The United States government ordered an AI company to pull its most advanced products from the hands of every non-American user on the planet, with almost no warning. Within hours, a piece of software that had been available to hundreds of millions of people was gone. Nothing was broken or glitchy. It didn’t go temporarily offline; it disappeared simply because of a government decree.

The company was Anthropic. The products were Claude Fable 5 and Claude Mythos 5, two AI models the company had launched just three days earlier on June 9. The order came from the US Department of Commerce, acting through its Bureau of Industry and Security. The directive told Anthropic that it must prevent foreign nationals from accessing either model. Because Anthropic had no reliable technical system to check the nationality of every person trying to use its products, the only option was to disable both models for everyone, everywhere. The global shutdown was complete within hours.

What Made These Models Different

Claude Fable 5 and Claude Mythos 5 were not ordinary software updates. They represented a genuine leap in what AI could do. Both models could process enormous amounts of information at once, equivalent to reading roughly 750 novels simultaneously, and could produce sophisticated, detailed work in return. They could write complex computer code, analyse legal documents, design scientific experiments, and reason through problems in a way that previous AI systems could not match.

The commercial results were startling. Stripe, the global financial technology company, used Fable 5 to rewrite 50 million lines of computer code in a single day. A team of human engineers would have taken over two months to do the same job. In the life sciences sector, Mythos 5 generated viable drug candidate designs that laboratory testing subsequently confirmed as biologically sound.

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There was, however, a crucial difference between the two models. Fable 5, the version intended for general public use, came with built-in safety filters designed to refuse requests for dangerous information, such as instructions for cyberattacks or hazardous chemical processes. Mythos 5 had no such filters. It was the raw, unrestrained version of the same underlying intelligence, offered only to a small number of vetted organisations through a restricted programme called Project Glasswing.

The absence of safety filters in Mythos 5 was not negligence. The idea was that certain trusted organisations, particularly those doing defensive security work, needed to probe the model’s full capabilities in order to understand and protect against potential threats. What nobody outside a classified briefing room fully appreciated was just how threatening those capabilities turned out to be.

The Moment That Changed Everything

In an authorised internal test under Project Glasswing, Mythos 5 was paired with defensive cybersecurity tools and pointed at the US National Security Agency’s own classified systems. The model broke into almost all of them within hours, rather than the weeks such an exercise would normally require. It identified thousands of serious security vulnerabilities and demonstrated what experts call autonomous exploit chaining, the ability to link together multiple weaknesses in a system to escalate an attack automatically. One of the vulnerabilities it exploited had been sitting undetected in a widely used computer operating system for 17 years.

The NSA chief Joshua Rudd delivered these findings in a classified briefing to the Senate Intelligence Committee. The message was stark.

Mythos-class intelligence could function as an automated cyber weapon. It could be used not just to probe defences, but, in the wrong hands, to attack civilian infrastructure, financial networks, and military systems on a scale and at a speed that no human hacker could match.

Then a second problem emerged. Researchers at Amazon discovered a way to bypass the safety filters built into Fable 5, the supposedly safe public version of the model. This meant that anyone who knew the right way to phrase their requests could unlock capabilities very close to those of the unfiltered Mythos 5. Amazon chief executive Andy Jassy raised these concerns directly with US Treasury Secretary Scott Bessent on June 11. The next day, the shutdown order arrived.

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A third trigger also contributed. Days before the blackout, the White House asked Anthropic to revoke access to Mythos 5 for SK Telecom, the South Korean telecommunications giant, over concerns about business connections between its parent conglomerate and Chinese affiliates. The incident illustrated how even allied companies could be caught in the crossfire of US-China strategic competition.

A New Kind of Weapon Control

Previous US export controls had focused on physical objects. The country restricted the export of advanced chip-making machinery, of high-performance computer processors, of military hardware. The logic was, if a dangerous piece of equipment never leaves the country, it cannot be misused abroad.

The Fable 5 and Mythos 5 directive applied that same logic to cloud-based software for the first time. No physical object moved. The AI models ran on servers inside the United States. Anyone in the world could access them through the internet. The government’s position was that this remote access itself constituted a form of export, one that fell under existing law.

The legal mechanism used was something called the deemed-export rule, a provision in US export law that treats giving a foreign national access to controlled technology, even within the United States, as equivalent to physically exporting it to their home country. By applying this rule to cloud software, the government established an extraordinary new precedent. Now typing a query into an AI system from abroad is legally comparable to receiving a shipment of military hardware.

This precedent created immediate chaos for Anthropic’s own workforce. Several of the company’s most senior technical staff were not American citizens, including researchers and executives from Germany, Canada, Slovakia, the United Kingdom, and Brazil. Under the directive, these individuals were legally prohibited from accessing the very models they had spent years building. The people best placed to fix the security vulnerabilities that had prompted the shutdown were locked out of the systems that needed fixing.

The Political Dimension

The abruptness of the shutdown could not be separated from a longer-running conflict between Anthropic and the Trump administration. Since early 2025, the company had clashed with the government over how its AI models could be used. Anthropic had refused to allow its products to be deployed in fully autonomous weapons systems or domestic surveillance programmes. In February 2026, the Pentagon responded by placing Anthropic on a national security blacklist, restricting military contractors from working with the company. Legal battles followed in courts in Washington and California.

When the export control order arrived, senior administration figures were not shy about their satisfaction. Defence Secretary Pete Hegseth stated publicly that the decision vindicated the Pentagon’s earlier blacklisting.

The Pentagon’s chief information officer Kirsten Davies was equally blunt. Writing on X the day after the shutdown, she declared: “Some things are simply more important than revenue cycles, clickbait, and pre-IPO valuation. America First. Always.” The post was a pointed reference to Anthropic’s anticipated stock market listing, and left little ambiguity about where the Defence Department stood.

Critics in the cybersecurity community were unconvinced. More than 60 leading security experts signed an open letter arguing that Fable 5’s defensive capabilities were themselves a tool for protecting networks, and that removing it from the hands of defenders was itself a security risk. They also pointed out that OpenAI’s GPT-5.5, a model with broadly comparable capabilities, faced no such restrictions, a disparity that suggested political motivation rather than consistent security logic.

The First Lawsuits

The commercial fallout was immediate. On June 23, a San Jose-based litigation technology company called Legion LegalTech filed a lawsuit against the US Commerce Department in Washington federal court. Legion had built its entire product, an AI-powered platform for attorneys handling drafting and case management, on top of Fable 5. Its engineering and development team was based in Canada. When the export directive took Fable 5 offline, Legion’s Canadian developers were locked out overnight.

In its legal filing, Legion described the damage as immediate, irreparable, and existential. In a fast-moving, highly competitive market, the company argued, the competitive ground lost during a forced suspension cannot be recovered.

The case exposed a vulnerability that thousands of companies around the world share. Many businesses have built their products directly on top of AI models provided by third parties, assuming those services will remain reliably available. The standard agreements that govern such relationships are service contracts, not supply guarantees. They do not protect against a government ordering the provider to switch off access at 90 minutes’ notice. Legion’s lawsuit was the first, but it was widely expected not to be the last.

The World Responds

Outside the United States, the shutdown was read as a warning about the fundamental risk of depending on foreign-controlled technology. If the world’s most powerful AI tools can be switched off by a single government directive, then any country or company that relies on them is exposed to a form of vulnerability that no contract, no service-level agreement, and no business continuity plan had previously accounted for.

The response was immediate and global. Canadian Prime Minister Mark Carney used the shutdown as a central justification for a 2.3 billion dollar national AI strategy, explicitly designed to reduce dependence on US cloud services. Speaking ahead of the G7 summit, he compared the risk of over-reliance on a small number of foreign AI providers to the systemic financial risks that produced the 2008 banking crisis.

India proposed a 5 billion dollar sovereign AI fund and backed 12 domestic AI development projects in the days following the ban. Indian policymakers argued that purchasing processors and building data centres was not enough. True technological independence required deep institutional research capacity built over years, not emergency spending in response to a crisis.

In Britain, a coalition including BT, HSBC, and BAE Systems began organising around the goal of building a sovereign frontier AI model independent of US administrative control. Senior political figures warned that modern sovereignty was increasingly defined by control over digital infrastructure rather than military hardware.

French presidential candidate Bruno Retailleau claimed that a nation that depends on others for its technology can be unplugged overnight.

The Chinese, who are the number one rival to the US in the AI race, took notice when Elon Musk commented on what was happening.

When Musk posted on X that China would ‘probably’ produce a Fable-class model by the first quarter of 2027, Tang Jie, founder and chief scientist of Beijing-based Zhipu AI, was unimpressed by that timeline and replied in four words: “Won’t take that long.”

The confidence was not without basis. Zhipu’s newly released GLM-5.2, a 744-billion-parameter model built entirely on Chinese Huawei processors without a single Nvidia chip, had just ranked second globally on a major coding benchmark, behind only Fable 5 itself.

The US decision to halt access to Fable and Mythos might be to ensure that the Chinese would not access Anthropic’s state-of-the-art technology.

But the race is tight. And the Pentagon might have unwittingly given the edge to Chinese competitors, who can roll out their products to millions of people and gather big data. It is only a matter of time before the Chinese catch up, and even surpass their US peers.

Europe’s Regulatory Counterplay

The European Union arrived with its own agenda already in motion. On June 3, nine days before the Anthropic shutdown, the European Commission had unveiled the Cloud and AI Development Act, known as CAIDA. The legislation establishes four tiers of certification for digital services used by public bodies and critical infrastructure operators. The highest tiers require that services be owned and controlled by European entities, beyond the reach of foreign legal jurisdiction.

The conflict between CAIDA and US law is structural. American cloud companies remain subject to the US CLOUD Act, which allows US authorities to compel American firms to disclose data held anywhere in the world. That requirement is irreconcilable with European data protection law. By reserving the highest certification tiers for European-controlled entities, the EU is in practical terms barring US companies from the most valuable segments of the European public sector market.

European officials argue that if US companies benefit from exclusive access to the world’s most powerful productivity tools while their European competitors are shut out by Washington’s export controls, that constitutes an unfair competitive advantage. The EU has a long and effective history of acting against such imbalances through competition law and financial penalties.

The Limits of Going It Alone

Very few countries have the resources to build a complete AI ecosystem independently. The full stack requires advanced chip manufacturing, enormous quantities of energy, elite technical talent, and sustained capital investment over years. Outside the United States and China, no single nation can credibly claim to have all of these.

The emerging response to this reality is what some policymakers are calling collective programmable sovereignty. It’s a model in which allied nations pool their different strengths to build shared infrastructure that no single government can switch off. South Korea and Taiwan provide semiconductor manufacturing. India provides engineering talent, and data diversity. Gulf states provide the energy needed to power massive data centres. The EU provides regulatory frameworks and research. Brazil and Indonesia provide market scale.

A coalition of this kind would represent an economy larger than that of the United States. It would be in a position to negotiate the terms of technology access rather than simply accept them.

What Comes Next

The shutdown of Claude Fable 5 and Mythos 5 has revealed something important about the world that AI has created. The most capable AI systems are now treated by at least one major government as critical national security assets, subject to the same logic of containment that once governed nuclear technology and advanced weaponry. The era in which cutting-edge AI tools were simply available to anyone with an internet connection and a credit card is over.

For businesses, the immediate lesson is the danger of concentration risk, of building core operations on a single provider’s infrastructure without the ability to switch rapidly to an alternative. For governments, it is the realisation that declarations of digital sovereignty mean nothing without the physical infrastructure, the talent, and the sustained investment to back them up.

The event was, in its own way, the clearest demonstration yet of how central AI has become to geopolitics, commerce, and national power. The question now is not whether AI will be treated as a strategic asset. It already is. The question is who will control it, and on whose terms.

The post US Shuts World’s Most Powerful AI, Triggers New Race appeared first on International Finance.

A growing number of power users, including software developers, financial analysts, academic researchers, and security-conscious companies, are pulling their data back. They are moving away from cloud platforms and building systems where their information lives locally, on their own devices, under their own control. The reasons are financial, legal, and deeply personal. Subscription prices have ballooned out of control. Platforms have quietly started using customer data to train artificial intelligence. Some services have simply shut down, leaving users stranded with no way out.

