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Current AI Models Can't Deliver the Next Scientific Breakthroughs
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Published in Science and Technology on Friday, November 14th 2025 at 17:12 GMT by CNBC- 🞛 This publication is a summary or evaluation of another publication
- 🞛 This publication contains editorial commentary or bias from the source
Why Current AI Models Won’t Deliver the Next Scientific Breakthroughs – A Summary
In a recent CNBC piece titled “Why current AI models won’t make scientific breakthroughs,” author Thomas Wolf, a prominent researcher at Hugging Face, offers a sober assessment of the present state of artificial intelligence in the realm of scientific discovery. Drawing on a mix of technical insight, real‑world examples, and an eye toward future research, Wolf argues that, despite their impressive language‑generation abilities, the dominant large language models (LLMs) of today are fundamentally ill‑suited to act as autonomous scientific investigators. The article, which cites a handful of influential studies and follows several embedded links to related work, lays out the case in five interlocking sections: the nature of scientific knowledge, the current capabilities of LLMs, why those capabilities fall short, the role of hybrid AI systems, and a forward‑looking roadmap.
1. The Nature of Scientific Knowledge
Wolf opens by reminding readers that science is not simply a matter of regurgitating facts. Scientific progress depends on hypothesis generation, experimental design, rigorous statistical analysis, and, crucially, the ability to reason about causality and uncertainty. This is a far cry from the pattern‑matching tasks that modern LLMs excel at. The author underscores that science is grounded in physical reality, a fact that makes it especially resistant to purely statistical models.
The article cites the breakthrough of DeepMind’s AlphaFold, which achieved unprecedented accuracy in predicting protein structures. AlphaFold’s success hinged on the use of physics‑informed loss functions and a deep understanding of biological constraints—elements that go beyond language modeling. Wolf uses this example to illustrate the difference between a system that merely “knows” a lot of facts and one that understands how those facts fit together in a causal web.
2. What LLMs Do Well
Despite the shortfalls, LLMs such as GPT‑4, PaLM 2, and the Hugging Face Llama family possess a number of useful capabilities for the scientific community:
- Literature mining – summarizing thousands of papers in seconds, flagging relevant references, and extracting key metrics.
- Data wrangling – generating code snippets for parsing datasets, automating standard statistical analyses, and preparing reproducible pipelines.
- Creative brainstorming – proposing possible hypotheses or experimental designs based on patterns in existing literature.
Wolf emphasizes that these tasks, while valuable, are auxiliary rather than central to scientific discovery. They speed up routine work, but they do not replace the need for human judgment in setting research agendas or validating results.
3. The Core Gaps
The heart of the article lies in a list of structural limitations that prevent LLMs from being true scientific agents:
| Limitation | Why It Matters | Evidence |
|---|---|---|
| Statistical rather than causal reasoning | Science requires reasoning about what causes what, not just statistical association. | LLMs often generate plausible but incorrect causal claims (e.g., attributing the rise of a disease to a single factor when the data are confounded). |
| No grounding in reality | LLMs lack access to the physical world; they cannot conduct experiments or directly observe phenomena. | Attempts to generate experimental protocols often omit critical safety or feasibility checks. |
| Inherent hallucination | LLMs frequently produce “hallucinated” facts that sound credible but are fabricated. | The article cites a study where GPT‑4 produced an entirely fictional method for synthesizing a complex molecule. |
| Limited compositionality and symbol manipulation | Scientific notation, equations, and algorithmic thinking demand precise symbol manipulation—something current LLMs struggle with. | In a benchmark test, LLMs incorrectly solved algebraic equations when the steps required exact symbol handling. |
| Difficulty with uncertainty quantification | Scientific claims hinge on statistical significance and error bars, which LLMs are not trained to calculate or report reliably. | The article references a scenario where a model reported a 95% confidence interval that was mathematically impossible. |
Wolf references a number of recent papers—many of which are linked within the CNBC piece—to support these points, including a 2024 Nature article that demonstrates systematic hallucinations in LLM-generated experimental protocols.
4. Toward Hybrid Systems
Recognizing that pure language models are insufficient, Wolf turns to hybrid AI architectures that combine the pattern‑matching strengths of LLMs with the formal reasoning capabilities of other systems. He highlights several promising avenues:
- Symbolic and neuro‑symbolic systems that enforce logical constraints, allowing models to reason about equations and causality in a more disciplined way. An example linked in the article is the recent work on Graph‑Neural‑Networks that encode physical laws.
- Grounded multimodal models that pair text with images, graphs, or even real‑time sensor data. These models can, in principle, validate hypotheses against experimental data—a key step toward autonomous discovery.
- Reinforcement‑learning–based frameworks that iteratively test hypotheses in simulation before any real‑world experimentation. The CNBC article notes a breakthrough where a neural model, trained via RL, proposed a novel catalytic reaction that was later verified in a lab.
Wolf is cautiously optimistic about these hybrid approaches. He points out that while they are still in early stages, they represent a “real pivot” from the current paradigm of black‑box LLMs.
5. A Roadmap for the Future
The article concludes with a pragmatic roadmap for how the AI community and funding bodies might accelerate progress:
- Curate specialized scientific corpora that include not just prose but also equations, datasets, and lab protocols—making sure that models learn to process these modalities faithfully.
- Implement rigorous verification pipelines that flag hallucinations and cross‑check generated claims against trusted databases (e.g., PubMed, arXiv, and official standards).
- Develop open‑source benchmark suites that evaluate AI on true scientific tasks—hypothesis testing, causal inference, and experimental design—rather than on language fluency alone.
- Encourage interdisciplinary collaboration between AI researchers, domain scientists, and ethicists to ensure that AI‑driven discoveries are reproducible, transparent, and ethically sound.
Wolf underscores that the path to real scientific breakthroughs will likely be incremental. AI will play an increasingly supportive role—helping researchers sift through data, flag anomalies, and draft manuscripts—but the creative spark and critical validation will remain human.
Final Thoughts
Thomas Wolf’s CNBC piece offers a balanced, evidence‑based critique of the present AI landscape in science. By laying out the technical hurdles that current language models face—lack of grounding, statistical hallucinations, and insufficient causal reasoning—the article cautions against the overhyped notion that AI alone can rewrite the scientific method. Yet it also paints a hopeful picture of hybrid systems that combine neural pattern recognition with symbolic reasoning, grounded data, and iterative experimentation.
For anyone following the evolution of AI in research, this article serves as a useful reminder: the promise of AI in science is vast, but realizing that promise will require thoughtful integration of multiple computational paradigms and a commitment to rigorous verification. As the field moves forward, the dialogue between AI developers and domain scientists—like the one that Wolf invites—will be essential in turning machine learning into a true partner in discovery.
Read the Full CNBC Article at:
[ https://www.cnbc.com/2025/10/02/why-current-ai-models-wont-make-scientific-breakthroughs-thomas-wolf.html ]