Decentralized exchange Hyperliquid’s permissionless platform, which lets anyone create perpetual futures tied to any asset, is more popular than ever.

Since its debut on Oct. 13, the so-called HIP-3 market has steadily gained traction, with open interest — the total value of all active contracts — hitting a record $1.2 billion on Sunday, according to data source ASXN. It has since remained at all time highs in a sign of growing adoption and activity on the platform.

The growth has been driven by booming activity in futures tied to equities and commodities, including oil, gold, and silver. It highlights how decentralized markets are increasingly being used to trade traditional assets, especially as a tool for price discovery over weekends when traditional exchanges are closed.

This story is worth discussing, Arca said in a weekly update, nothing the massive surge in activity on Hyperliquid.

“Interestingly, on Hyperliquid, just 7 of the top 30 markets are crypto pairs, while the vast majority are commodity and equity pairs on Trade.XYZ. This makes sense given the moves in silver, gold, and oil over the past few months, and it is a testament to Hyperliquid that we finally have a real platform where tokenized trading of RWAs is happening in meaningful size,” the firm said.

As of writing, the tokenized equity futures contract XYZ100-USDC led the pack, with open interest of $213 million, followed by the oil-focused CL-USDC at $169.8 million. Other top contracts included futures tied to Brent crude, the S&P 500, silver, and gold.

CL-USDC led in trading volume, seeing $1.62 billion in activity over 24 hours.

This follows the weekend surge in prices for select few crude oil grades, like the Murban crude, which traded at $103 per barrel, as conflict in the Middle East intensified, disrupting tanker flows through the Strait of Hormuz. Major oil benchmarks, such as Brent and WTI, surged above $110 per barrel on Monday, before crashing into two figures.

HIP-3, Hyperliquid’s builder-deployed perpetual futures, have shaken up how markets are made. Instead of limiting new contracts to a small set of validators, anyone can launch a market by staking 500,000 HYPE tokens — which serve as both a security deposit and a guard against spam.

This essentially puts the power to create markets in the hands of the community, opening the door to a far wider range of trading opportunities than traditional platforms allow.

The TRON network has joined the Agentic AI Foundation (AAIF), marking a new step in its push to integrate blockchain infrastructure with emerging artificial intelligence technologies.

According to an announcement from TRON DAO, the blockchain ecosystem has joined the foundation as a Gold Member and will serve on its governing board, participating in the organization’s oversight and development initiatives.

The AAIF operates under the umbrella of the Linux Foundation and aims to build open, interoperable infrastructure for agentic AI systems—autonomous AI programs capable of executing tasks, interacting with digital tools and collaborating with other AI agents.

The foundation was created to support the development of standardized tools and protocols that allow AI agents to operate across platforms and interact with real-world systems more efficiently. Major technology companies and open-source contributors have backed the initiative as part of a broader effort to ensure transparency and interoperability in the emerging AI-agent ecosystem.

TRON said its participation will focus on exploring how blockchain infrastructure can support machine-to-machine economic activity, particularly payments and settlement layers for autonomous software agents. The network processes large volumes of stablecoin transactions and positions its infrastructure as suitable for high-frequency micro-transactions that AI agents could require.

“Excited to see @trondao join @AgenticAIFdn! TRON continues to support and build for this next phase of autonomous economic innovation,” wrote Tron founder Justin Sun.

Agentic AI, systems capable of planning actions and executing tasks independently, has become a growing area of interest across the technology sector as companies explore how autonomous software could perform business processes, financial transactions and digital services.

By joining the foundation, TRON aims to collaborate with other technology organizations and open-source developers working on standards for this emerging “agent economy,” where autonomous AI systems may interact directly with blockchain-based financial infrastructure.

The move highlights a broader trend of convergence between blockchain networks and artificial intelligence, as both sectors experiment with decentralized systems capable of supporting automated digital economies.

Meanwhile, the news did not have much impact on the native token of the Tron (TRX) blockchain. TRX was trading at $0.28 at press time, down 0.7% in the last 24 hours.

