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Every technological revolution arrives wrapped in optimism. The steam engine would liberate us from drudgery. Electricity would transform living standards. The computer would make work faster and more efficient. The Internet would democratise knowledge and connect humanity.

Now, artificial intelligence promises to supercharge productivity, solve complex problems, democratise expertise and unlock unprecedented economic growth.

And it may do many of those things.

But beneath the glossy promises lies a more uncomfortable reality-one that business leaders, policymakers and investors are still largely unprepared to confront. As AI reshapes economies and societies at breakneck speed, it is also capable of entrenching old inequalities and creating entirely new ones.

The central economic question is therefore not simply how much wealth AI will create. It is who will capture that wealth, who will lose from the transition, and what happens to those who are unable to participate meaningfully in the new economy? What follows are seven sobering truths about our AI-driven economic future. They are not predictions of inevitable doom. They are warnings about risks that could become realities if today’s decisions are allowed to shape tomorrow’s economy without sufficient thought.

1. The AI divide will make the digital divide look tame

We’ve spent decades discussing the ‘digital divide’-the gap between those with access to computers, telecommunications and the internet and those without. The emerging AI divide could be far more consequential.

The numbers already tell a troubling story. According to Microsoft’s AI Diffusion Report, 24.7% of the working-age population in Global North countries had used generative AI by the end of 2025, compared with 14.1% in the Global South. The gap widened during the year rather than narrowing.

Within countries, the divide is equally striking. In the United States, AI usage is substantially higher in large urban areas than in rural communities. The geography of AI adoption also appears closely associated with educational and institutional concentration.

This matters because AI is not merely another digital service. A person with access to an AI assistant can potentially acquire capabilities that previously required years of training: translating documents, analysing data, writing software, preparing presentations, conducting preliminary research or creating marketing material.

But that advantage compounds. A well-educated professional with a powerful AI system, high-speed connectivity, good data and organisational support can become dramatically more productive. A worker without those resources may find the same technology inaccessible or incomprehensible.

The result could be a productivity divide layered on top of the existing digital divide. And the problem extends beyond individuals. Countries that possess advanced computing infrastructure, large datasets, research institutions, semiconductor access and AI talent can develop capabilities much faster than countries that merely consume AI services.

This creates a particularly difficult dilemma for developing countries. If AI becomes a major source of economic productivity, being an AI consumer rather than an AI producer could increasingly resemble the difference between importing technology and developing it domestically.

The United Nations Development Programme has warned that AI could contribute to a ‘Great Divergence’ reminiscent of earlier periods of industrial transformation. The danger is therefore not simply that some people will have AI while others do not. It is that those who have AI may increasingly pull away from those who do not.

2. Job displacement is no longer hypothetical

For years, warnings about AI-driven unemployment were dismissed as technological pessimism.

The standard response was reassuring: technology destroys some jobs but creates others.

Historically, that has often been true. Agricultural mechanisation reduced farm employment but created opportunities elsewhere. Computers eliminated certain clerical tasks but generated entire industries.

AI may follow the same pattern-but there is one important difference.

Previous waves of automation primarily attacked physical or routine cognitive tasks. Generative AI is increasingly capable of performing tasks associated with knowledge workers themselves. Lawyers, accountants, programmers, designers, translators, analysts, customer-service representatives, journalists and administrative professionals are all encountering systems capable of performing portions of their work.

More than 250,000 technology workers have reportedly been laid off since January 2025, while companies across the technology sector have been restructuring around AI and automation. Tata Consultancy Services, for example, announced plans to reduce its workforce by up to 12,000 employees. The important question is not whether every AI-exposed job disappears.

It is what happens to the number of people required to perform the same amount of economic activity.

A company that previously needed 100 analysts may eventually need 60 highly AI-enabled analysts. A software company that required 50 programmers may discover that 20 experienced developers working with AI can produce what 50 previously produced. That is an enormous economic gain for the company. It is not necessarily an equivalent gain for the 30 people whose jobs disappear.

And there is another subtle danger: entry-level jobs may disappear first. Young professionals traditionally learn by performing relatively simple tasks before progressing to more complex responsibilities. If AI can perform the simple tasks, organisations may have fewer incentives to employ large numbers of inexperienced workers.

That could create a strange paradox: AI makes experienced professionals more productive while simultaneously reducing the opportunities through which future experienced professionals acquire their experience. The result could be a broken career ladder.

