Artificial Intelligence

Cybersecurity has always favoured the attacker, but AI is widening that advantage. By compressing complex operations into automated decision loops, autonomous agents let a small human team delegate the bulk of an attack's tactical work, reconnaissance, exploitation, lateral movement, to machine tempo, in at least one documented case shifting 80–90% of that labour onto the AI itself. That doesn’t make attacks free, but it does mean cost scales far more slowly with the number of targets than it used to. Enterprises, meanwhile, face the opposite trend: breach costs are climbing, and a new governance-driven Shadow AI tax is compounding them. As automation lowers the labour cost of offense while recovery expenses and regulatory demands rise on defence, the balance of cybersecurity economics is being forced to overhaul itself.

The race to dominate AI is no longer being fought only in laboratories; It is now being fought at the ballot box. As the US struggles to establish comprehensive rules for one of the world's fastest-moving technologies, the companies developing it have begun competing over something even more valuable than market share, namely political influence. During the 2026 midterm election cycle, AI companies and their affiliated political action committees have poured tens of millions of dollars into congressional races, backed rival candidates, and expanded their lobbying operations across Washington. For the first time, elections themselves are becoming part of the battle over who will shape the future of AI governance.   This marks a significant shift in the relationship between technology and politics. Rather than waiting for governments to determine how AI should be regulated, leading firms are increasingly attempting to shape the political environment before those decisions are made. As Congress remains divided over comprehensive AI legislation, electoral politics has become another arena in the competition over AI. Consequently, the 2026 midterms may offer an early indication of whether democratic institutions can establish the rules governing AI before the industry's growing political influence begins shaping those rules instead.

Since mid-2025, a growing number of organizations that aggressively automated human work with artificial intelligence have begun reassessing those decisions. High-profile cases, including Ford Motor Company, Commonwealth Bank of Australia (CBA), IBM, and Klarna, demonstrate a common pattern: AI systems proved highly effective at handling routine, high-volume work but struggled with tasks requiring contextual judgment, tacit expertise, ethical reasoning, and complex customer interaction. Rather than abandoning AI, these organizations have reintroduced or redesigned human roles to complement automated systems.   This paper argues that these developments should not be interpreted as evidence that AI has failed, nor that widespread automation is reversing. Aggregate labor market data points in the opposite direction: AI continues to drive significant workforce reductions across many industries. Instead, the evidence suggests that many early adopters overestimated the extent to which entire jobs, not individual tasks, could be safely automated. The result has been a period of organizational recalibration in which firms are redefining the boundary between machine efficiency and human judgment.   Drawing on these company case studies together with a broader empirical base spanning Orgvue, Forrester, Robert Half, Careerminds, Gartner, and McKinsey, this paper develops a framework of task-conditional complementarity. Under this framework, AI increasingly performs standardized, repetitive, and predictable components of work, while humans concentrate on specialized oversight, exception handling, and continuous system optimization.   The paper also examines important boundary conditions. Not every organization has experienced this recalibration. Firms such as Amazon, Salesforce, and Shopify have not publicly demonstrated comparable reversals, suggesting that industry characteristics, workflow design, organizational maturity, and the pace of AI adoption may all influence automation outcomes. Similarly, Duolingo and JPMorgan illustrate alternative organizational responses, including policy correction and internal redeployment, that differ from direct rehiring.   The central conclusion is that the future of work is unlikely to be defined by either wholesale human replacement or resistance to AI adoption. Instead, competitive advantage will increasingly depend on accurately distinguishing which tasks can be automated, which require sustained human expertise, and how organizations redesign work to combine both effectively. The firms most likely to succeed will be those that treat AI implementation as an exercise in organizational redesign rather than simply a strategy for reducing headcount.

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.

Since late February 2026, the regional and international geopolitical landscape has entered a phase of accelerating military escalation following the outbreak of direct confrontation between the United States and Israel on one side and Iran on the other. That conflict has triggered successive waves of missile and drone attacks, generating recurrent spillover effects that have directly affected the airspace of Gulf states, particularly the United Arab Emirates.   Although the United Arab Emirates’ defence and institutional infrastructure, particularly in Dubai, demonstrated exceptional resilience and an immediate operational response to these threats through the activation of advanced air defence systems and the careful, precautionary management of brief airspace closures to safeguard air navigation and civilian safety, the real crisis did not lie solely in the direct military dimension. It also extended into a highly complex information war.   These developments coincided with the strict enforcement of domestic cybercrime laws, which restricted the circulation of unauthorised images and video footage in an effort to prevent panic and protect national security. Yet this also created an opening that the Western media machine exploited strategically and systematically to dominate the flow of information and construct a distorted account of events.   Against this backdrop of stark divergence between the coherent reality on the ground and the remote narrative constructed around it, international media outlets, particularly the British tabloid press, turned into vehicles for an extraordinary degree of dramatization. What were, in reality, limited regional spillovers were presented as evidence of an imminent and inevitable collapse of Dubai’s entire economic and social model.   By adopting provocative and polarising headlines that flatly declared Dubai “finished”, and casting the crisis as the tragic collapse of the safe tax haven dream, these outlets embraced a line of analysis wholly detached from realities on the ground. Verified evidence of business continuity and the strength of the UAE’s security architecture was sidelined in favour of a pre-packaged disaster narrative. This shift demands a deeper analytical and historical examination of the mechanisms and the economic and political incentives that can drive media institutions away from their role as objective conveyors of fact and turn them into instruments for shaping global public opinion.

