I am leaving academia despite enjoying the process of scientific discovery. I found real pleasure in asking questions, following ideas wherever they led, and uncovering pieces of the world that had previously been unknown. But over time, I began to feel a moral responsibility to direct that curiosity toward something that would meaningfully benefit society or humankind at large.

This raised a more difficult question:

What makes a problem meaningful enough to dedicate your life’s work to?

Growing up, I assumed the answer was human suffering. I entered medical sciences research in part because I believed I was meant to help alleviate it. If something causes people to suffer, then surely reducing that suffering is meaningful work.

I am less certain now. Suffering is an unavoidable part of being human. Some of it can and should be prevented or relieved, but much of it cannot simply be solved. Illness, loss, loneliness, disappointment, aging, and death will remain features of human life, regardless of how advanced our science or institutions become. Even when we eliminate one source of suffering, others inevitably take its place. We cannot escape it.

Perhaps, then, meaningful work does not always have to eliminate suffering. It can also create the conditions for a richer life alongside it.

There is value in creating beauty, connection, curiosity, play, and joy. These things do not eliminate the difficulties of being human, but they give us something to hold onto as we move through them. They can make life richer and more worth living.

This realization has changed what I find interesting. The questions that now seem most meaningful to me are increasingly social rather than scientific. I find myself thinking less about which biological mechanism I want to understand and more about the conditions in which people live: how communities are built, why institutions succeed or fail, and what helps people feel rooted in community. I am increasingly drawn to the idea of adding beauty to the world—not merely aesthetic beauty, although that matters, but anything that makes human life more worth inhabiting. I am interested in building environments that give people the freedom, support, and sense of belonging to discover and realize their potential—cultural institutions designed to help people flourish rather than simply extract value from them. Academia can certainly be a place to pursue these aims, and some people do. But I have found that the incentives and structures of academic science today make it difficult to succeed in this meaningfully, particularly at the scale I would like. I want to work in environments where building better communities and creating beauty are not peripheral to the work, but integral to it.

I don’t yet know exactly what form this will take in my career. But I feel a renewed sense of direction. I want to keep learning, developing new skills, and working alongside exceptional people who challenge how I think and expand what I believe is possible. I hope that, through those experiences, I will develop a clearer understanding of where I can contribute most meaningfully and what I ultimately want my life’s work to be.

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The Case for Beauty

Born in Halifax, Nova Scotia, maritime painter Jack Lorimer Gray (1927–1981) developed an early fascination with the sea, spending his childhood sketching ships around Lunenburg Bay. His talent was recognized by the Canadian painter Edmund Wyly Grier, and after the Second World War he studied at the Nova Scotia College of Art, where he was mentored by Elizabeth Styring Nutt and David Whitzman. There he also met fellow Nova Scotian artist Joseph Purcell, with whom he would later travel and paint along the province’s coast.

In 1947, Gray and Purcell rented a loft above a fish store in New Harbour, Nova Scotia, using it as a studio while they explored the surrounding coast. The following year, Gray traveled to Montreal to study life drawing under Arthur Lismer, a member of the Group of Seven whose paintings had helped establish a distinctly Canadian approach to landscape. Gray, however, was more interested in the boats outside the classroom than the human figure inside it. During one class, he was found sketching a boat hull instead of the model. The episode became emblematic of an artist whose attention was already firmly fixed on the maritime world.

Gray soon left formal art education to spend several seasons working aboard Nova Scotia’s dory-fishing schooners. The experience transformed his relationship with his subject. Rather than observing boats and fishermen from shore, he learned their construction, movement, and daily rhythms firsthand, accumulating sketches, photographs, and notes that would form the basis of his early paintings. His first major solo exhibition, held in Chester in 1948, established him as a promising young maritime painter.

The sea remained Gray’s great subject, but his surroundings changed throughout his career. In the early 1950s he often lived aboard boats that doubled as floating studios. After moving to New York in the middle of the decade, he turned his attention to the city’s harbor, painting tugboats, steamships, and working waterfronts from a small skiff. The resulting New York Harbor Collection brought him a wider American audience and demonstrated how naturally his maritime perspective could extend from the fishing villages of Nova Scotia to one of North America’s busiest industrial harbors.

Gray eventually returned to Nova Scotia, although he continued to travel and paint in Maine, Florida, and the Bahamas. His work remained grounded in the subjects he had known since childhood: working boats, sailors, harbors, and the changing conditions of the sea. Unlike many artists whose distinctive style emerged primarily through formal experimentation, Gray developed his perspective through immersion in his subject. His years at sea gave his paintings a particular physical accuracy, while his training under Lismer and his early associations with other Canadian artists provided a broader artistic foundation.

