I am grateful for the opportunity to comment on the timely manuscript by my distinguished colleague, David Donoho (2024).
Donoho argues that we are witnessing a fundamental shift in science, and perhaps society more broadly, due to the rapid advancements in data science and computational research. He believes this shift is not accurately captured by the popular concept of an “AI singularity.’’ Instead, he focuses on the idea of “frictionless reproducibility” (FR). The practical implication being that with FR, almost anyone can seamlessly reproduce scientific results with ease, thereby removing barriers, expediting validation, and facilitating building upon past work. It is argued that this transition drastically accelerates the spread of ideas, changes how researchers collaborate, and could ultimately erase the memory of “pre-FR” methodologies as their inefficiencies become starkly apparent.
Donoho paints a compelling, if arguably utopian, picture of a scientific landscape transformed by frictionless computational omnipotence. As a practitioner of conventional “pre-FR” approaches and a more recent machine learning practitioner-convert, I find myself both exhilarated by the potential Donoho outlines and troubled by a certain undercurrent of technological determinism in the article. Let me elaborate further.

The Devil Is In The Details
The pursuit of frictionless reproducibility is an undeniably worthy goal–a cornerstone of robust science. However, Donoho’s framing perhaps downplays the sheer complexity of ensuring true replicability in practice. Seemingly minor deviations in software libraries (e.g., PyTorch vs. TensorFlow), hardware configurations (e.g., numerical precision, GPU, CPU, TPU), or even the temporal order in which large data sets are processed can introduce nontrivial variance that is hard to control post hoc (Stodden & Miguez, 2014). These realities complicate the idyllic vision of effortlessly replaying any scientific computation from the past. Further, data sets in fields dealing with human systems, from social sciences to medical research, are notoriously dynamic (Sculley et al., 2015), with shifting, often tightening concerns around privacy rights. Distributions shift over time as well; behaviors evolve, and what was replicable yesterday might hold less relevance tomorrow. Thus, FR within Donoho’s paradigm needs to be tempered with an appreciation for context and change over time. I might offer a more practical framing: low-friction reproducibility.

The Human Factor
There is a risk in the article’s narrative that computational advancement may overshadow the enduring centrality of human expertise and judgment. Defining the very questions data science tools might answer remains irreducibly human. In fairness, one might argue this view (Holzinger, 2016) is now outdated; but if we stipulate that the goals of scientific progress are not just understanding the world, but principally, to improve the human condition, then neither machine learning nor data science are likely, on their own, to replace the deep domain knowledge and intuition essential for identifying truly impactful research directions.
While ever more powerful machine learning models achieve startling levels of predictive accuracy, they still often function as ’black boxes’ with limited explanatory power. This necessitates researchers capable of critically interpreting results and building trustworthy AI systems (Rudin, 2019).

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