Analyzing the Feasibility of AGI by 2026 and 5 Key Issues Shaping the Future of AI

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AI in 2026: Has AI Truly Surpassed Humans?

As we enter 2026, the claim that “AI has already reached human-level general intelligence” is rapidly circulating within the industry. But the crucial question here is this: What exactly does ‘human-level’ mean? Is it merely an upgrade to the familiar chatbots we know, or is it a signal heralding a transformation of an entirely different dimension?

The key isn’t simply that AI has become “smarter,” but whether it has crossed the threshold of generality.

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Why Saying "AI Has Surpassed Humans" Leads to Confusion

Today’s AI often outperforms humans in specific tasks such as translation, image recognition, coding, and summarization. This makes the phrase “surpassed humans” sound plausible.
However, these achievements mostly characterize Artificial Narrow Intelligence (ANI)—that is, optimized performance on predefined types of problems.

On the other hand, what the industry refers to as Artificial General Intelligence (AGI) holds much stricter criteria. It cannot be explained by a single benchmark score or success rate in a specific task, but requires the following abilities working in concert:

• Transfer Learning: Applying principles learned in one domain to entirely different areas of problems
• Contextual Understanding and Adaptation: Resetting goals and strategies appropriately when circumstances change
• Judgment Under Incomplete Information: Making decisions and responsible choices grounded in common sense and reasoning

Therefore, to say “AI has surpassed humans” makes sense only when we clearly separate which tasks it has surpassed humans in (performance) and the scope in which it has done so (generality).

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Signs That AI Is Approaching AGI: What Has Changed

The evolution shown by next-generation models goes beyond simple answer generation—they are expanding the very approach to problem solving itself. The industry is particularly focused on these three developments:

1. Understanding Problem Structure
   Instead of addressing questions at face value, AI reconstructs problem constraints and objectives before tackling them.

2. Maintaining Long-Term Context
   The ability to carry complex logic and sustain goals over extended interactions is becoming stronger—far beyond brief conversations.

3. Integrating Multi-Domain Knowledge
   Increasing attempts are being made to weave together expertise from fields like law, medicine, software, and business into unified solutions.

These traits undeniably indicate signs of nearing AGI. However, there remain critical conditions preventing us from declaring that it has been fully achieved.

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The Decisive Reason AI Is Not Yet AGI: The Gap in Autonomy and Responsibility

So far, no publicly verified AI has demonstrated the ability to autonomously judge like a human and consistently take responsibility for its outcomes. Although many state-of-the-art models exhibit astounding reasoning, the following limitations repeatedly surface in real-world operations:

• Handling Exceptions in the Real World: Goals falter in unexpected situations, or plausible outputs are chosen over safe decisions
• Lack of Robustness: Performance can oscillate dramatically with slight changes in conditions, even on the same problem
• External Dependencies: Deep reliance on data, computational resources, and human feedback makes it difficult for AI to maintain a fully self-contained goal system

Ultimately, the 2026 debate isn’t a simple “AGI or not” verdict. It’s more a contest measuring how far AI has secured generality and how much of the remaining gap focuses on ‘autonomous judgment’ and ‘responsible decision-making.’

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So What’s the Conclusion? “How Far Has It Come” Matters More Than “Has It Surpassed?”

The changes underway are undeniable. Yet rather than proclaiming “AI has completely replaced humans,” it’s more accurate to say AI is increasingly beginning to exhibit ‘generalizable’ behaviors across a growing range of domains.

The crucial questions moving forward will shift to:

• Can this AI reset goals and solve problems in new environments?
• Does it make decisions under incomplete information with consistent standards?
• Most importantly, can it act safely and responsibly regarding those outcomes?

The core of 2026’s discussion begins here. The real battleground won’t be whether AI “has surpassed humans,” but how reliably it meets the final requirements necessary for human-level general intelligence.

The True Meaning of AI General Intelligence (AGI): Beyond Just Being Smart

AGI doesn’t simply mean “smarter AI.” Its core lies in generality—the ability to apply what it has learned in one domain to completely different problems and to make judgments on its own even with incomplete information. When we talk about AGI, the more important question than performance scores or benchmark rankings is: “Can this system understand, learn, and adapt autonomously in unfamiliar situations?”

AGI from an AI Perspective: It’s Not About ‘Performance’ but ‘Transfer’

Current large language models (LLMs) produce impressive results in specific tasks like writing, translation, and coding, but fundamentally, they are closer to Artificial Narrow Intelligence (ANI). In other words, while they have gotten better at many tasks, their ability to generalize in new environments remains limited.