Gray’s work occupies an unusual place in Canadian art. He was trained within the country’s emerging modernist tradition, but his artistic identity was shaped less by movements than by experience. He did not simply paint the sea; he sailed it, worked on it, and repeatedly returned to it. His distinctive contribution was therefore not a radical new visual language, but an unusually intimate understanding of the maritime world he chose to depict.

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Five years is a long time to spend in any environment, and the experience inevitably becomes about more than just the work itself. In retrospect, my five years in graduate school at the University of Toronto fall naturally into distinct chapters.

Year 1: Learning the Craft

I arrived at graduate school with a mixture of uncertainty and excitement. I often questioned whether I was cut out for it. Yet, I was also exhilarated by the idea of entering an entirely novel world: a new city, university, intellectual environment, and community. I was surrounded by unfamiliar ideas, difficult questions, and people who seemed to know so much about things I wanted to understand. I read constantly, asked endless questions, and loved the feeling of discovering how much there was still to learn. At the time, the world of science felt expansive and full of possibility.

Year 2: Building Through Experimentation

The novelty gradually gave way to the harder, quieter work of becoming competent. Much of my second year was spent troubleshooting failed experiments, refining protocols, repeating what did not work, and eventually learning how to generate reliable data. I learnt that good science is painfully slow. There was little immediate reward, but knowledge accumulated and technical judgment developed through repetition and failure. Managing to sustain that high effort, despite the immense self-doubt, became an important foundation for everything that followed.

Year 3: From Learning to Discovery

Then the experiments started working. I became better at asking meaningful questions and, increasingly, at answering them. My confidence grew not only as hypotheses I developed were supported by the data, but as I began to contribute meaningfully to the work of others. I found myself increasingly engaged in scientific discussions across different environments, contributing ideas alongside principal investigators.

In many ways, this was the best year of my PhD. For the first time, I found the balance between difficulty and competence that makes challenging work deeply rewarding. I could become completely absorbed in any problem: moving between experiments, analysis, and ideas with a growing sense of fluency and momentum. I was genuinely excited to get out of bed each morning and work on the questions I had chosen to pursue. Every successful experiment felt like the best day of my life, and I could not wait to tell everyone around me what I had discovered. For the first time, the difficulty itself became part of the pleasure: I had developed enough competence to meet the challenge rather than simply endure it.

This was the most productive period of my PhD in terms of data generation, but it was also when I began to recognize that I could contribute intellectually beyond the boundaries of my own project. I was no longer simply learning science; I was beginning to see myself as someone who could meaningfully contribute to it.

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Year 4: Developing a Mind of My Own

My attention began to move beyond my own projects and research field. I became increasingly interested in connecting ideas across disciplines and understanding the broader intellectual context of my work. I also began to think strategically about what makes an idea compelling: how questions are framed, disparate findings connected, and arguments communicated with clarity and force. Preparing major research grants and manuscripts taught me to think about how ideas are positioned within the scientific landscape, and what makes others willing to invest in them.

I had already learned how to work with people, but I now had to learn how to hold onto my own judgment when others disagreed. I became more comfortable questioning assumptions, defending my reasoning, and navigating disagreement without allowing it to become personal. I also learned that logic and evidence, while essential to science, are not always sufficient for persuading people or moving a group forward. How an idea is communicated matters. Rhetoric, relationships, timing, and an understanding of how other people see a problem can be just as consequential as the strength of the underlying argument.

That was a difficult lesson for someone who had entered science believing that good reasoning should speak for itself. I began to understand that institutions are made up of people, and people do not respond to evidence in purely rational ways. Scientific environments are no exception.

I also began to recognize how deeply an environment can shape the way we think, work, and understand what matters. Graduate school was often an isolating experience, but it also gave me considerable space to develop my own judgment. Throughout the PhD, I remained conscious of holding onto my own priorities and convictions, even when they differed from those around me. I did not always feel compelled to adapt simply for the sake of fitting in, and over time I became more comfortable with that distinction. Rather than viewing difference as a tension that needed to be resolved, I came to see it as an important part of developing an independent mind. This made me think more deliberately about the kinds of environments I want to be part of: those in which intellectual independence is valued, criticism and dissent are welcomed, and different perspectives can strengthen rather than constrain the work.

Year 5: Reconciling Ideals with Reality

I had dealt with failure throughout my PhD, but in my fifth year it affected me differently. After years of hard work and investment in academia, I had begun to expect more from it: intellectual fulfillment, mentorship, and recognition that good work mattered. When reality fell short, setbacks felt less like isolated failures and more like a reckoning with the system I had invested in.