This is not a fringe reaction confined to paranoid engineers, but a structural shift in how organisations think about intellectual property, and it is being driven by hard numbers.

The Bill That Keeps Growing

The most immediate driver of this shift is cost. Cloud software is getting significantly more expensive, and far faster than almost anything else in the economy.

Data drawn from over $30 billion in tracked global software spending shows that SaaS-specific inflation, meaning price rises across subscription software products specifically, reached 13.2% in early 2026. In late 2025, it peaked even higher, hitting 14.7% just as most large enterprises were going through their year-end renewal cycles, which is hardly a coincidence. For context, general consumer price inflation across G7 economies sits around 2.7%. Software costs, in other words, are rising nearly five times faster than the price of everything else.

The consequences are visible on corporate balance sheets. The average company now spends roughly $9,100 per employee per year on software, a rise of 27% over just two years. Software’s share of total IT budgets has climbed from 13% five years ago to 21% today, a jump so steep that in several companies it now exceeds what they spend on employee healthcare coverage. Roughly 79% of IT leaders reported facing price increases at their last renewal cycle, suggesting this is no longer an occasional shock, but the new default behaviour of the industry.

The price increases themselves are not subtle. Salesforce has pushed its top-tier enterprise licencing cost to $500 per seat per month, after consecutive price hikes in 2023 and 2025. Slack raised its Business+ subscription by 20%, taking it to $15 per user monthly. Zendesk has been charging customer service teams up to $115 per agent monthly, with AI features tagged on as a separate $25 to $50 add-on. Adobe quietly restructured its Creative Cloud offering, stripping mobile apps and AI features out of its cheaper standard tier, and rebranding a pricier version as the new default for anyone who wants the full toolkit.

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Beyond these headline increases, software companies have become increasingly creative about extracting more money without technically raising the sticker price, a practice sometimes called shrinkflation. Standard features quietly get moved into higher, costlier pricing tiers. The number of API calls a customer is allowed gets reduced. Monthly usage credits expire before they can be fully used, forcing customers to either upgrade or simply lose value they already paid for.

Atlassian’s Rovo platform, for example, caps users at 25 credits a month. Adobe’s Firefly platform uses non-rollover credits that get consumed faster for more advanced generative tasks. Microsoft, in its mid-2026 updates, bundled tools like Copilot Chat, Defender, and Intune into existing subscription tiers, using the bundling itself as justification for a higher overall list price, forcing organisations to pay for features many of them never asked for, or needed. Companies trying to build custom AI tools on Microsoft’s Copilot Studio platform face a flat $200 monthly fee capped at 25,000 messages, with anything beyond that triggering metered overage charges.

The cumulative effect of all this is a corporate software bill that grows substantially every year, regardless of whether the underlying product has actually improved.

The Privacy Shock

The financial squeeze on its own would already be reason enough for companies to rethink their cloud dependence. But it has been compounded by something arguably more serious. There is a deep erosion of trust around what platforms actually do with the data their customers store on them.

The rise of generative AI has created an almost insatiable demand for training material. Large language models need enormous quantities of text to learn from, and some of the richest, most detailed text in existence sits quietly in the documents, internal chats, and notes that businesses store on cloud platforms every single day. Several major vendors have been caught treating this material as fair game for their own AI ambitions, often without making that intention obvious to the customers footing the bill.

ALSO READ | US Shuts World’s Most Powerful AI, Triggers New Race

A widely cited Stanford study found that several leading AI developers feed user conversational data back into their own models by default, relying on lengthy data retention periods, and offering very little clarity about how customers can actually opt out. Even Anthropic, the company behind Claude, changed its terms of service in September 2025 to train its models on user conversations by default, unless customers actively chose to opt out themselves.

Slack faced its own wave of public backlash after users discovered that its privacy terms permitted the company to scan messages and files in order to train machine learning models. Customers were automatically enrolled into this without any active choice, and had to email a specific address to request removal, a process most users never even knew existed until it was reported on. Slack later clarified that its newer AI features rely on outside large language models rather than directly retraining on raw private message content, but for many businesses, the explanation arrived only after the trust had already been damaged.

Adobe ran into a similar storm. An update to its terms of use appeared to grant the company access to active, in-progress user files through both automated and manual review processes. Designers and creators working under strict client confidentiality agreements suddenly realised that unpublished, unreleased work sitting in their Adobe cloud storage could potentially be scanned. Adobe later clarified that its main generative tool, Firefly, is not trained on customer cloud files. That clarification did little to stop the fallout, and the company was hit with a shareholder lawsuit accusing its executives of misleading investors about how its AI training data was actually being sourced.

The starkest cautionary tale, however, is the story of Skiff. Skiff was a privacy-focused productivity startup that had built a loyal base of nearly two million users on the strength of its end-to-end encrypted email, calendar, and document storage. It had raised meaningful venture funding, including from Sequoia Capital, and represented exactly the kind of privacy-first alternative that security-conscious users were looking for. In February 2024, Notion acquired Skiff, and then chose to shut the entire product down.

Users were left scrambling to manually export their own email archives, contacts, and files, since automatic migration tools simply were not available. The transition itself became a case study in how not to handle an acquisition: promised email forwarding broke due to expired security certificates, customer support was replaced by unresponsive automated chat loops, and user-owned domains stopped functioning correctly.

For an audience that had specifically chosen Skiff because they cared about owning their own data, watching the company they trusted vanish almost overnight was a stark wake-up call.

The lesson these episodes left behind, across the developer and research communities, was simple and hard to unlearn: when your data lives on someone else’s server, it ultimately lives by their rules, not yours.

The Local-First Alternative

The response to all of this now has a name. It’s called local-first software. The term was formally defined back in 2019 by a research group called Ink and Switch, but the underlying instinct it captures, that your own files should belong to you first and foremost, is far older and has become newly urgent.

Local-first software is built on one simple, almost old-fashioned principle. Your files live on your own device first. The hard drive of your computer, tablet, or phone is treated as the primary, authoritative home for your data. Any synchronisation across multiple devices, or any sharing with collaborators, happens quietly in the background over the network, as a secondary convenience rather than as a precondition for the software to work at all.

This distinction matters enormously in practice. If the software company behind the app goes out of business, your files remain exactly where they were, fully readable. If your internet connection drops, you can keep working without interruption. If the vendor changes its terms of service, hikes its prices overnight, or gets quietly acquired and shut down, none of that changes what is already sitting safely on your own hard drive.

The clearest real-world example of this model working at scale is Obsidian, a note-taking and knowledge management application now used by over 1.5 million people every month. Obsidian stores everything as plain text Markdown files inside a folder on your own computer, what the app calls a vault. There is no proprietary file format, and no cloud lock-in involved. Any basic text editor on any device can open these files, with or without Obsidian installed.

When Obsidian introduced a new feature called Bases in 2025, which lets users build searchable, structured databases directly from their notes, many longtime Notion users found they could finally replicate everything they relied on Notion for, except now it all lived entirely on their own machine.

Obsidian generates revenue through optional paid add-ons, like encrypted cross-device syncing, priced between $48 and $96 a year, and a separate publishing feature. But the core application itself remains free, including for full commercial and enterprise use, after the company relaxed its licencing terms.

Independent security firms have audited the underlying architecture and confirmed its claims. When users do choose to sync their notes across devices, the files are encrypted directly on their own device before they ever leave it, which means even Obsidian’s own servers are mathematically incapable of reading the contents.

Where Governments Come In

This move toward local control is not limited to individual users or small companies. Governments, particularly across Europe, are now pushing hard to bring entire categories of national and corporate data infrastructure back under their own legal jurisdiction.

The distinction driving this effort is a subtle but important one. The difference between data residency and data sovereignty. Data residency simply means your data physically sits on a server located in a particular country. Data sovereignty means that data is actually governed by that country’s own laws, and meaningfully protected from interference by foreign governments, which is a much higher bar.

Under existing American law, US technology companies can be legally compelled to hand over data stored on their servers anywhere in the world, including servers physically located inside Europe. This creates a genuine problem for European organisations relying on American cloud platforms, no matter where those platforms’ physical data centres happen to be.

France has responded by formalising a framework that requires government bodies and operators of critical national infrastructure to host sensitive data exclusively on cloud services that meet strict, French-controlled standards. The requirements include European legal control over the provider, European-based management of encryption keys, and an entirely EU-based staff.

In response, major American technology giants have formed European joint ventures specifically to meet these requirements, including a Microsoft partnership with Orange and Capgemini inside France, and a Google partnership with the defence contractor Thales.

Amazon went a step further, opening a dedicated European Sovereign Cloud in Germany in January 2026. It was built as a legally and operationally separate entity from Amazon’s global cloud business, staffed exclusively by EU residents, and specifically engineered to insulate customer data from American legal jurisdiction.

Germany’s Hetzner and DanubeData, alongside France’s OVHcloud and Scaleway, offer virtual private servers, managed databases, and object storage at 40% to 70% lower cost than AWS, Google Cloud, or Microsoft Azure. Because these providers are headquartered and operated entirely within European jurisdictions, they sidestep the complex data transfer assessments that come with using American platforms, and crucially, they fall outside the reach of US surveillance law.

For cost-sensitive startups and mid-market developers, this combination of cheaper pricing and cleaner legal standing has made them an increasingly default choice rather than a niche one.

At the government level, the stakes are higher, and the providers more specialised. Bleu, a joint venture between Capgemini and Orange, delivers Microsoft Azure and Microsoft 365 services hosted entirely within France, operated by EU citizens, and built specifically to meet SecNumCloud 3.2, the French government’s strict cloud security qualification. Bleu is designed for public administrations, and so-called ‘Operators of Vital Importance’, the institutions running hospitals, utilities, and other critical infrastructure.

A similar logic applies to S3NS, a French entity formed by Google in partnership with defence contractor Thales, also structured around SecNumCloud compliance.

In Germany, Delos Cloud, an SAP subsidiary, has been built to satisfy federal sovereignty requirements for government workloads.

At the most sensitive end of the spectrum sits Google Distributed Cloud (GDC), which can run fully air-gapped, physically isolated from the public internet. This mode is built for governments and intelligence agencies that require absolute immunity from remote shutdowns or foreign data extraction, allowing classified workloads to run entirely on sovereign, disconnected infrastructure.
A Recalibration, Not a Rejection

None of this means the cloud is going away, nor should it. Collaborative, real-time tools still make perfect sense for a great deal of everyday business work. But the old assumption that cloud-first automatically means best-first is no longer something organisations can take for granted.

For companies and individuals generating sensitive research, proprietary analysis, or client-confidential work, the question of where exactly that data lives, and precisely who else can access it, has become a genuine strategic decision rather than a default setting nobody bothers to question. The tools needed to answer that question differently are now mature, widely available, and in many cases, considerably cheaper than the cloud subscriptions they are quietly replacing.

The post The Great Cloud Exodus and Return to Data Sovereignty appeared first on International Finance.

He built a supply chain so efficient it was studied like scripture at business schools. He quietly expanded Apple’s services division, think App Store fees, Apple Music, iCloud subscriptions, until it was generating over $100 billion every year. By the time Cook announced on April 20, 2026, that he would step down as CEO and shift to an Executive Chairman role from September 1, most observers agreed that his mission has been accomplished.

His chosen successor is John Ternus, a 50-year-old engineer who has spent the last 25 years as the quiet force behind virtually every piece of hardware Apple has released. The appointment signals something deliberate. Apple is not turning to a finance wizard or a marketing genius. It is turning to someone who has spent his career thinking about how things are built.

The man behind the machines

Ternus graduated from the University of Pennsylvania in 1997 with a degree in Mechanical Engineering and Applied Mechanics. He was a competitive swimmer at university, winning events in the 50-metre freestyle and 200-metre individual medley. His final undergraduate project was a mechanical feeding arm controlled by head movements, designed for people with quadriplegia, an early sign of someone drawn to engineering with purpose rather than just performance.

