Why Academic Science Is Broken
And How We Could Fix It
Science is not broken. Academic science may be.
This distinction matters more than ever.
Science, in its purest sense, is humanity’s most powerful method for exploring reality: curiosity, falsification, iteration, synthesis. But the system we’ve built around it - academic science - is sometimes polluting its original essence. As a matter of fact, today, “science” often serves as political legitimacy, institutional authority, or unquestioned dogma. And that gap between the scientific method and the scientific bureaucracy has never been more consequential.
This article explores why the academic structure is failing, and how we could redesign a scientific ecosystem fit for the 3th millenium.
What “Science” Really Means (And Why We Misuse the Word)
The word science - from the Latin scientia, meaning “knowledge” - never meant certainty, dogma, or institutional authority. Today, however, we often confuse science with Science™, the bureaucratic machinery of peer-reviewed consensus, political endorsement, and formally impeccable models mistaken for immutable truth.
Yet the origins of science tell a radically different story. For thousands of years, knowledge grew not through committees or journals, but through curiosity-driven minds who dared to observe, question, and connect ideas across domains. The ancient Greeks used geometry to decode astronomy; Archimedes blended mathematics, physics, and engineering centuries before the concept of “interdisciplinarity” existed. In medieval Islam, polymaths like Ibn al-Haytham pioneered the scientific method before Europe even had universities. The early Renaissance saw figures such as Leonardo da Vinci move fluidly between anatomy, engineering, optics, and botany, reminding us that science was once a holistic enterprise, not a siloed profession.
Over time, this free-form exploration evolved into an increasingly institutionalised structure: grant committees setting boundaries on what may be researched, academic journals acting as arbiters of legitimacy, and prestige hierarchies determining whose ideas are allowed to exist. All of this reshaped science into something far more rigid than the tradition it came from.
We must remember a fundamental truth: science is not absolute. It is probabilistic. It provides models of reality, not reality itself. It is a lens through which we interpret the world - useful, powerful, but inevitably incomplete.
How Academic Science Really Works
Funding as Power
What gets funded ultimately determines what questions science is allowed to ask. Funding bodies tend to favour safe bets and predictable proposals, reinforcing a culture where risk-taking is quietly discouraged and unconventional ideas rarely survive the filtering process. The Matthew Effect operates in full force: established laboratories with prestige and history attract a disproportionate share of resources, while outsiders, younger researchers, and contrarian thinkers often find themselves excluded before their ideas can even be evaluated on merit.
Research Trajectory Misaligned with Curiosity
Today, many researchers function less like explorers pursuing truth and more like employees following rules and doing tasks imparted from above. The pressure to publish, summed up in the infamous “publish or perish”, pushes scientists toward micro-results, fragmented papers, and incremental progress rather than bold, synthetic breakthroughs. Careers are increasingly measured by publication counts, journal rankings, and funding metrics, not by the ambition or courage of the questions being asked, and the determination to find plausible answers and open scientific and philosophical discussions.
Peer Review: The Guardian or the Gatekeeper?
Peer review is often presented as the gold standard of scientific validation, yet in practice it functions as a highly conservative filter - turning our most powerful method for understanding reality into a self-referential loop that frequently produces redundant or uninspired outcomes.
The process is slow, unpaid, and largely unaccountable, and its purpose is frequently misunderstood: peer review evaluates whether a paper meets a set of criteria within a specific academic framework, not whether it reflects a universal truth.
Yet the label “peer-reviewed” has become a cultural badge of excellence, even though its rigor is ultimately limited by the biases, incentives, and assumptions of the community that upholds it.
Academia’s Overflowing Archives: Millions of Papers, Little Connection
We produce more scientific papers today than at any other point in history, yet only a small fraction contribute to a genuinely integrated or cross-disciplinary understanding of the world. Hyper-specialization fractures knowledge into narrow silos where researchers become experts in microscopic niches while losing sight of the broader landscape.
The system rewards what might be called “safe novelty”: slight variations on existing studies, small statistical deviations dressed up as breakthroughs, and an endless stream of incremental outputs that maintain academic momentum without significantly advancing insight.
Almost no incentive exists for synthesizers - those rare thinkers who connect disparate fields, construct integrative frameworks, or pursue bold, systemic questions that transcend departmental boundaries. The result is a vast and growing archive of information, but a shrinking reservoir of actual knowledge.