Artificial Intelligence is no longer just an algorithmic breakthrough; it is an infrastructure race. Around the world, governments and technology companies are investing billions into computing power, data centers, and specialized chips. India is rapidly positioning itself as one of the most strategic locations in this new AI economy.

From hyperscale data centers and GPU clusters to sovereign cloud environments and enterprise-ready AI platforms, the country is building the digital backbone required to support large-scale AI deployments. But behind the headlines lies a deeper story: massive capital investments, infrastructure gaps, and an evolving ecosystem where enterprises increasingly rely on specialized AI infrastructure services to operationalize AI at scale. This article explores the technical foundations, investment dynamics, costs, and long-term outlook of India’s evolving AI infrastructure ecosystem.

The Infrastructure Layer Behind AI Innovation

Most conversations about artificial intelligence revolve around models like GPT, multimodal AI, or generative systems. However, these systems depend heavily on large-scale compute environments. Training a modern large language model requires thousands of GPUs operating simultaneously, high-bandwidth networking, and distributed storage systems capable of processing petabytes of data.

These components collectively form what is now referred to as enterprise AI infrastructure. This infrastructure stack typically includes:

• GPU clusters for training and inference
• High-speed networking such as InfiniBand
• Distributed data pipelines
• Model training frameworks
• Scalable orchestration systems

Companies deploying AI at scale rely on an AI infrastructure platform that integrates these components into a unified environment capable of handling model development, training, deployment, and monitoring. In many cases, enterprises no longer build this stack from scratch. Instead, they partner with an AI Development company that provides end-to-end AI development infrastructure tailored to enterprise workloads.

Why India is Emerging as a Global AI Infrastructure Hub

India’s emergence as an AI infrastructure destination is driven by several macroeconomic and technological factors.

1. Explosive Data Generation

India generates nearly 20% of the world’s data, yet it currently accounts for only about 3% of global data center capacity. This imbalance is rapidly driving infrastructure expansion across the country.

As AI adoption increases across industries like banking, healthcare, telecom, and logistics, demand for computing infrastructure is accelerating.

To meet future demand, experts estimate India will require 45–50 million square feet of additional data center space and roughly 40-45 terawatt hours of power by 2030. This scale of expansion signals a massive opportunity for companies delivering AI infrastructure services.

2. Massive Global Investments

Global technology companies are pouring capital into India’s AI ecosystem.

For example, Microsoft recently announced a $17.5 billion investment to expand cloud and AI infrastructure in India between 2026 and 2029, including hyperscale data centers and sovereign cloud capabilities.

The initiative includes:

• New hyperscale cloud regions
• AI compute clusters powered by GPUs
• Sovereign cloud architecture for regulated industries
• National AI skilling initiatives

This type of investment is transforming India into a strategic node in the global AI cloud infrastructure landscape.

3. Data Center Expansion and GPU Clusters

India’s AI boom is strongly tied to the rapid development of GPU-powered data centers.

Specialized AI facilities now deploy thousands of GPUs connected through high-bandwidth networking to support large-scale model training.

Recent industry deployments indicate:

• AI data centers hosting 8,000-10,000 cloud GPUs per facility
• High-speed networking reaching 3.2 Tbps interconnect speeds
• Rack densities exceeding 200 kW per rack for AI workloads

Such environments form the backbone of modern AI infrastructure platforms used by enterprises building generative AI and predictive models.

The Rise of AI Infrastructure as a Service (AIaaS)

While hyperscalers build massive infrastructure environments, enterprises increasingly prefer consuming these capabilities as managed services.

This shift has led to the rise of AI infrastructure as a service, a model where organizations access compute resources, GPU clusters, and AI development tools on demand. Instead of investing millions into physical infrastructure, companies can deploy AI workloads through scalable cloud environments. Typical AIaaS offerings include:

• GPU-based compute clusters
• Model training pipelines
• Automated ML infrastructure
• Data engineering frameworks
• AI model hosting and inference services

This model drastically reduces the barrier to entry for enterprises adopting artificial intelligence. Organizations can focus on building AI applications rather than managing the underlying AI development infrastructure.