3. The rise of an “Unemployable class”

The most uncomfortable truth may be this: Not everyone displaced by AI will find a new role.

The optimistic version of technological change assumes that displaced workers can simply retrain.

But retraining is not magic. A 50-year-old administrative worker cannot necessarily become a machine-learning engineer after completing a six-month online course. Nor can every factory worker become a data scientist, every call-centre employee become a software developer, or every displaced accountant become an AI entrepreneur.

There is also a question of scale. If millions of people need to acquire entirely new skills simultaneously, who will provide the training? Who will finance it? Who will support workers while they retrain? And, perhaps most importantly, will the economy actually generate enough new positions for everyone who successfully retrains?

Historian Yuval Noah Harari has used the phrase ‘unemployable class’ to describe a possible future in which large numbers of people become economically redundant because machines can perform many tasks more effectively.

His argument raises a question that conventional economics struggles to answer: what happens when economic value is no longer sufficiently dependent on human labour?

For centuries, employment has been more than a way of earning money. It has provided social status, identity, structure and participation. If AI eventually reduces the demand for human labour substantially, simply distributing money may not solve the deeper social problem.

A person receiving income but feeling economically unnecessary may still experience exclusion.

This is why the AI debate cannot be reduced to productivity statistics. The future of work is also a question about the future meaning of work.

4. AI is devouring resources-and the environment is paying the price

AI may look weightless. We ask a chatbot a question, receive an answer and see nothing more than text on a screen.

Behind that apparently weightless interaction, however, exists a massive physical infrastructure: data centres, servers, chips, cooling systems, electricity grids, fibre-optic networks and water supplies.

The environmental cost is becoming difficult to ignore. Amazon disclosed that its global data centres consumed more than 9 billion litres of water in 2025. The United Nations University Institute for Water, Environment and Health has estimated that the global water footprint of data centres could reach trillions of litres annually by 2030.

And water is only one part of the equation. AI requires enormous amounts of electricity. As demand for computation increases, countries must decide how that electricity will be generated. If additional demand is met primarily through fossil fuels, some of the environmental benefits expected from digital transformation could be undermined.

There is also the environmental cost of manufacturing the hardware itself. Advanced AI requires increasingly sophisticated chips. Mining the minerals, manufacturing semiconductors, constructing data centres and eventually disposing of obsolete equipment all carry environmental costs.

This creates a contradiction. AI is frequently presented as a tool that could help humanity address climate change-through better energy management, improved weather modelling, optimised transport and smarter agriculture. But the technology designed to solve environmental problems can itself consume enormous quantities of energy, water and materials.

The answer is not necessarily to stop AI development. It is to recognise that ‘digital’ does not mean ‘immaterial.’ Every AI service has a physical footprint. And in countries already facing water shortages, electricity constraints or fragile infrastructure, that footprint could become economically significant.

5. Wealth and power are concentrating at an unprecedented scale

The economics of AI strongly favour scale. Training frontier models requires enormous computing resources, specialised chips, engineering talent, datasets and capital. Operating them at a global scale requires still more infrastructure.

This creates a powerful economic advantage for companies that already possess capital, cloud infrastructure, data and global distribution networks. The concentration is visible in financial markets. A relatively small group of technology companies has come to represent an extraordinary share of major stock-market valuations.

The AI economy therefore presents a paradox. AI is frequently described as a technology that democratises capabilities. A small business can use the same basic generative-AI tools that a multinational corporation uses.

But the underlying infrastructure is becoming increasingly concentrated. The small business may use AI. The corporation may own the AI infrastructure. That distinction matters.

If a handful of companies control the most advanced models, cloud platforms, chips, app ecosystems and distribution channels, they can capture a disproportionate share of the economic surplus created by AI.

This could produce a new form of economic inequality-not simply inequality between workers and employers, but inequality between owners of computational capital and users of computational capital.

There is also a competition issue. When the same companies control cloud infrastructure, AI models, operating systems, search engines, advertising platforms and distribution channels, their ability to shape markets can become extraordinary.

6. The race for AI sovereignty could spark a new Cold War

As nations awaken to AI’s strategic importance, a new geopolitical competition is taking shape. Countries increasingly want AI sovereignty-the ability to develop, control and deploy critical AI capabilities without depending entirely on another country.

The United States, China, the European Union and other countries are investing heavily in computing infrastructure, semiconductor manufacturing, research, talent and domestic AI ecosystems. Technology restrictions are becoming instruments of geopolitical policy.

