Argentina is set to become the host of Latin America’s first Stargate project, a major AI infrastructure initiative powered by clean energy. Led by Sur Energy with OpenAI, the plan aims to make Argentina a regional and global AI leader while boosting economic growth.
OpenAI and Sur Energy have signed a Letter of Intent to explore building a large-scale data centre in Argentina. Sur Energy will lead the consortium responsible for developing the project, ensuring that the ecosystem is powered by secure, efficient, and sustainable energy sources.
OpenAI is expected to be a key offtaker for the facility.
The project follows high-level talks in Buenos Aires between President Javier Milei, government ministers, and an OpenAI delegation led by Chris Lehane. With AI use tripling and millions using ChatGPT, Argentina ranks among Latin America’s top AI developers, making it an ideal choice for the project.
As part of OpenAI’s OpenAI for Countries initiative, discussions are underway to integrate AI tools into government operations. CEO Sam Altman said the project represents ‘more than just infrastructure’ and will help make Argentina an AI hub for Latin America.
Sur Energy’s Emiliano Kargieman called it a historic opportunity that combines renewable energy with digital innovation to create jobs and attract global investment.
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Oracle and NVIDIA have joined forces to advance sovereign AI, supporting Abu Dhabi’s vision of becoming an AI-native government by 2027.
The partnership combines the computing platforms of NVIDIA with Oracle Cloud Infrastructure to create secure, high-performance systems that deliver next-generation citizen services, including multilingual AI assistants, automatic notifications, and intelligent compliance solutions.
The Government Digital Strategy 2025-2027 of Abu Dhabi, backed by a 13-billion AED investment, follows a phased ‘crawl, walk, run’ approach. The initiative has already gone live across 25 government entities, enabling over 15,000 daily users to access AI-accelerated services.
Generative AI applications are now integrated into human resources, procurement, and financial reporting, while advanced agentic AI and autonomous workflows will further enhance government-wide operations.
The strategy ensures full data sovereignty while driving innovation and efficiency across the public sector.
Partnerships with Deloitte and Core42 provide infrastructure and compliance support, while over 200 AI-powered capabilities are deployed to boost digital skills, economic growth, and employment opportunities.
By 2027, the initiative is expected to contribute more than 24 billion AED to Abu Dhabi’s GDP and create over 5,000 jobs, demonstrating a global blueprint for AI-native government transformation.
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Nscale has signed an expanded deal with Microsoft to deliver about 200,000 NVIDIA GB300 GPUs across Europe and the US, with Dell collaborating. The company calls it one of the largest AI infrastructure contracts to date. The build-out targets surging enterprise demand for GPU capacity.
A ~240MW hyperscale AI campus in Texas, US, will host roughly 104,000 GB300s from Q3 2026, leased from Ionic Digital. Nscale plans to scale the site to 1.2GW, with Microsoft holding an option on a second 700MW phase from late 2027. The campus is optimised for air-cooled, power-efficient deployments.
In Europe, Nscale will deploy about 12,600 GB300s from Q1 2026 at Start Campus in Sines, Portugal, supporting sovereign AI needs within the EU. A separate UK facility at Loughton will house around 23,000 GB300s from Q1 2027. The 50MW site is scalable to 90MW to support Azure services.
A Norway programme also advances Aker-Nscale’s joint venture plans for about 52,000 GB300s at Narvik, along with Nscale’s GW+ greenfield sites and orchestration for target training, fine-tuning, and inference at scale. Microsoft emphasises sustainability and global availability.
Both firms cast the pact as deepening transatlantic tech ties and accelerating the rollout of next-gen AI services. Nscale says few providers can deploy GPU fleets at this pace. The roadmap points to sovereign-grade, multi-region capacity with lower-latency platforms.
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The rapid rise of AI has drawn a wave of ambitious investors eager to tap into what many consider the next major economic engine. Capital has flowed into AI companies at an unprecedented pace, fuelled by expectations of substantial future returns.
Yet despite these bloated investments, none of the leading players have managed to break even, let alone deliver a net-positive financial year. Even so, funding shows no signs of slowing, driven by the belief that profitability is only a matter of time. Is this optimism justified, or is the AI boom, for now, little more than smoke and mirrors?
Where the AI money flows
Understanding the question of AI profitability starts with following the money. Capital flows through the ecosystem from top to bottom, beginning with investors and culminating in massive infrastructure spending. Tracing this flow makes it easier to see where profits might eventually emerge.