The Cost of Building AI Infrastructure in India

Despite the rapid expansion, building AI infrastructure is an extremely capital-intensive process. A typical hyperscale AI data center includes several cost layers:

AI compute hardware is the most expensive component. Advanced GPUs used for AI workloads can cost between $25,000 and $40,000 per unit, depending on the architecture and memory configuration. Large training clusters often require thousands of GPUs operating in parallel.

AI data centers are highly energy intensive. High-performance compute environments require an enormous electricity supply for both compute and cooling. In many facilities, electricity consumption per rack can exceed 100-200 kW, far higher than traditional data centers. Power costs, therefore, become a critical factor when designing enterprise AI infrastructure.

Training large models requires an extremely fast networking infrastructure. Technologies such as RDMA networking and InfiniBand enable GPU clusters to communicate with minimal latency. At the same time, distributed storage systems must handle massive training datasets efficiently. These layers form the core of a scalable AI infrastructure platform.

• Talent and Operational Costs

Infrastructure alone does not guarantee AI success. Organizations must also invest in:

• ML engineers
• Data scientists
• Infrastructure specialists
• AI operations teams

This talent layer is often delivered through specialized AI infrastructure services offered by advanced AI Development companies.

Strategic Opportunities for Enterprises

India’s AI infrastructure expansion is creating a wide range of opportunities across sectors.

Banks, insurance companies, and healthcare providers are increasingly deploying AI to automate operations, detect fraud, and deliver predictive insights. Access to scalable AI cloud infrastructure enables these organizations to build enterprise AI capabilities without building data centers internally.

India’s startup ecosystem is rapidly embracing AI. Access to GPU clusters and AI infrastructure as a service allows startups to experiment with large models and generative AI applications that were previously accessible only to major technology companies.

• Sovereign AI and National AI Platforms

Governments and enterprises are also exploring sovereign AI strategies. These initiatives focus on building national AI models trained on local data and hosted on domestic infrastructure. This approach strengthens regulatory compliance, data privacy, and technological independence.

Challenges India Must Overcome

Despite strong momentum, India’s AI infrastructure journey faces several challenges.

AI workloads require massive GPU availability. However, global supply chains remain constrained, creating bottlenecks in infrastructure deployment.

AI data centers consume large amounts of electricity. Scaling AI infrastructure while maintaining sustainable energy usage will become a critical challenge.

While India produces a large number of engineers annually, the number of professionals with deep AI infrastructure expertise remains limited. Bridging this gap will require continued investment in AI education and skill development.

The Future Outlook: India’s AI Infrastructure in the Next Decade

India’s AI infrastructure expansion is still in its early stages. Over the next decade, several major trends will shape the ecosystem.

• Hyperscale AI Data Centers

Large-scale AI compute facilities capable of hosting tens of thousands of GPUs will become more common. These data centers will serve as regional AI hubs supporting enterprises, governments, and startups.

Businesses will increasingly rely on integrated AI infrastructure platforms that combine compute, data pipelines, model management, and deployment tools. These platforms will simplify AI adoption across industries.

• AI Infrastructure as a Strategic Industry

Infrastructure providers, cloud companies, and specialized AI Development companies will play a central role in enabling enterprise AI transformation. Companies that deliver scalable AI infrastructure services will become essential partners for enterprises navigating the AI economy.

The Road Ahead for AI Infrastructure

India is entering a decisive phase in the global AI economy where infrastructure capacity will determine how quickly innovation moves from research labs to real-world deployment. As enterprises adopt large language models, real-time analytics, and autonomous systems, the demand for scalable enterprise AI infrastructure will continue to accelerate. Organizations must therefore prioritize resilient compute environments, secure data pipelines, and high-performance deployment frameworks that support long-term AI initiatives. Building this capability often requires collaboration with a specialized AI Development company experienced in designing production-grade AI infrastructure platforms.

Antier enables enterprises to establish scalable AI development infrastructure, helping organizations deploy advanced AI systems with robust AI infrastructure services tailored for enterprise-scale innovation and operational efficiency.

