Artificial Intelligence

Elon Musk's promises about the future of artificial intelligence derive much of their appeal from offering a single answer to a range of human problems: poverty, arduous work, the cost of living and limited access to services. Smarter machines will produce more, costs will fall, humans will be freed from the necessity of work, and money will eventually lose its importance.   Yet this vision links technological possibilities, economic outcomes, and judgements about what constitutes a good life, presenting them as successive stages along a single path. Here lies the essence of the digital utopia: the assumption that advances in machine capabilities will almost inevitably bring comparable advances in social justice and human freedom. The speed of technological progress alone is not enough to prove such a relationship.   At the US-Saudi Investment Forum on 19 November 2025, Musk predicted that within ten to twenty years, work would become optional, something people might choose to do much as they play sports or grow vegetables at home even when they could simply buy them. He went further, arguing that artificial intelligence and robotics would eliminate poverty and offer a path to making everyone wealthy.   His comments on money, however, were more conditional. If advances in artificial intelligence and robotics continue, he argued, money will eventually lose its relevance, although he gave no timeframe for when this might happen. This distinction matters: predicting that money will become less relevant goes well beyond a change in payment methods, but it is not a time-bound declaration that currencies will be abolished.   His predictions extended into other areas. In an episode of "Moonshots" released on 6 January 2026, Musk predicted that artificial general intelligence (AGI) would be achieved within the year and that, by 2030, AI would surpass the combined intelligence of all humans. He also predicted that, within roughly three to four years, "Optimus" robots would outperform the world's best surgeons. He further linked the prospect of a future of abundance to retirement savings becoming irrelevant. Yet in the same conversation, he acknowledged that the transition would be bumpy and that he did not have all the answers. The point of criticism, therefore, lies in the breadth of the outcomes he promises relative to the evidence he offers for them, while taking his own qualifications into account.   This vision rests on three transitions, each of which requires evidence of its own: from improvements in system performance to the ability to perform the full range of human work; from expanding productive capacity to ensuring prosperity for all; and from freedom from economic necessity to achieving a more fulfilling life. Significant progress may occur at one level while the others falter. The economy may become more productive and some people wealthier, while others lose their income or their ability to influence the conditions of their lives. Grouping these possibilities under the banner of abundance obscures the questions that ultimately determine how its benefits are distributed.   Scientific evidence provides a solid basis for optimism about productivity, but it also points to its limits. A 2025 paper published in The Quarterly Journal of Economics examined the use of a generative AI assistant by 5,172 customer-support employees and found an average 15% increase in issues resolved per hour, with larger gains among less experienced workers. This is an important finding: it shows how technology can disseminate expertise and improve performance. Yet it measures the impact of a specific tool in a particular professional setting. It does not demonstrate that workers can be dispensed with across the economy as a whole, nor that productivity gains will automatically translate into higher incomes for them.   The importance of distinguishing between tasks and jobs is also evident in the International Labour Organization's 2025 estimates, which indicated that around a quarter of workers worldwide are employed in occupations exposed to generative AI to varying degrees. Yet the report suggests that jobs are more likely to be transformed than fully replaced by technology, given the continuing need for human input across many tasks.   This assessment cannot rule out the potential of future technologies that have yet to emerge. It does, however, illustrate the gap between what the current evidence allows us to conclude and claims that the need for work is ending. Exposure to automation is not synonymous with the disappearance of a job, and the disappearance of a job does not mean that the person who held it can afford to live without the income it provided.   A further gap remains between digital achievement and the economy's physical transformation. Reducing the cost of producing text or software is not, on its own, enough to build housing, power grids, hospitals or transport systems. These require investment, materials, infrastructure, maintenance and time to build. Musk himself acknowledged in Davos in January 2026 that electricity constrains expansion and that chip production could outpace the capacity to power those chips. This acknowledgement introduces a material constraint into the promise of abundance: the pace of progress in artificial intelligence does not, by itself, determine the pace at which the world it operates in can be transformed.   Similarly, the prediction that robots will outperform surgeons requires more than extrapolating from the rapid improvement of AI models. Demonstrating clinical superiority requires comparable outcomes, safety across a range of cases, the ability to manage complications, and clear accountability arrangements. The US Food and Drug Administration explains that the robotically assisted surgical systems covered by its guidance operate under the surgeon's control. The existence of robotic surgery does not, therefore, show that comprehensive surgical autonomy is close at hand. The claim that AI will "surpass the combined intelligence of all humans" raises a different problem. It first requires defining what exactly to measure and how to aggregate those measurements, and then establishing the connection between superiority on that measure and the ability to manage economic and social complexity.   