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Global technology groups including Microsoft, Amazon and Google have pledged billions of dollars to expand cloud and AI infrastructure across the country, betting on strong digital adoption, a vast developer base and policy support from New Delhi. At the same time, domestic conglomerates such as Reliance Industries and the Adani Group have unveiled plans to scale up capacity, signalling an arms race to capture the next phase of AI growth.

The surge is centred on established data centre hubs such as Mumbai, Hyderabad, Chennai and the National Capital Region. Industry estimates place India’s operational data centre capacity at more than 900 megawatts, with projections that this could double within a few years as hyperscale facilities come online. Much of the new supply is being designed for AI workloads, which require more powerful processors and higher rack densities than traditional cloud computing.

Microsoft has committed to expanding its Azure footprint in multiple regions, while Amazon Web Services continues to invest in its Mumbai and Hyderabad zones. Google Cloud has also deepened its presence, citing demand from financial services, healthcare and government clients. Nvidia, whose graphics processing units underpin many AI systems, has strengthened partnerships with Indian firms to support sovereign AI initiatives and localised computing.

Officials argue that this wave of investment will support economic growth, digital inclusion and advanced research. India’s data protection framework and semiconductor incentives have been positioned as part of a broader strategy to create a resilient digital ecosystem. The government has also outlined ambitions to develop domestic AI models trained on local languages and datasets, increasing the need for high-performance computing infrastructure within national borders.

Yet environmental experts warn that the geography of expansion raises difficult trade-offs. Mumbai and Chennai, two of the country’s largest data centre clusters, face periodic water shortages and are vulnerable to climate-related risks, including extreme heat and flooding. Hyderabad, another key hub, has experienced mounting pressure on groundwater resources amid rapid urbanisation.

AI data centres are particularly resource-intensive. Training large language models can consume vast amounts of electricity, and the cooling systems required to maintain optimal temperatures often rely on significant volumes of water. While operators increasingly use advanced cooling technologies and pursue renewable energy procurement, critics question whether efficiency gains can keep pace with the scale of expansion.

According to industry analysts, AI-optimised facilities can draw substantially more power per rack than conventional data centres. India’s peak power demand has been rising steadily, touching record highs during heatwaves. Coal continues to account for a large share of electricity generation, although solar and wind capacity have expanded rapidly. The tension between decarbonisation goals and surging digital energy needs is becoming more pronounced.

Water usage is another flashpoint. Traditional evaporative cooling systems can require millions of litres annually for large facilities, though some operators are shifting to air-cooled or liquid immersion systems to reduce dependence on freshwater. Companies state that they are investing in water recycling, rainwater harvesting and wastewater treatment, particularly in regions classified as water-stressed.

Environmental groups argue that even with mitigation measures, cumulative impacts could strain local communities. Urban residents in Chennai and parts of Maharashtra have already experienced water rationing during drought periods. The prospect of energy-hungry AI campuses expanding in these same areas has prompted calls for stricter environmental assessments and greater transparency around resource use.

Corporate leaders counter that hyperscale operators often have stronger sustainability commitments than smaller, on-premise data centres. Many global firms have pledged to run on 100 per cent renewable energy and to become water positive within the decade. In India, power purchase agreements for solar and wind farms are increasingly tied to data centre projects, supporting the build-out of clean energy capacity.

Policy makers face a delicate balancing act. Digital infrastructure is viewed as critical to competitiveness, especially as countries race to develop generative AI capabilities. India’s large pool of engineers and its expanding start-up ecosystem make it an attractive base for AI research and deployment. Delaying capacity expansion could risk ceding ground to regional rivals in Southeast Asia or the Middle East, where governments are also courting hyperscale investment.

At the same time, climate resilience is rising on the national agenda. The country has set ambitious renewable energy targets and emphasised sustainable development in international forums. Integrating AI growth with environmental safeguards will test regulatory frameworks at both central and state levels.

Lumen’s Advanced MDR offering delivers comprehensive, AI-powered threat detection and response across cloud, on-premises, identity, and OT environments – helping customers gain full-spectrum visibility and control over their security operations. Built on Palo Alto Networks Cortex XSIAM platform, which unifies XDR, SOAR, ASM, and SIEM capabilities, the solution simplifies SOC workflows, eliminates console switching, and accelerates incident response through automation and advanced analytics. This platform-centric approach not only improves detection accuracy but also reduces operational complexity and licensing overhead.

As a Select Partner, Lumen supports seamless deployment of Cortex XSIAM, empowering customers to transform their security posture with confidence. Customers benefit from continuous threat hunting, tailored playbooks, and asset discovery powered by Lumen’s SOC analysts and Black Lotus Lab. With deep regional expertise, a strategic global network of SOCs, and a customer-first success model, Lumen ensures proactive defence against evolving threats.

