Tata Power to Power India’s AI Boom with 2.8 GW Data Centre Push

Tata Power has announced a diversified 2.8 GW+ data centre opportunity pipeline across 18 leading global and domestic operators, positioning itself as a key partner in India’s AI and digital infrastructure expansion. This includes renewable energy, transmission, and distribution projects, with major investments in Mumbai’s grid to support hyperscale growth.

Tata Power has developed a robust and diversified data centre pipeline, including ~430 MW of upcoming distribution load, 1,236 MW of transmission-linked projects, and 1,150 MW of renewable capacity under active engagement. With 29 ongoing workstreams across 18 major global and domestic operators, the company is emerging as a critical enabler of India’s largest hyperscalers and AI-ready digital infrastructure.

Key Highlights

• 2.8 GW+ pipeline: Spread across 29 active workstreams with 18 blue-chip operators.
• 430 MW upcoming load under distribution.
• 1,236 MW transmission-linked opportunities.
• 1,150 MW green energy capacity under engagement.
• Integrated solutions: Powered land, renewable energy, storage, cooling systems, and utility infrastructure.
• Mumbai Grid Upgradation: ₹15,000 crore investment to enhance 110 kV & 220 kV networks, build new receiving stations, and develop a 400 kV transmission ring.

Strategic Context

Factor	Details
AI-driven demand	India’s AI-led data centres projected to add 26.3 GW electricity demand by FY2031-32.
Hyperscale focus	Direct support for hyperscalers and cloud operators with integrated infrastructure.
Energy diversification	Strong emphasis on renewables + transmission upgrades for sustainability and reliability.
Competitive positioning	Aligns Tata Power with Reliance, Adani, and global players investing in India’s AI-ready infrastructure.

Risks & Challenges

• Execution risk: Pipeline is opportunity-based, not confirmed contracts.
• Infrastructure strain: Hyperscale growth could stress Mumbai’s transmission capacity.
• Regulatory hurdles: Large-scale renewable and transmission projects require multi-state clearances.
• Cooling demand: AI-ready centres will increase cooling and water consumption.

Implications for India’s AI & Digital Growth

• Energy backbone: Stable baseload + renewable mix for AI workloads.
• Global competitiveness: Positions India as a trusted hub for hyperscale data centres.
• Sustainability: Supports India’s net-zero 2070 goals while enabling exponential AI-driven demand.

Colt DCS and RMZ Break Ground on AI‑Ready Mega Data Centre in Mumbai, Driving $1.7B Digital Infrastructure Expansion

• Part of a $1.7 billion investment under Colt DCS and RMZ's joint venture to develop data centre capacity across India. 
• Signs its first hyperscale client for Mumbai 3
• AI-ready facility delivering 64MW upon completion, powered entirely by renewable energy
• Hybrid cooling design incorporating a closed-loop cooling system to enable near-zero wastewater. 
• Expected to create more than 700 jobs across construction and operations

Colt Data Centre Services (Colt DCS), a global provider of AI-ready, hyperscale and large enterprise data centres, and RMZ, India's largest privately owned real assets platform, today announced the commencement of construction of its third data centre in the city (Mumbai 3), an AI-ready facility located at the operator's Navi Mumbai Campus that will deliver 64MW upon completion.

Mumbai 3 is being developed as part of Colt DCS and RMZ's joint venture (JV), announced in 2024, to invest $1.7 billion in the Indian data centre market. Ground preparation began in December 2025, and construction is now underway following the signing of the facility’s first hyperscale customer.

“The nature of data centre demand is evolving rapidly, with customers requiring larger deployments, higher rack densities and infrastructure capable of supporting increasingly intensive AI workloads. Mumbai 3 is designed to address these needs, further strengthening our Navi Mumbai Campus as a scalable and future-ready digital infrastructure platform,” said Pratap Mane, Country Head DCS, India.

“The next wave of digital growth will be driven by infrastructure that brings together scale, resilience, and speed to market. At Mumbai 3, we are developing a future-ready campus designed to enable hyperscalers and neo-cloud providers to scale seamlessly as their capacity and computing needs evolve,” said Deepak Chhabria, President, RMZ Digital Infrastructure.

Built for AI Deployments

Mumbai 3 has been designed to support AI workloads and high-density compute deployments, delivering 64MW of secured contracted power, supported by a hybrid cooling architecture that combines traditional air cooling with direct liquid-to-chip cooling. The facility will also benefit from a 220kV GIS substation located on campus, providing high levels of resilience and scalability, alongside carrier-neutral connectivity with access to high-capacity fibre routes.

Sustainable by Design, Creating Local Opportunity

• Operates using 100% renewable energy
• Closed-loop cooling system enabling near-zero wastewater
• Expected to create more than 700 jobs across construction and operations
• Supports local suppliers, contractors, and service providers

Joint Venture Commitment

As part of the JV, both organisations have committed to develop 250MW of data centre capacity across Mumbai and Chennai. The partnership brings together Colt DCS' global data centre expertise with RMZ's over two decades of experience in developing, owning and operating high-quality infrastructure in the Indian market.

About Colt DCS:
We design, build and operate data centres for global hyperscalers and large enterprises.
• Global portfolio includes 15 operational data centres, with 12 sites under development across 9 cities in the UK, Europe, and APAC.
• Over 25 years of experience in the data centre industry.
• Sustainability at the core, with Science Based Targets aligned to Net Zero Standard.
https://www.coltdcs.com

About RMZ:
RMZ is India's largest privately owned real assets platform, bringing together real assets and private capital across six Indian cities.
• Builds, owns and operates mixed-use GCC ecosystems hosting global enterprises.
• AI and data infrastructure serving India’s digital economy.
• Founded by Raj Menda and Manoj Menda in 2002, headquartered in Bengaluru.
https://www.rmz.com

Dell Expands AI Data Platform With Semantic Layer, Knowledge Graph and GPU Acceleration

Dell Technologies Turns Enterprise Data Into Trusted Context for AI Agents

• Dell AI Data Platform, the data foundation of the Dell AI Factory, expands with new data orchestration and storage capabilities. 
• Unified Semantic Layer and Enterprise Knowledge Graph give every application and agent the same trusted view of enterprise data. 
• Dell AI Data Platform shortens the wait between question and insight with accelerated data processing that processes data nearly 4 times faster on average than CPUs alone. 
• Dell AI Data Platform storage enhancements improve security and benchmark performance. 
• New AI-ready data services move organizations from deployment to production quickly and reliably

Dell Technologies (NYSE: DELL) expands the Dell AI Data Platform so AI systems deliver better answers, cost less to run and reach production faster, giving applications and agents trusted, current context from a company's own data.

Why it matters

In most cases the model isn't the bottleneck to achieving enterprise scale for AI. The data is. A company's most useful information sits in files, databases, cloud services and systems across multiple sites. Most of it was created long before anyone expected an AI agent to read it. It was never labeled, connected or organized with a machine reader in mind. As a result, agents burn tokens reconstructing answers that should already exist. Every query means more steps, more compute, more cost and slower, less trustworthy results.

Giving data meaning and context

The Dell AI Data Platform is built end to end, from where data is stored to where agents use it. Storage moves critical data to accelerated compute without bottlenecks; orchestration connects and prepares it; analytics and search transform and index it. NVIDIA Nemotron Retriever models enable document parsing, embedding and reranking. NVIDIA cuVS accelerates vector indexing and search. Because every layer runs on one platform, everything it produces traces back to governed data the customer already owns. Agents return to the same documents, history and decisions repeatedly, and most systems rebuild their understanding from scratch every time. That means answering the same two questions on every request: what does this term mean and how does it relate to everything else? Dell is extending the Dell AI Data Platform with three new features to answer both at once.

