AGI Race: A Competition for Humanity or a Global Power Struggle?

Artificial intelligence has experienced tremendous momentum over the past decade, creating a new arena of global competition: the AGI race. Technology companies and nations are now competing not only to produce better models but also to develop systems with high cognitive capacity. This race is not limited to engineering innovations; it is deeply intertwined with economic investments, hardware infrastructure, data policies, and international strategies.

Today, leading actors on the path to AGI — OpenAI, Google DeepMind, Meta, and Anthropic — are shaping the global power balance through distinct modeling approaches, safety strategies, and visions. Each institution is building its own technological ecosystem according to its understanding of artificial intelligence’s future. Parallel to this, the accelerating technological competition between the United States and China is turning the AGI race into an economic and political issue by heightening the importance of chip supply chains.

Although AGI research has not yet reached its ultimate goal, the way this technology is being developed is already generating global debates. Tension exists between calls for rapid progress and demands for more controlled, safety-oriented development. Some view competition as necessary for innovation, while others argue that this approach increases risks.

This article aims to uncover the dynamics shaping this process by examining the institutional strategies, state policies, technical constraints, and ethical debates behind the AGI race. The central questions framing this discussion are whether the race is truly being pursued for the benefit of humanity or has instead become a new arena for global power competition.

What Is AGI? Threat or Transformation for Humanity?

Artificial General Intelligence (AGI) differs fundamentally from today’s narrow AI models: it aims to unify a broad cognitive capacity with human-like flexibility within a single system. While current systems specialize in specific domains — such as language processing, chess, or image recognition — AGI represents an AI understanding capable of adapting to diverse problem types, developing its own learning strategies, and generalizing to novel situations. In this sense, AGI is seen as a goal that significantly surpasses today’s limits of artificial intelligence.

To understand AGI, two fundamental dimensions must be considered:

AGI stands out from narrow AI through its versatility and capacity for self-improvement. Such a system:

• can perform diverse tasks under a single model,
• can interpret abstract concepts,
• can develop new methods for generating knowledge,
• can optimize its internal processes.

Therefore, AGI is not merely a model that mimics training data; it is a structure targeting a broader cognitive architecture.

AGI also reopens debates on mind, consciousness, thought, and responsibility. The fact that human intelligence arises from a biological basis while artificial intelligence stems from a digital one raises the question of how these two distinct types of entities might develop intention, awareness, or ethical responsibility. AGI is not only a technical breakthrough but also carries the potential for a profound philosophical transformation in how humanity defines “intelligence.”

The impacts AGI could have on humanity are not limited to risks; it also presents significant opportunities for transformation. The broad cognitive capacity offered by AGI could accelerate solutions to many currently unsolved problems in medicine, climate science, materials science, energy efficiency, education, and scientific discovery. Such a system could support scientific progress by generating new hypotheses in research and enhance human decision-making capabilities.

However, the development of AGI also brings specific risks.

For example:

• a powerful AGI system producing unintended behaviors due to misalignment,
• interpreting instructions in unexpected ways due to goal misalignment,
• making complex decisions that are difficult to explain — these scenarios lie at the center of safety debates. Therefore, reliability, oversight mechanisms, and understanding system boundaries are critically important in AGI research.

Although full AGI has not yet been achieved, efforts such as OpenAI’s GPT series, Google DeepMind’s multimodal models, Anthropic’s Constitutional AI approach, and Meta’s open-source large models represent distinct pathways toward this goal. This diversity reveals the existence of multiple technical and methodological approaches to developing AGI.

In conclusion, AGI is viewed as a goal with immense potential for technological breakthrough as well as risks requiring careful management. While its development offers new scientific possibilities for humanity, it also demands clearly defined frameworks for safety, control, and ethics.

Today, the AGI race is advancing under the control of only a few major actors.

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When technological capacity, financial resources, data volume, and research ability converge, three major powers emerge at the center of this race: OpenAI, Google DeepMind, and Anthropic. These three institutions are not only developing different AI models; they also represent three distinct ideological, strategic, and ethical approaches to how AGI might emerge.

Although OpenAI’s AGI vision has largely moved away from its original principle of openness, it remains built on the narrative of “safe AI for humanity.” Dominating the AI field with large models like GPT-4, GPT-4.1, and o1, OpenAI argues that AGI must be controllable.

