Artificial Intelligence

Introduction: The Ambition at the Crossroads

Mexico currently faces an unparalleled economic juncture. Global geopolitical dynamics, driven by nearshoring and the imperative to diversify supply chains, have positioned the country for a development opportunity that far exceeds simple assembly manufacturing. The potential to build high-value ecosystems in artificial intelligence (AI) and semiconductor fabrication—the foundational pillars of the modern global economy—could fundamentally redefine Mexico’s standing in international trade.

But, this critical ambition is currently being stalled by a single, deeply rooted structural factor in the national infrastructure: the capacity, quality, and, above all, the reliability of the National Transmission Grid (RNT) operated by the Federal Electricity Commission (CFE). The power grid, therefore, is not merely an operational prerequisite; it has transformed into the primary strategic constraint jeopardizing Mexico’s technological sovereignty and its potential qualitative economic leap.

I. The Tensions of Demand: World-Class Requirements

The AI and semiconductor fabrication (FAB) industries impose energy demands that Mexico’s legacy infrastructure is struggling to meet. These sectors not only consume power on a massive scale but also require it with a precision and resilience that approaches technical perfection.

A. The Exponentials of AI and Data Centers

The core engine of AI is the data center. These facilities, especially those dedicated to training massive models using Graphics Processing Units (GPUs), require a constant power flow comparable to that of entire cities. Large hyperscale data centers can demand between 100 MW and 300 MW of installed capacity, and the aggregate demand from this sector in Mexico is projected to multiply tenfold in the near future.

This demand possesses one non-negotiable quality: 24/7 availability. AI operations cannot tolerate interruptions. A micro-power cut is more than just an economic loss; it represents the possibility of compromising the integrity of critical data or nullifying the progress of computation processes that have required weeks of execution—an unviable vulnerability for the industry.

B. The Precision Mandate of Semiconductors

Semiconductor manufacturing plants are arguably the industrial environments most sensitive to power quality. In the fabrication of microchips, where tolerances are measured in nanometers, a micro-unit of voltage fluctuation or an interruption lasting mere milliseconds can prove catastrophic. Such an event can instantaneously ruin entire batches of silicon wafers valued in the millions of dollars.

Therefore, the key to attracting advanced semiconductor fabrication facilities (FABs, typically requiring between 50 MW and 150 MW each) does not lie solely in guaranteeing the volume of energy but in certifying a power quality that the CFE, given constraints in transmission and distribution, struggles to consistently assure within the most desirable industrial hubs. The promise of availability must, by necessity, be a world-class guarantee.

II. The CFE Infrastructure: From Support to Barrier

The National Electric System (SEN) operates under a structural pressure that positions it as the decisive bottleneck. This barrier manifests across three critical dimensions that undermine the confidence of high-technology investors.

A. Saturation of Transmission and Distribution

Mexico’s fundamental problem is not a lack of total generation capacity but the systemic inability to move that power efficiently, a responsibility that falls squarely on the RNT. This infrastructure, much of which is aging or designed for industrial patterns of a past century, has simply failed to evolve at the pace required by nearshoring.

The consequence is severe congestion in substations and distribution lines, particularly in the vital industrial corridors of the north and center (such as Nuevo León, Coahuila, and the Bajío region). This congestion translates into something tangible and costly: industrial park developers face wait times exceeding a year just to obtain connection feasibility. This delay has led to a troubling phenomenon: the proliferation of “Dark Buildings”—industrial warehouses completely finished and ready for operation but lacking physical access to electrical power.

B. Reliability, Risk, and the Unacceptable Interruption

Recent waves of blackouts and recurrent service interruptions demonstrate that the SEN is consistently operating at its operational limit. Obsolescence in the generation fleet and deficiencies in transmission elevate the risk of system failures.

For any corporation managing mission-critical computing processes or high-value production lines like FABs, this level of risk is unacceptable. A multi-billion-dollar investment cannot depend on a grid that offers systemic uncertainty. Compounding this is regulatory volatility, where the perceived prioritization of fossil fuel generation over renewable energy dissuades global investors who seek clarity, stable long-term pricing, and a predictable framework for operation.

C. The Sustainability Imperative (ESG Factor)

Leaders in the technology industry (from Google and Amazon to major chip manufacturers) have adopted rigorous corporate commitments regarding sustainability and governance (ESG), including net-zero carbon goals or the use of 100% clean energy.

To establish AI or semiconductor operations in Mexico, these investors require contractual guarantees that a substantial portion of their consumption will be sourced from renewables. The difficulty imposed on the interconnection of private wind or solar energy projects to the RNT, coupled with the CFE’s reliance on generation based on natural gas and fuel oil, creates a sustainability impediment that automatically excludes Mexico from the list of viable destinations for many of these investments.

