Index quantifies architectural inefficiency.

When D exceeds 0.25, legacy electron-limited design constraints are strongly indicated, revealing fundamental limits in the chosen medium.

Autonomy Factor (A)
A system achieves genuine autonomy only when power generation, processing capability, and operational persistence are all native to the platform. The Autonomy Factor A = f(P₁, P₂, P₃) represents the geometric combination of power independence, processing independence, and persistence independence. Only when A = 1 can a system legitimately claim true autonomy—anything less is tethered dependence.
Doctrine I:
The Rim Analogy
"Computing with electrons is like driving on rims: motion is possible, but only through friction, drag, noise, and heat. There HAS to be a better way…"
— Jackson P. Hamiter, 2025
Consider the violent absurdity of a vehicle stripped of its tires, metal rims grinding against asphalt.

Forward motion remains technically possible, but at what cost?

The screech of metal on pavement, the shower of sparks, the rapid degradation of both rim and road—all announce that this is not transportation but trauma.
This is precisely the state of conventional computing.

Electrons, constrained by their mass and charge, traverse resistive pathways at glacial speeds compared to Photons.

Every instruction generates friction.

Every calculation produces heat.

The cooling infrastructure—fans, heat sinks, liquid loops, entire data center HVAC systems—represents our industrial-scale acceptance of computational injury.
The cooling trucks and megawatt refrigeration systems surrounding modern AI training facilities do not demonstrate engineering triumph.


They demonstrate that we are driving on rims at highway speeds, and the heat never stops.

The Law
If C < 0.75 (thermal inefficiency >25%), the system operates below Jacksonian coherence and exhibits thermodynamic protest requiring fundamental redesign.
The Vision
Legacy computation drives on rims.

PhotoniQ glides on light—frictionless, silent, coherent.
Doctrine II:
Profit vs. Physics
The Mirage of Market Success
A profitable error remains an error.

Revenue cannot negotiate with entropy.

Stock prices do not alter thermodynamic law.

The market may reward inefficiency for years or decades, but physics keeps an immutable ledger, and the bill always comes due.
Modern semiconductor companies generate billions in quarterly revenue while their products convert the majority of input energy into waste heat.

By financial metrics, they succeed spectacularly.

By physical metrics, they operate in continuous thermodynamic deficit—taking more from the grid than they deliver in useful computation.
The Real Return on Energy metric (R) exposes this fundamental disconnect between commercial viability and physical validity.

When R < 0, the physics is extractive.

The system survives not through coherence but through brute consumption, subsidized by cheap electricity and expensive cooling.
Jackson's Law States
R = (W_use / E_in) - (Q_loss / E_in).

When thermal losses exceed useful work, no amount of profit transforms physical incoherence into coherence.
The Market Cannot Bribe Heat
Thermodynamic reality operates independently of quarterly earnings, investor sentiment, or market capitalization.

Physics is the ultimate regulator.
Doctrine III:
The False Invention Principle
The Flying Car Fallacy
"If your flying car needs a helicopter to make it fly, you don't have a flying car—you have a press release."
— Jackson P. Hamiter, 2025

The technology sector suffers from a chronic condition: declaring breakthrough capabilities while maintaining silent dependence on legacy scaffolding.

This represents more than optimistic marketing—it is fundamental misrepresentation of physical reality.
True autonomy in any system requires simultaneous independence across three critical dimensions: power generation, processing capability, and operational persistence.

A drone that requires external charging is not autonomous—it is tethered with extra steps.

An "edge AI" device that depends on cloud connectivity for actual inference is not intelligent at the edge—it is a thin client with aspirations.
The Autonomy Factor A = f(P₁, P₂, P₃) quantifies this multi-dimensional independence.

Only when A = 1—when power, processing, and persistence are all genuinely native—can a system claim authentic autonomy.

Anything less is scaffolding with marketing.

Power Independence (P₁)
Native energy generation, not grid dependence or periodic recharging
Processing Independence (P₂)
Onboard computation without cloud offloading or external inference
Persistence Independence (P₃)
Continuous operation without mandatory downtime or maintenance cycles
Doctrine IV:
Intelligent Brute Force
Power divorced from feedback is not strength—it is chaos masquerading as capability.

