The rapid adoption of large language models (LLMs) in scientific research has led to a dramatic surge in the number of published papers, but evidence suggests that this increase comes at the cost of declining quality. A growing body of analysis shows that researchers who use LLMs tend to publish more papers, yet the scientific rigor, originality, and reproducibility of their work often do not keep pace. Studies tracking publication trends across multiple disciplines have found that labs and individual researchers incorporating LLMs into their workflows—particularly for drafting abstracts, generating hypotheses, writing methods sections, or summarizing literature—see a measurable uptick in output. In some cases, paper counts have doubled or tripled within a single year of LLM integration. However, deeper scrutiny reveals troubling patterns. Peer reviews and post-publication evaluations indicate that papers produced with heavy LLM assistance are more likely to contain factual inaccuracies, vague or nonsensical claims, and poorly structured arguments. Some studies have found that LLM-generated content often lacks proper citation context, misrepresents prior work, or fabricates references—phenomena known as “hallucinations.” Moreover, the scientific value of these papers appears to be diminishing. Metrics such as citation rates, replication success, and impact on subsequent research show little to no improvement, and in some cases, a decline. This suggests that while LLMs may accelerate the writing process, they are not enhancing the substance of scientific inquiry. The issue is compounded by the fact that many institutions and journals have yet to establish clear guidelines for the use of AI tools in research. Some authors use LLMs without proper disclosure, undermining transparency and accountability. Others rely on them to generate entire sections of manuscripts, reducing the role of critical thinking and independent analysis. Experts warn that unchecked reliance on LLMs risks turning science into a quantity-over-quality race, where the goal becomes publishing more rather than publishing better. As one researcher noted, “We’re seeing a flood of papers that look polished but lack depth—like well-written noise.” The scientific community is now grappling with how to balance the efficiency gains of AI with the need to preserve the integrity of research. Some journals are beginning to require disclosure of AI use, while others are developing new review protocols. But for now, the data is clear: LLMs are boosting publication numbers—but not necessarily scientific progress.