III. The Strategic Cost: Sovereignty and Dependency

If the electric infrastructure issue is not addressed with a decisive, long-term state vision, the cost to Mexico will be dual and profound:

Firstly, it will result in the loss of the value-added nearshoring opportunity. High-demand and high-precision firms will simply divert their investments to markets that offer solid power grids and transparent regulatory frameworks, such as the United States (driven by the CHIPS Act) or established Asian ecosystems.

Secondly, it will perpetuate technological dependence. Without the necessary energy infrastructure to host, power, and train large-scale AI models, and without the capacity to manufacture advanced components, Mexico will be relegated to being merely a consumer and assembler of technologies designed and produced elsewhere. This outcome has a direct, negative impact on national technological sovereignty and the capacity of Mexican research centers to compete at the global frontier of knowledge.

Conclusion: From Bottleneck to Catalyst

The CFE grid represents the single most fundamental challenge to Mexico’s digital ascension. While recent investments in transmission grid modernization signal a positive step, the sheer scale of the challenge necessitates a true paradigm shift that transcends institutional inertia.

To transform this bottleneck into a powerful catalyst, Mexico must execute a strategic course of action centered on efficiency and openness:

Agile Regulatory Reform: It is imperative to simplify procedures and drastically reduce the timelines for connection and feasibility studies for high-demand industrial projects.

Focalized Transmission Investment: The reinforcement of the RNT must be specifically prioritized in the industrial corridors that are the heart of nearshoring and the potential base for technological ecosystems.

Facilitating Clean Energy Integration: Creating mechanisms that not only permit but actively promote the interconnection of private renewable energy projects to meet the ESG demand and the volume required by technological leaders.

Deployment of Smart Grids: The massive adoption of AI-based technologies for distribution optimization, loss reduction, and ensuring resilient voltage quality is essential for the mission-critical needs of the AI and semiconductor industries.

Mexico’s technological future hinges upon the resolution of the CFE dilemma. It is the key that, when turned, will either open or definitively close the door to high-technology development.

US and China have long competed to become world powers, particularly in the technology sector. Since 2022, the US has systematically restricted the supply of high-performance NVIDIA chips to China. In today’s world, competition for power is no longer achieved through traditional means, such as military power. The US uses chips (semiconductors) as an instrument of political pressure. This policy is not just about economic or trade value, but has become part of technological statecraft designed to counter China’s military potential and its use of Artificial Intelligence (AI).

Semiconductors as a Provision of Power

The US policy of restricting high-end semiconductors to China shows a paradigm shift, chips (semiconductors) are not only industrial commodities, but have shifted to become a tool for achieving power. Export controls on high-performance chips and components that enable their production have been implemented by the Bureau of Industry and Security (BIS). These steps show that the US is restructuring the geopolitical arena of technology.

AI today relies heavily on chips that can process vast amounts of data. The US restricts the export of high-end chips, such as the NVIDIA H100 and A100. A country’s AI development capacity could be severely compromised without access to these chips. The H100 is more than just a technological component; it serves as a strategic enabler that determines a country’s ability to maintain military dominance.

NVIDIA and the Security Logic Behind Export Control

The Bureau of Industry and Security (BIS) on 2023 announcement expanded export oversight, not only targeting on specific chip models but also on component values, most notably in frontier algorithm development. The NVIDIA A100 and H100 are highly advanced datacenter and AI chips. The guidelines are particularly high for training complex AI models on supercomputers, even for military applications or demanding research.

To prevent misuse, the US government has implemented licensing requirements for chips like the A100 and H100 chips, which have put chips like the A300 and H800, made by NVIDIA, under increased scrutiny, despite being categorized as “weak service” chips. Export restrictions stem from concerns that NVIDIA GPUs could be used by China in training AI models related to the US military, not only to slow China’s technological progress but also to safeguard its own national interests.

The US understands very well that high-performance chips are “brain machines” that can accelerate the development of military superiority, intelligence analysis, and even autonomous systems. So it is very clear that limiting the capacity of computing and high-performance hardware is the way to go. To delay a rival’s capabilities without resorting to direct military confrontation. This is a concrete manifestation of the shift in the “battlefield” taking place in the technological and regulatory arenas.

Vulnerable Supply Chains and Dependence on Taiwan

In chip control, the US must recognize that there are undeniable realities. NVIDIA’s chip production goes through a fabrication process that is almost entirely carried out in Taiwan, a country that lies in the geopolitical conflict between Washington and Beijing. The Congressional Research Service (2024) shows that approximately 90% of global advanced semiconductor chip production is based in Taiwan, manufactured by the leading Taiwanese foundry, Taiwan Semiconductor Manufacturing Company Ltd. (TSMC). This creates a structural dependency that poses serious risks to US economic and technological security.