• Arthur Hayes predicts the Hyperliquid crypto price could reach $150.
• Hayes’ prediction is supported by strong trading activity, which fuels more buybacks.
• The immediate resistance levels to watch sit at $35.03, $39.87, and $43.82.

The price of Hyperliquid (HYPE) has climbed steadily as it responds to growing bullish sentiment around the fast-rising derivatives exchange.

At press time, the token was trading at around the $33 after a strong recovery from recent lows.

Why is the price of Hyperliquid crypto rising?

Much of today’s Hyperliquid crypto price surge can be attributed to the excitement around Arthur Hayes’ prediction that the HYPE token could surge to $150 this year.

My essay on why $HYPE is going to $150 by August 2026.

https://t.co/M1la2HpdzT

— Arthur Hayes (@CryptoHayes) March 9, 2026

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This bold forecast has quickly become one of the most talked-about topics in the crypto derivatives market.

Hayes believes the rally could unfold over the next few months as the Hyperliquid exchange continues to expand its ecosystem and attract new trading activity.

He even described HYPE as his largest liquid altcoin bet, a statement that immediately caught the attention of traders looking for the next major breakout project.

Notably, Hayes’ prediction comes at a time when decentralised derivatives platforms are gaining ground in the broader crypto industry.

More traders are exploring alternatives to centralised exchanges, especially platforms that offer deep liquidity and fast execution, and Hyperliquid has managed to capture that demand by focusing on high-performance infrastructure and a streamlined trading experience.

As a result, Hyperliquid has rapidly built a reputation as one of the most active decentralised derivatives venues in the market.

Strong trading activity supports the bullish HYPE outlook

One of the key factors supporting the bullish narrative is the platform’s growing trading activity.

Higher trading volumes translate directly into revenue for the protocol, and a large portion of this revenue is used to buy back HYPE tokens from the market.

These buybacks tighten the supply of HYPE tokens available on exchanges and help strengthen price momentum during periods of rising demand.

Nevertheless, analysts believe that reaching Hayes’s ambitious $150 target would likely require a major expansion in exchange revenue.

That kind of growth would depend heavily on continued adoption of derivatives trading within the crypto sector.

The key technical levels to watch

Beyond the fundamental story, technical indicators are also providing clues about where the Hyperliquid (HYPE) price could move next.

Recent price movements show that $32.28 has emerged as a short-term support zone since it has repeatedly held during recent pullbacks.

If that support gives way, the next support level appears near $28.98, which has acted as a historical price floor.

On the upside, traders should closely watch the $35.03 resistance level.

The cryptocurrency has tested this zone several times in recent sessions.

A clear breakout above that level could open the door for a move toward $39.87, which analysts say represents the next major resistance area.

If momentum continues beyond that point, the third resistance level sits around $43.82.

Breaking through these resistance levels would likely confirm a stronger bullish trend in the months ahead, likely towards the Arthur Hayes-predicted price target.

Institutional adoption of real-world assets (RWAs) is splitting between public and permissioned networks, exposing a divide between the liquidity advantages of blockchains like Ethereum and the privacy demands driving systems such as Canton Network.

The divergence is becoming more pronounced as tokenized assets gain traction among major asset managers.

Marcin Kaźmierczak, co-founder of blockchain oracle provider RedStone, said product development is likely to occur on public blockchains, while permissioned systems are better suited for institutional processes that require confidentiality.

“There are some operations between institutions that simply have to stay private, and this is the value proposition that Canton offers very effectively,” Kaźmierczak told Cointelegraph.

Digital Asset’s Canton Network lets banks and asset managers tokenize and settle RWAs while keeping transaction details visible only to involved parties. The network says it processed $6 trillion in RWA value in 2025.

Rather than converging on a single architecture, banks and asset managers are building parallel systems designed to serve different functions within the tokenized financial stack, according to Kaźmierczak.

Ethereum’s Merge was Wall Street’s tokenization moment

Tokenization has become one of the main narratives behind institutional blockchain adoption beyond spot crypto exposure and exchange-traded funds (ETFs).