The strongest objection to the promise that work will no longer be necessary, however, remains economic and political, even if we assume that automation succeeds on a large scale. The capacity to produce goods is not the same as having the right to access them. If a limited number of companies or investors own machines and platforms, their reduced need for workers may increase profits while weakening the primary source of income for other segments of society. Society as a whole may become wealthier without every individual becoming more secure. Eliminating poverty requires addressing deprivation of access to resources, something that the sheer quantity of resources produced cannot resolve on its own.   Economic research reinforces the need to maintain this distinction. In an IMF working paper published in April 2025, researchers used household-level data and an economic model to examine AI adoption. The findings showed that some effects could reduce wage inequality, while returns to capital and adoption patterns could increase wealth inequality. These findings depend on the model's assumptions and do not represent an inevitable account of the future. Their implication, however, is clear: economic efficiency can improve even as wealth becomes more concentrated. The promise of making everyone wealthy therefore requires a separate explanation of how ownership and returns will be distributed.   To be fair, Musk has proposed a distribution mechanism. In a post dated 17 April 2026, he proposed a high income for everyone, delivered through cheques issued by the federal government to address unemployment caused by AI. He added that the expansion in the production of goods and services would outpace the increase in the money supply, and that inflation would therefore not occur. But proposing transfers does not settle questions of their size, funding or sustainability, nor does it explain how the returns generated by automated companies would be converted into a stable entitlement for citizens. Once this task is assigned to government, abundance becomes contingent not only on technological progress, but also on fiscal decisions, legislation and the balance of political power.   The prediction that money will lose its importance, however, goes beyond what can be inferred from a reduced need for work. Money performs functions that extend well beyond paying wages: it serves as a medium of exchange, a unit of account and a store of value. Reducing the cost of labour involved in producing a good does not eliminate the need to settle transactions, compare alternative uses of resources or organise obligations over time. It is entirely possible to envisage an economy in which working hours decline and basic services are guaranteed, while money continues to perform these functions.   Similarly, abundance in some areas does not mean the end of scarcity in all others. The cost of digital services may fall dramatically, while housing in desirable locations or access to limited environmental resources remains subject to competition. Even if necessities were provided free of charge, decisions would still have to be made about how to allocate what cannot be made equally available to everyone.   In theory, such decisions could be made through non-monetary means, but they would still require institutions, rules and an authority to adjudicate between competing claims. The disappearance of money, then, if it can be envisaged at all, would require a transformation in the system for allocating resources and determining rights of access to them. It does not follow directly from the advent of more intelligent machines.   The claim that inflation would not occur faces a similar problem. Overall production may increase while certain sectors cannot respond to higher spending. The prices of digital services may fall even as the costs of housing, energy or other goods and services rise. European Central Bank analyses of supply bottlenecks have highlighted the importance of mismatches between demand and productive capacity in explaining price pressures. The possibility that higher productivity could help contain inflation is therefore different from a guarantee that inflation will not occur simply because high incomes are distributed.   There is no necessary contradiction between proposing cash payments during a transitional period and envisaging a future in which money becomes less important. What remains missing, however, is an explanation of the transition between the two stages: how do monetary incomes translate into guaranteed access to resources? What would lead owners to stop pricing their assets? And how would resources that remain scarce be managed? Calling the final stage "abundance" does not answer these questions.   The promise becomes more fragile when extended to humanity as a whole. An increase in global output does not, by itself, reveal how much of it will accrue to each country or group. Even if a wealthy country succeeds in financing a high income for its citizens, this does not explain how a country with limited resources could do the same, particularly if it imports the technology and pays foreign providers for its use. The International Telecommunication Union estimated that around 2.2 billion people remained offline in 2025. This does not prove that AI's benefits cannot reach them in the future. Still, it does illustrate the scale of the conditions assumed by the idea of a common future for all: infrastructure, the ability to pay, education, institutions, and the capacity to negotiate the terms on which the technology can be accessed and used.   