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Artificial intelligence can tune an experiment, accelerate calculations, prepare data, generate code scaffolding and make old datasets easier to search. It cannot take responsibility for deciding which questions matter, designing a convincing test, judging whether evidence is sound, crediting ideas correctly or signing a paper with a clear conscience. That is the durable core of physics, according to Sarah Demers, chair of Yale University’s physics department and a particle physicist working with experiments including Mu2e and ATLAS. Her message is neither that AI is harmless nor that it will replace physicists: use it for bounded, checkable work, while keeping scientific judgment and human conversation in human hands.
What does a physicist think will endure in the age of AI?
Demers’s answer is the practice of making and testing knowledge, not any particular calculation or software package. A model may produce a derivation, suggest an analysis or adjust an experimental parameter, but a physicist still has to decide what the result means and whether it deserves belief. In her words, “Confronting that — what is physics, really? What is enduring about the practice of physics itself? — that does not change with AI.”
That distinction matters because physics is more than obtaining a number. It is a chain of choices: selecting a consequential question, translating it into a measurable prediction, building or tuning an apparatus, identifying sources of uncertainty, comparing observations with alternatives and explaining the result to other people. Automation can shorten parts of that chain. It does not remove accountability for the chain as a whole.
Demers therefore rejects both easy extremes. AI is not merely a faster calculator, because language models and machine-learning systems can alter how people formulate problems and learn technical skills. Nor is it an autonomous scientist. “We need to remember our skepticism and be careful about the hype that we’re hearing. Human physics is not over.”
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Which parts of physics can AI automate or accelerate?
AI is most useful when the task has a defined objective, measurable inputs and an output that an expert can independently check. The following uses are already part of the discussion around modern experiments and theory.
| Task | What an AI system can do | What remains a physicist’s job |
|---|---|---|
| Experimental tuning | Search combinations of instrument settings or operating conditions to improve a specified signal or efficiency. | Define the objective, set safety and validity limits, and confirm that an apparently optimal setting reflects real physics rather than an artifact. |
| Calculations | Speed symbolic, numerical or repetitive computations and help explore many parameter choices. | Choose the right model, inspect assumptions, reproduce the result and determine whether the approximation applies. |
| Data preparation | Classify, clean, transform or organize large collections of measurements. | Decide what counts as a valid event, quantify selection bias and preserve enough provenance to audit the transformation. |
| Code frameworks | Draft boilerplate, translate an algorithm into a programming language or suggest tests. | Review every line that affects the analysis, test edge cases and take responsibility for the program’s output. |
| Historical-data access | Make older experimental records easier to query and compare, including material that is difficult to search manually. | Interpret the original measurement context, calibration and limitations before combining old and new evidence. |
These are support functions, not a transfer of authorship. A model can optimize a measurable target without knowing whether that target is scientifically important. It can also produce a plausible answer that fails outside the examples in its training or silently changes a definition in the middle of a calculation.
How does the Mu2e example show AI’s proper role?
The U.S. Department of Energy’s 2026 Genesis Mission illustrates the scale of institutional interest. In July 2026, the department selected 278 projects intended to incorporate AI into scientific work. One cited project uses AI to help physicists search for the exceptionally rare transformation of a muon into an electron in the Mu2e experiment at Fermilab.
In that setting, an algorithm can help control or tune experimental settings so that the apparatus spends more of its operating time in a useful region. The scientific question came first, and the experiment’s physical constraints still govern what counts as a meaningful observation. Researchers must establish that the system is measuring the intended process, not learning a shortcut caused by detector noise, changing conditions or an accidental correlation.
Mu2e is therefore a model of augmentation. AI can navigate a high-dimensional control problem faster than a person trying settings one by one, while physicists specify the search space, monitor the apparatus and interpret the candidate signal. The same logic applies to machine-learning analyses in large collaborations such as ATLAS: a classifier may separate likely events from background, but collaboration members must validate its behavior and explain how it affects the measurement.
Why can’t an AI model choose the important physics question?
