GTESI—the General Theory of Evolutionary Systems & Information—is a practical framework for understanding which systems persist, adapt, or collapse—and why.
Rooted in thermodynamics, information theory, and systems evolution, GTESI unifies insights from Shannon, Feynman, Einstein, and Ricardo to track how systems maintain coherence under pressure. It doesn’t replace spreadsheets—it explains why they sometimes stop making sense.
GTESI identifies the hidden architecture of persistence in technologies, policies, and organizations. It highlights failure points before collapse, and strengths before scale. In sectors where conventional models lose signal—GTESI clarifies the path forward.
GTESI’s Four Vectors
GTESI analyzes systems using four predictive metrics. Each one reflects a different axis of systemic resilience or risk.
| Vector | Description | Signal Interpretation |
|---|---|---|
| IPR – Inverse Persistence Ratio | “Value without memory” | Measures the gap between symbolic persistence (e.g. hype, valuation) and structural memory (plant, cashflow, execution). A high IPR warns of symbolic inflation—but a low IPR can signal deep-rooted resilience others miss. |
| SCD – Symbolic Compression Divergence | “When story breaks from system” | Tracks how aligned the narrative is with the actual operations. Rising SCD means story and system are drifting apart. Compression is leaking. Signal is fragmenting. |
| TRFI – Trust Ritual Friction Index | “Rituals keep systems sane” | Measures how well a system performs symbolic trust behaviors (filings, roadmaps, continuity). Rising TRFI means friction in rituals—like missed filings or quiet exits—signals adaptive stress or deeper strain. |
| EED – Entropy Export Delta | “Adaptation stalls, pressure builds” | Captures how effectively a system offloads entropy—through growth, licensing, simplification, alliances. A low EED is a valve. A high EED is a pressure cooker. Innovation without ventilation risks rupture. |
Why GTESI Matters
Most analytics describe what is. GTESI explains what’s becoming.
It shows why some platforms quietly survive when others collapse. It shows where narrative compression is failing before reputational collapse. And it highlights which ventures are poised to absorb shocks—or snap under them.
GTESI helps you ask:
- Is this system exporting entropy or hoarding it?
- Are the symbols still aligned with the structure?
- Are rituals stabilizing trust—or breaking down?
- Is persistence a result of structure—or the illusion of it?
In fields from finance to climate tech, pandemic planning to biomanufacturing, GTESI has already revealed early warning signals that traditional tools missed.
Examples:
- Spotting fragility behind inflated COVID preparedness narratives in 2019.
- Explaining why renewable diesel thrived while ethanol-to-jet faltered.
- Identifying hydrogen’s symbolic resilience—and its structural fragmentation.
- Tracking asset bubbles using symbolic coherence and adaptive friction.
Core GTESI Concepts
| Concept | Description |
|---|---|
| Entropy | The unavoidable cost of motion—disorder, energy loss, complexity |
| Compression | Turning motion into structure: stories, codes, habits, laws |
| Memory | What persists after motion: infrastructure, trust, symbolic fidelity |
| Entropy Export | Offloading systemic stress—via trade, growth, licensing, or simplification |
| Symbolic Trust | Faith in signals: filings, forecasts, brands, leadership, cadence |
| Narrative Compression | When story and system align—so signal is dense, clear, and persistent |
How GTESI Works
GTESI evaluates a system’s motion-memory balance. A healthy system:
- Exports entropy through productive motion.
- Retains coherence between symbols and structure.
- Performs rituals that maintain internal and external trust.
- Compresses narrative into usable, repeatable signals (manufacturing, contracts, guidance).
A brittle system exhibits:
- High IPR: Symbols outlasting operations.
- High SCD: Narrative drifting from delivery.
- High TRFI: Ritual cadence breaking down.
- High EED: Innovation without adaptive escape routes.
Background & Provenance
GTESI emerges from an interdisciplinary synthesis:
- Claude Shannon – Compression, information entropy
- Albert Einstein – Energy, curvature, and persistence
- David Ricardo – Comparative exchange and specialization
- Richard Feynman – Energy pathways and systemic modeling
Its full theoretical model appears in the forthcoming book Everything in Motion. But its use does not require a physics degree—just a willingness to ask: Why is this system still standing? And what might cause it to fall?
What GTESI Is Not
GTESI is not a deterministic model. It does not coerce. It clarifies.
It does not shame struggling leaders. It gives them earlier mirrors. It does not attack failing systems. It helps recompress what still has structure. It is not reactive analysis. It is proactive pattern recognition—for systems in motion.
There’s often a moment of hesitation:
“This feels dense. Who else is using it?”
That’s fair. GTESI is emergent. It’s not branded, credentialed, or legacy-approved. But it works. And the more it works, the clearer its value becomes.