The United States is the clearest focal point. The country has become the main hub for AI investment, where the technology is presented as the next major economic catalyst and treated by many investors as a potential cash cow.
The US market fuels AI through a mix of venture capital, strategic funding from Big Tech, and public investment. By late August 2025, at least 33 US AI startups had each raised 100 million dollars or more, showing the depth of available capital and investor appetite.
OpenAI stands apart from the rest of the field. Multiple reports point to a primary round of roughly USD 40 billion at a USD 300 billion post-money valuation, followed by secondary transactions that pushed the implied valuation even higher. No other AI company has matched this scale.
Much of the capital is not aimed at quick profits. Large sums support research, model development, and heavy infrastructure spending on chips, data centres, and power. Plans to deploy up to 6 gigawatts of AMD accelerators in 2026 show how funding moves into capacity rather than near-term earnings.
Strategic partners and financiers supply some of the largest investments. Microsoft has a multiyear, multibillion-dollar deal with OpenAI. Amazon has invested USD 4 billion in Anthropic, Google has pledged up to USD 2 billion, and infrastructure players like Oracle and CoreWeave are backed by major Wall Street banks.
AI makes money – it’s just not enough (yet)
Winning over deep-pocketed investors has become essential for both scrappy startups and established AI giants. Tech leaders have poured money into ambitious AI ventures for many reasons, from strategic bets to genuine belief in the technology’s potential to reshape industries.
No matter their motives, investors eventually expect a return. Few are counting on quick profits, but sooner or later, they want to see results, and the pressure to deliver is mounting. Hype alone cannot sustain a company forever.
To survive, AI companies need more than large fundraising rounds. Real users and reliable revenue streams are what keep a business afloat once investor patience runs thin. Building a loyal customer base separates long-term players from temporary hype machines.
OpenAI provides the clearest example of a company that has scaled. In the first half of 2025, it generated around 4.3 billion dollars in revenue, and by October, its CEO reported that roughly 800 million people were using ChatGPT weekly. The scale of its user base sets it apart from most other AI firms, but the company’s massive infrastructure and development costs keep it far from breaking even.
Microsoft has also benefited from the surge in AI adoption. Azure grew 39 percent year-over-year in Q4 FY2025, reaching 29.9 billion dollars. AI services drive a significant share of this growth, but data-centre expansion and heavy infrastructure costs continue to weigh on margins.
NVIDIA remains the biggest financial winner. Its chips power much of today’s AI infrastructure, and demand has pushed data-centre revenue to record highs. In Q2 FY2026, the company reported total revenue of 46.7 billion dollars, yet overall industry profits still lag behind massive investment levels due to maintenance costs and a mismatch between investment and earnings.
Why AI projects crash and burn
Besides the major AI players earning enough to offset some of their costs, more than two-fifths of AI initiatives end up on the virtual scrapheap for a range of reasons. Many companies jumped on the AI wave without a clear plan, copying what others were doing and overlooking the huge upfront investments needed to get projects off the ground.
GPU prices have soared in recent years, and new tariffs introduced by the current US administration have added even more pressure. Running an advanced model requires top-tier chips like NVIDIA’s H100, which costs around 30,000 dollars per unit. Once power consumption, facility costs, and security are added, the total bill becomes daunting for all but the largest players.
Another common issue is the lack of a scalable business model. Many companies adopt AI simply for the label, without a clear strategy for turning interest into revenue. In some industries, these efforts raise questions with customers and employees, exposing persistent trust gaps between human workers and AI systems.
The talent shortage creates further challenges. A young AI startup needs skilled engineers, data scientists, and operations teams to keep everything running smoothly. Building and managing a capable team requires both money and expertise. Unrealistic goals often add extra strain, causing many projects to falter before reaching the finish line.
Legal and ethical hurdles can also derail projects early on. Privacy laws, intellectual property disputes, and unresolved ethical questions create a difficult environment for companies trying to innovate. Lawsuits and legal fees have become routine, prompting some entrepreneurs to shut down rather than risk deeper financial trouble.
All of these obstacles together have proven too much for many ventures, leaving behind a discouraging trail of disbanded companies and abandoned ambitions. Sailing the AI seas offers a great opportunity, but storms can form quickly and overturn even the most confident voyages.
