This article was automatically translated from the original Turkish version.
In the last decade, artificial intelligence has experienced unprecedented momentum, creating a new arena of global competition: the race for AGI. Technology companies and governments 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 balance of power 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 manner in which this technology is being developed has already sparked global debates. A tension exists between calls for rapid progress and demands for more controlled, security-focused 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 underlying the AGI race. The central questions framing this analysis are: Is the race truly being driven for the benefit of humanity, or has it become a new battleground for global power balances?
What Is AGI? Threat or Transformation for Humanity?
Artificial General Intelligence (AGI) differs fundamentally from today’s narrow AI models in one key aspect: it aims to unify within a single system a broad cognitive capacity resembling human flexibility. 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 essential dimensions must be considered:
Unlike narrow AI, AGI stands out through its versatility and capacity for self-improvement. Such a system:
Therefore, AGI is not merely a model that imitates training data; it is a structure targeting a broader cognitive architecture.
AGI also reopens debates on concepts such as mind, consciousness, thought, and responsibility. The fact that human intelligence arises from a biological basis while AI stems from a digital one raises the question of how each type of entity 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 a significant opportunity 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:
are central to safety discussions. Therefore, reliability, oversight mechanisms, and understanding system boundaries are critically important in AGI research.
Although AGI has not yet been fully 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 demonstrates that there are multiple technical and methodological approaches to developing AGI.
In conclusion, AGI is viewed as a goal with immense potential for technological breakthrough and serious risks requiring careful management. While its development offers new scientific possibilities for humanity, it also demands clearly defined frameworks for safety, control, and ethics.
The AGI race today is advancing under the control of only a few major actors.
Image generated by artificial intelligence.
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.” OpenAI, which dominates the AI field with large models like GPT-4, GPT-4.1, and o1, argues that AGI must be controllable.
This approach rests on three key pillars:
OpenAI’s strategy advocates developing AGI first in “controlled environments.” While this approach appears security-focused, critics argue it creates a closed digital aristocracy. Concentrating control of a powerful AGI in the hands of just a few institutions could lead to massive power asymmetries in future information systems.
Google DeepMind is the institution that most defines its AGI vision through “scientific discovery.” Since its inception, its goal has not been to treat AGI as a commercial product but as a scientific breakthrough.
The key features of DeepMind’s approach:
Its Gemini models have delivered significant advances, particularly in multimodality. However, DeepMind’s approach to AGI 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 may ultimately slow AGI development. Bureaucratic processes, a culture of risk avoidance, and regulatory pressures could 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.”
This approach means:
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 power and financial resources. Nevertheless, it has managed to remain competitive thanks to massive investments from Amazon and Google.
Meta (Facebook) has adopted a different strategy in the AGI race: sharing large open-source models with everyone.
The LLaMA series has been publicly released, making it accessible to thousands of researchers worldwide.
This strategy has created two effects:
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 drivers lie in deeper, more powerful dynamics. No institution today can compete in the race for AGI merely with “good researchers”; it requires vast 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:
Therefore, companies treat:
as critical strategic assets.
Today, control over data also means cultural hegemony. Whoever controls the data shapes the mind of the future’s artificial intelligence.
The most decisive factor in the AGI race is hardware. Training large-scale AI models requires billions of operations. Consequently, GPU and specialized chip production have become a strategic battleground in recent years.
Today’s hardware power balance is as follows:
The computational power required to develop an AGI system is so immense that today only three to four specialized institutions and a handful of major states can reach this level.
Therefore, hardware in the AGI race means:
AGI is not only a strategic goal for private companies but also for governments. The U.S., China, the EU, the United Kingdom, and India view the AGI race as a matter of national security.
The U.S. vision for AGI:
China’s AGI vision:
The EU’s AGI policy:
The United Kingdom:
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 did in 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 as a new productive force that transforms human labor, reshapes exploitation relations, and radically changes modes of production.
According to Marx, the engine of history is the conflict between productive forces (technology) and production relations (economic-political structures). Today, AGI represents the highest level of productive forces; yet production relations—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:
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 companies leading the AGI race are centralizing all produced value within their own capital circles. This is a digital version of the process Marx predicted: the concentration of capital.
Today’s capitalism has transformed from a physical production economy into an economy of surveillance and behavioral manipulation based on data. 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 its new source of surplus value.
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 in today’s AI sector is precisely this.
these few tech giants possess all the resources of the AGI race:
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 theoretical perspective, this situation means:
will all be completely redefined.
According to Marx, ideology is the mechanism by which an economic system legitimizes itself. Today, discourses surrounding AI—“innovation,” “efficiency,” “security,” “progress”—have become tools for legitimizing capitalist competition.
Therefore, AGI is not merely a technical tool; it is also an ideological project.AGI
As AGI research accelerates, the potential risks of this technology 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 discussed 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 safety measures, an AGI-level system could:
This forces experts to focus on the control problem. The most debated issues include:
Managing these risks requires not only technical safety research but also ethical frameworks, institutional standards, and international cooperation.
One of the biggest debates surrounding AGI is who will develop and control it. Tech giants like OpenAI, Google DeepMind, Anthropic, and Meta possess unmatched capacities in data and hardware. This situation has generated an ethical debate around concentration of power.
The first actor to achieve AGI is expected to gain a significant advantage in determining:
This concentration of power is described by some thinkers as a risk of “digital feudalism”; the centralization of information and cognitive capacity could create a historically unique power structure.
The core issue 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 maintain social balance.
The risks posed by AGI are not limited to large institutions; they have the potential to transform society as a whole. Labor markets are at the center of these debates.
With AGI:
Because professions are central to individual identity in modern societies, this transformation could create not only economic but also cultural and psychological ruptures.
Therefore, social safety models, education systems, and definitions of work will need to be reimagined in the age of AGI.
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:
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; it becomes risky due to how it is developed and managed. The most critical problems arise from:
Therefore, the AGI development process must be viewed not merely as technological progress 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:
However, when evaluated from the perspective of global power balances:
are growing concerns. Therefore, many experts emphasize that cooperation is essential for the safe development of AGI. 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 information and power structures. Therefore, the decisions made today will determine the fundamental direction of tomorrow’s digital society.
Technical Dimension
Philosophical Dimension
OpenAI, Google DeepMind, and Anthropic: The Three Major Powers
OpenAI: Security-Centered, Controlled, and Commercial AGI Approach
Google DeepMind: Science-Centered, Long-Term, and Research-Oriented AGI Approach
Anthropic: A Security-Based Alternative with Constitutional AI
Meta: The Balancing Force Through Open Source
The Engine of the Race: Data, Hardware, and Geopolitical Competition
Data: The Fuel of AGI
Hardware: The Power That Defines AGI’s Limits
Geopolitical Competition: AGI as National Power
Capitalism and Artificial Intelligence: What If Karl Marx Read the Digital Age?
Digital Labor and Automation: Who Produces Value?
If AI replaces human labor, for whom does it produce surplus value?
Data Exploitation: Digitized Surplus Value
AGI and Capitalist Competition: Race or Monopoly?
Artificial Intelligence, Meta, and Ideology
Is the AGI Race Truly Dangerous for Humanity?
Control Problem: Autonomous Decisions and Superhuman Intelligence
Concentration of Power: Who Will Control AGI?
Societal Transformation: Labor, Inequality, and New Economic Structures
Lack of Global Regulation
The Real Risk: Not the Technology Itself, But How It Is Guided
Evaluation: Competition or Cooperation?