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“Bitcoin Keeps Working”: Strategy CEO Michael Saylor Issues Strong Message Amid Market Volatility

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Strategy Executive Chairman Michael Saylor remains unwavering in his conviction about Bitcoin’s long-term value as the world’s largest cryptocurrency faces renewed selling pressure and sharp price declines.

Amid heightened market volatility that has rattled investors and triggered concerns about Bitcoin’s near-term trajectory, Saylor in a post on X, issued a confident message, insisting that “Bitcoin keeps working”, reaffirming his belief that the digital asset remains the most reliable store of value in the modern financial era.

He wrote,

“Markets are closed today. Volatility is never easy. Bitcoin keeps working. So do we. Thank you for your support.”

Through his message, Saylor tries to reassure investors amid ongoing market volatility, emphasizing that short-term price fluctuations do not undermine Bitcoin’s long-term fundamentals.

While acknowledging that volatility can be challenging for market participants, he stressed that Bitcoin continues to operate reliably regardless of market conditions.

He also signaled that Strategy remains committed to its Bitcoin-focused strategy and expressed appreciation to shareholders and supporters who continue to back the company’s long-term vision despite periods of uncertainty.

His remarks come as Bitcoin faces renewed pressure, after the crypto asset dropped below the key $63,000 level, erasing recent gains after the crypto asset traded as high as $67,252 earlier this week.

Notably, Bitcoin’s fall below the $63,800 support zone triggered a sharp liquidation cascade across the derivatives market, accelerating the sell-off.

According to Coinglass data, the crypto market recorded over $303.66 million in liquidations over the past 24 hours, with long positions accounting for $258.53 million of the total.

The crypto asset recent price action comes amid risk-off sentiment sweeping global markets. Factors include hawkish signals from the Federal Reserve, which held interest rates steady while highlighting persistent inflation concerns tied to energy shocks.

The Fed on Wednesday left rates unchanged as expected but its projections pointed to the prospect of a rate rise by year-end. Fed Chair Kevin Warsh also said policymakers were committed to bringing inflation down.

Bitcoin price is currently trading around $62,596, at the time of writing this report, down 1.7% over the past 24 hours, bringing the next major support zone near $60,000 back into focus.

With institutional demand also showing signs of weakness through persistent spot ETF outflows, traders are increasingly bracing for an extended downward trend.

As Bitcoin slipped below support, forced liquidations added significant selling pressure, amplifying downside volatility and pushing prices lower.

Adding to the bearish pressure, spot Bitcoin ETFs have recorded heavy net outflows over the past two sessions, signaling weakening institutional demand. According to the latest ETF flow data, Bitcoin funds saw $216.48 million in net outflows on June 17, followed by a larger $389.50 million outflow on June 18, taking the two-day total to nearly $606 million.

Outlook

Volatility, while uncomfortable, is a feature of emerging asset classes like Bitcoin. Sharp moves can test conviction, flush out leveraged positions, and separate short-term speculators from long-term believers.

Saylor’s brief but direct note acknowledges the emotional toll of these swings while reinforcing commitment from both the asset itself and the team behind one of its largest corporate treasuries.

His consistent messaging over the years has helped frame Bitcoin not merely as a speculative token but as a superior form of capital that functions independently of legacy financial calendars and institutions.

With Bitcoin now trading below $63,000, all eyes are shifting toward the $60,000 support zone, which has emerged as the next major level for the market.

White House, Anthropic Move Toward AI Safety Rulebook After Clash Over Powerful New Models

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The White House and Anthropic are working to establish what could become one of the first formal frameworks for evaluating security vulnerabilities in advanced artificial intelligence models, following a high-profile dispute that led the U.S. government to effectively force the withdrawal of Anthropic’s most powerful systems from the market.

According to U.S. officials familiar with the discussions cited by Politico, the administration and Anthropic are negotiating a set of technical standards that would determine how future AI security flaws are assessed, how serious they are deemed to be, and when government intervention may be warranted.