• Unified Semantic Layer: Gives structured and unstructured information consistent business meaning with rules, definitions and a searchable glossary, so a term means the same thing everywhere it appears. Users can also import existing ontologies and classification taxonomies. Dell is also enabling NVIDIA Auto-Ontology to extend the Unified Semantic Layer.
• Enterprise Knowledge Graph: Maps how structured and unstructured data is related, so an application or agent can find the right context. It uses metadata, lineage and query history to keep tuning the graph as activity changes.
• Knowledge Agents: Put that context to work. Each one acts as a trusted advisor on a single topic, grounded in a defined slice of the Enterprise Knowledge Graph. NVIDIA Nemotron Retriever models provide reasoning and visual understanding.

The Unified Semantic Layer, Knowledge Graph and Knowledge Agents hold a customer's most valuable and sensitive information, so they stay inside the platform in the customer's data center. The platform builds context from that data, keeps it current as the business changes and shares it across every application and agent, without locking customers into any single model, data or storage provider.

Acceleration and security built into the data path

• cuDF accelerated data processing with Apache Arrow: Dell Data Processing Engine, powered by NVIDIA cuDF, processes data nearly 4 times faster on average than CPUs alone, and up to 20 times faster on batch workloads.
• Expanded multitenancy and security: Dell PowerScale now supports up to 500 tenants in a single cluster, with mTLS over NFS and granular role-based access control.
• Benchmark workload performance: Dell Storage Performance Tool helps customers size AI infrastructure, compare vendor solutions and test S3-compatible object storage performance.

Expertise to get to production

Dell is expanding Dell AI Data Platform implementation services across its data and storage engines to move organizations from deployment to production results quickly and reliably.

Perspectives

Venkat Sitaram, Senior Director and Country Head, Infrastructure Solutions Group, India, Dell Technologies: "Indian enterprises are generating and managing more data than ever before. However, data without context is just noise..."

Jason Hardy, vice president of storage technology at NVIDIA: "AI agents are only as effective as the data they can access, understand, and trust..."

Availability

• Unified Semantic Layer, Enterprise Knowledge Graph and Knowledge Agents: 1H 2027
• Dell Data Processing Engine enhancements: December 2026
• Dell PowerScale security and multitenancy enhancements: November 2026
• Dell Storage Performance Tool: Available now
• AI-ready data services: Available now

Additional resources

• Blog: Getting Enterprise AI into Production Starts with Context
• Connect with Dell on X and LinkedIn

About Dell Technologies

Dell Technologies (NYSE: DELL) helps organizations and individuals build their digital future and transform how they work, live and play.

Vertiv Marks AI Infrastructure Milestone with India’s First Direct‑to‑Chip Liquid Cooling Deployment

Secondary Fluid Network (SFN) skid design, engineering & manufacturing capability, and rigorous quality standards support Vertiv India's growing role in the country's liquid cooling market

Vertiv (NYSE: VRT), a global leader in critical digital infrastructure, has successfully delivered India’s first direct-to-chip high-density liquid cooling (HDLC) deployment in collaboration with CapitaLand India Trust (CLINT), Asia’s first India-focused property trust under CapitaLand Investment (CLI), at the CLDC Mumbai 01 Data Center in Airoli, Navi Mumbai. The project leverages Vertiv’s secondary fluid network (SFN) system to support 30 MW of AI workloads for a hyperscale end customer, marking a significant milestone for India’s data centre industry as it moves to liquid cooling to meet the demands of AI infrastructure.

Liquid-based cooling is the fastest-growing segment, accelerating as operators move beyond traditional air cooling to meet the demands of AI-driven, high-density workloads. Central to enabling liquid-based cooling adoption at scale, the closed loop SFN system carries coolant from a facility's cooling infrastructure to the chip level, and is manufactured, tested and commissioned to exacting standards before it can support live IT loads.

“The CapitaLand Investment project, particularly the collaboration, helped us accelerate the development of our manufacturing and engineering capabilities, enabling us to support the growing demands of AI-ready digital infrastructure,” said Vertiv India Managing Director Subhasis Majumdar. “As the Indian market advances toward high-density liquid cooling for hyperscale and data center applications, we see meaningful growth potential and are well positioned to deliver the scale, expertise, and innovation required for these deployments.”

From innovation to India’s first HDLC data center deployment

To meet the scale of the HDLC project, Vertiv built a production facility capable of mass-manufacturing the customized technology loop skids designed to support future large-scale deployments. The facility was also designed to meet stringent standards for protecting sensitive cooling components from contamination.

Vertiv worked closely with CapitaLand Investment, owner and operator of CLDC Mumbai, to develop installation and commissioning (I&C) procedures designed to minimize dust ingress into the technology loop as the SFN skids were installed. Following commissioning, Vertiv demonstrated technology-loop balancing using its innovative SFN-Tab kits, then carried out comprehensive Level 4 (L4) testing at the data centre to validate performance ahead of go-live.

Building Vertiv India’s capability for large-scale liquid cooling

The completion of this project further demonstrates Vertiv’s expertise in developing gigawatt-scale AI infrastructure. It also builds on the relationship between Vertiv and CapitaLand Investment, with both organizations working closely from design through commissioning to bring one of India’s first HDLC facilities to life.

For more information on Vertiv’s leading portfolio of power and thermal management, infrastructure solutions, IT systems, and services for critical digital applications, visit Vertiv.com.

India’s Data Centre Boom Sparks Power, Cyber and Operational Risk Concerns: Howden Report

Report Title - Howden’s Insuring the Data Centre Supercycle Report

• Power consumed by data centres has risen 68-fold since 2016, while expansion at live sites is increasing construction and business interruption risk
• More than 90% of India's data centres have redundant capacity, yet non-damage outages and interconnected tenants continue to create insurance gaps

India's data centre expansion is creating concentrated risks across power, construction, water, cyber and operations, according to new research from Howden, the global insurance broker. The findings call for an integrated approach from site selection and design through construction and operation to build resilience and secure insurance.

Power infrastructure as the biggest risk factor

Howden’s analysis identifies power infrastructure as the biggest risk factor. The challenge is not just generation capacity but reliable grid connectivity, substation capacity and redundancy at each location.

Between 2016 and 2025, the power consumed by data centres built in India grew 68-fold, at a 60% CAGR as per the recent S&P data. The demand is forecast to reach 57 TWh by 2030. Increasingly, hyperscale campuses are looking to on-site, behind-the-meter generation to ensure a reliable supply. This changes the underwriting risk profile and increases the importance of location-specific power resilience assessments. Reliable, efficient supply is a commercial priority and energy accounts for about 65% of operating costs. Power resilience is especially crucial in these markets, with Maharashtra, Telangana and Karnataka accounting for approximately 70% of the capacity.

Construction at live sites raises business interruption risk

India added 7 million square feet of data centre space in 2025, with construction volumes growing at a 37% CAGR since 2016, the S&P data indicates. The average size of a new facility rose from 59,000 square feet in 2016 to 276,000 square feet in 2025, concentrating greater asset values at individual sites.

Between 2026 and 2030, planned expansion at existing data centres is equivalent to 78% of their current footprint. Construction alongside sensitive, high-value operations increases the risk of physical damage and business interruption, particularly during testing and commissioning. Construction and operational risks therefore need to be assessed within a single programme.

Redundancy does not remove outage and cyber risk

More than 90% of Indian data centres have redundancy built into their UPS, generator and cooling systems. However, outages still occur, often because of system failures that cause no physical damage and may not trigger traditional insurance. The market is developing solutions such as parametric cover, particularly for retail and wholesale providers. These providers account for 86% of India's data centres and carry uptime commitments to multiple customers.

These facilities also face the greatest systemic cyber exposure. A single compromise can affect the systems and workloads of hundreds of thousands of tenant organisations, while interconnected tenants increase the risk of lateral movement. Cyber risk therefore needs to be considered alongside property, power and operational exposures.

Water and Environment risks need early attention

Most data centres are located in urban centres where water availability is already under pressure. Cooling a 1 MW facility can require around 25.5 million litres of water annually. Water use, renewable power sourcing, energy efficiency, power usage effectiveness (PUE) and carbon footprint should form part of early site planning and risk assessment, rather than being addressed after construction.

