|
Getting your Trinity Audio player ready...
|
For decades, modern science has assumed that management is indispensable: rectors run universities, administrators allocate grants, institutions coordinate research, and marketing departments “promote impact.” This model is rarely questioned. Yet its effectiveness is increasingly doubtful. Managers in Science
This article examines whether scientific management is genuinely productive—or whether it has become an artificial bottleneck—and explores an alternative: direct, algorithmic funding of scientists and science marketers via AI-driven systems such as AIIM.
Why Is Being a University Rector Considered So Important?
The rector (or president) of a university is typically portrayed as a strategic leader responsible for:
- Budget allocation
- Hiring and promotion policies
- Research priorities
- External relations and fundraising
In theory, this role exists to optimize scientific output. In practice, the rector’s success is often measured by non-scientific metrics: institutional rankings, political alignment, compliance, and administrative stability.
This creates a structural paradox:
The person with the least direct involvement in research often has the most power over research outcomes.
As universities scale, rectors become political managers, not scientific optimizers. Their incentives drift further from discovery and closer to risk minimization, bureaucracy, and reputation management.
Why Not Give Money Directly to Scientists?
A natural question follows:
Why not eliminate management layers and fund scientists directly?
Historically, the standard objection has been:
- “Scientists cannot manage money”
- “Coordination requires institutions”
- “Peer review must be centralized”
However, these assumptions increasingly fail under scrutiny.
Modern scientists already:
- Manage complex research pipelines
- Coordinate globally via open platforms
- Self-publish preprints and software
- Build reputations through demonstrable output
What they often cannot do is bypass institutional gatekeeping that delays or blocks funding for reasons unrelated to merit.
Direct funding would:
- Reduce latency between idea and execution
- Eliminate administrative rent-seeking
- Reward actual output rather than conformity
In economic terms, current science funding resembles central planning with delayed feedback—a model known to be inefficient.
Do We Need Managers—or Marketers?
If one function is genuinely missing from many scientists’ skill sets, it is communication, not management.
Science does not primarily fail because of poor internal coordination; it fails because:
- Results are poorly explained
- Value is not communicated to society
- Discoveries remain invisible to funders and users
This suggests a crucial distinction:
- Managers control scientists
- Marketers amplify scientists
A marketer’s incentives can align with scientific success:
- They benefit when work is impactful
- They are replaceable based on performance
- Their output is measurable (reach, adoption, funding raised)
In contrast, managerial power often becomes self-justifying and detached from outcomes.
Do We Need Research Institutions at All?
Large research institutions increasingly resemble kolkhozes in agriculture:
- Centralized control
- Weak personal incentives
- Risk aversion
- Output measured by formal compliance rather than productivity
Just as agricultural collectivization reduced efficiency despite good intentions, institutionalized science often suppresses:
- Unconventional ideas
- Independent researchers
- High-risk, high-reward work
This does not mean all institutions are useless—but it does mean their monopoly role is unjustified in a digital, networked world.
Institutions should be optional service providers, not mandatory intermediaries.
AIIM: Replacing Managers with Algorithms
The AI Internet-Meritocracy (AIIM) proposes a fundamentally different architecture:
- AI evaluates scientific output directly
- Funding flows to individuals, not institutions
- Reputation is proof-of-work–based, not credential-based
- Science marketers are funded alongside scientists
- Decisions are auditable, appealable, and adaptive
Instead of asking:
“Who should manage science?”
AIIM asks:
“What measurable signals indicate real scientific value?”
AI does not eliminate judgment—it compresses latency and reduces human bias. Unlike managers, algorithms do not accumulate political power or protect incumbency by default.
A Structural Shift, Not a Technological One
This is not about replacing humans with machines. It is about removing unnecessary intermediaries between:
- Discovery and funding
- Scientist and society
- Value creation and reward
Managers emerged when coordination was expensive. Today, coordination is cheap—but gatekeeping remains.
The real question is no longer whether we can fund scientists directly, but why we still pretend we cannot.
Conclusion
- Scientific management is largely a historical artifact
- Rectors optimize institutions, not discoveries
- Direct funding aligns incentives more efficiently
- Marketing matters more than managerial control
- Institutions should compete, not dominate
- AIIM offers a scalable alternative to centralized authority
Science progresses fastest when those who discover are those who decide—and when funding follows evidence, not hierarchy.
👉 SUPPORT AI Internet-Meritocracy project – salaries directly to scientists and science marketers