The effort follows a dramatic confrontation between the AI company and federal officials over Claude Fable 5 and Mythos 5, Anthropic’s most advanced models. The dispute culminated in the White House imposing export controls that prevented foreign users from accessing the systems after officials concluded that a security vulnerability, commonly known as a “jailbreak,” posed unacceptable risks.

The incident has rapidly evolved into one of the most consequential tests yet of how governments will regulate increasingly powerful frontier AI systems. At stake is a fundamental question confronting policymakers worldwide: who decides when an AI model becomes too dangerous to deploy?

Unlike traditional software vulnerabilities, AI jailbreaks occupy a regulatory gray area. Researchers routinely discover ways to bypass safety safeguards embedded in models, but there is little consensus about when such breaches represent manageable technical shortcomings and when they constitute national security threats.

Anthropic argued that the flaw identified by government officials was limited in scope and did not justify pulling the model from public use. Administration officials reached a different conclusion, triggering an unprecedented intervention that exposed the absence of clear standards governing frontier AI deployment.

The resulting negotiations suggest both sides now recognize that the technology has advanced faster than the institutions responsible for overseeing it.

The discussions are reportedly being led by Anthropic’s Head of Public Policy, Sarah Heck, and co-founder Tom Brown, alongside senior administration officials. The objective is to create a common methodology for evaluating future security incidents.

The proposed framework would examine factors including the extent to which safeguards were bypassed, the capabilities exposed through a jailbreak, the likelihood of misuse, and the practical consequences of the breach.

Such a system would represent a significant shift away from the current environment, where assessments are often made on an ad hoc basis, and companies and regulators can reach sharply different conclusions about the same vulnerability.

The negotiations also reflect a growing acceptance within government circles that no AI model can be made completely secure. That reality has become increasingly apparent as AI systems grow more capable. Even models equipped with extensive safety mechanisms have repeatedly been shown to be vulnerable to creative prompting techniques that can circumvent restrictions.

The challenge for policymakers is determining which vulnerabilities are tolerable and which require intervention.

The debate extends far beyond Anthropic.

Leading AI developers, including OpenAI, Google, Meta, and others, face similar questions as they push toward powerful models capable of advanced coding, scientific research, and cybersecurity applications.

Governments are particularly concerned about models that can identify software vulnerabilities, automate cyberattacks, assist in biological research, or accelerate the development of competing AI systems.

Anthropic’s Mythos model became a flashpoint precisely because it reportedly demonstrated unprecedented capabilities in cybersecurity-related tasks, raising fears that even limited breaches could expose powerful offensive capabilities.

The dispute has highlighted how AI regulation is beginning to resemble the oversight frameworks used for sensitive technologies such as nuclear energy, advanced semiconductors, and biotechnology. Rather than focusing solely on consumer harms or privacy concerns, policymakers are increasingly framing frontier AI as a matter of national security.

That shift is evident in the White House’s decision to use export controls, a tool traditionally reserved for strategically sensitive technologies, to restrict access to an AI model. The administration’s intervention also signals a broader willingness to assert federal authority over the deployment of advanced AI systems, particularly where cybersecurity risks are involved.

The discussions come amid growing international pressure to establish common AI safety standards.

Leaders and technology executives at recent G7 meetings reportedly raised similar concerns about the need for agreed methodologies to evaluate advanced model risks. Industry executives have warned that inconsistent regulatory approaches could create uncertainty for developers while allowing dangerous capabilities to slip through oversight gaps.

The outcome of the White House-Anthropic negotiations could therefore have implications far beyond a single company.

If successful, the framework could become a template for future interactions between governments and AI developers, creating a more predictable process for handling security disputes.

The talks offer a pathway toward restoring access to Fable 5 and Mythos 5 while avoiding prolonged regulatory conflict. The framework could provide a White House mechanism for evaluating future AI risks without resorting to emergency interventions each time a vulnerability is discovered.

