Rohit Krishnan turns a five-year-old's question about a dinosaur's sail into a rigorous test of how climate volatility shapes the very architecture of life on Earth. By treating the Paleobiology Database as a laboratory and large language models as fallible but tireless research assistants, he uncovers a hidden rule: when the planet shakes, ecosystems stop being unique and start converging on a narrow set of survival strategies. This is not just paleontology; it is a warning label for our own warming world, suggesting that the distinct regional flavors of marine life are already being sanded down into a global monoculture of the hardiest, most generic roles.
The Mechanics of Vibe Science
Krishnan's central premise is deceptively simple: he wanted to know if environmental pressure forces nature to repeat itself. He writes, "My hypothesis here was something like: 'if the landscape is less stable, we will see ecosystems seem more similar'." The logic follows that under extreme stress, only the most essential "job portfolios" for survival remain viable, causing disparate regions to look functionally identical even if they share no actual species. This approach reframes the concept of convergent evolution—a topic often explored in deep dives on the Paleobiology Database—moving it from a story about specific animals to a story about systemic constraints.
The data, drawn from the Paleobiology Database (PBDB) and climate reconstructions from the Community Earth System Model (CESM), delivered a result that was both surprising and nuanced. Krishnan notes, "Volatility doesn't make regions that already share species more functionally similar... But it does raise the minimum similarity between regions that share nothing taxonomically, it sets a floor on how different two ecosystems are allowed to be." This finding challenges the intuitive notion that shared species drive similarity; instead, the environment itself dictates the minimum complexity of life.
"When climates are volatile, ecosystems converge. And we can see it across 540 million years of prehistory."
However, the story isn't a straight line. The correlation Krishnan found was almost entirely driven by the Mesozoic era, specifically the aftermath of the Permian-Triassic extinction, where 96% of marine species vanished. This aligns with historical patterns seen in the Paleobiology Database, where the "reset" of the Permian-Triassic boundary created a unique window for convergence. As Krishnan observes, "The Mesozoic was in the sweet spot of transition and it had the extinction event in the middle, meaning there's enough range for convergence for volatility to have anything to correlate with." Critics might argue that relying on a single catastrophic event to drive a 540-million-year trend risks overfitting the data, but Krishnan acknowledges this limitation, noting that the signal drops off in the Cenozoic because modern ecosystems are too entrenched to be easily homogenized.
The Human Cost of Data and the AI Assistant
The most compelling part of Krishnan's piece is not the fossil data, but his candid autopsy of using AI to do the work. He describes a workflow where he acts as the principal investigator, constantly correcting the "lazy" and "mediocre" tendencies of the models. He writes, "The models just absolutely love mediocrity... They can't wait to sand the edges off any crazy ideas you have." This is a crucial insight for any professional considering automated research: the tools are indefatigable but lack the boldness to follow a hunch where it leads.
Krishnan details the friction of this new workflow, noting that "there was no substitute for actually looking myself, and LLMs ability to judge their own work remains remarkably bad." He had to manually clean the workspace, correct the models' presumptions, and even force them to delete the "enormous surplus of temp folders" they generated. The process was less like commanding a supercomputer and more like managing a brilliant but chaotic intern who needs constant supervision. "Constant vigilance is essential!" he warns, highlighting that the "final boss" of any analysis remains data quality, not the sophistication of the algorithm.
"It's brilliant, it's indefatigable, it's a little dumb, it's annoying, it believes weird things, but it'll do whatever you ask it to."
This "vibe analytics" approach allows a non-expert to test hypotheses that would traditionally require a PhD, but it introduces a new risk: the confidence of the user may outstrip the reliability of the tool. Krishnan admits that his initial theory about tectonic plates causing convergence was wrong; the data showed it was temperature change, not geography, that mattered. "The plates matter because they cause climate volatility, not because of the geography per se," he concludes. This correction underscores the value of the method: it forces the researcher to confront the data's reality rather than their own intuition.
A Warning for the Modern Era
The ultimate payoff of Krishnan's experiment is a prediction for the present. With current warming rates sitting in the top 10% of the Phanerozoic record, the same mechanism that homogenized ancient seas should be active today. Krishnan posits, "If this theory is right, marine ecosystems today should be losing their regional distinctiveness and converging on a narrower job menu." The data already shows that "suspension feeders" are expanding while "mobile predators" are shrinking during volatile periods.
This is not a neutral observation. It suggests that the rich tapestry of global marine life is being stripped away, replaced by a resilient but impoverished baseline of survival. Krishnan connects this back to his son's question about the Spinosaurus, noting that while he still doesn't have a perfect answer for the sail, he can now explain that the Cretaceous oceans were "converging on a limited menu of ecological jobs." The implication for the modern world is stark: we are not just losing species; we are losing the diversity of function that makes ecosystems robust.
"When in volatile climates the entire job portfolio homogenizes across regions regardless of which specific jobs expand or contract."
A counterargument worth considering is whether modern human intervention—such as fishing quotas or marine protected areas—could break this natural cycle. Krishnan's model assumes natural volatility, but the current crisis is anthropogenic and accelerating. If the "floor" of similarity is rising too fast, the entrenched incumbents of the Cenozoic may not have time to adapt, leading to a collapse rather than a convergence. The "liquid markets" analogy Krishnan uses for economics holds here: if the market is too choppy, nothing emerges at all.
Bottom Line
Krishnan's "vibe science" experiment succeeds not because it provides a final answer, but because it demonstrates a new way to ask questions of the deep past using modern tools. The strongest part of the argument is the identification of a "floor" for ecosystem similarity under stress, a non-obvious pattern that challenges standard evolutionary narratives. Its biggest vulnerability lies in the uneven quality of the fossil record and the current limitations of AI agents to handle truly exploratory research without human intervention. Readers should watch for the next phase of this work: testing whether the predicted convergence is already visible in modern marine data, a signal that would confirm we are entering a new, less diverse epoch of Earth's history.