What Europe’s Policy Reversals Mean for Sustainability, Business and AI

As Europe begins 2026, the continent finds itself at a crossroads in the governance of sustainability, technology and industry. Policymakers across the European Union and the United Kingdom are increasingly embracing deregulatory reforms, promoted as necessary to enhance competitiveness, stimulate investment and ease administrative burdens on business. Yet these reforms, when examined together, reveal a structural shift away from the sustainability frameworks that have shaped corporate accountability, environmental protection and long term innovation strategies over the past decade. This shift is more than a matter of regulatory calibration, reflecting a political economy in which deregulation is treated as an end rather than a means.

Recent policy changes, from the weakening of the EU’s sustainability reporting regime and shifts in nuclear regulation, to the potential rollback of the 2035 internal combustion engine ban and pressure to relax AI governance frameworks, suggest a broader reorientation. The cumulative effect is to elevate short term economic calculations over long term resilience and systemic stewardship.

1. The Retreat from Sustainability Reporting

Just last week, the European Council and the European Parliament agreed to significantly simplify the Corporate Sustainability Reporting Directive (CSRD) and the Corporate Sustainability Due Diligence Directive (CSDDD). Under the revised framework, only companies with over 1,000 employees and €450m in annual turnover remain in scope for mandatory sustainability reporting, while due diligence obligations now apply only to firms with more than 5,000 employees and €1.5bn in turnover. Moreover, mandatory climate transition plans and certain reporting requirements were eliminated, and a large proportion of smaller businesses were exempted from the rules entirely. This retreat removes approximately 90 % of companies from CSRD’s scope and 70 % from CSDDD’s remit, dramatically shrinking the regulatory perimeter of corporate accountability.

What was initially designed to standardise environmental, social and governance (ESG) disclosures now risks becoming an optional add-on. The scaling back of reporting thresholds reduces transparency and weakens the incentives for firms to integrate sustainability into core business strategies. Rather than equipping investors and stakeholders with reliable data on climate risk, supply chain impacts and human rights performance, the revised regime favours voluntary approaches, an outcome that benefits larger firms with entrenched reporting capacities but leaves rising enterprises and mid sized suppliers in a regulatory limbo.

2. Deregulation in High-Risk Sectors

The United Kingdom’s efforts to streamline nuclear regulation similarly illustrate the risks of deregulation in domains where environmental and safety stakes are high. Recent proposals to simplify planning, environmental and safety oversight for nuclear projects have drawn criticism for sidelining ecological expertise and reducing the scope of environmental assessments. While proponents argue that regulatory fragmentation has contributed to high costs and delays, critics warn that diminishing safety and environmental safeguards could erode public trust and undermine long term energy sustainability.

Similar tensions are visible across energy policy more broadly. Though the EU has prioritised energy grid upgrades and infrastructure resilience in recent years, the broader deregulatory frame risks reducing environmental assessment to procedural formality rather than substantive governance, especially when energy transitions intersect with local ecological concerns.

3. The Combustion Engine Backtrack

Shortly before Christmas 2025, yesterday to be precise, the European Commission announced a major shift in its automotive climate policy, proposing the easing of the 2035 ban on new internal combustion engine (ICE) vehicles, following intense pressure from Germany, Italy and major automakers. Under the original rule, all new cars and light vans sold in the EU from 2035 were to emit zero tailpipe CO₂. The revised plan now targets a 90 % reduction in CO₂ emissions from 2021 levels by 2035, instead of a full zero emission mandate, and allows continued sales of plug-in hybrids and vehicles powered by synthetic fuels or non-food biofuels.

The retreat from a hard combustion engine ban comes amid headwinds for the European auto industry, slower than expected electric vehicle (EV) adoption, intense competition from Chinese EV manufacturers, and rising costs for infrastructure and battery supply. Automakers have lobbied vigorously for flexibility, arguing that plug-in hybrids, biofuels and alternative compliance schemes are necessary to preserve jobs and industrial capacity.

Environmental advocates and many EV-focused companies, including Volvo, have criticised the shift as a setback for Europe’s climate leadership, a potential drag on investment in electrification, and as a move to hide the sector’s inefficiencies and poor business choices. Critics argue that diluting the target undermines regulatory predictability and could leave Europe lagging in the rapidly growing global EV market, especially as China accelerates battery vehicle deployment and U.S. policy oscillates between incentives and rollbacks.

From a sustainability perspective, this reversal illustrates how deregulatory pressures can reshape climate policy itself, not merely loosen reporting obligations or reduce paperwork, but recalibrate the very targets that define long term decarbonisation pathways.

4. AI Governance in a Deregulatory Era

Artificial intelligence poses similar governance challenges. AI technologies increasingly permeate business operations, supply chain optimisation, resource allocation and sustainability analytics. Their environmental footprint, particularly through energy intensive model training and data centre operations , is substantial, and their social impact, from labour displacement to bias amplification, is profound. Effective governance is essential to ensure that AI contributes to sustainability rather than undermines it.

Yet political pressure, particularly emanating from competitors with more permissive regulatory regimes and large corporations’ lobbying, pushed Europe toward weaker AI oversight. The result is a tension between the original EU’s risk based AI governance framework and the deregulatory narrative that frames oversight as antithetical to innovation. In practice, well designed regulation can enable innovation by providing legal certainty and aligning technological development with societal values; absence of regulation often results in fragmented standards, ethical harms and competitive disadvantage.

5. Competitive Pressures and Policy Drift

Across sectors, the deregulatory narrative shares a common rationale, and regulation is portrayed as a barrier to competitiveness; those who seek licence to profit over anything else, and to externalise their costs, have succeeded in equating regulation to sovietisation, when the truth is far from it. Whether in sustainability reporting, automotive emissions targets, nuclear licensing or AI oversight, the same fallacious claim resurfaces, regulatory simplification will catalyse growth. But this logic is flawed when it conflates short term cost reduction with strategic competitiveness. True competitiveness for businesses, particularly in the 21st century, depends on resilience, innovation rooted in environmental and social performance, and the ability to operate within predictable, transparent policy frameworks.

European firms have historically outperformed competitors in regulated spaces precisely because regulation provided structure for investment in long-term capabilities, from vaccines to aerospace and advanced manufacturing. Regulatory retreat does not inherently create advantage; it creates uncertainty.

In recent years, Europe delivered two of the COVID-19 vaccines that enabled the global economy to restart (and those invented outside the Europe also had a substantial, if not complete, state support via a pro innovation regulated environment), created the World Wide Web, and fielded aerospace technologies that continue to outperform global competitors. Airbus’ consistent lead over Boeing in deliveries, bolstered by its sustained investment in sustainable aviation and hydrogen propulsion, illustrates how regulated environments can support innovation more effectively than more permissive systems dominated by short term financial priorities that end in inefficiencies created by continuous diversion of funds and energy for damage control.

In defence technology, European capabilities such as the Meteor missile demonstrate innovation at the technological frontier, which is being adopted by other countries. In quantum communications, Europe is building coordinated sovereign capabilities, exemplified by the Eagle-1 satellite, which aims to provide secure continental networks based on quantum key distribution. These advancements are neither accidental nor the product of deregulation. They arise from structured governance, sustained investment and regulatory clarity.

Reframing Regulation as Sustainability Infrastructure

Europe’s recent policy shifts reflect more than political compromise; they signal a broader philosophical shift that elevates short term competitive narratives over the systemic goals of sustainability, transparency and innovation governance. Deregulation is not inherently harmful, but when it diminishes accountability frameworks, erodes environmental targets and reduces regulatory certainty, it undermines well-being, investor confidence and climate action.

Sustainability is not an add-on to economic policy. It is economic policy, a structural condition for resilience, competitiveness and societal stability in a world defined by the climate crisis, technological disruption and demographic change. To preserve Europe’s sustainability leadership, policymakers must recognise regulation not as a burden but as essential infrastructure, a basis on which responsible business, robust markets and trustworthy technology can thrive in the decades ahead.

The New US AI Action Plan or loosing race you declare

The Trump Administration just released America’s AI Action Plan, a bold, sweeping roadmap to secure what it defines as “unquestioned and unchallenged global technological dominance.” Framed as an existential race against geopolitical rivals like China, this plan sets out to transform every major sector of American life, industry, national security, infrastructure, education, through Artificial Intelligence. It is unapologetically ambitious, deregulatory, and ideologically driven, although these last factors with their clear anti science rhetoric may result in not achieving its stated aims.

The document is riddled with contradictions, selective interpretations of freedom, and a startling disregard for the pressing global challenge of sustainability. Yet, beneath the rhetoric and nationalist framing, there are pockets of pragmatic proposals, especially in sector-specific AI deployments, workforce development, and open source AI infrastructure, that deserve serious engagement.

At its core, the AI Action Plan reads like a manifesto for accelerationism without brakes. The opening pages reject previous efforts at cautious regulation, like Biden’s Executive Order 14110, and embrace full-speed deployment of AI, unburdened by red tape, environmental considerations, or ethical frameworks. The plan’s repeated insistence on removing regulatory barriers casts oversight itself as a threat, particularly oversight related to misinformation, diversity, climate change, and human rights. Paradoxically, based purely on ideology, the Office of Science and Technology Policy is tasked not with strengthening public interest safeguards but with rescinding rules deemed ideological or anti innovation.

This deregulatory zeal extends to infrastructure. Environmental protections under NEPA, the Clean Air Act, and the Clean Water Act are portrayed as inconvenient obstacles to building the data centres and energy systems AI needs. Climate considerations are not just omitted, they are actively scrubbed from public standards, with an explicit instruction to eliminate references to climate change from NIST frameworks. While this framing may excite Silicon Valley libertarians, and others poised to profit from unrestrained business activities, it raises the question of what kind of AI ecosystem will be the US building if the very values that ensure justice, accountability, and environmental sustainability are excised from its foundation.

One of the starkest contradictions in the plan is its call to defend freedom of speech in AI systems, followed immediately by a directive to suppress content or models that reflect so-called social engineering agendas or woke values. That is, according to the drafters of the policy, freedom of speech is guaranteed by prohibiting speech, which the equivalent of organising free orgies to promote virginity.

For instance, developers must ensure that their systems are “free from top-down ideological bias” a phrase used to justify banning government procurement of AI that acknowledges diversity, equity, climate change, or structural inequality . This narrow conception of objectivity suggests that any model reflecting progressive or globally accepted norms is inherently suspect. Accordingly, the Action Plan’s version of freedom seems to operate on a one-way street. It welcomes open dialogue, unless that dialogue challenges the current administration’s values. The implications for academic freedom, AI ethics research, and inclusive policymaking are profound, all of what are paramount for sustained innovation.