How AI can become profitable
While the situation may seem challenging now, there is still light at the end of the AI tunnel. The key to building a profitable and sustainable AI venture lies in careful planning and scaling only when the numbers add up. Companies that focus on fundamentals rather than hype stand the best chance of long-term success.
Lowering operational costs is one of the most important steps. Techniques such as model compression, caching, and routing queries to smaller models can dramatically reduce the cost of running AI systems. Improvements in chip efficiency and better infrastructure management can also help stretch every dollar further.
Shifting the revenue mix is another crucial factor. Many companies currently rely on cheap consumer products that attract large user bases but offer thin margins. A stronger focus on enterprise clients, who pay for reliability, customisation, and security, can provide a steadier and more profitable income stream.
Building real platforms rather than standalone products can unlock new revenue sources. Offering APIs, marketplaces, and developer tools allows companies to collect a share of the value created by others. The approach mirrors the strategies used by major cloud providers and app ecosystems.
Improving unit economics will determine which companies endure. Serving more users at lower per-request costs, increasing cache hit rates, and maximising infrastructure utilisation are essential to moving from growth at any cost to sustainable profit. Careful optimisation can turn large user bases into reliable sources of income.
Stronger financial discipline and clearer regulation can also play a role. Companies that set realistic growth targets and operate within stable policy frameworks are more likely to survive in the long run. Profitability will depend not only on innovation but also on smart execution and strategic focus.
Charting the future of AI profitability
The AI bubble appears stretched thin, and a constant stream of investments can do little more than artificially extend the lifespan of an AI venture doomed to fail. AI companies must find a way to create viable, realistic roadmaps to justify the sizeable cash injections, or they risk permanently compromising investors’ trust.
That said, the industry is still in its early and formative years, and there is plenty of room to grow and adapt to current and future landscapes. AI has the potential to become a stable economic force, but only if companies can find a compromise between innovation and financial pragmatism. Profitability will not come overnight, but it is within reach for those willing to build patiently and strategically.
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The UK government has issued a strong warning to company leaders to prepare for cyber incidents by maintaining paper-based contingency plans. The National Cyber Security Centre (NCSC) emphasised that firms must plan how to continue operations and rebuild IT systems if networks are compromised.
The advice follows a series of high-profile cyberattacks this year targeting major UK firms, including Marks & Spencer, The Co-op, and Jaguar Land Rover, which experienced production halts and supply disruptions after their systems were breached.
According to NCSC chief executive Richard Horne, organisations need to adopt ‘resilience engineering’ strategies, systems designed to anticipate, absorb, recover, and adapt during cyberattacks.
The agency recommends storing response plans offline and outlining alternative communication methods, such as phone trees and manual record-keeping, should email systems fail.
While the total number of cyber incidents investigated by the NCSC, 429 in the first nine months of 2025, remained stable, the number of ‘nationally significant’ attacks nearly doubled from 89 to 204. These include Category 1–3 incidents, ranging from ‘significant’ to ‘national cyber emergency.’
Recent cases highlight the human and operational toll of such events, including a ransomware attack on a London blood testing provider last year that caused severe clinical disruption and contributed to at least one patient death.
Experts say the call for offline backups may sound old-fashioned but is pragmatic. ‘You wouldn’t walk onto a building site without a helmet, yet companies still go online without basic protection,’ said Graeme Stewart, head of public sector at Check Point. ‘Cybersecurity must be treated like health and safety: not optional, but essential.’
The government is also encouraging companies, particularly SMEs, to use the NCSC’s free support tools, including cyber insurance linked to its Cyber Essentials programme.
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Scientists at the University of Oxford have achieved quantum teleportation between two quantum computers, marking a major step toward distributed quantum computing. The experiment successfully transmitted a quantum algorithm wirelessly between processors using quantum entanglement.
Rather than moving physical matter, the process transferred data instantaneously by linking qubits, the basic units of quantum information. The two computers, though separated by two metres, shared data as if operating as one, greatly enhancing their combined computing power.
The British breakthrough demonstrates how multiple quantum systems could one day work together as a single global supercomputer. Researchers say the approach could enable quantum networks and lay the groundwork for a future quantum internet capable of unprecedented speeds and security.
Quantum teleportation works by using pairs of entangled particles that remain connected across any distance. While humans and objects cannot yet teleport, the technology could soon allow scientists to connect remote machines into unified, ultra-powerful computing systems.
