Power, Merit, Money, and the AIIM Model

Two Modes of Hierarchy: Power and Merit

Every society contains at least two overlapping hierarchies:

Hierarchy of Power

This hierarchy answers the question:

“Who can make decisions that affect others?”

Power can come from:

  • Political authority
  • Administrative positions
  • Military force
  • Organizational control
  • Ownership of resources
  • Control of information

A person can possess great power even when their competence is limited.

Examples:

  • A bureaucrat who controls funding.
  • A minister who controls policy.
  • A corporate manager who controls hiring.
  • A university administrator who controls grants.

Power operates through dependency.

If many people depend on your decisions, your power increases.


Hierarchy of Merit

This hierarchy answers a different question:

“Who contributes the most value?”

In science this includes:

  • Discoveries
  • Theories
  • Proofs
  • Technologies
  • Explanatory power
  • Predictive power

Merit operates through achievement rather than authority.

Examples:

  • An unknown researcher who makes a major discovery.
  • A mathematician who solves an important problem.
  • An engineer who develops a revolutionary technology.

Merit creates knowledge.

Power creates decisions.

The two hierarchies may overlap, but they are not the same.


How Power and Merit Relate

In an ideal society:

  • Merit produces value.
  • Society recognizes value.
  • Recognition produces influence.
  • Influence produces power.

The chain is:

Merit → Recognition → Power

However, real systems often reverse the relationship:

Power → Recognition → Apparent Merit

People with power may receive:

  • More visibility
  • More citations
  • More funding
  • More media attention
  • More institutional support

As a result, merit and power can diverge.

A highly capable researcher may have little influence.

A highly influential administrator may contribute little original knowledge.


How Money Relates to Power

Money is one of the strongest forms of power because it creates dependency.

A simple relationship exists:

Whoever controls resources influences behavior.

Money affects:

  • Employment
  • Research funding
  • Investment
  • Education
  • Infrastructure
  • Information distribution

People naturally adapt their behavior to resource flows.

For this reason money can often be viewed as:

Stored decision-making power.

A funding agency can influence thousands of scientists without issuing direct commands.

A corporation can influence entire industries through investment choices.

Money therefore acts as a transmission mechanism for power.


Why Money and Merit Often Disagree

Historically, money does not necessarily flow toward the highest merit.

Money frequently follows:

  • Existing institutions
  • Reputation
  • Networks
  • Political interests
  • Risk avoidance
  • Short-term incentives

This creates a structural gap.

A scientist may create enormous long-term value while receiving little support.

Another project may receive large funding because it is institutionally safe.

The disagreement emerges because:

Merit measures truth or contribution.

Money measures willingness to allocate resources.

These are different mechanisms.


The AIIM Approach

AIIM proposes reducing the gap between:

Scientific Merit and Financial Support

The intended principle is:

The creators of scientific value should receive increasing influence over resource allocation.

Rather than relying entirely on administrative hierarchies, AIIM seeks to create a system where:

  • Contributions are evaluated.
  • Scientific achievements are recorded.
  • Financial support can follow demonstrated value.
  • Collaboration is incentivized.

The goal is not to eliminate power.

Power exists in every organized system.

The goal is to align power with merit more closely.

In simplified form:

Current model:

Institutional Power
↓
Money
↓
Scientific Work

AIIM vision:

Scientific Merit
↓
Money
↓
Power

This alignment attempts to make resource allocation more responsive to actual scientific contribution.


When Power and Merit Agree

The most productive periods of science often occur when:

  • Merit is recognized quickly.
  • Resources flow efficiently to contributors.
  • Institutions support discovery rather than merely preserving themselves.

In such situations:

Merit → Money → Power

becomes a positive feedback loop.

Discoveries attract support.

Support enables further discoveries.


Morality, Law, and Fear

A society also contains another distinction:

Morality

Morality asks:

“What should I do even when nobody is watching?”

It is internally motivated.

Law

Law asks:

“What am I prohibited from doing?”

It is externally enforced.

Law operates through consequences.

Morality operates through conviction.

When morality is replaced entirely by law, behavior becomes primarily fear-driven:

“I will not do this because I may be punished.”

rather than

“I will not do this because it is wrong.”

A legal system remains necessary in large societies, but law alone cannot generate trust, cooperation, or altruism. Those emerge primarily from internal moral commitments and shared values.

In that sense, law can restrain harmful actions, but morality is what makes sustained voluntary cooperation possible. Large scientific collaborations, markets, communities, and civilizations ultimately depend on both: external rules and internal ethical commitments.

👉 Help fund the next public test of AIIM.

Help Test a New Way to Fund Science

AI Internet-Meritocracy (AIIM) is an operational beta designed to allocate available donations to researchers and open-source developers using AI-assisted evaluation of documented contributions. Payment transactions are already recorded on-chain.

The next major evidence milestone is a proposed five-month public adversarial test of the allocation model, with $1,000 distributed to eligible funding recipients. Donations help pay for the development, infrastructure, reviewer and participant recruitment, outreach, and operating work needed to reach and evaluate that milestone.

You do not need to assume AIIM is already proven to support the project. Your donation helps turn the proposal into evidence: what works, what fails, and what should change.

Support the next testing milestone →   Read the test proposal →

Independent review

Researchers and technical reviewers: independent criticism is welcome, including negative conclusions. Review AIIM’s assumptions, governance, failure modes, and testing plan →

Research status: AIIM remains experimental. AI-generated evaluations can contain factual errors or biases, and decentralized governance and non-custodial components remain under development. That uncertainty is why public testing, auditability, and external criticism are central to the project.

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