I learned to separate rejection from identity. A failed experiment, rejected paper, or criticism could be disappointing without becoming a judgment of my worth. I had to learn to recognize the value of my own work without relying on others to validate it, and to let criticism inform me without allowing it to define me.

I became increasingly disillusioned not only with academia, but with aspects of science itself. I became more aware of how much research falls short of the standards we aspire to: questions that are poorly framed, experiments that are insufficiently rigorous, conclusions that extend beyond the data, and findings that are difficult to reproduce or meaningfully build upon.

This was one of the more unsettling realizations of the PhD. I had entered science with a fairly idealized view of the scientific process: that good questions, careful experiments, rigorous reasoning, and honest interpretation would naturally lead to good science. In practice, the process is much more human and much more imperfect. Incentive structures matter. Researchers are rewarded for novelty, productivity, and attention, and institutions operate under their own pressures. These forces do not necessarily align with the slower, less glamorous work required to produce robust knowledge.

The more I understood these dynamics, the more complicated my relationship with academia became. I still found the intellectual pursuit of science deeply compelling. I loved asking questions, working through uncertainty, discovering something that was not known before, and building an argument from evidence. But I became less convinced that the institutions in which science is practiced consistently optimize for those things.

And yet, this was also when I saw everything come together. After years of experiments, setbacks, analysis, and writing, seeing the culmination of the work was deeply rewarding. My defense felt particularly symbolic. It was no longer an examination in the way I had imagined it, but a genuine scientific discussion among colleagues. I had entered the PhD wondering whether I was cut out for science; I left knowing that I could think independently, do the work, defend my conclusions, and decide what questions I wanted to pursue next.

I do not yet know where these questions will lead me. But the curiosity that brought me to science remains. I am deeply grateful for the academic experience that allowed me to develop it—for the freedom to explore, the room to make mistakes, and the opportunity to learn not only about science, but about people and institutions. Over five years, I came to appreciate how deeply organizational structures, incentives, relationships, and culture shape what people can accomplish. The PhD taught me how to think independently, navigate uncertainty, believe in myself, and work patiently toward difficult answers. I look forward to carrying those habits of mind into questions beyond the boundaries of science.

If you are considering graduate school, or are already somewhere in the middle of a PhD and would like to talk through your experience, feel free to reach out. I am always happy to share what I have learned and to hear how the experience has been for others.

We live in a world increasingly preoccupied with efficiency and productivity. Even leisure is often given a utilitarian justification: exercise improves our health, reading develops our minds, learning a skill makes us more capable. Beauty is harder to defend on these terms because its effects are real but difficult to measure.

A sunset has no measurable utility. Neither does a painting, or an afternoon spent sitting beside a lake watching the light move across the water. And yet these experiences can leave us feeling calmer and more connected to the world around us. They soothe something in us that is difficult to name and even harder to quantify.

To appreciate beauty is to practice gratitude for what is already here—to pay attention to the present without immediately asking what it will produce in the future.

In a world increasingly organized around productivity and progress, beauty offers an antidote to the anxiety of always looking ahead. Beauty teaches us, if only for a moment, that the present does not need to become something else to be worth experiencing.

If we pay close enough attention to the world, it becomes vivid again. A tree is no longer simply a tree; we notice its particular shape, the texture of its bark, the movement of its branches. Light becomes something to observe rather than merely something we see. In this way, appreciating beauty allows us to experience the natural world with a greater degree of intimacy.

Attention, in turn, develops taste. The more carefully we look, the more distinctions we begin to perceive. We learn why one composition feels harmonious while another feels cluttered or why something fashionable may not necessarily be beautiful. Taste is the cultivated ability to perceive these differences.

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Our response to beauty is not entirely arbitrary. Across cultures and throughout history, people have shown recurring preferences for certain forms, colours, patterns, proportions, and natural environments. The particulars vary, but some aesthetic preferences appear remarkably persistent. Beauty may reflect something more fundamental about how humans perceive the world.

We are naturally drawn to order. Symmetry, repetition, rhythm, and proportion give us a sense that the world is intelligible. A pattern allows us to anticipate what comes next. There is something reassuring about recognizing structure in what might otherwise appear random—a signal that we are successfully processing and making sense of our environment, an ability that may have important implications for survival. Order is closely related to safety.

But we do not want everything to be ordered. A perfectly repetitive pattern eventually becomes boring, and a completely predictable piece of music leaves nothing to discover. We are also drawn toward the unfamiliar and uncertain, toward experiences that challenge our expectations and require us to make sense of what we do not immediately understand. Chaos introduces variation, uncertainty, and the possibility of discovery. The most compelling forms of beauty often contain enough order to give us a foothold, but enough chaos to keep us from seeing everything at once.