Perhaps the most glaring omission is the complete lack of any serious engagement with sustainability. Despite dedicating an entire pillar to AI infrastructure, including data centres, semiconductors, and the national grid, there is not a single reference to sustainable development goals, carbon emissions, or green AI. Instead, the plan explicitly promotes the expansion of energy intensive infrastructure while celebrating the abandonment of “radical climate dogma”. The phrase “Build, Baby, Build” is invoked as a national imperative, with energy consumption framed only as a barrier to be bulldozed through.

This omission is especially concerning given growing global awareness that AI, particularly large scale models, can have significant carbon footprints. The EU AI Act and many national strategies now link AI policy with broader climate objectives, not forgetting that the global investment in the low carbon energy transition reached $2.1 trillion in 2024. America’s plan, by contrast, treats environmental sustainability as a politically inconvenient distraction, and risks leaving the US out of the innovation fuelled by those funds. This leaves the U.S. not only misaligned with international efforts, but also vulnerable to long term economic and environmental risks.

However, amid the ideological rhetoric and the toddler-like phrases, there are components of the Action Plan that are thoughtfully constructed and potentially transformative, especially where the focus shifts from populism and geopolitics to sectoral applications and innovation ecosystems.

The plan calls for targeted AI adoption strategies in critical sectors such as healthcare, manufacturing, agriculture, national security, and scientific research. It supports regulatory sandboxes and domain specific AI Centres of Excellence, mechanisms that can help scale safe and effective innovation in complex environments.

Initiatives to modernise healthcare with AI tools, apply AI in advanced manufacturing, and support biosecurity research show a clearer understanding of AI’s potential for real world impact. If implemented with inclusive governance, these initiatives could significantly enhance productivity and resilience in key sectors, although as presented risk to leave outside of the funding pool those who focus on the environmental impact of their investments.

The Plan’s provisions to retrain and upskill American workers seem also well conceived, recognising the labour market disruption AI may cause and proposing concrete steps, from expanding apprenticeships and AI literacy in technical education, to tax incentives for employer-sponsored training. The establishment of an AI Workforce Research Hub could, if well supported, provide crucial data and forward looking analysis on job displacement, wage effects, and emerging skill demands. It has to be seen how the need for serious research is balanced with the constant attack to some of the world’s top research institutions.

The Plan’s strong endorsement of open weight and open source models may be one of its most forward looking elements. These models are essential for academic research, governmental transparency, and innovation outside the Big Tech ecosystem as, unlike closed source systems that concentrate power, open models allow more equitable access and experimentation.

Furthermore, the commitment to build a sustainable National AI Research Resource (NAIRR) infrastructure and improve financial mechanisms for compute access, especially for researchers and startups, is a rare bright spot. It signals an intention to diversify the AI innovation ecosystem but, again, it might collide with the constant defunding and battling of the White House with serious research institutions.

Finally, the Plan’s third pillar, international diplomacy and AI security, seeks to export “the full American AI stack” to likeminded nations while isolating rivals, particularly China. The aim is to create a global alliance built around U.S. developed hardware, standards, and systems. Here the plan may hit hard against reality, as the constant undermining of diplomatic principles and rules by the US government, and the growing lack of trust at global scale of American commitment to and international system based on rules, may result in countries looking for solutions elsewhere.

Without shared values of sustainability, fairness, and rights based governance, will the world want what America is selling? The EU, Canada, Brazil, and other global actors are increasingly anchoring AI governance in democratic accountability, inclusive participation, and climate conscious design. An American AI regime defined by deregulation and cultural exclusion may find limited traction outside its ideological bubble.

Ideology is the foundation of thinking, but when it replaces thinking, it may lead to a situation where the plans go against achieving the expected results, and some aspects of the America’s AI Action Plan might be a good example of that.

The fallacy of the AI debate in academic papers

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In the midst of the hand wringing over the use of artificial intelligence in academic writing, one fundamental truth seems to be getting lost: scientific papers exist to advance knowledge, not to pass a style audit or some sort of forensic analysis by not too keen peer reviewers. Whether a paper was written with the help of AI, edited by a colleague, or typed out manually at 2 a.m. is entirely irrelevant if the work it communicates is original, rigorous, and contributes meaningfully to the field.

And yet, paradoxically, many journals and peer reviewers remain locked in a self-defeating contradiction, where they claim to defend the sanctity of originality in research, while simultaneously enforcing rigid, performative standards of academic prose and citation that actively discourage innovation and insight. In doing so, they create a culture where form and appearance of science is privileged over function and actual scientific progress, where how a paper is written matters more than what it says.

The Real Purpose of a Scientific Paper

At its core, a scientific paper serves one purpose: to communicate the findings of a research process that either advances a theoretical understanding or offers a practical solution. The writing is the vehicle, not the destination.

We do not ask whether a microscope or a statistical software suite as the used in statistical analysis tainted the “authenticity” of a result, while a few reviewers, if any, actually checks whether the presented statistics add up. Yet we ask that of writing tools like AI, even when they’re used merely to structure or polish a piece, or help the writer better articulate complex ideas. The growing fixation on how a text is generated often obscures a more critical question: does the research move the field forward? We have reached the summit of ridiculousness by even questioning (and wasting journals’ space) if an abstract was written by AI (let me be as plain as possible: if you are not writing your abstracts using AI, the time consumed in writing an abstract should be discounted from your salary!).

Original Thought vs. Citation Performance

This problem is compounded by a deep contradiction in the peer review process. On one hand, reviewers and editors bemoan the lack of originality in submissions, urging authors to offer novel perspectives. On the other, they often reject papers that stray too far from the citation dense orthodoxy of academic writing, particularly when those papers provide fresh, compelling insights (they even call them “opinion pieces”). While it is important to have a robust literature review to show that what is the state of the art in a particular field, not all the research should be about literature, if not, better leave to AI to do the summary.

If a paper dares to deviate from the one citation per sentence model, or it synthesises across disciplines in a way that doesn’t fit the journal’s rigid schema, it risks an almost certain rejection not on scientific grounds, but stylistic ones, disguised as science. This pressure has only intensified in the AI era, where any sign of syntactic uniformity is now suspiciously scrutinised, as if clarity were a symptom of machine authorship, which in addition discriminates against the multilingual, who tend to use more flourished language, and some of the words usually employed by AI. Not forgetting that the so called AI uses probabilistic analysis based on what it has “learned” from previous papers, so if it uses a lot the word “nuance”, as I have done for years, it is because “nuance” has been used a consistently in previous mostly non AI papers, thus, using it now would also be highly probable even if not writing using AI. What it probably changed, is that those making now that important research about integrity, have never read or counted the papers those words before, for a variety of reasons, likely stylistic or for not being their type of science (which is based mainly about citations and not necessarily about original thought) .

The AI Moral Panic

The moral panic surrounding AI in academia is understandable but misdirected. Concerns about plagiarism, ghostwriting, and the erosion of critical thinking are valid, but they aren’t new, they’re just taking a new form, and the idea that now has increased substantially needs to be proved with the same rigour that those doing that research are demanding from the rest. The value of AI, like any tool, depends entirely on how it is used, and claiming that a change in the use of certain words implies the prevalence of some form of academic dishonesty is far more less rigorous and unscientific that many (most?) papers written using the aid of AI.

Using AI to fabricate results is fraud, plain and simple; using it to simply summarise what others have done and presenting as original is wrong, no discussion about that (although it seems to be preferred my some reviewers). But using AI to help articulate a novel, original contribution is no different from using grammar checking software or a thesaurus. Rejecting a paper on the mere suspicion that “AI helped with the wording” is akin to rejecting a paper because the figures were “too polished”, it is a non sequitur.

Reclaiming the Purpose of Academic Publishing

We need to return to a basic question in academic publishing: is this paper advancing the field? Is it offering a solution to a real theoretical or practical problem? Does it demonstrate methodological integrity? Is it grounded in evidence, even if it doesn’t reference every single author who ever touched the topic?

If the answer is yes, then the prose style, the citation density, and the grammatical polish are secondary at best. Reviewers should be encouraged to focus on the substance, not the scaffolding, although it is clear that it requires real effort by peer reviewers, instead of simply counting the number of references. Furthermore, the argument that writing properly is part of serious science or academia is as ridiculous as the one heard decades ago when having a good handwriting was also seen as a requirement to be a scholar.

At the end of the day, AI is not the enemy of science, rigid, anti-innovative gatekeeping is. Let’s not mistake performance for insight, or style for substance, as the the health of scientific inquiry depends on our ability to recognise and reward originality and rigour, no matter what tools helped communicate them.

(the subtitles were suggested by AI 😉 although the ideas are sadly mine)

The big selloff (a fictional story)

1 October 2027

The world stirred into a morning not unlike any other. Markets opened in Asia with a gentle hum of anticipation, commuters in Tokyo and Seoul spilled into subway cars, and diplomats in Brussels prepared their agendas for the EU’s routine morning briefings. But for a few sharp eyed analysts buried deep in think tanks and defence outposts, the data looked…wrong. And then, at 08:03 Beijing Standard Time, the first open source satellite images hit the public domain, posted by an amateur tracker on a Taiwanese message board. The post was titled simply: “Looks like they’re moving.”

It was more than movement. At first glance, it was subtle, a fleet of Type 055 destroyers departing the naval base in Qingdao, formation tight, heading southeast. But the next image was unmistakable: satellite photographs from an American commercial provider showed DF-21D missile launchers repositioning in Fujian Province, nose cones unmistakably trained toward the Taiwan Strait. Then, the PLA’s Southern Theatre Command issued an uncharacteristically aggressive communique, cryptic and ominous: “We are prepared to enforce the sovereignty of the Chinese people.”

By 09:15 CST, state run Xinhua had dropped the veil: China was initiating “Joint Sword 27,” a comprehensive set of “strategic exercises” encircling Taiwan. The name, oddly poetic for something so brutal. No warning, no gradual buildup. Just precision and shock. Jets screamed over the Bashi Channel, PLAAF J-20 fighters executing sharp turnarounds along Taiwan’s southern air defence identification zone. Dozens of PLAN warships were picked up fanning out in tight arcs between Hainan and the east coast of Taiwan. Submarine activity, especially from the Type 093 Shang-class nuclear-powered attack subs, spiked dramatically. Chinese fishing vessels, long suspected of dual use, formed thick, erratic swarms around Taiwan’s offshore islands.

