After dissing Anthropic for limiting Mythos, OpenAI restricts access to Cyber

Hype, “Dangerous Models,” and Marketing

• Many see the “too dangerous to release” positioning of Mythos and Cyber as a marketing tactic: artificial scarcity, velvet ropes, and “my model is more dangerous than yours.”
• Some argue labs would release these models if it maximized revenue; withholding suggests either overhyped capabilities or genuine risk.
• Others think companies want to appear responsible and prepared in case their models are later linked to real-world cyberattacks.

Cybersecurity Capabilities and Verification

• Claims: current models have strong vulnerability-research capabilities and can find large numbers of bugs or vulnerabilities.
• Skepticism: lack of broad, trusted third‑party evaluation; some “benchmarks” are called anecdotal (e.g., tiny code samples).
• Some links and projects are cited as partial evidence that Mythos‑style capabilities are not uniquely beyond existing pay‑as‑you‑go models.
• Unclear whether Mythos is truly exceptional or just good marketing around incremental improvements.

Economics, Pricing, and Compute

• Discussion of DeepSeek V4 pricing being dramatically lower than OpenAI’s models; some suspect state subsidy, others note US tech has long been effectively subsidized too.
• Debate over whether inference is actually being subsidized: some say nobody is profitable at scale; others insist third‑party hosts and major providers have healthy per‑token margins.
• Compute scarcity and long lead times are seen as a major strategic factor; pre‑locking capacity may matter more than model quality.

OpenAI, Leadership, and Trust

• Strong distrust expressed toward OpenAI’s leadership, citing past reversals (e.g., RAM capacity rhetoric) and the CEO’s reputation for ruthlessness.
• Some note employees and media heavily backed leadership during prior board drama, suggesting internal loyalty but also a susceptibility to narrative management.

Safety Filters and Cyber Programs

• Users report more refusals on legitimate defensive security tasks and describe the Trusted Access Cyber program and its outsourced verification as clumsy.
• Debate on whether it’s technically possible to reliably distinguish offense from defense via text alone; some say in principle yes, others point to current tools as evidence of practical failure.

Local and Open Models vs Frontier

• Several argue local models are now “good enough” for many tasks and lag frontier models by only 6–12 months, undermining big labs’ moats.
• Examples of strong local models and new architectures are mentioned, though some users report reliability and context‑length issues.