The same principle seems to operate in nature. Recognizable patterns are everywhere, but they are rarely perfectly repetitive. Waves follow predictable forms without ever being identical. Trees grow according to underlying biological patterns but are shaped by countless contingencies. Even our bodies develop according to evolutionarily conserved programs, yet variation in our genes and environment ensures that no two are quite alike.

Perhaps it is this interplay between order and chaos that gives complexity its depth. Meaning accumulates through time, effort, and experience. Mastery requires struggle; relationships acquire history; art gains significance as we come to understand the life behind it. Suffering, too, becomes part of the texture of a life. We are shaped by what we endure.

Our attraction to this balance reflects something fundamental about us. We need security, but we also need something to pursue. Too much uncertainty is difficult to bear, yet a life without challenge leaves little to discover or strive toward. What we pursue gives our lives purpose; the pursuit itself gives our lives meaning.

Beauty lies in the tension between order and chaos: enough structure to make the world bearable, and enough uncertainty to give us a reason to continue.

Our perception of beauty changes as we grow older. The painting has not necessarily become more beautiful. We have simply accumulated more of the experiences that allow us to see what is already there. What once looked plain can begin to look profound. Taste, then, may be less about learning what we should find beautiful than about becoming capable of seeing more. The richer our perception becomes, the richer the world becomes with it.

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Science is an Exploration, Not an Enterprise

Why I am Leaving Academia

Born in Blidsberg, Sweden, landscape painter and printmaker Sven Birger Sandzén (1871–1954) was the son of a rural Lutheran preacher and an accomplished watercolourist. He studied under the celebrated Swedish painter Anders Zorn and became part of Stockholm’s lively art scene in the 1890s, joining the Artists League (Konstnärsförbundet) in 1891, whose independent school challenged the conservative traditions of the Royal Swedish Academy.

In 1894, Sandzén moved to Paris, where he encountered the emerging currents of European modernism. He studied under Edmond-François Aman-Jean and absorbed the semi-scientific colour theories of Divisionism, in which separate points or strokes of pure colour are placed beside one another to produce more vibrant, luminous effects when viewed from a distance. He was particularly influenced by the work of Paul Signac and Georges Seurat, whose experiments with colour and light would remain evident in his work throughout his career. Sandzén ultimately fused these European influences with the vigorous brushwork of Zorn and his own fascination with the dramatic landscapes of the American West.

His great muse was the American landscape. After emigrating to the United States in 1894 and settling in Lindsborg, Kansas, Sandzén initially painted the surrounding prairie and rolling hills. A trip to Colorado in 1908 transformed his artistic direction. The Rocky Mountains offered the scale, geological drama, and changing light that would occupy him for the rest of his life. He returned repeatedly to Colorado and the Southwest, transforming mountains, rivers, forests, and rock formations into compositions of saturated colour and energetic, almost sculptural brushwork. In his later work, the distinction between landscape and abstraction begins to dissolve: the subject remains recognizable, but what matters increasingly is the sensation of light, colour, and movement.

Sandzén spent more than five decades in Kansas, teaching at Bethany College while continuing to paint and make prints. His work occupies an unusual position in American art: a Scandinavian-trained painter who carried the ideas of European modernism into the American West, and in doing so developed a distinctly personal vision of the landscape.

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There is a tendency to think of science as a production system: resources are invested, experiments are conducted, and those experiments are expected to produce useful outcomes. The better the system, the more efficiently it should convert investment into useful products. Under this model, an unsuccessful experiment is a failed investment, and a scientist who cannot articulate the eventual application of their work is difficult to justify funding.

But science was never meant to function as an enterprise for producing predictable returns. At its core, science is a formalized way of exploring the unknown.

Exploration is inherently uncertain. It is costly, resource-intensive, and often futile.

Most attempts will not produce anything immediately useful. But exploration has an asymmetric payoff: a small number of discoveries can generate benefits vastly greater than the resources invested in the many attempts that fail. The possibility of those discoveries—and the knowledge they add to civilization—justifies maintaining a capacity for exploration despite its uncertainty. You cannot know in advance which questions will matter, where an unexpected observation will lead, or what value a discovery may hold in the future. This is not a flaw; it is merely the nature of exploration.

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Part I — The Value of Science to Society

The pursuit of scientific knowledge creates several distinct forms of value for society.

1. Instrumental value

Scientific discovery has enabled more efficient agriculture, reduced food waste, improved housing and infrastructure, and transformed the prevention, diagnosis, and treatment of disease. It has expanded our capacity to improve the material conditions of human life in ways that would have been unimaginable to previous generations.

