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China Holds Lending Rates Steady for 13th Month as Policymakers Prioritize Targeted Support Over Broad Easing

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China’s central bank left its benchmark lending rates unchanged for the 13th consecutive month in June, signaling that authorities are in no hurry to deliver broad monetary stimulus despite persistent weakness in domestic demand and a deepening divergence in the world’s second-largest economy.

The People’s Bank of China (PBOC) kept the one-year loan prime rate (LPR) at 3.00% and the five-year LPR at 3.50%, in line with the unanimous expectations of 30 market participants surveyed by Reuters last week. The decision underscores a cautious policy stance that continues to favor incremental measures and fiscal support over aggressive rate cuts.

This steady approach comes as China grapples with a pronounced two-speed economy. Factory output and exports have shown surprising resilience, helped by global demand for Chinese goods and firms front-loading shipments amid trade tensions. However, domestic activity remains subdued, weighed down by a prolonged property sector downturn that continues to drag on household confidence and borrowing.

New bank lending in May rose less than expected, following a contraction the previous month, with household borrowing particularly weak amid the real estate slump. The data highlights the limited traction of monetary policy in reviving private sector demand, even as liquidity conditions remain relatively ample.

PBOC Governor Pan Gongsheng addressed these dynamics directly last week at the annual Lujiazui Forum in Shanghai. He noted that loan growth has slowed in recent years while bond and equity financing have gained ground — a development he described as evidence of “profound economic restructuring” and the emergence of new growth engines.

Rather than viewing the slowdown in credit as purely negative, Pan framed it as part of a necessary transition away from debt-fueled investment toward higher-quality, consumption- and innovation-driven expansion. This perspective helps explain why the central bank has held rates steady even as some analysts called for more support.

Analysts Expect Incremental Policy Response

Market observers largely see the decision as consistent with Beijing’s current playbook. Jing Sima, chief strategist at BCA Research, does not anticipate outright policy-rate cuts in the second half of the year.

“The persistent issue facing the aggregate economy is not a shortage of liquidity supply, but a lack of credit demand. Our base case is that fiscal policy becomes more supportive in the second half of the year, while the PBOC remains broadly accommodative but refrains from outright rate cuts,” Sima said.

Ho Woei Chen, economist at UOB, echoed this view, suggesting policy responses will stay measured unless growth threatens to undershoot the official target range of 4.5%-5.0%.

“Unless further evidence suggests that growth could slow below the official target of 4.5%-5.0%, we think policy responses will be incremental,” Chen said.

This approach reflects a deliberate strategy to avoid flooding the system with cheap credit that could exacerbate existing imbalances, particularly in the property sector, while directing support toward strategic areas such as advanced manufacturing, technology, and green industries.

By keeping rates on hold, the PBOC is effectively placing greater emphasis on fiscal tools and structural reforms to address the economy’s challenges. Beijing has already signaled more proactive fiscal measures in the second half of the year, including potential infrastructure spending and support for consumption.

The steady LPR also preserves policy space for future adjustments if downside risks intensify. However, it leaves the central bank navigating a narrow path: supporting growth without reigniting leverage concerns or property speculation, while managing external pressures from global trade fragmentation and shifting supply chains.

For businesses and households, the unchanged rates mean borrowing costs remain stable in the near term, providing some predictability. Yet some business leaders are concerned that the lack of broader easing may prolong the pressure on domestic demand, particularly in real estate and related sectors that have been key drivers of past growth.

China’s policymakers appear to be betting that a combination of targeted fiscal support, ongoing economic restructuring, and resilient external demand will be enough to keep growth within target without resorting to the kind of aggressive monetary stimulus seen in previous cycles.

Musk Predicts AI Will Trigger Severe Deflation, Proposes Direct Payments to Citizens Over Government AI Equity Stakes

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Elon Musk has outlined a provocative vision for the future of the United States economy shaped by advanced AI and robotics.

The tech mogul pushed back against a proposal that would see the US government acquire equity stakes in leading artificial intelligence companies including OpenAI, Anthropic, and his own xAI to form a sovereign wealth fund.

In a post on X, Musk argued that the U.S. Treasury should send money directly to the people rather than pursuing more complex interventions.

