🤖 Artificial Intelligence
Larus ArgentatusThree years after generative tools triggered a new era of artificial intelligence, AI is no longer a futuristic promise. By 2025, it has become one of the most transformative forces shaping business, science, geopolitics and everyday life.
What makes this moment different from earlier tech booms is scale.
AI is no longer confined to research labs or niche automation. It is now embedded across industries, influencing productivity, innovation, regulation, warfare, investment and even environmental resource use.
And while optimism is rising globally, so are concerns about power concentration, job displacement and societal risk.
I. AI Has Moved to Core Business Infrastructure
Nearly every major organization is now engaging with artificial intelligence in some form.
According to insights from McKinsey & Company, almost all surveyed companies report using AI, with the majority actively experimenting with advanced systems such as AI agents.
However, most organizations remain early in the transformation journey.
Nearly two thirds are still piloting or testing AI rather than scaling it across entire operations. While many companies already report productivity gains, cost savings and innovation benefits at individual use case levels, only around 39 percent currently see meaningful profit impact across the full enterprise.
The companies generating the strongest results share one common pattern. They do not treat AI as a tool layered on top of old processes. They redesign workflows entirely around AI.
High performers combine:
- efficiency improvements
- new revenue models
- faster innovation cycles
- automated knowledge work
AI is becoming less of a support feature and more of a structural business engine.
II. Capabilities Are Advancing at Historic Speed
Researchers at Stanford University and leading AI labs have shown that within just a single year, advanced models made breakthroughs that previously took decades in computing history.
New high difficulty benchmarks introduced in 2023 to stress test true reasoning, scientific understanding and real world problem solving were quickly surpassed in 2024 and 2025. Some of the most notable jumps occurred in:
- multimodal reasoning across text, images and data
- complex scientific question answering
- real world software engineering tasks
- long horizon problem solving
In several of these challenges, modern AI systems improved performance by dozens of percentage points within twelve months, a pace almost unheard of in traditional technology fields.
In controlled environments, AI agents now complete coding projects, debug software and solve engineering problems faster than human professionals working under time constraints. In scientific research, models are assisting in protein folding, drug discovery, climate modeling and materials science with increasing accuracy.
Beyond language and logic, AI has rapidly expanded into creative and sensory domains:
- high resolution video generation with realistic motion
- photorealistic image synthesis
- automated music and voice production
- medical imaging analysis rivaling specialists
- autonomous decision systems for logistics and robotics
What once required entire research teams, massive budgets and months of experimentation can now be achieved in minutes with properly trained models.
Perhaps most striking is that these improvements are no longer limited to giant corporate systems. Smaller, more efficient open models are now approaching the performance of closed proprietary systems, dramatically lowering barriers to access and innovation.
In practical terms, AI is shifting from being a powerful assistant to becoming an autonomous problem solver across many technical fields.
III. No Longer Experimental in Daily Life
Artificial intelligence has quietly crossed a crucial threshold. It is no longer being tested at the edges of society. It is now embedded directly into systems people rely on every day.
More than 220 AI enabled medical devices were approved in a single year, compared to just six less than a decade earlier. These systems now assist doctors in detecting cancer earlier than human radiologists in some cases, analysing heart conditions from imaging data, predicting patient deterioration in hospitals and accelerating drug discovery timelines by years rather than months.
AI is increasingly acting as a second medical brain inside clinics.
On public roads, autonomous driving has shifted from prototype to scaled service. Companies such as Waymo in the United States and Baidu in China now operate large commercial robotaxi fleets, collectively delivering hundreds of thousands of driverless rides each week across multiple cities. These vehicles navigate dense urban traffic, complex intersections and unpredictable pedestrian behaviour using continuously learning AI systems.
Europe is now following closely behind. In London, pilot autonomous taxis were already spotted in early 2026 as part of large scale testing programs, while the UK Government has announced regulatory changes planned for the second half of 2026 to formally allow driverless taxi services to operate across the city. Once approved, London is expected to become one of the first major European capitals with fully autonomous commercial transport.
In education, AI tutors are beginning to personalise learning paths in real time. In finance, algorithms monitor fraud, manage portfolios and execute trades at speeds no human can match. In logistics, AI optimises global supply chains down to individual delivery routes.
Meanwhile, conversational AI exploded into everyday use following breakthroughs from OpenAI, while platforms such as Google integrated AI directly into search results, online shopping, content discovery and digital assistants.
It is shaping how people search for information, make decisions, navigate cities, receive healthcare and interact with the digital world.
Artificial intelligence is rapidly becoming the new interface between humans and the internet itself.
