Bitcoin price briefly touched an intraday low of $65,727 on Monday, March 9, as market sentiment remained risk-off amid concerns surrounding rising oil prices and escalating tensions between the U.S. and Iran. 

According to data from crypto.news, Bitcoin (BTC) price fell 3.5% to an intraday low of $65,727 on Monday, extending its downturn for the fifth straight day and dropping nearly 11% in that period. The world’s largest crypto asset is down roughly 5% over the past month. 

Bitcoin price fell as investors continued to diverge from risk assets amid geopolitical tensions and macro volatility. 

The bellwether appears to be mimicking traditional equity markets. Notably, futures tied to traditional market indices such as the Dow Jones Industrial Average dipped 1,026 points to 46,696, while the S&P 500 and Nasdaq-100 dropped by 136 points and 440 points each before U.S. markets resumed.

Investor sentiment deteriorated as the ongoing military conflict between the U.S. and Iran successively led to a blockade at the Strait of Hormuz, a global chokepoint for oil distribution. This led to a sharp jump in oil prices. In fact, oil prices across the globe shot up above the $100 mark, the first time crude oil has surpassed this level in nearly four years. 

Market instability in the region began after Israeli fighter jets struck several fuel depots and refineries in the region on Saturday, March 7. Subsequently, Iran retaliated with missile and drone strikes of its own on vessels and military bases in the Gulf region. 

As market risk sentiment soured, investors are concerned about whether Bitcoin price will continue to decline in correlation with traditional equity markets. The bellwether has historically moved in tandem with equities, especially during periods of macro uncertainty. 

Against the backdrop, investors are concerned that rising oil prices could reignite U.S. inflation jitters and a potential delay in interest rate cuts. A hawkish stance from the Federal Reserve could dampen liquidity, which has often acted as a major tailwind for risk assets such as Bitcoin.

Bitcoin fell to an intraday low of $65,000 support during the late U.S. trading hours on Sunday. This level has acted as a strong demand zone over the past few months, and the asset managed to rebound as it retraced part of its weekend losses. 

At presstime, Bitcoin had recovered above $68,000. The quick recovery suggests that investors may have already soaked up the latest market shock.

A US federal court has dismissed a lawsuit accusing crypto exchange Binance of facilitating terrorism financing, ruling that the plaintiffs failed to establish the legal requirements needed to hold the platform liable under US anti-terror laws.

Federal judge throws out the terrorism financing lawsuit against Binance

In an opinion issued on March 6, Judge Jeannette A. Vargas of the US District Court for the Southern District of New York granted the defendants’ motion to dismiss the complaint brought by hundreds of victims and relatives of victims of terrorist attacks.

The plaintiffs, linked to 64 attacks worldwide between 2016 and 2024, alleged that Binance allowed accounts tied to terrorist groups and their intermediaries to operate on its platform. They argued that the exchange’s services enabled those actors to move funds and therefore amounted to aiding and abetting terrorism under the US Anti-Terrorism Act and the Justice Against Sponsors of Terrorism Act.

However, the court ruled that the complaint did not plausibly show that Binance knowingly provided substantial assistance to terrorist organizations.

According to the opinion, the allegations largely relied on claims that certain wallets linked to sanctioned groups had used the exchange, but they failed to demonstrate that Binance was aware of those connections at the time.

Judge Vargas also found that the plaintiffs did not sufficiently link the alleged cryptocurrency transactions to the specific attacks cited in the lawsuit. The court said the complaint relied on generalized claims about terrorist use of digital assets rather than concrete allegations showing that funds moving through Binance directly supported the incidents referenced by the plaintiffs.

Because of these shortcomings, the court concluded that the complaint failed to meet the legal standard required for aiding-and-abetting liability under US anti-terror statutes.

While dismissing the case, the judge granted the plaintiffs 60 days to file an amended complaint addressing the deficiencies identified in the ruling. If they succeed in presenting stronger allegations, the case could proceed in federal court.

Circle Internet Group has begun using its own stablecoin infrastructure to handle internal treasury operations, settling $68 million in intercompany transfers across eight corporate entities in under 30 minutes.

Jeremy Allaire says Circle settled $68M using USDC as firm “eats its own dog food”

The development was revealed by Circle CEO Jeremy Allaire in a recent post on X, where he said the company had started using USDC and the Circle Mint platform to replace traditional bank wires for internal settlements.

According to Allaire, the company’s treasury team processed the transfers across multiple internal entities in a single workflow that operated continuously, allowing funds to move at any time rather than during banking hours.

The process settled the $68 million in less than half an hour while maintaining full controls and auditability.

The stablecoin-based settlement replaces traditional fiat wire transfers that typically take one to three days to complete through conventional banking rails.

