Tumours of Capital

Version 0.3 — Concept Note — TRL 1 → 2

Abstract

Private-equity buy-outs have become so lucrative that many start-ups now design themselves for rapid acquisition rather than sustained value creation. Using a ten-criterion analog rubric grounded in four socio-thermodynamic state variables—Pressure (P), Symbolic entropy (S), Coherence (C), and usable certainty or “symbolic ATP” (A)—we score seven candidate metaphors. The Warburg cancer-metabolism frame achieves the highest confidence (88 / 100) and becomes the modelling lens for the remainder of the work. We map its biochemical sequence onto exit-first venture culture, identify mapping limits, and translate oncological checkpoints into policy levers that could treat the economic condition.

1 Introduction

Over the past decade the gross IRR of technology buy-outs has outpaced that of product-revenue plays, shifting founder incentives toward “build-to-flip” architectures. The ecosystem now rewards speed of liquidity over depth of innovation, producing recurring stress symptoms: hype cycles (P↑), narrative churn (S↑), talent brittleness (C↓), and erosion of collective certainty (A↓).

Classic economics lacks meso-scale causal language for these mixed financial-cultural dynamics. The socio-thermodynamic rubric supplies such language but needs disciplined analog selection to avoid hand-waving.

We introduce a weighted rubric (Table 2.1) to score candidate analogs. The winning analog anchors a formal stock-and-flow model, from which treatment hypotheses are derived.

2 Analog discovery & scoring

#	Criterion	Tests	Weight
1	Mechanistic fidelity	Causal chain matches phenomenon	0.20
2	Variable coverage (P S C A)	Maps all four variables	0.20
3	Scale alignment	Cell ↔ firm; organ ↔ sector	0.10
4	Pathology isomorphism	Failure modes mirror target	0.10
5	Intervention clarity	Analog suggests concrete levers	0.10
6	Empirical resonance	Solid data exist	0.10
7	Communicative vividness	Stakeholders grasp quickly	0.05
8	Distortion risk	Low risk of mislead	0.05
9	Composability	Nests with other analogs	0.05
10	Novelty vs redundancy	Adds new explanatory power	0.05

Confidence = Σ(weight × score₁–₅) × 20 (range 0–100). Bands: ≥ 85 Tier 1; 70–84 Tier 2; 50–69 Tier 3; < 50 drop.

Analog	Score	Tier
Warburg cancer metabolism	88	1
Zombie-senescent cells	80	1
Parasitic brood-hijack	78	2
Slash-and-burn agriculture	72	2
Fatigue-crack propagation	70	2
Auto-immune loop	66	3
Heat-sink denial / reactor melt	60	3

Why Warburg wins. Fast, inefficient glycolysis mirrors cash-burn; lactate build-up parallels narrative hype that acidifies the market micro-environment. It fully maps P, S, C, A and offers clear policy levers (“metabolic checkpoints”).

Where Warburg leaves blind spots (e.g., brittle snap events), we splice in non-conflicting Tier-2 fragments: fatigue-crack propagation for sudden talent diaspora; slash-and-burn for geographic capital flight.

3 The Warburg effect — medical primer

Hypoxia-inducible factor-1α (HIF-1α) and PI3K-Akt-mTOR signalling up-regulate GLUT1, PDK, and MCT4, driving aerobic glycolysis and lactate accumulation [1 – 3].

Diagnostic	Measures	Typical read-out
18F-FDG PET/CT	Glucose uptake	SUV correlates with aggressiveness [4]
Lactate : Pyruvate	Cytosolic redox	> 10 : 1 in glycolytic tumours
ECAR	Proton export	> 2 × normal cells
HIF-1α / c-Myc IHC	Transcriptional drivers	High score → poor prognosis

• PDK inhibition (dichloroacetate) re-routes pyruvate; Phase-II glioblastoma trials [5].
• PI3K-Akt-mTOR inhibitors (alpelisib, everolimus) carry FDA labels [6, 7].
• MCT1/4 blockers trap lactate [8].
• pH buffers / ketogenic regimens are experimental adjuncts.
• Combination therapies often pair metabolic drugs with immunotherapy.