IV. Business Investment Has Reached Unprecedented Levels
The economic momentum behind artificial intelligence is now surpassing nearly every previous technology wave, including the early internet boom and mobile revolution.
In 2024 alone, private AI investment in the United States exceeded $109 billion, nearly twelve times China’s total and over twenty times that of the United Kingdom. Generative AI alone attracted close to $34 billion globally in a single year, making it one of the fastest capital inflows in modern tech history.
But the most telling shift is happening quietly inside corporations. Major enterprises are no longer running small AI innovation labs.
They are restructuring budgets, halting legacy software projects and redirecting long term capital directly into AI infrastructure, proprietary model development and data strategy. Several Fortune 500 firms now allocate billions annually not just for AI tools, but for custom trained models built specifically on their internal data.
Across sectors, executives increasingly view AI not as an IT upgrade but as a competitive survival layer.
At the same time, real world adoption has accelerated at record speed. Company usage jumped from 55 percent to 78 percent in just twelve months, with many firms deploying AI across customer service, logistics, finance, marketing, cybersecurity and product development simultaneously.
What remains less visible publicly is the operational impact.
Internal studies within consulting firms, banks and manufacturing giants show:
- double digit productivity gains in knowledge work
- major reductions in processing times for compliance and analysis
- faster product development cycles
- significant cost compression in back office operations
A growing body of independent research confirms these effects, consistently finding that AI boosts output while often narrowing skill gaps by augmenting employees rather than eliminating roles outright.
In practice, many workers are now producing the equivalent of two to three times their previous output when supported by AI systems.
V. The Emerging AI Cold War and Global Power Race
Security analysts and governments increasingly frame today’s landscape as an AI Cold War between the United States and China, where dominance in advanced models, semiconductor supply chains and autonomous systems could determine military, economic and political power for the rest of the century.
China’s leadership has openly positioned AI as a national priority. Under long term state planning championed by Xi Jinping ( 習近平), the country is pouring tens of billions into domestic chip manufacturing, AI research hubs and military applications designed to reduce dependence on Western technology and surpass it by 2030.
Meanwhile, the United States has responded by tightening export controls on advanced chips, accelerating federal AI funding, and partnering closely with private sector leaders such as NVIDIA, OpenAI and Google to maintain model leadership.
One less visible consequence of this global AI arms race has been intense pressure on the hardware supply chain. Demand for high performance computing components, particularly advanced GPUs and server grade memory such as RAM, has surged so rapidly that prices have climbed sharply, in some cases making cutting edge hardware increasingly unaffordable for smaller businesses, researchers and everyday consumers.
At the heart of this race lies semiconductor control.
Advanced AI models depend on cutting edge chips produced by manufacturers such as TSMC, but those chips themselves rely on an even more exclusive layer of technology controlled largely by Europe. At the centre of this sits ASML in Veldhoven near Eindhoven, the world’s only company capable of producing extreme ultraviolet lithography machines required to manufacture the most advanced semiconductors.
Without ASML’s systems, today’s high performance AI chips simply cannot be built.
This reality makes both Taiwan and the Netherlands strategically vital in the global technology order. Behind closed doors, policymakers in Washington and Beijing increasingly view chip manufacturing capacity and lithography control as critical national security assets, comparable to energy supply or rare natural resources.
Interestingly, this strategic dominance has not prevented internal restructuring. Despite record revenues driven by massive global demand for its EUV machines, ASML recently announced plans to reduce roughly 1,700 jobs across its operations in the Netherlands and the United States.
Company leadership framed the move as a shift toward streamlining management layers and redirecting resources into core engineering and innovation, highlighting how even the most powerful players in the AI hardware race are under constant pressure to optimise speed, efficiency and technological focus.
The competition now stretches far beyond consumer technology into domains that directly shape national security, global influence and military power:
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Autonomous warfare and drone swarms
Modern conflicts, particularly the war between Ukraine and Russia, have already demonstrated how AI assisted drones can dominate surveillance, targeting and battlefield tactics. Rapid iteration cycles driven by real time data are transforming warfare faster than traditional weapons development ever could. -
AI driven battlefield decision intelligence
Military alliances such as North Atlantic Treaty Organization are actively integrating AI into strategic analysis systems to process intelligence, predict scenarios and accelerate command decisions, compressing timelines that once took days into minutes. -
Mass surveillance and information control systems
AI is increasingly deployed at scale to monitor populations in real time. Countries such as China use facial recognition and predictive analytics across cities, while places like Dubai operate AI powered smart surveillance networks in public spaces. In the UK, the Metropolitan Police Service has actively used facial recognition technology in parts of London to identify wanted individuals, illustrating how AI driven monitoring is expanding even within democratic societies. -
Automated cyber offense and defence
Advanced AI now powers real time cyber attack generation, phishing campaigns, vulnerability scanning and defence automation, with security leaders such as Microsoft repeatedly warning that generative AI is dramatically increasing the speed, scale and sophistication of digital warfare. -
Industrial scale autonomous weapons production
The United States Department of Defense is pursuing rapid deployment programs aimed at producing large volumes of AI guided autonomous systems, explicitly designed to overwhelm adversaries through speed and scale rather than individual platform superiority. -
Control of semiconductor and compute infrastructure
Beyond weapons themselves, strategic power increasingly lies in who controls chip manufacturing, advanced hardware supply chains and massive compute capacity, because AI dominance is ultimately constrained by access to processing power.