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California has become the first US state to regulate AI companion chatbots after Governor Gavin Newsom signed landmark legislation designed to protect children and vulnerable users. The new law, SB 243, holds companies legally accountable if their chatbots fail to meet new safety and transparency standards.
The US legislation follows several tragic cases, including the death of a teenager who reportedly engaged in suicidal conversations with an AI chatbot. It also comes after leaked documents revealed that some AI systems allowed inappropriate exchanges with minors.
Under the new rules, firms must introduce age verification, self-harm prevention protocols, and warnings for users engaging with companion chatbots. Platforms must clearly state that conversations are AI-generated and are barred from presenting chatbots as healthcare professionals.
Major developers including OpenAI, Replika, and Character.AI say they are introducing stronger parental controls, content filters, and crisis support systems to comply. Lawmakers hope the move will inspire other states to adopt similar protections as AI companionship tools become increasingly popular.
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Denmark will push for EU-wide age-verification rules to avoid a patchwork of national systems. As Council presidency, Copenhagen prioritises child protection online while keeping flexibility on national age limits. The aim is coordination without a single ‘digital majority’ age.
Ministers plan to give the European Commission a clear mandate for interoperable, privacy-preserving tools. An updated blueprint is being piloted in five states and aligns with the EU Digital Identity Wallet, which is due by the end of 2026. Goal: seamless, cross-border checks with minimal data exposure.
Copenhagen’s domestic agenda moves in parallel with a proposed ban on under-15 social media use. The government will consult national parties and EU partners on the scope and enforcement. Talks in Horsens, Denmark, signalled support for stronger safeguards and EU-level verification.
The emerging compromise separates ‘how to verify’ at the EU level from ‘what age to set’ at the national level. Proponents argue this avoids fragmentation while respecting domestic choices; critics warn implementation must minimise privacy risks and platform dependency.
Next steps include expanding pilots, formalising the Commission’s mandate, and publishing impact assessments. Clear standards on data minimisation, parental consent, and appeals will be vital. Affordable compliance for SMEs and independent oversight can sustain public trust.
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Google has announced a $15 billion commitment for 2026–2030 to build its first Indian AI hub in Visakhapatnam, positioning itself as a foundational partner in India’s AI-first push and strengthening US–India tech ties.
The hub will centre on a purpose-built, gigawatt-scale data-centre campus engineered to Google’s global standards for performance, reliability, and low latency. Partners AdaniConnex and Airtel will help deliver enterprise-grade capacity, enabling large companies and startups to build and scale AI-powered services.
Beyond compute, Google will anchor an international subsea gateway in Visakhapatnam, landing multiple cables to complement those in Mumbai and Chennai, adding route diversity, lowering latency across India’s east coast, and strengthening national connectivity for users, developers, and enterprises.
Clean growth is a core pillar of the plan, with work on transmission lines, new clean-energy generation, and storage in Andhra Pradesh. Google will apply its energy-efficient data centre design to expand India’s diverse clean power portfolio while supporting grid reliability and long-term sustainability goals.
The initiative aligns with the Viksit Bharat 2047 vision, targeting high-value jobs in India and spillover benefits to US research and development. By combining compute, connectivity, and clean energy at scale, Google aims to accelerate AI adoption across sectors and broaden digital inclusion nationwide.
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EU countries have agreed to step up efforts to improve child protection online by supporting Denmark’s Jutland Declaration. The initiative, signed by 25 member states, focuses on strengthening existing EU rules that safeguard minors from harmful and illegal online content.
However, Denmark’s proposal to ban social media for children under 15 did not gain full backing, with several governments preferring other approaches.
The declaration highlights growing concern about young people’s exposure to inappropriate material and the addictive nature of online platforms.
It stresses the need for more reliable age verification tools and refers to the upcoming Digital Fairness Act as an opportunity to introduce such safeguards. Ministers argued that the same protections applied offline should exist online, where risks for minors remain significant.
Danish officials believe stronger measures are essential to address declining well-being among young users. Some EU countries, including Germany, Spain and Greece, expressed support for tighter protections but rejected outright bans, calling instead for balanced regulation.
Meanwhile, the European Commission has asked major platforms such as Snapchat, YouTube, Apple and Google to provide details about their age verification systems under the Digital Services Act.
These efforts form part of a broader EU drive to ensure a safer digital environment for children, as investigations into online platforms continue across Europe.
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