In Taipei, sirens didn’t blare. The government knew better. It wasn’t war, not yet, but it was something worse than peace. Premier Lin Yicheng addressed the nation with calm and martial precision, standing before the blue-and-white flag. “We will not react with haste. We will not blink.” But as his address aired, Tainan’s coastal radar stations were already registering electromagnetic interference, the kind that suggested cyber operations, jamming, maybe even the deployment of high-altitude balloons or low-orbit drone constellations. Taiwan’s command centres went dark for thirty four minutes. Not offline, just quiet. Waiting.

In Washington, the day had barely begun when the President was briefed. By 02:45 EST, the day visit to the golf course had been suspended and the Air Force One had been readied for immediate contingency operations. The Secretary of Defence convened with the Joint Chiefs, and analysts at the Pentagon watched, with grim familiarity, the initial contours of an invasion play out in grainy real-time.

As the sun dipped below the South China Sea, and the silhouettes of Chinese amphibious assault ships loomed closer to the Pescadores, the world held its breath. For the first time in generations, war felt less like a relic and more like a schedule.

And someone, somewhere, had kept the time perfectly.

Section Two: Capitals on the Edge

However, the first reaction came not from Washington, Tokyo, or Brussels, but from New Delhi.

India, always wary of Beijing’s intentions, had spent the last five years deepening ties with the West and cautiously modernizing its military. By 1 October, 17:40 IST, the Indian Prime Minister had convened a National Security Council emergency session at South Block. Satellite imagery of Chinese naval movement across the South China Sea was projected onto a wall once adorned only with diplomatic flags. Inside the room, no one used the word “war.” But they all knew the script. For India, the implication was immediate: if the Taiwan situation escalated, the northern Himalayan border could again become a flashpoint. Troop deployments to Ladakh were quietly doubled. The Andaman and Nicobar Command, India’s easternmost tri-service outpost, shifted to readiness condition “Tango Red.” Radar stations along the Bay of Bengal watched the skies like never before.

In Tokyo, silence was louder than any alarm. The Japanese government had been caught in a dilemma it had long feared: the US needed its bases, its backing, and its moral authority, but any visible Japanese involvement risked enraging Beijing and inviting strikes within minutes. Furthermore, after the US initiated trade wars, Tokyo had grown closer to Beijing and Seoul, at expense of Washington. At Yokota Air Base, American F-35s sat in sleek silence, pilots on standby. Japanese destroyers, including the Izumo-class helicopter carriers, began repositioning from Sasebo and Kure, their courses concealed under “joint maritime training protocols.” But Prime Minister Fujimoto knew the line he walked was razor-thin. Japan released a carefully worded statement: “We are monitoring the situation with deep concern and are committed to regional stability in cooperation with our partners.” Internally, Tokyo knew that “monitoring” was code for counting missiles and measuring time in seconds.

In Brussels, the European Union was caught flat footed, as always. The early hours of 1 October had seen some murmurs in foreign affairs circles, but the official agenda for the day remained unchanged until well past midday. The rotating presidency scrambled to call an emergency meeting of the Foreign Affairs Council. France pushed for measured diplomacy. Poland demanded outright condemnation of China. Germany, wary of economic retaliation, stayed cagey, its automotive giants too entangled with the Chinese market to speak clearly.

At the Élysée Palace, the French President met behind closed doors with military advisers and economic ministers. “They’re not bluffing,” his intelligence chief said flatly. France ordered the Charles de Gaulle carrier to remain near Djibouti rather than head to the Indo-Pacific as planned. The world’s powers were hedging, shifting, recalculating. Not yet choosing sides, but drawing breath.

London, meanwhile, found itself in a familiar and unwanted position: a supporting actor in someone else’s crisis. Yet the stakes were real. The City started to absorb some of the shockwaves sent by the Chinese moves, with the pound sliding by nearly 2% before stabilizing. At 10 Downing Street, the Prime Minister met with MI6, the Treasury, and representatives of the Bank of England. Publicly, the UK called for “respect for international law.” Privately, the conversation was far more cynical. “The Chinese don’t seem to be playing games, and we need to be ready to act”.

In Moscow, the Kremlin had long predicted this day, and planned for it. President Putin, still ironclad in power despite growing internal rumblings and the Ukrainian retreat, appeared on state television that evening to express Russia’s “understanding” of China’s actions. “We have always respected the internal matters of sovereign nations,” he said with a slow, deliberate smirk. Russian diplomats began subtly pushing a narrative across international media: this wasn’t aggression, this was the end of Western hegemony in the region. For Moscow, it was more than spin. It was opportunity. In the past few years Russian gas exports to China spiked as new contracts were signed in yuan, not dollars, so supporting China was not without a view on economic gains.

In Tel Aviv, Riyadh, Ankara, and Cairo, the reactions were fragmented and self-interested. Regional power players saw the tremors but waited for aftershocks. Oil prices surged past $140 a barrel, briefly touching $150 before OPEC stabilized the futures with vague promises of increased production. But the mood in Middle Eastern capitals was clear: China decided that the end of the old world order, bulldozed by the US in recent years, implied a free reign to accommodate the region at its will.

And in Washington, the heart of the storm, the White House had become a fortress. Behind the thick walls of the Situation Room, the President sat with the Secretary of State, the Chairman of the Joint Chiefs, and the Treasury Secretary. Every screen showed a different theatre: troop movements near the Taiwan Strait, economic indicators flashing crimson, cyber threat assessments. The National Security Advisor spoke in clipped, practiced tones. “The consequences of their movements are in every front, military, economics, cyber. They’ve impacted the whole chessboard.”

The President, grave and weary, nodded slowly. “And what’s the board telling us now?”

A long silence.

“The massive moves, not yet an invasion, seems designed not to win,” the Treasury Secretary finally said, “but to end the game.”

Outside the White House, protestors had already begun gathering. On Wall Street, pension funds were staring to what had become usual since the trade wars, down brutal numbers. And in a quiet cyber facility in Maryland, an NSA analyst found an anomalous data packet originating from a Chinese server farm. It had penetrated a civilian water utility in California, not with malware, but with something worse: silence. It wasn’t a bug. It was a test.

The world had stepped through a threshold. Diplomats still called for restraint, and editors still published op eds demanding dialogue. But behind every placating sentence was the gnawing truth: the destruction of the global order by the US a couple of years before resulted, not only in years of economic pain for common Americans and the rest of the world, but in the other powers not trusting that the US was up to the game anymore.

And above them, high over the Pacific, Chinese satellites blinked awake.

Section Three: Echoes of Steel

Taipei, Taiwan, 10:22 AM (CST)

Yuan-Hao’s phone had already vibrated ten times before he answered it.

“Baba, they’ve locked our school down. We can’t even look outside the windows,” his daughter’s voice trembled through the line. “Are they really coming?”

Yuan-Hao Lin, thirty six years old, never wanted to return to uniform after his mandatory service. But he had. The past few years, he served in the reserves while working as a logistics engineer for a Taiwanese robotics firm. Yet this morning, everything else evaporated. The text came in at 05:46: Tier Two recall. Immediate reporting. No questions, no explanations. He had kissed his sleeping wife and daughter goodbye and slipped quietly into his uniform like it was 2015 again.

Now, in a reinforced command centre beneath a mountain in central Taiwan, he was staring at wall to wall screens tracking Chinese vessel movement. The map looked like it was bleeding red.

“Twenty four destroyers confirmed,” a lieutenant said behind him. “Submarines unknown. Airspace activity up seventy percent. We’re being painted every six minutes.”

“Civilians?” Yuan-Hao asked.

“Mostly calm. Power grid’s holding. But the news cycle is spinning. People want answers.”

He didn’t have them.

Outside, Taipei still hummed with life, markets open, scooters zigzagging through alleys, but it was a surreal calm, like a city waiting for an earthquake already registered on someone else’s Richter scale. No bombs had fallen. No invasions had begun. But the sense of impending fracture was everywhere. It sat behind every pair of narrowed eyes. In every extra bottle of water bought quietly at a corner shop. In the way shopkeepers glanced at the sky for no reason.

“Send our drone scouts out past Kinmen,” Yuan-Hao ordered quietly. “They need to know we’re not blind.”

And for a moment, as he stared at the creeping arcs of Chinese frigates converging in the Strait, he allowed himself to think the impossible: Maybe they aren’t bluffing this time.

Shenzhen, China, 11:55 AM (CST)

Captain Wu Lian sat aboard the Changsha, a Type 052D guided missile destroyer, slicing through the South China Sea like a blade through black silk.

“J20s crossing waypoint Echo,” his communications officer reported. “No resistance. Taiwanese jets just shadowing.”

Wu nodded without looking. The sea was calm. The operation, Joint Sword 27, had been rehearsed a dozen times under different names. Exercises. Simulations. But this was different. They were moving into a live corridor, where American satellites watched and enemy radar whispered back like ghosts.

And yet, the men weren’t tense. They were focused. Silent. Trained. They believed in the mission.

Wu, a fifty two years old veteran of China’s rapid military rise, had long kept his own counsel. He read Sun Tzu, but he reread Clausewitz. He knew politics sat at the end of the gun’s barrel, and he also knew how easily a gun could slip from one hand to another.

His orders were clear: move into a pre assigned grid, display naval presence, do not fire unless fired upon.

But he knew. Everyone knew. This wasn’t a rehearsal anymore. This was the shaping of a new epoch.

“Distance to Taiwan coast?” he asked softly.

“Seventy nautical miles.”

He stood, walked to the reinforced bridge window, and gazed out at the horizon. The sky shimmered, perfect and quiet. Almost deceitfully beautiful.

“We do not act first,” he reminded his officers. “But we do not hesitate.”

Behind his back, a young sailor murmured to another: “You think they’ll actually invade?”

Captain Wu didn’t answer. But he remembered the briefing the day before.

In twenty four hours, the West will blink.

New York City, 01:38 AM (EST)

Travis Berman hadn’t slept. Not since Tokyo’s Nikkei opened with a 900 point plunge.

Now he sat in the conference room of a mid-tier hedge fund in Lower Manhattan, sweating through his shirt as alerts pinged from his laptop like popcorn.

“This isn’t a selloff,” he snapped at the room. “It’s a damn exodus.”

