Analysis
China Overhauls the World’s Biggest Surveillance Network with Advanced AI
On a clear morning in Shanghai’s Pudong district, a camera detects a crowd assembling near a subway exit. Within seconds, an AI system flags the gathering, cross-references faces against a national database, and fires a pre-emptive alert to local police — before a single word has been spoken, let alone a permit requested. This is not a speculative scenario. It’s the operational reality of China’s surveillance architecture today, and it’s being rebuilt from the ground up with generative AI, large language models, and a political mandate to make authoritarian control faster, cheaper, and effectively invisible.
The Surveillance State Finds Its Intelligence Layer
China has spent two decades constructing what is almost certainly the world’s most extensive state surveillance infrastructure. Estimates put the country’s camera count at up to 600 million — roughly three cameras for every seven citizens. But raw hardware counts have never been the story. The real transformation is happening in the software layer.
Beijing’s 15th Five-Year Plan (2026–2030), unveiled at the March 2026 “Two Sessions” legislative meetings, enshrines AI-driven governance as a national strategic priority, carrying an explicit directive for China to seize the “commanding heights of science and technological development.” The plan formalises what researchers had already been documenting for two years: an accelerating fusion of generative AI, large language models, and legacy surveillance hardware into a single, predictive control apparatus.
Crucially, China’s amended Cybersecurity Law — the first major revision since 2017 — took effect in January 2026, weaving AI explicitly into the legal architecture of state surveillance for the first time. The Cyberspace Administration of China described the updated law as providing the foundational framework for “cyber sovereignty,” stressing its role in Xi Jinping’s directive for China to become a cyber superpower. When AI and censorship law merge, the implications don’t stay inside China’s borders for long.
1 — The Core Development: How China’s AI Surveillance Network Is Being Rebuilt
The China AI surveillance network upgrade is not a single programme. It’s a layered modernisation of interconnected systems, each accelerated by the same generation of tools now reshaping industries worldwide.
At its foundation sit two legacy projects. Skynet (天网工程), deployed primarily in urban centres including Beijing, Shenzhen, and Chengdu, operates as a high-precision facial recognition and automated tracking system — state media once claimed it could scan China’s entire population in under a second, though researchers at Georgetown’s Center for Security and Emerging Technology have noted that such claims “ignore glaring technical limitations.” Sharp Eyes (雪亮工程), launched in 2015 by the National Development and Reform Commission, extended surveillance into rural and semi-urban provinces including Hunan, Henan, Sichuan, and Guizhou, setting a target of 100% coverage of public space by 2020. It went further: integrating private household cameras into centralised monitoring platforms, and in some areas giving local residents access to live security footage — a model of what researchers now term “participatory surveillance.”
What has changed is the intelligence layer sitting on top of that hardware. According to a December 2025 report by the Australian Strategic Policy Institute (ASPI) — granted to The Washington Post for exclusive early access — the Chinese Communist Party is “harnessing AI to make its existing systems of control far more efficient and intrusive.” ASPI senior analyst Nathan Attrill stated: “AI lets the CCP monitor more people, more closely, with less effort. In practice, AI has become the backbone of a far more pervasive and predictive form of authoritarian control.”
The hardware supply chain is equally telling. Hikvision and Dahua together supply roughly one-third of the global market for security cameras and digital video recorders, and Hikvision directly implements Sharp Eyes infrastructure in cities including Xi’an. SenseTime, designated an official “AI Champion” by the party-state, provides facial recognition algorithms feeding into centralised police databases. The 206 System, developed by iFlyTek, analyses criminal evidence and recommends sentences to prosecutors. In Anhui province, prosecutors use AI platforms to draft indictments and flag inconsistencies in dossiers — an end-to-end automation of the charging process.
The architecture is converging toward what analysts described in March 2026 as an AI-driven criminal justice pipeline — surveillance that doesn’t merely observe, but actively adjudicates.
2 — The Analytical Layer: Predictive Control and the Logic of Pre-emptive Suppression
How Is China Using AI for Predictive Policing?
China is using AI for predictive policing through a combination of large language models, neighbourhood grid worker data networks, and real-time social media monitoring. Systems process individuals’ personality profiles, emotional states, and exposure to “negative cultural influences” to forecast social unrest before it occurs — a function previously requiring large human intelligence operations, now automated at scale.
The most significant shift is not the hardware. It’s the move from reactive surveillance — watching and recording — to predictive surveillance, which attempts to identify threats before they materialise. In August 2025, Guizhou Normal University filed a patent proposing the use of OpenAI’s GPT models as a “core reasoning tool” in a system designed to predict “social governance incidents” — the official euphemism for protests and collective petitions. The patent draws on inputs including individuals’ “long-term emotional states” and “degree of exposure to negative cultural influences,” without specifying how that last category would be measured. Any functioning implementation would depend entirely on the pre-existing surveillance infrastructure.
The human network feeding these AI systems is itself a revealing detail. Since early 2025, multiple Chinese institutions have developed tools built on reports from “grid workers” (网格员) — typically paid community workers who monitor assigned neighbourhood grids and upload incident reports in real time through a dedicated smartphone app. AI systems aggregate and analyse that granular social data, giving local authorities a dynamic, block-level picture of sentiment and risk. This is Xi’s concept of social governance operationalised through machine learning: citizens enlisted as data collection nodes in a system that processes their reports at a scale no human bureaucracy could sustain.