Semiconductors have become strategic assets. Access to advanced computing is increasingly treated not merely as a commercial issue but as a matter of national security.

The danger is that the AI economy could fragment into competing technological spheres.

Imagine a world in which American, Chinese, European and other AI ecosystems operate according to different technical standards, regulations, cloud infrastructures and data regimes.

Such fragmentation could increase costs for businesses and reduce the benefits of global interoperability. For smaller countries, the consequences could be particularly significant. A developing nation may be forced to choose between competing technological ecosystems-or become dependent on whichever external provider offers the cheapest or most accessible infrastructure.

There is another concern. If AI becomes strategically important to military, intelligence and cyber capabilities, the incentives for governments to accelerate development may become stronger than the incentives for international restraint.

That creates a classic security dilemma: one country’s defensive investment can look like an offensive threat to another. The AI race could therefore become a new form of geopolitical competition-less about tanks and territory, and more about chips, compute, data and algorithms.

7. Privacy is becoming a casualty of convenience

Every AI system runs on data. And increasingly, the most valuable data is personal data. Our faces, voices, locations, purchasing habits, online behaviour, professional histories and personal communications can all become inputs into increasingly sophisticated systems.

The bargain is seductive. Give the system more information, and it gives us greater convenience. But there is a hidden economic transaction taking place. We may be paying for supposedly ‘free’ services not with money, but with information about ourselves.

Facial recognition demonstrates the problem particularly clearly. In Brazil, hundreds of facial-recognition projects have been identified, potentially affecting tens of millions of people. Meanwhile, studies by the U.S. National Institute of Standards and Technology have documented demographic differences in the performance of facial-recognition systems.

The issue is not simply accuracy. It is power. Who gets to identify whom? Who stores the data? Who can access it? How long is it retained? Can an individual challenge an automated decision? What happens when an algorithm makes a mistake?

AI can make surveillance dramatically cheaper and more scalable. A human officer cannot watch millions of people simultaneously. An automated system can potentially analyse millions of images, transactions or communications continuously.

This changes the economics of surveillance. And once surveillance infrastructure exists, there is always the possibility that its use expands beyond its original purpose. The most dangerous erosion of privacy may therefore not happen through one dramatic decision.

It may happen incrementally. One convenient service here. One biometric database there. One automated identification system somewhere else. Until eventually we discover that privacy has become an exception rather than the norm.

The bigger problem: AI may change the distribution of economic power

These seven truths have something in common. They are not really about Artificial Intelligence.

They are about power. Who has access to AI? Who owns the infrastructure? Who controls the data? Who captures the productivity gains? Who bears the environmental costs? Who gets displaced? Who decides the rules? That is why simply measuring AI’s contribution to GDP will not be enough.

Suppose AI increases national productivity by 20%. That sounds wonderful.

But what if most of the additional income goes to a small group of technology companies and highly skilled workers? What if millions of workers experience stagnant wages or declining employment? What if electricity and water consumption rises sharply? What if smaller countries become increasingly dependent on foreign AI infrastructure?

The economy may become richer while parts of society become poorer.

GDP can rise while economic security falls.

That is perhaps the most important lesson for policymakers. The objective should not simply be to maximise the speed of AI adoption. It should be to ensure that the economic benefits of AI are broadly distributed and that its costs are not disproportionately imposed on those least able to absorb them. That requires thinking beyond technology policy. It means reconsidering education, taxation, competition policy, labour regulation, social protection, data governance, infrastructure investment and international cooperation.

“Ape Aanduwa; Wasara Dekayi, Ratata Subai” is the strategic narrative advanced by President AKD and the National People’s Power (NPP) Government to mark their two-year milestone in office.

The ruling dispensation formally translates it as “Our Government: Two Years – Good for the Country”.

The word “Subha” (???) in Sinhala literally means “auspicious,”. It is not just plain good. It is promisingly good. In English it conveys something such as ‘boding well’. It portends a bright future.

NPP rebranding

While “Ratata Subai” is simplified as “Good for the country” in everyday parlance, ‘auspicious’ delivers a nuanced message – optimistic and forward-looking.

It is my intuitive guess that the crafting of this slogan with its nuanced optimism and long-haul focus is the work of JVP’s veteran ideologues and carries the imprimatur of Comrade Tilvin.