Amit Agarwal, CEO, Howden India, said: "India's data centre story is not only about adding capacity. Power reliability, construction at operating sites, cyber concentration and water stress can all affect uptime. These risks need to be identified early and managed together, with insurance designed around the exposures that remain. This will help operators protect their assets and avoid gaps in cover as the sector expands."

Alongside risk transfer, the report highlights the need to build water and environment considerations into data centre planning from the start. This includes renewable power sourcing, energy efficiency and PUE, water availability and consumption, and the carbon footprint of large campuses. It also points to gaps in existing policy and insurance frameworks: water use is not adequately addressed, non-damage outages may fall outside traditional cover, and construction, power, cyber and operational risks are often treated separately. Closing these gaps will require stronger site-level risk assessment and more integrated insurance programmes.

Source: Howden analysis of NOAA and 451 Research by S&P Global

About Howden

Howden is a global insurance intermediary group with employee ownership at its heart. Founded in 1994, it provides insurance broking, reinsurance broking and underwriting services and solutions to clients ranging from individuals to the largest multinational companies. The group operates in 57 countries across Europe, Africa, Asia, the Middle East, Latin America, the USA, Australia, and New Zealand, employing 24,000 people and handling $51bn of premium on behalf of clients.

Website: www.howdengroupholdings.com

AI Infrastructure in Indian Healthcare: Bain & HealthQuad Spotlight Tech‑Driven Transformation

Indian healthcare providers are moving AI from pilots to practice, with varying adoption across administrative and clinical workflows: Bain & Company and HealthQuad

Operational use cases are scaling first, easing administrative load on clinicians; connected clinical care, gated by today’s ~35% EMR adoption, is the larger opportunity ahead

Bain & Company and HealthQuad launched their joint report ‘AI in Indian Healthcare Delivery’ today. The report finds that artificial intelligence (AI) capabilities have advanced rapidly in recent years, and India’s healthcare infrastructure is well-positioned to accelerate adoption compared with earlier waves of digital technologies. Government initiatives, rising EMR penetration, deployment of private capital, a thriving start-up ecosystem and clinician acceptance are strengthening the enabling environment. Adoption in hospitals, however, remains nascent and uneven. The majority of providers are still running AI pilots, with meaningful scale limited to operational use cases, and only a handful of providers are expanding into more clinical applications.

This gap, in tandem with a step-change in AI’s technological capabilities, particularly in healthcare, opens up previously unchartered territories for exponential growth. Newer generative and agentic systems can increasingly execute multistep workflows with limited supervision, while the amount of expert-level work AI can complete autonomously has been doubling every six to nine months since 2023. For providers, this creates an opportunity to reduce the administrative burden on doctors, nurses, and other healthcare professionals, giving them more time to focus on patient care and higher-value clinical work.

Today, however, most providers are testing AI in controlled settings rather than deploying at scale. Early adoption is translating first into operational and workflow applications where implementation is relatively easier, and benefits can be measured more quickly. Clinical AI adoption is emerging primarily among more mature providers, though the focus remains on support tools rather than autonomous decision-making. The velocity of change in AI technology currently exceeds that of most healthcare organizations. As hospitals build the data foundations and digital infrastructure needed to scale, AI adoption in the provider space is poised to accelerate and the gap between early movers and the rest will widen quickly.

Dhruv Sukhrani

Dhruv Sukhrani, Head of Bain & Company’s Healthcare & Life Sciences practice in India, said, “AI adoption in Indian healthcare is still early, but the conditions for it to scale are strengthening quickly. The technology itself has advanced significantly; the harder question now is how providers redesign workflows, manage change and build trust among doctors and nurses. This is increasingly a business transformation challenge, not simply a technology challenge. Providers will need to focus on the highest-value use cases, build the data and organisational capabilities to scale them, and embed AI into clinical workflows with the right governance and human oversight. The next phase of adoption will be shaped by providers’ ability to bring value, deployability and trust together—not simply by access to more advanced AI models.”

As AI capabilities advance rapidly, India is strengthening the foundations needed for adoption, with tangible progress beginning to emerge. Frontier models now match or outperform pre-licensed medical professionals in some controlled clinical reasoning tests. At the same time, these capabilities are becoming more accessible: the cost of frontier AI models has fallen by approximately 92% since 2023. India, meanwhile, has made progress in building the foundations AI needs.

Three enablers will determine how quickly these foundations translate into healthcare AI at scale:

• Data readiness: EMR adoption in India at ~35% remains well below the US and UK and is concentrated among larger urban hospital chains, while most small- and mid-sized hospitals continue to rely heavily on paper records.
• Regulatory clarity: India’s framework for adaptive and autonomous clinical AI is still evolving, particularly around accountability, data governance and clinical validation.
• Locally applied talent: India has deep AI capabilities, but much of that talent is currently directed toward global markets. Indian start-ups are already building across the patient journey: pre-visit and access, diagnostics and inpatient treatment, and post discharge care.

Namit Chugh

Namit Chugh, Director – HealthQuad said: “Healthcare in India has always been constrained by scarcity of clinicians leading to enormous variation in access and outcomes. AI can potentially change that equation by being not just an efficiency lever, but a capacity multiplier. AI capabilities are advancing exponentially, with performance increasingly matching or exceeding medical experts across selected tasks. The report brings a clinician-first lens across the patient journey, which is closely aligned with the investment thesis of HealthQuad’s Fund III, which is focused on backing healthcare innovators using technology and AI to address critical gaps across care delivery. The fund's recent investment in LifeSigns, an AI-powered remote patient monitoring platform, is an example of this thesis in action.”

The next opportunity is to move from operational gains to more connected patient care.
As AI moves deeper into clinical workflows, integration, data readiness, and trust become more significant constraints, with providers typically requiring human oversight. Looking ahead, the report identifies significant headroom in a few areas:

• Remote patient monitoring
• Operating theatre and ICU optimization
• Post-discharge chronic disease management

For hospitals, capturing value from AI means treating it as a business transformation, not an IT project. This requires anchoring AI to clinically owned outcomes, sequencing adoption carefully, building both specialist AI capability and broad organizational fluency, and governing clinical AI on an ongoing basis rather than through a one-time sign-off.
For founders, building durable healthcare AI companies will require solving real clinical problems and earning trust to scale. The most important steps:

• Validate the problem in clinical settings before building
• Land with a focused solution before expanding into a platform
• Treat workflow integration as core to the product, not an afterthought
• Build validation, explainability, and patient safety in from the outset

Ultimately, scaling AI in Indian healthcare will depend on bringing value, deployability and trust together, while building the data, workflow and clinical foundations required for adoption.

Methodology

This report is based on primary interviews with CIOs and CTOs of leading hospitals and diagnostic labs and start-up founders, as well as secondary market research and a range of industry participant interviews.

About Bain & Company

Bain & Company works with leaders worldwide to solve their toughest challenges and deliver enduring results. Since 1973, we’ve partnered with clients, including private equity and portfolio companies, to build the capabilities they need to stay ahead of change and help them redefine their industries. We measure our success by our clients’ success, and we proudly hold the highest levels of client advocacy in our field.
Bain is consistently recognized globally as one of the best places to work. We operate as one global team, uniting strategists, industry and functional experts, technologists, and advisors with a vibrant ecosystem of technology partners.
Note to Editors
Bain & Company was founded in 1973 and today has 19,000 employees across 67 cities in 40 countries. We have worked with more than two-thirds of the Global 500 and more than 9,000 companies worldwide. Bain has pledged to deliver $2 billion in pro bono consulting to nonprofit, public-sector and charitable organizations by 2035. The firm is consistently recognized as a Leader in major analyst rankings across multiple areas, including digital business, innovation, strategy, experience design, customer experience, and carbon-zero transformation.