The picture is more complicated when one considers the incentive structures for domestic AI firms. Alibaba, Baidu, and Tencent are building multimodal large language models that censor and reshape descriptions of politically sensitive content — not because they’re state-owned enterprises, but because commercial operating licences in China effectively require it. Private companies like SenseTime didn’t survive by resisting the surveillance state. They thrived by building it, and in doing so, became too strategically valuable for either side to disentangle.
What this produces is an AI ecosystem in which the line between commercial product and state instrument has effectively dissolved. That’s the structural condition that makes Beijing’s surveillance ambitions sustainable in a way that brute state spending alone never could have achieved.
3 — Implications and Second-Order Effects: The Export Problem
What Does China’s Surveillance Technology Export Mean Globally?
The global consequences are no longer a projection. Companies including Huawei, ZTE, and SenseTime have invested hundreds of millions of dollars in AI-related infrastructure across Asia, Africa, and Latin America, according to research by the Alan Turing Institute’s Centre for Emerging Technology and Security. These projects — ranging from broadband rollouts to city surveillance systems — come bundled with Chinese-built AI solutions, effectively embedding China’s technical standards and governance norms in host countries.
The Digital Silk Road has become the primary vehicle for this diffusion. When a government in Central Asia or sub-Saharan Africa purchases a “safe city” package from Huawei, it frequently receives the same underlying surveillance architecture deployed in Xinjiang, rebranded and repackaged for export. The technology transfer is also a norm transfer: the proposition that government surveillance of this kind is normal, desirable, and technically achievable.
The supply chain problem runs in both directions. A November 2025 congressional report found that American-made semiconductors, cloud computing resources, and AI development tools continued to flow into Chinese surveillance firms despite existing export controls. Representative Raja Krishnamoorthi argued that Washington had “deprioritised human rights protections in its China policy,” and that tightening controls would require coordination with European and Asian allies — because many of the most advanced AI systems and semiconductor manufacturing tools are produced collaboratively across borders. A unilateral American response, the report concluded, will be insufficient.
Inside China, the judicial implications are concrete and accelerating. Oxford University’s Institute of Technology and Justice has documented that China’s Supreme People’s Court declared all courts to be using AI tools in judicial proceedings by the end of 2025, with full AI integration across the justice system targeted for 2030. In Shanghai, an AI platform now recommends whether suspects should be arrested or granted bail. In at least one prison, facial recognition cameras monitored inmates’ expressions, flagging them for intervention if they appeared angry. Surveillance has moved inside the cell.
4 — The Counterargument: The System Is Less Unified Than It Looks
The instinct of outside observers is to imagine China’s surveillance state as a seamlessly coordinated machine, operated from a single console in Zhongnanhai. The operational reality is considerably messier.
Researchers who study the system closely note that China’s surveillance infrastructure is fragmented — a patchwork of overlapping jurisdictions, incompatible data standards, and uneven local implementation. Skynet and Sharp Eyes were rolled out by different agencies at different times; Xinjiang’s Integrated Joint Operations Platform (IJOP) was built largely in isolation from the national infrastructure. Police Cloud systems vary dramatically between provinces. Academic work published in Regulation & Governance in 2024 documented how platformised policing generated massive datasets that frequently couldn’t communicate with one another — information silos in the middle of a supposed information state.
That fragmentation limits actual predictive capability. Beijing wants unified AI surveillance; it has, for now, a collection of partially connected systems generating data that AI tools are only beginning to stitch together. The gap between the Chinese state’s surveillance ambitions and its operational architecture remains measurable — and that gap is precisely where privacy still partially exists.
Some Chinese legal academics have quietly raised accountability concerns, too. The Supreme People’s Court’s push for AI in sentencing has met internal scepticism from judges who ask: who is responsible when an algorithm recommends the wrong outcome? These aren’t dissident voices; they’re institutional concerns from within the apparatus itself.
None of this adds up to a reassuring counter-narrative. The trajectory is clear, the investment is sustained, and the 15th Five-Year Plan provides the political mandate to accelerate integration. But it does mean the gap between Beijing’s stated ambitions for its AI surveillance network and the system’s operational reality remains wider than official statements suggest. Ambition and capability are not the same thing — and in this domain, treating them as identical is its own form of error.
The Architecture Is Now Legal, Not Just Technical
China’s AI surveillance overhaul is, at its core, the industrialisation of authoritarian control — the application of the same machine learning techniques powering medical diagnostics and content recommendation to the problem of population management. The efficiency gains are real. The harms scale with the efficiency.
What makes this moment distinctively consequential is the legal architecture now surrounding it. The amended Cybersecurity Law, the 15th Five-Year Plan’s explicit directives, and the Supreme Court’s AI integration mandates have moved this from a technology project to a formal governance system. The apparatus is being institutionalised, not just expanded. That distinction matters: institutions survive their architects, outlast political cycles, and are far harder to dismantle than experimental programmes.
Whether democratic governments and technology companies can meaningfully slow the proliferation of these tools — across China’s borders, through the supplier relationships that sustain them, and into the legal frameworks of countries that may find them attractive — is among the defining policy questions of the next decade. Export controls, sanctions, and multilateral coordination are all on the table. None has yet proven sufficient.