“Travis, it’s military tension,” his boss, Andrea, said, trying to keep calm. “They haven’t fired a shot. It’s just fear. People will hedge back in when the smoke clears.”

But Travis wasn’t buying it. Not this time.

His screen showed the Hang Seng dropping 11%. Semiconductor indexes were cratering. Taipei’s stock exchange had halted twice already. The VIX, the volatility index, was spiking like a heart monitor in cardiac arrest. And the yuan was holding steady, unnaturally steady.

“That’s the part I don’t like,” he muttered. “Everything else is chaos. But the yuan’s anchored. It’s not supposed to be.”

No one responded. They were too busy staring at the red waterfall pouring down their Bloomberg feeds.

Outside, New York was still asleep, but already whispers moved through elite circles. “Taiwan.” “China.” “Could this really be it?”

Travis checked the time. In two hours, the pre-market would open. He sent an emergency note to his clients: Consider rotating out of East Asian equities until further notice. Hedge against Pacific exposure. Stay liquid.

He stared at the bolded sentence. Then deleted “until further notice.” Replaced it with: Indefinitely.

Taoyuan, Taiwan – 12:03 PM (CST)

Hsu Meiling adjusted the straps on her five year old son’s backpack while glancing nervously at the apartment television.

“Military drills,” the anchor was saying. “We repeat, these are military drills. There is no invasion.”

Her husband, Zhen, a civil engineer, came in from the balcony, where he’d been trying to get reception on his phone.

“I think we should take the car. Go inland. Just for the day.”

Meiling nodded. Her hands trembled as she zipped her son’s jacket. He looked up at her, not afraid, just confused.

“Are we going on vacation?” he asked.

She smiled, a small, brittle thing. “Yes, sweetheart. A little holiday.”

But as she looked out the window toward the distant coast, where the sky was just a little too empty, a little too blue, she felt it. The pause before history stirs. The weightless moment before the avalanche starts to fall.

And then her phone buzzed, an alert from the government: Prepare emergency kit. Stay informed. Avoid coastal areas. Do not panic.

Meiling didn’t scream. She didn’t cry. She packed rice, bottled water, and batteries. She grabbed birth certificates. She kissed her husband tightly. She knew what was coming, not the details, not the headlines, but the feeling. The tectonic rumble of a world beginning to split.

Section Four: The Flight to Safety

New York City, 03:07 AM (EST)

Travis had seen panics before. Not like 2008, he was still in college then, but enough mini crises to know what market fear should look like. This wasn’t that. This was colder. Cleaner. Less frenzy, more algorithm.

“We’re seeing a full flight to safety,” his assistant barked, reading off the trader chat. “Oil just hit $147. Spot gold is pushing two thousand. And Bitcoin’s dipping to unassailable lows.”

“Of course it is,” Travis muttered. “Because why not?”

He toggled between tickers. The Dow Futures were down over 800 points. Taiwan Semiconductor, the most valuable chipmaker on earth, had lost 18% in four hours of Asian trading. In after-hours trading in the U.S., Nvidia and AMD were haemorrhaging value. The tech sector as a whole was buckling.

But what really worried Travis wasn’t the drop. It was how fast capital was fleeing.

“This isn’t just Taiwan exposure,” he said aloud. “This is global recalibration. They’re pricing in more than a standoff.”

Andrea walked in again, coffee in one hand, tie askew, trying to stay above water.

“They haven’t even fired a shot, Travis,” she said. “No one’s sunk anything. Not a single plane downed. It’s bluster.”

“Markets don’t care. Markets read fear like tea leaves. And every goddamn leaf says: this isn’t a bluff.”

A red notification slid onto his screen: WTI Crude crosses $150/barrel.

“Jesus,” someone whispered behind him. Then silence. Not one person in the office, not the veterans of Brexit, not the crypto maniacs, not the macro guys, had seen oil spike that fast since the first Gulf War.

“It’s gonna choke supply chains,” Travis said, half to himself. “Shipping rates, fuel, air freight. All of it. That’ll hit inflation again. Fed won’t know what to do.”

“And gold?” Andrea asked.

“Up 6% since midnight. Everyone’s running to it like it’s 1941.”

The tension in the room was thicker now. The market wasn’t panicking because it thought missiles would fall. It was panicking because it no longer trusted that they wouldn’t.

Berlin, 10:18 AM (CET)

Klara Eisenberg, deputy director of risk assessment at the Bundesbank, stood at the edge of a small operations room normally used for cyber drills. But this morning, it was repurposed for something more primal.

On the main display: collapsing indices from Seoul, Tokyo, Taipei, and now Frankfurt, the DAX down nearly 5% in the opening hour. German industrials, so dependent on Chinese contracts, were being hit like dominoes. BMW, Siemens, BASF, all bleeding.

She sipped cold coffee and scrolled through ECB updates. The bond yields across the eurozone were oscillating in ways that made no fundamental sense.

“This is all reflex,” she said. “No one knows what to price in yet.”

“The Asian markets will drag everything down,” her analyst said. “Everyone’s trying to rotate out of equities. But there’s no clear safe asset.”

“They’re buying oil and gold like mad,” she said. “Everything else is tainted by risk. Even treasuries are shaky.”

“Why?”

Klara stared at him for a long second.

“I don’t know,” she admitted. “I don’t like that I don’t know.”

Tokyo, 06:26 PM (JST)

The Nikkei had closed 11% down. The worst single-day performance since the Fukushima disaster.

Inside the Ministry of Finance, Deputy Minister Shinobu Mori ran a trembling hand through her silver-streaked hair. She had been in office for twelve years, seen enough currency crises and political eruptions to know when something was truly novel.

“This isn’t just a confidence problem,” she told the room. “Investors are seeing this as systemic. Structural. They’re thinking: if Taiwan is hit, how much of the global economy survives intact?”

One of her aides added, “The yen’s spiking against the dollar. Speculative movement.”

“Speculation is reality now,” she replied. “We need to hold the line, not let this become a self-fulfilling prophecy.”

But no one in the room really believed it could be stopped.

From the window, she could see the lights of Tokyo Bay. Ships were still moving. Planes still taking off. But the city felt fragile now, as if a hairline fracture had appeared somewhere no one could quite locate.

Taipei,  02:11 PM (CST)

Meiling and Zhen had made it inland by now, a town near the central mountains, where things felt quieter. But the tension followed them.

They sat in a relative’s home watching news cycles loop the same grainy videos: Chinese destroyers on the water, jets streaking above offshore islands, the President of the United States holding a press conference with strong reassurances and bellicose tones.

The local market was still open, but stripped of bottled water and canned goods. Rumours ran through the town like wildfire, that Chinese drones had landed, that the Americans were preparing to evacuate consulates, that missile systems were already targeting critical infrastructure, and that the US was mobilising its entire Pacific fleet towards Taiwan.

None of it was confirmed. But Meiling could feel the edges of society beginning to peel, not in chaos, not yet, but in the way people moved: faster, sharper, quieter.

Their son was watching cartoons. Blissfully unaware. Zhen turned the volume down and looked at her.

“If they come,” he said, “it won’t look like the last time. We’ll have no warning.”

Meiling nodded. Her hands were still trembling, just barely.

“I think they’ve already come,” she said. “Just not the way we expected.”

Section Five: The Ghost in the Flow

New York City, 04:42 AM (EST)

The sun still hadn’t risen over the East River, but Travis Berman’s third coffee was already half gone. The screens around him glowed like stained glass windows in a church of fear. After the US reassurance that it would defend Taiwan with all its almighty power, everything was red, not just Asian markets anymore, but Europe, the S&P futures, even utilities and healthcare. Defensive sectors weren’t defending. The “safe zones” were crumbling.

Still, there were no missiles. No sinking ships. No downed aircraft. Just manoeuvres. Just pressure and America rhetoric.

And then something small, almost nothing, blinked into his peripheral screen.

Bond Movement Alert, Singapore / Zurich, USTs (10Y), Volume spike.

He blinked, leaned forward, and checked the line item again. Ten-year U.S. Treasury bonds, one of the safest, most liquid assets on earth, had just seen a sudden, unusual sell side volume coming out of Singapore. Not huge. But weird. The kind of weird that traders are trained to notice.

Then Zurich. Another dump. $2.4 billion total between the two.

He frowned.

“Hey,” he said to one of the analysts, “check the offshore bond flows. T-notes. Use our Asia Pacific overlays. See if anything’s coming out of China linked accounts.”

“Why would they be selling Treasuries now?” the analyst replied, incredulous. “That’s suicide.”

“I didn’t say they were. I said check.”

Travis turned back to his screen, mind ticking faster now. The markets were melting down, but that alone didn’t explain Treasury movement. Normally, in a geopolitical crisis like this, U.S. government bonds surged in demand. Investors fled to them like lifeboats in a storm. Yields should’ve been dropping. Instead, they were ticking up. Not wildly, but enough to be wrong.

Something didn’t fit.

He opened a private Bloomberg terminal and began digging. Twenty minutes later, he had three more anomalies. All small. All within offshore jurisdictions tied to Chinese banks or intermediary institutions often used for sovereign activity.

Not enough to confirm anything. But enough to feel it.

He leaned back and exhaled. A ghost was moving through the system.

And that’s when the old argument came screaming back into his brain, the one every economist, analyst, and politician had agreed on for decades: China would never sell its Treasuries.

It had been the backbone of the unspoken détente between the world’s two largest economies, Mutual Financial Destruction. China was the largest foreign holder of U.S. debt, with over $800 billion in Treasury securities. More, if you counted shadow accounts and sovereign proxies. Selling those en masse would tank the dollar, spike U.S. interest rates, cripple the very demand China needed to maintain its export driven economy. It was financial seppuku, using its Japanese neighbours word.

And so, the assumption had calcified into gospel: China won’t dump U.S. Treasuries. They can’t afford to.

Every major policy paper said the same. Every central banker echoed the refrain. Even during the 2025 trade war, Beijing had never weaponized its holdings; it was Japan that started selling theirs causing the US to backtrack partially on their destruction of the order they have created for their benefit. The logic was airtight.

Except Travis had always hated airtight logic.

He knew markets. And markets, especially in times of crisis, weren’t driven by reason. They were driven by fear, and sometimes, by purpose. Strategic purpose.

He opened another screen. Pulled up data going back ten years. Looked at China’s slow, steady reduction in UST holdings since 2013. Nothing drastic. But patient. Almost…methodical.

“Hey Andrea,” he called across the floor, voice quieter now, “you remember what everyone said during the last trade war? About the nuclear option?”

