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OpenAI has announced a sweeping expansion of its global education strategy by forging deep partnerships with a group of India’s most influential higher education institutions to embed artificial intelligence tools, training and research into campus life and academic workflows. The initiative, unveiled in New Delhi this week, aims for institutional transformation by offering enterprise-grade AI access and structured integration support across disciplines including engineering, management, health sciences and creative industries.

The programme brings together six premier universities — Indian Institute of Technology Delhi, Indian Institute of Management Ahmedabad, All India Institute of Medical Sciences New Delhi, Manipal Academy of Higher Education, University of Petroleum and Energy Studies and Pearl Academy — under a coordinated effort to build AI fluency and responsible usage skills for students, faculty and staff. The roll-out will provide secure access to ChatGPT Edu, detailed onboarding programmes, faculty training and discipline-specific implementation guidance aligned with institutional policies.

This shift marks a deliberate departure from ad-hoc access to consumer AI tools toward embedding artificial intelligence as an academic backbone to teaching, research, evaluation and campus operations. OpenAI’s Head of Education for India and Asia Pacific, Raghav Gupta, said the collaboration reflects a broader need to close the gap between what AI tools are capable of and how they are actually used. He referenced projections that by 2030 a significant portion of core skills relied upon by workers globally will be redefined by AI, underscoring the urgency for deep institutional readiness.

At IIT Delhi, the partnership will focus on engineering-led innovation and applied research, integrating AI capabilities into project-based learning, coding, simulations and prototype development. The institute also plans OpenAI-supported hackathons and ‘Industry Days’ designed to link student innovation with India’s manufacturing and deep-technology ecosystem. IIM Ahmedabad intends to embed AI fluency into core management education — spanning strategy, finance, operations and public policy — with scope for certification pathways backed by the collaboration.

All India Institute of Medical Sciences New Delhi is positioning AI as a tool for medical education and clinical training, exploring applications in clinical documentation, simulation-based learning and evidence synthesis. Officials at the institute said prioritising safe, ethical deployment of AI in clinical settings will be critical to maintaining educational and professional standards. Manipal Academy of Higher Education will pursue multidisciplinary AI literacy across engineering, health sciences and business programmes, while UPES and Pearl Academy will tailor integration to applied innovation and creative workflow environments respectively.

OpenAI’s strategy extends beyond campuses to partnerships with major education technology platforms, including upGrad, PhysicsWallah and HCL GUVI. These collaborations aim to launch structured courses on AI fundamentals and practical ChatGPT applications targeting students and early-career professionals who are not formally enrolled in partner institutions. The move reflects a growing industry recognition that AI skills must be scaled across the broader learner base, not just within elite universities.

Experts in education and technology policy see this initiative as part of a larger global trend where leading artificial intelligence firms are moving from consumer-oriented models toward institutional engagement that shapes long-term adoption and usage norms. By embedding AI literacy into the frameworks of higher education, these alliances could influence curriculum design, research priorities and the preparation of graduates for an AI-driven economy.

Industry analysts note that India’s higher education system represents one of the world’s largest talent markets, with millions of students entering the workforce each year. Integrating advanced AI tools into academic settings could enhance learning outcomes, deepen research capacity and create new pathways for innovation across sectors. However, some critics caution that rapid adoption must be matched with robust ethical and governance frameworks to address concerns around data privacy, academic integrity and equitable access to technology.

While OpenAI estimates that this phase of the initiative will directly benefit over 100,000 students, educators and administrators across its core partner institutions over the coming year, independent observers emphasise that the broader impact will depend on how well universities and ed-tech partners can operationalise the technology within existing academic structures. They point to the need for ongoing investment in faculty development, infrastructure and policy frameworks that ensure AI augments rather than disrupts pedagogical goals.

Built for this new phase, GLM-5 ranks among the strongest open-source models for coding and autonomous task execution. In practical programming settings, its performance approaches that of Claude Opus 4.5, particularly in complex system design and long-horizon tasks requiring sustained planning and execution.

The model rests on a new architecture aimed at scaling both capability and efficiency. Its parameter count has expanded from 355bn to 744bn, with active parameters rising from 32bn to 40bn, while pre-training data has grown to 28.5trn tokens. These increases are paired with advances in training methods. A framework called Slime enables asynchronous reinforcement learning at a larger scale, allowing the model to learn continuously from extended interactions and improve post-training efficiency. GLM-5 also introduces DeepSeek Sparse Attention, which maintains long-context performance while cutting deployment costs and improving token efficiency.

