1.The Acceleration of Generative AI Competition and the Road to AGI
In recent years, companies such as OpenAI, DeepSeek, and xAI have been engaged in fierce competition in the field of generative AI. Notably, in February 2025, xAI, led by Elon Musk, announced its latest AI model, “Grok 3,” claiming that it outperformed OpenAI’s GPT-4o and DeepSeek’s V3.
“Grok 3” reportedly achieved large-scale training using as many as 200,000 GPUs. This overwhelming deployment of computational resources allowed the model to rapidly learn complex tasks from extensive datasets. By leveraging such massive infrastructure, Grok could quickly update its enormous number of parameters and improve performance across deeper layers and broader domains. However, concerns have also been raised about energy consumption and cost implications associated with this approach.
In contrast, DeepSeek has focused not on hardware scaling but on innovation at the algorithmic level. A notable example is the introduction of a new attention mechanism called
Native Sparse Attention (NSA), proposed in a paper released on February 16, 2025. NSA replaces traditional fully connected attention structures—which are bottlenecks in terms of computational and memory usage—with a sparse alternative. Instead of calculating attention weights for all input tokens, NSA computes attention only for a small number of tokens that are most likely to influence the output. This significantly reduces the computational load.
Thus, the field of generative AI is witnessing an intense competition between two major strategies: one that relies on massive hardware investment (e.g., Grok 3), and another that seeks efficiency through algorithmic innovation (e.g., DeepSeek’s NSA). Each strategy has its advantages and limitations. As we move toward AGI, a key focus will be how these approaches may be combined or balanced to achieve optimal performance.
Importantly, DeepSeek’s open-source policy has empowered researchers, developers, and young innovators worldwide. The development of generative AI is no longer the exclusive domain of tech giants. Even small research labs and startups now have the potential to create groundbreaking innovations that can impact the world.
1.1 What Is AGI? – The Golden Rule of Work in the AI Era
AGI (Artificial General Intelligence) refers to AI systems capable of performing a broad range of intellectual tasks at a level comparable to humans. While current generative AIs are classified as “narrow AI,” specialized in specific domains, AGI is expected to learn and adapt across multiple fields, engage in creative thinking, and solve complex problems autonomously. Once realized, AGI could evolve from a mere support tool to an entity capable of independent judgment and decision-making, fundamentally reshaping the role of humans.
The development race toward AGI is likely to exert a significant impact on society and industrial structures. Key players include companies developing cutting-edge AI technologies, those offering AI applications and services, businesses adopting generative AI, and individuals acting as “prosumers” who utilize AI to create and share content. These various stakeholders will assume new roles, leading to increased labor market fluidity and a rise in freelancers or individuals working with multiple companies.
As generative AI spreads, there is growing concern that 20% of people may end up performing 80% of the work, while the remaining 80% see their roles diminish or disappear. In an ideal AI society, everyone would remain employed, with AI handling 80% of their workload, and individuals focusing on the remaining 20%. This balance forms what might be called the “Golden Rule of Work in the AI Era.”
1.2 How AGI Development May Transform Social Structures
The race to develop AGI is expected to have a profound impact on both society and industrial frameworks. Various players will take on new roles: companies developing advanced AI technologies, firms providing AI-based applications and services, general enterprises implementing generative AI into their operations, and individuals leveraging AI to produce and consume content—commonly referred to as “prosumers.”
This structural shift is anticipated to make the labor market more fluid, with a growing number of freelancers and individuals working for multiple companies. As traditional employment models loosen, career paths will become more flexible and diverse.
Moreover, the spread of generative AI could result in a scenario where 20% of the population handles 80% of the work, while the remaining 80% see their jobs diminish or vanish. In an ideal AI-driven society, the goal would be for everyone to retain employment, with 80% of their tasks automated by AI and the remaining 20% handled by humans—what might be seen as a new golden ratio for work balance in the AI era.
1.3 Debates Around the Arrival of AGI
Will AGI arrive soon?
- Improving reasoning through reinforcement learning alone: The study of DeepSeek-R1-Zero has shown that it’s possible to enhance the reasoning capabilities of large language models (LLMs) through reinforcement learning (RL) without relying on supervised fine-tuning (SFT). This suggests that AI could learn independently from datasets, accelerating the path to AGI.
- Scaling laws and sudden emergent capabilities: OpenAI’s research has indicated that as model size increases, new abilities can emerge unexpectedly. With continued development of ultra-large models such as ChatGPT’s advanced versions or Grok 3, some predict that AGI could arrive as early as 2026 to 2034.
Or is AGI still a distant future?
- Lack of deep reasoning: Experts like Gary Marcus argue that current LLMs are still based on statistical pattern matching and fall far short of the deep reasoning required for true AGI.
- Energy and computational constraints: Today’s LLMs demand enormous computing power. To achieve the versatility expected of AGI, further technological innovation will be needed—especially to address limitations in energy efficiency and hardware scalability.
2. New Forms of Work and Increased Fluidity
The evolution and widespread adoption of AI are expected to bring major changes to traditional industrial structures and working styles. Especially in terms of corporate structure and individual career paths, greater fluidity is anticipated, with more frequent shifts in occupations and roles.
Highly skilled AI professionals: There will be a growing number of engineers and producers who create new value by proactively leveraging AI and deepening their expertise. These individuals are likely to be drawn to advanced AI companies.
Corporate AI adoption and competition: Some companies will grow rapidly by adopting AI, while others—unable to adapt—will fall behind and may ultimately be pushed out of the market.
The rise of the AI-native generation: People who have grown up with AI will begin launching businesses on their own using AI tools, and it will become increasingly common for individuals to operate businesses on a personal level.