He wrote,

“Better just to send money directly to the people from the Treasury. So long as the increase in goods & services exceeds the increase in the money supply, which will be the case with AI & robots, there will not be inflation. In fact, my prediction is that we will desperately be fighting deflation”.

Musk’s statement comes in response to what U.S. Vice President JD Vance explained that President Trump supports the United States taking equity stakes in major artificial intelligence companies as a form of sovereign wealth fund.

Vance described the idea as unconventional for a Republican but consistent with Trump’s pragmatic style. He warned that allowing a handful of AI companies to become multi-trillion-dollar entities could concentrate enormous wealth in the hands of a few while ordinary workers see limited benefits.

Drawing parallels to the Industrial Revolution, he cautioned that such extreme wealth concentration has historically led to significant political backlash, including in parts of Europe.

Rather than relying solely on taxation and redistribution after wealth is created, Vance advocated for pre-distribution strategies.

He argued that workers should benefit directly from AI-driven prosperity upfront, rather than relying on government handouts that could leave them subservient to a small wealthy elite.

He emphasized giving workers “a seat at the table,” potentially through stronger labor representation, as the technology reshapes the economy. Vance cited the government’s equity investment in Intel under the CHIPS Act as a positive precedent that delivered returns for taxpayers.

He suggested a similar approach could be applied to leading AI firms such as OpenAI, Anthropic, and xAI. While noting Bernie Sanders’ proposal for roughly 50% public ownership, Vance clarified that Trump favored the general concept of government stakes without committing to a specific percentage.

The discussion framed the proposal as a potential evolution in American capitalism aimed at ensuring broader public participation in the massive economic gains expected from artificial intelligence.

This perspective prompted Musk’s counterargument. His reasoning centers on unprecedented productivity gains. As AI systems and physical robots like Tesla’s Optimus become widespread, production costs could plummet toward near-zero marginal levels for many items.

This abundance would flood markets with cheaper goods and services, potentially leading to deflationary pressure. In such a scenario, sustaining consumer demand becomes critical because widespread job displacement could reduce people’s ability to spend, even as prices fall.

Direct payments from the Treasury, according to Musk, offer a straightforward solution. By injecting money straight into citizens, the government could maintain economic circulation without distorting markets through targeted subsidies or complex programs.

He emphasized that as long as output growth exceeds the expansion of money supply which he believes AI will ensure, this approach would not spark inflation.

This idea aligns with Musk’s broader outlook on an AI-dominated future. He has previously suggested that AI and robotics represent the best path to addressing massive national debt by supercharging productivity.

Reactions And Implications

The proposal has sparked intense debate. Supporters view it as a pragmatic response to technological unemployment and a step toward shared prosperity in an era of plenty.

Critics raise concerns about government dependency, funding mechanisms, potential impacts on incentives to work, and long-term fiscal sustainability.

Some point out historical parallels to deflationary periods, while others worry it could concentrate power or fail to address deeper societal shifts.

Economists have long debated the effects of deflation. Mild deflation can benefit consumers through lower prices, but severe or prolonged deflation often discourages spending and investment as people delay purchases in anticipation of even lower costs, potentially slowing growth.

Musk’s comments comes amid rapid AI advancement. While the timeline for such transformative impacts remains uncertain, his influence as a leading figure in AI, robotics, and electric vehicles lends weight to the discussion.

Impact of Rising Global Energy, Insurance, and Shipping Costs on Domestic Economies

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The global economy is deeply interconnected, meaning that disruptions in one region can have significant consequences for countries thousands of miles away.

In recent years, escalating tensions in the Middle East have contributed to rising global energy costs, increased marine insurance premiums, and higher shipping expenses. These developments have created ripple effects across international trade networks.

As a result, businesses and consumers alike are facing growing economic pressures. Energy prices are particularly sensitive to geopolitical instability in the Middle East because the region remains one of the world’s most important suppliers of crude oil and natural gas.

Any threat to production facilities, shipping routes, or regional stability can trigger fears of supply disruptions. Even the possibility of interruptions often drives up oil prices in global markets.