In June 2024, McKinsey estimated that tokenized assets could reach around $2 trillion by 2030. More optimistic projections have much higher forecasts, including a $30.1-trillion target by 2034 set by Standard Chartered and Synpulse.

Regulatory clarity in the US has contributed to the shift. The GENIUS Act, passed in 2025, created a federal framework for stablecoins, which serve as the settlement layer for many tokenized assets.

Kaźmierczak said confidence in Ethereum began improving earlier, after the network transitioned to proof-of-stake in 2022.

“In 2022, when I was talking to institutions, the Merge was like a big question mark for those institutions,” Kaźmierczak said. “They saw it worked without any hiccups, so it gave them this confidence.”

Kaźmierczak claimed that RWA projects among institutions started in 2023 or 2024, but as institutions work with yearly budgets, developments and project launches don’t occur in weeks or months like they do in crypto. That led to a cluster of institutions announcing tokenization projects last December, he said.

“It’s not that they started in Q4 last year. No, they started a year before, and now we are seeing the fruits.”

Today, over $26.4 billion worth of RWA tokens use blockchains as distribution layers, and over $15 billion of those are on Ethereum. It also holds the deepest liquidity as the veteran in the smart contracts circle, with over $160 billion in stablecoins.

Related: Why institutions still prefer Ethereum despite faster blockchains

Banks are splitting activity across public and private chains

Institutions separate market-facing activity from internal operations. On one hand, public blockchains provide liquidity, composability and access to decentralized finance (DeFi) strategies such as lending and tokenized vaults. On the other hand, permissioned networks are preferred for settlement processes, bilateral transactions and internal asset management workflows that cannot be exposed on open networks.

Systems such as Canton allow financial firms to automate those processes while keeping transaction details restricted to counterparties. That structure is closer to existing traditional financial (TradFi) infrastructure.

That division suggests institutional blockchain adoption may not converge on a single network model. Instead, financial firms appear to be building parallel infrastructure, with public chains handling liquidity and permissioned systems supporting operational processes behind the scenes, according to Kaźmierczak.

“There are some operations between institutions that just have to stay private, and this is the value proposition that Canton offers very effectively. That’s the reason we want to be on both of those legs,” he said.

Several major financial institutions were involved in the Canton Network from its inception. Digital Asset and a consortium of firms, including Microsoft, Goldman Sachs and Deloitte, announced the network’s launch in May 2023. In September 2024, Digital Asset and the Depository Trust & Clearing Corporation completed a pilot of the US Treasury Collateral Network on Canton.

According to RWA.xyz, the Canton Network has over $313 billion in represented RWA tokens, referring to assets that use the blockchain as a recordkeeping layer.

Related: Privacy tools are rising behind institutional adoption, says ZKsync dev

ZK-proofs vs. permissioned privacy

One of the clearest distinctions between the two institutional tracks lies in how privacy is achieved. While many blockchain projects pursue confidentiality through cryptographic tools such as zero-knowledge (ZK) proofs, Canton relies on permissioned data sharing, where transactions are visible only to the parties involved.

Not everyone in the industry agrees that this is the strongest model. Matter Labs CEO Alex Gluchowski said in a social media exchange with Digital Asset’s Yuval Rooz that ZK systems strengthen blockchain security by requiring cryptographic proofs that every state transition follows the protocol’s rules. Even if operators or administrators are compromised, attackers cannot insert invalid transactions into the ledger without generating a valid proof of execution.

Rooz, in a blog post, claimed that fully opaque implementations of ZK systems could make it harder to audit activity in financial markets. If transaction data becomes entirely hidden, errors or fraud could remain undetected, potentially recreating the kind of “black box” conditions that once enabled corporate scandals such as Enron.

The disagreement highlights a broader architectural question for institutional blockchain adoption, as Kaźmierczak pointed out.

Financial firms are experimenting with multiple approaches to balancing privacy, verifiability and control. Public networks continue to host market-facing liquidity and DeFi activity, while permissioned systems replicate institutional processes that require confidentiality, forming parallel rails for the tokenized financial system.

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