Safety or Corporate Interest: Why Is Anthropic Holding Back the Mythos Release?

Safety or Corporate Interest: Why Is Anthropic Holding Back the Mythos Release?

Anthropic has traditionally positioned itself as a bastion of ethics and safety in the fast-moving AI industry. Now the startup has suddenly landed in the middle of a heated debate. The cause is its decision to sharply restrict access to its newest development, known by the codename Mythos.

Lab representatives are adamant: the new neural network is so powerful that releasing it openly could trigger an unprecedented cybersecurity crisis. But as the veil of secrecy around the project thickens, independent experts and market participants are starting to ask a natural question. Is concern for the safety of the internet really the true motive, or do mundane corporate protectionism and internal problems at a frontier lab hide behind a facade of noble intentions?

To understand the nature of the current conflict, it helps to look at Anthropic's own history. Former OpenAI staff founded the company as a research center that put safe, controllable AI first. Its approach, built on the concept of "constitutional AI", long served as something of a gold standard for the industry.

In the case of Mythos, the company says the model shows alarmingly strong abilities in writing complex code and automatically finding vulnerabilities in software. By its logic, in the hands of hackers or malicious state actors, this tool could become a perfect digital weapon capable of paralyzing critical infrastructure. That, the developers say, is why the full release is being postponed and access to the system is limited to a narrow circle of carefully selected specialists.

Still, the expert community is meeting these arguments with growing skepticism. Critics point to a complete lack of transparent metrics and independent audits that could confirm such apocalyptic forecasts. They note that technology giants increasingly use the word "safety" as a universal shield against any inconvenient question.

In Silicon Valley backchannels, a well-founded suspicion is growing that the real reason for the Mythos delay lies in technical difficulties, or in a shortage of compute that the lab ran into while scaling up. If the model runs unstably, suffers from critical hallucinations, or simply fails to justify the enormous financial investment, then declaring it "too dangerous" is a brilliant PR move. It lets the company not only save face with investors but also burnish its reputation as the creator of an AI so advanced that humanity is supposedly not yet ready for it.

Beyond possible hidden technical flaws, the harsh economics of the AI race also come to the fore. Building frontier models takes multibillion-dollar investments, and competition in the market has reached a historic peak. In these conditions, a full release of Mythos would inevitably lead competitors to dissect the model in detail. They could extract valuable information about the architectural decisions and new training methods Anthropic's engineers used. By restricting access, the company effectively protects its intellectual property and trade secrets, and it skillfully disguises this entirely pragmatic step as concern for the public good. As a result, the line between ethical restraint of a potentially dangerous technology and aggressive market behavior becomes almost impossible to see.

The consequences of such an opaque approach could be deeply damaging to the whole innovation ecosystem. If cloaking commercial interests in loud warnings about cyber threats becomes the norm, it will deal a crushing blow to open science and independent monitoring of algorithms. Academic institutions and outside researchers will lose the ability to study frontier systems properly, which will inevitably concentrate knowledge in the hands of a small group of private corporations.

What is more, the lack of outside oversight paradoxically reduces the real safety of the entire industry: without independent stress testing, society has to trust developers' press releases blindly. That creates fertile ground for a closed club of giants that set the rules of the game, assess the risks, and decide for themselves which innovations deserve to see daylight.

The delayed Mythos release exposes one of the sharpest paradoxes of the current technological era. Anthropic is undoubtedly right that the new generation of autonomous language models carries serious risks for global cybersecurity, and treating these threats lightly is unacceptable. But when the rhetoric of safety becomes a convenient tool of corporate marketing, trust in the industry begins to melt away fast. The further development of AI requires an honest and open dialogue between developers and society. Until labs offer verifiable mechanisms for proving their claims about the danger of their models, a shadow of doubt will follow their every step, and the question of whom they are actually protecting, the global internet or themselves, will remain open.