She looked up from her own mountain of chaos. “Yeah, that China would never nuke the bond market.”

“Right. Because it would destroy their own reserves, make their existing portfolio worth less, spike their borrowing costs.”

“And cripple their economy,” she added. “It’s been the assumption since 2001.”

Travis didn’t respond. Just stared at the numbers. Tick by tick.

“What?” she finally asked.

“I think they’ve found a way around it.”

She narrowed her eyes. “What do you mean?”

He turned the screen so she could see. Showed her the volume spikes. The seller profiles. The jurisdictions. The creeping uptick in yield on bonds that should’ve been gaining.

“I think,” he said slowly, “they’ve been preparing for this for years. Hedging quietly. Building buffers. Gold, euros, yuan reserves. Every time they bought Treasuries… I think they were covering the loss ahead of time.”

Andrea looked at the screen. Back at him. Then leaned in.

“If that’s true,” she said, “we’re not just dealing with a military feint.”

“No,” Travis said. “We’re in the opening moves of a coordinated economic war.”

He exhaled again. The office around them hummed with nervous motion. Screens flashing, analysts whispering, someone crying quietly near the coffee machine.

“Still early,” he muttered. “Still just tremors. But if they’re really pulling the trigger…”

He didn’t finish the sentence. Didn’t need to.

Because everyone knew what came after the tremors.

Section Six: The Quiet Realization

Washington, D.C. 06:23 AM (EST)

The conference room on the second floor of the Eisenhower Executive Office Building, adjacent to the West Wing, had the unmistakable energy of a room just behind the frontline of history. Not chaos, not yet, but the kind of taut alertness that made people speak with half breaths and blink too often.

No one wanted to be the first to say it.

Deputy Secretary of the Treasury Rachel Kaminsky stared at the Bloomberg dashboard that had been pulled up on the wall screen. The data was granular, broken down by market hours, jurisdictions, trading volume. Quiet. Too quiet. Except for a few disturbing outliers.

“Zurich and Singapore,” her analyst said, tapping his pen against the chart. “Volume anomalies. $6.1 billion in total so far. All in 10 year and 30 year T-bonds. No short term instruments.”

“No sales from domestic U.S. accounts?” Rachel asked.

“Not yet. But European funds are starting to move cautiously, not dumping, just rotating into cash.”

“And the Chinese angle?”

He hesitated. “We don’t have direct attribution. But… two of the firms involved are known proxies. Remember the ones we flagged back in ’21 for indirect Belt and Road financing?”

Rachel leaned back in her chair, lips tightening.

“It’s too early,” someone else said. “Too little volume. It could be pre-emptive hedging by funds exposed to Taiwan risk.”

Rachel didn’t respond. She was remembering a meeting from six years earlier, in an IMF backroom, when a Chinese delegate had casually said: “Every financial empire thinks it’s too big to be unplugged. You only realize it when the power goes out.”

She turned to the NSA liaison sitting in the back corner. “Are we seeing any cyber spikes in financial infrastructure?”

He nodded, flipping open a tablet. “No penetration. But there’s increased traffic, coordinated, patterned, clean. Recon level pings on Fedwire, SWIFT relays, and secondary liquidity hubs.”

“So,” Rachel said, fingers interlaced tightly, “they’re looking at the plumbing.”

London,11:34 AM (GMT)

In a fortified wing of the Bank of England, Charlotte Parris, Director of Global Risk Surveillance, wasn’t watching the pound. She wasn’t even watching the FTSE anymore. She was watching yields.

“Look at the curve,” she said to her deputy. “Ten-year moving up faster than the thirty. No safe haven behaviour. This is a targeted move.”

The younger analyst frowned. “Everyone’s saying it’s market noise. Military nerves.”

“This isn’t nerves,” she replied. “This is institutional. Intentional.”

She pulled up the historic spread curve between Treasuries and Bunds. The differential had narrowed overnight, not by much, but by enough to catch her eye.

“Someone’s forcing a revaluation of dollar trust,” she murmured. “Not selling off in panic. Trading out.”

Then, more softly: “This is what it looks like when someone tries to unwind a financial empire without making a sound.”

Frankfurt – 12:09 PM (CET)

The ECB’s Market Stability Council had been in session for four hours. They were supposed to break for lunch thirty minutes ago, but no one moved.

A German analyst with a thick Bavarian accent was presenting a breakdown of unusual gold transactions over the past eighteen months. “We believe these purchases were masked by intermediaries in non-reporting jurisdictions, but the origin trails back to Chinese-controlled entities.”

One of the French delegates leaned forward. “So they’ve been preparing for dollar turbulence?”

“Not preparing,” the analyst corrected. “Designing.”

The room went still.

They weren’t just witnessing a market crisis. They were watching the early contours of an engineered global reordering , still deniable, still faint, but with a shape that could no longer be ignored.

Someone whispered, “Is this the financial decoupling we were warned about?”

A British voice near the door said grimly: “No. This is what decoupling looks like. We’re already in it.”

New York City – 07:03 AM (EST)

Travis sat alone now, headphones in, watching the Japanese yen hit 138.5 against the dollar. It was supposed to be a win for the greenback, but it wasn’t behaving like one. Liquidity was thinning. Bid ask spreads on high-volume Treasuries were widening, slightly, but measurably.

His screen pinged again.

BREAKING: South Korea’s KOSPI down 14%. Emergency circuit breaker activated.

He barely glanced at it. His attention was now locked on a new message from a contact at a Geneva-based clearing house.

“We’re seeing longer duration USTs being unwound. Multiple desks. Looks surgical. Some of these desks haven’t moved positions in years.”

He closed his eyes.

This was the moment before the moment.

The markets hadn’t screamed yet. But the people who knew how to listen, to the flow of capital, the rhythm of safe havens, the murmurs of liquidity, they were starting to hear something.

And that something was a strategy.

Not a panic. Not a reaction.

A plan.

And plans, when executed at this scale and if originated in Beijing, didn’t reverse course.

Section Seven: Shattered Assumptions

Washington, D.C., 08:19 AM (EST)

It hit like a slow avalanche.

At first, the Treasury Department believed the initial dump could be managed, isolated volume. A correction. A market spasm in reaction to Taiwan tension. They’d seen worse. But by mid-morning in New York, it was no longer a trickle.

It was a rupture.

The data feeds were clear. By 08:07, $26 billion in U.S. Treasuries had been offloaded through accounts in Singapore, Luxembourg, and Zurich. Another $9 billion appeared to be moving via Hong Kong intermediaries, cleverly disguised as corporate reallocations. In aggregate, it was massive. In form, it was disciplined. Not a panic. A program.

The ten-year yield had shot up past 5.1%, a 74-basis-point jump in under six hours. Repo markets began flashing yellow. Treasury dealers widened spreads again.

Inside the West Wing, the National Economic Council met with the Fed Chair, the Secretary of the Treasury, and the National Security Advisor. What was meant to be a closed-doors discussion became something closer to a crisis war room.

Rachel Kaminsky dropped the paper in front of the room.

“We’re looking at nearly $70 billion in Treasuries sold in the last nine hours.”

The Fed Chair shook his head. “This makes no sense. They’re nuking their own portfolio. If they keep this up, they’ll wipe out half the value of their remaining holdings.”

“And yet,” Rachel said flatly, “they’re still selling.”

“But why?” asked a political advisor, pacing. “Why would they do it? We’ve always said this was their insurance policy. That dumping Treasuries would hurt them more than us.”

The room went silent.

Then a voice from the back, a young economist on loan from the IMF, spoke up.

“What if that assumption was based on the wrong premise? What if… every time they bought Treasuries, they were also buying a way out?”

Heads turned.

He went on. “They’ve been quietly accumulating euros, yuan-denominated debt, and physical gold. Not in headline numbers, but in the shadows. Through sovereign funds, intermediary banks, strategic swaps. Every purchase of a Treasury bond was balanced with a hedge.”

“Meaning what?” the Fed Chair snapped.

“Meaning they’ve spent twenty years building an escape pod, so they could cripple the US economy without affecting theirs, impeding any response when they decided to invade Taiwan.”

London, 01:44 PM (GMT)

In Canary Wharf, the crisis room at the UK Treasury was buzzing. Analysts and economists pored over secondary market data, trying to track the source nodes of the cascade.

Charlotte Parris, at the Bank of England, connected in via secure line.

“It’s too clean,” she told them. “This isn’t capitulation. This is sequencing. A deliberate unwinding of long duration notes first. Followed by targeted middle range bonds.”

“And short term?” someone asked.

“Untouched, for now. Which tells me they’re timing it. They’re managing the optics, trying to keep the dump just below overt panic, yet, but well above strategic shock.”

One of the senior political officials, his voice half panicked, asked, “But why now? We’ve known about Taiwan tensions forever. They’ve never done this before. Why now?”

Charlotte responded without hesitation. “Because they have decided to take Taiwan and are making sure that neither the American nor us have the resources to stop them, all while finishing the demolition of the preexisting world order, as started by the US in 2025. And because they’ve built their reserves and the alternative networks to a point where the damage is now survivable.”

Another analyst added, “And if they can shift international settlements into gold or yuan, even temporarily, the sell-off becomes a tool, not a cost.”

“Are you saying they want a dollar crisis?”

“I’m saying they’ve spent twenty years preparing for one., one that ends the dollar reign for good”

Beijing, 09:08 PM (CST)

Inside a secure wing of the People’s Bank of China, the Deputy Governor watched a live Bloomberg feed showing a red waterfall of global bond market chaos. There was no celebration. No visible satisfaction. Only quiet calculation.

“We’re at $81 billion,” a finance aide reported. “Another $20 billion queued by midnight. Zurich and Dubai are fully operational. The gold floor is absorbing 70% of the liquidity shift. Yuan demand from Belt and Road accounts has tripled.”

“Any feedback from Moscow and Tokyo?” the Deputy Governor asked.

“The Russians are holding their dollar reserves for now. But ruble-yuan swaps are active and expanding. In Japan, they are not selling their bonds, but our alliance after the 2025 trade wars seems to stay solid”

The man nodded. The design was working. The foundation had been laid long ago.

China’s strategy wasn’t to destroy the dollar outright. That would be too fast, too chaotic. Instead, the goal was subtler, to prove, first, that the dollar was not untouchable. That it could be targeted. That it could bleed.

And in that bleeding, the remains of trust that were left after the US dynamited the system that it had created, would further erode.