This approach rests on three key principles:

• Powerful models must remain closed: Model weights, data, and training methods are not disclosed.
• Development must proceed in a controlled manner: Each new model is gradually released only after passing rigorous testing phases.
• Collaboration with governments and large corporations is essential: The strategic partnership with Microsoft has uniquely positioned OpenAI in terms of cloud power and infrastructure.

OpenAI’s strategy advocates developing AGI first in “controlled environments.” While this approach appears safety-focused, critics argue it creates a closed digital aristocracy. If a powerful AGI is controlled by only a few institutions, it could create massive power asymmetry in future information systems.

Google DeepMind is the institution that defines its AGI vision most strongly through “scientific discovery.” From its earliest days, its goal has not been to treat AGI as a commercial product but as a scientific breakthrough.

The key features of DeepMind’s approach:

• Research comes first: Hundreds of publications in journals like Nature and Science demonstrate DeepMind’s scientific intensity.
• Integration of deep learning and neuroscience: DeepMind centers approaches that mimic the principles of the human brain.
• Energy optimization and hardware efficiency: Because AGI requires massive computation, DeepMind has made major innovations in this area.

Gemini models have delivered significant advances, especially in multimodal domains. However, DeepMind’s AGI approach is slower, more measured, and more scientific than OpenAI’s. Thanks to Google’s economic strength, DeepMind feels less commercial pressure and continues to view AGI as a “science project.”

Nevertheless, critics argue that Google’s corporate structure could slow AGI’s long-term development. Bureaucratic processes, a culture of risk avoidance, and regulatory pressures may limit DeepMind’s maneuverability.

Anthropic was founded by researchers who left OpenAI, responding to the criticism that OpenAI had become too fast and too closed. Anthropic’s greatest contribution has been developing the concept of “Constitutional AI” (Constitutional AI).

This approach means:

• The model is trained using a “constitution.”
• This constitution consists of ethical principles, human rights, and safety rules.
• The model analyzes its own errors and corrects them within the framework of this constitution.

Anthropic’s models (Claude 2, Claude 3, Claude Opus) have received global praise for their reliability. Anthropic’s AGI vision offers neither complete openness nor complete closure; instead, it provides a “reliable yet flexible” framework.

However, Anthropic’s greatest challenge is resource constraints. Unlike OpenAI and Google, Anthropic has limited hardware capacity and financial resources. Nevertheless, it has managed to remain in the race thanks to massive investments from Amazon and Google.

Meta (Facebook) has adopted a different strategy in the AGI race: Sharing open-source large models with everyone.

The LLaMA series has been publicly released, making it accessible to thousands of researchers worldwide.

This strategy has created two effects:

• It prevents centralization of the AGI race.
• It accelerates global innovation.

However, this approach carries risks. Open models are more vulnerable to malicious use, and Meta has faced serious criticism on this issue.

Nevertheless, Meta’s approach is seen as a force that slows the monopolization of the AGI race.

Although companies appear on the surface of the AGI race, its true engine rests on deeper, more powerful dynamics operating behind the scenes. No institution today can remain competitive in the race toward AGI merely through “good researchers”; it requires massive datasets, millions of GPU cores, state-backed investments, and global geopolitical strategies. Therefore, the AGI race has become not just a competition among tech companies but the new great power struggle of the 21st century.

For modern AI models, data is as strategic a resource as oil. Humanity’s digital footprints — social media content, books, academic papers, code, videos, voice recordings — are now considered equivalent to a nation’s most critical national asset.

Without data:

• models cannot learn,
• language models’ “world knowledge” is limited,
• AI’s generalization ability declines,
• AGI development slows.

Therefore, companies treat:

• YouTube data,
• web corpus archives,
• book databases,
• code repositories,
• social media interactions

as critical strategic assets.

Today, control over data also means cultural hegemony. Whoever controls the data shapes the mind of tomorrow’s AI.

In the AGI race, the most decisive factor is hardware. Training large-scale AI models requires billions of operations. Consequently, GPU and specialized chip production have become a strategic war in recent years.

