Privacy used to be the thing CT only cared about when $ZEC started running. Is it too much if I say privacy is now one of the real moats in AI?

Back in 2023 AI could get away with vibes and a Terms of Service nobody read anyway.
AI now managing capital, executing trades, reading your private docs, running full enterprise workflows, operating as autonomous agents with their own wallets.
The new bottleneck in AI is who can let models touch valuable data without leaking the thing that makes it valuable.
– Samsung engineers accidentally leaked source code to ChatGPT.
– DeepSeek got caught routing Korean user prompts directly to ByteDance servers in Beijing.
The deeper problem is that centralized inference has a structural leak by default. Our prompts go to someone else's server, get logged, potentially retained, potentially trained on.
If AI is helping me explain tech, summarize PDF or do research, that's fine.
But when AI is touching my trading strategy, deal flow, or private keys... suddenly that leak has a dollar value attached to it.
Agents make this exponentially worse. An agent's system prompt is its alpha. If it's readable, it's extractable. MEV, but for intelligence.
– 80% of orgs have already encountered risky AI-agent behavior, including unauthorized data access.
– Gartner projects 75%+ of processing on untrusted infrastructure will require TEEs by 2029.
– McKinsey's State of AI 2025 showed data security jumping 10pp YoY as the #1 scaling blocker for enterprise AI.
I also get that big tech is building its own privacy stack:
– NVIDIA confidential GPU mode on Blackwell is getting close to normal performance.
– Meta is building Private Processing for WhatsApp.
– Apple already has Private Cloud Compute.
– GCP and AWS both have confidential compute products live.
Crypto's edge is open coordination, verifiable markets, censorship resistance, and neutral infra without being locked into one cloud provider forever.
– $VVV: 2M+ users, around 50K DAU, 15K inference requests/hour, local encrypted memory, no-log private mode, E2EE for Pro users, plus access to open models and proxied frontier models.
– $NEAR: running AI Cloud on TEE-secured environments where even the GPU operator, cloud host, or NEAR itself can't see the data. Integrated into Venice, Brave, Abound...
– $NIL: their Blind Computer stack combines MPC, HE, and TEEs so data can be stored, queried, and computed on while staying encrypted. 643M+ docs stored, 1.4M+ inference calls, 112K+ users.
– $PHA: processing 1B+ LLM tokens daily through Intel TDX and NVIDIA H100/H200 GPU TEEs, where encrypted inference runs at roughly 95-99% of normal performance.
– $ROSE: shipped ROFL and Sapphire as production confidential EVM infrastructure.
Privacy coins don't just go up because the privacy narrative is hot.
They're making real moves because AI is creating the first mainstream reason for privacy infrastructure to become economically useful.