Life, complexity and the technologies learning to keep up
Spend enough time around the life sciences and you develop a tolerance for ambiguity. Experiments behave themselves, until they don’t. Patterns emerge, then blur at the edges. Results that felt solid yesterday look conditional today. None of this is treated as a crisis. It is simply how the subject reveals itself.
Life, after all, has never been particularly interested in being explained neatly.
This can be surprising to those encountering biology from the outside. There is an expectation, often unspoken, that beneath the apparent complexity there must be something orderly waiting to be uncovered. A set of principles that, once understood, make everything else fall into place. Biology, viewed this way, is a puzzle that just hasn’t been solved yet.
Those who work closest to it tend to think otherwise.
How life actually arrived here?
The earliest living systems were not innovations in any meaningful sense. They were arrangements of matter that happened to persist. Molecules interacted, structures formed, and some combinations survived long enough to interact again. There was no objective, no sense of improvement, no anticipation of what might come next.
Over time, these arrangements accumulated change. Some changes stuck. Others disappeared. What mattered was not whether a development was elegant or efficient, but whether it prevented immediate failure. Persistence was enough.
This is an important distinction. Evolution is often spoken about as progress, but progress implies direction. Life had none. What it had instead was continuity. New behaviours were layered on top of old ones. Ancient processes were retained even as new adaptations emerged around them. Nothing paused to simplify what already existed.
The result is a biological world shaped less by optimisation than by tolerance. Tolerance for inefficiency, redundancy and compromise.
The Inheritance of Complexity
Modern organisms still carry the marks of this history. They are built on mechanisms that predate them by vast stretches of time, interacting with adaptations that appeared much later and may yet disappear again. What we see today is not the product of a single, coherent design, but of countless overlapping solutions that happened to coexist.
At the cellular level, this history becomes immediately apparent. Cells are crowded, dynamic environments where interactions occur continuously and often simultaneously. Processes overlap. Signals interfere. Outcomes depend on context, timing and proximity as much as on structure.
There is no single point of control. No master process issuing instructions. Behaviour emerges from interaction, not coordination.
This makes life remarkably robust. Systems that tolerate variation are less fragile than those that depend on precision. Biological processes do not require ideal conditions to function. They adapt, compensate and continue even when circumstances are less than favourable.
It also makes life difficult to describe cleanly.
The compromises we make to understand it
Much of biological language is an attempt to impose order on something that resists it. We draw boundaries, label pathways and isolate mechanisms, knowing all the while that these distinctions are provisional. Diagrams flatten what is fluid. Models freeze behaviour that, in reality, never stops moving.
These compromises are not mistakes. They are necessities. Without them, communication would be impossible. But they also shape expectations. They encourage the belief that if we could only refine our descriptions enough, life would eventually become predictable.
In practice, that promise rarely materialises.
Understanding in the life sciences has always been probabilistic rather than absolute. Researchers develop a sense for what usually happens, what often happens and what is possible under certain conditions. Certainty is local and temporary. Confidence is earned through familiarity, not finality.
This has been accepted for generations. What has changed is the scale at which we now encounter complexity.
When Data Stopped Fitting Comfortably
As measurement improved and data volumes grew, biological systems became harder to summarise. Relationships that once looked stable revealed themselves to be conditional. Influences overlapped. Effects traced back to multiple causes, none of which could fully account for the outcome on its own.
Computational tools, for a long time, struggled with this. They worked best when problems could be clearly framed, variables controlled and outcomes traced along explicit paths. That approach delivered enormous value, but it also reflected a preference for clarity that biology does not always share.
The limitation was not a lack of data or effort. It was a mismatch of temperament.
Life is shaped by accumulation, context and interaction. Tools designed for tidy systems inevitably strain when asked to account for that.
A Different kind of Tool
Artificial intelligence enters the life sciences without insisting on simplification. It does not require complete explanations before it begins. It does not demand consistency as a condition for usefulness. Instead, it absorbs variation and looks for structure within it.
This makes AI unusually comfortable in environments where relationships are fluid and outcomes emerge from many interacting factors. Noise is not something to be eliminated. It is part of what the system learns from.
Rather than modelling life as a set of explicit rules, AI systems infer tendencies. They improve through exposure, refining their responses as they encounter more examples of how a system behaves across different conditions.
What emerges is not understanding in the traditional sense. There is no moment where the system can articulate why a particular pattern exists. What it offers instead is a growing confidence about what is likely to happen next.
The Discomfort of Useful Answers
This inversion can feel unsettling. We are accustomed to explanations arriving before results, not after them. In most domains, confidence is expected to follow understanding.
Biology has never fully complied with that expectation.
In practice, life scientists have always worked with incomplete explanations, guided by patterns that hold often enough to be useful. AI simply makes this implicit arrangement more visible. It produces results that work, even when the underlying reasons remain partially opaque.
This does not diminish the importance of interpretation. On the contrary, it increases it. Outputs still require context. Predictions still need to be weighed against biological knowledge and experimental reality. AI does not replace judgement. It amplifies the need for it.
Familiar Ideas in Unfamiliar Clothing
There is a temptation to describe this moment as revolutionary, but that risks overstating the change. Biology itself has not shifted. Living systems remain as contingent, adaptive and historically layered as they have always been.
What has changed is our willingness to use tools that do not demand that life behave differently.
In many ways, AI aligns naturally with how biological systems already operate. It improves through iteration rather than instruction. It tolerates imperfection. It does not expect a final, optimal state. Progress is incremental and provisional.
These ideas are not new to biology. They have simply found expression in a different form.
Working with Life, Rather than Against it
Seen this way, the growing role of AI in the life sciences is less about introducing intelligence into biology and more about adjusting our expectations of analysis. It reflects an acceptance that living systems cannot always be reduced to clean explanations without losing something essential in the process.
AI offers a way to engage with complexity without flattening it entirely. To notice patterns without insisting they be permanent. To make use of uncertainty rather than treating it as a failure of understanding.
This does not make life simpler. It makes our relationship with it more honest.
Life was never designed to be neat, predictable or easily summarised. It persists because it doesn’t need to be. The tools we use to study it are finally beginning to reflect that reality, not by imposing order, but by learning how to operate in its absence.
And that, perhaps, is less a technological breakthrough than a quiet adjustment in how we pay attention.