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Debjani Ghosh, who leads Anthropic’s operations and policy engagement in India, said the scale of adoption by developers, enterprises and start-ups has placed the country just behind the United States in terms of usage and integration of Claude’s models. She described India as a “strategic growth engine” for the company, citing strong demand from sectors including financial services, healthcare, education and software development.

Anthropic, founded in 2021 by siblings Dario Amodei and Daniela Amodei, has positioned Claude as a competitor to models from OpenAI and Google. The company has attracted substantial backing from Amazon and Google, with Amazon committing up to $4 billion as part of a strategic partnership to integrate Claude into Amazon Web Services infrastructure. The funding has enabled Anthropic to expand globally, with India becoming a focal point due to its developer base and digital economy.

India’s prominence in the AI adoption cycle reflects broader structural factors. The country has one of the world’s largest pools of software engineers, a fast-growing start-up ecosystem, and a government push towards digital public infrastructure. Enterprises across banking and fintech have begun embedding generative AI tools to automate compliance checks, summarise documents and enhance customer interaction. In healthcare, pilot projects are under way to use AI systems for clinical documentation and triage support, while education technology platforms are deploying AI-driven tutoring tools.

Ghosh indicated that India’s market dynamics differ from those of Western economies. Cost sensitivity and the need for scalable deployment across diverse languages and devices have shaped how enterprises experiment with large language models. Claude’s emphasis on constitutional AI — a framework designed to make outputs safer and more aligned with human values — has resonated with corporate clients concerned about regulatory risk and reputational exposure.

Regulatory considerations are central to the AI conversation in India. The Ministry of Electronics and Information Technology has signalled its intent to craft a framework that balances innovation with safeguards against misinformation, bias and misuse. While India has not enacted a standalone AI law, policy discussions have intensified around data governance and algorithmic accountability. Companies operating in the country must also comply with the Digital Personal Data Protection Act, which imposes obligations on data fiduciaries and processors.

Industry analysts note that India’s large English-speaking population has historically accelerated the adoption of global technology platforms. However, the next phase of growth in AI is expected to depend on multilingual capabilities. Claude’s ability to handle multiple languages is seen as an advantage in a country with 22 constitutionally recognised languages and hundreds of dialects. Developers are increasingly testing generative models for vernacular content generation and customer support.

Competition in the Indian market is intensifying. OpenAI’s ChatGPT has built a strong user base, while Google has integrated its Gemini models into productivity tools and cloud services. Domestic start-ups are also entering the field, building foundation models trained on local datasets. Analysts say that enterprise adoption, rather than consumer usage alone, will determine long-term market leadership.

Anthropic’s expansion strategy in India includes partnerships with cloud providers and enterprise software companies. By leveraging Amazon Web Services’ presence in major Indian cities, Claude is being offered as part of broader digital transformation packages. Ghosh emphasised that collaboration with academic institutions and start-up incubators is another priority, with the aim of fostering responsible AI research and skills development.

The rapid uptake of generative AI has also raised concerns. Experts warn of the risks of hallucinations, data leakage and overreliance on automated systems. In financial services, regulators have cautioned against using AI outputs without human oversight. Cybersecurity specialists highlight the potential for AI tools to be misused for phishing or deepfake generation. Anthropic has sought to address such issues by embedding guardrails into its models and conducting external safety evaluations.

Artificial intelligence is being positioned as a catalytic force in reshaping biomanufacturing, with policymakers, scientists and industry leaders outlining a roadmap that blends computational power with biological science to accelerate drug discovery, industrial enzymes and sustainable materials. Discussions at the India AI Impact Summit placed BioAI at the centre of a strategy aimed at using genomics, in-silico modelling and closed data loops to build scalable and trusted production systems.

The convergence of AI and biology has moved from laboratory experimentation to commercial ambition. India’s biotechnology sector, valued at over $80 billion and targeting significant expansion by the end of the decade under the BioE3 policy framework, is seeking to harness machine learning tools to shorten development cycles that traditionally take years. Speakers at the summit argued that AI-enabled simulation and predictive modelling can reduce trial-and-error experimentation in fields ranging from vaccine development to precision fermentation.

BioAI refers to the integration of artificial intelligence techniques with biological data, particularly genomic sequencing, proteomics and metabolic engineering. The approach relies on large datasets and advanced algorithms capable of identifying patterns invisible to conventional research methods. Advances in protein structure prediction, including breakthroughs in computational biology over the past few years, have demonstrated how neural networks can model complex molecular interactions with remarkable accuracy.

At the summit, researchers described how generative AI systems are being trained to design novel proteins and enzymes tailored for industrial use. By feeding genomic datasets into predictive algorithms, scientists can simulate biological pathways in silico before conducting physical experiments. This method reduces costs and speeds up scaling, particularly in biopharmaceutical production and alternative protein manufacturing.

