Why is AGI so powerful?

Unlike ANI, AGI is not confined to pre-programmed tasks or predefined responses within a limited domain. Instead, it has the potential to generate and apply knowledge across various contexts.

Imagine a self-driving car powered by AGI. It can collect a passenger from a train station but also personalize the journey with custom recommendations for pit stops, sightseeing avenues or navigating unfamiliar roads to arrive at the desired destination. And because it’s a machine, AGI would not experience fatigue and would continue learning and improving at exponential speeds. 

Here’s a definition of AGI by Vitalik Buterin, who highlights the sheer potential of AGI:

The example highlights some interesting features of AGI, which include:

• Learning capability: AGI can learn from experiences and improve its performance over time without a concerted effort by human programmers to perform additional data set training. This learning is not limited to specific tasks and instead encompasses a broad spectrum of activities.
• Problem-solving skills: AGI can solve complex problems by applying logical reasoning just as a human would. This includes consideration of non-traditional variables, such as emotional impact, which can highlight an even wider range of potential outcomes.
• Adaptability: AGI can adjust to new situations and environments without explicit programming, which means it can thrive in dynamic and unpredictable settings.
• Understanding and interpretation: AGI is equipped to comprehend natural language, abstract concepts and emotional nuance, allowing for sophisticated human-machine interactions.

Did you know? Blockchain timestamps could serve as a legal memory for AGI systems, allowing future audits to determine exactly what an AGI knew — and when.

The pursuit of AGI: Where does it stand as of April 2025?

AGI is currently the science-fiction version of AI. However, while still theoretical, the sheer potential of the concept makes AGI the science fiction equivalent of artificial intelligence. 

While existing models, such as ChatGPT, are constantly evolving and improving with each day, the journey to bringing AGI to life involves overcoming significant technical challenges, such as:

• Defining the tech stack: The purely hypothetical nature of AGI makes it exceedingly difficult, if not altogether impossible, to determine the precise nature of the technological stack required for practical implementation.
• Neural networks: Advances in deep learning have propelled this field forward, but AGI would also require specialist neural networks that mimic the human brain’s structure to process information and introduce a layer of emotion and nuance.
• Natural language processing (NLP): Significant advances are required in the field of NLP to enable machines to better understand and generate human language, incorporating nuance, emotion and complexities. This includes a more complex analysis of language syntax, semantics and context, which is still evolving in traditional machine learning models that leverage NLP. 
• Reinforcement learning: Using reward-based mechanisms to teach machines to make decisions would allow AGI to learn optimal behaviors through trial and error.

Despite advancements, creating AGI that can truly think like a human remains an elusive goal.

Did you know? DeepMind warns that not all AI risks come from the machines themselves — some start with humans misusing them. In its paper titled ‘An Approach to Technical AGI Safety and Security’, DeepMind identifies four key threats: misuse (bad actors using AI for harm), misalignment (AI knowingly going against its developer’s intent), mistakes (AI causes harm without realizing it), and structural risks (failures that emerge from complex interactions between people, organizations, or systems).

Can AGI think like a human?

The question of whether AGI can think like a human delves into the very core of human cognition. Human thinking is characterized by consciousness, emotional depth, creativity and subjectivity. While AGI can simulate certain aspects of human thought, replicating the full spectrum of human cognition is a formidable challenge.

Several dimensions of human cognition are particularly difficult to emulate:

• Consciousness and self-awareness: One of the defining traits of human thinking is consciousness, the awareness of oneself and one’s surroundings. AGI, as sophisticated as it may become, lacks the intrinsic human ability to introspect. AGI operates on an underlying set of algorithms and complex, learned patterns, without any subjectivity or genuine emotion.
• Emotional intelligence: Humans experience a wide range of emotions that influence their decisions, behaviors and interactions. While AGI can be trained to recognize and respond to such emotions, the lack of genuine emotional experience means that it cannot wholly replicate these emotions. Emotional intelligence in humans involves empathy, compassion and moral considerations, elements that are challenging to encode into machines.
• Creativity and innovation: Creativity involves generating novel ideas and solutions, often through intuitive leaps and imaginative thinking. AGI can mimic creativity by combining existing knowledge in new ways, but it lacks the intrinsic motivation and subjective insight that drive human innovation. True creativity stems from emotional experiences, personal reflections and cultural contexts, which AGI cannot authentically replicate.

Key benefits of AGI

The litmus test for AGI lies in its ability to holistically replicate a human experience. When realized, the potential benefits are enormous and stretch across various industries, spawning various aspects of daily life.

