Let me tell you something that’s been gnawing at me for a while: the way we talk about evolution often feels like watching a magic trick. We’re shown a sequence of events—simpler life forms becoming more complex, proteins gaining functions, information accumulating—and we’re supposed to accept it as a natural progression. But what if the whole premise is built on a flawed illusion? That’s exactly what Christoph Adami’s work on biological information makes me question, and it’s a conversation worth having.
Adami’s latest work tries to map the ‘evolution of information’ by analyzing two proteins: the homeodomain and COX2. On the surface, this sounds like a solid scientific endeavor. But here’s where it gets interesting: his conclusions rely on a method so fundamentally shaky that it feels like trying to measure the height of a mountain by averaging the elevation of every pebble on its slopes. He claims these proteins show a pattern of increasing information over time, but the way he calculates entropy is so convoluted it’s almost poetic in its absurdity.
Let’s unpack this. Adami takes all the sequences within a clade—say, all eukaryotes—and treats them as a single group, then calculates entropy based on that. But this is like trying to figure out what your great-great-grandmother looked like by taking a photo of your entire family and averaging the pixels. The result is a meaningless number that has nothing to do with the ancestor it’s supposed to represent. What makes this particularly fascinating is that Adami himself seems to recognize this problem, yet he doubles down on it anyway. Why? Because the numbers look neat, and neatness is seductive in science.
Now, here’s the kicker: the patterns Adami sees in his graphs—like certain lineages gaining information while others lose it—are not evidence of evolution’s directionality. They’re artifacts of his methodology. When you combine sequences from different subclades, the entropy calculation naturally averages out, creating the illusion of progress. It’s like claiming a river flows uphill because you’ve averaged the elevation of its banks. This raises a deeper question: how many other scientific conclusions are built on similarly flawed assumptions? The fact that Adami’s method produces results that align with the narrative of ‘increasing complexity’ is not a triumph of science—it’s a warning about the dangers of confirmation bias in data analysis.
What this really suggests is that our understanding of biological information is still in its infancy. We treat information as a measurable quantity, but in reality, it’s a concept that’s as slippery as it is vital. Adami’s work highlights a critical gap: we don’t have a reliable way to quantify information in ancestral proteins. Without that, any claims about ‘information increasing over time’ are just stories we tell ourselves to make sense of the chaos. A detail that I find especially interesting is how Adami’s argument hinges on the idea that ‘adaptive value’ explains the trends in his data. But if the data itself is unreliable, then the entire premise collapses. It’s like arguing that gravity exists because objects fall downward, but then realizing your measurements of ‘down’ were based on a tilted ruler.
If you take a step back and think about this, the implications are staggering. Evolutionary biology is built on the idea that complexity arises through natural selection, but Adami’s flawed methodology reminds us that complexity isn’t the same as information. A protein might become longer, but that doesn’t necessarily mean it’s more informative. It might be redundant, or it might be a relic of ancient pathways that no longer serve a purpose. This line of thinking opens up a terrifying possibility: what if the ‘progress’ we see in evolution is just noise, not signal? What if the story of life isn’t one of upward striving, but of random drift and occasional accidents?
In my opinion, Adami’s work is a cautionary tale. It’s not just about the specific errors in his analysis—it’s about the broader cultural obsession with finding direction in randomness. We want to believe that life is moving toward something, that information is accumulating like interest in a bank account. But the truth is messier, more chaotic, and far more beautiful in its unpredictability. The next time you hear someone cite ‘increasing information’ as evidence for evolution, ask yourself: what exactly are they measuring, and why should we trust their ruler?