The application of energy without informational constraint produces heat, noise, and waste with mathematical certainty.

Quality Control Law #1 establishes that force must be proportional to informational demand, not maximum available wattage.

The Principle
Consider a simple thermostat: it does not blast heating at maximum power regardless of ambient temperature.

It measures, responds proportionally, and maintains equilibrium.

Yet conventional computing applies full voltage to transistors whether the calculation requires it or not, dumps maximum current through channels designed for peak load, and treats energy as if abundance justified waste.
The design rule P_appl = k∇Φ formalizes feedback-constrained power application.

Applied power should track the gradient of the information field—the actual computational demand—not the thermal limits of the substrate.

Every watt beyond P_opt converts directly to Entropy.

01
Measure Demand
Quantify the actual informational gradient requiring computation
02
Apply Proportionally
Deliver power matching computational need, not substrate maximum
03
Monitor Response
Continuously assess output and adjust power dynamically
04
Minimize Excess
Treat unused wattage as entropy requiring elimination
Doctrine V: Electron Hard Limits
Physics imposes absolute constraints that no amount of engineering cleverness can circumvent.

Electrons possess intrinsic properties—mass, charge, drift velocity—that establish hard thermal ceilings for any architecture dependent upon them.

These are not engineering challenges to be overcome; they are physical boundaries that define the operating regime.
Electron drift velocity in silicon peaks near 10⁷ cm/s under high fields—roughly one percent the speed of light.

This fundamental speed limit, combined with unavoidable resistive losses in conductors and parasitic capacitance in any realistic geometry, means that electron-based computation generates heat as an intrinsic consequence of its physical mechanism.

You cannot negotiate with drift velocity.

You cannot optimize away resistance at the atomic level.

Drift Velocity Ceiling
~10⁶ m/s maximum electron speed imposes fundamental switching time limits
Resistive Dissipation
Joule heating is thermodynamically mandatory in ohmic conductors
Parasitic Capacitance
Unavoidable in realistic geometries, converting switching energy to heat

The Design Fault Index D = k·ΔT / P_out quantifies distance from coherence through temperature rise.

Every degree above ambient represents dissipation, wasted energy converted irreversibly to heat.

When D exceeds 0.25, the system is definitively electron-limited, operating within the hard constraints that define legacy architectures.

Design Efficiency Law #2
Electron-based architectures face insurmountable physical constraints. Temperature rise above ambient directly measures the thermodynamic cost of these limitations. Systems with D > 0.25 require medium transition—optimization within the electron regime yields diminishing returns.
"Do not scale inefficiency. Adding transistors, cooling, or watts to offset poor physics multiplies disorder."
The most seductive engineering mistake is scaling a flawed architecture.

When faced with inadequate performance, the instinctive response is to add more of everything: more transistors, more cores, more memory bandwidth, more power delivery, more cooling capacity.

This approach trades temporary performance gains for exponential increases in complexity, cost, and thermodynamic disorder.
Design Efficiency Law #3 captures this failure mode mathematically: η_n = η₀(1-ε)ⁿ. Each replication of an inefficient element (efficiency η₀ with defect ε) compounds the defect.

When the base inefficiency ε exceeds 5% and the replication count n grows large, the cumulative efficiency collapses catastrophically.

A system built from components with 95% individual efficiency sounds impressive until you chain 100 of them together—resulting in system efficiency of 0.59%, or barely better than random chance.


The Scaling Trap
  • Moore's Law assumed fixed efficiency per transistor
  • Dennard scaling promised constant power density
  • Both assumptions broke by 2005
  • Industry response: scale anyway, add cooling
  • Result: exponential energy growth for linear performance
The Coherent Alternative
Scale coherence, not capacity.

Improve the base efficiency η₀ and reduce the defect ε before replication.

A system built from components with 99.9% efficiency maintains 90% system efficiency even after 1000 replications.

The mathematics of compounding works in both directions—it amplifies excellence as readily as it amplifies defects.
Recognition
Identify base inefficiency in fundamental architecture
Remediation
Redesign to minimize ε before any scaling
Scaling
Replicate only after achieving η₀ > 0.95
Coherent System
Maintain efficiency through arbitrary scale
The Disruption Map
JCS-1 enables systematic identification of domains ripe for coherent transformation.