The cameras don’t blink. The question is whether anyone watching them will.
Discover more from The Economy
Subscribe to get the latest posts sent to your email.
Analysis
France opposes ‘anglicisation’ of EU trade talks
BRUSSELS — When the European Commission’s trade negotiators sat down last week to map out the next phase of talks with Mercosur, a familiar diplomatic tremor rippled through the room. It had nothing to do with tariff schedules or agricultural quotas. On the table was a procedural proposal to streamline the bloc’s negotiation practice by adopting English as the single working language for all trade talks. France’s deputy permanent representative, Anne-Marie Descôtes, scanned the document, then rose to speak. “This is not a technical adjustment,” she said, according to two officials present. “It is a cultural surrender.”
The proposal was shelved before the coffee arrived. By the end of the day, Paris had made clear it would veto any formal move toward what it calls the “anglicisation” of EU trade diplomacy. The episode, which might look like Brussels arcana, cuts to the marrow of a struggle that has intensified since Brexit removed the bloc’s largest native-English-speaking member: the contest over whose language shapes the terms of European power.
The dispute is not new. What has changed is the context. The departure of the United Kingdom in 2020 left English without a major state patron inside the Union, yet its institutional dominance only grew. English remains the lingua franca of the Commission’s trade directorate, the default language of legal drafting, and the working tongue of most technical committees. Ireland and Malta, the two remaining members with English as an official language, together account for less than 1% of the EU population. The irony is stark: a language that no longer belongs to any large member state now governs the bloc’s most sensitive external negotiations. For France, this is both a strategic wound and an ideological rallying cry.
A recent report by the French Senate’s European affairs committee estimates that French-language usage in EU institutions has fallen by 30% since 2004, with the steepest declines in the Commission’s trade and competition arms. Meanwhile, the EU spends roughly €1.1 billion annually on translation and interpretation services across all institutions, according to the European Court of Auditors’ 2025 language audit. The figure is often cited by proponents of linguistic streamlining, who argue that adopting a single working language for trade talks could save up to €180 million a year. French officials counter that the calculation is not merely financial.
The core development: a veto that signals a doctrine
French opposition to the English-only proposal crystallised on 9 June, when trade ministers gathered in Luxembourg for a Council meeting ostensibly focused on market access tools and WTO reform. What few expected was that Paris would use the session to lay down a red line that had been quietly hardening for months. According to a Reuters report on the closed-door exchange, French Trade Minister Sophie Primas told her counterparts that “linguistic diversity is not a barrier to be removed but a constitutional principle of the Union,” and that “any move to make English the sole procedural language would be challenged before the European Court of Justice.”
The statement was no rhetorical flourish. France has been building a legal and political architecture to defend multilingualism as a fundamental right of member states. Article 55 of the Treaty on European Union already guarantees equal linguistic status for all official languages, but its application to internal working practices has been murky. Primas’s intervention signalled that Paris is ready to test that ambiguity in court, a move that could drag trade negotiations into procedural paralysis for years.
The immediate trigger was a 45-page Commission discussion paper, seen by the Financial Times, that argued monolingual trade talks would cut negotiation timelines by up to 20%, reduce the risk of interpretative errors in legal texts, and align the EU with the practice of trade partners like the United States, Japan, and Australia — all of whom negotiate exclusively in English. The paper was careful to note that final treaty texts would still be translated into all 24 official languages before signature, but the working phase — often lasting four to six years — would function in a single language.
French officials were not mollified. “The distinction between ‘working language’ and ‘official language’ is a semantic trap,” one senior French diplomat told the Economist on condition of anonymity. “What happens in the working phase determines what is possible in the final phase. Excluding a language from the room is excluding the people who think in that language.”
The French position has gathered tacit support from Spain, Italy, and Germany — though Berlin’s enthusiasm is tempered by its business community’s preference for English as a neutral, efficient tool. A Bloomberg report noted that German Chancellor Friedrich Merz privately sympathises with the French cultural argument but will not jeopardise the Mercosur ratification timeline, which is already years behind schedule.
Why language is not merely a tool
The French resistance is often caricatured as nostalgic pique, but the strategic logic runs deeper. Language is a carrier of legal concepts, negotiating frames, and power asymmetries. A trade negotiation conducted entirely in English favours those who have mastered not just the vocabulary but the rhetorical conventions of Anglo-American legal and economic thought. A negotiator from a civil-law tradition, trained in French or German legal categories, can find herself forced to argue inside a conceptual grid that does not fully accommodate her own juridical instincts.
Why does France want French to remain a working language in EU trade talks? France argues that using English as the sole procedural language disadvantages non-native speakers, undermines linguistic diversity, and grants an unearned advantage to negotiators from Anglophone legal cultures. The French view holds that language shapes thought; when a trade rule is conceptualised in English, it tends to import common-law assumptions into a predominantly civil-law union. The linguistic choice, from this perspective, is never neutral.
That view is not fringe. A 2024 OECD trade policy paper found that language barriers can add the equivalent of a 9% to 15% tariff to trade in services, and that the effect is most pronounced in legal and financial services — precisely the sectors most sensitive to regulatory nuance. The study notes that when negotiators work in a non-native language, the risk of “conceptual misalignment” in final treaties rises measurably. The paper stops short of recommending any specific linguistic policy but makes clear that the costs of monolingualism are not zero.
