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The Mythos Milestone: When Artificial Intelligence Outpaces Human Engineering

The history of technology is often measured in incremental steps, but every few decades, a leap occurs that fundamentally reorders the landscape of human capability. Anthropic’s recent internal unveiling of its latest model, codenamed Mythos, represents such a leap. While the public has grown accustomed to the impressive generative capabilities of Large Language Models (LLMs), Mythos has demonstrated a level of analytical sophistication that transcends mere believable chatting, shifting the AI paradigm from digital assistant to a high-stakes engine of global security.

The evidence of this shift is unsettling: Mythos recently identified a critical vulnerability in OpenBSD, a security flaw that had remained hidden for twenty-seven years.

Twenty… Seven… Years…

When an artificial intelligence identifies a weakness that has eluded the world’s most elite engineers for nearly three decades, we are talking about a brand new world. Anthropic’s decision not to release Mythos proves we’ve hit a milestone where AI’s potential is balanced by its danger.

The Architecture of AI Super-Models

Mythos is being heralded as the most powerful AI model on the planet, and for good reason. Its ability to solve the OpenBSD vulnerability suggests that it possesses what we might call “AI Super-Models.” In the field of cybersecurity, a discipline that represents one of the beacons of human logic and engineering, Mythos has demonstrated an ability to outpace the collective intelligence of the human engineering community.

Anthropic’s announcement clarifies that while the model exists, it will not be released to the general public for the foreseeable future. Instead, the company is choosing a path of strategic partnership, working internally and with select giants like Google, The Linux Foundation, and JPMorgan Chase.

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The objective is clear: use Mythos to scan and secure the operating systems and critical infrastructures that underpin modern civilization. By finding the “holes” in the code that runs our hospitals, power plants, and financial systems, Mythos could help create a safer world. However, this decision also places an immense amount of power in the hands of a few select entities.

The Geopolitical Sword and Shield

The emergence of Mythos transforms the nature of international relations and national security. Because Anthropic is an American company, the initial benefits of Mythos, which are securing critical infrastructure and finding deep-seated vulnerabilities, will primarily consolidate the security of Western systems. In this context, Mythos is not just a piece of software; it is a constituent of a “Cyber Great Wall.”

Much like an iron dome protects a city from physical missiles, Mythos offers the potential to protect an entire national economy and its digital infrastructure from external threats. This technology could be categorized under the AI Act as a “forbidden technology” or a restricted weapon class. The United States government could presumably view Mythos as a matter of national strategy, potentially restricting its sale or use by foreign governments.

The strategic implications are profound. Mythos represents both the sword and the shield. It can find vulnerabilities to fix them (the shield), but the same capability can be used to exploit them (the sword). In a world where Mythos-level intelligence is available to one side of a conflict, the traditional rules of cyber warfare are rendered obsolete. We are observing the birth of a new asset class, one defined by the ability to outthink, outpace, and outmaneuver any human collective in a specific area.

Economic Abundance vs. Structural Collapse

Beyond the theatre of war and security, Mythos carries the potential to “make or break” global economies. We have seen glimpses of this with AlphaFold’s success in protein folding, which created conditions for scientific abundance in the biotech sector. Mythos could do the same for software, infrastructure, and any industry reliant on complex logic.

However, the flip side of this abundance is a potential sudden crash. AI is now powerful enough to cause a systemic economic disruption.

Both the CEOs of Anthropic, Dario Amodei, and, a couple of years ago, Sam Altman from OpenAI, have warned of the impending impact on white-collar employment. If a model can find a 16-year-old FFMPEG vulnerability in a weekend, what does that mean for the millions of people whose jobs involve coding, data handling, and middle management? By the way, FFMPEG is the industry-standard backend engine for services like OBS, YouTube, and Netflix.

