Tag Archives: science

For whom the bells toLLMs

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‘No Man is an Island’ by John Donne

No man is an island entire of itself; every man 
is a piece of the continent, a part of the main; 
if a clod be washed away by the sea, Europe 
is the less, as well as if a promontory were, as 
well as any manner of thy friends or of thine 
own were; any man's death diminishes me, 
because I am involved in mankind. 
And therefore never send to know for whom 
the bell tolls; it tolls for thee.

In 2015, I attended a graduate course on data analysis near Montréal, Québec, Canada. A senior researcher came to give a lecture, about his career and accomplishments. What could have been inspiring turned into a sad and bitter litany of complaints about how Science had changed and how it was accelerating at a pace that was unsustainable with expectations for scientists to “tweet” and contribute to “blogs” to get traction on their works. The professor, you can imagine them to represent the typical stereotype of a scientist, had had a career in which research manuscripts were typed by hand and sent by post mail; in which his dear wife stayed home with the kids while he worked and came back to a lovely meal; in which correspondence between researchers across the world was much slower and data points were put on grid paper. It made me angry to listen to such a “party pooper” (pardon my bluntness ^^) rain on my parade and turn to gloom a full room of next generation scientists that just wanted to believe that there is a space for them in this world of Academia. It made me feel like he was someone that could not adjust anymore to the novel ways, “time for him to retire” is what I thought…

Fast forward to 2026, and I am wondering what is happening with the publication system in Academia and wondering if this retiring professor was right…

We have known for a while that the publication system in Science is broken. It has turned into this massive capitalist business in which journals churn scientific papers faster and faster every year. The existence of predatory journals and their presence online has exploded; good reviewers that really take the time to evaluate and comment the value of a manuscript are harder to find than ever; scientists across the world are found guilty of cheating their data or performing blatant plagiarism; and the confidence in scientific results is going down down down the drain… And now I wonder if LLMs, Large Language Models, and AI tools, are the nails in the coffin.

Oh they are great, very efficient and quite the help if you have the knowledge to guide them and use them as a support rather than as the main source of your scientific content:

  • they can find mistakes in my code that would have taken me minutes, to hours, to days, to fix during my PhD (probably saving me some tears and a great amount of frustration against this one missing comma haha);
  • they can correct my typos and grammar mistakes (I once wrote “pubic” instead of “public”, never again!), you know English is not my first language just like for many other scientists across the world;
  • they can take my ideas and spit out a first draft of a text in mere minutes;
  • they can create the first version of a conceptual figure that I want to add to support my manuscript or presentation.

Oh they are good, sure. Because of this “enhancement” of our capacity, LLMs are accelerating the pace at which we can produce scientific manuscripts. And this amplifies even more the problems of our publication system:

  • Scientists that did not know how to analyze their data are now using LLMs to do that task for them, without reviewing the statistical choices made, the real code performed, and the robustness of the results.
  • Reviewers (let’s all remember this is a pro bono task that we do to ensure the quality of Science) that already did not have (or take) the time to review manuscripts well are now delegating LLMs to do that job, thus giving not so good reviews to already not so good manuscripts.
  • Predatory journals are publishing more shitty papers but they look more polished because of LLMs. Even international journals known to publish good quality manuscripts are now outputting works that show the remnants of LLMs and their misuse.
  • And no one reads papers anymore. No need, LLMs will find the important references, and summarize the literature.

So I wonder, if we are producing these papers and books at an ever accelerating pace, with the help of AI tools, then LLMs review these papers, and then it reads the papers for us to summarize the information, for whom am I doing Science anymore? For an AI company to train its own LLMs?

For whom the bell toLLMs? It toLLMs for thee?

Note: Thanks to Jonathan Rondeau-Leclaire for quick feedback including typos and suggestions 🙂

Oh and 2nd note: for those wondering, this post was also written with my own brain, no LLMs used in the process :))

Everything Evolves: Why Young Scientists Should Read Beyond the Literature

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As scientists, we are trained to read papers, and lots of them.