If semiconductor production were concentrated in a single region, it would create vulnerabilities that could destabilize the global technological system. Therefore, any tensions in the Taiwan Strait would disrupt US access to the computing infrastructure it maintains. Export restrictions are just one step in a much more complex strategy, requiring the US to diversify production locations and ensure that the chip supply chain is not concentrated in a single region.

Effectiveness and Adaptation Room for China

NVIDIA’s chip restrictions were intended to curb the pace of AI modernization in China, but China was still able to optimize the model’s efficiency. This demonstrates that limiting hardware performance doesn’t always equate to limiting innovation. On the other hand, unofficial market entities have emerged, allowing NVIDIA GPUs to remain accessible through third parties. This adaptation demonstrates that hardware control has limitations, especially when demand remains high and global distribution networks are not always transparent.

Looking at its overall effectiveness, US policy has been effective in slowing China’s computing capabilities, but it hasn’t stopped its strategic potential. Instead, it’s encouraging China to be self-sufficient in strengthening its technological foundation, even though the quality of local chips hasn’t yet matched NVIDA’s standards. In other words, restricting NVIDIA’s chip exports isn’t meant to end competition, but rather to transform it into a race toward technological independence. The policy’s effectiveness will only last as long as China finds a way to adapt, while China is working to fill that gap.

Policy Directions with Greater Strategic Opportunities

The effectiveness of the compute policy is based on a governance architecture that holds every allied country accountable to the same standards. Without a disciplined framework, export controls on China are merely an illusion that is easily penetrated by gaps in different economic and regulatory interests. By creating strategic alignment, which forces every democratic country to reduce the fragmentation of interests, it can open up greater policy opportunities to emerge. Many developing countries see this semiconductor race as a competition for dominance, not as an effort to maintain security.

In other words, a successful computing policy is not one that simply limits China’s space, but one that manages technological gaps without creating competing computing blocs. The geopolitical challenge is maintaining superiority without forcing the world into two technological divides that would be difficult to control. The US strategy to secure a leading position in future technologies requires flexibility in responding to global dynamics.

A Future Determined by Computational Capacity

The debate over NVIDIA chips demonstrates the growing integration of political and technological power. US policy aims not only to restrain the flow of strategic goods but also to build a new computing-based power architecture. However, this policy also presents challenges, including dependence on Taiwan, China’s flexibility, and economic pressure on US chip companies.

In a global world that continues to move toward an AI-driven economy, the future will be determined by who can manage geopolitical risks, understand supply chain dynamics, and design visionary policies. Ultimately, GPU regulation is no longer simply a matter of export control; it demonstrates how countries navigate a power struggle now measured in microchips.

Russia is framing artificial intelligence as a geopolitical technology on par with nuclear weapons, with Sberbank First Deputy CEO Alexander Vedyakhin warning that only nations capable of building their own large language models will hold real influence in the 21st century. Speaking at Moscow’s flagship AI Journey event, Vedyakhin said Russia considers it a strategic achievement to be among the few countries with home-grown AI and insists the state must rely exclusively on domestic models for sensitive sectors like public services, healthcare, and education. His comments echo President Vladimir Putin’s recent remarks that indigenous AI is essential for Russian sovereignty. While Sberbank and Yandex lead Russia’s push to compete with U.S. and Chinese AI giants, sanctions and limited computing power continue to restrain Moscow’s capability.

Why It Matters

Russia’s framing of AI as a sovereignty-defining technology signals a hardening global divide in the race for digital power. By likening AI to nuclear capability, Moscow is underscoring the strategic leverage it believes advanced models can confer over national security, economic competitiveness, and societal infrastructure. For Western policymakers, the statement highlights how AI is increasingly entwined with geopolitical rivalry, sanctions regimes, and technological self-reliance. For markets, the message is more nuanced: despite the rhetoric, Russia admits it cannot match global leaders in compute or scale, and it warns investors that AI infrastructure spending may not repay itself quickly, raising questions about the economic viability of high-intensity AI development.

Russia’s state institutions, security apparatus, and public-service sectors are central consumers of domestic AI models as Moscow seeks digital autonomy. Sberbank and Yandex are the primary corporate developers, tasked with building national-scale models under sanctions constraints. Western governments and AI firms remain part of the geopolitical backdrop, as Russia’s push for self-sufficiency follows restricted access to advanced chips and cloud hardware. Russian businesses, from healthcare to education providers, will increasingly rely on domestic AI systems while international partners watch how far Russia can expand its capabilities without global supply chains.

