Ethereum co-founder Vitalik Buterin has backed Moonshot AI’s decision to strip top-end hacking skills from its new Kimi K3.1 Pro model. In his view, limiting what AI can do beats limiting who may use it.
However, the safeguard comes at a measurable cost. Moonshot says the filtered model solved 81% of tasks on its internal exploit benchmark, while an unfiltered version solved 90.5%.
Why Vitalik Buterin Prefers AI Limits Over User Checks
Buterin shared his stance, arguing that discriminating against use cases is far less harmful than discriminating against users.
I am okay with Kimi 3.1 deliberately leaving out top-end offensive cyber capabilities from their training data.
Discriminating against use cases is MUCH less bad than discriminating against users. And the world does need all the time it can get to prepare, harden, check old…
— vitalik.eth (@VitalikButerin) October 26, 2026
Moonshot, a Beijing-based AI startup, plans to release the model’s open weights by November 4. Anyone can then download it and run it on their own hardware.
For that reason, the company says it removed cyber-related data from its training corpus and reinforcement learning environments. General coding tasks stayed in.
According to Vitalik Buterin, AI-powered attacks will eventually hit at full force. Until then, developers need time to harden systems, check legacy code, and verify new software.
The threat is already visible. Earlier this year, Binance Research found that AI exploits smart contracts roughly twice as well as it detects flaws.
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What Happens if the First Wave of AI Risk Goes Badly
Buterin also warned about the alternative. If this first wave goes badly, he expects locked-down models, Know Your Customer (KYC) checks on everything, and intrusive surveillance.
In that scenario, AI access could depend on credentials and nationality. Some people would become second-, third-, or fourth-class users, he wrote.
Vitalik Buterin has long favored running open AI models on his own hardware, as his local AI laptop tests showed.
Still, he does not expect the filtering to hold forever. When a user asked whether other labs would build such capabilities anyway, he replied that they would.
In fact, by Moonshot’s own count, DeepSeek’s rival open model V4.1-Flash already scored 85.7% on the same benchmark.
For Vitalik Buterin, AI safety has therefore become a race for time rather than a permanent block. That race favors defenders who adopt his redundant crypto security safeguards before attackers catch up.
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