Senior defense officials increasingly treat AI superiority as equivalent to nuclear advantage during the Cold War.
Whichever nation masters large scale AI deployment, controls compute infrastructure and secures semiconductor independence gains disproportionate leverage over global systems.
VI. Governments Are Responding With Regulation and Massive Funding
As artificial intelligence reshapes economies and national security, governments are accelerating both oversight and investment at unprecedented speed.
In the United States, federal agencies more than doubled the number of AI related regulatory actions in 2024 compared with the year before, reflecting growing concern over safety, transparency and market power. Globally, references to AI in legislation have risen more than ninefold since 2016, according to policy tracking data.
Countries are now treating AI as essential infrastructure, comparable to energy systems or defence networks, and are investing heavily to secure domestic capabilities.
Canada has launched multibillion dollar programs to support AI research centres and national computing infrastructure. France has announced long term technology investments exceeding €100 billion aimed at strengthening European leadership in AI and semiconductor manufacturing. China continues to pour tens of billions into chip production and industrial AI zones as part of its push for technological self reliance. India has introduced nationwide AI initiatives across education, public services and manufacturing. Saudi Arabia has committed roughly $100 billion through its Project Transcendence programme to build large scale AI data centres and research hubs.
Developments in 2026 further intensified competition in the AI industry. OpenAI faced growing criticism after reports that it had agreed to provide its AI models to the United States Department of Defense for use within classified government systems. OpenAI has published a statement, which you can find here.
The announcement triggered a strong reaction among parts of the technology community and the public. Many observers noted that OpenAI originally began as a research organisation promoting broadly accessible and safety-focused artificial intelligence, which made the move toward military collaboration feel like a dramatic shift in mission.
The backlash spread rapidly online. Campaigns such as “Cancel ChatGPT” and “QuitGPT” encouraged users to leave the platform, and reports suggested that more than 1.5 million users stopped using or cancelled their ChatGPT subscriptions within 48 hours of the announcement.
The company Anthropic publicly declined a potential collaboration with the United States Department of Defense, stating concerns about safeguards that would allow its models to be used for autonomous weapons or mass surveillance systems in negotiations with the Pentagon.
For many observers, the contrast between the two companies highlighted a deeper debate about the future of artificial intelligence. The industry is no longer only about productivity tools or consumer chatbots. Increasingly, it sits at the intersection of national security, military technology and geopolitical competition.
VII. The Environmental Cost Behind AI’s Expansion
The rapid growth of artificial intelligence is creating a surge in global energy demand, turning data centres into some of the largest electricity consumers in modern economies.
Analysts now estimate that worldwide data centre power use could more than double by the end of the decade as AI workloads expand, potentially reaching levels comparable to the total electricity consumption of major industrialised nations. High performance processors used for training and operating large AI models consume far more energy than traditional servers, pushing electricity grids under increasing strain.
The environmental impact extends beyond power alone. Cooling massive server farms requires vast quantities of freshwater, while producing advanced AI chips depends on energy intensive mineral extraction and contributes to growing electronic waste.
To put the scale into perspective, the cryptocurrency sector long criticised for excessive energy use consumes roughly 110 to 160 terawatt hours annually through Bitcoin mining. Recent studies suggest AI data centre demand has already reached or surpassed this level and continues to grow at a much faster pace.
In response, technology companies and governments are moving to curb the footprint.
Major firms such as Google, Microsoft and Meta are rapidly expanding renewable powered data centres and redesigning AI systems to require fewer computations per task. New specialised chips now deliver significantly higher performance per watt, while advanced cooling systems reduce both electricity and water consumption.
Governments are also stepping in. The European Union is introducing stricter energy reporting and sustainability requirements for large data centres, while the United States and several Asian countries are offering incentives for renewable powered AI infrastructure and funding research into low energy AI models. Some regions are beginning to link AI development grants directly to environmental performance.