Circle said the move reflects how blockchain-based payments can streamline corporate treasury management. Through Circle Mint, the company’s platform that enables businesses to mint and redeem stablecoins and move funds, treasury staff can initiate transfers, apply role-based approvals, and confirm receipt of funds in near real time.

The company’s treasury case study describes the workflow as a way to reduce the “cash-in-transit” gap common in traditional banking systems, where funds may be debited from one entity but not immediately confirmed at another due to settlement delays.

With USDC settlement, confirmations occur within minutes rather than days.

Circle said the new system has also accelerated accounting operations. Approximately 90% of the company’s intercompany transfer-pricing settlements were completed in a single day, significantly compressing the month-end close process.

The firm plans to expand the workflow as additional updates to Circle Mint roll out, with Allaire suggesting the model could eventually enable other businesses to adopt stablecoin-based treasury settlement systems.

Flow Foundation and Dapper Labs have filed a motion with the Seoul Central District Court to suspend the termination of trading support on major South Korean exchanges for the Flow blockchain’s native FLOW token.

According to a March 8 announcement, the firms are asking the court to temporarily halt the delisting decision by Upbit, Bithumb, and Coinone, which announced plans to end trading support for the token on Feb. 12 after a security incident on the layer-1 blockchain.

As previously reported by crypto.news, on Dec. 27, the Flow blockchain suffered a protocol-level exploit that allowed an attacker to mint about $3.9 million in duplicated tokens.

Although post-mortem reports published later confirmed that the incident did not impact user balances, it led to a temporary halt of the network and triggered emergency measures from validators, who were able to pause the chain and work with exchange partners to freeze and recover funds linked to the attack.

Flow initially proposed a full chain rollback recovery plan, which received opposition from ecosystem partners who were concerned it could create double balances for users who bridged assets out during the rollback window and losses for those who had bridged assets into the network during the same period.

Developers later opted for an isolated recovery plan by targeting and destroying the duplicate tokens in a bid to preserve legitimate user activity on the network.

Several exchanges suspended FLOW trading and services following the incident, including Upbit, Bithumb, and Coinone, which are at the center of the Flow Foundation’s court motion.

However, after conducting individual reviews of the incident and the remediation measures taken by the project, many of these platforms have since restored full services for the token.

The foundation contends that the FLOW token remains available on major global exchanges, including Binance, Coinbase, Kraken, while Korbit continues to support FLOW trading in South Korea.

“Given the weight of new evidence, Flow Foundation and Dapper Labs have filed a motion with the Seoul Central District Court requesting a suspension of the trading termination until a thorough review can be completed,” the foundation said.

“This step reflects the responsibility of the Foundation to advocate for the Korean community using every available pathway. The Foundation remains open to constructive conversation with all parties involved,” it added.

The court is set to review the application on March 9 and will determine the next steps in the case.

Further, the foundation said it would continue to pursue additional exchange listings in South Korea and would expand “self-custody access options” for impacted users.

As of last check, FLOW token was down 6.4% over the past 24 hours and was trading 99.9% below its all time high.

Artificial intelligence has entered a phase where infrastructure, not algorithms, is becoming the defining factor for enterprise success. Organizations are rapidly shifting their focus from experimentation to large-scale deployment of AI solutions. However, running modern AI workloads requires massive computing power, distributed storage systems, and specialized AI development infrastructure.

Industry research shows that enterprises are dramatically increasing their investments in AI foundations. According to research from Gartner, global AI spending is projected to reach $2.52 trillion by 2026, representing a 44% increase compared to previous years. A significant portion of this spending is directed toward AI infrastructure and enterprise AI platforms.

Infrastructure is now the backbone of enterprise AI adoption. Large organizations are investing heavily in high-performance computing clusters, AI cloud infrastructure, and scalable data pipelines to support generative AI and machine learning applications.

As John-David Lovelock, Distinguished VP Analyst at Gartner, explains:

“AI adoption is fundamentally shaped by the readiness of human capital and organizational processes.”

This shift toward infrastructure-led AI adoption has accelerated the rise of AI Infrastructure as a Service (AIaaS), enabling enterprises to build intelligent systems without managing complex underlying hardware.

What Is AI Infrastructure-as-a-Service (AIaaS)? A New Operating Model for Enterprise AI

AI Infrastructure-as-a-Service is a cloud-based delivery model that provides enterprises with on-demand access to computing resources, machine learning environments, and deployment platforms required to build and scale artificial intelligence systems.

Instead of investing in expensive hardware or building AI platforms internally, organizations can leverage managed AI infrastructure services delivered through cloud-based platforms.

