Thinknote

jhshin@thinknote.co.kr

[태그:] cybersecurity

Anthropic Mythos Shock: As AI Becomes a Strategic Asset, What Should Korea Prepare?

Anthropic’s “Mythos” issue is hard to treat as simple news about a new AI model. The colder point is that frontier AI models are now both cloud services and strategic assets.

Like semiconductor equipment or advanced GPUs, access to models itself is becoming a subject of diplomacy and security. Korea cannot dismiss this as another country’s regulatory news.

## What is the core of the Mythos issue?

The Mythos issue signals that access and control, beyond AI performance competition, have become matters of national strategy.

Anthropic introduced Claude Mythos 5 as a model strong in cybersecurity and biological research. Through Project Glasswing, it planned to use the model to find and defend against vulnerabilities in critical software.

According to the official explanation, early partners used Mythos Preview to find more than 10,000 high-risk or critical vulnerabilities in important software. For defensive purposes, that is a highly attractive result.

The problem is that the same capability can be used offensively. A model that quickly finds vulnerabilities is a weapon for defenders, but if control collapses, it can also become a weapon for attackers.

That is why Mythos was provided only to limited partners from the beginning. When the U.S. government then directed a suspension of foreign access to Fable 5 and Mythos 5, the issue moved from technology news to national-strategy news.

## Why did people say “AI is also a strategic asset”?

The U.S. directive showed that access to cutting-edge AI models can become a national-security judgment. The logic of semiconductor export controls has moved toward the model itself.

A key shift is underway. In the past, computing resources, chips, and equipment were the bottlenecks. Going forward, model weights, API access, the ability to remove safety measures, and data-retention conditions may also become targets of control.

For companies, this is more complicated. A model available yesterday may suddenly be blocked today. In high-risk sectors such as public administration, finance, healthcare, defense, and R&D, this becomes an operational risk, not just inconvenience.

## Three risks Korea must watch

Korea’s AI strategy must examine foreign model dependence, the dual-use nature of security AI, and the realism of sovereign AI together.

### 1. Dependence on foreign models

Korean companies and public institutions have quickly adopted global AI models. From a productivity perspective, that is natural. But if core work becomes deeply tied to a specific foreign model, supply interruption or access restriction can become work stoppage.

Areas connected to national functions—administration, defense, security, healthcare, energy, and finance—need separate standards. This does not mean using only domestic AI. It means areas that cannot be interrupted need fallback routes.

### 2. The dual-use nature of security models

Powerful security AI improves defensive capability, but without control it can also be converted into offensive capability.

The hardest question Mythos raises is: “If powerful security AI is widely released, does the world become safer or more dangerous?”

Vulnerability-detection AI is a major advantage for defense teams. But if verification, disclosure, and patching cannot keep up, vulnerability lists may simply pile up faster. Anthropic also explained that after vulnerabilities are found, verification, disclosure, and patching become bottlenecks.

Korea should not focus only on detection models when building AI security capability. Coordinated vulnerability disclosure, patch responsibility, supply-chain response, and incident-response training must be designed together.

### 3. The practicality of sovereign AI

Sovereign AI must not end as a slogan. It is not only about making one Korean-language model. Public data governance, domestic computing infrastructure, high-risk AI evaluation, industry standards, and procurement systems must be connected.

Korea is preparing systems and infrastructure such as the AI Basic Act, the National AI Committee, the AI Safety Institute, and the National AI Computing Center. The direction is right, but the Mythos issue demands more speed.

## Korea’s future strategy: before “securing a model,” build a controllable AI system

The core is not owning a specific model but having an AI operating system that can be stopped, changed, and verified when necessary.

Korea’s response should not end with “let’s build our own frontier model.” The more important question is: in which areas, at what level of control, and at what cost should control rights be secured?

### First, classify AI dependence in national core areas

Public institutions and critical industries should classify the AI services they use by work importance. A simple document-writing tool and a cyber, medical, or administrative decision-support tool cannot be judged by the same standard.

Core areas need at least three conditions: an alternative model, inference paths inside Korea or a trusted region, and manual operating procedures for failure.

### Second, make Korea’s AI safety evaluation system practical

AI safety evaluation must not end with document review. In areas with real potential harm—cybersecurity, biology, financial fraud, disinformation, and privacy leakage—red-team evaluation and repeated testing are needed.

