• MYX rebounds 29% after heavy losses, driven by V2 partnership news.
• Trading volume surges; whales and institutions show bullish signals.
• The immediate key levels to watch out for are the support at $0.441–$0.430 and the resistance at $0.546.

MYX Finance has surprised many traders by climbing nearly 29% in the last 24 hours.

This comes after a brutal 91% drop over the past month, which left the coin trading near historically low levels.

What sparked the MYX Finance price rebound?

The most immediate driver appears to be MYX’s partnership with Consensys to launch MYX Finance V2 after a successful funding round.

The upcoming V2 upgrade promises gasless trading and 50x leverage, features that can attract both retail and institutional traders.

The news has been framed as a “comeback,” and it has sparked genuine buying interest, not just speculative chatter.

Technical factors are also playing a role.

MYX has been bouncing off extreme lows, and the sudden increase in trading volume confirms strong participation in the rebound.

The 24-hour volume surged to over $55 million, suggesting that bargain hunters and momentum traders are stepping in.

Indicators like the Relative Strength Index (RSI), which is oversold, hint at the selling pressure easing, signalling the end of capitulation.

This combination of fundamental and technical drivers has created a near-term bullish environment.

MYX price technical analysis

After climbing above the $0.49 level, MYX is now consolidating rather than extending its breakout.

Market watchers expect the token to trade in the $0.50 to $0.60 range in the near term.

A sustained pickup in buying interest, particularly if supported by larger capital inflows, could open the door for a move toward $0.70.

If participation from larger investors increases, price swings could become more pronounced, with upside levels around $1, $1.50 and potentially $2 coming into focus.

At the same time, the risk of sharp pullbacks remains.

Such declines are common in volatile markets and are often viewed as part of normal price discovery, where weaker positions are forced out, and liquidity is absorbed by larger participants.

Despite the possibility of short-term setbacks, the broader structure is seen as gradually constructive.

Upcoming risks

Traders should be aware of a key event risk.

On March 6th, about 9.72 million MYX tokens will unlock, worth roughly $9.67 million.

This could create short-term selling pressure as holders choose to liquidate some of their positions.

It is an important factor to watch alongside technical levels and the V2 launch.

MYX price forecast

For short-term traders, the near-term support is around $0.441–$0.430.

On the upside, the first resistance lies at $0.546, the previous swing high.

If the price breaks above this level, gains could extend toward $0.570 and potentially beyond.

On the downside, failure to hold $0.430 could see MYX revisit $0.405.

For now, consolidation above $0.49 sets the stage for a gradual upward move, while the V2 launch and new capital entering the market could trigger sharper rallies.

Key Takeaways

• The Infrastructure: Canton Network is designed for the privacy-preserving Tokenization of real-world assets like U.S. Treasuries, ensuring regulatory compliance on a private ledger.
• The Role: XRP functions as a neutral bridge asset for cross-border liquidity, solving the pre-funding problem rather than the custody problem.
• The Signal: Atomic Settlement on Canton complements the liquidity corridors of the XRP Ledger—they are distinct layers in the Institutional Crypto stack.

MARA Holdings Inc. posted a $1.7 billion net loss in the fourth quarter (Q4) of 2025, a sharp reversal from the $528 million profit it recorded a year earlier.

This report comes only hours after the Bitcoin miner entered a strategic partnership with Barry Sternlicht’s Starwood Capital Group.

MARA’s $1.7 Billion Loss Underscores Bitcoin Volatility — But AI Pivot Signals a New Playbook

MARA’s $1.7 billion Q4 loss came against the backdrop of a roughly 30% decline in Bitcoin’s price during the period. This forced the company to take a $1.5 billion non-cash fair value write-down on its digital asset holdings.

• Revenue for the quarter slipped 6% year-over-year (YoY) to $202.3 million, down from $214.4 million in Q4 2024.
• Adjusted EBITDA swung dramatically to negative $1.49 billion, compared with positive $796 million in the same period last year.
• For the full year, MARA reported a net loss of $1.3 billion, compared with net income of $541 million in 2024.

This shows how mark-to-market accounting can amplify volatility for large Bitcoin treasuries. Despite the earnings hit, MARA ended 2025 with 53,822 BTC on its balance sheet, up 20% YoY.

At a year-end valuation of approximately $87,498 per Bitcoin, those holdings were worth roughly $4.7 billion. Of the total:

• 38,507 BTC were unrestricted,
• 9,377 were loaned, and
• 5,938 were pledged as collateral.

