Why AI needs zkML: the missing puzzle piece to AI accountability

From DeepSeek to Anthropic’s Computer Use and ChatGPT’s ‘Operator,’ AI tools have taken the world by storm, and this may be just the beginning. Yet, as AI agents debut with remarkable capabilities, a fundamental question remains: how do we verify their outputs?

The AI race has unlocked groundbreaking innovations, but as development surges ahead, key questions around verifiability remain unresolved. Without built-in trust mechanisms, AI’s long-term scalability — and the investments fueling it — face growing risks.

The Asymmetry of AI Development vs. AI Accountability

Today, AI development is incentivized for speed and capability, while accountability mechanisms lag behind. This dynamic creates a fundamental imbalance: verifiability lacks the attention, funding and resources needed to keep pace with AI progress, leaving outputs unproven and susceptible to manipulation. The result is a flood of AI solutions deployed at scale, often without the safety controls needed to mitigate ...