For high-performance models, there must be steps between “ban use” and “open without limits.” Limited partner access, log retention, high-risk query routing, independent evaluation, and incident reporting must move together.

### Third, the National AI Computing Center must become strategic infrastructure

The government is pursuing a National AI Computing Center worth up to two trillion won. This infrastructure should not simply rent GPUs; it should connect Korean models, safety evaluation, and public-sector AI demonstrations.

Accessibility is crucial. If only large companies can use the infrastructure, resilience across the whole industry will not increase. Universities, startups, security research organizations, and public agencies must be able to use it in practice.

### Fourth, cooperate internationally but assume a blocking scenario

Korea cannot build every AI technology alone. Cooperation with the United States, Europe, Japan, Singapore, and others remains necessary. But cooperation is not the same as dependence.

Contracts should include clauses on data location, model-access suspension, emergency patching, switching to alternative models, and audit rights. Public procurement should evaluate not only “the best-performing model” but also “the model that can operate during a crisis.”

## What should companies and individuals check?

Companies should inventory the AI tools they currently use: which work is connected to which model, where data is stored, and whether replacement is possible within days if the service stops.

Individuals can look at it more simply. The ability to use AI well matters. But trusting the answer of one specific model as-is is risky. In the AI era, having one’s own language and judgment criteria comes before prompts.

Related Thinknote articles worth reading include [In the AI Era, What You Need to Learn Before Prompts Is Your Own Language](https://www.thinknote.co.kr/ai-korean-prompt-literacy/) and [Metacognition in the AI Era: How to Check Your Thinking](https://www.thinknote.co.kr/metacognition-ai-thinking-checklist/). For AI-agent trends, also see [AI Agent Evolution](https://www.thinknote.co.kr/ai-agent-evolution-openclaw-action-oriented-ai/) and [AI-Native Workflows](https://www.thinknote.co.kr/ai-native-workflows-digital-brain-ai-agents/).

## Conclusion: Korea’s AI strategy must prepare for the politics of access

The message of the Mythos issue is clear. AI competition will not be only performance competition. It will also be competition over who can access models, who can adjust safety measures, and who can maintain service during failure.

Korea should use global models, but in core areas it needs controllable alternatives. Sovereign AI is not isolation; it is insurance. That insurance works only when models, data, compute, safety evaluation, and procurement move together.

## FAQ

### Can ordinary users use Anthropic Mythos?

No. Anthropic described Mythos 5 as a restricted-access model strong in cybersecurity and biological research. Fable 5 was intended for more general knowledge work with safety measures, but access was also suspended after the U.S. government directive.

### Does the Mythos issue immediately affect Korean companies?

Not all companies are affected immediately. But it is a warning signal for companies that rely on overseas frontier AI models for core work. They should check access rights, data location, alternative models, and failure-response plans.

### Does sovereign AI mean not using foreign AI?

No. Sovereign AI means securing control and choice in necessary areas. Global AI can be used, but public, security, and industrial core areas need replaceability and domestic operating capability.

### What is the Korean government already preparing?

The AI Basic Act, the National AI Committee, the AI Safety Institute, and the National AI Computing Center are being prepared. The computing center is especially important infrastructure for domestic AI research and industrial use.

### What should individuals prepare?

Do not depend on only one model. Verify important judgments through multiple sources and practice explaining AI answers again in your own words.

## References

– [Anthropic, Claude Mythos](https://www.anthropic.com/claude/mythos)
– [Anthropic, Statement on the U.S. government directive to suspend access to Fable 5 and Mythos 5](https://www.anthropic.com/news/fable-mythos-access)
– [Anthropic, Claude Fable 5 and Claude Mythos 5](https://www.anthropic.com/news/claude-fable-5-mythos-5)
– [Anthropic, Project Glasswing](https://www.anthropic.com/glasswing)
– [Anthropic, Project Glasswing: An initial update](https://www.anthropic.com/research/glasswing-initial-update)
– [Korea Policy Briefing, National AI Computing Center](https://www.korea.kr/news/policyNewsView.do?newsId=148938942)
– [MSIT, AI Basic Act passed at the National Assembly](https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&bbsSeqNo=42&nttSeqNo=1071)
– [NoCutNews, the warning from the U.S. Anthropic block](https://www.nocutnews.co.kr/news/6533333)

[Original Korean article](https://www.thinknote.co.kr/anthropic-mythos-ai-strategic-asset-korea/)

2026년 07월 10일
How Quantum Computers May Change the Next 10 Years: Reading the Next Technology Race After AI
# How Quantum Computers May Change the Next 10 Years: Reading the Next Technology Race After AI

After AI became an everyday tool, quantum computing is often named as the next candidate for technological power. The name is familiar, but the question “what changes in my work or industry?” remains vague.

