In accordance with the general trend of integrating artificial intelligence into almost every fieldresearchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists?
The Trump administration signed an executive order on November 24, 2025 announcing the Genesis missionan initiative aimed at building and training a series of AI Agents on federal science datasets “to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs.”
Although AI can contribute to tasks that are part of the scientific process, it is still far from automating science – and may never get there. As a philosopher who studies both the history and conceptual foundations of science, I see several problems with the idea that AI systems can “do science” without, or even better than, humans.
AI models can only learn from human scientists
AI models do not learn directly from the real world: they must be “tells” what the world is like by their human designers. Without the human scientists overseeing the construction of the digital “world” in which the model operates – that is, the data sets used to train and test its algorithms – the advances facilitated by AI would not be possible.
Consider the AlphaFold AI model. Its developers received the award Nobel Prize in Chemistry 2024 for the model’s ability to infer protein structure in human cells. Since many biological functions depend on proteins, the ability to rapidly generate protein structures for testing via simulations could potentially accelerate drug design, track disease development, and advance other areas of biomedical research.
As convenient as it may be, an AI system like AlphaFold alone does not provide new insights into proteins, diseases, or more effective drugs. This simply allows existing information to be analyzed more efficiently.
As philosopher Emily Sullivan said, to succeed as scientific tools, AI models must maintain a strong empirical link to already established knowledge. In other words, the predictions made by a model must be based on what researchers already know about the natural world. The strength of this link depends on how much knowledge is already available on a given topic and how well the model’s programmers translate highly technical scientific concepts and logical principles into code.
AlphaFold would not have succeeded without the existing body of human-generated knowledge about protein structures that the developers used to train the model. And without human scientists to provide a theoretical and methodological knowledge base, nothing AlphaFold creates would constitute scientific progress.
Science is an exclusively human enterprise
But the role of scientists in the process of scientific discovery and experimentation goes beyond ensuring that AI models are properly designed and grounded in existing scientific knowledge. In a sense, science as creative success draws its legitimacy from human capabilitiesvalues and lifestyles. These, in turn, are rooted in the unique ways humans think, feel, and act.
Scientific discoveries are more than just theories supported by evidence: they are the product of generations of scientists with a variety of interests and perspectives, working together through a shared commitment to their craft and intellectual honesty. Scientific discoveries are never the product of a single visionary genius.
For example, when researchers first proposed double helix structure of DNAthere was no empirical test to verify this hypothesis – it relied on the reasoning skills of highly trained experts. It took nearly a century of technological advancement and several generations of scientists to go from what seemed like pure speculation in the late 1800s to a discovery honored by a 1953 paper. Nobel Prize.
In other words, science is a resolutely social companyin which ideas are discussed, interpretations are offered, and disagreements are not always overcome. As other philosophers of science have noted, scientists more like a tribe that ““passive recipients” of scientific information. Researchers do not accumulate scientific knowledge by recording “facts”: they create scientific knowledge through competent practice, debate, and agreed-upon norms informed by social and political values.
AI is not a “scientist”
I believe the computing power of AI systems can be used to accelerate scientific progress, but only if done carefully.
With the active participation of the scientific community, ambitious projects like the Genesis mission could prove beneficial for scientists. Well-designed, rigorously trained AI tools would make the more mechanical parts of scientific research smoother and perhaps even faster. These tools would compile information about what has been done in the past so that they can more easily inform how to design future experiments, collect measurements and formulate theories.
But if the guiding vision for deploying AI models in science is to replace human scientists or automate the scientific process entirely, I think the project would only turn science into a caricature of itself. The very existence of science as an authoritative source of knowledge about the natural world depends fundamentally on human life: shared goals, experiences, and aspirations.
This edited article is republished from The conversation under Creative Commons license. Read the original article.