Benchmarks suggest strong gains. On SWE-bench-Verified and Terminal Bench 2.0, GLM-5 scores 77.8 and 56.2, respectively, the highest reported results for open-source models, surpassing Gemini 3 Pro in several software-engineering tasks. On Vending Bench 2, which simulates running a vending-machine business over a year, it finishes with a balance of $4,432, leading other open-source models in operational and economic management.

These results highlight the qualities required for agentic engineering: maintaining goals across long horizons, managing resources, and coordinating multi-step processes. As models increasingly assume these capabilities, the frontier of AI appears to be shifting from writing code to delivering functioning systems.

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Singapore’s drive to harness artificial intelligence as an engine of economic growth is entering a decisive phase marked by abundant ambition and clear signs of strain across company ranks. Government fiscal policy and private-sector deployment are converging on AI, but weak data infrastructure, skills gaps and uneven returns are tempering expectations even as enterprises escalate investments and policy support grows.

Last week, the nation’s Budget for the 2026 fiscal year unveiled a suite of measures aimed at elevating AI adoption across multiple industries. Prime Minister and Finance Minister Lawrence Wong confirmed the establishment of a National AI Council, to be chaired by himself, tasked with coordinating research, regulation and investment to align public and private interests in developing AI capability. The plan also includes National AI Missions focused on advanced manufacturing, connectivity and logistics, finance and healthcare, sectoral areas where government and business leaders see the most potential for productivity gains and value creation. The budget expands fiscal incentives with enhanced tax deductions for AI-related expenditures over the next two assessment years and widened grant schemes to support adoption, workforce training and enterprise transformation. Leaders of major consultancies said these measures signal a shift from experimentation to enterprise-scale adoption and give firms a clearer pathway to embed AI into core operations.

Despite this policy momentum, data from industry surveys and corporate research paint a more nuanced picture of utilisation and outcomes. Adoption of AI tools in Singapore is now near universal among larger firms, with about 96 per cent reporting some level of use. But a significant gap remains between deployment and measurable returns on investment. Only a minority of organisations consider themselves leaders in achieving strong, sustained ROI, indicating an emerging disconnect between AI deployment at scale and operational readiness to turn it into business value. Executives widely acknowledge that sprawling and complex data environments, coupled with cybersecurity concerns, are contributing to operational friction and risks that constrain potential gains.

Private forecasts and vendor research suggest that enterprises are committed to accelerating AI spending even as returns lag. Surveys of business leaders show average investments in AI of around SG$18.9 million, with expectations for investment growth of roughly 38 per cent over the next two years and ROI projections rising to an average of 29 per cent during the same period. Yet analysts note that these returns will depend significantly on firms’ ability to bridge gaps in data readiness and workforce capability. Many Singapore companies acknowledge limited internal training, fragmented data integration and widespread informal use of ungoverned AI tools, all of which undermine robust enterprise-level strategy and governance structures.

Structural issues are as salient as technical ones. Industry leaders point to weaknesses in reliable data foundations and the need for stronger leadership alignment to embed AI into strategic operations rather than relegated to pilot projects or isolated business functions. Senior executives contend that competitive advantage over the coming decade will be determined less by technology choice and more by how effectively organisations build hybrid human-AI teams and integrate AI into everyday workflows.

Talent acquisition and retention also feature prominently in conversations around Singapore’s AI trajectory. Advanced AI, cloud and cybersecurity skills are in high demand globally and Singapore is no exception, with firms reporting difficulty scaling teams with the specialised expertise needed to support complex AI systems. This talent shortage both drives up costs and slows the pace at which organisations can absorb and leverage new technologies.

Cloud infrastructure and advanced digital foundations are another determinant of success. Financial services institutions in Singapore, for example, are among the most advanced globally in cloud adoption, with a majority running core workloads in cloud or hybrid environments that facilitate large-scale AI deployment. This infrastructure supports use cases ranging from compliance and risk management to fraud detection and transaction monitoring. However, budget constraints, talent gaps and integration challenges remain headwinds even in these relatively strong pockets of adoption.

Commentators outside the corporate sphere emphasize the importance of inclusive growth, noting that small- and medium-sized enterprises must not be left behind in the national AI push. Business federation and consultancy leaders warn that without targeted support for smaller firms to sustain, not just adopt, AI technologies, widening capability divides could undermine broader economic innovation and productivity goals.

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.