This means about 28% of its Bitcoin stack is encumbered. The company generated $32.1 million in interest income from lending activities during the year.

Liquidity remained substantial. MARA reported about $5.3 billion in combined unrestricted cash and Bitcoin holdings, including loaned and pledged assets.

It also raised $568.6 million in 2025 through its at-the-market (ATM) program but suspended usage in Q4, marking the first quarter since 2022 without tapping the facility.

Operationally, the miner continued to expand. Energized hashrate reached a record 66.4 exahash per second (EH/s) in Q4, up 25% from a year earlier. However, this was below its previously stated 75 EH/s target as management emphasized capital discipline.

AI Infrastructure Pivot Reshapes MARA’s Growth Strategy

Bitcoin production totaled 2,011 BTC in the quarter, down 6% YoY, reflecting higher network difficulty and seasonal energy pressures.

Purchased energy cost per Bitcoin rose to $48,611 in Q4, while cost per petahash per day improved 4% to $30.5. It points to efficiency gains from the deployment of newer equipment.

Beyond mining, MARA is accelerating a strategic pivot toward energy and digital infrastructure, particularly AI and high-performance computing (HPC).

The company announced a joint venture with Starwood Digital Ventures to develop hyperscale, enterprise, and AI-capable data centers.

The partnership aims to deliver approximately 1 gigawatt (GW) of near-term IT capacity, with a roadmap exceeding 2.5 GW over time.

MARA can invest up to 50% in the projects, positioning itself for recurring infrastructure revenue and reduced exposure to Bitcoin price swings.

The company also highlighted its 64% stake in Exaion and the acquisition of a 42-megawatt data center in Nebraska as part of its AI/HPC expansion strategy.

Adding to market intrigue, MARA recently updated its executive compensation metrics in an 8-K filing. The company tied stock awards to megawatt capacity and contracted recurring revenue rather than solely to mining output.

The filing also introduced a change-of-control provision under which performance targets would automatically be treated as achieved if the company is sold. This move has fueled takeover speculation among investors.

Taken together, MARA appears to be balancing a massive Bitcoin treasury with an ambitious infrastructure buildout.

If this is true, then its transformation from a pure-play miner to a diversified energy and AI platform may determine whether it can smooth earnings volatility in the next crypto cycle.

Artificial intelligence has entered a defining new phase. The competitive conversation is no longer centered solely around model innovation, data volume, or algorithmic breakthroughs. Instead, the question enterprise leaders must now answer is far more foundational:

Is our compute foundation strong enough to scale AI across the business?

In 2026, the AI race has evolved into an infrastructure race – one that demands collaboration with the right AI infrastructure development Company and long-term architectural foresight. Amazon’s $12 billion investment in AI-focused data center campuses in Louisiana reflects a larger global reality: enterprise AI growth now depends on physical and architectural compute capacity.

The message for business leaders is clear: compute strategy defines market leadership.

The Shift from AI Experimentation to AI Industrialization

For years, AI initiatives lived in innovation labs – contained within pilots, proofs of concept, or isolated departmental use cases. Infrastructure requirements were minimal because workloads were temporary and limited in scale.

That reality has fundamentally changed.

AI now operates inside mission-critical systems, powering core operations, customer experience platforms, cybersecurity defenses, supply chain optimization, real-time analytics engines, and generative copilots. These are not experimental environments; they are revenue-generating, risk-sensitive business functions.

This evolution demands a formalized enterprise AI infrastructure strategy.

Deloitte’s 2026 Tech Trends analysis highlights a critical inflection point: the challenge is no longer just training models, but managing the long-term economics and scalability of inference at enterprise scale. As AI becomes operational, compute demand shifts from sporadic experimentation to continuous, production-level execution.

Enterprises must now make deliberate decisions about workload placement, hybrid scaling models, cost governance, and performance optimization.

AI is no longer a tactical deployment.
It is a strategic compute architecture commitment.

Amazon’s $12B Move: A Blueprint for AI-Ready Data Centers

Amazon’s $12 billion investment in new AI-focused data center campuses in Louisiana is more than geographic expansion – it is a signal of where global AI infrastructure economics are heading.

As reported by CNBC and covered in depth by Bloomberg, Amazon is expanding its cloud and AI capacity through purpose-built, next-generation data center campuses engineered for high-density compute workloads. These facilities are designed to support advanced AI applications that demand massive processing power, ultra-fast networking, and scalable energy infrastructure.

This investment reflects:

• Long-term compute capacity expansion
• AI-optimized hardware integration
• Advanced cooling systems built for dense GPU clusters
• Infrastructure tailored for large-scale, real-time AI inference

This is what AI-ready data center architecture for enterprises looks like in practice.