Every sector where legacy paradigms exhibit poor coherence (C < 0.75) represents an opportunity for categorical disruption through physics-first redesign.

Computation
Legacy: Binary electron logic with resistive dissipation
Coherent: Photonic ternary logic with minimal thermal signature
Power Distribution
Legacy: Resistive grids with transmission losses

Coherent: Autonomous distributed generation with local coherence
Thermal Management

Legacy: Heat rejection through refrigeration

Coherent: Entropy reformation converting disorder to useful work
Artificial Intelligence

Legacy: Data-hungry brute-force training on GPU clusters

Coherent: Coherence-driven learning with minimal energy footprint

The strategic maxim underlying this disruption map is unambiguous: The entity that masters Heat masters Intelligence itself.

In every domain, the coherent alternative offers not incremental improvement but categorical superiority—faster, smaller, quieter, longer-lived, and ethically superior through reduced environmental impact.
The Moat of Coherence
Sustainable competitive advantage in technology rarely comes from features, patents, or even substantial capital investment.

These can be copied, circumvented, or outspent.

True moats emerge from fundamental asymmetries that compound over time, creating gaps that widen rather than narrow as competitors respond.

Thermodynamic coherence represents precisely this kind of structural advantage.

When your rival burns kilowatts to perform inference that your system accomplishes with watts, you have not achieved a 1000× performance advantage—you have established a categorical difference in operational physics.

The competitor must not merely optimize their approach; they must abandon it entirely and rebuild from thermodynamic first principles.
The moat indicator ΔH = H_legacy - H_coherent quantifies this advantage through heat budgets.

For equivalent computational workloads, if ΔH ≫ 0 (your heat generation is dramatically lower) and your coherence index C > 0.90 (you maintain thermodynamic integrity), the gap is defensible.

The competitor cannot bridge it through clever cooling or improved power delivery—they must reinvent their fundamental architecture.
Heat Advantage
Lower thermal overhead enables denser integration
Density Advantage
Compact form factors enable new applications
Power Advantage
Reduced energy requirements enable autonomy
Persistence Advantage
Extended operation without maintenance enables reliability
Market Advantage
Superior capabilities generate revenue funding further coherence improvement
The Master of Heat
"The Master of the Heat will be the Winner of the A.I. Arms Race"
— Jackson P. Hamiter, 2025
Thermal inefficiency represents the last universal tax on intelligence.

Every watt dissipated as waste heat is a watt that could have driven computation, maintained memory state, powered sensors, or enabled communication.

In the race toward artificial general intelligence and beyond, the constraint is not algorithmic creativity or training data volume—it is energy efficiency at the physical layer.
Current AI training runs consume megawatts continuously for weeks or months.

The largest training clusters approach the power consumption of small cities.

This is not sustainable at scale, nor is it necessary.

It represents thermodynamic incoherence institutionalized as infrastructure—we have normalized computational pain and called it progress.


The Thermal Constraint
  • GPT-3 training: ~1,287 MWh total energy
  • Equivalent to 120 US homes for one year
  • >90% dissipated as heat
  • Cooling infrastructure cost exceeds compute cost
  • Scale limits imposed by power availability
The Coherent Future
  • Photonic coherence: C > 0.95 achievable
  • Heat generation reduced by 20× minimum
  • Training speed increases through density
  • Edge deployment becomes viable
  • Scale limited only by physics of light

Eliminate waste heat, and you unlock categorical dominance: faster training, denser deployment, edge autonomy, extended mission duration, and ethical superiority through reduced environmental impact.

The master of heat is not the entity with the largest cooling budget—it is the entity that needs no cooling at all because their physics is coherent.
JCS-1 Measurement Protocol
The theoretical framework of JCS-1 gains practical power through rigorous, reproducible measurement.

This public-safe protocol enables any laboratory to compute coherence indices, assign JCS grades, and compare designs across platforms without proprietary knowledge or specialized equipment.