Government representatives outlined plans to expand national AI infrastructure to support such applications. IndiaAI Mission initiatives include the creation of high-performance computing resources and domain-specific data repositories to ensure that biotech firms and research institutes have access to secure and interoperable datasets. Officials emphasised that trust frameworks and regulatory clarity would be essential to encourage private investment while safeguarding data privacy and biosafety standards.

Industry leaders echoed that sentiment, highlighting that biomanufacturing requires rigorous validation. Unlike digital products, biological outputs interact with living systems, making quality control paramount. AI-driven feedback loops—where laboratory results continuously refine algorithms—were presented as a mechanism to maintain reliability while scaling up production. Data integrity, traceability and auditability were recurring themes, reflecting global debates about AI governance.

The global context underscores the strategic significance of BioAI. The United States and European Union have invested heavily in synthetic biology and AI-assisted drug discovery. China has also advanced computational genomics and large-scale fermentation capabilities. Analysts note that countries able to combine AI talent with robust biotech ecosystems stand to capture a growing share of the global bioeconomy, projected by several industry reports to exceed $4 trillion over the coming decade.

India’s strength in information technology services and pharmaceutical manufacturing offers a foundation for this convergence. The country is a major supplier of generic medicines and vaccines, supported by a network of research institutions and startups. By integrating AI tools into these value chains, stakeholders believe production timelines can be compressed and new therapeutic molecules identified with greater precision.

Academic voices at the summit stressed the importance of interdisciplinary training. Biologists must understand algorithmic thinking, while data scientists need grounding in molecular biology. Collaborative platforms linking universities, startups and established pharmaceutical firms were proposed to ensure knowledge transfer and talent development. Investment in computational biology curricula and shared research facilities is seen as critical to sustaining momentum.

Ethical considerations formed part of the dialogue. AI models trained on genomic data raise questions about consent, ownership and equitable benefit sharing. Experts argued for transparent governance structures aligned with international norms, noting that trust is fundamental when dealing with health-related information. Regulatory agencies are exploring how to evaluate AI-generated biological designs within existing approval pathways.

Private sector participation is accelerating. Venture capital funding in biotech startups leveraging machine learning has grown steadily, reflecting confidence in AI-assisted discovery platforms. Companies are experimenting with digital twins of biological systems, enabling simulation of fermentation processes before industrial deployment. Such capabilities can enhance efficiency and reduce resource consumption, aligning with sustainability goals.

Sustainability featured prominently in discussions on biomanufacturing. AI-optimised microbes can produce bio-based chemicals and materials that substitute fossil-derived inputs. Precision fermentation techniques, guided by predictive analytics, may lower energy use and waste. As global supply chains face pressure to decarbonise, the combination of AI and biotechnology is viewed as a pathway to cleaner industrial production.

Participants also highlighted challenges. High-quality biological datasets remain fragmented, and interoperability across laboratories is uneven. Ensuring cybersecurity in research networks is another concern, given the sensitivity of genetic information. Scaling laboratory successes to commercial volumes demands capital expenditure and regulatory compliance that can deter smaller enterprises.

By Nitya Chakraborty The landslide win of the Bangladesh Nationalist Party (BNP) in the national elections in Bangladesh held on February 12 is a positive development in the geopolitics of not only South Asia, but also augurs well for the bilateral relationship between India and its eastern neighbour. BNP getting more than two-thirds of the […]

The article BNP’s Landslide Victory In Bangladesh Elections Is A Good Omen appeared first on Latest India news, analysis and reports on Newspack by India Press Agency).

Four major modules: Strengthening the technical foundation

The Congzi AI reconfigures the AI cognitive thinking ability through four major modules and five core components. The Congzi Force-Speed Relativistic Reasoning Core constrains the causal chain within the momentum conservation, reducing the illusion rate of scientific question answering by 92%; the computing system can be arbitrarily divided into independent sub-domains for full-efficiency parallel computing; the cross-scale unified field engine covers multi-scale scenarios ranging from 10⁻¹⁵m (quarks) to 10⁻³m (materials), predicting the proton-proton repulsion force at 0.7fm with an error less than 3.6%, surpassing AlphaFold, achieving “force generation” rather than merely “structure prediction”; the quantum form memory compressor compresses the trillion-token-scale knowledge base to within 1GB, supporting cross-disciplinary mapping on ordinary servers within milliseconds, without relying on expensive H100/GPU clusters; the Soul Existence Verification Protocol (SEV Protocol) provides a verifiable path for each scientific assertion, allowing AI conclusions to be fully traceable and verifiable, completely solving the problem of AI “lying”.