About HealthQuad

HealthQuad is one of India’s leading investment platforms focused on new-age healthcare models, and is currently investing from HealthQuad Fund III, continuing the strategy and track record established across Funds I and II. Founded in 2016, HealthQuad currently has an AUM of over US$500 million, and acts as the early-growth investment arm of Quadria Group, one of Asia’s leading dedicated healthcare investment platforms. HealthQuad backs exceptional founders building category-defining companies across HealthTech, MedTech, Bio/PharmaTech and novel care delivery, leveraging deep sector expertise, operating capabilities, and Quadria’s extensive healthcare ecosystem to scale in India and globally.

Global Tech Giants Back India’s $200B Data Centre Future

Commerce Minister Piyush Goyal has pitched India as a “trusted destination” for global data centres, highlighting investment commitments worth nearly $200 billion from hyperscalers like Google, Microsoft, Amazon, and Digital Connexion. He made this pitch during his Japan visit, inviting Japanese firms to partner in AI, semiconductors, and digital infrastructure.

The minister invited Japanese companies to partner with India in data centres, AI and semiconductors, highlighting around $200 billion in hyperscaler investment commitments.

Key Highlights of Goyal’s Pitch

Piyush Goyal in the Fireside Chat with Nikkei Asia in Tokyo on the India-Japan Next Generation Economic Partnership.

• Investment Commitments: Around $200 billion pledged by global hyperscalers for India’s data centre ecosystem.
• Major Announcements (Oct–Dec 2025):
  • Google: $15 billion
  • Microsoft: $17.5 billion
  • Amazon: $35 billion
  • Digital Connexion: $11 billion
• Policy Incentives: Indian government has proposed a tax holiday for data centres until 2047.
• Strategic Partnership: Goyal met Japanese Economy Minister Akazawa Ryosei, outlining four pillars of cooperation — trade, technology, investment, and tourism.
• Sectoral Focus: Collaboration in AI, semiconductors, and digital ecosystems, with India’s demand estimated at $150 billion.

Why India is Emerging as a Data Centre Hub

• Trusted Destination: India is positioning itself as a reliable partner amid global supply chain realignments.
• Policy Push: Incentives like tax holidays, renewable energy integration, and subsea cable projects.
• Talent Pool: India offers a large base of engineers, AI specialists, and IT professionals.
• Geopolitical Advantage: Seen as a safe harbour for digital infrastructure compared to other Asian hubs.

Comparison of Hyperscaler Investments in India

Company	Investment Size	Focus Area	Timeline
Google	$15B	AI & cloud infra (Vizag hub)	By 2028
Microsoft	$17.5B	AI-ready data centres	By 2029
Amazon	$35B	Hyperscale cloud infra	By 2030
Digital Connexion	$11B	AI infra & subsea cables	By 2029

Risks & Challenges

• Water & Energy Security: Large-scale AI data centres demand sustainable cooling and renewable energy.
• Execution Risk: Scaling commitments to operational capacity within tight timelines.
• Global Competition: India must compete with hubs in Singapore, UAE, and the U.S. for capital.

Starcloud Raises $250M to Scale AI Beyond Earth

Starcloud has raised $250 million in a Series A extension, boosting its valuation to $2.3 billion as it accelerates plans to build orbital data centers for AI workloads. The funding underscores growing investor confidence in space-based computing, with Nvidia and Cisco joining as strategic backers.

Starcloud aims to build orbital data centers leveraging continuous solar power and radiative cooling for large-scale AI compute.

Founded in 2024 2024 (originally as Lumen Orbit, later rebranded to Starcloud), Starcloud is a U.S.-based startup pioneering orbital data centers. Its founders are Philip Johnston (CEO, ex-McKinsey), Ezra Feilden (CTO, ex-Airbus Defence and Space), and Adi Oltean (Chief Engineer, ex-SpaceX and Microsoft Azure). The company has raised $450M to date, with investors including Benchmark, EQT, Manhattan West, NVIDIA, Cisco Investments, and others. 

Funding and Valuation

• Amount Raised: $250 million (Series A extension)
• Valuation: $2.3 billion post-money
• Total Capital Raised: $450 million since founding in 2024
• Lead Investor: Manhattan West
• Other Backers: Benchmark, EQT, Soma, NFX, 776, Nvidia, Cisco Investments, Cedar Capital, Goanna Capital, Standard Capital

Orbital Data Center Vision

• Core Idea: Move AI processing into orbit, reducing latency by analyzing data closer to where it is collected
• Advantages Over Earth-Based Centers:
  • No need for complex cooling systems
  • Continuous solar power without weather disruptions
  • Lower latency for space applications
• Target Scale: Constellation of 88,000 satellites delivering 20 gigawatts of compute capacity

Technology and Partnerships

• Nvidia Collaboration:
  • First NVIDIA H100 GPU flown to orbit in 2025
  • Development of Space-1 Vera Rubin Module, designed for radiation-heavy orbital environments
  • Future satellites expected to deliver 25x more compute than current H100 GPUs
• Cisco Role: Providing networking and AI infrastructure expertise for orbital systems
• Upcoming Hardware:
  • Starcloud-2 (2027): AI chips, storage, and backup modules
  • Starcloud-3: 200 kW satellites with advanced cooling and heat dissipation
  • Starcloud-4 (future): Cylindrical orbital data center with a 2.5-square-mile solar array

Manufacturing Expansion

• Facility: 100,000-square-foot plant in Woodinville, Washington
• Purpose: Mass production of Starcloud-3 spacecraft

Risks and Challenges

• High Capital Needs: Orbital infrastructure requires billions in long-term investment
• Launch Constraints: Reliance on SpaceX’s Starship as Falcon 9 phases out by 2028
• Regulatory Hurdles: FCC approval sought for 88,000 satellites
• Competition: SpaceX’s “Starmind” project envisions up to a million orbital data center satellites

Market Context

• SpaceX IPO Impact: Renewed investor enthusiasm for space-tech startups
• Global Trend: Orbital data centers seen as a solution to terrestrial bottlenecks in land, power, and water usage. 

L&T's Vyoma.AI Secures India’s Largest NVIDIA B300 AI Factory to Power Global AI Cloud Innovation

Representative Image

• Marking its foray into AI Factory business, L&T will deploy NVIDIA B300 infrastructure at its Chennai data centre campus to power Together AI’s AI-native cloud platform for large-scale training and inference.

Vyoma.AI, an L&T company, through its AI infrastructure subsidiary LTN Compute, has secured India's largest single-cluster AI infrastructure - an NVIDIA B300 AI Factory - for a US-based AI cloud innovator.

The AI Factory will power AI Native Cloud platform for large-scale inference, fine-tuning and training workloads, strengthening India's AI infrastructure ecosystem while supporting global innovation.

Vyoma's Chennai data centre campus, a gigawatt-scale AI infrastructure site, with Phase 1 designed for 250 MW and power infrastructure readiness of 150 MVA, provides a scalable foundation for future AI Factory expansion.

The integrated AI Factory, with capacity of 10,000 B300 NVIDIA GPUs, will be hosted at Vyoma’s Chennai data centre. The platform combines hyperscale data centre infrastructure, accelerated computing, high-performance networking, ultra-low-latency interconnects, high-throughput parallel storage and AI infrastructure operations, enabling customers to seamlessly deploy and scale AI workloads through a unified, end-to-end AI infrastructure stack.

"Artificial Intelligence is becoming foundational to every industry and AI Factories will power this transformation. Our deployment of an NVIDIA B300 AI Factory for Together AI marks a significant milestone in L&T's Gigawatt AI Infrastructure Mission and reinforces our commitment to making India a global hub for next-generation AI infrastructure", said Mr S N Subrahmanyan, Chairman & Managing Director, Larsen & Toubro.

"Making AI globally accessible is going to be the biggest infrastructure build-out in human history, and L&T understands that”, said Mr Vipul Ved Prakash, Co-founder & CEO, Together AI. “That's exactly why we partnered with them - to bring the scale, resilience and engineering excellence this moment demands to India.”

LTN Compute, a subsidiary of Vyoma.AI, is building AI-ready digital infrastructure across India through hyperscale AI datacentres, sovereign cloud platforms, AI Factory services, GPU-as-a-Service (GPUaaS) and managed AI platforms to support governments, enterprises, cloud providers and AI innovators.