Beyond economics, the French case draws on a broader European unease about cultural erosion. In a 2025 Eurobarometer survey, 62% of EU citizens said that “maintaining linguistic diversity is essential to European identity,” while only 31% agreed that “English should be the single working language of EU institutions.” The sentiment runs strongest in southern and eastern member states, where English proficiency remains lower than in the Nordic and Benelux countries. France has been quietly building a coalition of the linguistically disenfranchised, framing its position not as French exceptionalism but as a defence of pluralism.
Yet the picture is more complicated than a straightforward culture-versus-efficiency binary. The Commission’s internal surveys show that younger diplomats across the EU increasingly prefer English as a common working medium, not out of cultural submission but out of pragmatism. A 2026 internal staff poll, leaked to Politico Europe, indicated that 74% of trade-unit officials under 40 believe switching to a single working language would improve their professional effectiveness. For many Eastern European and Nordic capitals, the French position looks like an attempt to impose a linguistic hierarchy that benefits Paris and Brussels insiders who were educated in French-language grandes écoles.
Second-order effects: what a multilingual mandate would cost
If France succeeds in blocking the anglicisation of trade talks, the practical consequences will ripple across the EU’s negotiating machinery. The most immediate effect would be the retention — and likely expansion — of interpretation and translation services during the working phase of trade negotiations. Currently, the Commission uses a “pivot language” system for many internal meetings: interpretation is provided into French, German, and English, but smaller languages are covered only upon request. A French victory would probably force the Commission to provide full interpretation in at least the three procedural languages for all trade-related working groups, adding an estimated €240 million to the EU budget over the next seven-year cycle, according to an IMF working paper on institutional language costs.
The timeline implications are harder to quantify but potentially far larger. Trade negotiations are already glacial. The EU’s deal with Mercosur took more than two decades from initial talks to political agreement, and ratification is still pending in several member states. If every drafting session must accommodate simultaneous interpretation and cross-checking of legal terms in multiple languages, the median negotiation timeline could stretch by 18 to 24 months, according to a World Bank policy research note published in March. For agreements like the ongoing EU-India talks, where speed is seen as a geopolitical imperative to counter Chinese influence, that delay could have strategic costs that dwarf the financial ones.
Business groups are already signalling alarm. BusinessEurope, the continent’s largest employer federation, warned in a position paper this month that “any measure that prolongs trade negotiations weakens the EU’s ability to secure market access at a time when protectionist pressures are mounting globally.” The federation’s director-general, Markus Beyrer, cited the example of the EU’s stalled free trade agreement with Australia, where linguistic friction — particularly over the French translation of agricultural origin rules — contributed to a 14-month delay in 2024–25. “We cannot afford to make language another non-tariff barrier,” Beyrer said.
The legal dimension adds another layer of risk. Primas’s threat of a Court of Justice challenge is not empty. The Court has ruled before on linguistic rights in the EU — notably in Kik v OHIM (2003) — but has rarely been asked to adjudicate the internal working practices of the Commission in trade matters. A ruling that enshrines a broad right to multilingual working procedures could constrain the EU’s institutional flexibility for decades, creating a precedent that extends beyond trade to competition policy, financial regulation, and even the European Central Bank’s operational language. Legal scholars at the Max Planck Institute for Comparative Public Law have argued that such a case would force the Court to weigh the principle of linguistic equality against the treaty-recognised objective of an efficient common commercial policy — a balancing act with no clear doctrinal solution.
The pragmatic case for English: a counterargument worth steel-manning
The argument for adopting English as a single working language in trade negotiations is neither philistine nor intellectually unserious. It rests on three empirical pillars: efficiency, legal certainty, and global interoperability.
Efficiency is the most straightforward. The EU negotiates trade agreements with over 70 countries and blocs, and in virtually all of them the counterpart prefers English as the negotiating medium. Even China, which has invested heavily in French-language diplomacy in Africa, conducts its EU trade dialogue in English. Maintaining a multilingual negotiation framework means that every document must be translated, every oral intervention interpreted, and every legal term cross-checked against multiple linguistic versions — not just at the end but throughout the process. The Commission’s own impact assessment from January 2026 estimated that a shift to English-only working would reduce the average trade negotiation duration from 7.3 to 5.8 years.
Legal certainty is the second pillar. Trade agreements are technically bilingual or trilingual in their authentic versions, but when the working language is English, the English text tends to become the de facto master version, reducing the risk that courts or arbitration panels will later find irreconcilable differences between languages. The EU-Singapore Free Trade Agreement, for example, faced a three-month delay in 2022 when the French and English versions of the intellectual property chapter were found to contain a discrepancy that would have altered the scope of copyright protection. “One language from drafting to signature is not linguistic imperialism,” said Anu Bradford, a trade law professor at Columbia Law School, in a recent interview with the Peterson Institute. “It is a risk-management tool. Multilingualism in legal drafting has produced more arbitral disputes than any other single procedural factor.”