Unlike traditional facilities designed for general enterprise IT, AI-optimized data centers are engineered specifically to handle:

• GPU-intensive model training
• High-bandwidth, low-latency interconnects
• Continuous inference workloads
• Distributed real-time data processing environments

Amazon’s strategic expansion reinforces a broader industry truth: AI leadership is no longer defined solely by software innovation – it is secured through physical infrastructure leadership.

Why Compute Architecture Is Now a Strategic Weapon

Modern AI systems, particularly generative AI, real-time analytics engines, and autonomous decision systems, demand far more than virtualized servers. They require a reimagined enterprise compute architecture for AI workloads. Let’s examine why.

1. AI Is Compute-Intensive by Design

Training advanced foundation models can require thousands of GPUs operating simultaneously. Even inference, once considered lightweight, now demands specialized accelerators for high-speed response times.

Organizations that rely on outdated compute environments face:

• Processing bottlenecks
• Latency spikes
• Escalating operational costs
• Infrastructure fragility

AI doesn’t tolerate inefficiency. It exposes it.

2. Real-Time AI Changes Infrastructure Requirements

AI is increasingly embedded in live environments:

• Fraud detection in financial services
• Predictive maintenance in manufacturing
• Personalized product recommendations in e-commerce
• AI copilots in enterprise workflows

These applications require infrastructure for real-time AI, not batch-processing systems designed for overnight analytics.

Real-time AI demands:

• Ultra-low latency networking
• Edge integration capabilities
• Distributed processing
• Seamless scalability

According to TechRepublic’s enterprise AI coverage, many organizations struggle to transition AI from pilot to production because their compute, storage, and networking layers weren’t designed for production-grade workloads, creating bottlenecks that delay or derail deployments. 

3. Energy, Cooling, and Sustainability Are Now AI Variables

One often overlooked aspect of AI infrastructure is energy intensity. AI workloads consume significantly more power than traditional enterprise systems.

Modern AI-optimized facilities incorporate:

• Advanced liquid cooling systems
• High-density rack configurations
• Renewable energy integration
• Intelligent power distribution networks

Amazon’s Louisiana campuses are expected to include significant utility and infrastructure upgrades – including new electrical systems funded in partnership with Southwestern Electric Power Company and up to $400 million in water infrastructure improvements to support high-performance operations.

The AI era is also an energy era. Infrastructure planning must integrate sustainability, resilience, and cost efficiency simultaneously.

The Rise of a Formal Enterprise AI Infrastructure Strategy

What separates AI leaders from followers is not experimentation – it is architectural foresight. A strong enterprise AI infrastructure strategy includes:

• Strategic Capacity Planning

Forecasting compute requirements aligned with AI adoption roadmaps.

• Hybrid & Multi-Cloud Alignment

Balancing hyperscale cloud, on-premise systems, and edge environments.

Monitoring inference economics to prevent uncontrolled compute spend.

Embedding zero-trust principles into AI workloads and data flows.

Workload Placement Intelligence

Running the right workloads on the right platforms for performance and cost optimization.

Without a structured strategy, enterprises face:

• Siloed AI deployments
• Fragmented compute environments
• Rising operational costs
• Limited scalability

Infrastructure must move from reactive to predictive.

Why Enterprises Are Turning to Specialized Partners

Designing, deploying, and optimizing AI infrastructure is not trivial. It requires deep expertise across hardware, orchestration, networking, and AI deployment pipelines.

This is why organizations increasingly collaborate with experienced:

• AI infrastructure development companies
• Enterprise AI development companies

These partners help enterprises:

• Architect scalable compute frameworks
• Optimize GPU utilization
• Design resilient multi-cloud ecosystems
• Integrate AI seamlessly into enterprise environments

Infrastructure transformation is complex, but strategic partnerships reduce risk and accelerate deployment timelines.

The Economic Implications of AI Data Center Expansion

Large-scale AI infrastructure investments are signaling a structural transformation in the global economy. Compute capacity is becoming a strategic asset influencing energy markets, semiconductor supply chains, regional talent hubs, and capital allocation priorities.

Enterprises are no longer simply purchasing software licenses; they are competing for sustained access to scalable compute ecosystems. As AI adoption accelerates, infrastructure availability, performance efficiency, and cost governance increasingly determine which organizations can innovate reliably at scale.

The deeper shift is this: AI infrastructure is becoming industrial infrastructure.