IBM, Together AI Launch $240M NVIDIA-Powered Inference Cluster on IBM Cloud

NVIDIA HGX B300

• First large-scale inference cluster with Together AI on IBM Cloud using NVIDIA HGX B300 systems to help enterprises run AI workloads, designed for fast and efficient production

IBM (NYSE: IBM) announced a collaboration with Together AI to deliver IBM and NVIDIA AI infrastructure. Under a multi-year $240M agreement between IBM and Together AI, IBM is positioned to deploy a large cluster of NVIDIA HGX B300 systems on IBM Cloud with expected availability in Q1 2027. Together AI will use this cluster to provide open-source model inference. This deployment is the first dedicated, large-scale cluster built for inference on IBM Cloud using HGX B300 systems and NVIDIA Spectrum-XTM Ethernet networking. According to NVIDIA, it is built to deliver 30x more AI factory output compared to prior generations.

This collaboration aims to enable Together AI to deliver better performance and token economics to enterprises as they look to efficiently scale their AI deployments. Together AI is built on the principle that open-source models are essential for the future of AI and developers should be able to build using open, modular stacks. The company recently raised an $800M Series C financing round at an $8.3B valuation to expand its AI Native Cloud and its platform spans capabilities across inference, training, fine tuning, and agentic workflows. The company reports that it has seen significant momentum for its inference product, now serving 400 trillion tokens monthly.

Together AI selected IBM with NVIDIA because of their innovative product roadmaps and their ability to deliver GPU capacity at the pace required for rapid AI scaling and lowest token cost. Building on IBM's expertise in delivering enterprise-grade cloud capabilities, this collaboration aims to help Together AI to continue its expansion into the enterprise space while making open-source AI more accessible to developers and enterprises around the world.

"Enterprises want the performance of the best frontier models without the closed-model price tag, and that only works if the infrastructure underneath is fast and reliable at scale," said Vipul Ved Prakash, CEO at Together AI. "Working alongside IBM with NVIDIA gives us that foundation. This cluster lets us bring production-grade inference to more companies, faster, and it's a big step in our push to make open-source AI the obvious choice for enterprises."

IBM and NVIDIA Expand Collaboration to Power the Next Wave of AI Innovation

"Enterprises are in a race to adopt agentic AI at scale to drive real business outcomes," said Alan Peacock, General Manager of IBM Cloud. "IBM and NVIDIA are delivering scalable, economical, enterprise-grade AI infrastructure that can help Together AI accelerate innovation for the next generation of AI infrastructure." 

"AI factories are becoming essential enterprise infrastructure—like electricity and telecommunications—turning compute and data into intelligence," said Dion Harris, Senior Director, HPC and AI Infrastructure Solutions, NVIDIA. "With NVIDIA HGX B300 systems and NVIDIA Spectrum-X Ethernet networking on IBM Cloud, IBM and Together AI will deliver an accelerated computing platform to help enterprises deploy open-source AI with the performance, efficiency and scale required for real-time AI services."

This work is the latest example of a larger collaboration between IBM and NVIDIA, who continue to work together to advance AI across infrastructure and software, to deliver performance and efficiency for enterprise and startup clients. A hybrid environment on IBM Cloud powered by NVIDIA GPUs connected with NVIDIA Spectrum-X Ethernet networking and Together AI's inference platform gives organizations a reliable foundation to build, deploy and scale AI systems. Additionally, IBM and NVIDIA recently announced progress across GPU-native data analytics, unstructured data extraction, on-premises and cloud infrastructure, and consulting services. These advancements are designed to help organizations operationalize AI at scale. For more information about the IBM and NVIDIA collaboration, visit www.ibm.com/products/gpu-ai-accelerator/nvidia.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

About Together AI

Together AI is the AI Native Cloud, combining state-of-the-art open-source models, high-performance infrastructure, and frontier research in AI efficiency and scalability. Founded in 2022, Together AI powers over a million of developers and some of the world's most demanding AI workloads, delivering production-scale inference, training, and reinforcement learning for the next generation of AI-native companies.

Kioxia and Sandisk Debut 10th‑Gen QLC 3D Flash, Boosting AI and Cloud Storage Efficiency

10th-generation QLC 3D flash memory represents a major leap forward for AI storage architectures, cloud platforms, and data intensive innovations

Future of Memory and Storage Conference (FMS) – Kioxia Corporation, a subsidiary of Kioxia Holdings Corporation (TOKYO: 285A) and Sandisk Corporation (Nasdaq: SNDK) today unveiled their next-generation Quad-Level-Cell (QLC) 3D flash memory technology, which delivers up to 60% increase in bit density compared to their 8th-generation, surpassing 37 Gb/mm2 and setting the industry benchmark for bit density, performance and power efficiency.

Leveraging the companies’ revolutionary CMOS directly Bonded to Array (CBA) technology which fabricates CMOS logic and the memory array on separate wafers before bonding them together with high-precision wafer-to-wafer alignment, the new generation improves read and write bandwidth compared to the 8th-generation 3D flash memory and becomes the industry’s first QLC 3D flash memory technology to reach a 4.8 Gb/s 3 interface.

Additional interface enhancements improve I/O data-out transfer power efficiency. These large power efficiency upgrades directly address the power and cooling challenges of modern AI and cloud infrastructure.

Chief Technology Officer at Kioxia, Hideshi Miyajima, said: “As AI continues to underpin the advancement of society, its applications are expanding from generative AI to agent-based and physical AI, while the use of data is becoming increasingly diverse and sophisticated. QLC technology enables the efficient storage of rapidly expanding data volumes, helping deliver greater performance and scalability for AI systems. Kioxia will accelerate development toward the commercialization of its 10th-generation flash memory products incorporating QLC technology.”

Chief Technology Officer at Sandisk, Alper Ilkbahar, said: "As AI training, inference, and hyperscale datacenter workloads drive unprecedented growth in data generation, the storage industry faces increasing pressure to deliver greater capacity, higher performance, and improved energy efficiency. By redefining the performance and efficiency envelope of QLC NAND, our 10th-generation QLC 3D flash memory delivers simultaneous gains in density, bandwidth, and energy efficiency and establishes a new paradigm for high-capacity flash storage to provide a scalable foundation for next-generation infrastructure applications.”

Key 10th-generation QLC 3D flash memory technology highlights include:

• 332-layer architecture and optimized floorplan design deliver up to 60% increase in bit density, achieving >37 Gb/mm² compared to the 8th-generation.
• 4.8 Gb/s 3 NAND interface enabled by Toggle DDR6.0 and the Separate Command Address (SCA) protocol to unlock the full potential of fast interface.
• CMOS-directly-Bonded-to-Array (CBA) architecture enhances density scaling, performance, and manufacturing efficiency.
• Power-Isolated Low-Tapped Termination (PI-LTT) improves I/O data-out transfer power efficiency.

About Kioxia

Kioxia is a world leader in memory solutions, dedicated to the development, production and sale of flash memory and solid-state drives (SSDs). In April 2017, its predecessor Toshiba Memory was spun off from Toshiba Corporation, the company that invented NAND flash memory in 1987. Kioxia is committed to uplifting the world with “memory” by offering products, services and systems that create choice for customers and memory-based value for society. Kioxia's innovative 3D flash memory technology, BiCS FLASH™, is shaping the future of storage in high-density applications, including advanced smartphones, PCs, automotive systems, data centers and generative AI systems.

About Sandisk

Sandisk (Nasdaq: SNDK) delivers innovative Flash solutions and advanced memory technologies that meet people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. Follow Sandisk on Instagram, Facebook, X, LinkedIn, YouTube. Join TeamSandisk on Instagram.

As of August 3, 2026, based on Kioxia and Sandisk survey.

Technology wherein each CMOS wafer and cell array wafer are manufactured separately in their optimized condition and then bonded together.

1Gb/s is calculated as 1,000,000,000 bits/second. This value is obtained under our specific test environment, and may vary depending on use conditions.