What’s Next

Russia aims to expand from one or two national AI systems to several independent models, but its development will remain limited by restricted access to high-performance computing. Moscow will continue steering AI regulation toward data sovereignty, banning foreign models from handling state or sensitive information. As Russia ramps its rhetoric around AI power, expect greater global pressure for technological blocs, digital “non-alignment,” and AI export controls. Meanwhile, the Kremlin’s caution about an “AI bubble” hints that its investments will be narrower and more state-directed than those in the U.S. or China, potentially slowing innovation but avoiding the risk of overextension.

With information from Reuters.

The impact of Machine Learning (ML) on the global Foreign Exchange (Forex) is growing day by day. This results in a profound transformation of the algorithmic landscape, leading to a decrease in the dominance of human intuition, quantitative models, and macroeconomic analysis. These changes impact the growth of market efficiency, shift risk management patterns, and affect the very nature of global currency flow.

The ML-Driven Revolution in Forex Trading

Machine Learning, as an essential subset of Artificial Intelligence (AI), helps computer systems learn from extended datasets, identify sophisticated models, and make predictions without any pre-programmed patterns. Human traders simply cannot match the edge ML provides because the very environment of the currency market is getting faster and more data-rich.

Enhancing Predictive Analysis

ML models process vast volumes of market data pretty successfully. Their performance ranges from simple tick-by-tick price movements and trading volumes to social media responses and global news feeds. That is why forecasting with unprecedented accuracy becomes a reality. All this deals with the following:

1. Real-time data synthesis. The algorithms analyze time-series data, learn from historical market volatility, and immediately adapt to new information. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) are especially efficient for that.
2. Sentiment analysis. ML systems use Natural Language Processing (NLP). That allows them to scan thousands of new articles, economic reports, and political statements. Therefore, they never miss leading indicators based on market sentiment towards a specific currency.
3. Pattern recognition. ML can detect and observe subtle, non-linear relationships between disparate currency pairs and timeframes. In that way, they analyze all possible opportunities. 

Automation and Execution Speed

The most obvious impact of ML is observed in spreading algorithmic trading. Trades executed by automated systems are based on ML-driven insights. So, they are speedy, precise, and independent of human emotional bias.

Such automation is clearly observed in Expert Advisors (EAs), or trading bots. They can operate autonomously on various platforms, for example, on MetaTrader. The industry needs and is continuously introducing new top-rated Forex EAs. Their algorithms have already demonstrated perfect performance and resilience. These ML-powered EAs can manage such strategies as:

• High-Frequency Trading (HFT), processing thousands of trades per second;
• Adaptable trend following, used for adjusting stop-loss and take-profit levels to the shifts in real-time market schedules;
• Risk mitigation strategies, implemented through changes in hedging positions and reducing leverage according to predicted spikes in volatility.

Strategic Implications for Global Finance

ML integration into the Forex environment has far-reaching consequences. It affects international capital flows and requires enhanced risk management for financial institutions and states.

Redefining Currency Risk Management

ML provides high-quality tools for hedging and managing currency exposure. It is vital for multinational corporations and central banks. The significantly improved forecasting accuracy is crucial for optimizing forward contract planning and international payment strategies.

ML models can ensure dynamic hedging by continuous reassessment of risk-return profiles. They are capable of recommending dynamic adjustments to hedging ratios due to changing geopolitical or economic situations.

Moreover, advanced AI models can detect unusual trading patterns. That can diminish market abuse, like front-running or spoofing, much faster than conventional surveillance systems can. So, market integrity becomes better managed and more sustainable.

Geopolitical and Regulatory Challenges

So, we have examined the obvious benefits of the strategic deployment of ML. However, what about drawbacks? There are certain challenges here that require regulatory foresight and diplomatic engagement. They involve the following:

1. Algorithmic bias. An ML model may be trained on biased or incomplete historical data. That can cause systemic flaws and market instabilities, especially during unforeseen global events.
2. Concentration of power. Large hedge funds and financial institutions can concentrate large power in their hands. That may happen because the resources needed to develop, deploy, and maintain advanced ML infrastructure are hardly available beyond their authority. The need for specialized hardware and proprietary datasets may result in a systemic risk to market decentralization.
3. Need for explainability. All regulators require transparency. Complex neural networks cannot provide that due to their ‘black box’ nature. It creates a compliance hurdle that must be overcome with the help of explainable AI (XAI) frameworks.