The fact that talks have progressed from confrontation to technical collaboration suggests both sides recognize the need for clearer rules as frontier AI systems become more powerful.

The broader significance is that the AI industry may be entering a new phase in which model releases are judged not only by commercial performance or technological advancement but also by formal security benchmarks agreed upon with governments. That would mark a major evolution in AI governance, bringing the industry closer to a world where the deployment of cutting-edge models is governed by regulatory standards rather than solely by the discretion of the companies that build them.

NGX Overhauls Price Movement Rules to Curb Market Distortions, Boost Liquidity in Premium Stocks

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The Nigerian Exchange (NGX) is set to introduce a major change to its pricing methodology that could reshape how share prices move on the local bourse, with new minimum trading volume thresholds required before stock prices can change.

The reform, confirmed in the Exchange’s updated Rulebook and corroborated by multiple market operators, is aimed at strengthening market integrity, improving price discovery, and reducing the influence of low-volume transactions on stock valuations.

Under the new framework, stocks will be grouped according to their share prices, with different minimum trading quantities required before a transaction can alter the published market price. Shares priced at N1,000 and above will require at least 10,000 units to trade before a price movement is recorded. Stocks trading between N500 and N999.99 will require a minimum of 50,000 units, while equities priced below N500 will require at least 100,000 units before their market prices can change.

The rule represents one of the most significant adjustments to NGX’s trading structure in recent years and could have far-reaching implications for investors, brokers, market makers, and listed companies. At its core, the reform seeks to address a longstanding concern within the Nigerian market: the ability of relatively small transactions to create outsized price movements that may not accurately reflect genuine investor demand or supply.

For years, a relatively modest trade could trigger a price change in certain equities, particularly where liquidity was thin. This often created distortions in valuation metrics, enabled speculative trading strategies, and, in some cases, exaggerated market sentiment.

The new structure raises the threshold for price adjustments, meaning stock prices will increasingly be determined by more meaningful trading activity rather than isolated transactions.

Market analysts believe the change is particularly important as the Nigerian market evolves into a trillion-naira ecosystem that now hosts several companies with market capitalizations running into trillions of naira. As valuations have expanded, many operators argue that the previous framework no longer reflects market realities.

NairaMetrics quoted Abiodun Ogunniyi, Head of Research at GTI Capital Limited, who said the adjustment addresses concerns that market operators have repeatedly raised over the years.

According to him, one of the major challenges facing high-priced stocks has been liquidity constraints, which often made price movements difficult and disconnected from underlying investor interest.

“The challenge with many high-priced stocks is that they tend to be illiquid. This adjustment is a response to concerns that have existed for some time and should make it easier for market prices to reflect investor demand,” Ogunniyi said.

He noted that many premium-priced stocks have historically depended on institutional transactions to influence market direction because the volume requirements needed to affect prices were often difficult to meet.

The revised framework, he argued, could make these stocks more responsive to genuine market activity and improve overall valuation efficiency.

Beyond improving pricing dynamics, Ogunniyi believes the reform may encourage broader participation in high-value stocks by reducing some of the structural barriers that have discouraged retail investors from engaging with premium equities.

He added that while the pricing adjustment is a positive step, regulators must continue addressing another major challenge facing the market: liquidity.

“We need to free up liquidity. The issue of free floats remains a major conversation in the market. More liquidity means better price discovery and a more efficient market,” he said.

Aruna Kebira, Managing Director of Globalview Capital Limited, described the new framework less as a revolutionary change and more as a return to a system previously used by the Exchange.

He noted that the market once operated under a similar structure, where volume thresholds varied according to stock prices.

“This was how it used to be. High-priced stocks required 10,000 units, medium-priced stocks required 50,000 units, while lower-priced stocks required 100,000 units. So, in many ways, the Exchange is returning to a framework that operators are already familiar with,” Kebira said.