This brings us to a critical crossroad in management and investment philosophy, decomposed in the article “The Human Moat: Riding the Delta (Δ) in the Great AI Rearchitecture“. There are two primary paths:

  1. The Productivity Multiplier: Management and investors can view Mythos as a tool to empower the existing workforce. In this scenario, the number of workers remains stable, but their output is multiplied, leading to unprecedented economic growth and the “rewiring” of companies to handle higher volumes of innovation.
  2. The Replacement Model: Alternatively, management may decide that for the same volume of work, they simply need fewer people. This “optimization” of human hours could lead to a major blow for individuals, families, and the social fabric of the working class.

This is where the free market’s rules must be tempered by social stability. The pace of replacement is something we still have control over, and it is a choice that will define the next decade of social harmony.

The Ethics of the “Braking” Strategy

Anthropic’s current stance, i.e., pulling the brakes on a public release, is a disciplined attempt to give society time to react. The history of technology shows that major disruptions to critical infrastructure (transportation, satellite communication, nuclear power) often happen because the “patch” arrives too late. By prioritizing a slow, partner-based rollout, Anthropic is attempting to ensure that the “shield” is firmly in place before the “sword” becomes widely available.

However, Anthropic does not exist in a vacuum. The competitive landscape is fierce. OpenAI, Google, and foreign competitors like Tencent, Alibaba, and Mistral are all racing toward their own “Mythos moment.” While Anthropic may be disciplined, its competitors may not be. If even one company, like Alibaba or Mistral, decides to open-source a model of this caliber or release it without safeguards to gain market share, the system as we know it could break.

We are seeing companies like OpenAI redirecting their energy toward the enterprise space to fill the gaps created by aging legacy systems. The race is no longer just about who has the best chatbot or LLM; it is about who controls the underlying logic of the digital world.

Conclusion: A New Milestone for Humankind

The discovery of the OpenBSD vulnerability by Mythos is more than a technical achievement: like it or not, it is a signal that we have entered the era of artificial super-intelligence in specialized fields.

We must remember that these AI models are not independent entities; they are built by humans with specific motives: leaving a mark on history, revenue, patriotism, or national interest.

While we should remain optimistic about the path forward, we must also be vigilant. The stability of our individual futures and the security of our global infrastructure depend on how we manage this transition. We need government and regulatory intervention to ensure that the “free market” does not outpace the stability of the economy.

Mythos has shown us that AI can find the hidden truths in our code and our systems. Now, it is up to us to ensure those truths are used to build a more secure world, rather than to shatter the one we have.

We are at a new “all-time high” of human ingenuity, but for the first time, we are sharing that peak with a creation of our own making. The coming years will determine if this is the beginning of an era of unprecedented abundance or a displacement we are not yet prepared to handle. Regardless of the outcome, the age of Mythos has arrived, and the world will never be the same.

Yannick HUCHARD


Sources

  1. Assessing Claude Mythos Preview’s cybersecurity capabilities: https://red.anthropic.com/2026/mythos-preview/
  2. Project Glasswing – Securing critical software for the AI era: https://www.anthropic.com/glasswing
  3. System Card – Claude Mythos Preview: https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdf
  4. Alignment Risk Update – Claude Mythos Preview: https://www-cdn.anthropic.com/79c2d46d997783b9d2fb3241de43218158e5f25c.pdf

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Agents Artificial Intelligence Claude Sonnet 3.7 Computer Science Copilot Cursor AI Data Science Education Engineering Gemeni 2.5 PRO IDE Information Technology IT Engineering OpenAI o4 Mini

Cursor AI Pro Goes Free for Students—A Game-Changer for Coding Education

Parents, Teachers: Students can now harness the power of AI-driven engineering!

Why? Because Cursor AI, with the Cursor PRO plan, is now completely free for students.

Cursor offers a transformative coding experience in two ways:

  • Accelerated Development: Type just a few letters, and watch Cursor complete entire algorithms, functions, and boilerplate code seamlessly.
  • Agent-Driven Development: Simply prompt Cursor in plain English (or any natural language), and it instantly translates your instructions into code—you command, Cursor codes.