This is where the flaw in the generalisation lies: it concludes every individual from a prediction about aggregate productive capacity. The problem is not resolved simply by increasing the scale of the gains. What must be explained is the mechanism by which those gains would reach people who differ in wealth, geography, rights and influence. A cheque issued by a national government does not, in itself, constitute a global system for distributing the returns from AI. Then comes the deeper question: is it enough for the economy to no longer need human labour for people to have reached an optimal state of life? Precision here requires distinguishing paid work from human endeavour in its broader sense. When Musk says that work will become optional, it does not follow that people will stop learning, creating or caring for others.   Indeed, freedom from compulsory or arduous work could create more space for such activities. But an employer no longer needing a worker does not automatically give that worker freedom of choice. Choice requires a guaranteed income, genuine capabilities, opportunities to participate, and rights that protect individual autonomy.   To regard a life free from effort as an ideal, however, is a value judgement, not a conclusion that can be derived from equations of productivity. Technology cannot determine what makes a human life worth living. Learning, caring, achievement and participation retain a value that goes beyond obtaining the end product. They are practices through which people develop their capabilities, relationships and sense of self.   A machine may perform a task more efficiently without diminishing the value of a person performing it. Judging the worth of human activity solely by its ability to compete with a machine reduces human beings to the very standard of performance that technology is supposed to serve.   Research on motivation supports this argument without settling the larger question of life's ultimate purpose. Self-determination theory, developed by Richard Ryan and Edward Deci through a programme of empirical research, links motivation and well-being to the satisfaction of three basic psychological needs: autonomy, competence, and relatedness. These needs can be fulfilled both within and outside work.   The implication is that securing the means of consumption does not exhaust the conditions for human flourishing. Designing a future in which work is less necessary also requires institutions and opportunities that enable people to exercise their capabilities and feel that their participation has value.   The evidence also cautions against assuming that unconditional income will inevitably lead to inactivity. In Finland's 2017–2018 experiment, 2,000 unemployed people received €560 a month without being required to look for work. Survey respondents who received the income reported greater life satisfaction and lower psychological distress, although methodological limitations prevent conclusively attributing these differences to the income alone.   The experiment had only limited effects on employment. These findings provide a basis for serious consideration of the benefits of material security. Still, they do not test what would happen in a society that had dispensed with work altogether, nor do they demonstrate that financial transfers alone would be sufficient to build such a society.   Accordingly, the human question at the heart of an automated future concerns the kind of freedom that people will actually have. A guaranteed income may enable them to refuse degrading work and devote time to learning or caring for others. Under a different institutional arrangement, however, automation could instead be accompanied by growing dependence on a small number of entities that control income, services and platforms. The promise of liberation therefore requires guarantees of participation, the right to challenge decisions, and autonomy, alongside guarantees of material provision. Having enough to live on does not, by itself, determine how much control people have over their own lives.   History also shows the role institutions play in translating productivity gains into time and rights. The British Factory Act of 1847 limited the working hours of women and young people following a political campaign, and later legislation was needed to address loopholes exploited by factory owners. This episode shows that reducing the burden of work required intervention to change the terms of the relationship between employers and workers. The same mechanism remains relevant in the age of AI: technological gains create possibilities, while rules and institutions determine how their benefits are realised.   The transitional phase, which Musk himself acknowledges will be turbulent, matters more than simply waiting for abundance to arrive. People may lose their jobs before compensation systems are fully in place, pathways for gaining experience may erode before stable alternatives emerge, and countries may differ in their capacity to manage the transition. Even if a better future materialises decades from now, that does not erase the losses borne by those who paid the price of getting there. This is why tying long-term life decisions, such as preparing for retirement, to a conditional prediction of abundance gives the optimistic scenario practical weight beyond the certainty currently attached to it.   The problem with Musk's digital utopia is that it posits a desirable destination before fully establishing the path that leads to it. It assumes broad-scale technological success, sufficient expansion of material production, a fair distribution of returns, institutions capable of guaranteeing rights, and, finally, a form of human adaptation that gives abundance meaning. Some of these conditions may be realised, but their convergence is not an automatic consequence of advances in artificial intelligence.   A more appropriate standard for guiding such progress is how much it reduces compulsion, expands people's capacity to act, and protects human dignity and autonomy. A future in which production no longer needs humans becomes an ideal future only if humans can still determine its purposes and the conditions on which it operates.