We learn to scan abstracts, dissect methods, and critique results. Yet sometimes, stepping outside the narrow corridors of peer-reviewed literature can offer a much-needed breath of intellectual fresh air. That’s exactly what I found in Everything Evolves, the latest book by my colleague at UdeS: Mark Vellend.

Mark is known for his deep, deliberate thinking, what I see as a rare embodiment of “slow science.” In a profession where it’s easy to be swept away by the constant tide of tasks, deadlines, and metrics, Mark is my weekly reminder of the value of making space to think. Since arriving at Université de Sherbrooke, I’ve cherished the walks we’ve taken together along the trails of Mont Bellevue, where conversations about science and life unfold with ease. Like those lunch-hour strolls, his writing is not rushed, nor is it constrained by disciplinary boundaries. In Everything Evolves, he offers a conceptual framework that is both elegant and expansive: a generalized evolutionary theory that applies not only to biology, but also to culture, economics, technology, and beyond.

At the heart of the book is a metaphorical evolutionary soundboard, with four dials: variation, inheritance, movement, and differential success. These dials help us make sense of complex adaptive systems, from the distribution of sugar maple trees to the rise and fall of empires, from coral reefs to the dynamics of Coke vs. Pepsi. It’s a conceptual compass for navigating the messy, beautiful complexity of the world.

One of the more provocative ideas in Everything Evolves is Mark’s proposal to distinguish between what he calls First Science and Second Science. First Science refers to disciplines grounded in physicochemical laws, physics, chemistry, and parts of biology. Second Science, by contrast, encompasses fields that study complex adaptive systems shaped by evolutionary dynamics: ecology, economics, sociology, anthropology, and even artificial intelligence. These are systems where history, randomness, and feedback loops matter just as much as rules and equations.

Mark goes a step further and suggests that these Second Sciences deserve their own institutional home, a Faculty of Evolutionary Sciences. It’s a bold idea. Would reorganizing universities around conceptual foundations rather than traditional disciplines foster more meaningful collaboration? Could it help us break down the silos that often limit interdisciplinary thinking? Or is this just a thought experiment, a metaphorical nudge to remind us that the way we structure knowledge is itself a product of history and inertia?

Why Should Young Scientists Read This?

Because science is not just about data, it’s about ideas.

Reading books like Everything Evolves helps us:

  • Think across disciplines: Evolutionary principles apply to ecosystems, economies, and even AI. Seeing these connections fosters creativity and innovation.
  • Reflect on our role: As scientists, we’re not just technicians. We’re thinkers, interpreters, and sometimes storytellers. Books like this remind us of the bigger picture.
  • Embrace abstraction: Concepts like hysteresis, drift, and adaptation are not just theoretical, they shape how we understand resilience, tipping points, and change.
  • Challenge academic silos: Vellend’s idea of a “Second Science”, disciplines governed by evolutionary dynamics rather than physicochemical laws, invites us to rethink how universities are structured. What if we organized ourselves by conceptual kinship rather than historical convention?

Thinking Is Not a Luxury

In a world of metrics, deadlines, and publication pressure, taking time to think can feel indulgent. But it’s not. It’s essential. Reading conceptual books, especially those that blend science with philosophy, history, and personal reflection, helps us become better scientists and better humans.

In Everything Evolves, Mark doesn’t impose conclusions. He opens doors. He gives us space to ponder the role of community knowledge in innovation, the evolutionary roots of violence, and the dynamics of cooperation and competition. He even dares to ask how adaptation might apply to multiverses, not because it’s practical, but because it’s fascinating.

Final Thoughts

Everything Evolves is not just a book. It’s an invitation to think, to connect, to evolve.

For young scientists, especially those just beginning their academic journey, it’s a reminder that science is not just a career. It’s a way of seeing the world.

So read the papers. But also read the books.

Especially the ones that make you pause, reflect, and wonder.