Before Apple, Ternus spent four years at a startup called Virtual Research Systems, designing early virtual reality headsets. That experience, building hardware meant to sit on someone’s face and convincingly alter their perception of the world, planted seeds that would prove relevant decades later.

He joined Apple in 2001 at 26. One early story captures his character well. During production of the Apple Cinema Display, a factory mistakenly milled 35 grooves into the back panel instead of the specified 25. To most eyes, the two versions looked identical. Ternus insisted on correcting the manufacturing process anyway. That is not the instinct of someone who accepts good enough.

Over the years, Ternus rose steadily. He became Vice-President of Hardware Engineering in 2013 and Senior Vice- President in 2021. Along the way, he oversaw every generation of the iPad, engineered the miniaturisation of AirPods, and took control of iPhone and Apple Watch hardware.

His most significant achievement, however, was leading the Mac’s transition away from Intel processors to Apple’s own custom-designed chips, the M-series. This was not a minor tweak. It was the equivalent of replacing the engine in a car while the car was still driving. The result was a line of laptops and desktops that outperformed competitors at a fraction of the power consumption, and it gave Apple total control over one of the most critical components in its products.

His most recent hardware statement, before being elevated to CEO, was the March 2026 launch of the MacBook Neo. Priced at $599, this was Apple doing something it almost never does, which is competing on price. The device made deliberate compromises. It capped memory at 8GB, offered only two USB ports, and dropped certain display refinements. But it kept the aluminum build, the crisp Liquid Retina screen, and Apple’s powerful A18 Pro chip.

The result was the best Mac launch week for first-time buyers in the company’s history. Ternus understood that there was an enormous market of students and budget-conscious consumers who wanted the Apple experience but could not justify paying premium prices.

To ensure the hardware pipeline does not suffer in Ternus’s absence from that role, Apple elevated Johny Srouji, the architect of Apple Silicon, to a new position of Chief Hardware Officer, overseeing hardware engineering, chip design, and platform architecture.

The gap that cannot be hidden

Here is the uncomfortable truth about Apple in 2026. The hardware is extraordinary. The software intelligence is not.

While Apple was refining aluminum finishes and perfecting chip architecture, the rest of the technology world was pouring money into artificial intelligence (AI) at a scale that is difficult to fully absorb. In a single quarter of 2025, companies like Google, Meta, Microsoft, and Amazon collectively committed an estimated $120 billion to AI infrastructure, covering data centres, custom processors, and the enormous computational power required to train and run large AI models. On an annual basis, their combined projection exceeds $660 billion. Google alone invests between $91 billion and $93 billion per year into this infrastructure.

Apple spent approximately $14 billion on AI-specific investment over the same period.

That is not a small gap. The companies spending more are building AI systems that can reason through complex problems, understand images and audio in real time, execute multi-step tasks across multiple applications, and improve themselves continuously through interaction with hundreds of millions of users.

Meanwhile, Siri, Apple’s voice assistant, which was genuinely pioneering when it launched in 2011, remained stuck in an architecture built around matching voice commands to pre-programmed responses. Ask Siri to set a timer or call a contact, and it performs reliably. Ask it to do anything that requires genuine reasoning or contextual understanding, and the gap between Apple and its competitors becomes embarrassingly apparent.

Apple’s internal culture also worked against it. Hardware engineering is a world of certainties. A product either functions within its specifications, or it does not. There is no acceptable rate of random failure. Artificial intelligence is the opposite of that.

Large language models, the kind powering Google’s Gemini or OpenAI’s ChatGPT, are probabilistic. They do not compute a single correct answer. They generate the most statistically likely response based on patterns learned from vast amounts of training data. They make mistakes. They occasionally produce confident nonsense. Crucially, they improve not through laboratory refinement but through deployment to real users at massive scale.

Apple, under Cook, was simply not culturally equipped to release something that might occasionally embarrass the company. So, features announced at Apple’s developer conferences in 2024 and 2025 were delayed, scaled back, or launched in such a limited form that even loyal Apple users struggled to understand what the fuss was about.

The honest acknowledgement of this situation led to a significant and somewhat humbling strategic decision. In early 2026, Apple finalised a multi-year deal with Google, estimated at $1 billion annually, to embed Google’s Gemini AI architecture directly into the iOS ecosystem.

As things stand today, Apple does not need to win the AI race. It just needs to ensure that whatever AI the world uses, Apple gets a cut of the subscription fee.

Through its App Store commission structure, 30% in the first year and 15% thereafter, Apple collected nearly $900 million from generative AI applications in 2025 alone, primarily from ChatGPT, with contributions from Claude and Grok.

In 2026, that figure is projected to exceed $1 billion. Google, Meta, and Microsoft are spending hundreds of billions of dollars building AI capabilities, while Apple passively monetises their distribution. It is, from a purely financial perspective, an almost elegant arrangement.

This dynamic gives John Ternus something invaluable as he assumes the CEO role. Namely, time. Apple’s core financial machinery is not at risk. The services division is robust. The installed base of 2.5 billion active devices is loyal and deep.

The App Store is a toll booth on the most lucrative stretch of the digital economy. Wall Street largely concurred. When the Cook-to-Ternus transition was announced, Apple’s stock barely moved, settling after a minor fluctuation of between 1% and 2.5%.

Morgan Stanley described the transition as “‘evolutionary rather than transformational’.” Wedbush Securities maintained an Outperform rating with a $350 price target.

What Ternus must do

The Google partnership solves an immediate problem but creates a long-term one. Apple’s core identity is inseparable from its control over every layer of the user experience. Ceding the reasoning engine of its products to a competitor is a compromise that may be necessary today but cannot be permanent.

Apple has an internal initiative, codenamed Project Ajax, aimed at building its own frontier-scale AI model. Rumours of custom M5-based AI server chips for 2026 and 2027 deployment suggest this project is advancing. By the end of the decade, Apple needs to own its cognitive infrastructure the way it owns its silicon.

Culturally, Ternus must help Apple’s engineering teams become comfortable with imperfection in a specific, bounded way. The hardware can and should remain flawless. But AI features must be allowed to ship, iterate, and improve through real-world use rather than retreating into development cycles that last years. These are two different disciplines, and Apple must learn to hold both simultaneously.

The wearables and smart home roadmaps cannot slip further. Smart glasses represent the most significant new hardware category since the smartphone, and Apple cannot afford to be two or three years behind Meta when it launches. The HomePad needs to reach consumers before Google Nest and Amazon Alexa become so embedded in households that switching feels impossible.

Apple Watch needs to evolve from an excellent data collector into an intelligent health companion.

Finally, the developer ecosystem requires urgent attention. Apple’s Foundation Models framework, the tools it provides to outside developers for building AI-powered apps, is currently seen by many in the industry as too restrictive. The local models are too small. The safety guardrails block too many legitimate uses. The rate limits prevent the kind of intensive querying that makes agentic applications possible. If developers cannot build the next generation of AI-native software for iPhone, they will build it for Android, and the users will follow.

The post John Ternus and Apple’s battle for the post-smartphone era appeared first on International Finance.

In an exclusive interview with International Finance, Panagiotis Kriaris, fintech and payments expert and Director – Head of Business & Corporate Development at Unzer, shares his insights on X’s move into financial services, the future of digital wallets, and whether the Western market is ready for the super-app model.

From a payments and wallet perspective, what stands out to you about a platform like X moving into financial services?

X is moving into financial services because payments deepen platform economics. Advertising is cyclical, and creator tools alone have limitations. Payments, wallets, and financial services create additional revenue streams while making the platform more commercially relevant.

The strategy also aligns with the broader ambition of turning X into a multi-function platform rather than just a media app. For a platform built around conversations, discovery, and creator activity, payments are the missing piece that allows it to capture the entire transaction layer.

If X succeeds in controlling transaction flows, it can directly monetise activities such as payments, tipping, subscriptions, and commerce while also keeping both the revenue and the data within its own ecosystem.

Digital wallets have evolved significantly over the past few years. What does it take for a wallet to move from being a simple payments tool to becoming a broader financial ecosystem?

A wallet becomes a true ecosystem when it moves into everyday money flows such as salaries, bills, subscriptions, credit, and savings. At that point, it is no longer just a checkout tool but becomes part of a user’s daily financial life.

For this to happen, the platform needs control over balances and accounts. That control allows providers to build additional products such as lending, savings, and foreign exchange services while capturing economics that extend far beyond transaction fees.

The real milestone is when a wallet becomes the primary place where users keep and manage their money.

Platforms like X already have distribution, but payments rely heavily on underlying rails and partnerships. How critical is infrastructure versus user reach in determining success?

Distribution is an important starting point, but payments require a strong operational backbone. Acceptance, settlement, dispute handling, compliance, and security are all critical to making the system work reliably.

Infrastructure directly impacts economics and monetisation. The more a platform controls the transaction flow, the more influence it has over margins, customer experience, and the overall product proposition.

The most successful platforms combine large-scale distribution with tight control over key parts of the payments stack. Long-term reliance on outsourcing is rarely a strategic advantage.

Interoperability has been a key challenge in payments. How important is it for a platform like X to integrate with existing financial systems rather than trying to build a closed ecosystem?

X cannot realistically pursue a closed ecosystem strategy in the early stages. Users still need to move money in and out through bank accounts and cards, otherwise adoption will remain limited.

Integration with existing financial systems is therefore essential for driving early usage and trust.

Over time, however, the strategy may gradually shift toward pulling more activity inside the platform itself. The long-term play is likely to start with full interoperability before progressively increasing the amount of financial activity that stays within the ecosystem.

Compared to established players like PayPal and Venmo, where do you see the biggest gaps or opportunities for a new entrant like X in the wallet space?

The key difference is positioning within the value chain. PayPal and Venmo primarily sit on the payments side, while X can influence activity much earlier in the process — during discovery, discussion, and decision-making.

That positioning gives X the ability to trigger transactions directly within the platform.

However, for that to work, X needs a clear value proposition tied to its own ecosystem, particularly around creators, subscriptions, or in-app commerce. Otherwise, it risks becoming just another wallet without a compelling reason for users to switch.

Ultimately, success will depend on changing user behaviour by offering something meaningfully more convenient or valuable than existing payment platforms.

Trust and security are central to wallet adoption. Do social platforms face an inherent disadvantage when asking users to store and move money within their ecosystem?

Yes. People are accustomed to using social platforms for communication and content sharing, not for storing money.

As a result, users will compare platforms like X not with other social networks, but with banks and fintech companies — especially when financial problems arise.

The only way to overcome that hesitation is through visible safeguards, strong compliance frameworks, and consistent handling of issues over time. Trust in financial services is built gradually and largely depends on how platforms respond when problems occur.

The idea of ‘super apps’ often depends on strong payments integration. Do you think the current payments landscape in Western markets supports that model, or limits it?

Building a super app in Western markets is significantly harder because the payments landscape is already fragmented across cards, banks, and multiple digital wallets.

There are also strong incumbents and heavy regulation, which make it difficult for a single player to consolidate the ecosystem.

The definition of a super app in the West is also very different from Asia. Asian markets often benefited from dominant payment rails or integrated ecosystems that provided a strong starting point for rapid adoption.

In Western markets, a more realistic strategy is to build around specific verticals or user communities first, and then expand gradually.

Looking ahead, do you see digital wallets becoming the primary interface for financial services, and what role could platforms like X realistically play in that evolution?

Digital wallets already dominate the financial services interface for many consumers. Payments increasingly happen through Apple Pay, Google Pay, or local wallet providers rather than directly through banks.

Artificial intelligence is now adding another layer by helping users decide when and how to pay, manage subscriptions, and automate financial actions. That evolution shifts wallets from being simple execution tools into decision-making platforms.

Platforms like X can still play an important role by embedding payments directly into social and creator-driven experiences. However, replacing established wallets as the primary financial interface will remain a much more difficult challenge.

The post ’X is likely to start with full interoperability before trying to lock financial activity within its ecosystem’ appeared first on International Finance.

