Can AI Truly Replace Scientists? A New Mission Raises Fundamental Questions
AI is rapidly becoming integrated into nearly every field, and scientific research is no exception. As artificial intelligence models trained on vast datasets increasingly attempt to answer complex scientific questions, a critical debate emerges: can AI ultimately supersede the role of human scientists?
On November 24, 2025, the Trump administration announced the Genesis Mission, an ambitious initiative to construct and train a series of AI agents using federal scientific datasets. The stated goals are to “test new hypotheses, automate research workflows, and accelerate scientific breakthroughs.” However, the potential for fully automating the scientific process remains a contentious issue.
Early results from these “AI scientists” have been mixed. While AI excels at processing massive datasets and identifying subtle correlations often missed by humans, its lack of commonsense reasoning can lead to unrealistic or irrelevant experimental recommendations. Despite these limitations, the question remains: how far can AI truly go in the realm of scientific discovery?
One philosopher specializing in the history and foundations of science argues that fundamental problems exist with the notion of AI “doing science” independently, or even better than humans. A core issue is that AI models can only learn from human scientists.
These models don’t directly learn from the real world; they require human designers to define that world through the datasets used for training and testing. Without this human oversight, the breakthroughs facilitated by AI would be impossible. The success of AlphaFold, an AI model awarded the 2024 Nobel Prize in chemistry for its ability to predict protein structures, exemplifies this dependence. While revolutionary in its ability to accelerate drug design and biomedical research, AlphaFold doesn’t generate new knowledge; it efficiently analyzes existing information. As one expert put it, successful AI tools in science must maintain a “strong empirical link to already established knowledge.”
AlphaFold’s success hinged on the extensive body of human-generated data on protein structures used to train the model. Without this foundational knowledge, the model’s output would lack scientific significance.
However, the role of human scientists extends beyond simply providing data. Science, at its core, is a uniquely human enterprise. Scientific discoveries are not solely based on evidence-supported theories, but are the product of generations of scientists, diverse perspectives, and a shared commitment to intellectual honesty. Breakthroughs are rarely the result of a single genius, but rather collaborative efforts.
Consider the discovery of the double-helix structure of DNA. The initial proposal lacked immediate empirical verification, relying instead on the reasoning skills of trained experts. It took nearly a century of technological advancements and the contributions of multiple generations of scientists before this initial speculation was validated with a 1953 Nobel Prize. Science is inherently social, involving discussion, interpretation, and even disagreement. Scientists function more like a collaborative “tribe” than passive recipients of information, actively creating knowledge through skilled practice and debate.
While the computing power of AI can undoubtedly accelerate scientific progress, it must be approached with caution. Ambitious projects like the Genesis Mission could be beneficial with active participation from the scientific community. Well-designed AI tools can streamline the more mechanical aspects of research, aiding in experiment design, data collection, and theory formulation.
However, attempting to replace human scientists or fully automate the scientific process risks reducing science to a mere “caricature of itself.” The very foundation of science as a reliable source of knowledge depends on fundamental aspects of human life – shared goals, experiences, and aspirations.
