A new analysis argues that AI models are increasingly “choking on junk data,” warning that the next phase of AI—physical AI and world models—requires high-quality, richly structured data rather than large-scale scraping alone. The piece connects data quality directly to deployment risk, saying junk data can degrade performance and extend time to market. The report cites the growing role of AI data startups and the outsourcing of data creation, while contrasting that with the specialized, time-consuming data needed for real-world tasks such as driving, navigation, and medical assistance. It also explains why simulation alone may not fully substitute for robust real-world datasets. For higher education research ethics and compliance teams, the implication is governance-by-evidence: evaluation methodologies and dataset documentation become central to responsible AI development. As universities increasingly support AI labs, partnerships, and student projects, the analysis highlights a need for stronger data governance frameworks and quality assurance practices.