This is the form of scientific value that society tends to recognize and encourage most readily because its outcomes are comparatively tangible. A new therapy can be evaluated by the number of patients it reaches. A new technology can be measured by the efficiency or productivity gains it generates. These outcomes are visible, quantifiable, and relatively easy to translate into the language of policy and economics. Yet measurability is not synonymous with significance.

An invention can solve an immediate problem while creating consequences whose broader or longer-term effects remain unknown. Antibiotics, for example, transformed the treatment of infectious disease while simultaneously creating evolutionary pressure for antimicrobial resistance. AI illustrates the same broader principle: technologies developed to increase productivity, expand access to information, and automate difficult tasks may also introduce forms of misinformation, dependency, concentration of power, and social disruption that were difficult to anticipate at the outset.

Neither example makes the underlying scientific or technological advance a mistake. Rather, they illustrate how difficult it is to assess the ultimate value of an intervention when its consequences unfold across complex systems and over long periods of time.

The question of whether a scientific advance is ultimately good for humanity is considerably more complicated than we often acknowledge.

2. Epistemic value

Science also produces something more fundamental: knowledge about the world.

We learn something that we did not previously know. That knowledge may have no obvious application when it is discovered, but that does not mean its eventual value can be assessed as zero.

Scientific knowledge is cumulative and interconnected. A seemingly obscure observation can become the missing piece that makes a later discovery possible. A biological mechanism that appears irrelevant to human disease may become important when another researcher discovers how it interacts with a different pathway. A mathematical principle developed without any practical purpose may eventually become foundational to a technology that did not yet exist.

This is one of the fundamental difficulties of evaluating scientific knowledge by its immediate utility: we rarely know which pieces of knowledge will become useful until we have accumulated enough of them to see the connections.

Science serves not only to solve the problems we currently recognize, but to expand the body of knowledge from which future questions and solutions can emerge.

There is also a deeper reason to value the pursuit of knowledge. Exploration is one of the ways civilization has learned to confront the fundamental uncertainty of existence. We want to know where we came from, how life emerged, whether we are alone in the universe, and what governs the world around us. Science does not necessarily provide comforting answers to these questions, but it gives us a way to ask them rigorously and replace some measure of uncertainty with understanding.

In this sense, the pursuit of knowledge serves a purpose beyond its eventual applications. It helps us make sense of the world we inhabit. A civilization that can investigate its origins, understand its place in the universe, and continually expand the boundaries of what it knows is better equipped to confront the uncertainty inherent in existence.

This gives scientific inquiry an existential dimension: some questions are worth answering not for their practical utility, but because understanding our existence is itself a human concern.

3. Aesthetic value

There is a third form of value that is harder to quantify: aesthetic value.

Scientific training teaches us to observe—to look carefully, notice patterns and differences, and remain attentive to phenomena that might otherwise seem ordinary. In doing so, it changes not only what we know about the world, but how we see it.

A microscope can transform a drop of water into an ecosystem. Evolution can transform a forest from a collection of trees into the product of billions of years of history. Astronomy can transform the night sky from a scattering of lights into a universe containing billions of galaxies.

In this sense, science is not merely a way of understanding or controlling the world. It is a way of experiencing it. And there is value in that experience. Human beings spend much of their lives moving through a world that can easily become familiar and unremarkable. Scientific observation offers a way of continually rediscovering it.

Perhaps this is one reason the pursuit and teaching of science matter even when they produce no immediate practical benefit. To teach someone how to observe the world is, in a small way, to give them more of the world to experience.

The reward is not merely greater knowledge, but a richer encounter with the world and the complexity and beauty it contains.

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If science creates value in ways that cannot always be predicted, then we must also reconsider how we decide who should be entrusted to pursue it.

Part II — Who Should Perform Science?

Scientific exploration is expensive. It consumes public and private money, specialized infrastructure, equipment, institutional capacity, and enormous amounts of human time. Every scientific project therefore carries an opportunity cost.

If exploration is costly, then society has a responsibility to be selective about who is entrusted with it.

What should we select for? Not simply intelligence, technical competence, nor the ability to publish. A good scientific explorer should posess the following:

Curiosity: an insatiable need to ask questions and a desire to understand the world more deeply.

Independence: the ability to resist intellectual conformity and pursue contrarian questions on the strength of one’s own judgment.

Judgment: discernment of signal from noise, promising questions from trivial ones, and productive failures from wasted effort.

Humility: the willingness to recognize the limits of one’s knowledge and revise one’s assumptions when evidence demands it.

Tolerance for uncertainty: comfort with pursuing questions whose answers—and even their value—may remain unknown for years.

Imagination: the creative capacity to look beyond established questions and envision what might be possible.

