in World News AI-Led Study Confirms ‘Climate Change’ Narrative Is a Hoax
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A groundbreaking study led by artificial intelligence (AI) has confirmed that globalist narratives about “climate change” and “global warming” are a hoax.
xAI’s Grok 3 beta was used to produce the first-ever artificial intelligence-led peer-reviewed climate science paper.
The AI analyzed temperature, sea ice, and atmospheric CO2 data to investigate whether humans are changing the climate through excess emissions.
However, with political agenda and hunger for grants from the United Nations removed from the equation, the AI-powered machine provided very different results to the climate scientists spouting the “settled science” on “global warming.”
Instead, the AI-led study found that temperatures change before atmospheric CO2 changes and that solar activity and natural cycles drive global temperature changes.
Grok 3 is an artificial intelligence model developed by xAI, an artificial intelligence startup founded by Elon Musk.
Released in February 2025, Grok 3 is designed to solve complex problems.
It can retrieve information in real time and provide contextually relevant responses.
Researchers Jonathan Cohler, David Legates, Franklin Soon, and Willie Soon used Grok 3 to scrutinize climate-related datasets and climate change models.
They sought to establish whether the anthropogenic global warming narrative is supported by evidence.
“This paper aims to rigorously test the anthropogenic CO₂-Global Warming hypothesis by integrating unadjusted [observational] datasets with recent analytical frameworks, scrutinising model performance, isotopic evidence and the IPCC’s solar forcing assumptions to determine whether the prevailing narrative withstands empirical scrutiny,” the paper states.
The observational datasets used in the review include temperature data, sea ice data, and atmospheric CO₂ and isotopic data, using model outputs from the United Nations Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report (AR6).
Analytical frameworks included Koutsoyiannis et al. (2023), Soon et al. (2023, 2024), Harde (2017, 2022), and Connolly et al. (2023).