Despite its limitations, AGI is increasingly viewed as a force for good across a range of industries, including:

• Healthcare: AGI can assist in diagnosing diseases, developing personalized treatment plans and predicting customized health outcomes, leveraging a vast body of underlying training data.
• Education: It can provide customized learning experiences, tutoring and academic research support. AGI can adapt to individual learning styles and pace, enhancing educational outcomes.
• Economics: It can optimize financial models, predict market trends, and enhance productivity. It can analyze economic data to forecast market trends and guide investment decisions.
• Environmental Science: AGI can analyze climate data, model ecological impacts, and propose sustainable solutions.

Additionally, AGI’s potential extends to areas such as transportation, communication and entertainment, offering new frontiers for innovation.

Did you know? Some futurists believe AGI systems could eventually negotiate with each other autonomously using blockchain-based smart contracts — forming agreements, trading data or even co-developing solutions without human intervention.

Ethical and societal considerations

The rise of AGI raises significant ethical and societal questions. 

While powerful, AGI requires careful consideration for safe usage, which has prompted the creation of nonprofit societies, such as the AGI Society, as shown in the image below.

Fundamentally, it is crucial to address concerns such as:

• Safety: Ensuring AGI operates within safe and controlled parameters to prevent unintended consequences. This includes robust testing and the introduction of regulatory frameworks to oversee AGI deployment.
• Privacy: Protecting personal data from misuse by AGI systems. As AGI can process vast amounts of data, safeguarding privacy is paramount.
• Bias and fairness: Preventing discriminatory practices and ensuring equitable access to AGI benefits. Developers must ensure that AGI systems are free from biases that could lead to unfair treatment.
• Employment: Addressing the impact of AGI on job displacement and workforce dynamics. As AGI automates tasks, there is a need to consider its impact on employment and provide support for affected workers.

The integration of AGI into society requires a thoughtful approach to its governance, ensuring that it serves the common good and respects social values.

Can blockchain power AGI?

AGI could create computers as smart as humans, revolutionizing fields like cryptocurrency trading or market analysis. But AGI needs trust and fairness to work for everyone. Blockchain, the tech behind Bitcoin and Ethereum, offers a secure, transparent way to make this happen. 

Here’s how blockchain can supercharge AGI with crypto-inspired solutions:

• Clear training records: Blockchain works like Bitcoin’s open transaction log, recording every piece of data (e.g., crypto trading patterns) used to train AGI. This helps ensure the system is fair and free from hidden biases.
• Shared decision-making: Similar to Ethereum’s smart contracts, blockchain will allow developers, traders and users to vote on AGI’s rules, ensuring no single company controls it.
• Safe data sharing: Like crypto wallets safeguarding funds, blockchain could protect sensitive data from crypto exchanges, allowing secure sharing for AGI training without leaks.
• Rewards for fairness: Developers who build unbiased AGI, such as accurate trading predictors, could earn digital tokens, just like crypto mining rewards, encouraging ethical work.

However, ongoing challenges such as blockchain’s slow speed, delays in crypto transactions and limited storage capacity could make it hard for AGI to process data quickly or handle large datasets.

To make blockchain AGI-ready, researchers are already exploring:

• Offchain storage: Decentralized systems like InterPlanetary File System (IPFS) are used to store large files offchain, while the blockchain keeps only verifiable hashes, reducing congestion.
• Sharding and danksharding: Like Ethereum’s scalability upgrades, sharding splits data across multiple nodes, allowing AGI to process more information without slowing down the network. Also, danksharding, an advanced form of sharding being developed for Ethereum, combines rollups and data availability sampling to scale data access efficiently — ideal for real-time AGI applications.
• Data pruning: Advanced blockchain models like Decentralized Artificial Intelligent Blockchain-based Computing Network (DAIBCN) prune old or irrelevant data, keeping the system lean and optimized for high-demand tasks like AGI. DAIBCN also enables secure, distributed AI computing — blending blockchain trust with AI performance.

The future of AGI

Artificial general intelligence represents the pinnacle of AI development, promising capabilities that rival human intellect. 

While AGI can simulate aspects of human thinking, achieving true human-like cognition remains a distant goal. Consciousness, emotional depth and creativity are intrinsic to human experience and pose significant challenges for AGI. 

Nevertheless, the pursuit of AGI continues to drive innovation and reshape our understanding of intelligence. As we advance toward this frontier, it is imperative to navigate ethical considerations and societal impacts to responsibly harness AGI’s potential.

Ongoing research, identifying practical opportunities and technical requirements, and initiating dialogue across society are all essential steps to address the challenges and opportunities posed by AGI. 

The future of AGI holds promise, but it requires a balanced approach to ensure that its eventual integration into society enhances human well-being and respects ethical standards.