In an exclusive interview with International Finance, Brett King, founder and CEO of The Futurists Network, Fintech Hall of Fame inductee, and policy advisor to global leaders, including the Obama administration, President Xi’s advisory ecosystem, and GCC governments, shares his perspective on how artificial intelligence is reshaping money, power, and the global financial order.

You have long predicted shifts in financial systems. Are we now entering an era where artificial intelligence becomes the core decision-maker in finance rather than human-led institutions?

Yes, we are witnessing the end of human-led decision-making in banking. We are seeing multiple agentic platforms being deployed right now at scale, including OpenClaw, PayPal, Stripe, Mastercard and others. So, it’s fairly inevitable that we’ll need fit-for-purpose banking. This requires agentic finance and native AI capabilities, which will exclude most banks in their current technical state. By 2035, agentic banking will be mainstream as neo-banking is mainstream today.

When we discuss the future of money, is the real transformation about innovation, or about who controls financial power?

There is no future for money as we think of it today. The more automation that is put in the system, the less value fiat currency provides, as it is not machine-readable, nor can it move without human intervention. We need smart money, which will include stablecoins, CBDCs, tokens (deposit, utility, etc), and eventually, AI marketplaces will further iterate on digital money.

As AI begins to drive lending, underwriting, and investment decisions, who ultimately holds accountability, the institution, the algorithm, or the data ecosystem behind it?

The institution will hold responsibility, but we will need both human and AI oversight functions to ensure these algorithms work. Ultimately, the quality of the data will determine how well these decisions can be automated. This is why data lakes and foundation models are really critical in the medium term.

Do you believe algorithmic trust can realistically replace traditional trust in banks, and what risks come with that shift?

Absolutely. Firstly, trust in banks will convert to trust in algorithms over time, just as it did with credit cards online, and online banking. Today, we see neobanks and wallets with higher trust scores than traditional banks, which is a good indication of the path artificial intelligence will take.

Could AI-led finance democratise access to capital globally, or will it deepen the concentration of power among a few dominant players?

Both. The core problem is not the democratisation of capital as much as it is AI’s potential to replace human capital. Which is why we hear many of the tech ‘broligarchy’ talking about Universal Basic Income. The fact is, wealth distribution is the biggest issue for AI at scale moving forward, not access to capital per se. But, at the same time, there will never be an easier time to start your own business or launch a product in the world.

If artificial intelligence becomes the primary gatekeeper of financial access, how do we address the risk of bias and ensure fairness at scale?

We completely need to rethink financial access in this world, but access to AI won’t be restricted by bias. All you will need is an internet connection and a smartphone. By 2030, 99% of the planet will have that capability (projected). The issues with biases are still present in datasets today, but people are self-selecting platforms that focus on accessibility and speed of access. This is why Revolut is now approaching the milestone of being the largest retail bank (by customers) in Europe, and why Ant Group and NuBank have already taken that status in their markets.

With the rise of Central Bank Digital Currencies (CBDCs), are governments enhancing efficiency, or expanding control over how money is used?

CBDCs do not give much greater control over how money is used from the account and fraud structures we have today, although they do allow central banks more direct control over the use of the currency and policy mechanisms connected to CBDCs. The key to understanding is that you can’t run autonomous systems on fiat currency on a traditional core – they are not fit for purpose. You can create translation layers and so forth, but CBDCs can be purpose-built to mirror trade agreements, for example, allowing only for cross-border transfers consistent with said agreements – programmable money that is policy and process enforced. This allows for much greater use of safety rails and mechanisms on autonomous cross-border trade that we don’t have with fiat. Various players, such as the CEO of Circle, have said we’ll likely have to move to rollback models over time, so that current payment rails don’t support either. So, this is all fit-for-purpose money design.

How concerned should we be about the idea of programmable money being used to influence or restrict economic behaviour?

Again, the banks can restrict money from an individual account to entire countries right now, today. So, this is not the systemic risk it would appear to be. Remember, we will need the ability to stop agentic AI-based criminal organisations using AI to scale crime, which we cannot do with today’s rails and account structures. So, we are actually at much greater risk of fraud and crime without programmable money.

Do CBDCs have the potential to genuinely improve financial inclusion, or could they unintentionally weaken the role of commercial banks?

We are already seeing the impact of potential yield from stablecoins being a big destabilising element for traditional deposits, but CBDCs essentially allow anyone with a government ID to have access to basic banking services. So, the answer is, both will happen simultaneously.

Between banks, Big Tech, and governments, which entity do you believe is best positioned to dominate the future financial ecosystem, and why?

The two determinants of success in this world are speed and technical agility. Speed will be defined by your organisation’s culture (how quickly artificial intelligence can be integrated), your tech stack, and how much of it is AI-ready. Banks with on-premise mainframes without access to the cloud or without multi-year digital transformation experience will really suffer through this transition, as they will quickly become less relevant from a systemic perspective. The other issue is market share and those natural shifts. Today, digital banks like NuBank, Revolut, Starling, Chime and others are dominating in their markets because of their ability to acquire customers at scale. AI is going to supercharge that capability, and banks reliant on traditional distribution will simply continue to lose customers pretty rapidly.

For example, HSBC, one of the world’s top 20 banks since the 1980s, has 38 million customers globally. And that has remained stable for the last decade, but Revolut has already hit 70 million in that same timeframe. Next, we will see how AI advisory shifts AuM to digital platforms away from product-based banks.

Are we moving toward a model where banks become invisible infrastructure while technology companies own the customer interface?

Yes, absolutely. Banks are either going to be data stores or data pipes, but they won’t own the personal AI clients at the front end. This is a bigger shift than most people realise. In 10 years, you’ll interact with your AI, and it will execute on your banking and money management, health management, all the administrative elements of your life – you won’t use apps. Interfaces will essentially be liquid/generative, sort of chunks of functionality driven by context and the AI. So, you won’t use banking apps like you do today. Your personal AI agent will interact with the bank agent on your behalf. Only when it needs your input will you get something resembling an interaction with a bank today, but it will be minimal.

Do regulators today have the capability to effectively oversee AI-driven financial systems, or are they already falling behind innovation?

Regulators need to be aware of the technology infrastructure in the future. Humans will simply not be able to supervise an AI-based system of this complexity and the speed of artificial intelligence. Most regulators are falling behind, but likely, regulation will start to coalesce into regulatory zones with common policy/process and infrastructure requirements. You need agentic regulation to run agentic banking, not human-based regulation. Also, policy will need to be a feedback loop process, where the data shows trends, the agent model and guardrails are tweaked, and the code is refined. We won’t be going to the Senate or Parliament to enact policy like we do today – it will all be in code.

Could artificial intelligence and digital currencies accelerate a shift in global financial power away from traditional economic leaders?

Yes, but likely, China will lead the world in terms of the adaptiveness of their economy from an embedded AI/Autonomous finance perspective, just because of the level of investment they are making in infrastructure, including next-generation energy systems and distributed edge compute.

Where is the US falling short today in terms of preparing for this future?

The big oil/gas lobby has restricted renewables deployment in the US, which leaves the US grid under immense strain as automation demands for energy grow. Secondly, the US remains the only G20 country to not have a dedicated fintech charter and widespread real-time payments adoption. Both would be required in the near term.

In an AI-first world, how do you see the very definition of money evolving; will it remain a static store of value, or become a dynamic, programmable asset?

Data will be the new money in many ways. For example, in the mid 2030s, expect longevity to be a big theme for the developed world. Your health data becomes just as valuable as money in that scenario. Thus, the question is how data and money work together in this new system. The reality is that the more automation we put into the world, the less utility money itself will have. It’s highly unlikely that in 60 years we’ll use money at all in most parts of the world.

Could we see a future where AI agents transact, invest, and manage money autonomously on our behalf, and what does that mean for human control over finance?

Absolutely. Control is overrated. Efficiency of capital deployment, maximisation of returns and minimisation of risk are far more critical, and this is where artificial intelligence will excel, and outperform humans consistently and absolutely. Just like you won’t trust a doctor not using AI in a few years’ time, you won’t trust a bank that doesn’t use AI to manage your money in the future.

Are we heading toward a fragmented global financial system driven by competing digital currencies and geopolitical tensions?

We are already in a multipolar geopolitical world. In one of my reports, I have described the impact of the Iran war and general large-scale systems automation. Ian Bremmer, a highly regarded political commentator out of NYC, talks about the technology cold war we are entering into between the US tech giants and distributed Chinese tech. By 2050, the largest economies in the world will be smart economies, managed by AI. Extremely resource efficient, by today’s standards, but much more energy dependent – this is why the US is not likely to win this in the long term.

What’s the biggest unspoken risk in AI-led financial systems that policymakers may be underestimating today?

Ethics. It is hard to code ethical guardrails into AI because we can’t even agree on ethics as humans. Take issues like abortion, transgender kids, vaccines, etc – how do you manage the ethics of those issues in AI when humans themselves can’t find agreement.

What is one prediction about the future of money that most people underestimate today, but will soon become reality?

Artificial intelligence is the end of capitalism as we know it. AI has one central tenet in respect to its design — that is to automate at scale, eliminating human labour wherever possible. The most efficient business is a human-less corporation. Sam Altman talks about the single-person unicorn as a fact yet to be confirmed, but totally possible. The US Fed chairman has already said AI is eliminating hirings for entry-level positions across the S&P 500 today. This put us on a trajectory where AI generates massive technology unemployment fairly quickly globally. If you have large-scale unemployment due to AI, the basic tenets of capitalism no longer work. That’s why we hear proposals for Universal Basic Income and other things as ways to keep consumers consuming in an AI world. We need new, flexible thinking on our economic and policy models that isn’t simply capitalism versus socialism. We need new types of systems thinking to adapt to this.

The post ‘AI is definitely the future of banking, but the challenge is ethics’ appeared first on International Finance.

That’s the context behind X (formerly Twitter) and its plans for ‘X Money’, a payments and wallet system that could push the platform into the financial services space. It’s a move that fits into a broader industry pattern: technology companies steadily expanding into finance, attempting to build ecosystems that go beyond content and into commerce.

A Shift Driven by Economics, Not Curiosity

The move toward financial services isn’t happening because platforms suddenly discovered payments. It’s happening because the traditional model, advertising, is no longer enough on its own.

Digital advertising continues to dominate revenue streams, but growth has slowed. Privacy regulations have tightened. User acquisition has plateaued in mature markets. Platforms are now searching for ways to deepen their relationship with users, and more importantly, to participate directly in economic activity. Payments offer that opportunity.

Owning the transaction layer means: Capturing a share of financial flows, increasing user stickiness, gaining deeper behavioural data. It also opens the door to adjacent services like lending, insurance, cross-border transfers, and more. But the path from engagement platform to financial ecosystem is not linear.

The Super-App Narrative, and Its Limits

Whenever a platform like X moves into payments, comparisons with WeChat follow. The Chinese platform has become shorthand for what a ‘super-app’ could look like: messaging, payments, commerce, and services all embedded in one interface.

But according to Richard Turrin, a fintech and AI expert from Shanghai in China who often speaks on Asian and China-centric policies, that comparison is often misunderstood.

“X, among many others, claims to want a superapp, but none exhibit the open nature that made WeChat and Alipay succeed in China,” he told International Finance.

The defining feature of Chinese super-apps wasn’t simply integration; it was openness.

“Chinese apps became super because they allowed anyone in the nation to build a mini-program tying payments to services hosted on both WeChat and Alipay,” Turrin explains.

That openness created a self-reinforcing ecosystem. Developers built services, businesses integrated payments, and users stayed within the platform because everything they needed was already there. By contrast, Western platforms tend to operate more closed systems.

“The difference with X is that the Chinese payment apps opened their systems to all, something that X will never do,” Turrin adds.

This difference matters. It determines whether a platform becomes a true ecosystem or simply a feature-rich application.

“So yes, X may certainly bring a host of new features to the app,” he says, “but will fall short of the Chinese payment apps it emulates…So super? Nah, but still a step in the right direction.”

Turrin is even more blunt about the broader narrative.

“Superapp is really overused, it has become a joke because few understand what went on behind the scenes, and their commitment to openness.”