Conclusion: A New Era of Algorithmic Diplomacy

We need to understand and accept that Machine Learning is not an additional helping tool but a superior new operating system for global currency exchange. It strategically impacts everything related to international trading. Its ability to extract the most actionable intelligence from gigantic data volumes can result in hyper-efficient, instantaneous, and emotionless trade operations.

The rise of complex algorithmic systems, including top-rated Forex EAs, requires a new form of ‘algorithmic diplomacy.’ That is why global financial institutions and regulators must keep in touch, and their collaboration should be aimed at ethical frameworks and technical standards development. That can help them enhance the stability, transparency, and fairness of the international trading market for the benefit of the entire global economy.

Published on
20/11/2025 – 7:32 GMT+1

Shares in Nvidia rose more than 5% in after-hours trading after the chipmaker beat analysts’ expectations in its quarterly earnings report, released Wednesday.

In the three months to the end of October, Nvidia said its revenue jumped 62% to $57 billion (€49.49bn). The company reported $51.2bn (€44.43bn) in revenue from data-centre sales, beating expectations of $49bn (€42.52bn).

The firm also placed a forecast for the current quarter at $65bn (€56.41bn), surpassing Wall Street expectations of $61bn (€52.94bn).

“There’s been a lot of talk about an AI bubble,” said CEO Jensen Huang during an earnings call.

“From our vantage point, we see something very different. As a reminder, Nvidia is unlike any other accelerator. We excel at every phase of AI from pre-training to post-training to inference.”

Nvidia is now the largest stock on Wall Street, having momentarily surpassed $5 trillion in value. That means it has an outsized influence on the S&P 500 and can make or break the market’s daily performance.

The firm has also become a bellwether for the broader frenzy around AI, notably because other companies rely on Nvidia chips for this technology.

AI stocks have taken a hit in recent weeks as investors questioned whether certain tech companies had been overvalued, driving fears of a market crash.

Before Wednesday’s earnings report, Nvidia’s chips had dropped 11% from their peak in early November.

CEO Huang sought to ease concerns of a bubble on Wednesday, claiming: “AI is going everywhere, doing everything, all at once.” He noted that Nvidia was focused on major transition areas, namely generative, agentic, and physical AI.

Generative AI can create things, agentic can accomplish a specific goal with limited supervision, while physical AI relates to the physical world — for example through robots.

By&nbspEuronews

Published on
19/11/2025 – 12:14 GMT+1

European stocks showed mixed signals on Wednesday, somewhat easing fears of a global market crash.

At around midday, Germany’s DAX was up less than 1%, while the UK’s FTSE 100 and Spain’s IBEX 35 also saw modest lifts.

Italy’s FTSE MIB dropped less than 1%, as did France’s CAC 40.

Both the STOXX 50 and the wider STOXX 600 showed minimal movement.

Investors kept an eye on data releases on Wednesday, with UK inflation easing to 3.6% in October, down from 3.8% in July, August, and September.

The annual inflation rate in the eurozone, meanwhile, came in at 2.1% in October, a confirmation of a preliminary reading. That’s down from 2.2% in September.

“Investors will breathe a sigh of relief that the market sell-off has lost momentum,” said Russ Mould, investment director at AJ Bell.

“It’s the good news everyone wanted. The key question is whether this is simply the calm before the storm.”

In Asian trading on Wednesday, markets were broadly in the red.

Japan’s Nikkei 225 fell 0.34%, Hong Kong’s Hang Seng was down 0.38%, South Korea’s Kospi slid 0.61%, while Australia’s S&P/ASX 200 slid 0.25%. China’s SSE Composite rose 0.18%.

After a day of losses on Tuesday, Wall Street showed signs of optimism on Wednesday.

Ahead of the opening bell, S&P 500 futures were up 0.30%, while Dow Jones futures increased 0.12%. Nasdaq futures were trading 0.37% higher.

Investors around the world are awaiting third-quarter results from chipmaker Nvidia, set for release later on Wednesday.

Nvidia’s performance matters disproportionately because its immense size means it’s the most influential stock on Wall Street. Its financial report will also influence the narrative around an AI bubble and fears that tech stocks may be overvalued.

“Nvidia reports tonight and the slightest bit of news to disappoint investors has the potential to whip up a tornado across global markets,” said Mould.

“Investors will be hanging on Jensen Huang’s every word and looking for clues that big investment in AI is worth it.”

The Old Global Arrangement Is Breaking Apart

For decades the world economy rested on a clear arrangement. Wealthy nations consumed, financed innovation and set global standards. Developing nations supplied affordable labour, delivered production capacity and powered the rise of outsourcing. This structure created jobs, raised incomes and guided national strategies across Asia, Africa and Latin America.