Humanity today confronts one of the most striking and complex paradoxes of the modern age. While the world's leading technology companies race to present artificial intelligence as a silver bullet for saving the planet and addressing the climate crisis, a troubling physical reality is quietly taking shape beneath the surface. The very technology that promises a sustainable future is silently consuming one of the most vital and scarce resources on which human survival depends on freshwater.   This profound contradiction between the dazzling promises of the digital age and harsh environmental realities goes beyond a passing technical problem. It raises a fundamental strategic and geopolitical question: will the boundless ambitions of artificial intelligence ultimately collide with the planet's hard physical limits? This silent struggle between technological advancement and the scarcity of natural resources raises a critical question: which will ultimately give way to the other, the immense power of data servers or the life-sustaining drops of water?

President Trump returned to office with a clear promise to the technology industry, namely that the federal government would get out of the way. He had campaigned on dismantling what he called the Biden administration's overreach on AI safety, installed venture capitalist David Sacks as White House AI and crypto czar, and welcomed Big Tech CEOs to his inauguration as a signal of the partnership he intended.   For Silicon Valley, the message was unambiguous. The deregulatory era had arrived, and American AI companies would be free to race ahead of China without bureaucratic friction slowing them down. Almost 18 months later, those same companies cannot release their most advanced models without first receiving a phone call from the Commerce Secretary. The story of how that reversal happened, and what it means for US national security, allied trust, and the global AI race, is one the administration has never fully explained.

In February 2025 the AI researcher Andrej Karpathy named something programmers had already started doing: describing what they wanted in plain English and letting an AI model write the code, rather than reading and understanding it themselves. He called it "vibe coding." A year later, a version of the same habit turned up somewhere far less forgiving of error. A recent Economist report on "vibe lawyering" describes how ordinary people, guided by AI chatbots rather than legal training, are drafting complaints, contesting disputes, and pursuing litigation they would once have needed a lawyer for. Research cited in that report, by Anand Shah at MIT and Joshua Levy at USC, examined 4.5 million federal civil cases and found that the share of self-represented litigants, flat at around 11% for two decades, climbed to 16.8% by fiscal year 2025, while the number of self-filed suits roughly doubled. The chatbots involved don’t just help people write; they tend to invent case law outright, encourage litigation, discourage settlement, and inflate people’s sense of how likely they are to win. Courts have started responding in kind: nearly a thousand reprimands have gone out over improper AI use in filings, and a federal appeals court recently suspended two lawyers over fabricated citations.   Law makes an unusually good case study, because courts keep a public, searchable record of what happens when confidence outpaces competence. But it is a case study, not the whole story. The same dynamic, a fluent AI answer standing in for judgment someone doesn’t actually have, is turning up wherever people now use AI to make decisions they used to need real expertise for. That is what this piece is actually about: not litigation specifically, but what AI does to a person’s sense of their own competence once it is quietly doing part of the thinking for them, and what that could mean as the pattern spreads well past the courtroom.

The global security landscape is undergoing a fundamental transformation driven by the rapid advancement of artificial intelligence technologies, which have evolved from purely technical tools into strategic forces reshaping patterns of power and conflict. Artificial intelligence has emerged as a transformative capability offering substantial societal benefits, yet its inherently dual-use nature renders it a double-edged instrument.     A careful examination of historical precedents reveals a recurring pattern in which terrorist organisations demonstrate a high degree of adaptability in exploiting emerging technologies to advance their radical agendas. Just as these groups previously leveraged online forums and encrypted communication platforms, they are now actively exploring and adopting artificial intelligence capabilities. This shift is no longer confined to speculative concern or theoretical risk. Rather, AI-enabled terrorism has moved from conceptual discussion into an experimental phase characterised by repetition and rapid diffusion, raising acute concern among security institutions and governments that the technology may become a strategic enabler of unprecedented operational capability.     The convergence between artificial intelligence and the logic of asymmetric warfare is fundamentally altering the balance of power between states and non-state actors, significantly lowering the barriers to entry that were historically imposed by advanced military technologies. Emerging fields and intelligence evidence indicate the development of a multi-domain adoption strategy spanning informational, physical, and cyber spheres, necessitating a deeper analytical examination of how terrorism is being re-engineered in the age of intelligent systems.