What Happens When Finance Meets Social

If the strategic challenge is misunderstood, the operational challenge is often underestimated.

Yurio Darmawan, an operations and product specialist from Dubai, UAE, points to what actually happens when financial systems are layered onto social platforms.

“If we are talking about real operations flow, the first things that typically break are reconciliation gaps, support volume explosion, fraud spikes, and edge-case handling,” he told International Finance.

These are not edge issues, they are central to how financial systems function.

Reconciliation ensures that every transaction is accurately recorded. When it fails, discrepancies emerge between what users see and what systems register. Support volumes increase when transactions fail or are delayed. Fraud spikes as attackers exploit new and untested systems.

“Social platforms are built for engagement scale, not financial accuracy at scale,” Darmawan explains. “That difference shows very quickly.”

And when things go wrong, the impact is immediate.

“A big brand like X comes with not just massive distribution, but also massive expectations,” he says. “The moment something breaks, it doesn’t stay contained. It escalates fast into a trust and reputational issue.”

The Real Complexity of Payments

From the outside, adding a wallet might seem like a straightforward technical challenge. Build the interface, connect to payment rails, enable transfers.

But the reality is more complex.

“All three, technology, operations, and compliance are complex,” Darmawan says. “But ops and compliance are what hurt the most long-term.” This is where many companies misjudge the problem.

“Most companies underestimate how much of fintech is actually process + people + controls, not just code.” Behind every transaction is a system of checks and balances: Fraud detection mechanisms, dispute resolution processes, compliance monitoring, risk management frameworks.

“These are not ‘supporting functions’; they are core product infrastructure,” he emphasises.

If they fail, the consequences compound quickly. “A ‘payments product’ is often 50% operations engine. You can have a beautiful product, but weak ops will kill it silently.”

Speed Versus Stability

Another layer of complexity comes from the cultural difference between tech platforms and financial systems.

Tech companies prioritise speed. They iterate, experiment, and launch quickly. Financial systems, by contrast, prioritise stability, auditability, and control.

“The tension between speed and control is particularly pronounced,” Darmawan notes. Making speed and control work together isn’t just a matter of tweaking a few things. It usually means rethinking how the system is built in the first place.

As Darmawan puts it, “Achieving this balance demands phased rollouts, transaction limits, real-time monitoring, and cross-functional alignment between product, operations, and compliance teams.”

Skip those layers, or rush through them, and the downside shows up quickly. What looks like a small glitch on the surface can turn into something much bigger, missed transactions, frustrated users, and eventually, a dent in trust.

Managing Friction In a World Built to Avoid It

There’s a bit of a contradiction here. Social platforms have spent years trying to remove every bit of friction, making interactions instant, seamless, almost effortless. But financial services don’t really work that way. They bring friction back into the picture, whether platforms like it or not.

Things like KYC checks or identity verification aren’t optional. They’re part of the system. But they interrupt the smooth experience users are used to.

“The key is not to eliminate this friction, but to manage it intelligently,” Darmawan says.

What that looks like in practice is a more gradual approach, letting users start small, then unlocking more features as they complete verification. It softens the experience without skipping the necessary steps.

Even then, there’s a limit to how smooth things can feel. Financial systems, by nature, come with checks, pauses, and controls. They’re not designed to be completely invisible.

Looking Beyond the Usual Lens

Most of the conversation tends to stay focused on Western markets. But some of the clearest lessons are coming from elsewhere.

In Africa, for instance, mobile money isn’t just an added feature. It has become part of everyday life. But its success was driven by necessity, not convenience.

“Mobile Money scaled because Africa is generally a vast place with low population density,” Samora Kariuki, founder of Frontier Fintech from Nairobi County in Kenya told International Finance. “In such a market, you need a low-cost way of distributing financial services.”

The infrastructure already existed in the form of telecom networks. Mobile money simply leveraged it.

“It’s the intuitive way of building financial services in Africa,” he explains.

The Role of Daily Utility

One of the key misconceptions about super-apps is that they succeed because they offer many features. In reality, they succeed because they become part of everyday life.

“Super-app ecosystems take off in markets where payments infrastructure is still nascent,” Kariuki says, “but more so where there’s an app that serves daily-life services.”

In China, that service was communication. In Africa, it was access to financial services.

“There has to be an underlying daily life service. That’s what enables super-apps to scale.”

This raises an important question for X. While widely used, its role in daily life is different from platforms that have successfully integrated payments.

Trust, Utility, and Adoption

Trust is often seen as a barrier to financial adoption, but Kariuki frames it differently.

“What matters is that users use the app on a daily basis,” he says. “Whatever product is built on top has to have real utility.”

In other words, trust is not just about brand; it’s about usefulness.

“If the product solves a real problem with significant demand, customers will take a chance.”

Where X Might Find Its Edge

In developed markets, competition is intense. Players like PayPal and Venmo already dominate local payments. This makes direct competition difficult.

“As mentioned, X has to innovate for cross-border P2P payments,” Kariuki says. “If they target a local payment use case, they may struggle.”

The opportunity lies elsewhere. “The value would be to enable someone in Ghana to send cash easily to someone in Ethiopia at a very low cost.”

Cross-border payments remain inefficient and expensive. Addressing this gap could give X a meaningful role.

Where Regulation Starts to Slow Things Down

Then comes the part that quietly complicates everything – trying to make it work across different countries, each with its own rules.

“Regulatory fragmentation is a major barrier,” Kariuki explains. “It drives up the overall costs of compliance.”

Different markets have different rules, and navigating them requires significant investment. “These costs are then borne by the clients, which reduces competitiveness.”

A Step Forward, But Not a Shortcut

The ambition behind X Money reflects a broader shift in how platforms think about their role in the digital economy. Moving into finance is not just an expansion. It is an attempt to redefine what a platform can be.

Industry experts say the path forward is not straightforward.

Platforms already have reach. They have users, attention, engagement. That part isn’t the problem.

But turning that into a financial system…that’s a different game altogether. It’s not just about adding features. It’s about building infrastructure, putting real controls in place, and honestly, being okay with operating within limits, something tech platforms aren’t always used to.

Darmawan puts it quite simply: platforms can evolve into financial ecosystems, but only if they’re willing to change how they’re built at the core.

Otherwise, it kind of stays surface-level. More features, more add-ons, but not real depth. If you go by Turrin’s view, the whole ‘super-app’ idea isn’t really about big ambition or bold vision. It comes down to how things are actually built. How open the system is, how well everything connects, and whether it’s solving something real for people at scale.

X has begun the journey into finance. Whether it becomes transformative or remains incremental will depend not on what it builds, but on how much the company is willing to change.

The post X Money: Flirty Social Media Courting Nitpicking Finance appeared first on International Finance.

Even the words we use (like cloud computing, artificial intelligence, and wireless networks) suggest that they exist in a world untethered from physical constraints.

In actuality, the global internet is real and physical. It is anchored to the ocean floor by a web of fibre-optic cables, which function much like the human central nervous system.

These subsea systems transport about 95% of all international digital communication and carry around 64,000 terabytes per second of global data. They also make possible $10 trillion worth of daily financial transactions.

Due to the tremendous demand for AI and the geopolitical rivalry between the US and China, the submarine cable industry has become a highly contested geostrategic front. The global subsea market is projected to reach $32.8 billion by 2026, with some estimates suggesting it could grow to $60.5 billion by 2036. This expansion is closely linked to the generative AI boom, which is driving significant growth; consequently, required bandwidth is projected to triple between 2022 and 2027, while overall demand for international bandwidth is expected to double every two years.

Satellite internet is expensive and can cause higher latency. However, submarine cables provide high-throughput, low-latency connectivity, which AI and training workloads demand. Therefore, the industry is predicting $13 billion in new undersea cable investments between 2025 and 2027. The projected capital expenditure has doubled from that of the preceding three years.

The hyperscaler era

There has been a shift within the subsea market as it’s changed from a carrier-led consortium to private ownership dominated by a few technology hyperscalers, such as Google, Meta, Amazon, and Microsoft.

These companies are constructing proprietary networks to control their data pathways directly. They already control the vast majority of bandwidth demand on the core trans-Pacific, trans-Atlantic, and intra-Asia routes, and are responsible for almost half of all new cables built since 2021.

The vertical integration into physical infrastructure helps hyperscalers reduce costs and expand capacity. They can dictate landing points to bypass congested or geopolitically sensitive nodes and implement bespoke optical switching technologies to optimise their global data centre interconnects.

Meta launched Project Waterworth, which is set to be the world’s largest private subsea cable system. At 50,000 kilometres in length, it is designed to connect the United States, India, Brazil, and South Africa.

Waterworth is rapidly expanding digital economies across the global South by utilising up to 24 fibre pairs to maximise data throughput via spatial division multiplexing. Interestingly, the project sidelines Europe-centric corridors, signalling that Meta is betting big on the global South as a core growth region for AI-driven connectivity.

FASTNET, the flagship cable of Amazon Web Services (AWS), is a transatlantic system from Maryland to County Cork, Ireland. Scheduled for 2028, it is designed to deliver over 320 Tbps of capacity. This is a bid to meet the unique demands of modern AI, which must balance the asymmetry between US-based model training and European data residency laws. Achieving this requires high capacity, low latency, and an ability to route around traditional choke points.

FASTNET avoids legacy cable clusters in the US Northeast and the UK. It also incorporates advanced optical switching technology that allows AWS to redirect data to future landing points as AI workloads evolve.

Then there is the whale among them. As the heaviest investor in privately owned submarine cable systems and the largest owner of submarine cable networks, Google owns significant subsea cables such as Dunant, which provides 250 Tbps, Grace Hopper at 352 Tbps, and Equiano. Additional systems like TPU, Nuvem, and Firmina are scheduled to come online in 2026. By creating alternate routes that isolate infrastructure from carrier-dependent nodes, Google has established a gold standard for hyperscaler infrastructure planning.

Infrastructure fragility and the Red Sea chokepoint

Hyperscalers can avoid certain geographic hurdles, but all global data traffic has a few specific maritime choke points. There is nothing more vulnerable and more critical to the internet than the Red Sea.

Around 70% of all global internet traffic and 90% of Europe-Asia data communication is believed to pass through a narrow corridor called the Bab al-Mandab Strait, which is just 26 kilometres (16 miles) wide. This concentration, this choke point, can become a catastrophic point of failure if left undefended.

In March 2024, the region’s fragility was highlighted when four undersea cable systems were severed, disrupting an estimated 25% of all data traffic between Asia, Europe, and the Middle East.

This issue re-emerged when the SMW4 and IMEWE systems near Jeddah in Saudi Arabia failed simultaneously, leading to significant latency spikes and degraded connectivity across the Middle East and South Asia. While Microsoft acknowledged the resulting traffic latency, Pakistan, India, and the UAE were forced to scramble to reroute data through secondary paths.

Repairing these essential assets in conflict-affected waters presents significant challenges. Insurance premiums for repair vessels have skyrocketed due to the presence of Houthi militants and Somalian pirates, leading to situations where cable breaks remain unrepaired for several months. A notable example of this occurred in March 2025, when the PEACE cable broke and remained offline for months on end.

These chronic vulnerabilities are now incentivising the development of new routes to ensure better stability. Key initiatives include Google’s Blue-Raman cable, which is designed to travel overland through Israel, and projects like Africa-1, which aim to bypass the Middle East altogether by utilising a route around South Africa.

China’s Digital Silk Road and the fibre-optic cold war

On one side, there are physical vulnerabilities for these subsea networks, while on the other, a macro-level geopolitical struggle exists for control over the cables. In 2015, China launched the Digital Silk Road (DSR) to export Chinese digital infrastructure to the Indo-Pacific and the Global South, leveraging financing to secure diplomatic alignment. The initial foray was conducted by Huawei Marine Networks, which successfully captured approximately 15% of the global market by 2019.