That system is now weakening. Wage gaps that once justified outsourcing are closing rapidly. Factory wages in China have more than doubled over the past decade. Salaries in Vietnam, Bangladesh, Mexico and Eastern Europe have risen as these economies matured. Service wages in the Philippines and several African nations have also increased enough to erode the advantage that global firms once assumed was permanent. The global labour discount is disappearing and honestly the logic of offshoring is losing strength faster than many expected.

The New Producer Is Not a Country

Artificial intelligence is accelerating this shift. AI systems now complete tasks that once required large numbers of workers in the Global South. Customer support, document processing, routine software maintenance, claims handling, financial verification and data entry are already moving to automated systems that operate at scale with high accuracy and very low marginal cost.

This is not simply a productivity gain. It represents a substitution of labour itself. The International Monetary Fund estimates that about forty percent of global jobs contain tasks that can be automated. Surveys show that nearly thirty percent of companies plan to replace entire categories of work with AI within a year. These numbers are not abstract. They reflect changes that are already underway inside Western corporations, and many leaders barely talk about it publicly yet.

The Global North is becoming a producer again, but the production now happens through models rather than offshore workers. When a system can perform a task at a fraction of the cost of a remote employee and without coordination risk or geopolitical uncertainty, outsourcing collapses quickly and sometimes silently.

A New Global Divide Is Emerging

The world once divided neatly into high income consumers and low income producers. That divide is being replaced by a new line of separation. The decisive factor now is control over compute infrastructure and ownership of data and advanced models.

Compute is becoming the new labour force. Data is becoming the new export commodity. Intellectual property is becoming the new foundation of national power.

Research shows that developing countries face the highest automation exposure because they supply the kind of predictable and repetitive work that AI can absorb easily. Scholars describe this as a dual vulnerability because these nations depend heavily on sectors with high substitution risk while lacking the resources to adopt advanced technology at an equal pace. The risk is clear but the response has been slow.

The Global South Faces a Narrow Window

The consequences are immediate. The Philippines depends heavily on outsourced services. Bangladesh and Vietnam rely on labour intensive manufacturing. Kenya, Rwanda and several West African nations have built emerging digital service sectors under the assumption that global firms would continue sending work for decades.

An African regional analysis warns that up to forty percent of tasks in outsourcing roles could be automated by the year twenty thirty, with women and low income workers facing the highest risk. If Western companies reduce labour demand sharply, millions of workers across the Global South will face disrupted futures at the same moment and many governments are not prepared for that scale of change.

What The Global South Can Still Do

AI does not remove opportunity. It moves opportunity. Developing nations can remain competitive if they shift quickly.

They can strengthen their position in rare earth minerals and strategic metals that power batteries, servers and large data centres. By building refining and processing capacity instead of exporting raw ore they can capture higher value in the AI supply chain. They can also use their geography to become low cost energy hubs that attract global compute infrastructure, something that is slowly becoming a huge competitive advantage.

Nations can treat local data as a strategic national asset. Agricultural data, healthcare records and cultural archives can be structured into national datasets that foreign firms must license. This turns data into a renewable export product and helps retain control over how information is used.

They can also specialise in scientific and technical niches where talent matters more than capital, such as precision agriculture, advanced materials or climate analytics. Countries do not need to dominate entire industries. They just need one area that the world depends on.

Finally they must adopt AI internally to raise productivity. Early adoption helps nations move workers into higher skill roles before the full force of automation arrives, and without waiting for external pressure.

Reinvention Is the Only Path Forward

Competing on price alone is now impossible. Humans cannot become cheaper than algorithms that operate at almost zero cost. Developing nations must move beyond labour based strategies. They must build value in areas that reward expertise, judgement, culture and creativity. They must invest in local compute, protect intellectual property and build their own data resources.

The choice is not between the old model and the new model. The old model is ending on its own. The only choice is what must replace it, and that decision cannot be delayed much longer.

A New Chapter in Globalisation

Globalisation is not disappearing. It is shifting into a new form. The earlier version relied on inexpensive labour in developing nations. The new version relies on intelligent systems concentrated in wealthier nations. The global consumer now has a new producer that is faster, cheaper and infinitely scalable.

Countries that once supplied the workforce must now decide whether they will redefine their place in the global economy or allow their relevance to decline. Some countries may adapt. Many might not.

A new chapter has begun. The nations that understand this shift will shape their future. The nations that do not will be written out of the story far quicker than they realise.

BRITS returning home will face shorter wait times at passport control after a facial recognition trial passed with flying colours.

Border Force conducted a successful trial of the technology that would allow for contactless passage when arriving back at UK airports.

The trial run was held at Manchester Airport in October which would replace the traditional passport checks with facial recognition technology.