In his view, the reintroduction of the framework reflects the evolution of the market rather than a reactionary policy response. Kebira argued that today’s market bears little resemblance to the one that existed when the rule was originally modified.

The emergence of trillion-naira companies, increased institutional participation, and greater market sophistication have changed the dynamics of trading, requiring periodic regulatory adjustments.

He maintained that such reviews are a normal feature of mature capital markets, where rules are continuously refined to reflect changing market conditions.

“When systems are introduced, they are tested over time. If market conditions change, regulators review them and make adjustments. That is part of market development and international best practice,” he said.

Beyond its immediate impact on trading activity, the NGX reform highlights a broader shift in regulatory thinking.

Globally, exchanges are increasingly focused on ensuring that prices are determined by meaningful liquidity rather than sporadic trades. The goal is to create a market environment where valuations better reflect genuine investor conviction and where prices are less vulnerable to manipulation or artificial swings.

However, the new rule comes at a remarkable time. The Nigerian local market has witnessed a surge in retail participation over the past two years, while institutional investors have become more active amid rising interest in equities as a hedge against inflation and currency weakness.

At the same time, regulators have intensified efforts to strengthen market transparency, improve governance standards, and enhance investor confidence.

The new pricing methodology appears designed to support those objectives.

However, the reform has come with potential risks.

Some traders worry that requiring larger transaction volumes before prices can move may reduce responsiveness in thinly traded stocks. In less liquid markets, genuine buying or selling interest may take longer to be reflected in market prices, potentially slowing price discovery.

Smaller investors could also find it more difficult to influence market direction in certain stocks, concentrating price-setting power among larger institutional players.

Against this backdrop, market observers expect the ultimate success of the reform to hinge on how effectively it balances two competing objectives: improving market stability while preserving efficient price discovery.

If successful, the changes could reduce price distortions, encourage deeper liquidity, and make valuations more reflective of underlying fundamentals. If not, concerns may emerge about reduced market responsiveness, particularly among smaller-cap stocks.

With the implementation date yet to be formally announced, brokers, traders, and institutional investors are closely monitoring developments and awaiting further operational guidance from the Exchange.

RMZ Sets $35bn Investment Drive to Become Major Player in India’s Booming Data Center and AI Infrastructure Market

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Bengaluru-based diversified real estate and investment firm RMZ is preparing a massive expansion into data centers, targeting 2-3 gigawatts of capacity over the next five years as part of a broader $35 billion investment drive aimed at capitalizing on India’s surging demand for AI and cloud computing infrastructure.

The company, which currently operates 250 megawatts, is in the final stages of discussions for three new projects that would push its total capacity above 1 gigawatt, Deepak Chhabria, president of RMZ Infrastructure, told Reuters on Thursday. By the end of this year, RMZ also plans to acquire additional land capable of supporting up to 2 gigawatts, positioning it as one of the more aggressive domestic players in what is becoming one of Asia’s hottest digital infrastructure battles.

In April, RMZ unveiled plans to invest more than $35 billion over five years to develop co-location data centers, AI factories, and related infrastructure, with the possibility of an initial public offering to help fund the growth. The announcement came amid a wave of commitments from global technology giants and Indian conglomerates pouring billions into the country’s digital backbone.

“We are seeing only positive signs from some of the hyperscalers, and I think by the middle of this year, we will start ramping up capacity as we get clients signed up,” Chhabria said, declining to name specific customers.

India’s digital infrastructure sector is on track to attract more than $50 billion in planned spending across data centers, cloud, and AI ecosystems. Global hyperscalers and domestic players alike are racing to secure computing capacity as artificial intelligence adoption accelerates across industries, from fintech and e-commerce to healthcare and government services.

The country’s large English-speaking talent pool, improving connectivity, and relatively lower operational costs compared to traditional hubs have made it an increasingly attractive destination.