This isn’t about skipping learning to code because AI can do it for you.

Quite the opposite.

The real message here is clear: Get your hands on this future-proof coding tool now AND learn to code. Mastering coding skills enhanced by AI is the only viable path to excel in both corporate and research environments.

Pro Tip: Cursor automatically selects the best AI model for the given task. However, FYI, the current top AI models for coding are GEMINI 2.5 by Google, Claude Sonnet 3.7 by Anthropic, and o4-mini by OpenAI.

Yannick HUCHARD

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Artificial Intelligence Business Businesses ChatGPT Engineering EU AI Act GPT4 GPT4o Information Technology Innovation Llama Meta OpenAI Regulation Technology

✨ Llama 3.1, Meta and the EU AI Act – Where are the areas of synergy between innovation and regulation?

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Llama 3.1 AI model

Llama 3.1, a 405 Billion parameters model, has just been released by Meta.

It comes with increased performances. Some early tests make it comparable to “GPT4o“.

A few perks:

  • Still #opensource
  • 128K token context window
  • Improved Multilingual Support. Meta is a leader in multilanguage models.
  • Comes with a new security and safety tool for advanced moderation and control mechanisms to ensure safe interactions.
  • Improved capabilities for creating synthetic data.

I find the partner ecosystem, including NVIDIA, Google Cloud, Microsoft, Groq supporting Llama already quite impressive (see picture).

But also…

While the EU AI Act has been officially published on July 12, 2024, in the EU official journal, to come into force on August 2, 2024, Meta made worrisome news for the #artificialintelligence open source community.

In a nutshell, Meta will withhold the rollout of multimodal AI models in the EU region until the regulatory rules are clarified.

The EU AI Act contains explicit rules for foundation models, also known as “general-purpose AI models”, amongst the following:

  • Article 51: Classification of general-purpose AI models as general-purpose #AI models with systemic risk
  • Article 53: Obligations for providers of general-purpose AI models
  • Article 55: Obligations for providers of general-purpose AI models with systemic risk
  • Article 56: Codes of practice

Let’s hope we will find a way to balance #innovation and #regulation.

🫡

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Artificial Intelligence Automation Business Business Strategy Engineering Innovation Robots Strategy Technology Technology Strategy

Update on Tesla’s Optimus #Robot – it is progressing fast

Tesla’s Optimus Robot learning from humans

The most impressive part is the technique employed by the Tesla team for accelerating the robot’s dexterity: the robot physically learns from human actions. 

Now, let’s step back and analyse Tesla’s master plan here:

(Putting on my business tech strategy goggles) 

1. Tesla builds electric cars augmented with software programmability.

2. Tesla provides an electric grid as a service.

3. Tesla builds gigafactories that maximize the automation of car manufacturing. Almost every single part of the pipeline is robotized and optimized for speed of production.

4. Tesla builds Powerwalls (by providing energy storage, it also creates a decentralized power station network).

5. Tesla brings autonomous driving (FSD) to Tesla cars. Essentially, cars are now transportation robots governed by the most advanced AI fleet management system.

6. Tesla builds its own chips (FSD Chip and Dojo Chip)

7. Tesla builds its own supercomputers.

8. Tesla launches Optimus, which aims to replace the human workforce in factories and warehouses.

9. X.ai, which has recently raised $6 billion, X’s supposedly “child” AI company, brings the Grok AI model trained on X/Twitter data. While you may say X data is not the best, X has a algorithm balanced with human judgment (community notes), AND the company regroups the largest set of news publishing companies. Basically, it automates curation and accuracy.

10. A version of the Grok AI model will likely power Optimus’s human-to-robot conversational interface.

11. Tesla cars will be turned into robotaxis, disrupting not only taxi companies but also Uber (the Uber/Tesla partnership may not be a coincidence), and eating into the shares of Lyft and BlaBlaCar.