Border Force boss, Phil Douglas, said the trial in Manchester “considerably reduced” waiting times.

“So people approach the e-gate, it recognizes them [as] already on our database, and they’re checked through,” Douglas told The Times.

The facial recognition was fitted into existing passport e-gates and reduced waiting time as passengers no longer had to scan their passports.

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“The border has really changed over the last few years and that work is picking up pace. Public expectations have changed and technology has changed,” Douglas added.

“We now have AI facial recognition, the use of biometric identifiers in parallel with the more traditional forms of identification, like visas and passports.”

Douglas explained that Border Force wanted to make use of the existing 270 e-gates at airports and ports around the country by fitting them with the new technology.

 “It’s our intention that almost everybody will go through an e-gate of one description or another,” Douglas said.

“The Manchester pilot has shown that we can actually reduce transaction times considerably as well.”

He did warn that while this was a huge leap in technological advancement and would reduce waiting times, there was “something important about the ‘theatre’ of the border.”

Douglas said passengers should still expect to feel a sense of a border and scrutiny when entering the UK and when “they’re stopped it’s a moment they know they’re being checked.”

The UK is not the only country to introduce facial recognition technology at airports with the United Arab Emirates allowing passengers from 50 countries to enter using it.

Australia and the US were also considering trialling the software.

The Sun contacted Border Force for comment.

Facial recognition technology was also being considered at ports which would remove the need to even step out of your car to go through passport control.

The technology will be used at ports to match their faces with passport and car details already logged in government databases.

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The cameras, which are being trialled at four ports since November 2024 – are designed to cut queues that build up during busy holiday periods.

Only “passengers of interest” highlighted as a risk because of intelligence, safeguarding concerns or questions over their identity will have to undergo manual checks by a Border Force officer on arrival.

An estimated half a trillion dollars was wiped out from the financial markets this week, as some of the biggest tech companies, including Nvidia, Microsoft, and Palantir Technologies saw a temporary but sizeable drop in their share prices on Tuesday. It may have been just a short-lived correction, but experts warn of mounting signs of a financial market crash, which could cost several times this amount.

With dependence on tech and AI growing, critics argue that betting on these profits is a gamble, stressing that the future remains uncertain.

Singapore’s central bank joined a global chorus of warnings from the IMF, Fed Chair Jerome Powell, and Andrew Bailey about overvalued stocks.

The Monetary Authority of Singapore said on Wednesday that such a trend is fuelled by “optimism in AI’s ability to generate sufficient future returns”, which could trigger sharp corrections in the broader stock market.

Goldman Sachs and Morgan Stanley predict a 10–20% decline in equities over the next one to two years, their CEOs told the Global Financial Leaders’ Investment Summit in Hong Kong, CNBC reported.

Experts interviewed by Euronews Business also agree that a sizeable correction could be on the way.

In a worst-case scenario, a market crash could wipe out trillions of dollars from the financial markets.

According to Mathieu Savary, chief European strategist at BCA Research, Big Tech companies, including Nvidia and Alphabet, would cause a $4.4 trillion (€3.8tn) market wipeout if they were to lose just 20% of their stock value.

“If they go down 50%, you’re talking about an $11tr (€9.6tr) haircut,” he said.

AI rally: Bubble or boom?

The US stock market has defied expectations this year. The S&P 500 is up nearly 20% over the past 12 months, despite geopolitical tensions and global trade uncertainty driven by Washington’s tariff policies. Gains have been strongest in tech, buoyed by optimism over future AI profits.

While Big Tech continues to deliver, with multibillion-dollar AI investments and massive infrastructure buildouts now routine, concerns are growing over a slowing US economy, compounded by limited data during the government shutdown. Once fresh figures emerge, they could rattle investors.

AI enthusiasm is most evident in Nvidia’s extraordinary stock gains and soaring valuation. The company is central to the tech revolution as its graphics processing units (GPUs) are essential for AI computing.

Nvidia’s shares have surged over 3,000% since early 2020, recently making it the world’s most valuable public company. Between July and October alone, it gained $1tr (€870bn) in market capitalisation — roughly equal to Switzerland’s annual GDP. Its stock trades at around 45 times projected earnings for the current fiscal year.

Derren Nathan, head of equity research at Hargreaves Lansdown, said: “Much of this growth is backed by real financial progress, and despite the massive nominal increase in value, relative valuations don’t look overstretched.”

Analysts debate whether the current market mirrors the dot-com bubble of 2000. Nathan notes that many tech companies that failed back then never reached profitability, unlike today’s giants, which generate strong revenues and profits, with robust demand for their products.