Frankfurt , 02:11 PM (CET)

At the Bundesbank, Klara Eisenberg leaned forward, hands folded under her chin.

She’d just received word from a confidential source: the Swiss National Bank had begun quietly shifting some of its reserve mix out of dollar-denominated assets. Not yet a policy move, just “precautionary rebalancing.” But the message was clear.

Confidence was faltering.

“We’re approaching a monetary pivot point,” she said to the room. “Not because the dollar is crashing, but because people are realizing that it can be made to crash.”

A French economist near her said softly, “The idea of U.S. invincibility, it’s what’s been holding the system together. Once the idea breaks…”

“It doesn’t come back,” Klara finished.

New York City, 09:31 AM (EST)

Markets opened to chaos.

The Dow dropped 1,300 points in the first fifteen minutes. Trading halted on five major banks due to volatility. Gold hit $2,100. Oil cracked $158 per barrel. And the ten year Treasury yield was pushing 5.7%, a death knell for mortgage rates, for credit markets, for any future soft landing.

Travis sat at his desk, shoulders rigid, jaw tight.

“It’s happening,” he said aloud.

Andrea didn’t need to ask what “it” was.

Around them, traders yelled, phones rang, screens blinked like warning lights in a failing aircraft. But Travis felt oddly still. Almost frozen.

All those years of dismissing the threat. All those policy briefings, journal articles, academic panels, all grounded in the belief that China was too smart to cut off its own legs.

They weren’t cutting off anything.

They’d just built themselves a new pair.

Section Eight: Architects of the Quiet Sword

Beijing, 09:53 PM (CST)

People’s Bank of China, Inner Directorate Chamber

The room was cold, windowless, and silent. Phones were left outside. Devices turned off. There were no transcripts, no minutes, just memory, discipline, and old loyalties.

At the head of the table sat Luo Min, Deputy Governor of the People’s Bank and one of the principal architects of the operation codenamed Project Qingxuan, loosely translated: the Clarity After the Mist.

He was seventy one years old, silver haired, with the quiet demeanour of a professor more than a financier. For most of his career, Luo had been underestimated, too soft-spoken, too methodical. But in the cracks of the 2008 financial crisis, when the dollar buckled and the Fed printed its way out of the abyss, Luo had seen the future.

He had written it in a memo that only three people ever read:

“The empire is built on a promise of return. But if we reshape the conditions of belief, the promise fails.”

Now, fifteen years later, the reshaping was in motion.

“Zurich reports full transition from phase two to three,” said a younger man to Luo’s right, Qi Haoran, a strategist from the State Administration of Foreign Exchange (SAFE). He was in his forties, fluent in German and Python, and had spent two years embedded in the IMF under a false identity. He’d written the original code that allowed the obfuscation of sovereign backed shell companies and layered financial positions across Singapore, Mauritius, and the Caribbean.

Luo nodded.

“Confirm gold buffer levels?” he asked.

“Over targeted by 11%. Physical delivery schedules intact. Dubai, Vladivostok, and Djibouti all reporting compliant storage volumes. No interruptions.”

“And the Belt and Road accounts?”

“Most of the African and Central Asian recipients have shifted to yuan-denominated settlement. Latin America is slower, but moving.”

A woman across the table, Gao Yini, Deputy Minister of Commerce and the bridge between the financial operation and the foreign policy apparatus, leaned forward. “The Americans are still clinging to the belief that this is emotional. A lashing out. They haven’t yet realized it’s a conclusion.”

Luo sipped tea, bitter, cooling.

“They have long assumed we are prisoners of their rules,” he said softly. “Because we used their currency, bought their bonds, followed their markets. They believed this meant dependency. But dependency only matters when there is no alternative.”

Gao nodded. “And now there is.”

Silence returned. On the wall behind them, a live feed ticker showed U.S. Treasury yields rising in slow but steady pulses, like an accelerating heartbeat.

Qi broke the quiet.

“There will be pain for us too. Exports will contract. Dollar based deals will be renegotiated. Our own real estate sector is still brittle.”

“Yes,” Luo said, “but pain is not collapse. And pain is survivable when prepared for.”

Gao added, “The Americans assumed their pain would always be our pain.”

Luo turned to her. “They forgot what it meant to plan for twenty years instead of for the next quarter.”

Ten Years Earlier , 2017, Hangzhou, An Unmarked Office on the West Lake

The first models were tested here, in a side building of the China Academy of Social Sciences. A small team of ten economists and mathematicians ran simulations under the direction of Luo Min, who was still then only a senior advisor to the PBoC.

They fed in datasets: China’s Treasury holdings, historical bond volatility, global reserve flows, oil prices, geopolitical events. Then they designed a game theoretic engine: How long could the US hold before having to stop all military operations due to the economic impact of a sudden Treasury bonds sell off? How long could China sell Treasuries, in various masked forms, before it triggered self-damaging blowback?

The results surprised even Luo. If done slowly, over decades, building gold reserves, shifting Belt and Road debts to yuan, locking in oil contracts outside the dollar, a day could come when the pain was bearable. And on that day, while even a partial dump of U.S. Treasuries would stop US military operations, a substantive sell off would not crash China’s economy.

It would wound it.

But it would mortally undermine trust in the U.S. financial system, and it would make it unable to fund a military operation like the one needed to defend Taiwan against a Chinese invasion.

The plan was born.

Back in 2027, Beijing, Now

Qi was reviewing updated sell numbers: another $14 billion queued. The pace had quickened. Not fast enough to collapse the markets, just fast enough to force doubt.

Gao handed Luo a document from the Ministry of State Security. Western intelligence agencies were just beginning to align the anomalies. Articles were leaking. Market strategists were starting to write the unthinkable: that China was dumping U.S. Treasuries, not reactively, but methodically. Deliberately.

“It will take them two weeks to say it aloud,” she said. “Three more to name it what it is: a financial offensive.”

“And by then?” Luo asked.

Qi smiled, almost sadly. “By then the psychology will have changed. The dollar will still stand, but not as a god. Just as a currency., and the US will not be able to rely in its fallen deity to keep its global military presence.”

Luo closed his eyes and leaned back.

It wasn’t vengeance.

It wasn’t chaos.

It was design.

Section Nine: The Sound of Surrender

Washington, D.C., 5 October 2027, 08:12 PM (EST)

White House, Situation Room

There were no raised voices anymore. No outrage. No pounding of tables. Just silence.

The room smelled faintly of coffee, sweat, and defeat.

The Chairman of the Joint Chiefs had just finished presenting a revised assessment. PLA forces had completed a full encirclement of Taiwan, a maritime and aerial quarantine more than a blockade. No military action had occurred. No missiles fired. No troops landed. But nothing could get in or out of the island without passing through the teeth of China’s military.

The President sat motionless, hands folded beneath his chin. He looked like someone attending a funeral he’d long feared. His childish discourse and attitudes did not serve against a rational, mature and calculating adversary.

“And there’s nothing they’ve done we can officially call an act of war to rally our allies?” he asked.

The Secretary of Defence shook his head. “Not by the book. They’re operating in international waters and airspace. Aggressive? Absolutely. But legal. Barely. And, what allies are we going to call? We told them only two years ago that they needed to take care of themselves. They kicked Russia out of Ukraine without us”.

“And the bond markets?”

Rachel Kaminsky, Treasury, gave a brief nod. “Yields are stabilizing, at high levels. $134 billion in USTs liquidated over four days, and they still hold more than $650 billion that they can sell. Gold continues to surge. The Fed has burned through $480 billion in emergency facilities just to restore liquidity. We’ve still got tools, but…” She paused. “Not for long.”

“And the dollar?”

“Dented. Very dented. International settlements in yuan have quadrupled since Monday. Smaller central banks are hedging out of dollars. We’re not going to lose reserve currency status overnight. But the illusion of untouchability is gone.”

The President didn’t speak for a while.

Finally: “So what you’re all telling me… is that we can’t defend Taiwan.”

No one answered.

Because everyone knew.

They couldn’t.

They couldn’t move a carrier group into the Strait, it would invite escalation and test a defence perimeter already sewn shut. They couldn’t issue economic sanctions, not when the global markets were already absorbing the shock of China’s decoupling manoeuvre. They couldn’t isolate China diplomatically, not when half the Global South was suddenly trading in yuan or receiving loans from newly leveraged gold-backed instruments.

China hadn’t attacked Taiwan. It had surrounded it, with steel, silence, and solvency, while the US had abandoned the world with twits, shouts and mounting debts. So it soon discovered that its arsenal, tanks, planes, broken treaties, and Treasury bonds, was not calibrated for this kind of war.

London, 6 October, 02:47 AM (GMT)

UK Foreign Office, Emergency Advisory Council

Foreign Secretary Robert Finch looked up from his notes.

“Well then,” he said flatly, “they’ve won.”

No one corrected him.

A German ambassador muttered, “They’ve changed the rules of engagement. And we didn’t realize until we were already playing.”

The French delegate, normally eloquent, simply stared at the map.

Taiwan was encircled. Its skies monitored, its ports frozen in fear of miscalculation. And yet, there was no formal declaration, no images of war, only drones, satellites, and economic rupture.

“But the people of Taiwan…?” someone asked quietly.

“What do we do?”

There was no answer. Because there was no policy tool left that didn’t carry the risk of planetary financial collapse or actual war with nobody being able to foot the bill.

Taipei, 6 October,11:19 AM (CST)

Yuan-Hao stood atop a concrete outpost in the central highlands, watching through binoculars as a contrail arced far overhead, one of dozens circling above the island like hawks. He hadn’t slept more than two hours in five days.

His radio crackled with updates, troop drills, electronic jamming, brief incursions.

But there were no bombs.

Just pressure. Relentless, silent pressure.

His daughter was back in the city now. Her school remained open, but she hadn’t left the building in three days. His wife texted him short, brave messages.

And he knew, deep in his chest, what was coming.

Not a war. Not an invasion.

A wearing down. A slow suffocation of options until Taiwan became not conquered, but irrelevant.

Beijing, 6 October,12:02 PM (CST)

Zhongnanhai Compound

Luo Min stood by the koi pond, an old place of reflection in the heart of power, a present from their Japanese counterparts.

A young party official approached him with a message from the Central Committee. Praise, gratitude, triumph. Luo accepted it politely, nodded, and waved the man away.

He looked up at the sky, pale with smog and history.

They had not fired a shot.

And yet, the outcome was complete.