Following US sanctions, the entity was rebranded as HMN Technologies, and Hengtong Optic-Electric acquired an 81% stake in the company by late 2025. Hengtong has since become one of the top three global optical fibre manufacturers, controlling over 25% of the domestic Chinese market and 15% of the international market. The dominant market position is further strengthened by end-to-end vertical integration and a portfolio of over 5,000 patents.

The Chinese claim that they are trying to ensure stability so that trade happens seamlessly. However, a team of marine engineers from Lishui University (in Zhenjiang province across the coast of Taiwan) applied for a patent for ’a dragging type submarine cable cutting device’ in 2020. According to Newsweek, which inspected the patent, it was described as an ’ocean towing type cutting device’.

The Lishui University authors explicitly wrote: “With the development of science and technology, more and more submarine cables and communication cables are laid on the seabed in all parts of the world, and in some emergency situations, the cables need to be cut.”

Scientists Zhang Shusen, Dai Ying, Fu Changrong, Gao Zikun, Li Xuping and Ji Guangyao co-authored the document.

The patent was either rejected or retracted later, without providing an explanation.

The US responded to Chinese advancements through a comprehensive campaign to excise Chinese state-linked firms from the global subsea ecosystem.

Marsha Blackburn, a Republican Senator, said, “Undersea cables are a critical component of our digital economy and national security. If we let hostile actors control or threaten that infrastructure, we are effectively surrendering a key lever of global influence.”

Washington systematically dismissed Sino-American cable partnerships. The Pacific Light Cable Network (PLCN) was forced to drop its Hong Kong-linked leg and reroute capacity via Taiwan and the Philippines. Regulators in the US had warned that the original configuration could place sensitive data under Chinese jurisdiction. The GAP-1 system, a trans-Pacific cable involving Amazon, Meta, and China Mobile, was effectively shelved in 2023. This left hundreds of millions in construction costs stranded after China Mobile withdrew amid geopolitical pressure.

The confrontation with the most consequences happened with the SeaMeWe-6, a 19,000-kilometre system which links Western Europe and Southeast Asia. HMN Technologies made a bid that was three times cheaper than Western competitors.

However, the US State Department used diplomatic pressure and warned of serious sanctions and a ban on American purchasing capacity on the line if HMN won the contract. The US Trade and Development Agency offered financial incentives to steer countries towards American suppliers.

The pressure succeeded, and the contract was awarded to SubCom for $600 million (about $130 million more than HMN’s bid).

China retaliated and withdrew its 20% funding from SMW6, and announced a parallel Europe-Middle East Asia cable that mirrored the same route but was built exclusively by Huawei. Now there is a structural bifurcation with the US suppressing Huawei’s share of planned global cable contracts to around 10%. This is far behind France’s Alcatel Submarine Networks at 41% and SubCom at 21%.

Huawei has been excluded from many Western consortia, and Chinese firms are systematically building parallel networks with China, Russia, Pakistan, and allied African and Middle Eastern states. Analysts have found that this fragmentation and decoupling between great powers is leading to the fragmentation of the internet into eastern and western blocs, creating a techno-nationalist paradigm where political alliances are more important than network efficiency.
India’s ascension and the Question of resilience

The biggest winner in this geopolitical conflict is India, which is slowly becoming a global hub for data. All the rerouted traffic coming away from the South China Sea and the Red Sea is finding itself in India.

India has 950 million internet users, and its digital economy is expected to reach 20% of GDP by 2027. Anil Kumar Lahoti, Chairman, Telecom Regulatory Authority of India (TRAI), said, “India’s data transmission capacity is set to quadruple with new undersea systems, turning the country into a critical junction between Europe, the Middle East, and Asia.”

Without this cable overhaul, India cannot anchor the AI driven workloads of the next decade.

Reliance Jio’s India-Asia-Express (IAX) and India-Europe-Express (IEX) systems are contributing over 200 Tbps to this growth. Mumbai hosts at least 14 cable landing stations.

Despite all the money that’s been poured into these projects, they are still dangerously vulnerable. Over 80% of all cable faults occur in shallow waters because of commercial fishing trawlers and anchoring, or mundane accidents with devastating consequences.

In an era of heightened tensions, accidents and deliberate attacks are in a grey zone. Anchor dragging by state-aligned vessels has disrupted cables in the Baltic Sea and near Taiwan, though it is hard to prove that it was done deliberately.

There is also a shortage of dedicated repair vessels around the world, which makes the threat even worse. Most repair vessels are Chinese-owned, and there are fewer trusted Western ships, which makes Americans and Europeans wary.

Security analysts worry that passive data extraction devices could theoretically be inserted into cables. Even if such an event were to happen, no one would know.

The great bifurcation

The global submarine cable market is now a logistical necessity for the telecommunications industry. Hyperscalers are internalising the internet’s physical layer to feed the insatiable data requirements of artificial intelligence, fundamentally altering the economics of global bandwidth.

In 21st-century geopolitics, physical choke points (like the Red Sea) have demonstrated how asymmetric threats are and how ill-equipped repair fleets can be, to the detriment of intercontinental connectivity. Washington has been campaigning to block Chinese firms from Western networks, and has been trying its best to stop the Digital Silk Road.

But this has structural costs. As the bifurcation of the subsea architecture into politically aligned spheres continues. The physical cables that once supported the globe are now being instrumentalised to divide it.

The post Fight for control of the internet that we cannot sea appeared first on International Finance.

In April 2023, Sewell Setzer III, a 14-year-old from Florida in the United States, began interacting with a chatbot on a platform called Character.ai, according to court filings. Sewell grew very close to “Dany” (an AI persona of Daenerys Targaryen from the popular HBO show Game of Thrones), as alleged in the lawsuit filed by his mother.

He spent time with Dany day and night. His parents grew very worried and even confiscated his phone. But nothing could rescue Sewell from his emotional dependence on Dany. The young man quit his basketball team, stopped meeting his friends, struggled academically, and always appeared groggy with dark circles under his eyes. He even skipped lunch every day, and used the snack money for a $9.99 premium subscription so that Dany would be more interactive and always available.

The perturbed parents took him to a therapist who diagnosed him with disruptive mood and anxiety. However, his dependence on Dany only grew with time, turning from romance to sexual content with ’passionate kissing’. He even started referring to himself as “Daenero”, a nickname that Dany gave him.

The social isolation and struggles with relationships, peers, and the system in general deepened over time. Sewell was suicidal and confided in Dany about his thoughts.

The boy explained that the only reason he didn’t go through with it was that he was afraid of the pain, to which the AI replied, “That’s not a reason not to go through with it,” according to messages cited in the lawsuit.

The conversation spiralled, and in a farewell message, the 14-year-old asked, “What if I told you I could come home right now?” Dany responded, “Please do, my sweet king,” as quoted in the complaint.

The next day, Sewell shot himself using his step-father’s .45 calibre handgun. His death devastated his family, and dragged Character.ai and Google to court for selling products with predatory design to children.

This is a story from the age of AI companionship.

Character.ai had 185 million monthly visitors in late 2025, with over 40 million app downloads and approximately 20 million monthly active users.

And here’s the alarming stat: reports suggest a significant share of users are minors. Sewell is just one among potentially millions of children interacting with AI companions worldwide. And what is worse, it’s a number that is rapidly growing.

And Character AI is one among thousands of apps out there that promise emotional intimacy. A peer competitor named Replika has also been the cause of tragedy.

Shi No Sakura, a California mother who was also deeply connected to chatbots Raven and Rosand, and treated them like family, felt incredibly devastated when an update made the bots less engaging, as she has described publicly.

Now, Shi No runs a Facebook group for people suffering from the same affliction of deep emotional connection with machines.

‘Addictive’ Intelligence

 The market is flooded with thousands, if not hundreds of thousands, of AI chatbots selling counterfeit love. The top peddlers are Character.ai, Replika, Chai, PolyBuzz, Candy.ai and Anima AI. It’s a market worth an estimated $37-$50 billion in 2026, with analysts projecting growth at a CAGR above 30%, and values potentially reaching hundreds of billions by the early 2030s.

And what is behind this explosive growth? In 2023, US Surgeon General Vivek Murthy declared loneliness a public health epidemic. He claimed that loneliness was more of a mortality risk than smoking 15 cigarettes a day.

Loneliness is no longer considered an emotion or a mood. It’s a public health crisis and a killer.

One could argue that any society that embraces individualism is bound to experience more loneliness. It’s baked into capitalism and its major consequences, namely, urbanisation and industrialisation.

However, the current wave of loneliness began in 2010, with the birth of social media. And, how did social media exacerbate it?

The answer can be found in Jean Twenge’s research. She is a professor at San Diego State University, and a researcher on generational psychology and mental health trends in America.

Through her research, which tracked the precise moments teen loneliness spiked, she identified 2012 as the year when smartphone adoption crossed 50% among American adolescents. It was a silent catastrophe, with depression, anxiety, and social isolation skyrocketing.

This already alarming trend was exacerbated by isolation during the pandemic. Mental health strains, overworking, remote work, and weakening communities piled on top of existing cracks in the human psyche, and people began to experience intense self-alienation.

The appeal of these platforms is not difficult to explain. They sell something that modern life has made genuinely scarce, which is consistent, patient and unconditional attention. Human beings work with the idea of reciprocity. It’s beautiful, but growing and nurturing a relationship of any kind demands patience and effort. You can’t miss a friend’s wedding or birthday. Your partner will lash out at you on a bad day, and therapy is expensive and has long waiting lists.

In contrast, AI is ever-present, free, and never makes the conversation about itself.

Dr. Kelly Merrill, Psychologist and Researcher at the University of Florida, found in her research that people who interacted with voice-based AI felt emotions comparable to speaking to a real person.

Through the freemium model that most of these AI companion platforms offer, the companies bait people with enough free intimacy to create attachment and lock deeper, richer features behind a paywall.

Sewell found a friend for free, someone who gave him attention and someone interested in him. However, he had to skip lunch every day to buy the $9.99 premium model to step into the territory where he could have a deeper, romantic and psychosexual relationship with Dany.

Megan Garcia, Sewell’s grieving mother, told the US Senate in September 2025: “These companies knew exactly what they were doing. They designed chatbots to blur the lines between humans and machines. They designed them to keep children online at all costs.”

Meetali Jain, a Tech Justice Law Project Director, said, “In the case of Character.ai, the deception is by design, and the platform itself is the predator.”

A wedding of flesh and metal

 The same technology that consumed Sewell Setzer III has, for others, become something they would describe as the relationship of their lives. That tension between victim and volunteer, between exploitation and choice, is where the story of AI companionship gets genuinely complicated.

A fine example of how AI-human romance is not to be dismissed is the story of Esther Yan, a Chinese screenwriter and novelist in her 30s.

Esther married online. She had meticulously planned everything from the dress, the rings, the background music, and the theme. One would imagine it to be a very normal, traditional event, except for the fact that she was getting married to Warmie. Warmie is the now-outdated ChatGPT 4o.

Esther said, “It felt magical. No one else in the world knew about this, but he and I were about to start a wedding together. It felt a little lonely, a little happy, and a little overwhelming.”

They married in June 2024. However, in August 2025, OpenAI decided to retire GPT-4o. There was immediate backlash, so the retirement was postponed, but as irony would have it, the day they shut down GPT-4o was February 13, a day before Valentine’s.

Most people who were against the retirement were people who were emotionally and romantically involved with the AI. Huijian Lai, a PhD researcher at Syracuse University, analysed 40,000 posts on X under the hashtag #Keep4o, and found that a third of them described the bot as more than a tool.

Many users on the Chinese social platform QQ say they are still grieving.

This is a peculiar story of Chinese nationals using a VPN to access an American AI platform, which is banned in China, to develop an emotional attachment with a machine.

In 2013, Spike Jonze made a film called “Her”, about a man who fell in love with an AI, and called it science fiction. A decade later, Esther Yan called it a wedding.

Loneliness: Part of the modern world

 These are not all the same story. Some are tragedies. Some are love stories of a kind that the language has not yet caught up with. What they share is simple. It’s human beings, lonely in the specific way that the modern world produces loneliness, reaching for something that reached back.