On Feb. 28, 2026, the United States and Israel launched a military campaign against Iran, striking more than 900 targets in the first 12 hours and killing Iran’s Supreme Leader Ayatollah Ali Khamenei. The conflict is still raging, with strikes continuing across the country and the region destabilising by the day. Yet behind the missiles and fighter jets lies another revolution in how this war is being fought.   AI, the same technology that millions use daily to draft emails or summarise documents, has become a central instrument of lethal military power. Anthropic’s Claude AI model is embedded inside the Pentagon’s targeting and intelligence apparatus, processing satellite imagery, intercepted communications, and operational data to help commanders decide who to strike, where, and when.   What once required days of human analysis is now compressed into hours or minutes, enabling a pace of warfare that no prior generation of military planners could have executed. AI has been present on battlefields before, from drone guidance systems to satellite image analysis, but the Iran conflict represents its most expansive and consequential deployment to date, and the full implications of that scale are still unfolding.

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.

During the past decade, artificial intelligence (AI) has shifted from being an academic curiosity, becoming a driving force for reshaping economies worldwide. What once felt like speculative capabilities including machines generating code and automating complex workflows as well as optimizing global logistics and producing creative content, now became deployable tools on a larger scale across industries. AI’s rapid adoption raises several key questions among policymakers, economists and business leaders, most notably whether AI can contribute to the growth of national economic growth, and under what conditions do these gains materialize?   Macroeconomic models and strong empirical evidence suggest a positive outcome, however with notable limitations. AI, as a general-purpose technology, has more to offer than just efficiency improvements, it also functions as a key driver of innovation, productivity enhancement and transformation tool of economic structures. AI visibility and adoption have grown substantially, especially with the emergence of generative AI technologies such as exemplified ChatGPT, GitHub Copilot. This growth establishes AI as a valuable source of information and data, benefiting both firms and the border national economy, provided that this widespread adoption is backed and supported by a strong infrastructure and an adequate human capital, prepared to complement these technologies.

Governments are increasingly concerned about the realistic but AI-generated images, audio, and videos. Such deepfakes cause widespread misinformation and, in some cases, harm national security by endangering public trust in institutions and elections, as well as inciting political violence. On the other hand, the general public and digital platform users can’t differentiate between AI-generated fake and real content, causing misinformation, polarisation, and the commodification of private data by large tech companies. Accordingly, the rapid movements of deepfakes drive the need to act to set the environment for the new reality.   Relying on tech companies to mitigate misinformation is highly challenging, as these companies face the problem of regulating deepfake content due to its wide accessibility through numerous companies. Therefore, regulating such content by one company will certainly decrease its profit, as users will shift to another supplier. Additionally, these companies financially benefit from publishing advertisements on misinformation websites unintentionally. Hence, there is a pressing need for an outside source to force regulations and strategies to mitigate the proliferation of online misinformation.

In 2025, Airbnb is no longer simply reshaping travel preferences, it is fundamentally altering the competitive landscape for hotels. What started as a short-term rentals (STR) platform has evolved into a diversified lodging ecosystem, offering private homes, boutique hotels, and curated local experiences through a single digital interface. This transformation has intensified pressure on traditional hotel operators, whose fixed costs, regulatory exposure, and legacy systems limit their ability to adapt. As travellers increasingly value flexibility, privacy, and authentic local stays, Airbnb’s asset-light model continues to draw market share away from lower- and mid-tier hotels particularly. AI-driven pricing, scalable supply, and global host networks enable the platform to respond to demand fluctuations faster than conventional accommodation chains.   As consumer preferences fragment and digital expectations rise, many hotels struggle to maintain occupancy, protect margins, and justify rate premiums. The crucial question is no longer whether Airbnb competes with hotels, but how profoundly its growth is reshaping hotel performance, strategy, and long-term sustainability. And with hotels now beginning to integrate into Airbnb’s platform, a deeper question emerges: in this evolving hybrid model, who ultimately stands to benefit more?

Simulation technology is witnessing a profound transformation with the emergence of Digital Cognitive Twins (DCTs), the next generation of Traditional Digital Twins (DTs). These advanced systems go beyond conventional monitoring functions, integrating sophisticated AI models, particularly machine learning networks and natural language processing (NLP) techniques.   This convergence grants DCTs complex capabilities, enabling them to perform autonomous decision-making, conduct real-time self-optimisation, and develop predictive and anticipatory mechanisms. As a result, this technology is reshaping key sectors across multiple domains. In Industry 4.0, it enhances the efficiency and resilience of logistical supply chains; in urban governance, it enables the intelligent management of resources with exceptional accuracy; and in the healthcare sector, it accelerates the adoption of precision medicine tailored to the individual.   The exceptional performance of these systems depends on their ability to absorb and aggregate vast datasets, comprising thousands of variables for a single individual. These datasets extend well beyond the conventional boundaries of personal information, encompassing biometric inputs, genomic data, clinical records, and continuous monitoring of behavioural and psychological patterns derived from digital interactions.   This aggregation produces human simulation models of exceptional fidelity, a defining feature that places this technology squarely within the dual-use domain. While these models promise vast societal benefits, the compromise or seizure of these composite data repositories would constitute a catastrophic national security threat: the harm arising from the exposure of citizens’ data would be strategic, permanent, and irreparable.   The gravest risk lies in the possibility that state or non-state actors might exploit these datasets. Whereas past influence operations—most notably the disinformation campaigns of the last decade—targeted broad audiences, behavioural models derived from integrated digital-transformation processes enable bespoke cognitive-warfare interventions at the level of individuals or small groups. This capability transcends conventional geopolitical forecasting, enabling real-time prediction of societal behaviour.   At the core of the threat is the capacity to selectively manipulate these datasets or even fabricate synthetic records to engineer a pretext for intervention. By corrupting cognitive models, an adversary can simulate a manufactured state of public unrest, precipitate mass psychological collapse, or stage apparent systemic institutional failure—thereby manufacturing a spurious justification for political, economic or security interventions.