The NPP just didn’t win the parliamentary elections. They were swept into office after AKD’s razor edge performance in the presidential election. Sri Lanka’s voters surprised themselves.

At that point in history, when Aragalaya gathered its own momentum, the NPP relied on the demand for ‘System change’ in silent solidarity with the Aragalaya movement. So, they ingeniously offered the captivating theme ‘Let the Real People Win.’

Two years in the business of governance, the new slogan of ‘Ratata Subhai’ marks a new phase of rebranding. It moves away from the rhetoric of anti-establishment Opposition. Now it is focused on a legacy of stable governance.

That said, a rational analysis of their two-year track record cannot escape their overriding dilemma.

They walk a tightrope between the JVP’s foundational Marxist-Leninist ideology and its modern, governance-focused rhetoric of cozy comforting relations with the many and varied chambers of commerce and industry.

NPP’s economic policy, if there is one such, is in aimless drift. Its policy ambiguity captures the central paradox of the NPP Government’s approach to the economy.

Like Lewis Carroll’s Alice trying to pin down a vanishing Cheshire Cat with its smile, observers and investors analysing Sri Lanka’s economic landscape find that the closer they look, the more the policy specifics end up in broad, reassuring smiles of President AKD.

To be blunt, AKD has come to terms with Ranil W’s orthodox reality. Once in power under President AKD, the cheshire cat has turned into a protean creature.

AKD has emphatically maintained ‘macroeconomic continuity’ with Ranil’s fiscal program. They have consistently met IMF review targets, preserved the core of the Economic Transformation Act, and retained major revenue consolidation measures.

AKD and the NPP Government are heavily dependent on governance reform as its central policy platform.

Their rhetoric heavily emphasises anti-corruption, systemic overhaul, and democratic accountability. That offers ample space to opponents both on the right and left. They gleefully point to the historical, more rigid ideological framework of the party of Rohana Wijeweera.

This is strategic political ambiguity. NPP supporters claim that it is a process of necessary evolution to address contemporary economic realities dictated by a diverse electorate.

There is a class war

There is a ‘Class War’ that is raging under the thin ice of reassuring economic indicators released by the Treasury and the Central Bank. The opposition to the 22nd Amendment is classic class war in disguise.

This is how it all began

The sight of Ranil Wickremesinghe, a former Sri Lankan President escorted into a prison bus on 22 August 2025, marked a historic semiotic turning point.

It was a piece of quasi-street theatre, choreographed or otherwise by the new political order to signal the end of an elite class and a decisive collapse of political impunity in Sri Lanka.

For Ranil, a man, manor born and steeped in power and privilege, lunching with Swaraj Paul in Wolverhampton was an ordinary and harmlessly simple affair-made even simpler by a touch of presidential discretion that took from Peter to pay Paul. The law is yet to determine how simple or complex that encounter was.

For the first time in modern Sri Lankan history, a former executive president experienced physical detention and prison transport.

It was a distinctive message of the leveling of power. Wickremesinghe-a veteran five-decade political operator who outmaneuvered rivals-was visually reframed inside a standard prison vehicle, equalised under the law with ordinary detainees.

The imagery signaled a potent message to the public about the new administration’s anti-corruption drive, transforming a legal proceeding into a powerful public theater of accountability.

To supporters of the ruling party as well as many neutral middle-class people with no inherited wealth the Prison Van represented long-delayed justice.

To RW’s supporters gathered outside the courthouse, it was perceived as a harsh, politically motivated humiliation of a national leader.

With the heir apparent of the Rajapakse family that brokered massive economically unviable infrastructure investment, in remand custody the SLPP has responded with a show of strength in Anuradhapura.

SLPP’s Anuradhapura shindig

The SLPP’s Anuradhapura display of its organisational capacity and its ability to enlist a disparate list of political wanderers under its ample shade holds a lesson. It strikes at the heart of contemporary Sri Lankan politics.

It pits the NPP’s transformative revolutionary intent against deeply entrenched structural traps of repeated history.

NPP and AKD may view the downfall of the old elite as a permanent rupture in history. But as Marx pointed out on his brilliant analysis of the French revolution, failure to make deep structural changes, the old regime might simply reappear in a new, farcical disguise.

Marx’s celebrated essay ‘Eighteenth Brumaire of Louis Napoleon ‘is famously known among students of Marx who aren’t necessarily ‘Marxists’ for the adage that history repeats itself, ‘the first time as tragedy, the second time as farce.’

