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.

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.

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.

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.

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.

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.

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.

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.