The AGI Imagination Gap: Societies That Predict Next Year vs. Societies That Design It

Many people consume the statement "AGI could arrive around next year" like a prophecy.

It's coming, it's not. It's 2027, it's not. Which company will make it first, or not.

But this perspective sees AGI's essence as far too small.

AGI around next year is not just a smarter chatbot. It's the possibility that part of the judgment, execution, coordination, research, and design that humans have handled can shift to machine infrastructure. So the imagination around AGI is not a question of "when does it arrive." It's a question of "assuming it arrives, what must society and organizations redesign?"

The difficulty in AGI discussion lies in the fact that AGI is not a technology that switches on all at once like a light. Google DeepMind's AGI leveling framework also explains that AGI should be viewed not as a single event but as a combination of performance, generality, and autonomy. AGI is not a monster that suddenly appears one day, but reveals itself along a continuum of systems that handle broader tasks for longer periods more autonomously.

So the phrase "next year AGI" is not a prediction but pressure.

This phrase asks us:

If in 2027 an AI system can autonomously handle a significant portion of the analysis, coding, research, planning, customer response, and experimental design that one person used to do, can today's companies still operate in the same way?

Looking at history, massive technological transitions always appeared as mere functions at first. Electricity too was initially just brighter lighting. But electricity's true significance lay in changing factory structures. Factories in the steam engine era had no choice but to arrange around a central power shaft. When electricity arrived, machines could be placed in different ways, and production lines were redesigned.

Railroads were no different. Railways were not faster carriages. They were infrastructure that bound cities, logistics, timetables, finance, and military strategy into a single national market. The internet also initially looked like homepages and email, but actually changed the entire structure of distribution, advertising, media, community, payment, and identity.

AGI should be viewed the same way.

AGI is not a smarter search box.

AGI is the possibility of becoming an operating system for judgment.

Here the important concept is the "imagination gap."

The imagination gap comes before the technology gap. Some organizations imagine AGI as merely a productivity tool that employees use. Some organizations imagine AGI as operational infrastructure that changes the design principle of the organization itself. The former adds AI on top of existing processes. The latter rewrites processes under the premise that AI exists.

This difference is by no means small.

When systems approaching AGI emerge, the core bottleneck in organizations is no longer "not enough people." The bottleneck shifts to goal-setting, authority delegation, verification, responsibility, and trust. If AI can conduct research, write code, design experiments, draft contracts, and analyze customer segments, human work does not disappear. It only shifts. Humans change from beings who do everything directly to beings who decide what to delegate, determine what to permit, and judge which results to trust.

The core of this change is not automation.

The core is that the cost of judgment drops.

Until now, judgment has been expensive in many organizations. To make good judgments required gathering materials, holding meetings, creating reports, and receiving inter-departmental approvals. So organizations economized on judgment. Important decisions went upward, small experiments were delayed, and the cost of failure grew large.

But if AGI-like systems make all preceding stages of judgment nearly free, organizational structure changes. Instead of people gathering to write reports, dozens of scenarios are automatically generated and humans choose which hypotheses to actually execute. Strategy becomes not a quarterly document but a living system updated daily. Companies become organizations competing not on headcount alone, but on how often, how accurately, and how safely they can repeat judgment.

The direction is already visible. METR introduced the "task-completion time horizon" metric that measures AI agent capabilities based on the length of tasks they can complete. In a 2025 study, they revealed that from 2019 to 2025, the task time horizon of frontier AI showed a trend of doubling approximately every seven months. Time Horizon 1.1, released in January 2026, expanded data and evaluation infrastructure while also presenting faster growth estimates in the post-2023 period. However, METR itself makes clear the limitation that results shake sensitively depending on baseline time estimation for long tasks and task composition.

This does not mean "AGI has already arrived." Rather, it reveals a more important fact. AI advancement is not measured only by rising accuracy rates. It is being measured in the direction of "how long a task can be delegated." It means AI is shifting from answer machine to task performer.

Agent infrastructure also points in the same direction. OpenAI's Agents SDK documentation describes agents as applications that make plans, call tools, collaborate with multiple specialized systems, and maintain state to complete multi-step tasks. The A2A protocol, which Google started and passed to the Linux Foundation, also aims for a common language for agents from different vendors and frameworks to communicate and collaborate safely.