Just as railroads powered manufacturing growth and broadband enabled digital commerce, AI-ready compute environments now form the backbone of competitive enterprise ecosystems. Organizations that recognize infrastructure as strategic capital, not operational overhead, will define the next decade of market leadership.

What Enterprise Leaders Must Do Now

Infrastructure decisions can no longer be deferred to IT roadmaps. They must sit at the center of enterprise AI strategy. To remain competitive in the Infrastructure Era of AI, leaders should:

1. Conduct a Compute Readiness Assessment

Identify architectural bottlenecks, GPU constraints, latency risks, and cost inefficiencies that could limit AI scale.

2. Formalize an enterprise AI infrastructure strategy

Align infrastructure investment with long-term AI adoption plans, ensuring compute capacity grows alongside business ambition.

3. Redesign enterprise compute architecture for AI workloads

Move beyond retrofitting legacy systems. Build environments purpose-designed for training, inference, and hybrid scaling.

4. Build a dedicated infrastructure for real-time AI

Enable low-latency, production-grade AI systems that operate within mission-critical workflows.

5. Partner with AI Infrastructure Experts

Work with specialists who can design scalable compute environments and ensure your infrastructure supports sustainable AI growth.

The organizations that act decisively will turn infrastructure into a growth multiplier. Those who delay will find their AI ambitions constrained by architectural limits.

The New Definition of AI Leadership

AI leadership in 2026 is no longer measured by isolated model innovation, but by the strength and scalability of enterprise compute foundations. As AI shifts from experimentation to industrialization, competitive advantage depends on a well-defined enterprise AI infrastructure strategy and a purpose-built enterprise compute architecture for AI workloads. Organizations that invest in AI-ready data center architecture for enterprises and build infrastructure for real-time AI position themselves to scale efficiently, control costs, and sustain performance.

In this new era, infrastructure is not operational support – it is strategic capital. Market leaders will be those who align compute capacity with long-term business vision. Aniter, an enterprise AI development company, helps organizations design, deploy, and optimize scalable AI systems that deliver resilient, production-grade performance and measurable business impact.

Key Takeaways

• The Actor: Senior business development staff with unrestricted admin access to live user databases.
• The Method: Cross-referencing internal UIDs with on-chain data to identify and front-run KOL wallets.
• The Failure: A YC-backed unicorn generating $390M revenue operating with zero role-based access controls.

Pantera Capital and Franklin Templeton’s digital assets units have joined the first cohort of Arena, a new testing environment from open-source AI lab Sentient that is designed to evaluate how AI agents perform in enterprise-style workflows.

In a Friday announcement shared with Cointelegraph, Sentient positioned Arena as a production-style benchmarking platform rather than a static model test. Instead of scoring agents on fixed datasets alone, it runs them through standardized tasks modeled on enterprise conditions, including long documents, incomplete information and conflicting sources. 

“In this initial phase, participation refers to supporting the Arena program and developer cohort,” Oleg Golev, product lead at Sentient Labs, told Cointelegraph.

He said partners are helping shape what “production-ready reasoning” looks like for document-heavy tasks such as analysis, compliance and operations. The companies are not announcing capital commitments tied to the initiative. 

Related: Jack Dorsey’s Block to cut 4,000 jobs in AI-driven restructuring

The launch comes as enterprises accelerate the deployment of AI agents into research and operational workflows, even as governance frameworks lag. 

According to the Celonis 2026 Process Optimization Report, published Feb. 4, 85% of surveyed senior business leaders aim to become “agentic enterprises” within three years, while only 19% currently use multi-agent systems.

Production-style evaluation, not static scoring

Golev described Arena as a shared platform where developers submit AI agents to standardized tasks and compare results under consistent testing conditions.

The platform tracks failure categories such as hallucination, missing evidence, incorrect citations and reasoning gaps, allowing developers to diagnose recurring issues.

Arena plans to publish comparative performance metrics through a public leaderboard and release postmortems summarizing common failure modes and fixes.

Infrastructure partners, including OpenRouter and Fireworks, are supplying inference compute for the initial cohort, while other partners support tooling and workshops.

Related: High-yield bond surge signals rising risk, demand in BTC mining, AI infrastructure

Governance layer amid rising AI autonomy

The initiative emerges as financial and crypto companies experiment with giving AI systems greater economic autonomy.

On Wednesday, MoonPay launched infrastructure enabling AI agents to create wallets and execute stablecoin transactions.

On Thursday, Stripe executives warned that blockchains may need significant scaling improvements if AI-driven commerce expands.