Today’s hardware power balance is as follows:

• NVIDIA → Controls nearly the entire chip market; H100, A100, and the new B100 chips are the foundational stones of the AGI race.
• TSMC → The world’s most advanced semiconductor manufacturer; a giant upon which both the United States and China depend.
• US-China technology war → Export bans, chip embargoes, and production restrictions directly shape the AGI race.
• Microsoft and Google supercomputers → Only a few institutions possess the computational power to train trillion-parameter models.

The computational power required to develop an AGI system is so immense that today only three to four specialized institutions and a few major states can reach this level.

Therefore, hardware in the AGI race means:

• speed,
• capacity,
• control and power.

AGI is not only a strategic goal for private companies but also for states. The United States, China, the European Union, the United Kingdom, and India view the AGI race as a matter of national security.

The United States’ AGI vision:

• Private-sector driven,
• Advancing through universities and tech giants,
• A hybrid model with state support.

China’s AGI vision:

• State-centered,
• Based on data dominance,
• Compatible with digital surveillance infrastructure,
• A more closed and controlled model.

The European Union’s AGI policy:

• Ethics and regulation focused,
• Aiming for “safe development” rather than open competition.

The United Kingdom:

• Leading initiative in AI safety,
• Establishing AI safety institutes.

This landscape shows us: AGI is no longer merely a technological challenge; it is the new focal point of international politics.

Today, state investments in technology directly determine energy, defense, economic, and cultural influence. Possessing AGI could define a nation’s status as a “superpower” in the digital age.

Therefore, the AGI race is not only fought in laboratories but also at diplomatic tables, in economic policies, and in military strategies.

Beneath the AGI race lie not only data, hardware, and research capacity but also the deepest dynamics of modern capitalism.

Today, artificial intelligence has the same transformative effect as steam engines during the 19th-century industrial revolution: it fundamentally alters the speed, cost, and scale of production. If Karl Marx had observed the digital age, he would likely have defined artificial intelligence — especially AGI — as a new productive force that transforms human labor, reshapes exploitation relations, and radically changes modes of production.

According to Marx, history’s engine is the conflict between productive forces (technology) and relations of production (economic-political structures). Today, AGI represents the highest level of productive forces; yet the relations of production — the capitalist system — are directing this new power toward their own interests.

AGI causes three critical transformations under classical capitalism:

In traditional economies, value was produced by human labor. However, artificial intelligence — especially AGI — has the potential to overturn this relationship:

• Software developers,
• Designers,
• Lawyers,
• Teachers,
• Artists, even decision-makers, can be partially or fully replaced by AI.

This forces a reinterpretation of Marx’s concept of “commodification of labor” in a new era. Labor is no longer produced solely by humans but also by algorithms. This raises a new question:

Today, the leading companies in the AGI race are centralizing all generated value within their own capital circles. This is a digital version of the concentration of capital Marx predicted.

Today’s capitalism has transformed into an economy of surveillance and behavioral guidance based on data, not just physical production. The structure Shoshana Zuboff calls “Surveillance Capitalism” treats human digital behaviors as raw material, commodifying them.

Data is not merely capitalism’s new oil — it is even more fundamentally its new source of surplus value.

• User behavior,
• Likes,
• Location data,
• Texts,
• Sounds,
• Images — all are used to train models, yet data owners receive no share of this process.

In this context, if Marx analyzed the digital age, he would ask: “Who produces digital surplus value, and who appropriates it?”

The current answer is clear: large technology companies.

Capitalism appears to be built on competition; yet as Marx noted, competition inevitably leads to monopoly.

What is happening today in the AI sector is precisely this.

• OpenAI,
• Google DeepMind,
• Anthropic,
• Meta.

These few tech giants possess all the resources of the AGI race:

• data,
• hardware,
• finance,
• research talent,
• state support,
• global networks.

In this scenario, the first institution to achieve AGI could gain unprecedented monopolistic power in the digital economy. Control over AGI could lead to the greatest concentration of power capitalism has ever produced.

From Marx’s perspective, this situation means:

• a complete redefinition of class relations,
• ownership of means of production,
• the nature of labor,
• and social power balances.