In 2025, the rapid acceleration of artificial intelligence (AI) is no longer just expanding digital capabilities, it is reshaping the physical infrastructure that underpins the global economy. Data centres are becoming “AI factories,” designed for unprecedented computational intensity and continuous, large-scale workloads. Nearly 11,800 facilities were operating worldwide by 2024, with an increasing share built or retrofitted to power AI-grade computing. This shift has triggered a structural rise in energy consumption, placing extraordinary pressure on land, water, electricity systems, and financially straining grids and supply chains worldwide.   The defining constraint on the future of AI is no longer hardware or algorithms, it is energy. Without a rapid global shift to renewable and clean power, AI data centres will collide with resource shortages, grid instability, and economic risk, threatening the very growth they are meant to enable. As AI becomes foundational across industries, the challenge is no longer whether data centres will expand, but whether the world can generate enough clean power to sustain them. With demand already outpacing conventional grid capacity in major regions, energy availability not technological innovation will determine global competitiveness in the AI era.  

This Pulse survey, conducted in November 2025, explores public attitudes toward AI in Arab societies, with a particular focus on reliance on foreign AI models and their impact on Arab values and identity. The findings highlight growing concerns about how AI systems may influence cultural norms, shape collective identity, and redefine societal priorities. By examining perceptions of the need for Arab-developed AI and views on who should lead its development, the survey offers insight into how technological dependence is increasingly understood as a strategic, cultural, and identity-related issue across the Arab world.

Artificial intelligence systems and data centres have increasingly become an integral part of modern day society. A KPMG survey focused on AI use found that 66% of respondents use AI for work and personal reasons, of which 38% of respondents claim to use AI on a daily or weekly basis and 28% use it semi-regularly. According to these results, a majority of the respondents rely on AI to carry out day to day functions whether it be for work, study, or personal reasons. Moreover, the reliance on AI has been extended to governments, global financial systems, and states, as these entities rely on AI systems to improve efficiency and speed of services provided. This shows how AI has become integrated into the fabric of global society.   Now imagine one day all AI systems and programs cease to function. While the chances of such an event happening are low, it is not impossible and the consequences of being overly reliant on AI systems can be devastating. The consequences of a global AI shutdown will impact the global economy as well as global geopolitics, which could lead to trillions disappearing from the stock market and national security disasters across the globe.

Semiconductors have become the arena of a global strategic contest among the world’s major powers, a rivalry so intense that some now refer to these tiny chips as “the new oil.” Their economic and security value is immense, for microchips permeate every aspect of modern life, from consumer electronics and automobiles to advanced weapons systems.   This pivotal role has fuelled a fierce technological race between the United States and China, one whose repercussions extend deep into the geopolitical sphere. Specialist reports underscore that the outcome of this innovation race in semiconductor manufacturing will ultimately determine which nation leads the development of artificial intelligence and its applications, a contest with profound strategic and economic implications.   The United States has taken decisive steps to safeguard its technological supremacy. Since 2022, it has gradually imposed stringent export controls on semiconductor technologies and equipment bound for China. These restrictions include bans on exporting advanced chipsets, supercomputers, and manufacturing tools, while dozens of Chinese firms have been placed on blacklists. The declared objective is to cripple China’s ability to access the technologies required to produce next-generation chips that could enhance its military or intelligence capabilities. This policy has been partially effective: it disrupted China’s semiconductor sector and pushed up the prices of certain rare chips. Yet it also provoked an assertive Chinese response aimed at mobilising domestic resources. Beijing has channelled vast financial and institutional support into developing indigenous innovations capable of surpassing existing technologies, raising the possibility of a sudden Chinese leap that could upend Western technological dominance. In other words, the weaponisation of technological sanctions has made Beijing even more determined to achieve semiconductor self-sufficiency, despite the formidable challenges it continues to face.   