These qualities are difficult to measure using conventional academic metrics and are unevenly distributed among scientists. Although they can be cultivated, they cannot be reliably produced through training alone. Many scientists can conduct rigorous experiments; far fewer can identify consequential questions, pursue them independently, and generate genuinely unexpected insights. Yet as the number of researchers pursuing independent work grows, our academic system increasingly relies on metrics such as publications, citations, grants, and demonstrated impact to evaluate scientific potential—precisely because these are easier to measure than the qualities that make someone an effective scientific explorer.

The problem, therefore, is not that too many people participate in science, but that independence has become the default endpoint of scientific training rather than something earned through demonstrated capacity for exploration.

This does not mean that independent scientists should be expected to produce predictable outcomes. They should, however, be held to a high standard of intellectual honesty, rigor, and judgment even when the outcomes of their work are uncertain. Failure is inevitable. A failed hypothesis is not necessarily a failed scientist. What matters is whether uncertainty is approached with rigor, judgment, and a willingness to learn from the outcome.

We cannot predict which questions will prove fruitful. We can, however, become better at identifying the explorers best equipped to pursue them.

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The Case for Beauty

Why I am Leaving Academia

One of the more surprising realizations I had during my PhD was that the scientific community has built extraordinary institutions for producing knowledge, but comparatively weak ones for evaluating its quality. Every day, journals decide which papers to publish, funding agencies allocate billions of dollars in research support, hiring committees shape the next generation of scientists, and investors determine which discoveries are worth commercializing. Few professions devote more time to evaluating ideas.

Yet despite the enormous influence of these decisions, much of scientific evaluation still relies on tradition, intuition, and imperfect proxies rather than an explicit philosophy of what constitutes good science.

Ask a room full of scientists what they value in a paper and the answers are reassuringly consistent: originality, rigor, careful interpretation, reproducibility, and a meaningful advance in knowledge. At the level of principle, there is broad consensus.

The difficulty begins when those principles have to be translated into decisions. Which grant deserves funding? Which paper belongs in Nature? Which applicant should receive the faculty position? Which company is worth investing in? These are not questions with objective answers; they are judgments made under uncertainty, using imperfect measures of quality.

Why, then, do our decisions so often diverge from the ideals we claim to value?

I suspect the answer lies in incentives.

Economists have long understood that incentives shape behaviour. Scientists are no exception. If we want to understand why research looks the way it does, we should spend less time asking whether scientists are making the “right” decisions and more time asking what, exactly, the system rewards.

We reward publication in prestigious journals, so scientists learn to write papers that appeal to prestigious journals. We reward citation counts, so researchers gravitate toward questions likely to attract attention. We reward novelty, so incremental but essential work often struggles to find a home. Funding agencies increasingly reward translational potential, so even basic science proposals promise clinical relevance long before such claims can reasonably be justified.

None of this requires bad actors. Rational people respond to rational incentives. If we want different scientific behaviour, we need to think more carefully about the incentive structures we create.

The publishing ecosystem illustrates this particularly well.

The most prestigious journals were never designed simply to publish the most rigorous experiments. Journals such as Nature, Science, and Cell built their reputations by identifying discoveries that were surprising, broadly relevant, and capable of changing conversations across disciplines. Their editors are not simply evaluating methodological quality; they are making editorial judgments about what readers will find compelling and what will attract attention. Those are entirely legitimate objectives, but they are not identical to identifying the highest-quality science.

The papers that transform science are often both exciting and rigorous. Yet history is equally full of influential discoveries that failed to replicate, alongside more humble studies that gradually became foundational. Editorial decisions inevitably balance scientific merit against novelty, breadth of appeal, and perceived importance. That is not a criticism of elite journals; it is simply an acknowledgment that they optimize for multiple objectives, only one of which is scientific quality.

The same tension exists throughout academia. Grant panels predict which projects are most likely to succeed before the experiments have even been performed. Hiring committees select scientists not only for the quality of their work, but for the value they are expected to create for the institution itself—financially, strategically, and socially. Venture capital firms evaluate biotechnology companies according to their expected financial upside. Every institution responsible for selecting science is optimizing for many things simultaneously, and scientific quality is only one of them.

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If science is serious about improving the quality of its discoveries, it should devote as much creativity to building institutions that evaluate knowledge as it has to building institutions that produce it.

One of the most interesting recent attempts comes from QED Science. Their AI-based framework evaluates life science manuscripts along two independent dimensions: originality, defined as how far a study advances what the field already knows, and validity, defined as how well the evidence supports the conclusions being drawn.