An enterprise-grade AI infrastructure platform typically provides:

• GPU and AI accelerator clusters for large-scale computation
• Distributed storage for large datasets
• AI development infrastructure for model training
• MLOps pipelines for lifecycle management
• AI deployment and inference environments

This service-based model enables organizations to build advanced AI applications while focusing on innovation rather than infrastructure management.

Industry analysts highlight that AI-optimized infrastructure services are becoming one of the fastest-growing segments of enterprise technology.

According to Gartner research, spending on AI-optimized Infrastructure-as-a-Service is expected to reach $37.5 billion by 2026, driven by the increasing demand for specialized computing hardware such as GPUs and AI accelerators.

The Rise of AI Cloud Infrastructure: Powering the Next Generation of AI Applications

Modern AI systems rely heavily on scalable cloud environments capable of handling massive datasets and complex machine learning workloads. As a result, AI cloud infrastructure has become the foundation of enterprise AI deployment.

Unlike traditional cloud environments, AI cloud infrastructure is optimized for high-performance computing and large-scale model training. It integrates advanced hardware components such as GPUs, tensor processing units, and AI accelerators with distributed storage and networking systems.

Key capabilities of AI cloud infrastructure include:

• Scalable GPU clusters
• Distributed computing frameworks
• High-speed networking for parallel processing
• Automated model deployment environments

These capabilities allow enterprises to train complex machine learning models, process massive datasets, and deploy AI-driven applications across global markets.

According to reports from Deloitte and Gartner, enterprise spending on AI infrastructure is accelerating as organizations scale generative AI and machine learning deployments. Major technology companies are investing hundreds of billions of dollars into data centers designed specifically for AI workloads.

This growing infrastructure ecosystem is enabling enterprises to build AI systems that can process vast amounts of data in real time.

Building Enterprise AI Infrastructure: Key Architectural Components

A modern enterprise AI infrastructure consists of multiple interconnected layers designed to support the complete lifecycle of AI development.

These layers form the foundation of AI development infrastructure used by data scientists, machine learning engineers, and enterprise technology teams.

High-Performance Compute Layer

AI workloads require specialized hardware capable of handling parallel computations. GPU clusters and AI accelerators enable organizations to train deep learning models and generative AI systems efficiently.

These compute environments are particularly critical for large language models and advanced neural networks that require thousands of parallel operations.

Data Engineering and Storage Layer

AI systems rely on vast volumes of data. Enterprise AI platforms include advanced data pipelines that support data ingestion, storage, transformation, and governance.

These systems allow organizations to process structured and unstructured data at scale while maintaining security and compliance.

Machine Learning Development Environment

AI engineers require sophisticated development environments that allow them to experiment with models, test algorithms, and collaborate across teams.

These environments are an essential component of modern AI development infrastructure.

They typically include:

• model training frameworks
• experiment tracking tools
• collaborative development environments

These capabilities accelerate innovation while ensuring consistency across AI projects.

MLOps and Model Lifecycle Management

As AI systems move into production environments, organizations must manage the entire lifecycle of machine learning models.

MLOps frameworks provide automation for:

• model deployment
• monitoring and performance tracking
• continuous model retraining

These systems ensure that AI applications remain reliable and effective over time.

The Role of AI Development Companies in Accelerating Enterprise AI

For many organizations, building AI infrastructure internally can be both technically complex and financially demanding. As a result, enterprises increasingly collaborate with specialized AI development company partners that provide expertise in building scalable AI ecosystems.

An experienced AI development company can help enterprises:

• Design scalable AI infrastructure platforms
• Implement AI cloud infrastructure environments
• Build custom AI models and data pipelines
• Deploy AI applications across enterprise systems

By combining infrastructure expertise with advanced AI engineering capabilities, these companies enable organizations to accelerate AI adoption while minimizing operational risks.

Business Advantages of AI Infrastructure-as-a-Service

Adopting AI infrastructure as a service provides multiple strategic benefits for enterprises looking to scale AI initiatives

AIaaS eliminates infrastructure bottlenecks, allowing organizations to focus on building intelligent applications rather than managing hardware.

• Scalable Computing Resources

Enterprises can dynamically scale computing resources based on demand, enabling them to handle large AI workloads efficiently.

• Reduced Capital Investment

Organizations avoid large upfront investments in specialized hardware such as GPU clusters and AI accelerators.

• Improved Operational Efficiency

Managed AI infrastructure services reduce operational complexity and simplify the management of AI environments.

• Faster Deployment of AI Applications

AIaaS platforms accelerate the development and deployment of AI solutions across enterprise systems.

Emerging AI Infrastructure Trends Shaping 2025-2026

The evolution of enterprise AI infrastructure is being shaped by several transformative trends.