On the other hand, China has not remained idle in this confrontation. In addition to massive investments in its domestic firms, Beijing has leveraged trade pressure as a strategic tool against the West. A recent example came in October 2025, when China banned the export of chips produced by Nexperia, a European company operating factories in China, in retaliation for a Dutch decision to place it under state supervision and remove its Chinese chief executive. This abrupt Chinese move triggered a crisis for automobile manufacturers across Europe and Japan. The European Automobile Manufacturers Association warned that factories were on the brink of shutdown within days due to an acute shortage of essential chips. Major firms such as Volkswagen and Nissan cautioned that their semiconductor reserves were nearly depleted unless a swift diplomatic solution could be reached. Europe was also affected by other Chinese restrictions on the export of rare earth minerals, a countermeasure to U.S. sanctions. This escalating technological trade war is now threatening global industrial supply chains and prompting high-level political manoeuvres. Senior European Union officials have since sought negotiations with Beijing to mitigate the severity of these measures.   The politicisation of semiconductors has reached a point where they have become a strategic bargaining chip in the global geopolitical arena. The United States is not merely exerting pressure on China; it is also expanding the scope of its restrictions to encompass third countries, driven by fears that sensitive technologies might reach Beijing. In October 2023, Washington broadened its export controls to include any state suspected of re-exporting chips to China or other competitors, effectively requiring special licences even for the transfer of advanced semiconductors to its own allies in the Middle East. This position generated implicit discontent among regional states, which viewed it as an obstacle to their technological aspirations. Yet it also encouraged them to deepen cooperation with Washington while simultaneously developing their own national capacities in semiconductor manufacturing, a path both Saudi Arabia and the United Arab Emirates have recently pursued with growing determination. It is widely recognised that the epicentre of the global semiconductor industry lies in East Asia - specifically Taiwan, South Korea, and Japan - a reality that heightens the political sensitivities surrounding Taiwan, home to the semiconductor giant TSMC. Any military escalation in the region could send shockwaves through global chip supply chains, binding the semiconductor issue to international security as closely as to economic stability.   Thus, the competition over semiconductors has evolved into a driver of policy and diplomacy alike, shaping new trade alliances and security-linked agreements that grant technological privileges in fields such as artificial intelligence in exchange for strategic assurances. It is, in essence, a technological confrontation bearing the hallmarks of a modern Cold War, one that will profoundly reshape the maps of global alliances and conflicts in the years to come.   Investment in artificial intelligence (AI) has become a global priority, reflecting its immense economic potential and its transformative impact across virtually every sector. Recent analyses suggest that AI could add around $15.7 trillion to the global economy by 2030, a staggering figure that underscores the profound structural transformation expected to accompany its widespread adoption. The Middle East, too, anticipates substantial gains. AI is projected to contribute approximately $320 billion to the region’s economy by 2030, equivalent to 11% of its total GDP. The figure is even higher in technologically advanced Gulf states, where AI is expected to account for about 13.6% of the UAE’s economy (roughly $96 billion) and 12.4% of Saudi Arabia’s (around $135 billion) by 2030. These projections explain the mounting competition among governments and corporations to channel investments into AI and to design national strategies that will secure a foothold in the fast-evolving digital economy.   Globally, spending on artificial intelligence research and applications has more than doubled in recent years, as leading technology giants, including Microsoft, Google, and Amazon, compete to embed AI models into their services and products. This competition has sent semiconductor company valuations soaring. A striking example is Nvidia, whose market capitalisation reached $2.6 trillion by mid-2024 following its overwhelming success in the AI chip market. This technological revolution has also compelled policymakers to act. Today, more than sixty countries have adopted national AI strategies or established dedicated agencies to oversee their implementation. Major economies are now racing to secure the largest share of a global AI market projected to exceed $1 trillion annually over the coming decade.   For the Middle East, artificial intelligence represents a historic opportunity to diversify economies and transition toward knowledge-based societies. The Gulf states, particularly Saudi Arabia and the United Arab Emirates, stand at the forefront of this transformation, investing heavily in education, training, and AI-related research and development. In 2020, Saudi Arabia announced its National Strategy for Data and Artificial Intelligence, aiming to rank among the world’s top 15 nations in AI by 2030. The Kingdom has allocated substantial financial and human resources to realise this ambition, including the launch of a national company with a capital of $15 billion as a “national AI champion,” as well as a $40 billion global investment fund established in partnership with international investors. These figures reflect the seriousness of Saudi Arabia’s efforts to restructure its economy around knowledge, innovation, and technological leadership.   