This change is not product competition.

It is rail competition for new economic actors.

While AI was merely generating answers, model performance was central. But when AI starts performing tasks, negotiating with other AI, calling APIs, making payments, editing files, handling customers, and conducting research, the questions change. Who will grant identity to these agents. Who will manage authority. Who will audit action records. Who will take responsibility for accidents. Who will create standards for inter-agent transactions.

Core infrastructure in the AGI era is not only models.

On top of models must come identity, authority, payment, security, auditing, responsibility, data access rights, and organizational operating systems.

Global competition also unfolds at this layer. The United States attempts to dominate the upper layers of the AGI economy by bundling models, cloud, semiconductors, capital markets, and Big Tech distribution networks. Stanford HAI's 2026 AI Index tallied that private AI investment in the U.S. in 2025 reached $285.9 billion, far larger than China. The same report explains that generative AI reached a 53% global population adoption rate in just three years.

What these numbers indicate is not simple investment fervor. AI is already becoming not a technology in the laboratory, but general-purpose infrastructure simultaneously changing mass user experience, capital market expectations, and corporate work structures.

Korea's question must start here.

It is difficult for Korea to fight in AGI model competition the same way as U.S. Big Tech. The differences in capital scale, data centers, cloud distribution networks, and global developer ecosystems are large. But Korea's strategy should not stop at "let's import good models and use them well." That is voluntarily choosing the lowest position in the AGI economy.

The position Korea must seize is not "user nation" but "application infrastructure design nation."

Korea has semiconductors, manufacturing, financial infrastructure, telecommunications networks, gaming, content, fandom economy, public services, and fast consumer adoption culture. All are testbeds where AGI's actual operation within industries can be tested. What Korea must do does not stop at creating one massive general-purpose model. It is structuring how industry-specific agents can move safely with authority.

For example, AGI in manufacturing sites is not a chatbot that reads manuals. It is an operating system that understands process data, detects equipment anomaly signs, calculates parts procurement risk, and proposes action plans to field engineers. AGI in finance is not consultation automation. It is a trust system that handles customer context, regulation, risk, product structure, and post-incident responsibility together. AGI in content is not a tool that quickly generates images and videos. It is creative infrastructure that weaves IP planning, fandom response, global localization, and distribution strategy in real-time.

In all these areas, the core is trust over performance.

The smarter AGI becomes, what becomes more important is not "what can it do" but "what will we permit it to do."

Korea already has a national-level AI strategy coordination body. The National AI Strategy Committee presents a role of coordinating AI policy and trust-based ecosystem building. However, strategy in the AGI era is insufficient with declarations alone. Companies and government must together design agent identity, data access rights, task audit logs, responsibility allocation, industry-specific sandboxes, and public procurement standards.

If Korea views this trend only as a regulatory target, the risk is clear. Korean companies and individuals will use overseas models, run agents on overseas clouds, and process work on overseas protocols. Usage occurs domestically, but standards, data, and auditability remain on overseas infrastructure. At that moment, Korea may become a country that uses AI heavily, but will find it difficult to become a country that makes the rules of the AGI economy.

Conversely, if Korea views AGI not as a "threat that may arrive next year" but as "operational infrastructure to design starting now," there is opportunity. Korea can experiment with AGI-native workflows fastest in the world in specific industries. In hospitals, manufacturing plants, financial firms, game studios, entertainment companies, and public institutions, Korea can test within systems how humans and agents work together.

This is real AGI imagination.

Not imagining a scene where superintelligence drops from the sky.

Imagining how my company's approval lines will change, how my industry's cost structure will change, what my country's trust infrastructure must protect.

Whether AGI will really arrive around next year cannot be determined. Rather, the moment we make determinations, thinking becomes impoverished. What matters is not whether AGI is completed in 2027. When drawing the near future of 2027, it is whether we will still see AI only as a "tool people use," or as "new actors that operate the economy together with people."

Societies that wait for AGI try to guess the date.

Societies that design for AGI create roles, authority, responsibility, and trust first.

In the end, there is one question.

Will we wait for AGI, or will we design the society in which AGI will operate.