I find this framework compelling—not because I believe artificial intelligence can completely replace scientific judgment; I still believe that meaningful evaluation of science will always require human judgment and scientific taste, much as criticism in art, literature, or film ultimately depends on human discernment rather than algorithms. Rather, I find it compelling because it provides a structured way of thinking about questions that scientists have been asking all along.

Did we learn something genuinely new?

Should we believe it?

Originality asks whether a study introduces an idea, observation, or mechanism that meaningfully extends what was previously known. Does it ask a genuinely new question, uncover an unexpected phenomenon, or offer a novel explanation? Originality is about the intellectual contribution a paper makes at the moment it enters the scientific record.

Validity asks a different question. Assuming the finding is novel, do the data actually support the conclusions? How well do those conclusions fit within our current understanding? If they challenge that understanding—as many important discoveries eventually do—is the evidence sufficiently strong to justify doing so? This is where scientific rigor lives. Experimental design, appropriate controls, statistical analysis, reproducibility, consideration of alternative explanations, and methodological transparency are not independent virtues—they are all different ways of evaluating the same underlying question: how much confidence should we place in these conclusions?

These two dimensions help us distinguish between a compelling idea and a compelling demonstration.

The final dimension is impact.

Unlike originality and validity, impact cannot be meaningfully assessed at the moment of publication. It is a judgment made in retrospect, emerging over years—or often decades—as a discovery is challenged, replicated, extended, and ultimately influences science or society.

Scientific impact asks whether a discovery fundamentally deepened our understanding of biology. Did it reshape how we think about the principles of life? Did it establish a new paradigm or become foundational for future discoveries?

Societal impact asks a different question. Did the discovery meaningfully improve human lives? Did it change clinical practice, enable transformative technologies, influence public policy, or solve an important real-world problem?

There is a reason the Nobel Prizes are typically awarded decades after the original discoveries. Their purpose is not simply to recognize outstanding papers, but to recognize discoveries whose influence has stood the test of time. That judgment requires hindsight.

I think of these three dimensions as operating on different timescales.

Originality is potential.

At the moment a paper is published, we can ask whether it contributes a genuinely new idea. Originality reflects the potential of a discovery to advance a field, not whether that potential will ultimately be realized.

Validity is credibility.

Validity asks whether the available evidence justifies believing the claims being made. Unlike originality, however, validity is not fixed. As new evidence accumulates—through replication, contradictory findings, improved methodology, or deeper mechanistic understanding—our confidence can increase or decrease. Scientific knowledge is provisional by design, and our assessment of validity should evolve alongside it.

Impact is legacy.

Impact asks whether the discovery ultimately mattered. Did it fundamentally change our understanding of biology? Did it meaningfully improve human lives? Unlike originality and validity, impact cannot be judged contemporaneously. It can only be assessed with the benefit of hindsight, once history has had an opportunity to reveal the true influence of a discovery.

Even if we agreed on how to evaluate science, we would still face another problem: creating conditions where scientific criticism can be expressed openly.

Modern research is an intensely social enterprise. We collaborate across institutions, review one another’s grants, write recommendation letters, serve on hiring committees, and depend on professional relationships throughout our careers. These connections strengthen science, but they also make criticism more complicated than we often acknowledge. The person whose paper you reject today may review your grant tomorrow. The investigator whose conclusions you publicly challenge may later evaluate your promotion package.

In such an environment, criticism acquires a cost. And whenever a behaviour becomes costly, it becomes less common. Diplomacy becomes the rational strategy.

Science advances through criticism, yet our incentive structures often reward collegiality more than candour. As a result, many papers receive less critical scrutiny than they probably should—not necessarily because scientists lack integrity, but because open disagreement carries professional consequences. One of science’s most important corrective mechanisms is therefore weakened by the very social structures that make collaboration possible.

To be clear, this was not always such a serious problem. Peer review evolved in an era when science was smaller, slower, and less professionally intertwined. Researchers could evaluate one another's work without the dense web of collaborations, grant competitions, hiring committees, and institutional relationships that characterize modern academia. The system was never perfect, but it was better aligned with the incentives of its time.

I wonder whether science would benefit from developing its own culture of independent criticism.

In almost every other domain, criticism has evolved into an independent profession. Film critics do not work for movie studios. Political journalists are not employed by governments. Their value lies precisely in their independence. Science, by contrast, relies almost exclusively on active participants to evaluate one another. There are good reasons for this—scientific criticism requires deep technical expertise—but it also means that nearly all evaluation is performed by people with professional stakes in the same system they are assessing.

There may be room for a complementary model: independent scientific critics, professional methodologists, retired investigators, or carefully moderated post-publication review. More fundamentally, the internet has made it possible to create entirely new forums for scientific evaluation outside traditional academic structures. Thoughtful online communities, moderated discussion platforms, and reputation-based review systems could create spaces where scientific claims are debated more openly, rigorously, and independently than is often possible within the professional networks of academia.

