Clay McLeod
Beyond bioinformatics rewrites | Clay McLeodSkip to main content All postsRecently, I came across rewrites.bio, a manifesto proposing<br>principles for rewriting bioinformatics tools with AI. It begins with a prediction: “A<br>wave of rewrites is coming.” The authors argue that this work should credit the original<br>developers, disclose the use of AI, validate results carefully, preserve compatibility,<br>and include a real plan for maintenance.
Much of that advice is thoughtful and responsible. Still, I felt some hesitation as I<br>read it, particularly around the principle to “emulate exactly.” I could not immediately<br>articulate why. Faster and more maintainable versions of important tools sound<br>unambiguously useful, especially in a field where performance has often taken a back<br>seat and analyses can cost more and take longer than they should. Even as each<br>recommendation seemed reasonable, my attention kept moving beyond the manifesto's focus<br>on rewrites, toward what else we might create with the new capacity AI affords us.
Making sense of that inclination has required me to work through what I believe AI is<br>useful for, where I want human judgment to remain, and what kind of software our field<br>actually needs. Allow me to share some background so you can see where I'm coming from.
A brief aside
Though my title now includes “Director,” I am a software engineer at heart. Overall, I<br>would not consider myself a true early adopter, but I am also not afraid to try something<br>new after a bit of a settling period (I have long had a rule that I will try any new tool<br>for two weeks before I dismiss it!).
After a few months of tinkering, AI has become a regular part of my workflow for writing<br>and reviewing code. I am not quite as bullish as many on my LinkedIn timeline: working<br>carefully with AI as a partner, not as an independent agent, strikes the right balance<br>of control and quality for me. Perhaps curmudgeonly, I still review every line of code<br>produced by either the AI or me; no<br>loop engineering utopia for me.
More broadly, I see how differently people are responding. Some are experiencing<br>significant productivity gains and breaking into new fields; others are working through<br>technical concerns and deeper uncertainty about what AI means for their work and the<br>world at large. I do not think its impact on each person's life requires a uniform<br>response, and my own view ultimately remains unsettled.
However, a few pieces of my worldview have begun to solidify with time. I want to<br>record them here, both to remind my future self and to establish the philosophical<br>underpinnings for the rest of this post.
To the extent that we do use AI, we should use it to make better things rather<br>than become more productive at making mediocre things. AI has made implementation<br>easier, but making something amazing still requires a great deal of ingenuity and<br>sweat equity. Building something once you have decided what to build has become<br>easier; deciding what deserves to exist remains hard. This is not a claim about<br>whether AI should be used, only how I believe it should be used when it is.
We should use AI to emphasize our individual humanity. Every use of AI involves a<br>choice: whether it extends your ability to express your own judgment and individuality<br>or substitutes some aggregate form of everyone else's humanity for your own. Using AI<br>exists on a spectrum. Typing a question into an LLM and passing its response along<br>verbatim without critically evaluating it or contributing thinking of your own does<br>little to express your individuality because the model has supplied both the substance<br>and its expression. Having AI implement code that you have architected and whose<br>details you review is different: the tool helps with implementation, but the direction,<br>judgment, and accountability remain yours. As I'm defining it here, creation is a human<br>act of deciding what should exist and giving it form. AI should help you do more of<br>that, not make it easier to present work you have neither understood nor made your own.
AI does not solve the general planning problem for us. In classical AI, planning<br>is often formulated as a search through a state space: begin with the current state<br>of the world, define the actions available, describe how each action changes the<br>world, and search for a path to some desired goal. Even simplified planning problems<br>become difficult quickly because the number of possible states and paths grows<br>combinatorially (Berkeley offers a good<br>introduction).
The planning problem we face in the real world is harder still because the goal<br>itself is often unsettled. The space of possible tools, methods, experiments, and<br>questions we could pursue is unimaginably large. AI may allow us to explore parts of<br>that state space faster and make individual paths cheaper to follow, but it cannot<br>search the space exhaustively. Human judgment remains responsible for choosing where<br>to look, which possibilities seem promising, what a...