When “Institutional Science” Becomes Political: the case Covid Vaccines
The pandemic accelerated vaccine development in unprecedented ways. While this is often hailed as a triumph - and rightly so - the process also exposed structural tensions:
• The typical timeline for vaccine development (sometimes 10+ years) was compressed to under a year.
• Emergency Use Authorization (EUA) and overlapping clinical trial phases (e.g., combining phases) raised concerns about long-term safety and side-effects.
• Public funding played a massive role: estimates suggest tens of billions were committed by governments, significantly reducing financial risk for manufacturers, which, by the way, have been found guilty and fined multiple times for drug development malpractices.
• Redundancy and waste: a retrospective analysis found that, during the pandemic, several vaccine formulations candidates were prioritized based on fewer regulatory hurdles, leading to potential neglect of other promising but less bureaucratically safe options.
Taken together, these factors hint that scientific decisions during COVID-19 were not purely empirical, but shaped by political risk, funding power, and institutional urgency. That doesn’t delegitimize all vaccine science - but it warns us against conflating “accelerated regulatory success” with pure, slow, deliberative truth.
The Epistemological Trap: Replicability ≠ Reality
One of the most dangerous myths in modern science is the belief that if something cannot be replicated under controlled conditions, it cannot be real. Yet reality does not always bend itself to laboratory protocols. Complex and emergent systems - such as ecosystems, human societies, or even consciousness - often defy strict replication because their behaviour depends on variables we cannot fully isolate or reproduce. Scientific inquiry gives us models, approximations, and working frameworks, not the ultimate ground of reality, and mistaking these models for truth leads us directly into the trap of scientism: the assumption that science represents the only, or the absolute, form of knowledge. True scientific humility requires recognising that our instruments, methods, and paradigms are inherently limited, and that the world often exceeds the boundaries we impose on it.
A New Model for Science
If academic science is structurally misaligned, we don’t just need incremental reform, we need a paradigm shift.
To execute this, I propose starting from the following open list of actionable steps.
1. Open the Doors to Independent Thinkers & Outsiders
Some of history’s greatest breakthroughs came from outsiders:
• Giordano Bruno (1548–1600): a philosopher and cosmologist who proposed an infinite universe and cosmic pluralism. His ideas anticipated relativity and challenged dominant religious and scientific dogmas.
• Nikola Tesla: an inventor and engineer far removed from the academic mainstream. Tesla’s visionary work in electricity, wireless systems, and energy transformation was often undervalued by his contemporaries.
• Modern tech innovators (like the founders of current digital giants) were not experts in their industry, but they dared to innovate and find breakthroughs that revolutionised the whole global landscape. Their overall impact can be questioned, the fact that the greatest innovations came from young outsiders cannot.
2. Encourage Radical Interdisciplinarity
Promote roles like scientific synthesizers: people whose job is to bridge disciplines, connect findings, and build systems-level insights.
Fund research that is not just deep, but wide: integrating physics, biology, AI, philosophy, systems thinking.
3. A Scientific Ethical Oath
Scientists swear nothing akin to the Hippocratic Oath. What if there were:
• A Truth & Human Flourishing Oath committed to transparency, humility, long-term thinking, and service to humanity rather than academic prestige or profit?
• An ethical framework that prioritizes knowledge that empowers, rather than knowledge that just “earns” citations.
4. Better Science Communication
Teach scientists to communicate uncertainty, not hide it behind technical jargon, and sometimes arrogance
Elevate public discourse: treat audiences as capable of understanding nuance and probabilistic reasoning.
5. Leverage AI & ML for Discovery
AI offers a transformative opportunity to rebuild the connective tissue science has lost. Instead of treating it as a mere automation tool, we can use AI to evaluate vast volumes of scientific papers, detect hidden patterns, and generate new hypotheses that humans alone would struggle to see. Its ability to cross-reference massive and diverse datasets makes it uniquely suited to synthesise scattered information into coherent knowledge or even to illuminate unconventional research paths that traditional academia overlooks. Crucially, this synthesis should extend beyond modern scientific literature into ancient sources: philosophical texts, early medical treatises, forgotten engineering insights, cosmological narratives, and pre-modern observations. Not everything modern is functional, and not everything ancient is obsolete; progress often comes from bridging the two.
In this model, AI becomes a partner rather than a replacement - a tool that amplifies the scientist’s ability to think across disciplines, map conceptual terrain at unprecedented speed, and explore ideas that would otherwise remain buried in the noise. The future of science may well depend on such collaborative intelligence, where human intuition and machine pattern-recognition evolve together, enabling discoveries neither could achieve alone.