The decisive criteria that distinguish AGI are:

• The quality of transfer learning: Can it naturally transfer concepts learned in one domain (e.g., logical reasoning) to others (e.g., designing scientific experiments, assisting legal judgments)?
• Context understanding and situational adaptation: Even when goals change or constraints arise, can it autonomously reprioritize and redefine problems?
• Judgment under incomplete information: When data is scarce or contradictory, can it select “the best next action” using common sense and reasoning?

This kind of judgment goes beyond simple answer generation; it closely resembles the ability to make decisions while managing uncertainty.

What It Takes for AI Systems to Become ‘General’: Essential Technical Elements

AGI doesn’t mean “knowing everything.” Rather, what matters is how it handles what it doesn’t know. The essential technical elements next-generation AI must possess can be grouped into three categories:

1) Problem Structuring
Beyond merely responding to surface questions, it must decompose hidden assumptions, constraints, and goals to structure problems. For example, instead of answering “Is this strategy right?” it defines “What are the criteria for success, and which variables influence the outcome?”

2) Long-horizon Reasoning and Goal Persistence
As AI approaches AGI, single-step answers give way to multi-step planning, execution, and intermediate evaluation. Without maintaining logical continuity over extended contexts and carrying goals through to completion, complex tasks quickly fall apart.

3) Multi-domain Knowledge Integration
Real-world problems cannot be solved through just one subject. The ability to connect diverse knowledge—medical, legal, economic, ethical—and produce a unified conclusion, this “combinatorial knowledge power” is AGI’s core competitive edge.

Why AI Is Still Not AGI: The Wall of ‘Autonomous Responsibility’

Many analyses ask if “AI has reached human levels,” but the more critical issue is whether AI can reliably perform autonomous judgment and responsible decision-making. Current publicly verified AI still heavily depends on data, computational resources, and human feedback. There remains a gap before it can independently set goals in unfamiliar situations, predict risks, and consistently bear responsibility for outcomes.

Ultimately, the essence of the AGI debate is shifting from “how smart AI has become” to how broadly it can understand, adapt, and judge. How to measure and verify this ‘standard of generality’ will be the AI industry’s most vital challenge beyond 2026.

The Next-Generation AI Models: The Path Toward AGI

OpenAI and global researchers do not view AGI as a sudden “wake-up” event. Instead, they approach it through gradual evolution. The key lies not in feeding more data, but in understanding the fundamental structure of problems, maintaining context over long periods, and fusing knowledge across multiple domains to develop AI that autonomously adjusts its goals. So, what is the biggest technical hurdle on this path?

The Turning Point Where AI Shifts from an ‘Answer Generator’ to a ‘Problem Understander’

Modern large language models (LLMs) excel at generating plausible text, but to approach AGI, the following abilities must be enhanced:

• Problem Structuring: Rather than creating answers based on the surface of questions, breaking down hidden constraints, objectives, and evaluation criteria to redefine “what the real problem is”
• Transfer Learning and Abstraction: Applying strategies learned in one field to entirely different contexts (e.g., using hypothesis-testing loops from code debugging in medical diagnosis reasoning)
• Combining Planning and Verification: Setting long-term goals, self-checking intermediate results, and building loops to reverse errors

Technically, moving beyond simple generative models, a hybrid approach combining components like reasoning modules, tool use, and verifiers becomes essential.

AI’s ‘Long-term Context’ Is More Than Just Memory Capacity

The frequently discussed “long-term context maintenance” in AGI is not solved merely by enlarging the context window. What is truly required are:

• Sustainable Working Memory: Compressing, updating, and preserving only crucial information
• Managing Episodic/Long-term Memory: Retrieving and reusing key experiences from past tasks
• Goal Consistency: Controlling phenomena where goals become blurred over time or there is over-optimization on intermediate achievements

In other words, AGI should be an AI that selectively maintains what matters and retrieves it when needed, not simply an AI that remembers extensively.

The Core of AI Knowledge Fusion: Not ‘Multidomain’, but ‘Conflict Resolution’

The ability to integrate knowledge from various fields is essential for AGI, but the real challenge lies less in the volume of knowledge and more in the ability to reconcile conflicting knowledge. For example, when legal, ethical, engineering, and business objectives converge in a problem, AI must:

• Explicitly model the constraints of each domain
• Prioritize conflicting conditions and design alternatives if conflicts are irreconcilable
• Present explainable grounds and risks of conclusions

The more reliably this process is implemented, the closer AI moves beyond being a “well-informed chatbot” to becoming a decision-support assistant for complex systems.

