A version of this essay has been published by Open Magazine at https://openthemagazine.com/essays/the-new-knowledge-war/ Generative AI, as exemplified by chatGPT from Microsoft/OpenAI and Bard from Google, is probably the hottest new technology of 2023. Its ability has mesmerised consumers to provide answers to all sorts of questions, as well as to create readable text or poetry and images with universal appeal.  These generative AI products purport to model the human brain (‘neural networks') and are ‘trained’ on large amounts of text and images from the Internet. Large Language Models or ‘LLMs’ are the technical term for the tools underlying generative AI. They use probabilistic statistical models to predict words in a sequence or generate images based on user input. For most practical purposes, this works fine. However, in an earlier column in Open Magazine, “Artificial Intelligence is like Allopathy”, we pointed out that in both cases, statistical correlation is being treated by users as though it were causation. In other words, just because two things happened together, you can’t assume one caused the other. This flaw can lead to completely wrong or misleading results in some cases: the so-called ‘AI hallucination’.  To test our hypothesis, we asked chatGPT to summarise that column. It substantially covered most points, but surprisingly, though, it completely ignored the term ‘Ayurveda', although we had used it several times in the text to highlight ‘theory of disease’. This is thought-provoking, because it implies that in the vast corpus of data that chatGPT trained on, there is nothing about Ayurveda. The erasure of Indic knowledge Epistemology is the study of knowledge itself: how we acquire it, and the relationship between knowledge and truth. There is a persistent concern that Indic knowledge systems are severely under-represented or mis-represented in epistemology in the Anglosphere. Indian intellectual property is ‘digested’, to use Rajiv Malhotra’s evocative term. For that matter, India does not receive credit for innovations such as Indian numerals (misnamed Arabic numerals), vaccination (attributed to the British, though there is evidence of prior knowledge among Bengali vaidyas), or the infinite series for mathematical functions such as pi or sine (ascribed to Europeans, though Madhava of Sangamagrama discovered them centuries earlier). The West (notably, the US) casually captures and repackages it even today. Meditation is rebranded as ‘mindfulness’, and the Huberman Lab at Stanford calls Pranayama ‘cyclic sighing’. A few years ago, the attempts of the US to patent basmati rice and turmeric were foiled by the provision of ‘prior art’, such as the Hortus Malabaricus, written in 1676 about the medicinal plants of the Western Ghats.  Judging by current trends, Wikipedia, and presumably Google, LinkedIn, and other text repositories, are not only bereft of Indian knowledge, but also full of anti-Indian and specifically anti-Hindu disinformation. Any generative AI relying on this ‘poisoned’ 'knowledge base' will, predictably, produce grossly inaccurate output.  This has potentially severe consequences: considering that Sanskrit, Hindi, Tamil, Bengali (and non-Latin scripts) etc. are underrepresented on the Internet, generative AI models will not learn or generate text from these languages. For all intents and purposes, Indic knowledge will disappear from the discourse. These issues will exacerbate the bias against non-English speakers, who will not think about their identity or culture, reducing diversity and killing innovation. More general problems with epistemology: bias, data poisoning and AI hallucinations Generative AI models are trained on massive datasets of text and code. This means they are susceptible to inherent biases. A case in point: if a dataset is biased against non-white females, then the generative AI model will be more likely to generate text