The Dawn of AGI: How AI Will Reshape the Future

Imagine a future where, upon waking up in the morning, your AI assistant has already planned the best travel route based on your schedule and real-time traffic conditions; at work, AI collaboration tools help you efficiently handle complex tasks, allowing you to focus on more creative work; when you return home, the smart home system has adjusted the indoor environment according to your preferences and even prepared your favorite dinner. This is not a scene from a science fiction movie, but a glimpse of the era of Artificial General Intelligence (AGI).

As Lukas Petersson stated in his 2025 blog post “AI Founder’s Bitter Lesson. Chapter 1 – History Repeats Itself”^[1], “General methods always prevail in the end.” This insight profoundly reveals the inherent laws of AI development and foreshadows the inevitable arrival of the AGI era. The leap from specialized AI to general AI is not only a technological breakthrough but will also profoundly change human society.

Golden Age of Specialized AI: Limited Glory

Looking back at the development of AI, the achievements of specialized AI in specific fields are remarkable. In 1997, IBM’s “Deep Blue” defeated world chess champion Garry Kasparov, proving that AI’s computational ability in specific domains can surpass human capabilities. Deep Blue’s victory relied on its powerful computational capacity: 30 PowerPC processors and 480 custom chess chips, evaluating 100-200 million chess positions per second, combined with human expert chess knowledge for strategic reasoning.

Nearly two decades later, DeepMind’s AlphaGo made headlines again by defeating Go world champion Lee Sedol 4:1 in 2016. The complexity of Go far exceeds that of chess, with possibilities surpassing the total number of atoms in the universe, once considered a field difficult for AI to conquer. Unlike Deep Blue, AlphaGo adopted a combination of deep neural networks and Monte Carlo tree search, selecting the next move through a “policy network,” assessing the situation with a “value network,” and continuously learning and improving through self-play, demonstrating AI’s learning ability in complex strategic games.

Specialized AI has excelled not only in gaming but also achieved significant results in medical diagnosis and financial forecasting. For example, AI’s accuracy in breast cancer diagnosis has surpassed that of human radiologists. Google’s AI system in breast cancer diagnosis has an accuracy rate 5.7% higher than that of human radiologists while reducing the false positive rate by 1.2% and the false negative rate by 2.7%. These achievements demonstrate the powerful capabilities of specialized AI and bring tangible benefits to human society.

However, the limitations of specialized AI are becoming increasingly apparent. They lack flexibility and generalization ability, unable to transfer knowledge learned in one domain to another. While AlphaGo excels at Go, it cannot be directly applied to autonomous driving. The deeper reason is that specialized AI often relies on specific domain rules and knowledge, lacking abstract understanding and reasoning about the world, which is the key breakthrough AGI aims to achieve. As Lukas Petersson pointed out, these specific domain rules and knowledge may help AI systems achieve results quickly in the short term, but in the long run, this dependency on domain-specific knowledge will limit the development of AI systems and ultimately be surpassed by more general methods.

Dawn of General AI: The Rise of Large Models

In recent years, the rise of large language models (LLMs), represented by the GPT series, has illuminated the path to AGI. From GPT-1 in 2018 to GPT-3 in 2020 and now to GPT-4, the model’s parameter scale and capabilities have shown exponential growth. GPT-3 has 175 billion parameters and performs excellently on various natural language processing tasks, even demonstrating astonishing few-shot learning abilities, mastering new tasks quickly with just a few examples. For instance, on the SQuAD question-answering dataset, GPT-3 achieved an accuracy of 88.8%, very close to the 89.5% level of human experts.

The outstanding performance of the GPT series models is attributed to their adoption of the Transformer architecture. The core innovation of Transformer lies in the self-attention mechanism, which allows the model to dynamically focus on different parts of the input sequence, achieving efficient modeling of long-range dependencies. This mechanism enables the GPT series models to better understand and generate natural language, showcasing their strong versatility across various tasks. If you want to dive deeper into the Transformer model, you can refer to this article “The Illustrated Transformer”^[2].

The rise of large models gives us hope for AGI. However, the path to AGI remains fraught with challenges.

Technical Challenges of AGI: From “Black Box” to “White Box”

Despite the tremendous success of large models, they still face numerous technical challenges. Among them, the “black box” problem is particularly prominent. Due to the complexity of model structures and the vast scale of parameters, it is difficult to understand their internal workings and decision-making processes, which not only hinders the further development of models but also poses safety and ethical risks.

To address this issue, researchers are exploring various methods to improve model interpretability and transparency. The rise of neuro-symbolic AI offers new ideas for solving this problem. This approach combines the perceptual learning capabilities of neural networks with the reasoning abilities of symbolic logic, aiming to create AI systems that are both interpretable and flexible. For example, the neuro-symbolic concept learner (NSCL) developed by an MIT research team can learn abstract concepts from a few examples and perform complex combinatorial reasoning, such as understanding “the circle to the left of the red square” and finding the corresponding objects in an image. This ability is crucial for achieving true language understanding and visual reasoning.