01
Instrumentation Setup
Power analyzer for E_in measurement, calorimeter or thermal model for Q_loss, precision temperature sensors, optional entropy instrumentation
02
Stabilization
Operate device at defined workload until thermal equilibrium achieved, typically 30-60 minutes for computing systems
03
Data Collection
Measure input energy E_in and heat loss Q_loss simultaneously over minimum 10-minute window, record ambient and case temperatures
04
Computation
Calculate C = 1 - (Q_loss / E_in), assign JCS grade from standard table, compute R and D indices if applicable
05
Documentation
Report all parameters, measurement uncertainties, ambient conditions, and cooling configuration for reproducibility

Measurement Fidelity
The accuracy of JCS grading depends critically on precise heat loss measurement. Calorimetric methods provide gold-standard accuracy but require specialized equipment. Thermal resistance modeling (Q_loss = ΔT / R_th) offers practical alternative with 5-10% uncertainty when R_th is well characterized. Document your method clearly to enable cross-laboratory comparison.
Competitive Benchmarking Example
Theory becomes tangible through concrete comparison.

Consider a standard machine learning workload executed on contemporary hardware versus a coherent photonic alternative—a thought experiment grounded in measurable thermodynamic reality.
Legacy GPU Architecture
450W
Power Draw
Continuous consumption under ML training load
102°C
Junction Temperature
Operating temperature requiring active cooling
90%
Heat Conversion
Input energy dissipated as thermal waste
Coherence Analysis: Q_loss / E_in ≈ 0.90 → C = 0.10 → JCS Grade: F
This is not a design flaw—it is a thermodynamic indictment. The physics is in continuous protest.
Coherent Photonic Alternative
22W
Power Draw
Equivalent computational workload through optical coherence
32°C
Operating Temperature
Minimal rise above ambient, passive cooling sufficient
1%
Heat Conversion
Input energy dissipated, remainder drives computation
Coherence Analysis: Q_loss / E_in ≈ 0.01 → C = 0.99 → JCS Grade: A+
Near-perfect thermodynamic alignment. Physics in harmony with mission.

The contrast is categorical, not incremental.

The coherent system achieves 20× power reduction and 50× better coherence index.

Service life extends by orders of magnitude.

Deployment scenarios previously impossible—edge AI, mobile robotics, space applications—become trivial.
This is the dividend of thermodynamic mastery.
Quality Control
&
Design Efficiency Laws
JCS-1 consolidates fundamental principles into actionable engineering laws that guide coherent system design.

These are not suggestions—they are thermodynamic imperatives that determine whether architectures succeed or fail.

1
Jackson's Law of Thermal Inefficiency
Excess heat is not a byproduct; it is a protest.

The magnitude of thermal dissipation directly measures the mismatch between medium and mission.

Coherence resolves the protest by aligning physics with purpose.
2
Intelligent Brute Force (QC Law #1)
Power must be feedback-constrained.

Apply energy proportional to informational gradient (P_appl = k∇Φ), not substrate capacity.

Unused wattage converts directly to entropy and disorder.
3
Electron Hard Limits (DE Law #2)
Drift velocity, resistance, and capacitance impose absolute thermal ceilings on electron architectures.

Temperature rise quantifies distance from coherence.

Systems with D > 0.25 require medium transition.
4
Parasitic Upscaling (DE Law #3)
Never scale inefficiency. Efficiency compounds as η_n = η₀(1-ε)ⁿ.

When base defect ε > 0.05 and replication n is large, system efficiency collapses.

Scale coherence, not capacity.
5
Heilmeier Catechism (Adapted)
State objectives without jargon.

Declare risks, costs, and validation tests explicitly.

Performance claims require measurement methodology.

Coherence before commercialization.
Strategic Implementation
JCS-1 provides more than measurement—it enables strategic decision-making across research, development, and deployment.

Organizations can use coherence indices to prioritize investments, evaluate acquisition targets, and identify market opportunities.
Research Prioritization
Direct funding toward architectures exhibiting C > 0.75 in preliminary tests.

Terminate programs that cannot articulate path to B-grade coherence within 18 months.

Thermodynamic viability precedes feature development.
Manufacturing Strategy
Design production processes that preserve coherence at scale.

A component with C = 0.95 in lab must maintain C > 0.90 in volume manufacturing.