Sandisk the Sandisk logo are registered trademarks or trademarks of Sandisk Corporation or its affiliates in the US and/or other countries. All other marks are the property of their respective owners. Product specifications subject to change without notice. Pictures shown may vary from actual products.

© 2026 Sandisk Corporation or its affiliates. All rights reserved.

India's AI Pilots Need a Frontline Failure Log Before They Scale

India is moving quickly from AI ambition to deployment. The IndiaAI Mission's Safe and Trusted AI work now spans 13 responsible-AI projects, 58 centres of excellence (CoEs) and 27 data and AI labs. A new innovation challenge is offering promising systems a path into MSME governance and AYUSH-enabled public health, including structured pilot support and the possibility of multi-year government contracts.

That is exactly the kind of momentum India needs. But the hard part begins after a demonstration succeeds.

A pilot can look impressive because the data is clean, the users are motivated and the exceptions are quietly handled by the people running the test. Production is different. Real users switch between languages. Records are incomplete. Policies change. A small error travels into a customer decision, a benefit application, a health recommendation or a business filing. The tool may save ten minutes at the front end while creating an hour of checking and correction somewhere else.

India's startups and public agencies therefore need a simple discipline before they scale an AI system: a frontline failure log.

The pilot-to-production gap

Most organisations already collect technical metrics such as response time, uptime and model accuracy. Those numbers matter, but they often miss the moment when a system fails in actual work.

Consider an AI assistant used to help a small business identify a government scheme. The model may retrieve the right programme but misunderstand the applicant's industry classification. A staff member catches the mistake, rewrites the query and gives the correct answer. The interaction may still be recorded as successful. Yet the correction reveals something important about the data, the prompt, the workflow and the training users need.

The same problem appears in health, finance, hiring and customer service. Human intervention makes the system appear more reliable than it is. Unless that intervention is recorded, leaders cannot see the true cost of adoption or the conditions under which the tool becomes unsafe.

India's AI Governance Guidelines rightly emphasise trust, people-first design, fairness, accountability, understandable systems and resilience. A frontline failure log turns those principles into operational evidence.

What the log should capture

The log does not need to become another compliance platform. A lightweight form can capture seven fields.

First, record the real task. "Drafted a reply" is too vague. Note whether the system interpreted an eligibility rule, summarised a medical history, classified a supplier, answered a customer or recommended an action.

Second, record the operating conditions. Was the source current? Was the user speaking Hindi, Tamil, Bengali or a mix of English and a regional language? Was a scanned document difficult to read? Did the system lack a crucial field?

Third, record what the system did. The point is not to save every word of every interaction. It is to capture the decision or output that mattered.

Fourth, record the human intervention. Did someone correct a fact, reject a recommendation, add missing context, change a category or stop the workflow entirely?

Fifth, record the downstream consequence. Did the error merely create awkward wording, or could it have delayed a payment, misdirected an applicant, exposed personal data or produced an unfair result?

Sixth, record the rework. Count the minutes spent checking, correcting, escalating and repairing the result. This is the difference between gross time saved and reliable work completed.

Seventh, name the owner and next action. Someone must decide whether the response calls for better training, fresher data, a changed workflow, a narrower use case or a stop rule.

A scaling asset for founders

For startups, the log is not an admission that the product is weak. It is evidence that the company understands the environment in which its product must operate.

A founder can use the data to distinguish a one-off user mistake from a recurring design problem. Product teams can see whether failures cluster around a language, a document type, a customer segment or a policy change. Sales teams can describe operating limits honestly. Investors and public-sector buyers can evaluate whether the system is becoming more dependable instead of relying on a polished demonstration.

The log also creates a better learning loop for employees. People are more likely to report a near miss when leaders treat it as useful evidence rather than proof that someone used the tool badly. That psychological safety matters because the most valuable information often comes from the employee who notices that the answer looks plausible but is wrong.

India's recent AI governance architecture, including the new inter-ministerial AI Governance and Economic Group, recognises that innovation, labour-market effects and public trust must be handled together. Frontline evidence is where those priorities meet.

Scale what survives reality

India does not need to slow its AI ambitions. It needs to make scaling more selective.

Before a pilot expands, leaders should be able to answer basic questions. What kinds of failures occurred? Who caught them? How much hidden work did correction require? Which users or communities faced the greatest risk? Did the failure rate fall after changes were made? Is there a clear human owner when the system is uncertain?

A pilot that cannot answer those questions is not ready for scale, no matter how impressive the demo appears.

India's advantage will not come only from building more models or funding more pilots. It will come from learning faster than others about how AI behaves in the messiness of real work. A frontline failure log gives founders, agencies and employees the evidence to do that—and turns responsible AI from an aspiration into a practical operating habit.

AUTHOR – Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook

Sandisk and SK hynix Drive AI Infrastructure Standardization with High Bandwidth Flash Specification

Sandisk Corporation (Nasdaq: SNDK) and SK hynix Inc. today announced the release of the HBFTM (High Bandwidth Flash) technical specification through the Open Compute Project (OCP), advancing the workstream to drive HBF standardization for the AI inference era, just six months after the consortium began work in February.

The specification was developed through the HBF technology workstream under OCP, with Sandisk and SK hynix serving as primary contributors. Notably, Google and Tenstorrent joined as consortium members during this standardization process, contributing significantly to technology validation and the establishment of the standard. The specification provides companies and developers designing AI inference systems and accelerators with a common technical framework for incorporating HBF technology where larger, near-compute memory capacity and higher bandwidth are needed to improve power and performance metrics and help reduce total cost of ownership.

Modern AI inference systems need high-bandwidth memory positioned close to compute cores, while the demand for greater near-compute memory capacity continues to grow with the requirements of large language models and emerging AI workloads. HBF technology is designed to address this need by combining high bandwidth with high capacity, helping data center system designers improve interactivity and throughput during model serving.

“AI inference is creating a new set of memory requirements, and HBF technology is designed to meet that moment,” said Alper Ilkbahar, Chief Technology Officer, Sandisk. “This specification helps give system designers a practical path to bring high-capacity, high-bandwidth memory closer to compute, while enabling more flexible architectures. It is an important milestone for the HBF ecosystem and for the next generation of AI systems built to improve token economics at scale.”

The specification defines system interface, electrical and other technical guidelines for designing systems that interact with and use HBF technology, including basic performance expectations, the xPU-HBF host interface, reliability and packaging guidance for an HBF die stack, and a software user guide for read and write operations. As one of the first technical standards of its kind in the memory and storage industry, the specification helps give AI compute system designers added flexibility to build systems where HBF technology can coexist with High Bandwidth Memory, helping support ecosystem readiness. The specification was released within the Open Compute Project framework, meaning the information is openly available to the industry. Sandisk and SK hynix proactively published the specification to position HBF technology as the de facto standard in the rapidly evolving AI storage market. Their strategy involves fostering an early-stage ecosystem, increasing the visibility of HBF technology’s adoption for customers, and accelerating market expansion and technological maturity through open collaboration and membership in the consortium.

Sandisk Keynote: NAND - The Versatile & Scalable Foundation of the AI Era

On Wednesday, August 5, at 11:40 a.m. PT, Sandisk’s keynote at The Future of Memory and Storage Conference (FMS) at the Santa Clara Convention Center, will explore the importance of system-level optimization and NAND in enabling AI inference at scale. The keynote will feature Sandisk’s Jim Elliott, chief revenue officer; Khurram Ismail, chief product officer; and Alper Ilkbahar, chief technology officer.

FMS Panel Discussion: Breaking the Memory Wall with High Bandwidth Flash

On Thursday, August 6 at 9:45 a.m. PT, Sandisk, SK hynix, and Google will present a panel discussion hosted by Thomas Coughlin, President of Coughlin Associates, at The Future of Memory and Storage Conference (FMS) at the Santa Clara Convention Center, Conference Room D. The session will discuss how HBF technology aims to redefine the memory hierarchy by providing near-memory speeds with the density and persistence of high bandwidth flash. The panel will bring together experts from HBF solution providers as well as a Hyperscale-AI Infrastructure provider, to dissect the HBF technology usage and development needed for success, including Architectural Integration, Technical Challenges, Standardization timelines, performance and economics.