Lumen has also recently been recognised in the Major Players Category in the IDC MarketScape: Asia/Pacific (excluding Japan) Managed Detection and Response (MDR) Services 2025 Vendor Assessment^[1]. Lumen believes this recognition further validates its leadership in delivering scalable, outcome-driven security solutions.

“Our Advanced MDR offering is designed to give customers clarity and control in the face of growing cyber complexity. Partnering with Palo Alto Networks allows us to deliver a unified, intelligence-driven platform that not only strengthens security operations but also aligns with our customers’ business goals. This specialisation validates our ability to deliver trusted, outcome-focused cybersecurity, backed by deep regional expertise and a commitment to proactive protection,” said Ignatius Wong, Senior Director, Managed & Professional Services, APAC at Lumen Technologies.

Michelle Saw, Vice President, GTM Shared Services and Ecosystems, Asia-Pacific and Japan at Palo Alto Networks, said, “Lumen’s achievement is a significant milestone that reinforces our commitment to a partner ecosystem focused on delivering world-class security outcomes in APAC. In today’s complex threat landscape, customers need the power of a unified, AI-driven platform like Cortex XSIAM, combined with the deep regional expertise and managed services of a trusted partner like Lumen. We are proud to collaborate with Lumen to empower businesses across the region to transform their security posture and confidently face the next generation of cyber threats.”

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Lumen is unleashing the world’s digital potential. We ignite business growth by connecting people, data, and applications – quickly, securely, and effortlessly. As the trusted network for AI, Lumen uses the scale of our network to help companies realise AI’s full potential. From metro connectivity to long-haul data transport to our edge cloud, security, managed service, and digital platform capabilities, we meet our customers’ needs today and as they build for tomorrow.

Agentic artificial intelligence is forcing a fundamental reassessment of how enterprise software is sold, bought and priced, with implications for licensing models, cloud economics and the broader SaaS market worldwide. As autonomous AI agents that can plan, execute and optimise complex workflows proliferate across enterprises, traditional per-seat software licensing is coming under pressure, major vendors are redesigning pricing frameworks and investors are recalibrating valuations around AI’s transformative potential.

Industry executives and analysts say that agentic AI – autonomous systems that operate beyond simple generative tasks to perform multi-step functions without constant human direction – is altering the value proposition of enterprise software. Organisations are increasingly demanding pricing that reflects outcome-based performance and consumption rather than fixed seat counts, pushing large software providers to experiment with enterprise licence agreements that bundle AI capacity with predictable costs and shared risk. This shift is expected to reshape cloud economics by altering how revenue and costs are aligned in the era of pervasive AI automation.

The software industry’s long reliance on per-seat or subscription models is facing disruption as AI agents demonstrate the ability to automate work historically done by humans. Gartner forecasts that around 40 per cent of enterprise software applications will feature embedded task-specific AI agents by the end of this year, exponentially up from a fraction only a short time ago. Enterprises are seeking simpler, more transparent pricing arrangements that reflect AI usage and business outcomes, rather than paying for seats that may no longer correspond to human users in traditional roles.

Salesforce and other leading software vendors have moved to reframe their pricing strategies for an agent-centric world. Executives have described new Agentic Enterprise Licence Agreements that offer “all you can eat” access to AI-powered tools and platforms at agreed-upon costs, designed to give customers confidence to scale AI deployments without unpredictable usage charges. These agreements represent a departure from legacy licence structures and a bet that long-term customer engagement will outweigh short-term licence profitability concerns.

Investors are already pricing in uncertainty about how this transition will unfold. Episodes of sharp stock sell-offs in global software equities have highlighted market anxiety about whether traditional enterprise software will maintain its relevance as agentic AI systems gain traction. While some market observers argue that fears of widespread obsolescence are overstated, the volatility reflects genuine debate about how software demand will evolve as autonomous AI agents become more capable of developing, integrating and executing workflows.

The transformation extends beyond pricing into cloud infrastructure economics. AI workloads, particularly those driven by agentic systems, impose heavier and more variable demands on cloud compute and data storage, challenging conventional cost forecasts. Infrastructure expenses tied to training and operating autonomous agents can grow faster than top-line revenue if not carefully managed. Finance teams are rethinking cost allocation, forecasting and real-time billing to provide clearer visibility into consumption by AI agents, a trend that underscores the complexity of cloud economics in an AI era.

Enterprise readiness for agentic AI varies. While adoption continues to expand rapidly, many organisations are cautious about the risks of autonomous decision-making, prioritising data trust, governance and integration challenges. A significant number of leaders report that scaling fully autonomous agents remains an enterprise priority, but that confidence in data integrity and risk controls are the biggest bottlenecks for broad deployment. The interplay between promise and practical reality is shaping cautious but forward-looking strategies.