Process yield metrics should include thermal performance alongside functional testing.
M&A Due Diligence
Require JCS measurement of target company products as standard acquisition due diligence.

A profitable product line with F-grade coherence represents obsolescence risk, not value.

Thermal analysis reveals hidden technical debt.
Policy & Procurement
Government agencies should require minimum coherence standards (C > 0.75) for data center procurements and defense systems.

Taxpayer dollars should fund thermodynamically sound technology, not institutionalized inefficiency.



The geopolitical stakes are substantial.

Nations that establish coherence standards and incentivize C-grade-or-better technology will achieve AI capability advantages while reducing energy infrastructure burden.

The master of heat gains strategic independence.
Path Forward:
Adoption & Evolution
JCS-1 establishes a foundation, not a final answer.

The standard will evolve through open collaboration, empirical validation, and theoretical refinement as the community engages with coherent design principles.

1
Phase 1: Publication & Review
Release JCS-1 as public-safe theoretical standard. Invite academic and industry critique.

Refine definitions based on community feedback.
2
Phase 2: Experimental Validation

Independent laboratories measure coherence indices across device classes.

Calibrate JCS grades against real-world thermal performance.

Build empirical database.
3
Phase 3: Standards Body Engagement
Submit to IEEE, ISO, or IEC for formal standardization.

Develop certification protocols. Enable third-party JCS testing and verification.
4
Phase 4: Market Integration

Manufacturers adopt JCS grading as product specification.

Procurement policies reference coherence requirements.

Industry recognizes thermodynamic integrity as competitive differentiator.

Call for Collaboration
PhotoniQ Labs invites researchers, engineers, and institutions to engage with JCS-1 through:
  • Independent measurement campaigns
  • Theoretical extensions and refinements
  • Application to novel device classes
  • Critique and improvement proposals
Open Science Commitment

JCS-1 is public-safe and non-proprietary. All measurement protocols, equations, and grading criteria are freely available.

This standard belongs to the community that validates and improves it.
Conclusion:
Coherence as Imperative
"One does not simply accept the Second Law of Thermodynamics—one masters it."
— Jackson P. Hamiter, 2025

The Jackson Coherence Standard represents more than a measurement framework—it articulates a fundamental shift in how we conceive, design, and evaluate technology.

For decades, the computing industry has operated under the implicit assumption that waste heat is inevitable, that cooling infrastructure is merely overhead, that thermodynamic inefficiency is the price of progress.
JCS-1 rejects this premise entirely. Heat is not inevitable—it is diagnostic.

Thermal dissipation is not overhead—it is feedback.

Inefficiency is not the price of progress—it is the definition of its absence.

The Second Law of Thermodynamics does not forbid coherence; it demands it for any system claiming optimality.

The Challenge
Current AI infrastructure consumes gigawatts and requires industrial cooling.

This scales linearly with capability growth—an unsustainable trajectory.
The Opportunity
Coherent architectures break the thermal scaling law.

Capabilities grow while energy requirements remain bounded by physics, not by brute force.
The Imperative
Organizations that achieve coherence first establish compounding advantages—technical, economic, and strategic—that widen over time.
Intelligence
Sustainability
Accessibility
Reliability
Performance


The entity that masters heat will not merely win the AI arms race—they will redefine what winning means.

They will deploy intelligence where others require infrastructure.

They will operate continuously where others require downtime.

They will scale economically where others encounter thermodynamic cliffs.
JCS-1 provides the language, metrics, and principles to make coherence measurable and mastery achievable.

The path forward is clear: design with thermodynamic integrity from first principles, measure coherence rigorously, and scale only what deserves scaling.
The age of thermodynamic coherence has begun. Will your organization master heat, or be mastered by it?
Jackson's Theorems, Laws, Principles, Paradigms & Sciences…
Jackson P. Hamiter

Quantum Systems Architect | Integrated Dynamics Scientist | Entropic Systems Engineer
Founder & Chief Scientist, PhotoniQ Labs

Domains: Quantum–Entropic Dynamics • Coherent Computation • Autonomous Energy Systems

PhotoniQ Labs — Applied Aggregated Sciences Meets Applied Autonomous Energy.

© 2025 PhotoniQ Labs. All Rights Reserved.