Higher energy costs affect nearly every sector of the economy because fuel powers transportation systems, industrial machinery, and electricity generation. Consequently, businesses must absorb increased operational expenses or pass them on to consumers through higher prices.

Another major consequence of Middle East tensions is the rise in marine insurance costs. Shipping companies operating through strategic waterways such as the Red Sea, the Strait of Hormuz, and the Suez Canal face elevated risks during periods of geopolitical uncertainty.

Insurance providers respond by charging higher premiums to cover potential losses resulting from conflict, attacks, or disruptions. These increased insurance costs add another layer of expense to international trade, making the movement of goods more costly and less predictable.

Shipping expenses have also surged due to security concerns and logistical challenges. Some shipping companies choose to reroute vessels away from high-risk areas, resulting in longer journeys, greater fuel consumption, and delayed deliveries.

Alternative routes often require additional resources and increase transportation costs.

Since modern supply chains depend heavily on efficient and timely shipping, any disruption can have significant consequences for manufacturers and retailers. Delays in receiving raw materials, components, or finished goods can slow production schedules and reduce overall economic efficiency.

The impact of these global developments is felt strongly at the domestic level. Logistics companies face higher fuel bills and transportation expenses, making it more expensive to move goods within national borders. Manufacturers must contend with increased costs for imported raw materials, machinery, and intermediate products.

Industries that rely heavily on energy-intensive processes, such as steel production, chemicals, and construction materials, are particularly vulnerable. As production costs rise, businesses often increase prices to maintain profitability, contributing to inflationary pressures across the economy.

Consumers ultimately bear much of the burden. Higher transportation and manufacturing costs translate into more expensive food, household goods, electronics, and other everyday products. Inflation reduces purchasing power, making it harder for households to manage their budgets.

Lower-income families are often the most affected because they spend a larger share of their income on essential goods and services. Rising global energy costs, marine insurance premiums, and shipping expenses linked to Middle East tensions demonstrate how geopolitical events can influence domestic economic conditions.

Through their effects on logistics, manufacturing, and supply chains, these global pressures contribute to higher production costs and consumer prices. Addressing these challenges requires stronger supply chain resilience, diversified energy sources, and strategic investments that reduce vulnerability to external shocks.

China Retaliates Against U.S. Blacklists With New Restrictions on Dozens of American Companies

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China has imposed fresh restrictions on dozens of American companies, targeting rare earth producers, drone manufacturers, and defense contractors in response to Washington’s latest effort to blacklist Chinese technology firms with alleged links to the country’s military.

The measures, announced Monday by Beijing, highlight the increasingly entrenched nature of the U.S.-China economic and technology confrontation, even as both sides attempt to stabilize broader diplomatic relations following recent high-level engagements between President Donald Trump and President Xi Jinping.

At the center of the dispute is the Pentagon’s updated 1260H list, which earlier this month added several major Chinese companies, including Alibaba Group, Baidu, and BYD, to a roster of firms Washington believes are supporting Beijing’s military modernization efforts.

While the U.S. designation does not immediately impose sanctions, it bars the Department of Defense from awarding direct contracts to listed firms beginning June 30 and will extend procurement restrictions indirectly across supply chains from 2027.

In response, China’s Ministry of Commerce placed 10 American companies on its export control list, prohibiting the export of Chinese-origin dual-use goods to those firms. Among the most notable targets are rare earth producers MP Materials and USA Rare Earth, both viewed as central to Washington’s efforts to reduce dependence on Chinese critical mineral supplies.

Drone manufacturers Teal Drones and Jaia Robotics were also included, alongside aerospace and defense-linked firms such as Ball Aerospace & Technologies, Oshkosh Defense, and California-based electronics manufacturer Aveox Inc.

In a separate move, China’s Finance Ministry barred 46 U.S. companies, largely defense contractors, from participating in Chinese government procurement projects. However, Beijing stopped short of imposing broader commercial restrictions. Foreign-invested entities registered locally in China and associated with the affected companies will remain exempt from the procurement ban.

Rare Earths Remain a Strategic Pressure Point

The inclusion of MP Materials and USA Rare Earth stands out, given the strategic importance of critical minerals in the global technology race. Rare earth elements are essential inputs for electric vehicles, advanced semiconductors, defense systems, renewable energy equipment, and artificial intelligence infrastructure.