The video from “This Science, That Science” addresses that point well. A quantum computer is not a faster laptop. It is a technology that handles certain calculation problems in a fundamentally different way.

The key is balance between hype and indifference. Not every encryption system collapses tomorrow, but quantum computing is not pure science fiction either.

## Why look at quantum computing again now?

Scene explaining quantum computing research and experimental environments

Quantum computing is drawing attention for the same broad reason AI did: infrastructure, investment, talent, and national strategy move together around the technology.

Professor Kim Beom-jun describes it as a computer based on quantum mechanics. Ordinary computers calculate with bits, 0 and 1; quantum computers handle qubits. But this does not mean they are always faster. They may open new paths for certain problems, not replace everyday document work or web browsing.

## What do qubits change?

Scene showing quantum chip and circuit implementation

Qubits are the starting point. The video explains superposition and interference in accessible language: instead of following only one path, quantum computation handles many possibilities and draws out meaningful results at the end.

But a mysterious process does not guarantee perfect output. Quantum states are fragile and sensitive to error. Qubit count, error correction, and control technology all matter. The competition is not only “how many qubits,” but who can control them stably and connect them to useful algorithms and software.

## The first area to shake is cryptography and security

Scene discussing quantum computers and encryption/security risk

Security may be the first area the public feels. The video raises questions about Bitcoin, encryption, and certificate systems.

The issue is preparation, not panic. If sufficiently powerful quantum computers appear, some existing public-key cryptography could become vulnerable. NIST has already released post-quantum cryptography standards to prepare for that transition.

For companies, the realistic question is not “Will a quantum computer break my system today?” but “When should we change long-term data protection and authentication systems?”

## Commercialization bottlenecks: equipment, cost, and ecosystem

Cryogenic quantum computer equipment that looks like a chandelier

Quantum computers look like chandeliers because of physical requirements: cryogenic environments, control lines, and noise suppression.

For some time, quantum computing will likely remain cloud-based research and industrial infrastructure rather than a personal device. Like high-end GPUs, it may spread through access rights and usage capability rather than direct ownership.

Korea’s preparation should be judged the same way: not by whether it owns one machine, but by whether researchers, software, industrial problems, security transition, and education move together.

## The next technology after AI, or a technology that goes with AI?

Scene discussing Quantum 2.0 and future technology competition

The video title asks whether quantum is “after AI.” More precisely, AI and quantum computing meet at different layers. AI changes judgment and generation through data and models. Quantum computing tries to handle difficult calculations in drug discovery, materials, optimization, cryptography, and simulation.

The key question for the next decade is not who first makes a consumer product. It is who first connects quantum computing to real industrial usefulness.

## What individuals and organizations should do now

Most people do not need to learn quantum computing immediately. But they should understand the questions it will change. Security teams should review post-quantum roadmaps. Strategy teams should identify calculation-heavy areas such as drug discovery, materials, logistics, and financial optimization. Educators should prepare simple language for bits versus qubits, probabilistic computation, error correction, and limits of application.

## Related reading
## FAQ
### Are quantum computers always faster than ordinary computers?
No. They are expected to have advantages for specific calculation problems, not ordinary office or web use.
### Will encryption collapse immediately when quantum computers arrive?
No. But data and authentication that require long-term security should prepare for post-quantum transition.
### Is quantum computing really the next technology after AI?
It is better seen as strategic infrastructure after AI, not just the next trend. It addresses different problems and may connect with AI in industry.
### What should Korean companies prepare first?
Security transition, industrial problem discovery, talent and partnerships, and cloud-based experimental access before buying hardware.
## References
Original Korean article
2026년 07월 10일

[태그:] cybersecurity

Anthropic Mythos Shock: As AI Becomes a Strategic Asset, What Should Korea Prepare?

How Quantum Computers May Change the Next 10 Years: Reading the Next Technology Race After AI

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