The objective would not be to replace peer review, but to complement it by strengthening one of science’s most important self-correcting mechanisms. The aim is to create intellectual spaces where scientific ideas can be evaluated as independently as possible from the incentives, relationships, and institutional pressures that inevitably shape modern research.

That is the experiment I want to run with Beyond the Abstract.

My goal is to create a space where scientific ideas are evaluated from first principles, discussed openly, and debated on their merits.

Science gradually converges on a more accurate understanding of the world, but that process can take years or even decades. Because decisions cannot wait for perfect knowledge, we need ways to evaluate the credibility of new evidence as it emerges.

I don't expect to provide definitive answers. My hope is simply to ask better questions—and I hope you'll join me in exploring them.

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But translating research into understanding has become increasingly difficult.

Studies are often oversimplified—or taken out of context—by the media. Within academia, the pressure to publish in prestigious journals has driven studies to become larger, more complex, and more data-rich than ever before. While this has advanced science in many ways, it has also made it harder to distinguish genuine signal from noise, robust conclusions from overreach, and findings that meaningfully push the needle from those that simply add another data point.

Beyond the Abstract is an attempt to slow down.

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In this series, I’ll take a closer look at influential and timely scientific papers—not only to summarize them, but to examine the questions they ask, the methods they use, the strength of their evidence, and what their findings actually mean in the context of the broader scientific literature and the real world.

My academic training is in biology and the medical sciences, but the analytical tools of science travel well. Since many of the most interesting questions transcend disciplinary boundaries, I’ll occasionally venture into lateral fields—from psychology and economics to artificial intelligence and computer science. Where appropriate, I’ll use google/AI as a research assistant to help navigate unfamiliar literature, while grounding every analysis in the primary sources.

Too often, the conversation ends when a paper is published. I hope to reinvigorate it—examining influential studies with curiosity and healthy skepticism, placing their findings in context, and discussing what they truly add to our understanding.

Whether you’re a scientist, student, or simply someone who enjoys thinking critically about evidence, I’d love for you to join the discussion in the comments section. Often, diverse perspectives reveal assumptions that specialists overlook.

If there’s a recent paper you’d like to see analyzed, leave a suggestion below. And if you’re interested in collaborating on a future discussion, I’d be happy to hear from you.

Welcome to Beyond the Abstract. I look forward to our conversations ahead.

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I’ve accumulated too many thoughts and not enough people to share them with. I’ve been on Twitter for years, but found myself increasingly craving long-form writing.

I recently submitted my PhD dissertation. For the first time in years, my mind has space again. Instead of being consumed by lung immunology, it’s wandering in every direction. I’m quickly realizing that these new ideas need somewhere to live.

Descartes argued that our capacity for thought establishes our existence: Cogito, ergo sum—I think, therefore I am. When everything else is called into question, the thinking self remains. For someone who questions everything, there is something grounding in that simple certainty: I am here, thinking.

For me, though, thought rarely happens in isolation. More often than not, I discover what I think through writing.

This is the central thesis of this Substack: writing forces belief formation.

Thoughts are fluid. They contradict one another, wander without direction, and rarely demand a conclusion. In your own mind, you’re free to entertain every possibility without committing to any of them.

Writing changes that.

The moment an idea reaches the page, you’re forced to make sense of it—to organize it, interrogate it, defend it, and ultimately decide what you actually believe. Writing isn’t simply the expression of thought; it’s the process by which thought becomes belief.

Thinking shapes who you are. In that sense, what you write becomes who you become.

That realization feels especially relevant at this stage of my life. I’m in my late twenties, recently married, and about to finish graduate school. For the first time, there isn’t a prescribed path in front of me. I’m finding myself asking not just what I want to do, but who I want to become.

I’m hoping writing will help me think through that question.

So what will I write about?

Simply put: my observations about the world around me.

I’m endlessly curious about the fundamentals of life—purpose, motivation, love, family, ambition, science, philosophy, art, technology, and whatever else captures my attention that week. Nothing is really off limits. If I find something interesting enough to think about, I’ll probably want to write about it.

Although I’m writing primarily for myself, the internet has a funny way of bringing together strangers who have been asking the same questions. If these essays happen to resonate with a few people along the way, I'll consider that a happy surprise.

Publishing publicly is intimidating; it’s much easier to keep ideas private until they’re polished. However, I think of writing the same way I think of science: every idea is a hypothesis. Once it’s public, it’s open to scrutiny. Some ideas will emerge stronger; others will fall apart. Either outcome is useful.

So here we go.