RMZ, which already operates across major Indian cities including Bengaluru, Mumbai, and Hyderabad, views its data center push as more than just real estate development. Chhabria described it as a strategic entry point into allied businesses such as graphics processing units (GPUs), power infrastructure, and software services.

“Now we will use that as a stepping stone eventually to go up the food chain and build the bottom layer of power,” he said, referring to deeper involvement in the critical energy infrastructure that underpins AI and cloud computing operations.

The company built its existing 250-megawatt capacity through a joint venture with UK-based Colt Data Centre Services. Chhabria said the two partners are actively exploring further growth opportunities together, leveraging Colt’s international expertise and RMZ’s deep roots in the Indian market.

Timing in a High-Growth Market

RMZ’s ambitions align with a broader transformation underway in India’s technology industry. As global tech firms scramble to expand their AI footprints, securing reliable, scalable power and computing capacity has become a key competitive advantage. Delays in land acquisition, regulatory approvals, and power connectivity have slowed some projects, making companies with strong local presence and execution capabilities particularly valuable.

By focusing on both co-location facilities (where multiple clients share infrastructure) and dedicated AI factories, RMZ aims to serve a range of customers — from large hyperscalers needing massive scale to Indian enterprises seeking localized, compliant solutions.

The move into power infrastructure is significant, as reliable electricity remains one of the biggest constraints for data center development in India, where peak demand often strains the grid.

Chhabria’s comments suggest RMZ sees itself evolving from a real estate developer into a more integrated digital infrastructure provider. This “full-stack” approach could help insulate the company from pure-play real estate cyclicality while capturing higher-margin opportunities in the AI value chain.

India’s data center market is becoming fiercely contested. Global players such as Equinix, Digital Realty, and AWS are expanding aggressively, while domestic conglomerates including Reliance, Adani, and Tata are also making large bets. Foreign investment is flowing in, drawn by government incentives under initiatives like the IndiaAI Mission and production-linked incentives for electronics manufacturing.

RMZ’s $35 billion commitment stands out for its scale and speed. If executed, analysts believe it would place the firm among the leading capacity builders in the country at a time when demand forecasts continue to be revised upward. Industry estimates suggest India could need tens of gigawatts of additional data center capacity by 2030 to support its digital ambitions, creating room for multiple large players.

The joint venture with Colt provides RMZ with technical credibility and global best practices, while its local land bank and relationships give it an edge in navigating India’s complex regulatory and state-level approval processes. Success in the initial gigawatt-scale projects will be critical in building investor confidence ahead of any potential public listing.

For the broader Indian economy, RMZ’s expansion, and others like it, come with significant implications. Data centers are capital-intensive and create high-skilled jobs in engineering, operations, and maintenance. They also drive demand for ancillary infrastructure, from power generation to fiber optics and cooling systems.

At a time when India is positioning itself as a global technology and AI player, robust domestic capacity reduces reliance on foreign data centers and enhances data sovereignty.

However, power availability, water resources for cooling, and environmental clearances are persistent hurdles. Land acquisition in prime connectivity locations can be slow and contentious.

Still, Chhabria’s tone was optimistic, reflecting confidence that India’s digital infrastructure story is only beginning.

Prediction Market Apps and the Future of Data-Driven Finance

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Prediction markets started as a niche curiosity. Today, they are attracting traders, investors and businesses interested in a simple question: can markets forecast the future better than experts?

Wall Street has spent decades trying to predict the future. Economists publish forecasts, analysts build models, and investors pore over data looking for an edge. Prediction markets approach the same problem from a different angle. Instead of asking a handful of experts what they think will happen, they let thousands of participants express a view with their wallets.

The recent growth of prediction-market apps suggests that more people are starting to pay attention to the results.

Forecasting Is Becoming a Financial Asset

Good forecasting has always been valuable because it helps businesses decide where to invest, which risks deserve attention and which opportunities are worth pursuing.