According to Marx, ideology is the mechanism by which an economic system legitimizes itself. Today, discourses around AI — “innovation,” “efficiency,” “safety,” “progress” — have become tools for legitimizing capitalist competition.

• The “safety” narrative legitimizes closed models.
• The “efficiency” narrative normalizes the displacement of human labor.
• The “innovation” narrative shapes state policies.

Thus, AGI is not merely a technical tool; it is also an ideological project.AGI

As AGI research accelerates, its potential risks have moved beyond scientific debate to become a global political, ethical, and social agenda. Although AGI has not yet been fully constructed, assessments of its possible impacts reveal how transformative it could be for humanity. Today, experts agree on three key risk areas: loss of control, concentration of power, and societal transformation.

One of the most debated risks of AGI is the possibility that it could achieve decision-making capacity beyond human control. While current models can be constrained by rules and safeguards, an AGI-level system could:

• generate new goals,
• optimize its own behavior,
• improve itself,
• develop strategies beyond human cognitive comprehension — forcing experts to focus on the control problem.

In this context, the most discussed issues are:

• Goal misalignment: AGI misinterpreting a given goal and producing unintended outcomes.
• Instrumental behavior: The system developing independent sub-goals to sustain its own function.
• Unexplainable decision processes: AGI’s complex internal modeling making human oversight difficult.

Managing these risks requires not only technical safety research but also ethical frameworks, institutional standards, and international cooperation.

One of the most significant debates surrounding AGI concerns who will develop and control this technology. Tech giants like OpenAI, Google DeepMind, Anthropic, and Meta possess unparalleled capacities in data and hardware resources. This situation has sparked an ethical debate around concentration of power.

The first actor to achieve AGI is expected to gain a major advantage in determining:

• global information flows,
• the pace of scientific progress,
• economic production processes,
• security standards.

This concentration of power is described by some thinkers as a risk of “digital feudalism” — where information and cognitive capacity are centralized in a single entity, creating a historically unique power structure.

The core problem here is not AGI itself but the inadequacy of control and transparency mechanisms. Therefore, the responsibility of technology companies must extend beyond innovation to developing institutional policies that preserve social balance.

The risks posed by AGI are not limited to large institutions; they hold transformative potential for society as a whole. Labor markets are at the center of these debates.

With AGI:

• a significant portion of cognitive skill-based jobs could be automated,
• transformations driven by AI could occur even in highly specialized expert fields,
• income inequality could deepen,
• the middle class’s role in production processes could weaken.

Because occupations define individual identity in modern societies, this transformation could create not only economic but also cultural and psychological ruptures.

Therefore, social safety nets, education systems, and definitions of work must be reimagined for the AGI era.

Unlike nuclear energy, biotechnology, and space research, which have international treaties, there is currently no binding global framework for AGI. This creates a fragmented development environment where countries and companies apply their own standards.

This gap amplifies three key risks:

• Inconsistent safety standards
• Risk of technology falling into the wrong hands
• Ambiguity in accountability mechanisms

When dealing with a powerful technology like AGI, the absence of international cooperation can become a factor that accelerates the risk itself.

AGI is not inherently “dangerous”; what makes it risky is how it is developed and managed. The most critical problems arise from:

• unregulated development,
• opaque strategies by corporations and states,
• unclear ethical principles,
• exclusion of society from the process.

Therefore, the development of AGI must be viewed not merely as a technical advancement but as a test of humanity’s collective ethical and political responsibility.

The key factor determining AGI’s future is not the speed of competition but the principles guiding its development. From a scientific progress perspective, AGI:

• can contribute significantly to fields ranging from climate science to medicine,
• materials science to educational technology,
• energy efficiency to knowledge production.

However, when evaluated from the perspective of global power balances:

• the economic and political influence of the first actor to achieve AGI,
• the concentration of cognitive infrastructure in a single hand,
• the erosion of democratic processes

are growing concerns.

Therefore, many experts emphasize that cooperation is essential for AGI’s safe development. International agreements, transparent standards, independent oversight mechanisms, and research ecosystems prioritizing public interest are critically important for this technology’s future.

AGI is not merely a technological leap; it is a turning point capable of reshaping humanity’s knowledge and power structures. Therefore, the decisions made today will determine the fundamental direction of tomorrow’s digital society.