Bengaluru Shop Owner Claims "Torture" By Man Over Signboard In English
Bengaluru:
A new incident highlighting the ongoing language row in Karnataka has come to light, where an elderly man clashed with a shop owner over the compliance of the state government's rules for a commercial signboard. A video of the incident has gone viral on social media.
In the 44-second clip, the woman, a shop owner, accused an elderly man of "torture" over an English-only commercial signboard. The man confronted her, demanding to change the signboard to comply with Kannada language rules. While the woman insisted that a text in Kannada is already written on the board, the man argued that the Kannada text must cover at least 60 per cent of it.
He was then seen filming the signboard, saying that he would file a complaint.
The woman, in the video, questioned the elderly man over his request, asking him who he was to tell her about the signboard rules. To this, he said he is a "public citizen from Karnataka". The woman then argued, "This is India".
The Kannada Language Comprehensive Development (Amendment) Act, 2024 requires that 60 per cent of all commercial, industrial, institutional, and public signboards in Karnataka be in Kannada. The Kannada text must occupy the upper half of the board, with the remaining 40 per cent allowed in any other language.
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Imagine a farmer in eastern Uttar Pradesh calling a helpline and interacting with a chatbot that understands and replies fluently in Bhojpuri, while also generating a clear summary for a government officer to act on. Or an AI tutor generating regional-language lessons, quizzes, and spoken explanations for students in languages like Marathi, Tamil, Telegu, or Kannada. These efforts fit into India's broader digital stack, alongside Aadhaar (digital identity), UPI (unified payments interface), ULI (unified lending interface) and ONDC (the Open Network for Digital Commerce). In a world where AI models are fast becoming a symbol of digital leadership, 'a sovereign LLM is also about owning the narrative, the data, and the future of its digital economy", said Akshay Khanna, managing partner at Avasant, a consulting firm. 'Sovereignty will be a key requirement in all nations including India," says Mitesh Agarwal, Asia-Pacific managing director at Google Cloud. 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For enterprises, it means unlocking intelligence without sending data beyond borders." Sarvam is developing three model variants—a large model for 'advanced reasoning and generation"; a smaller one for 'real-time interactive applications", and 'Sarvam-Edge for compact on-device tasks". It is partnering with AI4Bharat, a research lab at the Indian Institute of Technology (IIT)-Madras, supported by Infosys co-founder Nandan Nilekani and his philanthropist wife Rohini, to build these models. Sarvam has already developed Sarvam 1, a two-billion parameter multilingual language model, trained on four trillion tokens using Nvidia H100 GPUs. The company claims its custom tokenizer (that breaks text into small units, like words or parts of words, so a language model can understand and process it) is up to four times more efficient than leading English-centric models when processing Indian languages, hence reducing costs. Sarvam 1 supports 11 languages: Hindi, Bengali, Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Oriya, Punjabi, and English. It powers various generative AI (GenAI) agents and is also hosted on Hugging Face, enabling developers to build Indic-language apps. Hugging Face is a platform for sharing and hosting open-source AI models and datasets. meanwhile, is building voice-to-voice foundational LLMs that aim to produce near instant autonomous voice conversations, with very low latency. The models also aim to enable 'emotion aware conversations", which preserve intonation, stress and rhythm in the conversations, said Ganesh Gopalan, co-founder and CEO of 'The model will enable realistic conversations in governance, healthcare and education," he added. Wait and watch Sovereign LLMs and SLMs are likely to find strong acceptance in public service delivery and citizen engagement services across the country, just like it happened with UPI. 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Moreover, there are limited safeguards against hallucinations. 'Without deeper fine-tuning, cultural grounding, and linguistic quality assurance, these models are too brittle for nuanced conversations and too coarse for enterprise-scale adoption," Gogia added. 'The ambition is clear—but execution still needs time and investment." The missing millions Building sovereign models without government or venture capital funding could also pose a big challenge since developing a foundational model from scratch is an expensive affair. For instance, OpenAI's GPT was in the works for more than six years and cost upwards of $100 million and used an estimated 30,000 GPUs. Chinese AI lab DeepSeek did build an open-source reasoning model for just $6 million, demonstrating that high-performing models could be developed at low costs. But critics point out that the reported $6 million cheque would have excluded expenses for prior research and experiments on architectures, algorithms, and data. Effectively, this means that only a lab which has already invested hundreds of millions in foundational research and secured access to extensive computing clusters could train a model of DeepSeek's quality with a $6 million run. Ankush Sabharwal, founder and CEO of CoRover, says that its BharatGPT chatbot is a 'very small sovereign model with 500-million parameters". He has plans to build a 70-billion parameter sovereign model. 'But, we will need about $6 million to build and deploy it," Sabharwal says. Long way to go A glance at the download numbers for the month of May from Hugging Face underlines the wide gap between some of India's local language models and similar-sized global offerings. For instance, Sarvam-1's 2-billion model saw just 3,539 downloads during the month. Krutrim, a 12-billion model from Ola-backed Krutrim SI Designs, fared similarly with only 1,451 downloads. Fractal AI's Fathom-R1 14-billion model showed the most promise with 9,582 downloads. In contrast, international models with comparable or slightly larger sizes saw exponential traction. Google's Gemma-2 (2-billion) logged 376,800 downloads during the same period, while Meta's Llama 3.2 (3-billion) surpassed 1.5 million. Chinese models, too, outpaced Indian counterparts. Alibaba's Qwen3 (8- billion) recorded over 1.1 million downloads, while a fine-tuned version of the same model—DeepSeek-R1-0528-Qwen3-8B—clocked nearly 94,500 downloads. The numbers underline the need for a stronger business case for Indian startups. The senior government official quoted earlier in the story said that sovereign models must stand on their own feet. 'The government has created a marketplace where developers can access and build apps on top of sovereign models. But the startups must be able to offer their services first to India, and then globally," he said. 'API revenue, government usage fees, and long-term planning are key," Aakrit Vaish, former CEO of Haptik and mission lead for IndiaAI until March, said. API revenue is what a company earns by letting others use its software features via an application programming interface. For example, OpenAI charges businesses to access models like ChatGPT through its API for writing, coding, or image generation. Nonetheless, API access alone won't cover costs or deliver value, Gogia of Greyhound Research said. 'Sovereign LLM builders must focus on service-led revenue: co-creating solutions with large enterprises, developing industry-specific applications, and securing government-backed rollouts," he suggested. Indian buyers, he added, want control—over tuning, deployment, and results. 'They'll pay for impact, not model access. This isn't LLM-as-a-Service; it's LLM-as-a-Stack." In short, capability alone won't cut it. 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