The world still functioned, the internet was on, markets were trading, aircraft flew. But something deeper had shifted, irrevocably. The illusion of economic invincibility, of Western permanence , was gone.

Taiwan might resist. Its people were proud, intelligent, determined. But in the years to come, it would face the same silent war being waged now: diplomatic strangulation, economic redirection, global fatigue.

Time would do the rest.

Luo dropped a crumb into the pond. The koi surged forward, briefly, then calmed again.

New York City, 6 October, 10:37 PM (EST)

Travis Berman left the office early for the first time in six days. He didn’t even remember what day of the week it was anymore. Midtown was still lit, New York never really went dark, but it felt quieter now. The kind of quiet that happens after something’s cracked but hasn’t fallen yet.

People passed him in murmured conversations,“…Taiwan’s ports still shut…” “…gold hit $2,150…” “…China sold how much?” No one had answers. Just questions, headlines, half-wrapped theories. But Travis had stopped asking.

He stood still for a long moment. The lights of the city still buzzed outside. The world was still spinning. Markets would reopen. Statements would be made. Panels convened. Strategies adjusted.

They had not been outgunned.

They had been out thought.

He walked three blocks north, almost on instinct, and stopped in front of his apartment building. Instead of going in, he turned and walked another block, to a 24 hour bookstore he sometimes ducked into when the markets were too loud.

He stepped inside and made his way to the philosophy section. There, untouched in the dim yellow light of the late-night shop, was a familiar spine: The Art of War, Sun Tzu’s original, bound in black cloth with gold script. He pulled it from the shelf. He turned to a section he remembered from years ago, during a late night cram session at Queen Mary in London. Back when these ideas felt like intellectual games. Now they read like prophecy.

He found the line. Read it three times before whispering it aloud almost laughing:

“The supreme art of war is to subdue the enemy without fighting.”

Students, you need to use AI in your assignments

Graphic created using ChatGPT 4o with the prompt “draw a picture for the blog that follows, including diverse students”, and the whole text of the blog.

A new teaching semester has started, and most of my students were surprised by my overencouragement for them to use AI for their assignments (at least in my modules), meaning that there are still some (many) teachers around that are telling them that that the use of AI should be avoided and that it would/may be cheating.

Both in February and April 2023, at the inaugural Technology Enhanced Learning Community of Practice event and BILETA 2023 respectively, and when the cover of the newspapers still were presenting large language models as the end of literacy, I insisted on the need to adapt assessment to the rise and rise of AI in general and large language models in particular. Many of my colleagues rose up in arms to the chants of “Cheating institutionalisation!” (with different words, though), and claiming that the already high proportion of cheaters in Higher Education would become astronomical. I simply replied that the vast majority of my students were not cheaters, and asked whether they would pass a student that submitted work with numbers as fabricated as those they were mentioning, which contradicted all the literature and available data at university level. Some didn’t like my question, none replied but all got the message.

The central issue is that we are not at the dawn of the AI age, we are in the morning of it, and those who cannot master it will be replaced by AI. So, students need to know how to use it, while understanding that if AI alone can write their assignments, the market will not need them because, at individual level in certain jobs, AI is cheaper than an employee. The challenge is to produce work that uses AI but goes beyond it, and there is where teachers (and professions’ regulators) come in the picture.

The question is not whether a particular AI tool can pass some country’s examination, but if the bar examination is a valid method to assess whether some is ready to be a lawyer, to give a blunt example. And the same applies to almost every module/class/course or whatever name subjects are given in different institutions.

It has become clear, and that somehow seems to be missing in certain discussions about AI and copyright (if you cannot distinguish between human and AI produced work, you may need to rethink the concept of originality instead of insisting on some formalities that reality will render obsolete very soon, like the courts’ repeated mantra “no human no copyright”), that AI is extremely good for many things and surpasses human in many others, but it is not match for human intelligence. Accordingly, instead of trying to stop the incoming waves with a bucket, many of us need to get up from the lounge chair, leave the comfort of the beach, and learn to surf.

For my first seminar of Business Law, the task is “Using ChatGPT or similar, answer the questions given at the end of Lecture 1. Prepare to discuss”, and I explained to the students that they will have to deconstruct the LLMs given answers justify, support or refute them, with particular attention paid to the hallucinations.

Part of the module’s assessment used to be a Self-reflective journal, where students needed to critically reflect upon some area of law, and the learning process that took them from where they were in relation to it before the start of the module and to where they are at the end of it. Now, the same task consists on asking a LLM to critically analyse a particular area of law, to explain what prompts they used to order the task and why those were the appropriate prompts, to justify, support or refute the AI critique, and to explain how their semester learning process allowed them to do so.

And there is much more to come…

AI and environmental damage

In the previous entry the issue of the environmental and climate change impact of AI use and development was presented as important, and in need of urgent treatment by policy makers (who are squarely ignoring it in most proposals of AI regulation). Those impacts are real, considerable and multifaceted, involving major energy consumption, resource depletion, and a variety of other ecological consequences.

Training large AI models requires immense computational power, leading to the use of large quantities of energy. Just as example, training a single model like GPT-3 can consume 1287 MWh of electricity, resulting in about 502 metric tons of CO2 emissions, which is comparable to the annual emissions of dozens of cars. But while the energy consumed during the training phase is significant, quite more energy is used during the inference phase, where models are deployed and used in real-world applications. There have been interesting forms to justify such a use, mainly by comparing pears not with apples but with scissors, but they seem to obviate the fact that the general human emission that are compared with the AI ones will be there regardless of the activity, so the improper use of AI adds emissions without subtracting much of them. In a world that the development and deployment of AI is bound to keep growing at bubble-like rates, this implies that the location of data centres plays a crucial role in determining the carbon footprint of AI, as they are bound to double their energy consumption by 2026 (if 170 pages is too much to read, simply go to page 8). Data centres powered by renewable energy sources, have a lower carbon footprint compared to those in regions reliant on fossil fuels, and there is an argument about making such use compulsory.

From the resource depletion and e-waste point of view, AI hardware, including GPUs and specialized chips, requires rare earth elements and other minerals. The extraction and processing of these materials can lead to environmental degradation and biodiversity loss. AI is been currently used to find ways to replace those rare earth elements, but even then, as AI technology evolves, older hardware becomes obsolete, contributing to a steep increase in the amount of electronic waste. Besides the global inequalities generated by the mountains of e-garbage currently dumped in developing countries, E-waste contains hazardous substances like lead, mercury, and cadmium, which can contaminate soil and water if not properly managed.

A less obvious but equally significant impact is water usage. Training AI models like requires abundant amounts of water for cooling data centres, with some studies claiming that the water consumed during the training of algorithmic models is equivalent to the water needed to produce hundreds of electric cars.

To add to the energy and resources consumption, the uncontrolled, improperly regulated widespread use of AI can have severe ecological impact, particularly and paradoxically, when used in activities where proper use of AI can minimize them. Not making sustainability a key aspect of algorithm design, training and AI deployment, may lead to situations that it is more profitable to carry on with environmentally harmful AI driven activities, like the overuse of pesticides and fertilizers, harming soil and water quality and reducing biodiversity, not mentioning that AI-based applications like delivery drones and autonomous vehicles can disrupt wildlife and natural ecosystems without giving much benefits (beyond increasing the already fat profits of few).

All this supports the idea that AI regulation must address sustainability issues and not leave to general environmental legislation, because it is important to know who owns what AI produces, but only if we have a planet where you can enjoy those works…

Algorithmic systems and sustainability

After more than a year not even opening this almost twenty years old blog, several changes in my private and job life imply that I will return to this old pastime. I have decided to spend less time on planes and managerial roles in Higher Education, and more in research, teaching and engagement activities, meaning more time to write (with, of course, some academic and policy related travelling).

Last year we were somehow in awe for the rapid development of AI, although one could argue that what we saw was just a very fast adoption of a particular type of algorithmic systems, generative AI, while even that type of algorithmic systems have been around people’s lives for quite longer than a year and a half.

However, it is true that the irruption of generative AI and Large Language Models made algorithms a super-hot issue, so much that it seems that the whole IT law field has been swamped by AI discussions, and that there is no much else to talk about. But if the different scenarios and the obvious challenges that algorithmic systems presented to the law, seemed to quickly create a consensus (really?) in the need of regulating them, the usual tendency of lawyers, law academics, judges and policy makers to focus on what it allowed them to modify less the current legal status quo, resulted in important (fundamental) areas of law left outside of the analysis and or regulatory frenzy. One of them is the dilemmatic relationship between algorithmic systems and sustainability, which will have deep effect both in the environment and in the businesses operating in the AI field.

The argument has been that sustainability and climate change implications of AI are common to any technological and economic activity and that, at best, there should be a generic sustainability legal framework that applies to all of them, not specifically to AI. The counterarguments to that are various and can be made from different angles. From the sectorial point of view, the same could be said for the oil, the cement and the transport industries, but there is a growing body of discussions and case-law that says that their situation is not a generic one, even when generic rules are been applied to them. If we focus on the substantial issues and emissions, the old view that a different in degree big enough implies a change in class, seems to apply squarely here: something that emits substantially more than other activities and or vast amount of greenhouse gasses emissions are intrinsic to its functioning, does not share common characteristics with any technological and economic activity. Algorithmic systems are in this category, and regulating them with a focus on sustainability and climate change is essential.

In the coming days I will start to dissect the why and how that is true, coupled with the potential application of current rules, which are being used to deal with other heavy-emitter industries.

The AI letter (and the fallacy of not shooting the messenger)

Several days have past since a group of academics and business people released a letter asking for a moratorium on AI development and deployment. As presented, the letter represents too little too late from a group of people that includes some of those with the least legitimacy to say things like “[s]uch decisions must not be delegated to unelected tech leaders”. Really? Do we need to quote 1990s’ Lessig again when referring to a letter signed by some of those who fought so hard to be uncontrolled from any form of regulation by elected leaders that their responsibility in the current situation, astonishly ignored by some of the less cynical signatories, cannot be understated?