6. Depoliticize Key Research Institutions
Create truly independent scientific auditors: third-party bodies that validate major scientific claims, especially when tied to public policy.
Establish firewalls between public funding agencies and political narratives, reducing the risk of research being co-opted for short-term agendas.
Conclusion: Reclaiming Science for the Third Millennium
Academic science may be struggling, but the deeper scientific spirit - the human drive to understand the world - remains fully alive. And that distinction matters. What’s faltering is not curiosity, not the scientific method, not the human instinct to explore, but the institutional scaffolding we’ve built around it: the incentives, the funding bottlenecks, the political entanglements, the rigid peer-review rituals, the fetishization of replicability, and a culture that confuses consensus with truth.
Yet everything outlined in this article points to a single, liberating idea: science can be rebuilt. It has been reinvented many times across history - by ancient polymaths who worked across disciplines, by Renaissance thinkers who blended art and engineering, by outsider innovators who reshaped entire industries. Let’s make the current crisis in a crisis intended with its etymological meaning: a turning point.
The opportunity before us is extraordinary. We can build a scientific ecosystem that rewards bold questions instead of safe answers; that values synthesis as much as specialization; that invites independent thinkers, contrarians, and young outsiders to participate meaningfully; that embraces interdisciplinarity not as a slogan but as a working methodology. We can cultivate a culture where humility replaces dogma, where scientific models are understood as tools rather than sacred truth, and where ethical commitments guide discovery toward human flourishing rather than bureaucratic prestige.
And we now possess something no previous era could even imagine: AI as a thinking partner, capable of sifting through humanity’s entire archive of knowledge and helping us connect dots at a scale our ancestors would have called magic. Used wisely, AI can help us see what is missing, ask new questions, and accelerate discoveries that transcend the limits of any one discipline or institution.
The future of science will not be built by gatekeeping committees or prestige journals. It will be built by communities of thinkers who collaborate across borders, institutions, and paradigms; by systems that reward courage rather than conformity; by a generation of scientists who are unafraid to admit uncertainty, to explore the unknown, and to elevate knowledge above politics.
Academic science may be broken.
But science has never been more alive.
If we choose to rebuild it with clarity, humility, creativity, and purpose, the third millennium could mark not the decline of scientific integrity, but its greatest renaissance yet.
Bibliography & Explanatory Notes
1. Philosophy & Origins of Science
Aristotle – Posterior Analytics
Takeaway: Establishes early foundations of empirical reasoning, causality, and systematic inquiry. Demonstrates how ancient thinkers approached knowledge without institutional science.
Archimedes – Works, ed. T. Heath
Takeaway: Shows how mathematics, physics, and engineering were seamlessly integrated in ancient science, illustrating the original interdisciplinary nature of scientific inquiry.
Ibn al-Haytham – Book of Optics
Takeaway: Often cited as the first true scientist. Introduces experimentation, falsification, and systematic observation centuries before European scientific institutions existed.
Leonardo da Vinci – Notebooks
Takeaway: Embodies science as holistic curiosity: anatomy, mechanics, hydrodynamics, optics. Demonstrates how breakthroughs emerge from cross-disciplinary thinking, not bureaucratic structures.
Thomas S. Kuhn – The Structure of Scientific Revolutions (1962)
Takeaway: Paradigm shifts often require breaking away from institutional consensus; “normal science” resists new ideas. Supports the article’s point about institutional conservatism.
2. Sociology of Science, Institutions & Incentives
Robert K. Merton – “The Matthew Effect in Science” (1968)
Takeaway: Famous paper explaining how prestige snowballs in academic systems, giving disproportionate visibility and funding to already-famous scientists.
Richard Horton – “Offline: What Is Medicine’s 5 Sigma?” The Lancet (2015)
Takeaway: Editor-in-chief of The Lancet states that up to 50% of published biomedical research is “untrue.” Highlights systemic flaws in peer review and publication incentives.
John Ioannidis – “Why Most Published Research Findings Are False” (2005)
Takeaway: One of the most cited papers in science studies. Argues structural biases, small sample sizes, and publication pressure undermine reliability. Supports the article’s thesis on redundancy and poor replicability.
Daniele Fanelli – “Do Pressures to Publish Increase Scientists' Bias?” (2010)
Takeaway: Demonstrates that “publish or perish” environments increase questionable research practices and reduce scientific quality.
