AGI, the holy grail of AI development, masters any cognitive task a human can do, learns across domains, and improves itself. And the core building blocks are already here.
But when systems self-improve, even 1% misalignment = P(doom) 💀
Let's make sure we get it right.
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AGI = AI that performs at human-expert level across most cognitive domains. Unlike today's narrow AI (great at one task), AGI solves unfamiliar problems, transfers knowledge across domains, and sets its own goals.
Plot twist: The core building blocks of AGI were already discovered in 2020-2024.
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DeepMind separates what AI can do from how independently it’s allowed to act. They define six autonomy levels, from human-controlled tools to fully autonomous agents.
Crucially, powerful AGIs don’t need high autonomy, and even simple AI can be risky if left unsupervised. Safe deployment hinges not just on capability, but on control.
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DeepMind's 6-layer AGI safety stack: Interpretability + Monitoring + Training interventions + Deployment safeguards + Secure architectures + Governance.
But these safeguards need a trusted execution layer...
Full research:
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Enter Confidential Computing 🔐
💁♀️ Apple's Private Cloud Compute (Siri)
💁♀️ Anthropic's Trusted VMs (Claude)
💁♀️ Azure's confidential containers
All converge on one goal: Run AI where neither cloud providers nor attackers can access model internals or sensitive queries.
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Phala Network: Confidential Infrastructure for Safe AGI. Key components:
☁️ GPU TEE support for high-perf AI
☁️ Private ML SDK (secure LLM inference)
☁️ Redpill Gateway (auditable AI API)
☁️ On-chain attestation for verification
Phala proves your AGI agent actually did what it claimed, securely.
AGI agents will run outside labs — in edge devices, finance, governance, science, etc.
Phala ensures they run privately, verifiably, and with built-in audit trails.
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