Learning from Marx

Marx analyses how the French Revolution of 1848, which sought to overthrow a corrupt monarchy, inadvertently created a power vacuum filled by Louis Bonaparte-a mediocre adventurer who weaponised populism to establish a dictatorial regime.

NPP’s presumed clean break with the past may turn out to be illusory. Marx warned that revolutionaries often believe they are creating something entirely new, yet they remain haunted by the past!

‘The tradition of all dead generations weighs like a nightmare on the brains of the living.’

The NPP has installed its own team in power. But it has inherited the same bankrupt state apparatus, debt structures, and bureaucratic inertia left by the ‘ancien régime.’

If the NPP merely replaces elite figures without changing the underlying economic architecture, the ghosts of the old system will inevitably re-emerge.

There are several candidates for the role of Louis Napoleon in our ‘milieu’.

Louis Bonaparte succeeded because he positioned himself above the class struggle, presenting himself as a savior to a fractured society.

If the NPP focuses too heavily on the spectacle of punishing the old guard rather than resolving the material grievances of the poor, the hungry and the wage-earning classes, they risk slipping into a form of ‘Bonapartism.’

True systemic change requires dismantling the socioeconomic conditions that allowed the old elite to thrive, not just staging moral victories.

If a reformist movement fails to deliver on its revolutionary promises due to systemic constraints, it creates public disillusionment.

In the aftermath of the French revolution that beheaded a monarch, popular disillusionment allowed a farcical imitation of Napoleon to seize absolute power.

The warning to the NPP is clear: if they fail to manage the current historical moment with ideological rigour, they open the door for a populist, right-wing backlash that could be even more destabilising than the original ancien régime.

The NPP’s political project relies heavily on a classic Marxist leaning social-democratic assumption: that enlightened material self-interest will lead the working and peasant classes to reject corrupt oligarchs.

The Lumpen Class

However, the Anuradhapura Rally exposes the limit of this assumption.

The lumpenproletariat is often deeply conservative and skeptical of systemic, structural overhauls.

To a class accustomed to navigating survival through clientelism, the NPP’s promise of ‘system change’ and meritocracy feels abstract, distant, and cold. They prefer the flawed certainty of a known patron over the revolutionary uncertainties of a reformed state

The SLPP’s Anuradhapura rally provides contemporary proof of Marx’s assertions in the Eighteenth Brumaire regarding the stubborn loyalty of the lumpenproletariat.

While the organised middle classes and class-conscious proletariat demand systemic legal accountability for the old regime, the marginalised underclass views the prosecution of Namal Rajapaksa through the lens of disrupted patronage.

For this group, anchored in informal economies and bound to the state via clientelism, the fall of the Rajapaksa dynasty represents a terrifying vacuum. By rallying around Mahinda Rajapaksa in the ancient capital, they demonstrate how easily a ruling class can weaponise cultural hegemony to turn the most economically exploited layers of society into the fiercest defenders of their exploiters.

A word of advice to President AKD

You and your party, the NPP, have chosen the capitalist path. Very Good. But you must also pay a little attention to Karl Marx. I will tell you why.

Transformative movements should study Karl Marx not as a dogmatic blueprint, but as a dynamic method for understanding power, organising collective action, and sustaining systemic change.

Political experiments carried out in Karl Marx’s name have historically varied and sparked deep ethical debates. But Marx’s foundational texts offer modern activists’ indispensable tools to diagnose structural problems and move beyond surface-level reforms.

In simpler terms, proof of the pudding is in the eating. What matters finally is not how many are in jail. What matters is how many homes are subsisting on just one meal a day.

As Amartya Sen said ‘Human life does not stand or fall on the success of the market, nor on the growth of the gross national product. It depends on the freedoms we have and the lives we can lead.’

Sri Lanka will send a 156-athlete contingent to the 20th Asian Games in Aichi-Nagoya, Japan, with 100 men and 56 women set to compete across 25 sports. The size of the team provides Sri Lanka with an opportunity to look beyond the traditional medal-winning disciplines and discover new heroes on one of Asia’s biggest sporting stages.

Team Sri Lanka will travel to Japan with a contingent of 156 athletes, comprising 100 male and 56 female athletes, who will compete across 25 sports disciplines.