In addition to neuro-symbolic AI, quantum machine learning also shows great potential. The parallel computing power of quantum computers is expected to solve the computational bottlenecks in AI development. For instance, Google announced in 2019 that it had achieved “quantum supremacy”^[3], with its 53-qubit processor Sycamore completing calculations that would take a classical supercomputer 10,000 years in just 200 seconds. Although current quantum computers are still in their early stages, their immense potential in solving specific problems suggests that quantum machine learning could become an important pathway to AGI.

Furthermore, federated learning, as a privacy-preserving distributed machine learning method, also provides new ideas for the development of AGI. Through federated learning, multiple participants can collaboratively train models without sharing raw data, which not only helps protect data privacy but also promotes broader data collaboration, accelerating the development of AGI. For example, Google has begun applying federated learning to Android phones^[4], improving predictive text models while protecting user privacy.

Social Impact of AGI: Opportunities, Challenges, and Ethical Boundaries

The development of AGI is not only a technological leap but will also have far-reaching impacts on society. In the medical field, AGI-driven diagnostic systems can diagnose diseases more accurately and formulate personalized treatment plans. For example, SmartHealth AI uses AGI-driven diagnostic systems to analyze vast amounts of medical data, accurately diagnosing diseases and recommending personalized treatment plans, reducing diagnostic errors, improving treatment outcomes, and enhancing overall patient care. In the financial sector, AGI can help financial institutions assess risks more accurately and detect fraud, such as Vectra AI and Capital One using AI to prevent and address cyberattacks and identify suspicious transactions. In education, AGI can provide personalized teaching content and tutoring based on each student’s learning pace and preferences, such as LearnX’s personalized learning case utilizing an AGI-driven adaptive learning platform to deliver personalized learning experiences, analyzing individual learning patterns, preferences, and cognitive abilities to optimize learning outcomes.

A study by the McKinsey Global Institute simulates that AI could drive GDP growth by 26% and contribute up to $15.7 trillion to the global economy by 2030, equivalent to the total GDP of China and India today. However, AGI brings not only economic growth but also deeper social changes.

The World Economic Forum predicts that by 2025, AI could replace 85 million jobs but may also create 97 million new jobs. This means that large-scale occupational transitions will be inevitable. Compared to specific job skills, abilities that are difficult for AI to replace, such as critical thinking, creativity, communication skills, and emotional intelligence, will become increasingly important. We need to rethink our education system and social security systems to help workers adapt to the new employment landscape.

The development of AGI may also exacerbate social inequality. If AGI technology is monopolized by a few countries or companies, it could lead to a global imbalance of power and an expansion of wealth gaps. Therefore, we need to advocate for an open, collaborative, and inclusive development model for AGI, ensuring that the benefits of AGI development can benefit all humanity.

Ethical issues surrounding AGI require our attention. We must prepare in advance, establishing effective mechanisms to ensure the safety and controllability of AGI systems. This requires joint efforts on both technical and institutional levels to develop reliable safety technologies and formulate comprehensive regulatory laws. For example, we can refer to the “Oxford Principles for AI Ethics”^[5] to guide the development and application of AGI. At the same time, we need to enhance public education and discussion on AGI ethical issues. The development of AGI is not just the concern of scientists and engineers; it relates to everyone’s future. We need to engage more people in understanding AGI and participating in discussions about AGI ethical issues to shape the future of AGI together. Among these, establishing ethical guidelines and safety protocols for AGI systems, designing corresponding regulatory and governance frameworks, and balancing AGI development with ethical risks will be important challenges we face.

Conclusion: Embracing the Dawn of the AGI Era

We stand at the dawn of the AGI era, witnessing the transformation of AI from specialized tools to general intelligence. This path is filled with challenges but also holds infinite opportunities. Although there is no definitive answer to when true AGI will arrive, experts predict a 50% chance of AGI emerging before 2060.

The development of AGI requires global cooperation, bringing together the strengths of academia, industry, and government to jointly promote AGI development and establish corresponding ethical guidelines and regulatory frameworks to ensure its safety, reliability, and controllability.

The future of AGI is in our hands. Let us embrace the arrival of the AGI era with an open, inclusive, and responsible attitude, shaping a harmonious future of human-machine coexistence together. In this future, AGI will be a powerful assistant in exploring the mysteries of the universe, solving global challenges, and advancing human civilization. As Lukas Petersson foresaw, “General methods always prevail in the end,” and AGI is the general intelligence that will lead us into the future.