China dominates the sector, accounting for roughly 70% of global rare earth mining and approximately 90% of refining capacity. That dominance has increasingly become one of Beijing’s most effective geopolitical tools as competition with the United States intensifies.

The move comes only days after reports emerged that China was increasing scrutiny of exports of indium and other strategic materials used in advanced optical chips and AI data centers. Together, the measures suggest Beijing is continuing to build a layered framework of export controls that can be activated when geopolitical tensions escalate.

A Calculated Response Rather Than Full Escalation

Analysts largely view China’s latest actions as a measured response rather than a major escalation. Han Shen Lin, China country director at Eurasia Group’s affiliate consultancy The Asia Group, said many of the targeted firms have limited commercial exposure to China.

The restrictions, therefore, carry more political significance than immediate economic consequences. The approach allows Beijing to demonstrate resolve while avoiding actions that could undermine the tentative improvement in bilateral relations that followed recent diplomatic engagement between Trump and Xi.

Dan Wang, China director at Eurasia Group, described the measures as a “model example” of how Beijing is likely to respond to lower-level U.S. escalations while maintaining overall stability in the relationship.

However, the development is largely seen as an expansion of national security concerns between Beijing and Washington. What began several years ago with restrictions on telecommunications firms has expanded to encompass artificial intelligence, electric vehicles, biotechnology, semiconductors, cloud computing, and advanced manufacturing.

The addition of Alibaba, Baidu, and BYD to the Pentagon list demonstrates how companies that operate largely in commercial markets are increasingly being drawn into competition between the world’s two largest economies.

Chinese officials have repeatedly criticized such actions.

Following the Pentagon’s latest designations, Beijing said it would take all necessary measures to protect Chinese companies’ “legitimate and legal rights and benefits” and accused Washington of “drawing up discriminatory lists under the pretext of national security.”

Several Chinese companies have already indicated they intend to challenge the designations. Past experience suggests such challenges can succeed. Chinese smartphone maker Xiaomi successfully fought its inclusion on a U.S. government blacklist in court, resulting in the designation being removed in May 2021.

That precedent has encouraged other companies to pursue legal avenues while continuing efforts to reassure investors that the restrictions will not materially affect operations.

The latest exchange signals that the U.S.-China confrontation has moved well beyond tariffs. The competition now centers on control of advanced technologies, supply chains, critical minerals, artificial intelligence infrastructure, and future industrial leadership.

While Monday’s measures are unlikely to have an immediate economic impact, they reinforce a longer-term shift: both Washington and Beijing are steadily building parallel systems of economic security restrictions that can be expanded whenever geopolitical tensions rise.

Navigating the AI Frontier: Why Nigeria is Rewriting Its Data Protection Act for the Era of Algorithms, Artificial General Intelligence and Emerging Technologies

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The world is in the midst of a technological transformation unlike any it has experienced before. Artificial intelligence (AI), machine learning, robotics, big data analytics, and emerging autonomous systems are rapidly changing the way societies function, how businesses operate, and how governments deliver services. Algorithms now influence decisions that affect employment opportunities, access to credit, healthcare outcomes, educational prospects, and even the information citizens encounter online. As technology increasingly becomes embedded in every aspect of human life, legal and regulatory frameworks around the world are struggling to keep pace.

Nigeria is no exception. In June 2026, as the country marked the third anniversary of the Nigeria Data Protection Act (NDPA) 2023, the Nigeria Data Protection Commission (NDPC) announced plans to initiate a comprehensive review of the legislation to address the challenges posed by artificial intelligence, robotics, and other emerging technologies. The move reflects a growing recognition that while the NDPA 2023 represented a significant milestone in Nigeria’s digital governance journey, the rapid evolution of technology demands a more nuanced and future-oriented legal framework.

The enactment of the NDPA in 2023 was rightly regarded as a watershed moment for privacy regulation in Nigeria. The legislation established a comprehensive framework for the processing of personal data and elevated the country’s standing among jurisdictions that have adopted modern data protection regimes. It introduced important principles relating to lawfulness, fairness, transparency, accountability, purpose limitation, and data minimisation, while also establishing the Nigeria Data Protection Commission as the central authority responsible for enforcing data protection obligations.