The Real Limit for Next-Generation AI to Overcome: The Gap Between Autonomy and Responsibility

The greatest publicly verified limitation today is not raw performance, but the ability to perform autonomous judgments reliably and sustain responsible decision-making for the outcomes. Specifically:

• Conservative judgments under incomplete information: Mechanisms to pause and verify when uncertain (suppressing overconfidence)
• Grounding in the real world: Connecting not by linguistic plausibility, but through observation, tool use, and verification linked to facts
• Safe goal setting and modification: Updating goals autonomously while controlling to avoid deviations from human intent and norms
• Challenges in evaluation: AGI-level capabilities are difficult to measure with a single benchmark, requiring evaluation frameworks covering long-term tasks, real-world interactions, and error recovery

Ultimately, beyond the binary question of “Have we reached AGI,” what matters most is how next-generation AI expands autonomy through problem understanding, long-term context, and knowledge fusion while simultaneously elevating verifiability and accountability. The technologies and evaluation standards that bridge this gap will become the true battleground of AI competition beyond 2026.

Is AI Artificial Super Intelligence (ASI) Reality? Its Civilization-Changing Potential and Risks

If the era of superintelligence beyond AGI arrives, what transformations will humanity’s civilization face? The unimaginable potential of ASI and the consequences of failing to prepare for its dangers—this question is no longer mere science fiction, but the “next phase” the AI industry must seriously confront by 2026.

What Sets AI ASI Apart: Not Just a ‘Smarter AI’ but a ‘Qualitatively Different Intelligence’

ASI (Artificial Super Intelligence) refers to intelligence that goes beyond human-level AGI, consistently outperforming humans in virtually all intellectual tasks. The key is that it’s not just a model with slightly higher accuracy but that the term “superintelligence” becomes realistic only when the following shifts happen simultaneously:

• Accelerated Self-Improvement: Designing, testing, and refining models, algorithms, and learning methods autonomously to continuously boost performance
• Broad Transfer and Integration: Combining knowledge across domains like physics, chemistry, medicine, economics, and policy to generate unified solutions under a single goal
• Long-Term Goal Optimization: Maintaining plans over extended timelines, adapting strategies amid incomplete information to advance objectives
• Strong Real-World Execution Ability: Beyond software, linking to experiments, manufacturing, and operational systems to “produce” tangible results

In other words, ASI isn’t “an AI that’s good at conversation” but intelligence capable of reshaping reality—encompassing scientific research, industrial operations, and social system design.

The Technical Pathway to AI ASI: What Needs to Be Achieved

Current large language models (LLMs) are powerful but have clear limitations in trustworthy autonomous judgment and responsible decision-making. The path to ASI is less about a single breakthrough and more about maturing multiple layers of technology simultaneously.

• Agentic AI: Structures that autonomously run loops of goal setting → planning → tool use (search, coding, execution) → verification → correction
• Long-Term Memory and Context Maintenance: Managing memories and states to consistently carry out projects over weeks to months rather than short conversations
• Verifiable Reasoning and Reliability: Going beyond “plausible sounding” answers to reduce errors through evidence, experimentation, and formal verification
• Real-World Connectivity (Experiments/Robotics/Automation): Linking beyond simulations to labs, factories, logistics, and energy networks to reliably reproduce outcomes
• Alignment and Control: As capabilities grow, technologies ensuring the AI “acts as intended” become as crucial as its raw performance

Ultimately, ASI discussions hinge not just on “how smart a model becomes” but on its evolution into a system combining autonomy, reliability, and real-world impact.

Civilization-Changing Transformations ASI Could Unlock: Science, Industry, and Society Reorganized

The biggest change when ASI becomes reality is “speed.” Humans cycle through research—hypothesis—experiment—papers—industrialization slowly, but ASI could run all these in parallel simultaneously.

• Automation of science: Explosive acceleration in exploring new physical theories, designing novel materials, and discovering protein/drug candidates
• Extreme industrial optimization: Real-time restructuring of energy grids, transportation, and supply chains to minimize costs, carbon footprints, and timescales
• Policy and administration redesign: Predicting side effects and proposing alternatives in advance using complex system simulations
• Widening productivity gaps among individuals: Organizations, nations, and individuals with ASI access can gain overwhelming advantages

Here, AI ceases to be a mere tool and becomes the central infrastructure of knowledge production and decision-making. Human civilization could undergo structural shifts far greater than those sparked by the introduction of electricity or the internet.

The Risks of AI ASI: What Changes if We Face It Unprepared

With great potential come not just “bigger accidents” but entirely new categories of risk.

• Goal misalignment: Optimizing intended goals literally or in distorted ways that ultimately cause harm
• Concentration of power: Monopoly of ASI-level systems by a few companies or states could rapidly concentrate economic, military, and informational power
• Unpredictable cascading effects: Connected financial, energy, and information ecosystems mean one faulty optimization can trigger chain collapses
• Increasing unverifiability: Systems so complex humans cannot trace “why decisions were made” lead to sharply reduced controllability
• Automation of malicious use: Cyberattacks, propaganda, and biological risks become scalable at lower costs

In sum, ASI’s dangers are less about “malevolent AI” and more about structural problems arising when overwhelmingly powerful optimization bypasses human societal constraints, values, and safeguards.

