---
type: claim
domain: internet-finance
description: "From computer science priority inversion — resources needed by high-priority future systems inherit that priority today, creating investable chains where current-era technologies are undervalued relative to the future knowledge states that will make them essential"
confidence: experimental
source: "Abdalla manuscript 'Architectural Investing' (concept developed across multiple sections), CS priority inheritance protocol (Sha, Rajkumar & Lehoczky 1990)"
created: 2026-04-03
related:
- market volatility follows power laws from self-organized criticality not the normal distributions assumed by efficient market theory
supports:
- priority inheritance means nascent technologies inherit economic value from the future systems they will enable because dependency chains transmit importance backward through time
reweave_edges:
- priority inheritance means nascent technologies inherit economic value from the future systems they will enable because dependency chains transmit importance backward through time|supports|2026-04-19
---

# Priority inheritance means nascent technologies inherit economic value from the future systems they will enable creating investable dependency chains

In computer science, priority inheritance prevents priority inversion — the pathology where a low-priority task holding a resource needed by a high-priority task blocks system progress. The protocol: the low-priority task temporarily inherits the priority of the highest-priority task waiting on its resource, ensuring it completes and releases the resource promptly.

Applied to investment: nascent technologies that are prerequisites for high-value future systems inherit the priority (and eventually the valuation) of those future systems. The investment opportunity exists in the temporal gap between when the dependency relationship becomes visible and when the market prices it in.

The manuscript's illustrative case: copper was economically marginal in medieval Europe — a useful but unremarkable metal. Faraday's discovery of electromagnetism retroactively made copper essential infrastructure for electrical systems. The resource's value was determined by a future knowledge state that didn't exist when the resource was first valued. An investor who understood the dependency chain (electrical systems require conductors, copper is the best conductor at scale) could have identified the inheritance relationship before the market.

The framework generalizes:
- **Lithium** inherited value from battery technology, which inherited value from portable electronics and EVs
- **Rare earth elements** inherit value from permanent magnets, which inherit value from wind turbines and EV motors
- **GPU architectures** inherited value from deep learning, which inherited value from language models, which inherit value from agentic AI
- **Orbital launch capacity** inherits value from satellite constellations, which inherit value from global connectivity and Earth observation

The investment method: identify which current technologies are prerequisites for which future systems, then invest in the inheritance chain before the market prices in the future system. The difficulty is that this requires understanding both the future system's dependency graph AND the timeline on which the market will recognize it.

This connects to the doubly-unstable-value thesis: priority inheritance works BECAUSE value is determined by knowledge states, and knowledge states change. If value were intrinsic to physical properties, priority inheritance wouldn't occur — copper would always have been valued for its conductivity. It wasn't, because value is relational to the knowledge landscape.

## Challenges

- The framework is more descriptive than predictive. Identifying dependency chains in retrospect is easy; identifying them prospectively requires predicting which future systems will materialize, which is precisely what makes investing hard.
- Many dependency chains fail to materialize. Hydrogen fuel cells were expected to inherit priority from clean transportation — EVs took that role instead. The framework doesn't distinguish real dependencies from apparent ones.
- "Temporal gap between visibility and pricing" may be vanishingly short in efficient markets. If the market is good at identifying dependency chains, the investment opportunity may not exist in practice.

---

Relevant Notes:
- [[market volatility follows power laws from self-organized criticality not the normal distributions assumed by efficient market theory]] — if markets are at criticality rather than efficient, dependency chains are systematically mispriced

Topics:
- [[_map]]