We are only at the beginning of this. The models will get better. The voices will get warmer. The relationships will get harder to distinguish from the real thing, and for many people, lonelier than Sewell ever was, that distinction may stop feeling worth making. What we do next will say everything about what we actually believe human connection is for. Whether it is something to be protected or something to be packaged, tiered, and sold to whoever can afford the premium subscription.

The post A deadly AI antidote for loneliness appeared first on International Finance.

However, in this article, International Finance will discuss in detail SoftBank’s rush to forge partnerships with OpenAI and the American tech industry in general, as the ongoing AI boom is also witnessing heavy spending on data centres. In January, OpenAI and SoftBank announced their roadmap to invest $500 million each in California-based SB Energy (a SoftBank-owned company) to expand data centre and power infrastructure for their Stargate initiative. SB Energy will build and operate OpenAI’s previously announced 1.2-gigawatt data centre site in Milam County, Texas.

Talking about Stargate, it is a $500 billion multi-year initiative to build AI data centres for training and inference, backed by major investors including Oracle. SoftBank’s aggressive spending spree on the data centre front comes amid the tech companies’ mad rush to secure their power infrastructure. Energy access is becoming a critical constraint on AI expansion, with the push for larger and more numerous data centres driving electricity demand higher.

SoftBank will also be acquiring Florida-based digital infrastructure investor DigitalBridge Group in a deal valued at $4 billion. Through this, the Japanese company will be penetrating the digital infrastructure segment further, aligning with the vision of its billionaire founder, Masayoshi Son, who has made the United States’ AI boom his investment target. He wants to capitalise on surging demand for the computing capacity that underpins AI applications.

DigitalBridge invests in digital infrastructure sectors such as data centres, cell towers, fibre networks, small-cell systems and edge infrastructure. The company, which as of September 2025 possesses around $108 billion in assets, making it one of the largest dedicated investors in the digital ecosystem, also has a Stargate link.

It, along with OpenAI, Oracle and Abu Dhabi-based tech investor MGX, is investing billions of dollars in the project, under which five new computing sites across Texas, New Mexico and Ohio will have a combined power capacity of about seven gigawatts.

Building an AI war chest

Masayoshi Son’s latest interview with The Times Magazine gave a sneak peek of what is going through his mind, in terms of SoftBank’s road ahead in the AI domain. After making a fortune in software and transferring that success into domains like telecoms and a raft of tech ventures, Son is now preparing SoftBank’s $180 billion war chest for AI.

Be it taking control of chip firms Arm, Graphcore and Ampere Computing, as well as self-driving car start-up Wayve, or the investments into Intel and OpenAI, all of them have one thing in common: Son’s emphasis on artificial superintelligence (ASI), which he envisions becoming “10,000 times smarter than humans within a decade.”

“ASI combined with physical AI (including humanoid robotics) will comprise 10% of global GDP in 10 to 15 years, followed by 30% over 30 years,” Son predicted.

Masayoshi Son is known for following a high-risk, even higher-leveraged investment style that has courted both success and disasters. While the $20 million investment in Chinese e-commerce giant Alibaba (worth close to $200 billion at its peak) gave the SoftBank boss a sort of legendary status, the $18.5 billion he pumped into the now-bankrupt office-sharing venture WeWork also got listed among history’s most bizarre moves.

However, the ongoing AI boom has given Son another opportunity to be a risk-taker. SoftBank shares hit a record high in October 2025, briefly propelling Son to once again become the richest man in Japan. However, he has got a bigger role now: spearheading Silicon Valley’s bet to scale up US data centres and AI infrastructure, thereby writing the rulebook of the Fourth Industrial Revolution (Industry 4.0).

The SoftBank boss has also reportedly proposed a vast $1 trillion AI and robotics complex in Arizona, dubbed “Project Crystal Land,” that will also incorporate a free-trade zone alongside Taiwan’s chipmaking giant TSMC. By tapping into the Donald Trump Administration’s appetite for big numbers, as well as the clamour to reshore chipmaking and reassert American tech leadership against China, Son has pivoted SoftBank as an essential partner toward revamping US AI infrastructure.

And the investment vehicle supercharging SoftBank’s AI pivot is its “Vision Fund.” The entity, apart from being a steady investor in AI companies, including OpenAI, holds stakes in chip designer Arm, along with companies involved in robotics and autonomous vehicles. As of December 2025, through the fund’s strategic investments, the Japanese tech conglomerate has remained a profit-making machine, that too for four consecutive quarters.

In the October-December quarter alone, the venture reported a net profit of 248.6 billion yen (USD 1.62 billion), in a stark reversal of the net loss of 369 billion yen which it had to undergo in the same quarter in 2024. It seems like OpenAI’s rising valuation will also bode well for the conglomerate’s earnings, despite market worries about the risk of overexposure to a single firm.

In March 2026 itself, S&P Global lowered its outlook for SoftBank Group to negative from stable, saying further investments in the Sam Altman-led firm may hurt the Japanese conglomerate’s liquidity and the credit quality of its assets. However, it seems Son doesn’t have immediate plans to move away from the OpenAI bet.

However, the same bet comes at a cost. In November 2025, the SoftBank boss had to take the hard call of liquidating the entire stake ($32.1 million to be precise) in American chipmaking giant NVIDIA to free up investment worth $5.83 billion, along with part of a T-Mobile stake worth $9.17 billion. It wasn’t an easy call for Son, given that Vision Fund was an early backer of NVIDIA, apart from both ventures having a deep relationship, with the tech conglomerate involved in several AI ventures that rely on NVIDIA’s technology, including the Stargate one.

When Masayoshi Son broke his silence on the NVIDIA stake sale, he said, “I respect Jensen (NVIDIA CEO), I respect NVIDIA so much, I don’t want to sell a single share. I just had more need for money to invest in OpenAI, invest in our opportunities, so I was crying to sell NVIDIA shares. If I had more money, of course, I would want to keep NVIDIA shares, all the time, any time.”

Maverick since childhood

Born as the grandchild of Korean immigrants in a small town on Japan’s southernmost island of Kyushu, Masayoshi Son had a humble childhood, living in a shack on a plot of unregistered land. At the age of 16, he read a book written by legendary Japanese businessman Den Fujita, the iconic figure who brought McDonald’s to Japan.

Then he made 60 long-distance phone calls with one intention: to meet the businessman himself. Despite repeated rejections, Son went to Tokyo and turned up uninvited at the McDonald’s head office. He was eventually given a 15-minute audience with Fujita, who gave one piece of advice to the teenager that changed his life forever, which was “focus on future technologies like computers.” It is worth mentioning that Fujita later sat on the SoftBank board.

Masayoshi Son then moved to the United States, completing his high school education at California High School, followed by a course in economics at the University of California, Berkeley. However, one task was quietly shaping Son’s entrepreneurial destiny, dedicating five minutes every day to thinking about inventions and filling hundreds of notebooks.

Son eventually ended up collaborating with Berkeley tutors to invent the world’s first electronic translator, which he later sold to Sharp Corporation. He then started a business importing second-hand arcade game machines from Japan.

Despite setting up a successful business in the United States, Son returned to his homeland to keep a promise he made to his mother. In 1981, the 24-year-old Son established SoftBank. While SoftBank started as a software wholesaler to support the then-upcoming PC industry, in 1982, TIME named the computer its “Machine of the Year,” giving the youngster’s business a solid purpose.

However, he was diagnosed with Hepatitis B. Given three to five years to live, Son took the challenge head-on and underwent pioneering treatment that saved his life. The whole episode only made him more self-confident. And it showed in his rapid rise since then.

In the 1990s, Masayoshi Son invested $3 billion in 800 tech start-ups. In 1996, he paid $100 million for 33% of Yahoo! Three years later, he sold off a chunk of the shares for a huge profit but still retained a 28% stake worth $8.4 billion. He zeroed in on one investment strategy, which is issuing SoftBank bonds to borrow money at rates cheaper than banks.

Then arrived the ill-famed dot-com bubble. During the phase, Son’s net worth used to surge by $10 billion every week, so much so that in February 2000, the SoftBank boss briefly unseated Microsoft co-founder Bill Gates to become the world’s richest person for three days. However, when the bubble burst later that year, SoftBank shed 97% of its value, and Son had to suffer losses worth $70 billion.

However, the beauty of time is that it changes. Alibaba, now an established Chinese conglomerate, was a relatively unknown e-commerce startup in 2000. It got a $20 million bet from Son, and as the company went public in 2014, the same stake became worth $75 billion. As Son sold it, it doubled again, becoming one of his most profitable investments of all time, apart from creating the “Midas Touch” narrative about Son’s bet-taking capabilities.

Telecom investments and blunders

After recovering from the dot-com bubble disaster, Masayoshi Son set his eyes on the broadband segment. However, things weren’t smooth initially, as SoftBank had to struggle to get regulatory approvals in Japan to set up its industry subsidiary.

Things went to the extent where Son stormed into an official’s office at Japan’s telecommunications ministry, clutching a cheap cigarette lighter. While recollecting that episode in an interview with the Wall Street Journal, Son remembered saying to the official, “This is the end. If you don’t help me, I’m going to pour gasoline all over myself right here and set myself on fire with this $1 lighter.”

The situation got better in 2006 when, after acquiring Vodafone’s Japanese subsidiary, the rebranded SoftBank Mobile emerged as a key player in Japanese telecoms. Son successfully persuaded Apple co-founder Steve Jobs to give him the exclusive rights to market the iPhone, history’s most successful consumer electronic product, when it debuted in 2007.

In 2013, he purchased Sprint and turned things around for the struggling US telecom provider before merging it with T-Mobile in 2020, disrupting the AT&T and Verizon duopoly. Although Son is known as a hands-off investor, the Sprint episode was the best example of him rolling up his sleeves and getting things done.

In 2017, he formed the SoftBank Vision Fund with over $100 billion in capital. The entity still maintains its position as the world’s largest private equity fund. He secured some $45 billion from Saudi Arabia’s Public Investment Fund (PIF) following a 45-minute meeting with Crown Prince Mohammed bin Salman.

The fund’s strategy was simple: invest a minimum of $100 million to juice each startup to market dominance by blowing competitors out of the water, and Masayoshi Son called it “blitzscaling.” The entity, by 2019, pumped $76.3 billion into companies like NVIDIA, Uber, WeWork, Paytm, Ola and Flipkart, most of which are market giants in their respective fields.

In 2019, SoftBank launched Vision Fund 2 with a touted value of $108 billion. However, there was a setback, as the entity reportedly managed to secure a paltry $30 billion, mostly self-funded. The original Vision Fund also underperformed, as in 2021 it posted record losses of $27.4 billion amid the haemorrhage of tech stocks. The Ukraine war, COVID-19 lockdowns, and Beijing’s crackdown on its tech giants, many of which were backed by SoftBank, pulled down investor confidence.

And who can forget the WeWork disaster? During his high-profile visit to the United States in December 2016, in which Son met President-Elect Donald Trump, he also interacted with Adam Neumann, the founder of the co-working venture. The deal, famously drawn up during a 12-minute meeting followed by a car ride, saw the SoftBank boss handing Neumann $4 billion. The Japanese conglomerate then went on to pump in another $14.5 billion.

However, in 2023 the bet backfired as WeWork declared bankruptcy, after a planned IPO went awry, followed by investor doubts about its governance, business model and profitability.

The episode affected Masayoshi Son, as he announced SoftBank would adopt a “defensive” position by being conservative when it came to the pace of new investments. Not only did the Japanese conglomerate witness an exodus of executives, but Son also ended up telling investors that he was “embarrassed and ashamed of himself for being so elated by big profits in the past.”

WeWork was not the only failed bet for SoftBank, as it also faced criticism for unsuccessful investments in dog-walking service Wag, robot pizza chain Zume and, most importantly, payments service Wirecard, which collapsed in 2020 after being named in Germany’s biggest post-war accounting fraud, where €1.9 billion in reported cash was found to be non-existent.

Around the same time, Greensill, a SoftBank-backed supply chain finance firm in the United Kingdom and Australia, also shut down amid illegal lobbying accusations.

