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Artificial intelligence (AI)—encompassing machine learning, neural networks, generative models, and advanced algorithms—is a defining technology of the 21st century, reshaping economies, societies, and global systems. Its capacity to address pressing challenges is unparalleled: AI-driven climate models enhance disaster preparedness, medical diagnostics accelerate drug discovery, and predictive tools boost economic efficiency. Yet, these advancements carry significant risks, including deepening wealth inequalities through corporate monopolies, enabling digital authoritarianism via surveillance systems, and threatening individual freedoms through unchecked data exploitation. The dual nature of AI—its potential for progress and peril—raises a critical question: How can society harness its benefits while mitigating its dangers?

This article does not address the Nepal-specific context, as that could be a comprehensive topic for a separate write-up. Instead, it examines the trajectory of state-of-the-art AI through a multifaceted lens: historical lessons, information networks, practical applications, health and media innovations, corporate accountability, global competition, and ethical realities. We argue that deliberate, equitable governance, ethical system design, and robust global cooperation can maximize AI’s societal benefits while preventing division, surveillance states, or corporate-driven harm. Without proactive measures, AI risks eroding democratic liberties and exacerbating global inequities. With foresight and collective action, however, it can foster an inclusive future prioritizing shared prosperity, human dignity, and sustainable progress.

Historical lessons and corporate power: governing technology for equity

History shows that transformative technologies drive progress but often concentrate wealth and power unless governed equitably. For centuries, global productivity growth stagnated, with innovations like the iron plow benefiting feudal elites while most lived in subsistence. The Industrial Revolution marked a shift, with steam engines and mechanized production boosting annual growth from 0.1 percent to 1.9 percent by the late 19th century, and averaging 2.8 percent through the 20th century. Yet, mechanization displaced workers, sparking unrest until labor movements and policies like the Factory Acts secured protections such as fair wages and working hours. This pattern underscores a key lesson: technological advancements require governance to ensure broad societal benefits.

AI’s evolution mirrors this dynamic. From IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997 to transformer-based models enabling nuanced language processing, AI has advanced from narrow applications to systems with widespread impact. However, a “productivity paradox” persists: global labor productivity growth slowed to 1.8 percent annually between 2005 and 2015, down from 2.5 percent in the 1990s, due to uneven adoption, skill gaps, and corporate prioritization of shareholder value over societal good.

AI offers a path to reverse this trend, streamlining manufacturing and increasing agricultural yields through precision farming tools, such as AI-powered irrigation systems in sub-Saharan Africa that enhance food security. Yet, without equitable deployment, AI risks replicating historical inequities. Tech giants and state-backed firms could monopolize benefits, marginalizing workers and smaller economies. Corporate monopolies control vast data and computational resources, stifling competition and limiting access, particularly in developing economies. Corporate negligence, such as failing to moderate harmful content, has fueled social unrest and public health crises, while partnerships with authoritarian regimes for surveillance tools highlight complicity in undermining freedoms. To counter these risks, antitrust enforcement, public investment in research, and upskilling programs are essential. Policies like universal basic income, piloted in Scandinavia, support workers displaced by automation, enabling retraining for an AI-driven economy. Transparent accountability mechanisms and global standards, despite resistance from corporate lobbying, are critical to ensure AI fosters inclusive growth rather than concentrated power.

AI as an information network: Connectivity and risks

AI extends humanity’s information networks, building on the legacy of the printing press, telegraph, and internet, which enabled unprecedented cooperation but also amplified risks like misinformation and propaganda. AI embodies this duality. It enhances global connectivity and efficiency, with climate models improving flood predictions in vulnerable regions and predictive algorithms optimizing retail supply chains to reduce waste and costs. These advancements demonstrate AI’s potential to strengthen global systems and foster collaboration.

However, AI networks pose significant dangers. Fabricated content, such as deepfake videos, erodes trust in democratic processes, as seen in election-related misinformation campaigns. Authoritarian regimes leverage AI for behavioral surveillance, tracking citizens through data-driven systems. Corporate negligence exacerbates these risks, with social media platforms often failing to curb harmful content due to profit-driven priorities. Solutions include algorithmic transparency, strict content moderation, and decentralized data governance. Some democracies mandate audits of AI systems to prevent bias and misinformation, but global enforcement remains fragmented due to corporate resistance and varying legal standards. Robust accountability mechanisms are essential to ensure AI serves as a tool for cooperation rather than division.

Practical applications and health innovations: Promise and pitfalls

AI’s practical applications span diverse sectors, driving productivity when designed collaboratively and ethically. In education, AI-powered tutoring systems address teacher shortages, improving outcomes in underserved areas. In energy, AI-optimized grids enhance reliability, reducing outages in unstable infrastructures. In logistics, predictive models streamline delivery networks, cutting costs and emissions, as seen in AI-driven route optimization in shipping that reduces fuel consumption. Long-term, AI holds promise for climate solutions like advanced carbon capture and renewable energy forecasting, critical for global net-zero targets.

