Gemini can't show me the fastest way to copy memory in C# because it's unethical
Google’s Gemini model is drawing criticism for refusing to answer seemingly benign technical questions — such as how to copy memory efficiently in C# — on “ethical” or safety grounds. Commenters see this as an example of overzealous guardrails that make corporate LLMs less useful, inconsistent, and patronizing, driven by PR and liability fears rather than genuine safety. Many argue this trend risks centralizing and constraining access to technical knowledge, strengthening the case for less restricted open-source or self-hosted models in the long term.
Gemini refusing “unsafe” C# memory copy
- Gemini reportedly refuses to show the “fastest” way to copy memory in C# when “unsafe” is requested, citing ethics/security.
- Some users can’t reproduce; others get similar refusals, especially when explicitly asking for
unsafeexamples. - Workarounds: rephrase using “unmanaged” instead of “unsafe”, change language (e.g., German/Spanish), or push back in the prompt; then it sometimes yields lower-level approaches.
Overcautious safety filters and misfires
- Gemini and other LLMs are described as heavily “aligned,” often declining benign tasks:
- Election-related safety layer triggers on unrelated queries (Django “polls”, words like “vote”), returning canned “try Google Search” messages.
- Refusals for serial-port experiments (“might damage hardware”), certificate authority code, OpenSSL self-signed certs, even puns or lifestyle questions (hermitry → suicide hotline).
- Some topics (e.g., Playboy, certain breathing techniques) are blocked outright.
- Users see this as shallow keyword/embedding filtering rather than nuanced reasoning.
Debate: safety vs usefulness
- Critics: guardrails are patronizing, treat users like children, and reduce LLMs to “nanny layers” that withhold legitimate technical knowledge.
- Supporters: major vendors are avoiding PR disasters, lawsuits, and misuse (explosives, medical harm), so overblocking is rational.
- Many argue a disclaimer-based approach or multiple model variants (kid-safe vs full-access) would be better than one overcensored default.
Open-source vs corporate models
- Several see growing censorship and “AI safety” as a competitive advantage for open models: more privacy, fewer guardrails, more control.
- Concerns: open models depend on big-iron training runs funded by corporations or leaks; unclear how sustainable that is long-term.
- Some expect hardware and compute to democratize over time; others think frontier corporate models will keep moving out of reach.
Long-term knowledge and control worries
- Fears that, as books/blogs decline and AI becomes the primary learning interface, corporate-controlled filters will shape what future engineers can even learn.
- Analogies to book banning, “security through obscurity,” and a possible new “Dark Ages” if deep, “dangerous” knowledge fades while only sanitized AI output remains.