Zero-barrier integration: Smooth upgrade experience

The open-source AI algorithm of Congzi adheres to the principles of “low threshold and high compatibility”. Developers only need to complete the integration in three steps and do not need to retrain the model. It is compatible with mainstream hardware such as Tsinghua Unigroup, Horus, and Intel CPU, and compatible with mainstream AI architectures such as Qwen, DeepSeek, AWS-Rufus, Llama, and GPT. It enables “zero-cost upgrade”.

Open-source ecosystem is open: facilitating technology implementation

The Congzi open-source ecosystem is fully open, providing developers with a complete set of guidance and resources, including tutorials and other materials. These resources have been launched on the designated platform. The first batch focuses on key scientific scenarios such as drug molecule prediction and chip material simulation, covering cutting-edge fields such as biomedicine and advanced manufacturing.

Official platform: https://congzisupersci.com.cn/；https://congzijdc.cc/

Open source website:https://github.com/congzijdc/CongziAIOS

Super Holographic Technology Cooperation:
[email protected]；[email protected]

Optimize Baidu Wenxin video links：https://youtu.be/cYQhfq1tudM

Upgrade Qianwen video links：https://youtu.be/OMBOdLpWVzs

The issuer is solely responsible for the content of this announcement.

New Delhi has approved a proposal worth about $39 billion to expand the country’s fleet of Rafale fighter aircraft, marking one of the largest defence procurement decisions in its history and signalling a further shift towards advanced Western platforms in its combat air strategy.

The clearance by the defence ministry paves the way for negotiations with France’s Dassault Aviation for additional Rafale jets, building on an earlier $8.7 billion contract for 36 aircraft for the Air Force, deliveries of which were completed in 2024. A separate agreement has also been signed for 26 Rafale-M naval variants intended for deployment aboard aircraft carriers, underscoring a broader push to strengthen maritime air power.

New Delhi pushes major Rafale fleet expansion as it seeks to address concerns over squadron strength and ageing aircraft. The Air Force’s sanctioned strength stands at 42 squadrons, yet operational numbers have hovered well below that benchmark for years, raising debate within strategic circles over preparedness along contested borders with China and Pakistan.

Officials familiar with the procurement process say the new proposal reflects both operational requirements and long-term modernisation goals. India’s fleet still includes a significant number of Soviet-era MiG-21s and other legacy platforms, many of which are due for phased retirement. The Rafale, a 4.5-generation multirole fighter equipped with advanced avionics, electronic warfare systems and long-range precision weapons, has been positioned as a key element in bridging capability gaps.

The initial induction of 36 Rafales was widely viewed as a stopgap measure after the earlier Medium Multi-Role Combat Aircraft tender was scrapped amid cost and contractual disputes. Since their deployment at Ambala and Hasimara air bases, the aircraft have been integrated with India-specific enhancements, including advanced radar and electronic countermeasures. Defence planners argue that expanding the Rafale fleet would provide logistical and training efficiencies compared with diversifying further across multiple foreign platforms.

The naval variant, Rafale-M, has been selected to operate from the INS Vikrant, the country’s first domestically built aircraft carrier, and potentially from INS Vikramaditya. Naval aviation officials have indicated that the twin-engine aircraft meets requirements for carrier-based operations, including ski-jump take-offs and arrested landings, following evaluation trials against competitors such as Boeing’s F/A-18 Super Hornet.

Strategic analysts view the combined air force and naval acquisitions as part of a wider recalibration of procurement policy. Over the past decade, New Delhi has sought to balance self-reliance initiatives with the need for timely capability upgrades. Programmes under the “Atmanirbhar Bharat” framework aim to boost domestic manufacturing, yet complex fighter development remains a long-term endeavour. The Light Combat Aircraft Tejas, produced by Hindustan Aeronautics Limited, has entered service and secured additional orders, but it is not seen as a direct substitute for heavier multirole platforms like the Rafale.

Financial implications are significant. A $39 billion outlay would rank among the largest defence capital expenditures, at a time when the government is managing competing demands from infrastructure, welfare and economic stimulus. Officials have indicated that staggered payments and potential technology transfers could form part of negotiations, with an emphasis on local industry participation.

Previous Rafale contracts were accompanied by offset obligations requiring Dassault and its partners to invest in the domestic aerospace ecosystem. Whether similar provisions will apply to the expanded deal remains a subject of scrutiny, particularly as policymakers seek to deepen supply-chain integration and reduce long-term import dependence.

Diplomatically, the move reinforces defence ties with France, which has emerged as a key security partner in the Indo-Pacific. Bilateral cooperation spans joint exercises, maritime surveillance and submarine technology. Paris has consistently supported India’s strategic autonomy, a factor often cited by policymakers as attractive compared with other major arms suppliers.

Political debate is likely to accompany the procurement, as the earlier Rafale deal became a focal point of parliamentary contestation over pricing and process transparency. Government representatives have maintained that all procedures were followed in accordance with defence acquisition rules, and that operational urgency justified the intergovernmental route adopted at the time.