Some of the those signing the letter, both business people making billions and academics getting chairs and winning prizes, are directly responsible for the “profound change in the history of life on Earth” created by technologies that have already contributed significantly to environmental catastrophe, greatest ever income inequality, growth of undemocratic movements and continuous degradation of average intelligence. Are they serious when the say “dramatic economic and political disruptions (especially to democracy) that AI will cause”? Do they know that the, then, more robust democracy in the world elected a misogynistic fraudster thanks to first the more progressive groups ignoring masses of people, but then the capture of them by the immoral with the help of the technologies they sell or helped develop with their papers? Have they heard about mass manipulation by the media exacerbated by technology, and the impact on Brexit? Do they know about the use of current technologies for the rise and rise of autocratic tendencies around the word (and, no, the use of social platforms in the Arab Spring does not show the democratic impact of technological developments…even a broken clock gives the correct time twice a day and in that case the current scenario is not that promising) So, “will cause”?! Sorry to say it, but the correct phrase is “it has already caused” and, many of the signatories were/are part of it, not forgetting that many before them have already pointed out the potential harmful impact of AI.

Furthermore, when someone in the past 30 years would point to the need to either stop and or regulate some technological developments until “we are confident that their effects will be positive and their risks will be manageable”, many of those signing this letter would respond fallaciously that such a moratorium or regulation would stifle innovation and a plethora of non-sense that, via billions spent in lobbyists and lawyers, and prestigious papers, they managed to infuse at all levels, making sure that they got their billions and their prizes. What happens now? Wasn’t technological innovation more important than, almost, everything, so they lobbied and argued to be allowed to reign in society as robber barons reigned during the twilight of the Wild West?

Here is when many (most) will say, “focus on the message and not in the messenger”, which is a fallacy that needs to be sent to the Averno with many other phrases produced or used by, coincidentally, many signatories of the letter. A metaphorical example will elucidate the paramount importance of the identity and past behaviour of the messenger even with correct, reasonable and 100% true messages (of which the letter is not one of them). Let’s assume that a flock of sheep is desperate looking for water; lost, thirsty and near starvation and a wolf comes telling them the exact location of a water well, which happens to be correct. Should the sheep focus on the message or should check whose coming from? Has the wolf suddenly found a fondness for every form of life that decides to make sure that the sheep survive, or it the wolf leading them to the well where the pack is waiting to slaughter them all? Or is just making sure that they survive a little longer so the pack has a longer term provision of meat? So, yes, the message is important, but many times the messenger is as important too, and with the letter, there are plenty of examples to show that the sudden interest in “the clear benefit of all, and [to] give society a chance to adapt” is indeed very sudden for many of the signatories.

Just as example as the letter has been widely promoted using one of the owners’ names; will Tesla pause all sale of cars until all their systems “implement a set of shared safety protocols […] that are rigorously audited and overseen by independent outside experts” and that “[…] systems adhering to them are safe beyond a reasonable doubt”? Will the academics return their chair and prizes, and refrain from publishing papers until they “are confident that [the] effects [of what they are producing] will be positive and their risks will be manageable”, again, “beyond a reasonable doubt”? Until that day that such a pause is implemented, the signature of the mentioned letter by some signatories seems, at best, shameless. Some may say that this situation is different and or that when they were developing the theories and technologies that led to today’s situation and today’s AI, they didn’t know to what may lead, but that only reinforces the point: they should have known, or the billions and the chairs and prizes need to be given back. It is impossible to not remember when a number of scientists, many whom had been part of the nuclear weapons program, signed a letter committing to create conscience in other scientists and the public on the dangers to humanity of the nuclear weapons…such a huge number of Nobel prizes and distinguished scientists did not about the dangers when they were designing nuclear weapons, what did they think were designing them for? A science fair?!

The letter has few good points; but as the technological moratorium is unlikely to happen (and many signatories have zero moral authority to request one), the focus should be in a clear and strong regulatory framework, because as some ignotus academic said in 2018 (down in page 66), “[t]he challenge is not technical; it is sociopolitical”, and the same academic already said back in 2008, “current law and principles are ill-equipped to deal with further radical changes in technology, which could imply the need of a more proactive approach to legal developments”.

In conclusion, as ChatGPT likes to say, it is not time for billionaires and respected academics to sign letters, but to put their money where they mouth is, and focus their work on the actual achievement of a robust regulatory system, for example by using their lobby power and money to that effect instead of doing exactly the opposite, so we can all “enjoy a long AI summer” instead of having hordes of people suffering sunburns.

ChatGPT, the Skynet moment, and Judgement day without steel robots

There was a time when we imagined that the end of times would be marred with steel robots crushing the humans that tried to disconnect them, or enslaved them as a source of energy, but while science fiction has provided plenty of accurate predictions of things to come, it seems that end of the society as we know it may come from something less muscular and more subtle. We are going through days when it is almost impossible to open any social media site or publication without bumping into a discussion about the uses, benefits and problems from the use of freely available Natural Language Processing algorithmic systems that seem to create texts of almost anything better than humans.

Leaving aside that it is actually not true that the available systems create text better than humans, as they are quite basic, rigid and plagued of errors, let’s assume for a moment that they are indeed better than humans in creating those texts. There is a pervasive mistake making the algorithm the centre of the discussion, and the hero of the imagined saving-all AI. As many know and have pointed out, the centre, the middle and the periphery of everything that AI can do is the DATA (yes, with capital letters!). There are plenty of writing about the ownership and privacy of such a data and, therefore, the resulting text (or image), but the crucial issue, the one with the capability of creating judgment-day scenario, is the quality of the data.

There is some true in the statements that AI is not biased per se, but there is even more true in the fact that by using biased data AI can replicate and reinforce the original bias, so much that it can convert it in the new, accepted as unbiased, reality. The same applies to any form of AI, including descriptive, predictive and prescriptive, where the description, the prediction and the prescription is based on data that is biased, or incomplete, or plainly wrong, or a combination  of all or any of those.

Let’s use as example Facebook and the way that it shows users news in the feed and advertising. There are many “studies” that say that with XX number of “likes” Facebook knows what are your preferences and with a YY number of them it can predict better than yourself what you like and want, but that is not entirely true. It is more accurate to say that, based on what you actually like, Facebook shows you news and adds that are close to it but usually with a tendency to move towards what the advertiser wants you to like, in such a subtle manner that YY likes later you are actually liking what Facebook or the advertiser wanted you to like in the first place. Do you really think that millions of Americans woke up one day and just for their dislike of Hillary decided to vote for a misogynistic, fraudster, liar and megalomaniac like Trump? If you believe that you are not getting the gravity of the Cambridge Analytica scandal and how by knowing the actual preferences of people, an algorithm can start crawling-pegging their interest until they are somehow remote and even the opposite of what they originally wanted (yes, some of those who originally were sincerely abhorred by the idea of a sitting US president having an affair with an intern and then lying under oath, were the same who then supported a women-grabbing, “friend” of a paedophile, who knowingly tried to subvert the basis of their democracy).

Now let’s imagine that it is not “friends” news or adds what the algorithm is showing you, but your whole consumption of news, data, science and information, which is written specially for you; that every time you want to know something you have a system that does not give you a link to a page but gives you a text saying what actually “is” (in the ontological sense). Although it sounds great and the possibilities seem endless, yes you guessed, it all depends on the quality of the data to which the system has access to. Currently most AI systems are “fed” data to train them so they know how to behave in certain situations, even in a biased form, but the ultimate goal is to unleash them in the wealth of data constituted by Internet, mainly because there is where the money is. As you are already guessing, here is where we have the judgement day moment may arise, that moment when Skynet is connected to Internet and the machines kill everyone around them. No killing here, but the effects can be also daunting.

If social media and its algorithmic exacerbation of unethical and unprofessional press has led to massive manipulation, making people choosing social self-harm at the levels we’ve seen in Brexit, Trump, Bolsonaro, different forms of Chavism and a long list of people choosing what will damage them and their community, only imagine if all information they receive, everything that they “write” or ask the system to write come from this data-tainted algorithmic description, prediction and prescription. A different, soon coming, post is needed to the issue of data quality and the role of the established press in the misinformation campaings, but it is clear that we need to start discussing that, besides how bad AI chats are supposed to be for essays as form of assessment, there is a prospect of real social dissolution by misinformation and manipulation at scale not seeing until now.

ICT, farming and law

The population of the planet is going to growth from the current 7.7 billion to 9.7 billion in 2050 and nearly 11 billion by 2100, meaning that on one hand, a higher pressure to the availability of land for agriculture, and on the other need to greater agricultural production for food, raw materials and energy. Global climate change due to human activity and environmental degradation implies that extending the agricultural frontiers by further depleting existing forests is not an option. Smart farming consists of a suite of technologies rather than a single technology, and its global market stood at nearly 5 billion US dollars in 2016, expecting to reach 16 billion US dollars by 2025.  AI can be used to process that data, forecast production output and anomalies for better distribution, financial planning and mitigation; smart sensors can collect vast amount of data to forecast production outputs and anomalies; driverless machinery can perform different tasks around the clock, with replicable precision and subject to adverse environmental conditions; drones are being used to gather data and control both crops and animal production; geographic information  systems allow farmers to increase production to map and project fluctuations in environmental factors; and digital veterinary applications include telemedicine, trackers, wearable, monitoring and identification devices, and visual and sound recording. The use of these technologies in agricultural production refer to a range of legal issues, some of which have currently clear definition and others that might need some adaptations and reform. Artificial intelligence in agriculture attracts all the legal issues currently being pointed to artificial intelligence in general, including contractual data issues, with some that might have specific impact on agricultural production. The main use of systems based on algorithms is to forecast different scenarios, using current and past data to find patterns, and, for example, there might be legal uncertainty when management decisions lead to severe variations in agricultural output, due to the lack of transparency and accountability found in some AI. The vast amount of data produced by sensors in fields and animals lead to the need of retailoring agricultural contracts to identify the different responsibilities and limitation of liability arising from the negative consequences of wrong decisions based on faulty data. At the same time, some of the data may result in the identification of the producers, which would attract the whole set of data protection laws to data that seems to be unrelated to it in ways not foretold by legislators. Furthermore, due to the sensitivity of the data recollected, the security of the data needs to be a clear legal requirement, not only at contractual level but also with some public safety and market transparency considerations. The use of mechatronics, drones, geographic information systems and the whole set of digital veterinary applications brings back the issue of privacy, liability for both malfunction and, more importantly, undesirable production results, adding a strong need to cybersecurity and a set of regulatory compliance, which may bring into question some fundamental rights issues. For example, can a farmer use technology based on drones near an airport? If not, would the airport operator or the state compensate the farmer for the potential losses or lack of profits? What are the security requirements for veterinary applications that have the potential to put unhealthy products in the consumer market through third party malign interference? These are few of the many issues raised by ICT use in agricultural production, all of which deserves further analysis, so, keep an eye on Electromate