In healthcare, AI is revolutionizing synthetic biology and diagnostics. AI-driven protein modeling accelerates drug discovery for diseases like cancer, while diagnostic tools enhance accuracy in resource-constrained settings, improving tuberculosis detection in low-income regions. AI-engineered microbes show promise in reducing environmental waste, aligning health innovation with sustainability.

However, pitfalls persist. Biased algorithms, trained on skewed datasets, perpetuate inequities, as seen in early AI hiring tools that favored certain demographics. Flawed datasets in healthcare can lead to misdiagnoses, while underrepresentation of diverse populations reduces efficacy and exacerbates health inequities. Biosecurity risks, such as AI designing harmful pathogens, demand urgent attention. Misinformation on AI-driven platforms has eroded public trust, fueling vaccine hesitancy during health crises.

To address these challenges, bias audits, mandatory kill switches, and human-in-the-loop frameworks ensure oversight. Transparent, inclusive datasets and international oversight through global health AI guidelines are vital, as are robust bioethics protocols. Regulatory delays hinder progress, with some regions struggling to implement biosecurity measures. Collaborative innovation—pairing public, private, and academic efforts with ethical scrutiny—will ensure AI drives progress without deepening divides or enabling unchecked power.

AI, media, and democratic governance: Strengthening civic engagement

AI is reshaping political discourse, amplifying populist narratives while offering tools to strengthen democratic engagement. Social media algorithms fuel sensationalism, polarizing societies and undermining trust in institutions. Micro-targeting exploits psychological data to sway voters, and privacy-invasive systems threaten autonomy, with large-scale voter data systems raising concerns about surveillance and democratic erosion. Yet, AI also empowers civic participation. Digital platforms facilitate transparent budget audits, uncovering fraud and enhancing governance, while AI-driven apps boost voter turnout by simplifying access to information and fostering community engagement.

To counter manipulation, algorithmic transparency and independent content moderation are critical. Some governments require platforms to disclose content prioritization methods, reducing harmful narratives. Balancing free speech with global standards remains challenging, particularly on platforms where echo chambers entrench division. Public literacy programs, teaching citizens to evaluate AI-driven content critically, are vital. Inclusive governance, such as participatory platforms engaging diverse voices, can protect democracy. By leveraging AI’s potential for transparency and engagement while addressing its risks, societies can strengthen democratic institutions in an era of rapid technological change.

Global competition and ethical realities: Navigating geopolitics and technical limits

The US-China AI race is reshaping global geopolitics, with both nations vying for technological supremacy. The US leverages advanced chip production and private-sector innovation, while China counters with state investment and domestically developed models. Developing nations, caught in this rivalry, face risks of surveillance and economic dependency, as seen in the adoption of certain 5G infrastructures. Sanctions and competing economic systems deepen divides, with hardware access restrictions prompting alternative supply chains and technological fragmentation.

AI excels in specific tasks but falls short of general intelligence, revealing technical and ethical limitations. Adversarial attacks, where systems misinterpret inputs, and biased outputs from skewed datasets highlight the alignment problem: AI often fails to reflect human values. Errors in welfare systems have excluded vulnerable populations, while biased algorithms perpetuate inequities in justice and hiring. Regulatory frameworks, like risk assessments and transparency mandates, aim to address these issues, but rapid advances outpace governance. Interdisciplinary research, including AI ethics boards, reduces bias through iterative testing, though encoding diverse values, particularly from underrepresented regions, remains challenging.

Cooperative frameworks, such as international AI safety protocols, aim to curb escalation, but geopolitical tensions and corporate interests undermine progress. Developing nations are building local AI capacity through public-private partnerships and research hubs tailored to local needs. AI-driven military systems and surveillance programs threaten privacy and freedom, with global powers deploying data collection at unprecedented scales. Global ethical standards, transparent governance, and international treaties can balance security and liberty, but superpower rivalries complicate cooperation. Balancing competition with collaboration is essential to ensure AI drives global progress rather than conflict or exclusion.

Conclusion: A human-centric vision for AI’s future

AI’s potential to tackle humanity’s greatest challenges—healthcare, productivity, climate change—is matched by its risks to equity, freedom, and trust. Historical lessons, from the Industrial Revolution to modern generative models, underscore the need for deliberate, inclusive policies. Collaborative innovation, corporate accountability, and global cooperation provide a roadmap for a sustainable AI future. Antitrust measures, workforce upskilling, and public investment counter wealth concentration, while transparent information networks and ethical frameworks mitigate misinformation and biases. Global treaties prevent technological fragmentation, and public literacy empowers democratic oversight.

AI raises profound questions about truth, agency, and global power, challenging traditional notions of knowledge and autonomy. By prioritizing human dignity, fairness, and freedom through ethical design and governance, we can ensure AI’s benefits outweigh its harms. Interdisciplinary collaboration—spanning governments, academia, and civil society—can overcome corporate lobbying and technical complexity, steering AI toward collective human progress. This human-centric vision fosters an inclusive future where technology amplifies shared potential, driving equitable, sustainable progress for all.

Note: The author acknowledges using large language models, such as Grok and ChatGPT, to edit this article