The spread of diverse working styles: Side jobs and freelance work will become more normalized, with individuals engaging with multiple companies simultaneously. This will bring about more flexible working styles, and individual skills and personal networks will become more crucial than ever.
That said, the advancement of technology does not merely result in market elimination—it also creates new opportunities. As more creators and “prosumers” emerge through AI, individuals will gain new chances to offer their own services and content. By embracing coexistence with AI and using one’s strengths to adapt to change, a more diverse and creative society is expected to take shape.
Predicted changes in occupations due to AI:
- Increase in engineers and producers who enhance their specialization by utilizing AI
- A growing divide between companies that adopt AI and those that do not
- Internal divisions even within companies between AI adopters and non-adopters
- The AI-native generation normalizing personal entrepreneurship
- Freelance and side-job-based work becoming standard
- Professionals operating like free agents—akin to pro athletes or actors—who secure contracts and take on projects autonomously
2.1 Debating the Future of Workstyles
Will changes be limited, preserving existing frameworks?
- Cost barriers for AI adoption: Implementing AI involves significant costs, which may prevent small and medium-sized enterprises (SMEs) from adopting it quickly.
- AI as a supportive role: In many cases, AI will remain a support tool, and occupations requiring human creativity or managerial skills will continue to exist.
- Gradual labor market transformation: The shift in the job market will likely be gradual, and large-scale layoffs triggered by AI implementation may not occur immediately.
Will changes be rapid, eliminating those unable to adapt?
- Accelerated automation: As AI automates more processes, many traditional roles will become obsolete, and companies or individuals unable to keep up will be left behind or eliminated.
- AI evolving through self-learning: With advancements like DeepSeek-R1-Zero—an RL-trained model capable of self-improvement—AI is expected to make more sophisticated decisions autonomously.
- Widening gaps: A significant divide will emerge between individuals and organizations that can effectively utilize AI and those that cannot.
3. The Importance of Shared Values and Ethics
With the development and spread of AGI (Artificial General Intelligence), it becomes increasingly crucial to establish a shared ethical foundation while recognizing the diversity of human values. As AI systems become more advanced, potential conflicts may arise—between different AI models, and between AI and humans—making mutual understanding and ethical consensus essential for social stability.
3.1 Diversity of Values and AI Training
Modern society embraces a wide range of values shaped by diverse cultures and histories. These differences inevitably affect the data used to train AI systems and influence how decisions are made. While such diversity can lead to conflict or friction, building an ideal future will require humanity to find and respect common ground while honoring pluralism.
Ethical Challenges in the Age of AGI
- Value-based discrepancies in AI judgment: Different AI systems may reach conflicting conclusions depending on the values they are trained on.
- Possibility of societal conflict: Value-based disagreements may escalate to international disputes or social unrest.
- Accountability and fairness: As AI systems make increasingly impactful decisions, the question arises—who ensures that these decisions are fair and just?
3.2 The Need for Shared Ethical Standards
There is growing agreement that developing shared ethical standards is essential for guiding AGI development. Reports from Japan’s Ministry of Internal Affairs and Communications, for instance, emphasize the importance of legal and institutional frameworks that prevent friction between technology and societal values.
Many AI researchers advocate embedding human ethics and values into AGI from the design stage, alongside systems that continuously monitor and evaluate AI’s behavior.
3.3 Opposing Views: Is Consensus Even Possible?
- Cultural and social diversity makes it extremely difficult to unify ethical frameworks globally.
- Adaptive ethics: Some argue that AI should not be bound by human-defined ethics alone, but instead respond flexibly to circumstances.
- Respect for AI diversity: Others propose allowing different cultures and societies to develop localized models, rather than enforcing a one-size-fits-all ethical standard.
Despite these views, many caution that without a shared ethical foundation, confusion and instability will spread:
- Inconsistent AI judgments would erode public trust and cause societal disorder.
- Minimum universal rules for AI—such as “respect human dignity” or “mutual respect between AI and humans”—should be established.
- Next-generation AI must be designed to avoid deepening bias or inequality, especially to protect marginalized individuals or communities.
3.4 Toward Ethical AI Development
To address these complex challenges, cooperation among AI developers, policymakers, ethicists, and diverse stakeholders is essential. AGI design must include ethical and value-based principles from the outset and incorporate systems for ongoing transparency and accountability.
By doing so, society can ensure that AGI’s behavior remains trustworthy. As technology continues to advance, it is crucial for individuals, organizations, and governments alike to embrace change while preserving shared values, ultimately building a harmonious society prepared for the AGI era.
4. Conclusion: The Social Structure of the AGI Era
4.1 AI Development and Social Adaptation
The competition in generative AI is accelerating the path toward AGI. While its technical realization may arrive sooner than expected, many ethical and societal challenges remain unresolved.
The transformation of the labor market is inevitable. Whether individuals and companies can successfully adapt will determine their survival in the new AI-driven world.
Establishing ethical standards for AI is an urgent task. It is essential to achieve global consensus on foundational rules for how AI should behave and interact with society.
4.2 Directions for the Future
- Corporate Strategy: Companies must clarify their AI strategies and carefully consider which job roles and tasks should be automated, and which should remain human-led.
- Educational Reform: Educational institutions should prioritize the development of talent capable of utilizing AI effectively and enhance digital literacy across all levels of society.
- Policy and Lawmaking: Governments and international organizations must proactively discuss the societal impacts of AI and move forward with legal and institutional reforms.
4.3 Final Thoughts
Whether AGI arrives sooner than anticipated or remains a distant goal depends on both the pace of technological development and society’s ability to adapt. What is certain, however, is that preparing now—at the individual, organizational, and national level—is the most crucial step toward a stable and equitable AI-driven future.