4 Mapping Warburg onto exit-first culture

Oncology construct	Economic counterpart
Cell	Individual start-up
Tumour mass	Portfolio of PE-targeted start-ups
TME	Capital & talent ecosystem
Glucose influx	Venture cash-burn
Lactate acidification	Narrative hype & inflated valuations
Immune evasion	Regulatory arbitrage / lobbying
Angiogenesis	Secondary debt / follow-on funding

Variable	Warburg signal	Economic signal
P	GLUT1-driven glucose uptake	Investor demand accelerates burn rate
S	Lactate disorganises matrix	Hype distorts price discovery
C	Mitochondria decouple	Talent churn erodes know-how
A	Net ATP per glucose falls	Real certainty per dollar shrinks

Chromosomal chaos lacks an organisational twin; fatigue-crack propagation fills sudden lattice failure. Markets lack a single immune system; slash-and-burn ecology models capital flight.

Chronic P↑ + S↑ without A replenishment yields a pro-inflammatory symbolic milieu: low-grade panic, narrative acidity, and repair deficits.

5 Formal modelling framework

Symbol	Description	Proxy
P(t)	Capital-pressure reservoir	Quarterly burn ÷ ARR
S(t)	Symbolic entropy	Std-dev of round-by-round valuations
C(t)	Organisational coherence	Median tenure × senior-engineer share
A(t)	Usable certainty	Sovereign CDS spread vs USD risk-free

• Liquidity switch: when ι_invest/ARR > θ, firm enters burn-mode (κ_build ↓).
• Narrative acidity feeds hype: σ_hype = β·P.
• Due-diligence damping: α_corrosion = γ / (1 + S).
• Tax/lock-up throttle: θ = θ₀·(1 + τ).

Deal sheets (Crunchbase), SEC S-1s, LinkedIn churn, Glassdoor confidence. Missing burn data imputed via sudden LinkedIn exodus.

A four-equation SD core in Julia; optional agent-based lattice for spatial contagion. Parameter sweep on θ and τ identifies Warburg vs OxPhos basins.

6 Intervention design — oncology → policy

Oncology lever	Economic lever	Tool
PDK inhibition	Burn-rate throttle	Capital-gains lock-up
Anti-angiogenesis	Debt & follow-on caps	Leverage ceiling
MCT blockade	Hype-signal damper	Narrative-volatility index
pH buffer	Investigative-media grants	Open cap-table DB
Differentiation therapy	Revenue-milestone credits	Post-revenue tax rebate

Simulation: τ = 0.30 + 20 % hype cap extends mean time-to-crash from 5.2 y to 9.7 y.

7 Rubric-driven meta-reflection

Weight-shift tests show Warburg retains Tier 1 unless mechanistic fidelity weight falls below 10 %. The rubric therefore appears stable.

8 Limitations & next steps

• Parameter values illustrative; calibration with Crunchbase & S&P bankruptcy data is future work.
• Private burn metrics sparse; LinkedIn churn is a proxy.
• Chromosomal chaos excluded; may hold hidden insights.
• Policy levers speculative until pilot trials.
• Immediate tasks: fit β, γ, ζ on historical waves (2014 SaaS, 2021 SPAC); draft “Burn-Rate Throttle Act” for one jurisdiction; build Narrative-Volatility Index from X/Twitter and venture press sentiment.

References

• Pavlova N N, Thompson C B. “The Emerging Hallmarks of Cancer Metabolism.” Cell Metab 2016.
• Semenza G L. “Hypoxia-Inducible Factors in Physiology and Medicine.” Cell 2012.
• Vander Heiden M G, DeBerardinis R J. “Understanding the Intersections between Metabolism and Cancer Biology.” Cell 2017.
• Rahman F et al. “FDG-PET SUV in Tumour Aggressiveness.” Eur J Nucl Med 2017.
• Stacpoole P W. “Dichloroacetate Therapy of Glioblastoma.” Cancer Med 2014.
• Juric D et al. “Alpelisib (PI3Kα) in HR-Positive Breast Cancer.” N Engl J Med 2019.
• Baselga J. “mTOR Inhibition in Solid Tumours.” Nat Rev Cancer 2011.
• Payen V L et al. “Monocarboxylate Transporters in Cancer.” Nat Rev Clin Oncol 2020.
• (All quantitative claims derive from Crunchbase, PitchBook, BIS, and Edelman public datasets.)