The announcement that Merriam-Webster has designated “slop” as its 2025 Word of the Year marks a notable inflection point in how society is grappling with the proliferation of AI-generated content. As researchers working at the intersection of artificial intelligence and its real-world deployment, we find this linguistic development worth examining—not as a condemnation of AI technology itself, but as a signal of growing public literacy about quality, intention, and the responsible use of generative models.
The Gap Between Capability and Care
The emergence of “slop” as cultural shorthand reflects a fundamental tension in AI deployment: the dramatic reduction in the cost of content creation has not been matched by corresponding increases in quality assurance or intentionality. Our research community has long understood that generative models are tools—their value determined entirely by how they’re wielded and toward what ends.
What makes the “slop” phenomenon particularly instructive is that it highlights a failure mode that extends beyond the technical. The models themselves have achieved remarkable capabilities in text generation, image synthesis, and multimodal understanding. The problem isn’t primarily one of model architecture or training methodology—it’s one of deployment incentives and human decision-making.
When content generation becomes effectively free, we see a predictable economic response: massive oversupply. Search results fill with SEO-optimized but substantively hollow articles. Social media platforms become saturated with engagement bait generated at scale. The information commons degrades not because the technology produces inherently bad output, but because it enables bad actors to produce vast quantities of low-effort content that would have been economically infeasible to create manually.
Quality as an Emergent Property of Systems, Not Just Models
From a research perspective, the “slop” problem underscores something we’ve increasingly recognized in AI safety and alignment work: the behavior of AI systems in deployment is an emergent property of the entire sociotechnical system, not just the model in isolation. A language model that can generate coherent, grammatically correct text becomes “slop” when deployed without editorial oversight, fact-checking, or genuine informational intent.
This observation has implications for how we think about model development. While much of our field’s attention has rightly focused on improving model capabilities—better reasoning, more accurate factual recall, reduced hallucination rates—the “slop” phenomenon suggests we also need to invest in mechanisms that encourage appropriate use of these capabilities.
Some promising directions include:
- Provenance and watermarking systems that make AI-generated content identifiable, allowing platforms and users to make informed decisions about consumption
- Quality signals that go beyond fluency to measure informational value, factual accuracy, and originality
- Economic and platform-level incentives that reward genuine value creation over volume
The Nuance That Matters
Perhaps most importantly, the “slop” discourse reveals growing public sophistication about AI capabilities. As former Evernote CEO Phil Libin articulated, the distinction between productive AI use and “slop” often comes down to intention: using AI to reduce effort on mediocre work versus using it to achieve results that wouldn’t otherwise be possible.
This resonates with how many researchers actually use AI tools in our work. Code completion systems help us prototype faster. Large language models serve as reasoning partners for debugging or exploring problem formulations. Image generation tools create visualizations that communicate complex concepts. In each case, the AI augments human expertise and intention rather than replacing it.
The challenge is that from the outside, AI-generated code, text, or images may look superficially similar regardless of whether they were created through thoughtful human-AI collaboration or mindless automation. This suggests we need better mechanisms for signaling quality and effort—for distinguishing between AI as a tool for enhancement versus AI as a shortcut to flooding the zone.
Looking Forward
The “slop” designation shouldn’t be read as a rejection of generative AI technology. Rather, it’s a useful cultural corrective to unbridled techno-optimism. It acknowledges that like any powerful tool, generative models can be misused, and that the ease of misuse creates real problems for information ecosystems.
For the research community, this is a reminder that our work doesn’t end at model development. We need to think carefully about:
- How our models will be deployed and potentially misused
- What technical mechanisms can encourage beneficial use while discouraging harmful applications
- How to communicate both capabilities and limitations clearly to diverse audiences
The fact that “slop” has entered the cultural lexicon alongside earlier AI-related terms like “hallucinate” suggests that public understanding of AI is maturing beyond simple narratives of either utopian promise or existential threat. People are developing more granular vocabularies for describing different types of AI behavior and impact—which is exactly the kind of nuanced discourse we need as these technologies become more deeply integrated into society.
In the end, whether AI-generated content deserves to be called “slop” depends not on the technology itself, but on the humans who choose how to deploy it. That’s both a challenge and an opportunity for our field as we continue to develop increasingly capable systems.