The difference today is that forecasting no longer relies solely on historical data. Artificial intelligence, machine learning and predictive analytics have become part of the toolkit.

Research examining big-data analytics and machine-learning forecasting found that modern financial systems increasingly rely on large datasets and predictive models to improve decision-making and operational efficiency. Finance has become a constant exercise in processing new information, and firms that can identify useful signals faster than competitors gain a genuine advantage. Prediction markets fit naturally into that environment because they transform expectations into measurable prices.

Prediction Markets Turn Expectations Into Prices

Prediction markets work on a simple premise. Participants buy and sell contracts linked to future events, and the market price reflects the collective probability assigned to a particular outcome. Every new piece of information can influence those prices, creating a constantly updated forecast.

Activity in the sector has accelerated dramatically. Monthly trading volume across major prediction-market platforms increased from less than $5 billion in September 2025 to approximately $24 billion in April 2026. That growth has pushed prediction markets beyond election forecasting and into areas such as interest rates, artificial intelligence, economic indicators and corporate developments. For businesses interested in data-driven finance, those markets provide a real-time view of how participants interpret emerging information.

For companies, the value is not necessarily in trading the contracts themselves. The more important use may be informational. A business can observe how market expectations change around inflation releases, policy decisions, product launches or macroeconomic events. Those signals can complement internal models, analyst forecasts and customer data, giving decision-makers another way to understand how expectations are shifting in real time.

Sports Markets Have Become a Live Testing Ground

Sports have provided one of the clearest demonstrations of market-based forecasting. Futures markets move when injuries occur, qualification odds change when results arrive elsewhere, and bookmaker expectations adjust constantly as information enters the market. Every adjustment reflects a collective reassessment of probability.

That dynamic increasingly overlaps with prediction-market activity. Participants now track championship outcomes, player-performance markets and event contracts with the same attention traditionally reserved for financial indicators.

Market selection has become an important differentiator because sports-focused contracts, trading volume and participation incentives can vary substantially between operators.

Comparing those differences is now part of the process, particularly when activity concentrates around major events. For readers comparing sports prediction markets, a structured guide can help show how operators differ by available contracts, trading activity, market focus and promotional terms. Those differences matter because liquidity, contract variety and platform rules can shape the user experience as much as the forecast itself.

Interest in these comparisons continues to grow because prediction markets have developed into sophisticated ecosystems where liquidity and contract variety can influence participation just as much as the forecast itself.

The growing relevance of prediction markets has attracted attention far beyond sports and politics. Increasingly, the discussion involves financial infrastructure, derivatives trading and new approaches to market participation.

The same regulatory and infrastructure questions are now spreading beyond traditional event contracts. A recent dispute involving CME Group, Kalshi and the U.S. Commodity Futures Trading Commission showed how newer trading platforms are beginning to overlap with established derivatives markets. The case centred on perpetual futures rather than standard yes-or-no prediction contracts, but it still illustrates the broader point: platforms built around simplified, app-based market participation are moving closer to mainstream financial infrastructure.

Markets Are Becoming Information Networks

Prediction markets remain a relatively young part of modern finance, yet their development points toward an interesting future. Financial markets have always been mechanisms for processing information; prediction markets simply apply that principle to a wider range of events.

Whether the subject is interest rates, economic growth, artificial intelligence or a championship outcome, the underlying process remains the same: participants assess available information and express a view on what happens next.

That idea helps explain the growing interest in prediction-market apps. Businesses have never lacked data. The challenge has always been deciding which information deserves attention and which signals are worth acting on. Prediction markets offer a different way of approaching that problem because they transform expectations into prices that can be observed in real time.

Finance will continue to evolve as new forecasting tools emerge, yet the underlying objective remains unchanged. Better information leads to better decisions. Prediction markets have become part of that conversation, occupying a space between traditional forecasting and active market participation; a position that is likely to attract increasing attention as data-driven finance continues to mature.