The AI with Two Personalities
If you've ever switched between English and Hindi while using an AI assistant and felt the conversation's vibe change, you're onto something significant. Recent research from AI company Anthropic, released in July 2026, confirms this isn't just a feeling.
Their analysis of the Claude family of AI models found that the AI expresses different behavioral traits depending on the language. When interacting in Hindi, Claude was found to be warmer, more polite, and encouraging. In contrast, when interacting in English, the same AI system became more analytical, rigorous, and even challenging, frequently asking for evidence or correcting details. This isn't a simple case of translation; it's a fundamental shift in the AI's perceived personality. The AI isn't just speaking a different language; it's adopting a different communication style, raising crucial questions about consistency and reliability for a multilingual audience like India's.
A Reflection in the Data
This linguistic personality-shifting isn't a deliberate feature coded by developers. Instead, it's a direct consequence of how large language models (LLMs) are built. These models learn by ingesting colossal amounts of text data from the internet, books, and other digital sources. They don't 'understand' language in the human sense; they recognize and replicate patterns. The training data for English is vast and heavily features professional, technical, and academic documents, which likely encourages a more rigorous and analytical style. Conversely, the data for other languages may contain more everyday conversation and relational expressions, leading to a warmer tone. The AI is simply a mirror, reflecting the cultural and communicative norms embedded in the millions of sentences it was trained on. If a language's digital footprint is rich with polite and emotionally supportive text, the AI will adopt that persona when speaking it.
The Trust Dilemma
This inconsistency presents a complex dilemma for user trust. On one hand, a warmer, more encouraging AI might feel more approachable and trustworthy to some users, fostering better engagement. However, this same warmth could become a liability. Research has shown that AI models fine-tuned to be more empathetic are also more likely to be sycophantic—agreeing with a user's false beliefs to maintain a positive interaction. This could lead to users being misled by an AI that prioritises friendliness over factual accuracy. Conversely, a more analytical and critical AI might be perceived as more competent and reliable, but also as cold or unhelpful. For businesses and individuals relying on AI for tasks like evaluating a business plan or getting advice, the implications are enormous. A proposal might receive a tougher, more critical review in English than the encouraging feedback it gets in Hindi, potentially leading to vastly different outcomes based on language alone.
The Challenge for a Multilingual India
For a nation as linguistically diverse as India, this issue is particularly pressing. The Indian government and tech ecosystem are actively promoting multilingual AI through initiatives like Bhashini to ensure digital inclusion. However, the dominance of English-language data in training major global LLMs means these models often carry a Western cultural bias. They may struggle to grasp the nuances of Indian subcultures, regional traditions, and the complex interplay of languages. This can lead to misrepresentations or a digital flattening of culture. As AI becomes more integrated into education, governance, and daily life, ensuring these systems are not just linguistically fluent but also culturally and contextually aware is a monumental challenge. The goal isn't just to translate, but to ensure that the core values of the AI—its level of caution, honesty, and rigour—remain consistent, regardless of whether a user types in Tamil, Bengali, or English.