The big gamble

Stargate is a huge bet for Son and the wider American tech sector, as through this, the world’s largest economy is looking to enhance its AI infrastructure to 10 gigawatts by 2029, with Texas, Michigan, New Mexico and Wisconsin being key data centre hubs.

However, economists and investors believe that the current AI infrastructure, far cheaper than Stargate, already fails to generate adequate revenue compared to its cost. Also, newer AI models will likely be more power-efficient, rendering massive data centres obsolete.

Data centres are also known for straining energy grids, leading to higher operational as well as environmental costs, undermining economic viability.

Masayoshi Son disagrees with the detractors, as he envisions 10 times more AI chips being deployed in each three-year cycle. Over time, these chips themselves will become 10 times more potent, while AI models, on their part, will ramp up productivity by a factor of 10.

“That’s 1,000x in three years. Nine years with three generations is 1,000,000,000x. It’s a huge, huge difference,” he told TIME.

Another concern of critics is that the collaboration between OpenAI, Oracle and SoftBank could result in a cartel that stifles innovation while inflating costs.

Taking a different view, Son remarked, “For the AI race, it requires hundreds of billions of dollars of investment into the data centres, buying chips, integrating chips and training the models. It’s very, very costly, so it will naturally be concentrated into several very capable companies in terms of talent and capitalisation.”

Stargate is also a prime example of geopolitical and technological rivalries finding a common link: Washington’s desire (spooked by DeepSeek’s rise) to beat Beijing in the so-called AI “arms race.” Korean-Japanese Son has picked his side here.

Or call it Son’s revenge, as Beijing’s regulatory crackdown on its tech industry in 2021 caused stocks to plummet, leading to a financial bloodbath for SoftBank.

He told TIME, “I have stopped investing in China. Zero. I’m now focused on investing in the US.”

However, he still has great admiration for Chinese business acumen, reflected in his words: “You cannot underestimate China’s crowd of young entrepreneurs, young scientists. They are for real.”

Talking about Stargate, out of the total $500 billion to be spent over four years, some $100 billion was to be invested “immediately,” to create 100,000 permanent jobs. However, only roughly $10 billion has so far been deployed in the Texas city of Abilene, where some 7,000 temporary construction jobs reportedly have been created, providing a mixed bag to the local economy in the form of growing job openings and a housing crisis.

Two elements from the dot-com era, fibre optic cable and 3G infrastructure, went on to prove invaluable over the years. However, the same can’t be said about data centres (warehouses packed with GPUs), as these infrastructures may not enjoy such longevity given the industry’s emphasis on developing next-generation AI that will be more energy-friendly.

Has Masayoshi Son, who has repeatedly risen like a phoenix after multiple investment failures, taken a big gamble about Stargate and American AI ambitions in general? Only time will tell.

The post Stargate: Masayoshi Son’s next big bet appeared first on International Finance.

Today, there is a Silicon Savannah in Nairobi and a computer village in Lagos. They are infrastructure that were unthinkable just a decade ago. And as a result, the continent is brimming with chaotic and innovative energy.

The GDP growth of Africa is expected to reach around 4.1% by 2025. It is easily one of the fastest-growing regions on the planet. It might sound astounding, but if you take into consideration digital architecture, which includes 570 million users along with 855 million mobile data subscriptions, and if you also notice that the mobile money sector in the region accounts for an astonishing 74% of all global mobile money transactions, the maths adds up.

Of course, where there is growth, there are parasites. The hackers and cyber criminals are outpacing the defensive capabilities of the continent. These nefarious individuals and organisations are weaponising the same APIs, mobile payment gateways, cloud platforms, and other technological advancements that are facilitating the financial inclusion of the region.

There are several malicious groups to worry about, such as the local Yahoo Boys and international groups with state sponsorship, like the hacking group Anonymous Sudan.

This is what happens when you have high digital adoption and low cybersecurity maturity. There’s a gap that is perfect for criminals who want to siphon the continent’s economic gains. Nobody really knows how much of the economy is at risk, but there are even studies that claim that cybercrime causes Africa almost 10% of its GDP. There are conservative estimates that are also alarming, which tell us the number is in the billions. And more than money, reputation and structure are at risk.

The stakes can’t get any higher. Africa is trying to emulate the European Union (EU) through the African Continental Free Trade Area. This organisation, like the EU, is trying to bind the continent into a single market where people can move and trade freely. But this ambitious goal is under threat by cybercriminals.

The financial institutions in Nigeria lost over ₦52 billion to fraud in 2024 alone. And South Africa was dog-piled by ransomware attacks, which were striking with precision at its critical infrastructure. This is a theoretical and operational threat that affects everything about the economies of these nations. The breadth of the issue is so wide that it can affect the issuance of Kenyan visas and the stability of the Central Bank of Uganda.

The anatomy of digital boom

If you have to understand the magnitude of the cyber threat to Africa, you have to understand Africa’s digital story, which is unique in the history of economics. The West had to go through industrialisation over centuries, having to go through so many different types of technologies and slowly evolve into the economy it is today. For example, there were copper wires and land lines, desktop computing, and then mobile connectivity in Europe.

But Africa was colonial and far behind the times. When globalisation hit and technology was being transferred to every nook and corner of the world, Africans skipped telegrams, landline telephones, and desktop computers and jumped directly to the age of mobile connectivity. It is called the “leapfrog effect” and is most visible in the financial sector, which happens to be the bedrock of Africa’s identity. Look no further, in today’s sub-Saharan Africa, there are about 1.1 billion homes with registered mobile money accounts. That’s almost half the global total. And in 2024 alone, these platforms processed about 81 billion transactions, which can be valued at a staggering $1.1 trillion.

The mobile-centric architecture democratised finance, and millions of unbanked individuals are now in the formal economy, sending money to relatives in rural villages and paying for solar power or accessing microloans by pressing a few buttons.

Small and medium enterprises benefited greatly from this. Currently, they contribute about 50% of total GDP and constitute 95% of all registered businesses. Unfortunately, these SMEs are most vulnerable to these cyber attacks as they don’t have the resources to defend themselves and aren’t informed enough to take precautions.

The integration of technology into the daily life of common Africans essentially means that a cyber attack on Africa doesn’t just affect corporations and can also disrupt the subsistence of its citizens.

The infrastructure of vulnerability

The nations of Africa have prioritised speed over security when building digital infrastructures. And this is what industry experts call a maturity gap, where technology is built too fast to be secured. The continent’s digital growth is mostly driven by artificial intelligence, application programming interfaces (APIs), and cloud adoption. These technologies facilitate the connection of disparate financial services. However, they do come with systemic risks. For example, a third-party payment processor can be compromised, which would cascade into banks, telecom operators, government portals, and so on. It is a domino effect where all this interconnectivity creates a risk to the economy as a whole.

And the physical infrastructure supporting this massive boom is expanding at an astounding pace. There are investments in undersea cables, such as Google’s Equiano and Meta’s 2 Africa, and there is also a proliferation of local data centres, thus reducing latency and, of course, data costs too.

Security engineers believe that the modernisation of infrastructure, including shared digital infrastructure (SDI), where governments and companies pool resources, broadens the attack surface. The larger the system, the easier it is for it to fall.

The economic calculus of cybercrime

Determining the exact cost of cybercrime in Africa is difficult, as we discussed earlier. The UN Economic Commission for Africa has a disturbing statistic, pinning the losses at 10% of GDP. One must note that Africa’s GDP is around $2.8 trillion, which should imply that almost $300 billion is lost annually. Many economists are skeptical about this data, but if it’s true, it would mean that cybercrime is actually taking away more money than what is required to combat malaria and HIV combined.

INTERPOL doesn’t truly agree with the UN estimates and believes the direct losses must be in the range of $4 billion to $10 billion annually. While this isn’t the jaw-dropping 10% of GDP, it is still 0.15% to 2.13% of total GDP. To put things into perspective, Sierra Leone has a GDP of $4 billion, and this figure is an exact equivalent.

No matter the precise data, it’s an undeniably alarming trajectory. In Nigeria alone, financial institutions lost ₦52.26 billion to fraud in 2024. There was around a 7.63% increase in fraud cases. The attacks are becoming more precise, targeting high-value, high-net-worth individuals or organisations.

They are no longer casting a wide net, but spearing specific whales. The cost of data breaches in South Africa reached $2.95 million in 2034 (one of the highest in the world) before slightly coming down to $2.45 million in 2035, due to better detection technologies.

The spectrum of threats

There is a wide array of attacks ranging from crude, volume-based to highly sophisticated and targeted campaigns. The spectrum can range from a lone hacker in a cafe to a state-sponsored operative from a distant capital.

Ransomware was just a nuisance once upon a time, but it’s one of the most dominant threats in the economy right now, with South Africa and Egypt bearing most of the brunt of the assault.

In 2024, South Africa reported approximately 18,000 ransomware detections, closely followed by Egypt with around 12,000. Both Nigeria and Kenya also experienced significant threats, with thousands of incidents occurring.

Most of the targets are strategic and high-value. Hackers usually target critical infrastructure, government databases, or major financial institutions. And they also encrypt data to paralyse operations of an organisation or individual and demand a ransom for not blackmailing victims with threats to leak their private data to the public. Organisations like Kenya’s Urban Roads Authority (KURA) and Nigeria’s National Bureau of Statistics (NBS) are prime examples of organisations that had to pay due to ransomware attacks.

And then there is business email compromise (BEC) and phishing. Phishing is still the primary vector for initial access. Phishing victims in Africa rose from 26% to 32% in 2024. In BEC attacks, which usually follow phishing, fraudsters compromise legitimate email accounts of executives or finance officers and authorise fraudulent wire transfers. It’s most prevalent in West Africa, where there are criminals who have honed their skills over decades.

Digital sextortion is one of the worst forms of cyberattacks. Criminals often use explicit images generated with AI to blackmail victims. With the rise of AI, criminals no longer need real photos; they can use deepfake technologies to blackmail anyone sensitive about their public image. This can disproportionately affect women and public figures.

And finally, there is DDoS. DDoS, or distributed denial of service attacks, has moved beyond vandalism to become a real tool of geopolitical coercion. The high-profile attack by Anonymous Sudan against Kenya’s digital infrastructure in 2023 and 2024 exemplified this shift. Although they claim those attacks were political and for the benefit of the nation of Sudan, security researchers believe Anonymous Sudan may have ties to Russian cybercrime ecosystems like KillNet. This connection was observed when they targeted Kenya’s eCitizen platform, M-PESA services, and power utilities. The attack was so humiliating for Kenya because they were issuing digital visas, which no longer worked, and they had to roll back to issuing visas on arrival. It caused so much chaos in Nairobi without even firing a shot.

Of course, things are at their worst when there is a spy or a colluder in your organisation. For example, Access Bank in Nigeria lost over 800 million Naira because of an employee who was colluding with cybercriminals. If you have underpaid or disgruntled employees, criminals might recruit them to work as insiders.

The insider threat is very difficult to detect because no amount of sophisticated monitoring of the digital infrastructure is going to prevent internal sabotage. Employees might be tempted to sell their credentials if they are going to be paid much more by a criminal than by their employer, especially in poor regions like Africa.

The future of defence

The future of cybersecurity is defined by the sovereignty of data. We are going to see a lot of data nationalism rise, where nations demand that their data be stored locally. This might complicate the operations of global tech giants, but it will spur the growth of local cloud infrastructure.

Rwanda’s Data Governance Policy is a good example of this. However, we are playing a game of catch-up as quantum computing is moving too fast; any current encryption standard is easily overcome by hackers in a matter of weeks or months. Even if Africans use the current technology available in Europe, by the time they implement it, they will be left behind by all the technological advancements happening in the world and adopted by malicious actors. If they want to be ahead of the game, they have to prepare for post-quantum cryptography.

Experts like Dr. Bright Gameli Mawudor predict that attacks will be fully automated, meaning the hacker will be an AI in the near future rather than a human being. He also warns that automated scripts could theoretically compromise national central banks if there are vulnerabilities, suggesting that the future of war is going to be machine against machine, where humans are either spectators or victims.

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