However, the technological environment in which the NDPA was enacted has evolved significantly. The law was designed primarily for a digital ecosystem in which data is collected, processed for specific purposes, stored, and eventually deleted. Artificial intelligence systems, particularly modern generative AI models, operate in an entirely different manner. They ingest enormous amounts of data, identify patterns and correlations, and embed information into complex neural networks comprising millions or even billions of parameters. In such systems, data is not simply stored in a database but transformed into mathematical representations that may become difficult, if not impossible, to isolate or erase.

This reality exposes fundamental questions that existing data protection laws struggle to answer. Can a data subject exercise the right to be forgotten once their personal information has contributed to the training of an AI model? How can an individual correct inaccurate information that has been integrated into a machine learning system? Who bears responsibility when an autonomous system makes a discriminatory or harmful decision? These questions highlight the growing tension between traditional privacy frameworks and the emerging realities of artificial intelligence.

The issuance of the General Application and Implementation Directive (GAID) 2025 represented an important effort to strengthen the operational framework of the NDPA. The Directive introduced structured compliance mechanisms, including tiered classifications for Data Controllers and Processors of Major Importance, mandatory audit requirements, and enhanced governance obligations such as the appointment of Data Protection Officers and the conduct of Data Protection Impact Assessments and Legitimate Interest Assessments. The GAID significantly improved the practical implementation of data protection obligations in Nigeria and consolidated various compliance requirements into a unified framework.

Yet, despite these achievements, the Directive remains largely anchored in a traditional understanding of data processing. It assumes a relatively linear lifecycle in which information is collected, processed for predetermined purposes, and eventually deleted. Artificial intelligence systems, however, do not operate within such straightforward parameters. They continuously evolve, learn from data, and generate new insights and inferences. Consequently, the current framework offers limited guidance on issues such as algorithmic accountability, automated decision-making, the secondary use of data for AI training, and the rights of individuals affected by algorithmic systems.

At the centre of this regulatory challenge lies the issue of data itself. Modern AI systems are powered by enormous quantities of information, much of which consists of behavioural data generated through people’s interactions with digital services. Every online search, click, purchase, location signal, and social media interaction contributes to a digital footprint that can be analysed and utilised to train increasingly sophisticated algorithms. Generative AI models, in particular, often rely on vast datasets compiled through the scraping of publicly available information and other digital content.

This practice raises significant legal questions under the NDPA. The Act requires that personal data be processed on the basis of a valid legal ground, including consent or legitimate interests. Yet obtaining informed and specific consent from millions of individuals whose information may be scraped for AI training purposes is practically impossible. Relying on legitimate interests presents its own difficulties, as it requires a balancing exercise between the commercial objectives of AI developers and the privacy rights of individuals. Whether the economic benefits of developing powerful AI systems can justify the extensive use of personal information without explicit consent is a question that regulators around the world are still grappling with.

The stakes become even higher when one considers the growing use of algorithmic profiling and automated decision-making systems. Artificial intelligence increasingly influences decisions in sectors such as finance, healthcare, insurance, education, and public administration. Credit scoring algorithms determine who qualifies for loans and at what interest rates. Recruitment software screens job applicants and identifies suitable candidates. Predictive analytics systems assist healthcare professionals in diagnosing diseases and recommending treatments.

While these technologies offer substantial efficiencies and benefits, they also carry significant risks. Many AI systems operate as “black boxes,” producing outcomes that are difficult for even their developers to explain fully. Individuals subjected to automated decisions often have little understanding of how those decisions were reached, what data was considered, or how they can challenge an adverse outcome. In the absence of adequate safeguards, algorithms can perpetuate historical biases, reinforce discriminatory practices, and create new forms of exclusion.

As Nigeria considers reforming its data protection framework, it has the advantage of learning from the experiences of other jurisdictions. The European Union has developed one of the world’s most sophisticated digital regulatory frameworks through instruments such as the Digital Services Act, the Digital Markets Act, and the Artificial Intelligence Act. These regulations impose obligations relating to algorithmic transparency, systemic risk assessments, and the protection of minors, while establishing heightened responsibilities for dominant digital platforms.