The third pillar is global interoperability. The multilateral trading system is an English-language system. WTO dispute panels, UNCITRAL arbitration, and ISDS tribunals operate overwhelmingly in English. A European trade negotiator who cannot think and argue fluently in English is professionally handicapped not because of Anglo-American hegemony but because of path dependency. The EU’s own trade defence instruments — anti-dumping investigations, safeguard proceedings — already function almost entirely in English. Singling out the negotiation phase for special linguistic treatment is, from this perspective, an inconsistency that serves no one’s interest except that of a small cadre of French-language diplomats who, as one unnamed Commission official told the Wall Street Journal, “are defending their own relevance more than any cultural principle.”
This pragmatic case does not dismiss cultural identity, but it draws a sharp distinction between areas where identity should govern and areas where function should govern. A trade negotiation is not a parliamentary debate or a citizen-facing regulatory process; it is a technical exercise in maximising joint welfare under constraint. To burden it with symbolic politics, proponents argue, is to make the EU slower, less coherent, and less competitive at a moment when it can afford none of those things.
The synthesis, and what comes next
The French veto, for now, holds. The Commission has no appetite for a fight it would likely lose in the Council, where the linguistic coalition Paris has assembled — even if soft — makes a qualified majority improbable. The proposal for a single working language is effectively dead, though officials say it may resurface in a diluted form, perhaps as a pilot programme for minor trade dialogues with English-speaking partners. That would allow Paris to save face while letting the Commission claim a modest efficiency gain.
The episode, however, is about more than procedure. It exposes a fault line that runs through the European project at a moment of profound external pressure. The Union is trying to position itself as a geopolitical actor capable of striking strategic trade deals at speed, yet it remains constitutionally committed to a vision of linguistic equality that makes speed structurally difficult. Both commitments are genuine. Neither can be casually discarded.
What follows, however, is not a simple trade-off. Linguistic diversity is not merely ornamental; it is a mechanism that distributes power among member states, shapes the cognitive frames of policy, and connects the technocratic work of Brussels to the domestic publics that must ultimately ratify its results. To strip it away in the name of efficiency would be to centralise influence among those who are already linguistically privileged — a move that would almost certainly deepen the legitimacy deficit the EU suffers in precisely those regions where Euroscepticism is most virulent.
On a rain-slicked afternoon in the European quarter, a young French trade attaché, who had spent the morning arguing the case for multilingualism with his German and Danish counterparts, offered a thought that cut through the institutional noise. “We are not asking everyone to speak French,” he said. “We are asking that the room be large enough for more than one way of thinking.” The sentence lingers because it captures the true stakes of the dispute, which are not about languages at all but about whether a union of 27 distinct nations can negotiate as one without losing the distinctiveness that makes the union worth preserving.
Discover more from The Economy
Subscribe to get the latest posts sent to your email.
Analysis
Goldman and JPMorgan Ease Office Working Rules to Counter World Cup Disruption
Eight World Cup matches will be played at MetLife Stadium in East Rutherford, New Jersey — including the final on July 19. Nearly a million people commute into New York City every day, many of them crossing the Hudson from New Jersey. Goldman Sachs and JPMorgan Chase, whose towers anchor Lower Manhattan and Midtown respectively, have spent four years enforcing some of the strictest return-to-office mandates on Wall Street. Now, with gridlock, security perimeters, and match-day crowds threatening to turn the commute into an endurance event, both banks are making a pragmatic concession they once seemed constitutionally incapable of: temporary flexibility.
It’s a small retreat. But on Wall Street, small retreats tend to mean something.
The Stage Is Set for Disruption
The 2026 FIFA World Cup, co-hosted by the United States, Canada, and Mexico, is the largest in the tournament’s history — 48 nations, 104 matches, running from June 11 to July 19. The US is absorbing 78 of those games across 11 host metros: New York/New Jersey, Los Angeles, Dallas, Houston, Miami, Atlanta, Seattle, Philadelphia, Boston, Kansas City, and San Francisco. Together, those cities account for roughly one-third of US GDP and one quarter of national employment, according to Goldman Sachs’s own economists.
The disruption isn’t theoretical. A Boston Consulting Group projection estimates the tournament could generate more than $5 billion in short-term economic activity across North America, with individual host cities seeing between $160 million and $620 million in incremental activity. Five to seven million international visitors are expected to pass through those same cities over six weeks. The transportation networks they’ll strain are the same ones that Wall Street’s workforce depends on every morning.
1: The Core Development — Wall Street’s RTO Emperors Blink
Goldman Sachs and JPMorgan have been the two loudest champions of the five-day office mandate in global finance. Goldman Sachs CEO David Solomon called remote work an “aberration” as early as 2021 and began recalling staff before most of America had even accepted the pandemic was winding down. JPMorgan CEO Jamie Dimon pushed further: in January 2025, he issued an internal memo instructing all 316,000 of the bank’s global employees to return to the office full-time from March of that year, shutting down the comments section after hundreds of employees responded within the hour. As of mid-2026, both banks maintain official five-day-a-week office policies — among the strictest of any employer in the US.
That context makes the World Cup accommodation notable. Both banks have signalled to employees in host city offices that temporary flexibility around match days will be permitted for the duration of the tournament. The move is framed internally as a logistics response rather than a policy shift — an acknowledgement that the commute into Midtown or Lower Manhattan on a day when a match is being played at MetLife, with security perimeters rippling out across the New Jersey Transit network, is materially different from a normal Tuesday.