A model can rank, combine or rephrase questions found in existing material. Choosing a question that changes what the field understands requires a sense of significance, feasibility and timing that is not reducible to fluent text generation. It also requires noticing when an accepted framing is incomplete or when an inconvenient result deserves attention.
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Experimental design makes this limitation concrete. A physicist has to decide which control measurement would distinguish competing explanations, which uncertainty dominates the result and what observation would count against the favored hypothesis. Those decisions involve values about evidence and consequences, not just pattern matching. An optimizer can maximize sensitivity to a target selected by a human; it cannot justify why that target should matter.
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The same is true after data collection. A statistically unusual feature may be a discovery, a calibration problem or an unmodeled background. Deciding among those possibilities requires knowledge of the instrument, the history of the run and the behavior of independent checks. The human process of skepticism is not a decorative final review; it is part of the method.
How should researchers verify AI-generated scientific work?
Any output that influences a publication or a consequential experimental decision must be understood and validated by the scientist who signs the work. A practical review sequence is:
- Define the task and constraints before prompting. Write down the physical quantity, allowed inputs, units, boundary conditions, safety limits and the test that would falsify the result.
- Keep an independent baseline. Solve a small case by hand, with a trusted analytical method or with an established program. The baseline gives you something to compare against instead of treating the model’s first answer as ground truth.
- Reproduce the output. Run the calculation again with controlled inputs, inspect random seeds or sampling settings where relevant, and record the model version, prompts, code and data used.
- Probe failure cases. Try limiting values, known symmetries, dimensional checks, noisy inputs and deliberately adversarial examples. A convincing answer on a typical case says little about behavior at the edge of the domain.
- Trace every external claim. Check equations, references, data provenance and quotations against the original source. Language models can invent citations or merge statements from different papers.
- Use an independent review. Ask a colleague who understands the physics to inspect the assumptions and results, not merely the prose. For a major measurement, compare with a separate analysis path.
- Document the human decision. State what the system did, what the researchers changed, which checks passed and where uncertainty remains. The record should allow another scientist to reproduce the reasoning.
Demers draws a line at responsibility: “A physicist is responsible for what is in their publication if they’re going to sign their name.” A generated paragraph or script does not dilute that responsibility. If the author cannot explain an output well enough to defend it under questioning, it is not ready to support a scientific claim.
What are the attribution and intellectual-property risks?
Large models are trained on extensive bodies of other people’s writing and code, yet their outputs may not identify the sources that shaped them. That creates two separate problems. First, a researcher can unknowingly present another person’s wording, idea or implementation without proper credit. Second, confidential or proprietary material entered into an external service may be exposed beyond the project’s intended boundaries.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAttribution is part of discovery, not an administrative afterthought. “It’s also part of the scientific process that we correctly give attribution to the ideas.” Researchers should preserve source notes, inspect generated references and distinguish their own contribution from an algorithm’s transformation of existing material. A model’s output should never be treated as evidence that an idea is original.
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Institutions may impose additional rules for unpublished data, controlled information or collaboration agreements. Even when no formal rule is violated, a lab should decide who may enter data into a tool, what retention settings apply and how generated material will be reviewed. If a model cannot provide a reliable provenance trail, its output is a drafting aid at most, not an authority.
Does AI change what physics students need to learn?
Yes, but not by making foundational knowledge obsolete. Demers says AI is rapidly changing theoretical physics by speeding calculations and changing the skills needed to solve problems. She compares this transition with older expectations such as reading German papers: training programs have always had to decide which skills are essential and which can be supported by tools.
One possible benefit is earlier access to meaningful data work. Undergraduates who spend less time writing routine code or learning the mechanics of a repetitive calculation could analyze real measurements sooner. That time is valuable only if it is redirected toward framing questions, understanding uncertainty and learning to think like physicists. Removing practice without adding judgment would leave students dependent on systems they cannot evaluate.
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Why does expertise determine whether an AI tool helps?
Experienced researchers may use a language model as a “thought partner” because they can spot a hidden assumption, reject a fabricated reference or redirect an unproductive line of reasoning. A junior researcher may not yet have the background needed to notice those failures. Fluency can therefore increase risk when it is mistaken for correctness.