The United States has adopted a more fragmented approach, combining federal initiatives focused on national security and innovation with state-level legislation addressing issues such as algorithmic discrimination, consumer privacy, and child safety. Singapore, on the other hand, has pursued a pragmatic and innovation-friendly model centred on co-regulation, technology neutrality, and privacy-preserving solutions. Its approach to age assurance and digital safety offers particularly valuable lessons for jurisdictions seeking to balance child protection with privacy rights.

Certain sectors in Nigeria deserve special attention as the country revisits its data protection laws. The healthcare sector increasingly relies on AI-driven diagnostics, genomic analysis, and biometric technologies. These innovations have the potential to revolutionise healthcare delivery but also involve the processing of highly sensitive personal information. An amended legal framework should require enhanced oversight and rigorous audits for AI systems operating in the health sector, ensuring that algorithms are tested for bias and that training datasets accurately reflect the diversity of the Nigerian population.

The financial services sector presents another compelling case for reform. Nigeria’s thriving fintech industry relies heavily on automated systems for credit assessment, fraud detection, and behavioural profiling. While these technologies have expanded financial inclusion and improved service delivery, they also carry the risk of creating self-reinforcing cycles of exclusion if left unchecked. Individuals denied loans or subjected to adverse financial decisions by automated systems should have the right to receive meaningful explanations and to request human review of such decisions.

The protection of children in digital spaces has also emerged as a major policy concern. Educational technology platforms and social media services increasingly collect vast amounts of behavioural data from minors. Although the NDPA contains provisions relating to the processing of children’s data, the emergence of AI-driven platforms and conversational systems creates new challenges that require more targeted intervention. The law should provide stronger safeguards against behavioural profiling and targeted advertising directed at children while ensuring that privacy notices and disclosures are communicated in a manner that children can understand.

One of the most complex issues in this regard concerns age verification. Digital platforms need reliable methods to determine whether users are minors, particularly when providing access to potentially harmful or age-inappropriate content. Traditional approaches that require individuals to upload identity documents create significant privacy risks by encouraging the widespread collection and storage of sensitive information. The solution increasingly lies in privacy-enhancing technologies that can verify age without requiring individuals to surrender unnecessary personal data.

Technologies such as on-device age estimation, cryptographic zero-knowledge proofs, and adaptive verification systems offer promising alternatives. These approaches allow platforms to establish whether a user satisfies an age threshold without collecting excessive personal information or creating large repositories of sensitive identity data. They demonstrate that privacy and child safety need not be competing objectives but can instead be pursued simultaneously through thoughtful technological design.

Looking beyond immediate challenges, Nigeria’s lawmakers must also prepare for the emergence of Artificial General Intelligence and increasingly autonomous frontier systems. The country’s next generation of data protection laws must be sufficiently flexible and forward-looking to govern technologies that may not yet exist. This will require a shift from purely principles-based regulation towards more specific obligations relating to algorithmic accountability, transparency, and risk management.

Among the reforms deserving consideration are mandatory algorithmic impact assessments for high-risk AI systems, the establishment of a specialised registry for frontier AI models, and the expansion of the existing Data Protection Compliance Organisation framework to include algorithmic and AI safety auditing. Nigeria should also consider developing a sovereign training data commons that would provide legally compliant and culturally representative datasets to support the development of indigenous AI systems and reduce reliance on foreign data sources.

The proposed review of the Nigeria Data Protection Act represents a defining moment in the country’s digital evolution. It offers an opportunity to create one of Africa’s most forward-looking and technologically sophisticated regulatory frameworks, one capable of protecting fundamental rights while simultaneously encouraging innovation and economic growth.

Artificial intelligence is transforming societies at an unprecedented pace. The laws that govern data and digital rights must evolve just as quickly. The challenge for Nigeria is not simply to regulate today’s technologies but to build a legal framework capable of adapting to tomorrow’s innovations. If approached thoughtfully, the forthcoming reforms could position Nigeria as a continental leader in digital governance and demonstrate that technological advancement and the protection of human rights are not competing priorities but mutually reinforcing objectives in the age of algorithms and Artificial General Intelligence.