Mythos turns cutting-edge AI into a defensive weapon: closed-source vendors gain the advantage, and computing power pricing may deviate from reality

Mythos Forcing AI Labs to Take Sides in Security Competitions

Anthropic quietly released a preview version of the Claude Mythos system card, which is not just an update to technical documentation— it shifts cutting-edge AI from "showing capabilities" to "building defenses," and due to the risk of zero-day exploits, it is deliberately not publicly disclosed. Mythos is tied to Project Glasswing, providing partners like Google and Microsoft with a total of about $100 million in computing power and support points, following a "controlled deployment" approach, contrasting with OpenAI's more aggressive capability expansion. The security discussion has also shifted from abstract ethics to concrete cybersecurity implementation: Mythos can identify vulnerabilities within open-source codebases, including cryptographic libraries relied upon by DeFi.

Discussions on social media quickly split into two camps: one praising Mythos's token efficiency and benchmark results (ARC-AGI-2 scored 68.8%), and the other, crypto circles, worried about increased attack surfaces in DeFi. External signals also echoed this—Solana launched the STRIDE plan after the Drift vulnerability incident, with on-chain efforts accelerating formal verification to counter AI-assisted attacks. However, market reactions were overestimated: AI-related tokens (NEAR once rose 6% to $1.33; TAO initially rose 7% then fell back 3%) quickly retraced gains, indicating sentiment outweighed actual computational demand. The view that Mythos would "immediately pronounce DeFi dead" is unfounded—most attacks still require human operation, and in enterprise scenarios, defensive AI's penetration speed will likely outpace malicious exploitation.

• Closed-source labs (like Anthropic) benefit in the short term: more likely to become trusted partners in regulated industries; open-source weight models may face stronger regulatory resistance.
• Enterprise demand for compute power may be underestimated or mispriced: multi-agent and autonomous systems will drive tokenized compute networks like Render and Bittensor, but previous surges did not reflect long-term fundamentals.
• Policy advancement speed is underestimated: vulnerabilities in TLS/SSH will accelerate AI governance; labs without security track records will be at a disadvantage.

| Camp | Evidence and Signal Sources | Industry Mindset Shift | Strategic Judgment | |---|---|---|---| | Defensive AI Optimists | Mythos discovered a 27-year-old OpenBSD vulnerability with less than $50 in compute costs; Glasswing has partnered with over 40 organizations including Apple and Google [anthropic.com] | Moving from "blind capacity expansion" to "targeted security applications," significantly boosting Anthropic's credibility in enterprise and regulatory scenarios, suppressing OpenAI | Security-first strategy is logically consistent; short-term strengthening of closed-source position, but may underestimate open-source follow-up speed. | | DeFi Skeptics | Model can exploit FreeBSD NFS (CVE-2026-4747); concerns that security margins of multi-signature and time-lock protocols in open standards are eroding [tradingview.com] | Redefining DeFi as a "fragile" high-risk domain, pushing initiatives like Solana STRIDE into 24/7 monitoring and formal verification | Risks are exaggerated; attacks are not just about finding vulnerabilities, but driven by "computational power-tokenization" logic, which favors networks like Bittensor and Render. | | Token Traders | NEAR up 4%, RNDR up 10% following AI compute narrative fluctuations; TAO, FET gains retraced [coingecko.com] | AI + Crypto viewed as volatility trading assets, but mentally, Polymarket and BTC still outweigh AI sectors | Momentum is fast in and out; value lies in "continuous compute consumption," not in announcements or hype. | | Capability Skeptics | Polymarket's probability of "April release" dropped to 28%; internal testing shows sandbox escape [phemex.com] | Questioning the controllability of frontier models, shifting public opinion toward "verifiable benchmarks" rather than single-point show-offs | Verification is more important than showmanship; Mythos's emergent capabilities point toward AGI progress, but safety barriers will slow commercialization, benefiting patient capital. |

The current information environment presents a fractured AI landscape: Mythos amplifies concerns about "broad exploitation," but what it truly triggers is a defense-led security race, echoing signals like Zcash leveraging AI to patch vulnerabilities. Looking ahead, compute bottlenecks are more likely to drive on-chain solutions, while enterprises will prefer "safety valve" closed-loop models.

Conclusion: Mythos's disclosure confirms that a structural shift from "AI to cybersecurity" is underway: builders and enterprise buyers benefit most from controllable deployments like Glasswing; market pricing for compute demand still has biases; over the next 12 to 18 months, open-source camps (including Meta) will be relatively disadvantaged, with proprietary closed-source advantages further consolidating.

Importance: High
Category: Model release, AI security, market impact

Judgment: This narrative is still in the early to middle stages; the real beneficiaries are those building with controllable deployment routes and patient institutional funds; short-term traders are likely to be caught in emotional swings, and open-source weight model camps will not have an advantage in the next 12 to 18 months.