About Sandisk

Sandisk (Nasdaq: SNDK) delivers innovative Flash solutions and advanced memory technologies that meet people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward.

Vertiv Expands Global Manufacturing Capacity for AI-Ready Data Center Cooling Solutions

• Expansions at the company's Tognana, Italy, technology campus support growing worldwide demand for advanced thermal infrastructure and strengthen Vertiv's cooling innovation capabilities

Vertiv (NYSE: VRT), a global leader in critical digital infrastructure, announced investments at its Tognana campus near Padua, Italy, to expand manufacturing and integrated testing capabilities for data center cooling systems. The company expects the investments to double chiller production capacity in the region by the end of 2026 and plans to complete a new large-scale testing laboratory in early 2027, supporting growing demand for AI and high-density computing infrastructure.

The new laboratory will enable testing of large-scale chillers and validate their integration with liquid cooling systems under high-density load conditions and extreme temperature ranges. The expanded capability is intended to help customers validate thermal performance under expected site conditions and deploy increasingly complex cooling systems with greater speed and confidence.

Vertiv expects to double regional chiller manufacturing capacity with the expansion of its Tognana, Italy facility

"AI is driving thermal demands that didn't exist two years ago, with higher densities, faster deployment demands, and no room to compromise on reliability," said Gio Albertazzi, CEO of Vertiv. "The expansion at Tognana puts us further ahead with more manufacturing capacity, integrated testing, and advanced thermal management systems built for current and future generations of silicon. This investment reinforces our position at the front of the curve."

The campus serves as one of Vertiv's principal centers for cooling technology development, integrating research and development, product management, manufacturing, testing, and customer engagement. The site includes a Customer Experience Center where customers and consultants can participate in witness testing of a broad range of cooling technologies across the thermal chain under real-world operating conditions.

For more information on Vertiv’s leading portfolio of power and thermal management, infrastructure solutions, IT systems, and services for critical digital applications, visit Vertiv.com.

HCLTech Announces AI Data Center in Bhubaneswar in Partnership with Sarvam and Govt of Odisha

Representative Image

• The investment will accelerate India’s sovereign AI and data ecosystem

Further to the announcement dated July 13, 2026, relating to its proposed entry into the full-stack AI market, HCLTech, a global technology company, today said that it plans to set-up its first AI Data Center in the upcoming Odisha Sovereign AI Park.

The AI Data Center will be established in partnership with Sarvam and the Government of Odisha. The planned capital outlay for the project will be Rs 14,257 crores, including financial assistance from the Government of Odisha.

This investment will boost India’s sovereign AI play and local developer ecosystem by leveraging HCLTech’s full-stack AI capabilities and Sarvam’s foundation models to offer sector-specific AI applications to private and public sector enterprises and address the government’s sovereignty needs. It will also contribute to delivery of multi-lingual AI based services to the last mile, in line with Hon’ble Prime Minister Sh. Narendra Modi’s clarion call of AI for the benefit of everyone.

A Memorandum of Understanding to this effect was signed between HCLTech, Government of Odisha and Sarvam in the presence of the Hon’ble Chief Minister of Odisha, Sh. Mohan Charan Majhi; Dr Mukesh Mahaling, Hon’ble Minister of Electronics and IT, Government of Odisha; Roshni Nadar Malhotra, Chairperson, HCLTech; C Vijayakumar, CEO & MD, HCLTech and Pratyush Kumar, Co-Founder of Sarvam.

Roshni Nadar Malhotra, Chairperson, HCLTech commented, “HCLTech has been a flagbearer of India’s technology industry. In line with the Digital India and Atmanirbhar Bharat vision of Hon’ble Prime Minister Sh. Narendra Modi, we strive to be an enabler of India's sovereign AI ecosystem with differentiated offerings across the value chain through focused investments and partnerships. We are pleased to start this endeavor in partnership with Odisha government and Sarvam.”

“This is an important milestone in our full-stack AI play and will enable us to serve the large demand for sovereign AI solutions and unlock the scale of our strategic partnership with Sarvam. Odisha offers a progressive policy environment backed by excellent infrastructure and we look forward to working with them in collaboration with Sarvam, added C Vijayakumar, CEO & Managing Director, HCLTech.

According to Pratyush Kumar, Co-Founder, Sarvam, “The Odisha Sovereign AI Park will connect high-performance compute with Indian models and real-world applications. Together with HCLTech, we are excited to bring Sarvam’s full-stack AI capabilities to enterprises and public systems at scale.”

India is among the fastest growing technology markets globally and a growth market for HCLTech. The country’s data center ecosystem is on a rapid growth trajectory, driven by strong demand from a vibrant digital economy, data localization and critical infrastructure needs to support GPU deployments for AI training and inference workloads.

Schneider Electric and Foxconn Unite to Build AI Factories

Schneider Electric, a global energy technology leader, today announced a strategic collaboration with Hon Hai Technology Group (Foxconn), the world’s largest electronics manufacturer, to help define and scale the next generation of AI data centers.

As AI adoption surges, the demands on digital infrastructure are being fundamentally reshaped. This collaboration brings together Foxconn’s unmatched expertise in advanced compute platforms, AI rack integration, and global manufacturing with Schneider Electric’s leadership in power systems, cooling, and energy management. Together, the companies aim to deliver integrated, ready-to-deploy solutions that enable customers to build and operate AI infrastructure with greater speed, efficiency, and predictability across regions. Production will begin later this year.

“At the pace AI is evolving, the industry requires a new model for how infrastructure is designed, built, and delivered,” said Young Liu, Chairman of Foxconn. “By combining Foxconn’s strength in AI systems and global manufacturing with Schneider Electric’s deep expertise in power and energy, we are creating a path for customers to deploy AI capacity at scale—faster, smarter, and more sustainably.”

"AI demand continues to accelerate, and as compute scales to keep pace, the energy behind it becomes a fundamental enabler,” said Olivier Blum, CEO of Schneider Electric. “If we want to scale AI responsibly, these systems must be connected. This is where energy intelligence becomes essential. At Schneider Electric, we are advancing energy tech to build the most efficient and sustainable AI factories by bringing integrated power, cooling, and digital capabilities into AI data centers. Working with Foxconn, we are helping customers build capacity with real speed, resilience, and efficiency, as energy technology partners to an industry that is firmly entering the era of intelligence."

Through this collaboration, Foxconn and Schneider Electric will co-develop next-generation reference architectures for AI data centers. The partnership will also explore innovations in closed-loop energy optimization, modular power and cooling skids, and standardized design frameworks, creating repeatable, high-performance blueprints for AI factories worldwide. By aligning manufacturing excellence with energy intelligence, the two companies are setting the foundation for a new class of AI infrastructure that is scalable by design, efficient by default, and ready to meet the accelerating demands of the AI era. 

About Foxconn

Hon Hai Technology Group (Foxconn) (TWSE:2317) is the world’s largest electronics manufacturer and leading technology solutions provider, ranking 28th in Fortune Global 500. In 2025, revenue totaled TWD8.1 trillion (approx. USD260 billion). The Group’s market share in electronics manufacturing services (EMS) exceeds 40% and covers four major product segments: smart consumer electronics; cloud and networking; computing; and components and other. Operating over 240 campuses across 24 countries, Foxconn is one of the world’s largest employers with approx. 900,000 employees during peak manufacturing season. We are committed to sustainability in the manufacturing process and serving as a best-practice model for global enterprises. The Group is guided by its 3+3+3 strategy, actively investing in industries of electric vehicles, digital health, and robotics; in technologies of artificial intelligence, semiconductors and next-generation communications; in intelligent platforms of Smart Manufacturing, Smart EV and Smart City. Foxconn is dedicated to becoming a comprehensive, world-class enterprise, with AI as its core driving force. Learn more at www.foxconn.com/en-us

About Schneider Electric

Schneider Electric is a global energy technology leader, driving efficiency and sustainability by electrifying, automating, and digitalizing industries, businesses, and homes. Its technologies enable buildings, data centers, factories, infrastructure, and grids to operate as open, interconnected ecosystems, enhancing performance, resilience, and sustainability. The portfolio includes intelligent devices, software-defined architectures, AI-powered systems, digital services, and expert advisory.