The danger is not limited to spectacular mistakes. A model might use a convention from a neighboring subfield, omit a correction that matters at the experiment’s precision or produce code that works on a sample while leaking information between training and test sets. An expert sees the warning signs because they know what an instrument, equation or data distribution should look like.
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Labs can reduce the gap by pairing novices with reviewers, requiring reproducible notebooks and treating model-generated work as untrusted until checked. The goal is not to ban assistance but to ensure that responsibility grows with the system’s influence on a result.
Why does Demers avoid using language models for personal writing?
Demers says, “I have enough objections that I’ve never typed anything into Claude or ChatGPT.” She does not use an LLM to write emails or plan vacations. Her reason is not that every generated sentence is wrong; difficult communication often forces a person to work through a problem, and outsourcing that work can evade the problem itself.
She describes email as an example: “Why are some emails difficult to write? Because I’m navigating something that’s tricky, that I need to figure out.” Writing can reveal what the sender actually believes, what they owe another person and which trade-off they have not resolved. A polished draft may conceal that unfinished thinking.
She has also worried that AI answers based on a physics-and-dance book she co-wrote with Emily Coates could replace direct conversations with readers. Those conversations provide feedback and understanding that a generated summary cannot reproduce. In this view, human contact is not merely a slower interface; it is part of how knowledge and relationships develop.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a research group decide before adopting AI?
A group can make adoption safer by matching each proposed use to the amount of verification it permits. The following questions turn broad enthusiasm into an operational decision.
- What is the task? Separate drafting, coding, data transformation, experiment control and scientific interpretation. The closer a tool comes to interpretation or control, the stricter the review.
- Can the output be checked independently? Require a baseline, a test dataset or a physical conservation law before relying on the result.
- Who will review it? Name a person with enough domain expertise to detect subtle errors, rather than assigning review to the same novice who generated the output.
- What information may leave the lab? Classify unpublished measurements, personal information, controlled data and collaborator-owned material before using an external service.
- How will credit be recorded? Keep a prompt and code log, preserve original sources and state the system’s role in internal records and, where required, in the publication.
- What happens if the system fails? Maintain a manual or established software path so an experiment or analysis can continue without the model.
This approach treats AI as infrastructure that needs monitoring, not as a colleague whose confidence substitutes for evidence. It also protects the parts of training that make future physicists independent.
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What is the enduring standard for progress?
Physics advances when a community can inspect a claim, reproduce its basis, challenge its assumptions and learn from the result. Faster computation and broader access to old data can increase the number of claims a community examines. They do not lower the standard those claims must meet.
Demers’s warning is blunt: “Otherwise, we’re just not playing in the realm of making progress in science anymore; we’re just adding noise to the system.” AI is valuable when it reduces routine friction while leaving question selection, experimental judgment, attribution, validation and conversation visible and accountable. Those responsibilities are what endure.
Frequently Asked Questions
Can AI discover a new particle without a physicist?
An AI system can flag unusual events or optimize a search, but the available data does not establish autonomous discovery. Physicists must define the signal, rule out detector and background effects, and decide whether the evidence supports a new claim.
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Should AI-generated code be listed as a co-author?
Code generation does not meet the human responsibility implied by authorship. The people who understand, test and take responsibility for the code and its scientific consequences should receive credit; the tool’s role should be documented according to the collaboration or journal’s rules.
What records help make an AI-assisted analysis reproducible?
Keep the model or service version, prompts, input and output files, generated code, data-preparation steps, random seeds where applicable, and the independent checks used to accept or reject results. Preserve the original data so a reviewer can distinguish a model transformation from a measurement.
Why might searching older physics data be as important as collecting new data?
Earlier experiments may contain measurements that were difficult to query with older software or metadata. Better retrieval can reveal comparisons or constraints without repeating an expensive run, provided researchers recover the original calibration and collection context.
What human skill is hardest to replace in scientific collaboration?
The interview points to problem-solving through conversation: explaining an uncertainty, negotiating a design choice and learning from another person’s response. Those interactions generate context and trust that a fluent automated answer cannot supply.
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