• Generative AI Infrastructure

The rise of generative AI has significantly increased demand for computing power and data processing capabilities. Enterprises are building infrastructure specifically designed to support large language models and multimodal AI systems.

• AI Supercomputing Clusters

Large-scale AI clusters capable of connecting thousands of GPUs are becoming the backbone of enterprise AI platforms.

Organizations are increasingly deploying AI models closer to data sources to enable real-time processing for applications such as smart manufacturing and autonomous systems.

As AI adoption grows, enterprises are implementing governance frameworks and financial operations strategies to manage the cost and performance of AI workloads.

Experts highlight that infrastructure readiness is becoming a critical factor for successful AI implementation.

Challenges Enterprises Must Address When Building AI Infrastructure

• Data Security and Compliance

Enterprises must ensure that sensitive data remains protected when deploying AI workloads in cloud environments.

Training large AI models can require significant computing resources, increasing operational expenses.

Many organizations struggle to find professionals with expertise in AI infrastructure engineering.

Relying heavily on a single AI cloud provider can create long-term operational dependencies. Addressing these challenges requires careful planning and a well-defined enterprise AI strategy.

The Future of AI Infrastructure Platforms

AI infrastructure is rapidly evolving as enterprises push the boundaries of machine learning and generative AI technologies.

Future enterprise AI platforms are expected to incorporate:

• autonomous AI operations
• distributed AI networks
• edge computing infrastructure
• AI-native cloud environments

Researchers predict that the number of AI agents and intelligent systems could increase dramatically over the next decade, placing even greater demands on global computing infrastructure.

This means that scalable AI infrastructure platforms will become essential digital foundations for the next generation of intelligent systems.

Why AIaaS is Becoming the Backbone of Enterprise AI

Artificial intelligence is transforming how organizations operate, compete, and innovate. However, the ability to scale AI initiatives depends heavily on the availability of reliable and high-performance infrastructure. AI Infrastructure-as-a-Service provides enterprises with a powerful solution for building and deploying intelligent systems without the complexity of managing hardware environments. By leveraging scalable computing environments and modern AI platforms, organizations can accelerate innovation, reduce operational complexity, and unlock new opportunities in the AI-driven economy. As AI adoption continues to expand, AIaaS will play a critical role in enabling enterprises to build the intelligent digital ecosystems of the future.

As a trusted AI Development company, Antier helps enterprises design and implement scalable AI environments that support modern AI workloads and intelligent applications. With deep expertise in enterprise AI deployment, Antier empowers organizations to transform ideas into production-ready AI solutions.

The excessive use and oversight of artificial intelligence in the workplace is giving workers “AI brain fry,” contrary to the technology’s assurance that it would ease job pressures.

Workers who are using AI tools report that the technology is “intensifying rather than simplifying work,” researchers from Boston Consulting Group and the University of California wrote in the Harvard Business Review on Friday.

A study of nearly 1,500 full-time US workers found 14% said they had experienced “mental fatigue that results from excessive use of, interaction with, and/or oversight of AI tools beyond one’s cognitive capacity,” or what the researchers called “AI brain fry.”

Respondents described having a “mental hangover” with a “fog” or “buzzing” and an inability to think clearly, along with headaches, slower decision-making, and difficulty focusing.

AI companies have pushed their products as a productivity booster, allowing workers to offload some or part of their workloads, a message that some companies have taken on and started to measure AI use as a performance metric.

Crypto exchange Coinbase CEO Brian Armstrong has said he fired engineers who didn’t want to use AI, and set a goal late last year to have AI generate half of the platform’s code.

“As enterprises use more multi-agent systems, employees find themselves toggling between more tools,” the researchers wrote. “Contrary to the promise of having more time to focus on meaningful work, juggling and multitasking can become the definitive features of working with AI.”

AI carries “significant costs,” but can improve burnout

The researchers said this AI-induced mental strain “carries significant costs in the form of increased employee errors, decision fatigue, and intention to quit.”

Study respondents who said they had brain fry experienced 33% more decision fatigue compared to those who didn’t, which researchers said could cost large companies millions of dollars a year. Those with AI brain fry were also around 40% more likely to have an active intent to quit.

Those reporting AI brain fry also self-reported making nearly 40% more major errors than those who did not, with a major error defined as one with “serious consequences, such as those that could affect safety, outcomes, or important decisions.” 

The researchers found, however, that the use of AI to replace repetitive and routine tasks decreased burnout, a state of chronic workplace stress that leads to negative feelings about the job and decreased effectiveness.

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Respondents who used AI to reduce time spent on routine and repetitive tasks reported their levels of burnout were 15% lower than those who didn’t use AI in such a way.

The researchers said company leaders looking to reduce AI brain fry should “clearly define AI’s purpose in the organization” and explain how workloads will change with the tool.