News India Times

www.newsindiatimes.com – that’s all you need to know News India Times (July 11, 2026 - July 17, 2026) July 17, 2026 4 Opinion/ Analysis H-1B hiring approach,” a spokeswoman for the retailer said at the time. Other employers followed suit. The number of employers filing H1-B visas has fallen by nearly 40% in the last fiscal year, according to US Citizenship and Immigra- tion Services. Walsh, the immigration attorney, said that the financial, professional services, and technology firms she represents still need to hire foreign-born workers and are finding the new restrictions “enormously frustrating.” Those barriers pushed 68% to consider nearshoring or offshoring their foreign-born recruits, according to a survey by Envoy Global, a immigration services provider affiliated withWalsh’s firm. She suspects most are going to Latin America or India. “This is what they need to grow their business and thereafter contribute to the US economy, create more jobs,” she said. But “they’re just being met with these bar- riers.” - Bloomberg Fewer US Companies AreWilling To Hire Foreign Grads - Continued From Page 3 Disclaimer:The views and opinions expressed on this page are those of the authors and Parikh Worldwide Media does not officially endorse, and is not responsible or liable for them. IndiaMust Ride The Next AI Wave, Not Chase Past Ones Next revolution will come from smaller models, specialised models, reasoning systems, neurosymbolic AI, edge AI, agentic workflows. W hile India debates — duly impassioned by China’s recent AI strides — spending billions on building its own foundation model, a company that has already spent billions on the world’s most famous AI laboratory is now looking at using Chinese models. That company is Microsoft and its CEO, SatyaWith- out Indian data, Indian AI will be foreign AI wearing Indian clothes. It needs datasets drawn from India, and grounded in Indian realities (Reuters)Nadella, has said that the world cannot allow a handful of AI giants to eat the economy. Microsoft has invested billions in OpenAI, helped turn ChatGPT into a household name, and made AI the centre of its future. Yet, he recently told theWall Street Journal that he is considering using technology from DeepSeek, the Chinese AI company that shocked Silicon Valley by showing that powerful models could be built and run far more cheaply than anyone believed. Indeed, China’s Z.ai recently revealed capacity that rivals Anthropic’s frontier models in terms of cybersecurity implications. Nadella’s intuitiveness should sober up India’s AI chest-thumpers. If Microsoft, with all its money, cloud infrastructure, engineers, enterprise customers, and inside access to OpenAI, is preparing to use cheaper Chinese models, why should India spend billions trying to build a giant model of its own? Microsoft has seen the future, and it does not look like billion-dollar models with trillion-dollar egos. It looks like cheap, interchangeable intelligence running inside platforms that own the data, security, workflows, com- pliance, pricing, and customer relationship. The model builders may have the glamour; the platform owners will collect the rent. Sam Altman and his kin have been insisting that only a few chosen labs can build the models that matter, and that they, therefore, deserve more chips, more data centres, more electricity, more capital, and less interfer- ence to steer humanity through the AI future. Nadella is puncturing that myth. He is saying customers must have choice, companies should be able to use many models, including cheaper ones, and AI should not be controlled by a handful of companies doing all the learning for the world. He is right. Large language models (LLMs) are also nearing the top of their exponential S-curve, the pattern every exponential technology follows as it moves from slow early progress to breathtaking acceleration and then to the painful plateau where each incremental gain costs vastly more and delivers much less. LLMs have had their miracle phase. They stunned the world by writing essays, generating code, summarising documents, passing exams, and speaking like humans, but making them bigger is not making them wiser. They still hallucinate, make basic reasoning errors, invent facts, fail to understand truth, causality, ethics, or con- sequences, and burn astonishing amounts of hardware, electricity, and capital to produce answers that often sound authoritative but are simply wrong. This does not mean AI has stopped advancing expo- nentially. It means this particular S-curve is flattening and the next one is beginning, which is precisely why India must not waste its energy chasing yesterday’s news. The next revolution will come from smaller models, specialised models, reasoning systems, neurosymbolic AI, edge AI, agentic workflows, and systems that can verify their own output instead of producing fluent nonsense. The trillion-dollar opportunities will be in the platforms on which these applications run: Trusted data layers, model-routing systems, AI clouds, workflow engines, security frameworks, compliance tools, and domain-specific applications that make intelligence use- ful in hospitals, schools, courts, farms, factories, banks, and government offices. This is where India has natural strengths: Software, systems integration, frugal engineering, digital public infrastructure, and the ability to solve messy real-world problems at national scale. It would be stupid for India to spend billions chasing the fading curve of giant LLMs just as this party is coming to an end. None of this means India should depend on for- eign AI; that would be dangerous. If Indian hospitals, ministries, courts, universities, and companies pour their data into foreign APIs, India will become a digital colony again. The model may speak Hindi or Tamil, but the value, data, and control will sit elsewhere. India needs small models that run on phones, hospital machines, school networks, local servers, and edge de- vices; models cheap enough for government schools and reliable enough for primary health centres. It also needs a secure national AI cloud and a mar- ketplace that routes each task to the right model: Cheap models for routine work, powerful ones for complex work, Indian models for sensitive contexts, open models where possible, and foreign models only when data and security rules are clear. All of this must rest on domestic data platforms. Without Indian data, Indian AI will be foreign AI wear- ing Indian clothes. It needs datasets drawn from India’s languages, dialects, hospitals, farms, courts, schools, and government systems, and grounded in Indian realities. It must embed India’s values, history, philosophical tradi- tions, scientific achievements, social complexity, and ci- vilisational memory, because AI that does not understand India will eventually distort India. The obsession with size is Silicon Valley’s disease; In- dia’s genius has always been doing more with less. The government should build rails, not monuments. UPI succeeded because it created open infrastructure on which others could innovate. IndiaAI should do the same — provide compute, datasets, benchmarks, evaluation tools, procurement pathways, safety testing, and open interfaces, then let startups, IITs, hospitals, universities, and state governments build. India’s IT giants need to wake up or become toast. AI will surely erode the old labour-arbitrage model and decimate their billing hours. But it also gives them the chance to become the world’s AI transformation partners because they know enterprise systems, workflows, and imple- mentation better than almost anyone. If they wake up and move fast, they can help every company rewire itself for the AI age and lead India — and them- selves — onto the next S-curve. Vivek Wadhwa is CEO, Vionix Biosciences. ByVivekWadhwa PHOTO:CourtesyVivekWadhwa PHOTO:MICHAEL NAGLE/BLOOMBERG Pedestrians in New York City.