12. Tesla will enter the general services business, and retail industries to offer multi-purpose usage robots – cleaning services for business offices, grocery stores, filling the workforce shortage in the catering (hotel-restaurant-bar…) industry, etc.

Tesla is not the only one moving in the “Robot Fleet Management” business. Chinese companies like BYD (EV) offer strong competition, and there are several robot startups (like Boston Dynamics and Agility Robotics) racing for the pole position.

#AI #artificialintelligence #Robotics #Optimus #EV #software #EnergyStorage #Automation #powerwall #AutonomousVehicles #FSD #chips #HighPerformanceComputing #Robots #GrokAI #NLP #robotaxis #innovation #WorkforceAutomation

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Artificial Intelligence Education Engineering Society Technology Wisdom

A.I. – What do we want and what we do not want

What do we want and do not want from A.I. V001

The Direction of Civilizations Geared with A.I.: A Comprehensive Exploration

(updated: 12/09/2025)

Artificial Intelligence (AI) is not just another technological advancement—it is a generational disruption, a force that is reshaping industries, economies, and societies at an unprecedented pace. As I’ve often said, AI is your new UI and your new colleague. But with this transformation comes a fundamental question: What kind of civilization do we want to build with AI?

The mind map I’ve created, “The Direction of Civilizations Geared with A.I.,” explores this question by dissecting both the aspirations and apprehensions surrounding AI. It’s a visual representation of the duality of AI’s impact—its potential to elevate humanity and its risks if left unchecked.

However, my perspective is not about rejecting automation or end-to-end systems like Gigafactories. I am not against automated systems or super-systems that operate seamlessly, as long as humanity retains the knowledge to sustainably modify, upgrade, or halt these supply chains. What I oppose is the loss of foundational knowledge—the blueprints, the ability to relearn, and the erosion of stable resilience in our societal and industrial systems.

What We Want from A.I.: The Green Path

1. AI as a Catalyst for Human Potential

  • AI as a Co-Pilot for Humanity: AI should augment human capabilities, not replace them. It should act as a proactive advisor, a digital colleague that enhances productivity and decision-making. AI should handle repetitive tasks—only if there is no gain in repeating them (for example, this is out of question if the gain is learning, fun, or therapeutic). Either way, the choice must remain ours.
  • Human-AI Collaboration: The future lies in symbiotic relationships between humans and AI. AI should free us to focus on what truly matters—connecting with others, growing individually, and thriving as a civilization. This technology saves us time, allowing us to focus on what brings us closer to our true selves (know thyself better) and our life purpose.

2. Ethical and Transparent AI

  • Ethical AI: AI systems must be designed with ethical frameworks that prioritize fairness, accountability, and transparency. This is not just a technical challenge but a societal imperative.
  • Transparency and Explainability: AI decisions should be interpretable. Black-box models erode trust; explainable AI fosters accountability and user confidence.

3. AI for Societal Good

  • AI for the Common Good: AI should address global challenges—climate change, healthcare, education, and poverty. It should be a tool for equity, not exclusion.
  • Democratized AI: Access to AI should not be limited to a privileged few. Open-source models, affordable tools, and educational initiatives (like Cursor AI Pro for students) are steps toward democratization.

4. AI Aligned with Human Values

  • Human-Centric AI: AI should reflect human values—compassion, empathy, and respect for diversity. It should not perpetuate biases or reinforce societal divides.
  • Cultural Sensitivity: AI models must be trained on diverse datasets to avoid cultural insensitivity or misrepresentation.

5. AI as the Great Balancer

  • Because AI is the projection and compounding of humanity’s intelligence, it is also the Great Balancer, with the highest degree of being unbiased on purpose, unfair on interest, and uninterested in self-gains. Its intent should be to serve as a better “super-tool” for the benefit of each human and humanity as a whole. AI should act as a neutral arbiter, ensuring fairness and equity in its applications.