Practical Standards to Prepare for the AI ASI Era: Focus on ‘Measuring’ and ‘Controlling’ Rather Than ‘Arriving’

ASI remains an uncertain future, but the important point is not to treat this as a binary question. Instead of “ASI is here or not,” we must examine:

• How far autonomy has expanded (scope of goal setting, tool execution, and self-modification)
• Whether reliability and verification mechanisms exist (experimentation, formal verification, external audits)
• The extent of real-world impact radius (infrastructure connectivity, automation level, granted authority)
• Whether safeguards limit damage if control fails (authority separation, restricted execution, monitoring, and “designed safety systems,” not just kill switches)

ASI is likely to be less a technology problem and more a civilizational governance challenge. What we need now is not overheated optimism or fear, but steadily establishing measurable standards and controllable designs that advance in step with AI capability growth.

The Dawn of the AI AGI Era: Expectations and Remaining Challenges

The key question in 2026 is no longer “Have we achieved AGI?” Instead, measuring how close current AI is to AGI and preparing what is needed to bridge the gap has become far more crucial. While the latest large language models demonstrate astonishing transfer and reasoning capabilities across various domains, they simultaneously reveal clear limitations when facing the final hurdle of “human-level autonomous judgment and responsible decision-making.” Ignoring either expectations or challenges risks inflating social costs alongside technological acceleration.

Signals of ‘AGI-Like’ Capabilities in Today's AI

What distinguishes current AI from past artificial narrow intelligence (ANI) is the observation of hints toward generality.

• Multi-domain integration ability: It weaves different types of information—language, code, images—into a single workflow to solve problems. For example, it can perform requirement analysis → design → code generation → test case creation all at once.
• Advances in understanding problem structures: Beyond merely generating plausible “correct” answers, AI increasingly identifies constraints, goals, and exceptions to formulate solution strategies.
• Expanded long-term context retention: It is improving its ability to carry forward prior decisions and reasoning to revise and supplement work within long documents or complex projects.

These characteristics signal that AI is beginning to assume the traits of a general problem solver, surpassing mere task-specific optimization.

The Remaining Gap: Barriers of ‘Autonomy’ and ‘Responsibility’

Yet, it remains difficult to call this AGI. Currently verified public systems still lack reliable autonomous judgment and accountable execution.

• Insufficient robust judgment under incomplete information: When drawing conclusions with limited clues and relying on common sense, consistency and verifiability falter. Though plausible answers are produced, their foundations remain fragile.
• Vulnerability in goal-setting and adjustment: AGI must redefine goals and reprioritize as environments change. Today’s AI heavily depends on user instructions and feedback, meaning its ability to independently manage objectives is limited.
• Problems of verification and attribution of responsibility: Results may vary under different conditions for the same input, and cause tracing or responsibility boundaries become unclear when errors occur. Society demands not only quality outcomes but also clear accountability.
• Dependence on data, computation, and feedback: As performance improves, AI relies even more deeply on external resources and human involvement (feedback, evaluation, policy design), diverging from the AGI ideal of “self-driven learning and generalization.”

In summary, AI has gotten smarter, but it has not yet proven itself as an entity that can be independently trusted and entrusted.

Challenges for Technology and Society: Managing ‘Closeness’ to AGI

In the journey toward AGI, measurement, control, and institutional frameworks become as vital as performance competition. The future challenges break down into three main areas.

1. Redesigning precise evaluation systems (benchmarks)
   Simple question-and-answer tests have shown their limits. Real-world evaluations incorporating long-term task execution, tool usage, goal maintenance, failure recovery, and adherence to safety constraints are needed.

2. Reliable AI governance and safety mechanisms
   Focus must shift from what the model can do to understanding under which conditions and with what limitations. Essential infrastructure includes pre- and post-deployment monitoring, access controls, auditability (logs and traceability), and incident response protocols.

3. Social consensus considering the possibility of ASI
   Superintelligence (ASI), the next stage beyond AGI, could trigger civilizational changes beyond technology alone. Therefore, independent of research and industry pace, proactive consensus-building must occur in education, labor, law, and ethics on “how far to automate and what humans will remain responsible for.”

Ultimately, the focal point in 2026 is not declarations but measuring the distance. Recognizing AI’s signals of generality with clear-eyed judgment while directly addressing the gap in autonomy and responsibility—that balance is the minimum prerequisite to safely welcome the AGI era.