Despite these improvements, experts warn that efficiency gains may struggle to keep pace with the sheer scale of AI expansion.
Whether its environmental impact can be contained will depend on how quickly clean energy, efficient hardware and regulatory frameworks evolve alongside it.
VIII. A World Divided Between Optimism and Concern
Public sentiment around artificial intelligence is increasingly split between enthusiasm for innovation and deep anxiety about economic security.
In countries such as China, Indonesia and Thailand, large majorities continue to view AI as a net positive, often associating it with economic growth, technological leadership and improved public services.
In contrast, concern is rising sharply across United States, Canada and much of Europe, where workers increasingly connect AI adoption with job insecurity rather than opportunity.
Behind this shift lies a growing wave of automation driven layoffs.
Across sectors such as customer support, sales operations, marketing, content creation and even software development, companies are quietly replacing large portions of human labour with AI systems that operate continuously at a fraction of the cost. Major firms now openly cite AI efficiency gains when announcing workforce reductions, particularly in administrative and knowledge based roles once considered stable career paths.
The impact is being felt most sharply among younger professionals.
University graduates in fields like marketing, communications, business analytics and junior IT roles are facing shrinking entry level opportunities, as tasks traditionally used to train early career workers are now automated. Many spent years studying for careers that rapidly transformed just as they entered the job market.
For decades, education was framed as a near guarantee of long term employment, home ownership and financial stability. Today’s workforce faces far more dynamic conditions, with faster skill obsolescence, short employment cycles and rising living costs that make the traditional life trajectory increasingly unreachable.
While AI is boosting productivity and corporate profits, its benefits are unevenly distributed.
Highly specialised engineers, data scientists and AI strategists see rising demand and salaries, while large segments of middle skill white collar work face compression or elimination.
How societies manage this transition, through reskilling, social policy and labour protection, may ultimately determine whether artificial intelligence becomes a broad engine of prosperity or a driver of deeper inequality.
🎓 Artificial Intelligence Is a Structural Shift
AI in 2025 is not another digital tool. It is a structural shift that is beginning to change how work is organised, how value is created and which skills remain economically rewarded.
Most credible labour research suggests the immediate story is not a single mass wipeout of jobs, but uneven disruption. Some roles shrink, others are redesigned, and new work appears around AI deployment, governance, security and infrastructure. Even McKinsey’s global survey signals uncertainty rather than consensus: 32 percent of respondents expect workforce decreases in the coming year, 43 percent expect no change, and 13 percent expect increases.
Entry level work is being squeezed. A Stanford Digital Economy Lab paper found that workers aged 22 to 25 in the most AI exposed occupations saw a 6 percent employment decline from late 2022 to September 2025, while older workers in the same fields saw growth. This matches what many graduates are experiencing: fewer junior marketing, support, analysis and content roles, because the tasks that used to train beginners are exactly the tasks AI can now assist with.
Customer support is the clearest near term target. Many companies are already using AI to absorb basic tickets, refunds, booking changes and routine troubleshooting, keeping humans for escalation and high trust cases.
Cashiers are likely to keep declining, but mostly through a mix of self checkout, ordering kiosks and automation rather than humanoid robots.
Police work is more complicated. AI is expanding in surveillance and identification, but replacing officers is unlikely in the near term because policing involves judgement, accountability, public trust and legal responsibility.
In practice, the most common pattern is not full replacement. It is “fewer humans per workflow,” with AI handling first contact and humans handling exceptions.
The most important lever is not banning AI. It is controlling how it is deployed.
Companies can reduce damage by committing to “human in the loop” service standards, retraining pathways and redesigning jobs rather than simply cutting headcount. The backlash to overly aggressive “AI first” messaging, such as Duolingo’s controversy and contractor replacement debate, shows that consumers still value human quality, trust and cultural nuance.
Governments set the boundaries. They decide what must remain human controlled, what must be audited, and what organisations must disclose. That includes rules on hiring automation, consumer transparency, liability when AI makes harmful decisions, and the use of biometric surveillance. When regulation lags, companies set the default rules.
Governments are responsible for enforceable boundaries and labour protections.
Companies are responsible for deployment choices and workforce transition plans.
Individuals are responsible for adapting skills, but they cannot carry the full burden of a system level shift.
The likely future is a reshaped job market, not a single collapse. But without clear policy and responsible deployment, the reshaping will be harsher, more unequal and more destabilising than it needs to be.
What do you think about the rapid development of artificial intelligence and its impact on the future of work and society? Share your thoughts in the comments.🤖