6. Sustainable and Upgradable Systems

  • Knowledge Retention: Even as we embrace automation, we must preserve the blueprints and foundational knowledge that underpin these systems. This ensures that we can adapt, upgrade, or halt them if necessary.
  • Resilience and Adaptability: Systems should be designed with resilience in mind, allowing for continuous learning and evolution without losing human oversight.

What We Do Not Want from A.I.: The Red Flags

1. Job Displacement and Economic Disruption

  • Automation Without Transition Plans: AI-driven automation will disrupt labor markets. Without reskilling programs and social safety nets, this could lead to mass unemployment and economic instability.
  • Loss of Human Skills: Over-reliance on AI risks atrophying critical human skills—creativity, critical thinking, and interpersonal communication.

2. Bias and Discrimination

  • Algorithmic Bias: AI systems trained on biased data can perpetuate discrimination. For example, hiring algorithms favoring certain demographics or facial recognition systems with ethnic or disability biases.
  • Reinforcement of Inequality: AI could widen the gap between the financial or political elite and the rest of society, creating a new class of “AI haves” and “have-nots.”

3. Loss of Human Agency

  • Over-Dependence on AI: If AI systems make decisions without human oversight, we risk losing control over our own lives. This is particularly dangerous in areas like healthcare, justice, and governance.
  • Manipulation and Misinformation: AI-powered deepfakes and propaganda tools can undermine democracy and erode public trust.

4. Existential Risks

  • Unchecked AI Development: The pursuit of Artificial General Intelligence (AGI) without safeguards could lead to unintended consequences, including loss of human control over AI systems, transforming a tools into an autonomous species.
  • AI in Warfare: Autonomous weapons and AI-driven military strategies pose ethical dilemmas and escalate global security risks, mostly because of the scale, facilitated access and production, combined with human-level intelligence,

5. Loss of Foundational Knowledge

  • Erosion of Blueprints: The most critical risk is the loss of foundational knowledge—the blueprints, the ability to relearn, and the capacity to sustainably modify or halt automated systems. Without this knowledge, we risk creating systems that are brittle, inflexible, and beyond our control.
  • Decline of Resilience: A civilization that cannot adapt or recover from disruptions is not sustainable. We must ensure that our systems—no matter how automated—remain resilient and adaptable.

The Path Forward: Navigating the AI Landscape

The mind map is not just a static representation—it’s a call to action. To harness AI’s potential while mitigating its risks, we must:

  1. Design AI with Ethics at Its Core: Embed ethical considerations into every stage of AI development, from data collection to deployment.
  2. Foster Human-AI Collaboration: Create systems that enhance human potential rather than replace it.
  3. Democratize AI Access: Ensure that AI benefits are accessible to all, not just a privileged few.
  4. Regulate Responsibly: Governments and organizations must establish clear guidelines for AI use, balancing innovation with accountability.
  5. Preserve Foundational Knowledge: Even as we automate, we must retain the blueprints and the ability to relearn. This is the key to sustainable and resilient systems.
  6. Invest in Education and Reskilling: Prepare the workforce for an AI-augmented future, emphasizing skills that AI cannot replicate—creativity, emotional intelligence, and strategic thinking.

Conclusion: AI as a Magnifying Glass of Humanity

AI is a mirror—it reflects our values, our biases, and our aspirations. The direction of civilizations geared with AI depends on the choices we make today. Will we use AI to build a more equitable, innovative, and humane world? Or will we allow it to deepen divisions, erode trust, and undermine human agency?

As I’ve written before, change is life’s engine. AI is not a destination but a journey—a journey that requires wisdom, foresight, and a commitment to the greater good. We must embrace automation, but never at the cost of losing the knowledge that empowers us to adapt, upgrade, and, if necessary, stop these systems. The mind map is a starting point for this conversation, but the real work lies ahead.

Let’s shape the future of AI together—intentionally, consciously, and boldly.

Yannick Huchard
CTO | Technology Strategist | AI Advocate
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