The Sri Lankan contingent will compete across Aquatics – Swimming and Diving, Archery, Artistic Gymnastics, Judo, Rowing, Rugby Sevens, Table Tennis, Squash, Weightlifting, Wrestling, Basketball 3×3, Wushu, Kabaddi, Beach Volleyball, Athletics, Boxing, Karate, Sailing, Taekwondo, Surfing, Badminton, Cricket, Golf, Modern Pentathlon and Hockey.

The Asian Games begin on 19 September and run until 4 October, bringing together the continent’s strongest sporting nations. For Sri Lanka, the challenge will be considerable, but there are several areas where the contingent can realistically aspire to make an impact.

Athletics carries the biggest individual hopes

Athletics remains central to Sri Lanka’s Asian Games ambitions. Historically, it has been the country’s most successful sport at the Games, producing 11 of Sri Lanka’s 12 Asian Games gold medals. Cricket has supplied the other gold. Sri Lanka has won 51 medals overall at the Asian Games.

The spotlight, naturally, will fall on Rumesh Tharanga Pathirage.

The 23-year-old javelin thrower has emerged as one of the most prominent athletes in world athletics. His recent performances include throws of 92.62 metres in Rome and 92.07m in Zurich, while he also captured the Diamond League Final with 89.04m. World Athletics lists him as Sri Lanka’s leading javelin athlete.

His Asian Games campaign, however, has been complicated by an unusual setback. Three of his key competition javelins were reportedly damaged during air transportation. Those implements were associated with his 92.62m, 92.07m and 91.09m performances.

The equipment problem will therefore be an additional challenge, but Tharanga enters Nagoya with a formidable competitive record and considerable international experience.

Cricket remains a major medal opportunity

Cricket will again be one of Sri Lanka’s major medal avenues. The sport has already produced an Asian Games gold for the country, and the return of T20 cricket gives Sri Lanka an opportunity to use its depth and experience in the shortest format.

The cricket competition also has wider significance for Japan. The country is using the Asian Games to promote the sport, with Sri Lanka among the major cricket nations participating in the tournament.

Rugby Sevens can produce a surprise

Sri Lanka’s rugby Sevens teams have traditionally possessed the speed and attacking ability to trouble stronger Asian opponents. In a short tournament, the format can produce unexpected results, particularly when a team is able to maintain discipline and fitness across successive matches.

A strong start could put Sri Lanka into contention for the latter stages, although the Asian competition remains extremely demanding.

Rowing, badminton and squash

Rowing is another discipline where Sri Lanka will look for individual performances to translate into finals and potential podium opportunities. The sport demands exceptional preparation, and even a place among the leading crews would represent an important benchmark.

In badminton, Sri Lanka’s challenge will be particularly demanding given the strength of countries such as China, Indonesia, Japan, South Korea, India and Chinese Taipei. Nevertheless, the exposure gained by Sri Lankan players against this level of opposition could be valuable for the next generation.

Squash is another sport capable of producing competitive performances. Sri Lanka has a history of producing technically accomplished players, and the Asian Games provide an opportunity to measure that talent against some of the world’s strongest squash nations.

Combat sports and strength disciplines

Sri Lanka’s prospects should not be confined to the headline sports.

Boxing, judo, karate, wushu, wrestling and weightlifting all provide opportunities for athletes to progress through the draw and challenge established Asian competitors.

Combat sports can be particularly unpredictable. A single outstanding performance can change an athlete’s tournament, making these disciplines important to watch even when Sri Lanka enters without the same international profile as the continent’s leading nations.

Sailing also deserves attention. Sri Lanka has previously won an Asian Games silver medal in the sport, demonstrating that the country has the ability to compete at continental level.

The bigger picture

The significance of Nagoya should therefore extend beyond the final medal tally.

Sri Lanka is taking a sizeable group of athletes across 25 disciplines, giving many competitors the opportunity to experience a level of competition that cannot be replicated domestically.

For some, the target will be a medal. For others, reaching a final, establishing a personal best or defeating a higher-ranked opponent could become the foundation for future international success.

And above them all stands Rumesh Tharanga.

If the javelin star can reproduce anything close to his extraordinary 2026 form, his performance could become one of the defining stories of Sri Lanka’s Nagoya campaign. But Sri Lanka’s Asian Games challenge should not be reduced to one athlete.

The 156-member team represents 25 different sporting journeys – and Nagoya could reveal that Sri Lanka’s next generation of Asian Games heroes extends well beyond the javelin runway.