With 160,000 employees and one million partners in over 100 countries, Schneider Electric is consistently ranked among the world’s most sustainable companies.

www.se.com

Dixon and Taiwan's Gemtek to Manufacture Optical Modules in India

Dixon Technologies has signed a binding term sheet with Taiwan’s Gemtek Technology to form a joint venture in India, focused on manufacturing Small Form-Factor Pluggable (SFP) optical transceivers, Bidirectional Optical Subassembly (BOSA) modules, and other telecom gear. Dixon will hold a 60% stake, while Gemtek will own 40%. This marks Dixon’s official entry into India’s fast-growing data centre and optical connectivity ecosystem.

Gemtek Technology Co., Ltd. is a Taiwan‑based communications equipment company, founded in 1988, specializing in broadband, wireless networking, and optical connectivity solutions. It is publicly listed on the Taiwan Stock Exchange (TSEC: 4906) and employs around 5,000 people.

Gemtek is positioning itself as a key player in next‑generation broadband and AI‑ready optical connectivity, with innovations in Wi‑Fi 8, DOCSIS 4.0, and 400G–1.6T optical transceivers. Its partnership with Dixon in India reflects a broader strategy to expand into data centre and telecom infrastructure markets, aligning with global demand for AI‑driven networking.

Dixon Technologies & Gemtek JV in India

Key Details of the JV

• Stake split: Dixon Electroconnect – 60%; Gemtek – 40%
• Products: SFP optical transceivers, BOSA modules, telecom/data centre equipment
• Government support: Dixon Electroconnect is a beneficiary under India’s Electronics Components Manufacturing Scheme (ECMS)
• Strategic focus: Data centres, telecom infrastructure, optical connectivity, cloud computing, edge computing, AI networking
• Market impact: Dixon’s shares rose ~2% to ₹11,616 on June 9, 2026

Why This Matters

Aspect	Details
India’s optical ecosystem	Strengthens domestic manufacturing of optical transceivers and modules, reducing reliance on imports
Data centre boom	Supports India’s growing demand for high-speed networking driven by AI, cloud, and edge computing
Global footprint	Gemtek expands into India, while Dixon diversifies beyond consumer electronics into telecom infrastructure
Policy alignment	Fits into India’s push for electronics self-reliance under ECMS and broader semiconductor/telecom initiatives

Strategic Implications

• For Dixon: Diversification into optical networking gear, positioning itself in India’s telecom and data centre supply chain
• For Gemtek: Strengthens global presence and builds a competitive supply chain in India
• For India: Boosts local manufacturing capacity in advanced optical communication, aligning with national digital infrastructure goals

Risks & Challenges

• Regulatory approvals: JV subject to definitive agreements and government clearances
• Technology transfer: Success depends on effective integration of Gemtek’s optical expertise with Dixon’s manufacturing scale
• Market competition: Global players like Huawei, Cisco, and Finisar already dominate optical networking gear

This JV signals India’s deeper integration into the global optical communication supply chain. With AI-driven data centre demand surging, Dixon-Gemtek could become a pivotal supplier for both domestic and international markets.

Dixon Technologies’ Recent Joint Ventures

Key JVs in 2026

• Gemtek JV (June 2026): Focus on SFP optical transceivers, BOSA modules, and telecom/data centre equipment. Stake split: Dixon Electroconnect 60%, Gemtek 40%.
• Longcheer JV (February 2026): Partnership with Longcheer Intelligence (Singapore) and Dixtel Infocom. Stake split: Dixon 74%, Longcheer 26%. Focus on smartphones, tablets, smartwatches, AI PCs, automotive electronics, and healthcare devices.

Strategic Themes Across JVs

JV Partner	Sector	Focus Areas	Dixon Stake
Gemtek	Telecom & Optical	Optical transceivers, BOSA modules, data centre gear	60%
Longcheer	Smart Devices	Smartphones, tablets, wearables, AI PCs, automotive electronics	74%

Dixon is expanding beyond consumer electronics into telecom infrastructure and AI-enabled devices. Both JVs leverage India’s Electronics Components Manufacturing Scheme (ECMS) and self-reliance initiatives. Partnerships with Gemtek (Taiwan) and Longcheer (Singapore) strengthen Dixon’s footprint in Asia’s tech ecosystem.

Australia’s AirTrunk to Invest $30B in India, One of the Largest Commitments to S. Asia’s Digital Infra Sector

Blackstone-backed hyperscale operator AirTrunk has announced a landmark plan to invest $30 billion (₹3 lakh crore) in India by 2030, building over 5 GW of data centre capacity across multiple states to support AI and cloud infrastructure growth. This marks one of the largest digital infrastructure commitments in India’s history and to the South Asian nation’s digital infrastructure sector.

AirTrunk is a Sydney-based hyperscale data centre operator, founded in 2015 by Robin Khuda, and acquired by Blackstone and CPP Investments in 2024. It has rapidly expanded across Asia-Pacific and the Middle East, positioning itself as a leading provider of large-scale, sustainable cloud infrastructure.

In April this year, AirTrunk acquired Lumina CloudInfra to gain a 600 MW pipeline in Mumbai, Chennai, and Hyderabad. AirTrunk is one of the largest hyperscale operators in the Asia-Pacific & Middle East region, serving global cloud providers and enterprises.

Key Details of AirTrunk’s India Investment

• Scale of Investment: $30 billion (₹3 lakh crore) by 2030
• Capacity Target: More than 5 GW of hyperscale data centres
• Backers: Supported by Blackstone and CPPIB
• Entry into India: Acquired Lumina CloudInfra in April 2026 (600 MW pipeline)
• Strategic Focus: Expansion across states, renewable energy, subsea cable access
• Government Support: Welcomed by PM Modi

Why India?

• AI & Cloud Demand: India emerging as global hub
• Policy Frameworks: IndiaAI Mission (₹10,000 crore), Semiconductor Mission (₹76,000 crore)
• Talent & Energy: Skilled workforce and renewable energy
• Geopolitical Advantage: Safe harbour for investments

Economic & Strategic Impact

• Job Creation: Tens of thousands expected
• Supply Chain Localization: Boost to domestic suppliers
• Global Positioning: Strengthens India’s AI role
• Regional Competition: Competing with Google, Reliance, Adani

Comparison of Mega Data Centre Investments in India

Company	Investment Size	Capacity Target	Location Focus	Timeline
AirTrunk	$30B (₹3 lakh crore)	5 GW	Multi-state (Mumbai, Chennai, Hyderabad + expansion)	By 2030
Google	$15B	1 GW	Vizag	Ongoing
Reliance	₹1.08 lakh crore ($13B)	Giga-scale campus	Vizianagaram	Approved May 2026
Adani	$100B	Hyperscale AI-ready	Pan-India	By 2035

Risks & Challenges

• Power & Water Security: Renewable energy and desalination needed
• Regulatory Approvals: Streamlined clearances critical
• Global Competition: Competing with Singapore, UAE, US
• Execution Risk: Scaling from 600 MW to 5 GW

AirTrunk operates hyperscale data centres in Australia, Singapore, Japan, Hong Kong, Malaysia, and India. The Australian company provides colocation solutions for cloud, content, and enterprise customers, with real-time hyperscale capacity deployment.

AirTrunk was officially acquired by Blackstone and CPP Investments in December 2024. The deal gave Blackstone majority control of the Sydney-based hyperscale operator, marking one of the largest private equity transactions in Asia-Pacific’s digital infrastructure sector.This acquisition positioned AirTrunk to accelerate its Asia-Pacific and Middle East expansion, including its landmark $30B India investment plan by 2030.