The numbers back that framing. NJ Transit has imposed a $150 special round-trip fare on match days — applicable only to match ticket holders — while regular commuters face altered routes and delays across the eight match days hosted at MetLife Stadium. In Boston, comparable transport costs have run to $95 for a round trip on match days, four times the standard price. Challenger, Gray & Christmas, the outplacement firm, has calculated that a single missed workday in the 11 host metros could cost US employers $8.2 billion in lost productivity, with the New York/New Jersey metro alone carrying a $2.14 billion exposure.
Against that backdrop, telling bankers they can work from home on a handful of match days isn’t generosity. It’s operational risk management.
2: Why This Matters Beyond the Scoreline — The Return-to-Office Ratchet
The World Cup accommodation is a data point in a larger argument that Wall Street’s RTO ideologues have long refused to make: that blanket mandates, however sincerely held, will always encounter events that mandate flexibility.
What does the Goldman and JPMorgan World Cup policy actually mean for return-to-office norms?
It means that even the most rigidly enforced attendance mandates contain implicit carve-outs for force majeure — and that those carve-outs, once granted, create precedent. For now, the banks are characterising the adjustment as time-limited and event-specific. The policy won’t survive the July 19 final. But employees who spent six weeks working productively from home during the tournament will have experienced, firsthand, that the sky did not fall.
The US federal government moved first, and faster. In early June, the Office of Personnel Management issued guidance permitting federal agencies in all 11 World Cup host cities to allow employees to work remotely for the duration of the tournament — a notable move from an administration that had spent the previous 18 months aggressively clawing back remote work from the federal workforce. Across the private sector, the picture has been similar: human resources consultancy Brightmine’s employer guide for the World Cup explicitly advises companies to permit temporary changes to working patterns and allow holiday requests at short notice where operationally feasible.
What distinguishes Goldman and JPMorgan from the majority of employers making similar adjustments is their symbolic weight. These are the institutions that set the cultural tone for professional-services return-to-office globally. Their accommodation, even temporary, tells the rest of Wall Street — and the firms that watch Wall Street’s every HR move — that the five-day doctrine isn’t absolute.
3: The Second-Order Effects — Productivity, Culture, and the Precedent Problem
The immediate market implications of a few weeks of flexible banking are minimal. Trading desks will still trade. Investment bankers will still pitch. Risk managers will still run their models. The technological infrastructure that made remote work viable in 2020 hasn’t degraded; if anything, it’s better. AI-assisted workflows mean that a junior analyst at home during a match day is arguably more productive than they were in the office in 2019.
That’s the uncomfortable truth the RTO orthodoxy has always struggled to absorb. A 2025 CBRE study found that 37% of companies were enforcing strict office attendance requirements, up from 17% the previous year — a surge driven largely by finance and professional services. Yet the correlation between office presence and measurable output has never been cleanly established for knowledge workers. What RTO mandates clearly do achieve is cultural signalling: the message that seniority, presence, and visibility are linked, and that the old hierarchies of face time and floor proximity still operate.
The World Cup accommodation, temporary as it is, chips at that signal.
There are downstream consequences for talent, too. Goldman Sachs estimates the tournament will add 40,000 nonfarm payroll jobs in June alone — predominantly in hospitality, retail, and transportation — with modest upward pressure on GDP and retail sales through July. What the bank hasn’t publicly calculated is how much of that temporary economic energy will translate into employee expectations about flexibility once the tournament ends. Workers who’ve spent six weeks watching their employers accommodate commute disruption will not forget that accommodation simply because the final whistle has blown.
The cities themselves are recalibrating. Everbridge’s host-city risk analysis notes that every host city will face significant transportation disruption, with road closures around stadiums rippling outward to affect commute times and delivery routes — and recommends that employers pre-establish remote-work triggers tied to specific disruption thresholds. That language — normalised trigger-based flexibility — is precisely what the five-day mandate camp has resisted for four years.
4: The Counterargument — Presence Has a Price That Absence Can’t Pay
The case for in-office work at Goldman and JPMorgan isn’t merely cultural vanity. It’s a serious argument that deserves to be made seriously.
Solomon’s position — and Dimon’s, articulated more bluntly — rests on the view that investment banking, like surgery or litigation, is an apprenticeship craft. Junior analysts learn by proximity: by sitting next to a managing director during a live deal, by absorbing the texture of a negotiation, by being in the room when a client calls with a problem at eight in the evening. That transmission of institutional knowledge doesn’t happen reliably over Zoom. It requires physical co-presence, serendipitous corridor conversations, and the accumulated small moments that eventually produce someone who can run a deal on their own.
The Raconteur’s 2026 survey of companies enforcing five-day mandates found that finance sector firms overwhelmingly cited mentorship quality and junior development as primary rationales — not monitoring or distrust. Dimon put it plainly in his January 2025 memo: the benefits of in-person work are “substantial and irreplaceable.”
There’s also a client-service dimension. Hedge funds and corporate treasurers don’t typically appreciate discovering that the banker managing their portfolio was watching the Brazil match from a home office in Hoboken when a margin call came through. Perception, in financial services, is often indistinguishable from reality.
The counterargument to the World Cup accommodation, then, is straightforward: this is exactly the kind of precedent that erodes culture incrementally. One exception becomes a template. A template becomes a norm. A norm becomes a negotiating chip. The firmness of the five-day rule has always derived precisely from its lack of exceptions. Once you start carving out events — a World Cup today, a child’s school play tomorrow — you have a hybrid policy. You’ve just chosen not to call it that.