For software providers, the transition to AI-centric offerings is pushing a broader rethinking of product strategy and business model evolution. Traditional vendors are integrating agentic capabilities into existing platforms, expanding from assistive features to autonomous operations, while newer AI-native firms are building products around AI agents from the ground up. Software incumbents face a dual challenge: reinventing core products to leverage agentic capabilities while maintaining service quality, compliance and continuity for enterprise customers.

Beyond commercial implications, the rise of agentic AI is prompting discussions about its broader impacts on organisational structures, work roles and human-AI collaboration. Proponents argue that autonomous agents can dissolve rigid organisational silos, enhance productivity by taking over repetitive and complex tasks, and free human workers to focus on higher-order decision-making. Skeptics warn of ethical, legal and accountability challenges associated with handing over greater autonomy to AI systems, including liability, bias and governance risks.

The gathering brought together senior executives from global technology firms, domestic start-ups, academic leaders and policymakers, signalling a concerted push by New Delhi to position the country as both a major market and a development hub for AI. Modi framed the technology as central to economic growth, public service delivery and national competitiveness, arguing that India’s digital public infrastructure and vast talent pool provide a foundation for leadership in the field.

Speaking at the event, Modi said AI must be “inclusive and responsible”, emphasising applications in healthcare, agriculture, education and governance. He pointed to platforms such as Aadhaar, the Unified Payments Interface and the CoWIN vaccination system as examples of large-scale digital architecture that could integrate advanced analytics and machine learning.

The summit’s optics were carefully managed to reflect global engagement. Executives from leading US technology companies shared stages with founders of domestic firms working on generative AI models, language tools and enterprise software. Representatives from multilateral bodies and research institutions discussed governance frameworks, data protection and the need for cross-border collaboration.

Yet the conference also exposed tensions between ambition and execution. Several sessions ran behind schedule, and some industry participants privately expressed concern over policy uncertainty, particularly around data localisation, cross-border data flows and regulatory oversight. Draft frameworks on AI ethics and safety remain under consultation, and companies are awaiting clarity on compliance requirements.

The event unfolded against a backdrop of intensifying global competition. The United States continues to dominate foundational AI research and chip design, while China has invested heavily in domestic models and semiconductor capacity. The European Union has advanced comprehensive legislation through its AI Act, seeking to set global standards on risk and accountability.

India’s approach has been more incremental. The Ministry of Electronics and Information Technology has outlined plans to support compute infrastructure, encourage domestic large language models and expand skilling initiatives. Budget allocations for semiconductor manufacturing and design-linked incentives aim to reduce dependence on imports, although fabrication plants remain years away from full-scale production.

Industry analysts note that India’s strength lies in its software services sector and a large base of engineers. According to data from industry bodies, the country produces hundreds of thousands of engineering graduates annually, many of whom work in IT and business process outsourcing. Major domestic firms have announced internal AI integration plans, embedding generative tools into consulting, customer support and software development workflows.

At the same time, structural constraints are evident. High-performance computing resources are limited compared with those available in the US and China, and advanced chips are largely imported. Electricity reliability, data centre capacity and high-speed connectivity vary across regions. Venture capital funding for deep-tech ventures has fluctuated amid global market volatility.

Civil society groups and some academics used the summit to call for stronger safeguards. They warned that rapid deployment of AI systems without robust oversight could exacerbate misinformation, bias and privacy violations. India’s data protection law, enacted last year, sets out obligations for companies handling personal data, but implementing rules and enforcement mechanisms are still being developed.

Opposition figures have questioned whether headline events translate into durable policy outcomes. They argue that sustained investment in research universities, independent regulators and judicial capacity will be essential to manage the societal impact of AI. Some economists have also cautioned that automation could disrupt segments of the services workforce, requiring targeted reskilling programmes.

Global investors, however, continue to view India as a growth market. International cloud providers have expanded data centre footprints in cities such as Mumbai, Hyderabad and Chennai. Partnerships between domestic start-ups and multinational firms aim to build AI systems tailored to local languages and contexts, addressing a market of more than a billion people with diverse linguistic needs.

Modi’s messaging emphasised sovereignty alongside collaboration. He has repeatedly advocated for a global framework on AI governance that reflects the priorities of emerging economies, not solely those of advanced industrial nations. At the summit, he called for shared principles that balance innovation with ethical safeguards, while ensuring access for developing countries.

Diplomatically, the conference reinforced India’s profile as a convening power in technology policy, following its presidency of the G20 and engagement in forums on digital public goods. Whether that translates into tangible influence over global AI standards will depend on sustained coordination with like-minded partners and domestic regulatory coherence.

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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Open-Source Repositories

GitHub: https://github.com/zai-org/GLM-5
Hugging Face: https://huggingface.co/zai-org/GLM-5

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GLM-5 Technical Blog: https://z.ai/blog/glm-5
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