The United Arab Emirates has likewise taken an early lead. As far back as 2017, it appointed a Minister of State for Artificial Intelligence and launched the UAE Artificial Intelligence Strategy. In 2023, Abu Dhabi’s Technology Innovation Institute (TII) unveiled its large language model, Falcon 180B, trained on 180 billion parameters —a milestone that positioned the UAE firmly on the global map of algorithmic innovation. Meanwhile, Abu Dhabi’s G42 Innovation Hub has drawn worldwide attention after announcing a partnership with American technology companies to establish a supercomputing complex for artificial intelligence with a capacity of five gigawatts — the largest of its kind outside the United States. Under a special U.S.–UAE agreement, the facility will import up to 500,000 advanced Nvidia AI chips annually starting this year. The deal has been hailed as a major triumph for the UAE in its quest to become a global AI powerhouse, particularly after a period in which it was constrained by U.S. export restrictions under the previous administration.   Hence, nations that invest early and decisively in artificial intelligence stand to reap immense economic and strategic rewards. They will possess the capacity to boost productivity, enhance public services such as healthcare, education, and transportation through intelligent solutions, and create entirely new industries and employment opportunities. In other words, the balance tilts toward innovation-driven growth and job creation that far outweigh the potential risks, provided that sound legislation and adaptive policy frameworks accompany this technological evolution. It is from this perspective that the region’s determination to achieve AI leadership becomes clear: a drive embedded within a broader global race that will define the contours of the world’s most resilient and competitive economies in the decades to come.

On September 17th, the UAE President Sheikh Mohamed bin Zayed Al Nahyan met with OpenAI CEO Sam Altman to discuss strengthening cooperation in the field of Artificial Intelligence (AI) research and application. This meeting underscores the UAE’s commitment to becoming the world’s leader in the field of AI, grounded in the country’s 2031 vision and 2071 centennial goals.

The rapid evolution of artificial intelligence, propelled by transformer models like OpenAI’s ChatGPT, has reshaped industries and redefined human–machine collaboration. Beyond generating language, AI now powers psychological assessments, financial sentiment analysis, and synthetic empathy—making emotional intelligence a critical asset. Within this shift, emotional audio intelligence has emerged as especially strategic, enabling machines to both recognize affective states and reproduce them in synthetic voices. Meta’s 2025 acquisition of WaveForms AI reflects this trend, securing early control over “programmable affect” and underscoring both the economic promise and geopolitical risks of affective computing. By turning AI from a diagnostic tool into a simulation system, the deal positions Meta to create digital agents capable of projecting warmth, urgency, or reassurance—reshaping the future of human–machine interaction.   Meta’s 2025 acquisition of WaveForms AI marks a turning point in artificial intelligence: the rise of emotionally intelligent audio as both an economic opportunity and a national security risk. AI has evolved rapidly, propelled by transformer models like OpenAI’s ChatGPT, and now extends far beyond language generation. From psychological assessment to financial sentiment analysis, its ability to interpret emotions has become a critical asset. Emotional audio intelligence pushes this further by enabling machines not only to recognize affective states but also to reproduce them in synthetic voices. With WaveForms, Meta secures early control over this capability—transforming AI from a diagnostic tool into a simulation system, capable of projecting warmth, urgency, or reassurance in ways that could redefine human–machine interaction.

As enthusiasm for Artificial Intelligence (AI) grows each day, so too does anxiety about its potential impact on jobs and overall societal stability. Several studies highlight the possibility that full automation could disrupt economic and political systems. While these concerns are valid and should not be dismissed, it is important to remember that, like any other transformative technology, AI can be both celebrated for its potential and feared for its risks.   A stable society cannot function without a labour force; removing it entirely would violate basic economic principles such as supply and demand, while also undermining political stability, which depends on the resilience of the middle class. These structural realities suggest that, rather than erasing human work altogether, AI will likely be both automatically and deliberately integrated in ways that preserve social and economic balance. From this perspective, the future shaped by AI is not as dire as some anticipate.