Goldman and JPMorgan’s World Cup accommodation is, in isolation, a minor operational footnote. In the longer arc of the return-to-office story, it’s something more revealing: evidence that even the most doctrinaire workplace mandates are ultimately subject to the same force that disrupts everything else in financial markets — events that no internal policy can anticipate, and no memo can override.
The tournament runs until July 19. On July 20, both banks’ five-day mandates will reassert themselves, and the trading floors will fill again. The commuters will file back through the turnstiles. MetLife will fall quiet.
But the employees who spent six weeks working from home — productively, demonstrably, without the sky falling — will remember. And in the long game of office politics, memory is the asset that compounds.
Discover more from The Economy
Subscribe to get the latest posts sent to your email.
AI
AI Is Revolutionising the Stock Market — But the Risks Are Scaling Too
The machines are winning. That much is settled. What isn’t settled is what happens when they start losing together.
On the morning of August 5, 2024, Japanese and American equity markets shed trillions of dollars in a matter of hours. It wasn’t a corporate scandal. It wasn’t a central bank error. Tobias Adrian, the IMF’s Financial Counsellor and Director of Monetary and Capital Markets, suggested the rout may have been shaped in part by AI-driven trading strategies — automated systems reacting to the same signals, at the same moment, in the same direction. It was a preview, not an anomaly.
The Acceleration Nobody Planned For
For most of the twentieth century, stock markets moved at human speed. Traders on exchange floors, analysts with Bloomberg terminals, fund managers reading earnings releases over morning coffee — the rhythm was set by biological limits. That era didn’t end gradually. It collapsed.
Financial markets are no longer the exclusive domain of human intuition or simple, static algorithms. The decisions to allocate billions of dollars are now made in fractions of a second, supported by multimodal neural networks, reinforcement learning, and advanced semantic analysis. The transition from rules-based automation to genuinely adaptive AI systems has happened across a single decade — faster than any regulatory framework has been able to absorb. Barchart
The algorithmic trading market grew from $21.89 billion in 2025 to an estimated $25.04 billion in 2026, a compound annual growth rate of 14.4%. That figure, drawn from Research and Markets data, likely understates the actual deployment footprint — it captures licensed platforms, not the proprietary systems built in-house at Citadel, Renaissance Technologies, or Two Sigma. Algorithmic strategies now execute between 60% and 70% of equity volume, and the market is growing at 13% annually. Research And MarketsMedium
The question isn’t whether AI is reshaping markets. It is.
How AI Trading Actually Works in 2026
The phrase “AI trading” gets used loosely, covering everything from a retail investor’s sentiment-scanning app to Renaissance Technologies’ Medallion Fund. The reality is a spectrum, and where an institution sits on that spectrum determines its competitive position in ways that weren’t true five years ago.
At the institutional end, AI in stock markets today means something quite specific. Pre-trade analysis that once required teams of analysts — parsing earnings transcripts, mapping sentiment across news sources, reading regulatory filings — is increasingly handled by NLP systems that deliver synthesised insights, compressing hours of analyst time into minutes. Buy-side desks are shifting from isolated AI pilots to embedding these tools across the full investment lifecycle: research, portfolio construction, order execution, risk management, and compliance. Medium
The performance data supports the investment. Academic research on generative AI in asset management found that hedge funds with higher reliance on generative AI showed a statistically significant improvement in quarterly portfolio returns — with a one-standard-deviation increase in AI reliance associated with a 2.2% annualised performance gain, equivalent to roughly 21% of the average quarterly return. Cafr
That’s not a marginal edge. In a world where institutional funds compete for basis points, 2.2% annually is transformational — provided it persists, and provided everyone isn’t running the same model.
Retail adoption has accelerated in parallel. By February 2026, over 76% of Coinrule’s users were integrating AI-driven execution into their strategies, a figure that signals how quickly sophisticated tools — once the preserve of quant desks — have diffused downmarket. The analytical gap between a high-net-worth individual with access to AI-powered portfolio tools and a mid-tier fund manager has narrowed considerably. Kavout
What Does AI-Driven Trading Actually Mean for Markets?
The short answer is that it means faster price discovery, tighter spreads, and deeper liquidity — but also compressed time horizons for human oversight and a growing tendency for correlated systems to amplify rather than dampen volatility.
AI trading accelerates the incorporation of information into prices, which in theory benefits all participants. When AI reads an earnings release at 5:30am and repositions a portfolio before human traders have finished their coffee, the market becomes marginally more efficient. That’s the case for it.
The case against it is structural. The AI-driven repricing of global equities collided with geopolitical shocks and shifting interest-rate expectations in early 2026, making the first quarter “particularly disruptive for global markets and multi-asset portfolios,” according to MSCI’s global head of index regional research solutions. When all systems respond to the same inputs — the same training data, the same macro signals, the same risk thresholds — the diversity that stabilises markets disappears. CNBC
Spring 2026 survey data from the Federal Reserve’s Financial Stability Report showed that 50% of market contacts identified AI as a possible shock to financial stability — compared with just 9% a year earlier. That’s a fivefold jump in perceived systemic risk in twelve months. Aicerts News
Regulators responded. On April 17, 2026, the interagency SR 26-2 letter updated model risk management guidance for large banks — but the carve-out for generative and agentic models left a policy gap that many observers questioned. Aicerts News
The Geography of the AI Trading Revolution
The competitive map of AI in stock markets doesn’t follow the old financial geography.