David Graeber – Bureaucracy (2015)
Takeaway: Explores how bureaucratic structures create stagnation, risk-aversion, and conservatism—core issues mirrored in academic science.
3. Peer Review & Knowledge Fragmentation
Tom Nichols – The Death of Expertise (2017)
Takeaway: While focused on public culture, Nichols outlines how expert systems often become rigid, insulated, and self-legitimising—mirroring issues in peer review.
Naomi Oreskes – Why Trust Science? (2019)
Takeaway: Argues that scientific consensus is valuable but not infallible; institutions must remain open to critique. Clear support for the idea that “peer-reviewed” ≠ “true.”
Milojević, Filip – “The Principles of Scientific Boom and Bust Cycles” (2015)
Takeaway: Shows that scientific output grows exponentially while genuine innovation does not, reinforcing the point about overflowing archives and declining knowledge synthesis.
James Evans – “Electronic Publication and the Narrowing of Science and Scholarship” (2008)
Takeaway: Digital publication paradoxically reduces citation diversity, shrinking intellectual breadth and increasing siloization.
4. Replicability, Epistemology & Scientism
Karl Popper – The Logic of Scientific Discovery
Takeaway: Introduces falsification as a core scientific principle; reminds readers that no scientific model is absolute—only falsifiable.
Paul Feyerabend – Against Method (1975)
Takeaway: Argues that overly rigid scientific methods hinder progress and that historical breakthroughs often violated methodological norms. Good support for epistemological humility.
Steven Shapin – Never Pure (2010)
Takeaway: Demonstrates that science has always been shaped by culture, politics, and institutions. Undermines the myth of objective, isolated science.
Open Science Collaboration – “Estimating the Reproducibility of Psychological Science” (2015)
Takeaway: Large-scale study finding that only ~36% of psychology studies replicated. Key evidence for the replicability crisis.
Nature – “Challenges in Irreproducible Biology” (2016)
Takeaway: Reports widespread replicability issues in preclinical biomedical research. Supports the point on complex/emergent systems and limited replicability.
5. COVID-19, Biomedicine & Institutional Pressures
FDA – Emergency Use Authorization Framework
Takeaway: EUA allows for accelerated approval when traditional timelines are incompatible with emergency needs; illustrates compression of vaccine development timelines.
Moderna, Pfizer Clinical Trial Protocols (2020–2021)
Takeaway: Show overlapping phases, rapid scaling, and unprecedented public funding—highlighting institutional urgency and political influence.
BMJ Investigations on Vaccine Transparency (2021–2022)
Takeaway: The British Medical Journal published multiple articles flagging issues of data access, regulatory capture, and lack of independent oversight.
Public Citizen – “The Real Story of Big Pharma’s Criminal Penalties”
Takeaway: Documents decades of fines for misconduct (off-label marketing, data suppression), providing context to trust issues during COVID-19.
WHO Landscape of COVID-19 Candidates (2020–2021)
Takeaway: Shows redundancy in vaccine development, heavy reliance on certain platforms, and political prioritization of “low regulatory risk” candidates.
6. Innovation Outside Academia
Isaacson, Walter – The Innovators
Takeaway: Demonstrates how major tech breakthroughs were driven by outsiders, hobbyists, and interdisciplinary thinkers—not by academic orthodoxy.
Nasaw, David – Andrew Carnegie / Tesla: Man Out of Time
Takeaway: Tesla’s outsider status and creative autonomy exemplify breakthroughs emerging outside institutions.
Peter Thiel – Zero to One
Takeaway: Emphasizes contrarian thinking and outsider innovation; relevant to the argument that major breakthroughs often come from non-experts.
7. AI, Knowledge Systems & Scientific Discovery
Bengio, Goodfellow, Courville – Deep Learning (2016)
Takeaway: Provides theoretical foundations for ML systems capable of pattern-extraction and hypothesis generation—key for AI-enhanced science.
Schmidt & Lipson – “Distilling Free-Form Natural Laws from Experimental Data” (2009)
Takeaway: Early demonstration of AI autonomously discovering scientific laws from raw data.
Kosinski – “Large Language Models Develop Theory of Mind” (2023)
Takeaway: Suggests AI can perform high-level reasoning tasks, indicating potential for conceptual scientific assistance.
King et al. – “The Robot Scientist” (2009)
Takeaway: First demonstration of a system autonomously forming hypotheses and running experiments—a foundational case for AI as scientific partner.
If you appreciated this content consider supporting us here.