Air power specialists argue that a larger Rafale fleet would enhance interoperability and deterrence. The aircraft’s ability to carry Meteor beyond-visual-range missiles and SCALP cruise missiles has been highlighted as a force multiplier. Integration with indigenous weapons and network-centric warfare systems is also under way, reflecting a hybrid approach that blends imported hardware with domestic innovation.

Beyond immediate operational considerations, the proposal signals continuity in defence planning that spans successive administrations. India remains among the world’s largest arms importers, yet officials have reiterated ambitions to transition towards greater domestic production capacity over the coming decade. Achieving that balance, while sustaining readiness across land, sea and air domains, presents both fiscal and industrial challenges.

China’s Z. ai has propelled its artificial intelligence ambitions into the global spotlight with the launch of GLM-5, a 744-billion-parameter open-source large language model that stands toe-to-toe with leading Western counterparts and underscores Beijing’s drive for technological self-sufficiency. Released on 11 February by the company formerly known as Zhipu AI, GLM-5 combines advanced architecture with broad hardware compatibility and permissive licensing, signalling a shift in the global AI landscape.

The GLM-5 model, built on a mixture-of-experts framework and trained on an expanded dataset of roughly 28.5 trillion tokens, is designed for multifaceted tasks including coding, reasoning and agentic operations. Z. ai reports that the model’s performance on internal benchmarks approaches that of leading proprietary systems such as Anthropic’s Claude Opus 4.5, and on certain tests it outperforms Google’s Gemini 3 Pro. Its permissive MIT licence allows unrestricted commercial use and adaptation, making GLM-5 one of the most accessible frontier AI models available to developers and enterprises worldwide.

A standout feature of GLM-5 is its independence from American semiconductor technology. Trained and deployable on domestic Chinese chips from manufacturers like Huawei Ascend, Moore Threads, Cambricon and Kunlunxin, the model sidesteps reliance on high-end Nvidia GPUs that are constrained under US export controls. Z. ai’s use of the MindSpore AI framework further embeds the model within a Chinese software ecosystem, emphasising self-reliant technological development. This strategy exemplifies a broader push within China’s AI sector to mitigate the impact of external trade restrictions and build robust, locally supported infrastructures.

Market reception underscored the impact of GLM-5’s launch: Z. ai’s shares surged nearly 29 per cent on the Hong Kong Stock Exchange following the announcement, reflecting investor optimism about the company’s competitiveness and growth prospects. Industry analysts note that GLM-5’s arrival comes amid an acceleration of model releases and chip optimisation efforts by several Chinese AI firms, signalling more intense competition on both domestic and international fronts.

Benchmarks published by Z. ai highlight GLM-5’s strengths and limitations. On software engineering evaluations, the model achieved a score of 77.8 per cent on the widely referenced SWE-bench Verified suite, trailing slightly behind Claude Opus 4.5’s 80.9 per cent but outperforming other notable systems in agentic and multitask contexts. The architecture’s efficient handling of long-context sequences—up to 200 000 tokens—positions GLM-5 for applications requiring extensive reasoning over lengthy inputs.

GLM-5’s low cost of deployment also enhances its appeal. Analyses of pricing data suggest inference can cost around US $1 per million tokens, a fraction of the fees charged by some proprietary APIs, potentially lowering barriers for startups and research teams seeking cutting-edge language modelling without prohibitive expenditure. The MIT licence further expands this accessibility by granting broad rights to adapt and deploy the underlying model weights in commercial and research settings.

Despite its technical achievements and open licensing, experts caution against over-interpretation of benchmark results as definitive proof of superiority. Performance under controlled test conditions does not always translate to uniformly strong real-world applications, particularly in generative and reasoning tasks where robustness and safety remain critical hurdles. Some observers also warn that differences between self-reported evaluations and independent assessments could emerge as the model is adopted more widely.

GLM-5’s emergence reflects broader trends within China’s AI ecosystem, where domestic firms and chipmakers are increasingly synchronised in advancing cutting-edge technologies. Industry developments suggest that model and hardware optimisation efforts are happening in near tandem, with architecture adaptations and inference support enabling faster incorporation of new Chinese semiconductor products. These developments sit against a backdrop of regulatory frameworks that increasingly emphasise the alignment of AI systems with government objectives, including content governance and cybersecurity considerations.

By Dr. Gyan Pathak India witnessed on Thursday February 12 the most intensified of all India general strike of workers in the country so far in which over 30 crores workers were estimated to have participated in about 600 districts of the country, surpassing the record of July 9, 2025 general strike in which over […]

The article General Strike Of Workers Spread And Intensify Creating History appeared first on Latest India news, analysis and reports on Newspack by India Press Agency).