A global reshuffling in stock-market hierarchy is underway, with AI propelling Taiwan and South Korea past several long-established Western financial centres. The reason is hardware: Taiwan’s TSMC manufactures the chips that power the models; South Korea’s Samsung and SK Hynix supply the memory. The supply chain advantage is translating into equity advantage, as investors bid up the enablers of AI infrastructure. CNBC
HSBC’s Asia-Pacific head of equity strategy, Herald van der Linde, warned that many Asian portfolios are now facing concentration risk — too much exposure to a small number of stocks in the region. That’s the paradox of an AI-driven rally: the very systems optimising for returns are collectively creating the fragility that will eventually unwind them. CNBC
In the United States, the top ten companies now comprise over 35% of S&P 500 weight, and mega-cap tech companies poured nearly $300 billion into AI capital expenditures in 2025, with spending projected to reach $1.6 trillion through 2029. The concentration is unprecedented. So is the potential for correlated drawdown. Financer
The Dissenting Case: AI as a Stabiliser
The systemic risk argument is compelling. It’s also contested.
Tyler Cowen of the Mercatus Center at George Mason University takes a different view. Cowen argues that increased AI use by traders may actually diminish the likelihood of a crash, because the number and diversity of models will increase over time, reducing rather than amplifying herding effects. In his framing, the proliferation of different AI approaches creates a more resilient market, not a more fragile one. Medium
The argument has historical support. Markets have absorbed successive waves of automation — electronic order routing, direct market access, high-frequency trading — without the systemic collapse that critics predicted at each stage. The flash crash of May 6, 2010, when the Dow Jones Industrial Average briefly fell 998 points in minutes due to algorithmic cascade effects, is routinely cited as evidence of AI fragility. Yet markets recovered within the same session. The plumbing held.
What’s changed since 2010, Cowen’s critics would say, is scale. In the short term, model diversity is limited — most production trading systems rely on a small number of foundation models and similar training data. Architectural diversity may increase in the long term, but the practical reality depends on timescale. Medium
The IMF’s position sits somewhere in the middle. The Fund warns of opacity in AI strategies, susceptibility to social media disinformation, and uncertain stress-test performance. AI-driven portfolios using social media sentiment achieved 13.4% annualised returns in one study — but also amplified risks of market destabilisation, as seen in the GameStop episode of 2021. arxiv
What Follows When the Models Agree
The deepest risk isn’t that AI trading systems fail. It’s that they succeed — all at once, in the same direction.
The IMF’s most recent assessment, published in May 2026, concluded that as AI reshapes the cyber landscape, the central question for authorities is whether the financial system can continue to function under severe stress. That’s a careful formulation. What the IMF is describing is not the possibility of a rogue algorithm or a single bad actor. It’s the possibility of a globally synchronised response to a common shock — millions of AI systems, trained on overlapping data, reaching the same conclusion at the same moment. International Monetary Fund
The policy response remains fragmented. Europe’s MiFID II framework requires firms to distinguish between AI decision-making and execution algorithms, but does not address real-time monitoring of autonomous systems. The SEC mandates developer registration. The Fed’s SR 26-2 letter took a step toward standardised model risk management but left generative AI largely unaddressed. There is no Geneva Convention for algorithmic trading.
The crucial difference from the dot-com era, analysts argue, is that current valuations rest on actual earnings rather than pure speculation: S&P 500 companies project 15% earnings growth in 2026, with 75% of companies showing growth that’s broadening beyond tech. The fundamentals are real. Still, the structural fragility is real too. Financer
Markets have always run on the collective behaviour of participants who tend, in extremis, to act alike. AI has made that tendency faster, deeper, and harder to interrupt.
The machines aren’t going anywhere. The question for the next decade isn’t whether to allow them — that debate is over. It’s whether the humans nominally overseeing them can build the circuit breakers before the next cascade runs faster than they can respond.
Discover more from The Economy
Subscribe to get the latest posts sent to your email.
-
Markets & Finance5 months agoTop 15 Stocks for Investment in 2026 in PSX: Your Complete Guide to Pakistan’s Best Investment Opportunities
-
Analysis4 months agoTop 10 Stocks for Investment in PSX for Quick Returns in 2026
-
Analysis4 months agoBrazil’s Rare Earth Race: US, EU, and China Compete for Critical Minerals as Tensions Rise
-
Banks5 months agoBest Investments in Pakistan 2026: Top 10 Low-Price Shares and Long-Term Picks for the PSX
-
Investment5 months agoTop 10 Mutual Fund Managers in Pakistan for Investment in 2026: A Comprehensive Guide for Optimal Returns
-
Analysis4 months agoJohor’s Investment Boom: The Hidden Costs Behind Malaysia’s Most Ambitious Economic Surge
-
Global Economy6 months ago15 Most Lucrative Sectors for Investment in Pakistan: A 2025 Data-Driven Analysis
-
Global Economy6 months agoPakistan’s Export Goldmine: 10 Game-Changing Markets Where Pakistani Businesses Are Winning Big in 2025