Ben Barringer, global head of technology research at Quilter Cheviot, added: “With governments investing heavily in AI infrastructure and rate cuts likely on the horizon, the sector has solid foundations. It is an expensive market, but not necessarily a screaming bubble. Momentum is hard to sustain, and not every company will thrive.”

BCA Research sees a bubble forming, though not set to burst immediately. Chief European strategist Mathieu Savary said such bubbles historically peak when firms begin relying on external financing for large projects.

Investments in assets for future growth, or capital expenditures, as a share of operating cash flow, have jumped from 35% to 70% for hyperscalers, according to Savary. Hyperscalers are tech firms such as Microsoft, Google, and Meta that run massive cloud computing networks.

“The share of operating earnings is likely to move above 100% before we hit the peak,” Savary added. This means that they may soon be investing more than they earn from operations.

Recent examples of Big Tech firms turning to external financing for such moves include Meta’s Hyperion project with Blue Owl Capital and Alphabet’s €3 billion bond issue for AI and cloud expansion.

While AI investment growth is hard to sustain, Quilter’s Barringer told Euronews: “If CapEx starts to moderate later this year, markets may start to get nervous.”

Other factors to watch include return on invested capital and rising yields and inflation pressures, which could signal a higher cost of capital and a bubble approaching its end.

“But we’re not there yet,” said Savary.

Further concerns and how to hedge against market turbulence

Even as tech companies ride the AI wave, inflated expectations for future profits may prove difficult to meet.

“The sceptics’ main problem may not be with AI’s potential itself, but with the valuations investors are paying for that potential and the speed at which they expect it to materialise,” said AJ Bell investment director Russ Mould.

A recent report by BCA reflects the mounting reasons to question the AI narrative, but the technology “remains a potent force”, said the group.

If investor optimism does slow, “a sharp correction in tech could still have ripple effects across broader markets, given the sector’s dominant weight in global indices,” Barringer said. He added that other regions and asset classes, such as bonds and commodities, would be less directly affected and could provide an important balance during a downturn.

According to Emma Wall, chief investment strategist at Hargreaves Lansdown, “investors should use this opportunity to crystallise impressive gains and diversify their portfolios to include a range of sectors, geographies and asset classes — adding resilience to portfolios. The gold price tipping up is screaming a warning again — a siren that this rally will not last.”

Authors: Christopher Jackson and Aaron Spitler*

Digital technologies, particularly AI, are accelerating democratic backsliding and revitalizing authoritarian governments. AI-focused companies have been forming close partnerships with government actors, often in ways that undermine democratic norms. Around the world, private firms are supplying or co-designing technologies that enhance mass surveillance, predictive policing, propaganda campaigns, and online censorship. In places like China, Russia, and Egypt, a blurring of boundaries between the state and the technology industry has led to serious consequences. This collusion has undercut privacy rights, stifled civil society, and diminished public accountability.

This dynamic is now playing out in the United States. Companies like Palantir and Paragon Solutions are providing government entities with powerful AI tools and analytics platforms, often under opaque contracts. In September, U.S. President Donald Trump approved the sale of TikTok to U.S. private entities friendly with the administration. Unchecked public-private integration within the technology industry poses serious risks for democratic societies, namely that it offers increased power to unaccountable actors. The focus of this article is to examine case studies on how these emerging alliances are enabling authoritarian practices, as well as what they might mean for the future of democratic societies.

Russia: Manipulating Digital Tools

In Russia, democratic norms under Vladimir Putin have eroded while Russian tech companies continue to work hand in glove with state authorities. Sberbank, the country’s largest financial institution, and their development of Kandinsky 2.1, an AI-powered, text-to-image tool owned by the firm, illustrate this long-running trend.

Despite the quality of its outputs compared to rivals like DALL-E, the solution came under fire in 2023 from veteran lawmaker Sergey Mironov, who argued that it generated images that tarnished Russia’s image. He would go on to charge that Kandinsky 2.1 was designed by “unfriendly states waging an informational and mental war” against the country.

Not long after, some in the tech space noticed that Kandinsky 2.1’s outputs changed. For instance, while the tool previously churned out images of zombies when prompted with “Z Patriot,” users noted that it now repeatedly produced pictures of hyper-masculine figures. Critics claim that this alteration not only represented an overt manipulation of the technology itself but also an attempt to curry favor with those in the government.

This episode shows how AI-powered tools are being specifically tailored to serve the needs of authorities. The modifications made to the model transformed it into an invaluable resource the government could use to amplify its messaging. As a result, users are no longer likely to see Kandinsky 2.1 as a tool for creativity, particularly if its outputs remain blatantly skewed. Developers in countries like Russia may look to this case for inspiration on how to succeed in restrictive political contexts.