AGI will not be a chatbot.
It will be an institution.
I share my article: AGI ALPHA: An evolutionary substrate for intelligence organizations The central idea is simple: The Transformer scaled intelligence inside models.
AGI ALPHA aims to
@ceobillionaire
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AGI as an evolving institution: AGI ALPHA
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Proof-Carrying Evolution: Secure AI Propagation via Strict Selection Gates
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The RSI era should not be ungated self-modification. It should be proof-carrying evolution: Every agent action emits proof.
Every proof enters a Selection Gate.
Only what passes eval, risk, scope, canary, monitoring, and rollback earns propagation. Private intelligence stays -
CEO Billionaire Proposes Proof-Gated Evolution Constitution for AI: GoalOS
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RSI was missing a constitution. Not “AI improves itself.” Proof-gated evolution: Aim → Act → Prove → Evolve. No proof, no evolution.
No eval, no propagation.
No rollback, no release. AEP-001: GoalOS https://
github.com/MontrealAI/pro
of-gradient/blob/main/docs/standards/AEP-001/GoalOS_Proof-of-Evolution-Constitution_AEP001_v12.1_Institutional_Final.pdf
… #GoalAI #MontrealAI #QuebecAI -
GoalOS: AI Improvement, Governance, and Usability for Institutions via Proof-Gradient
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GoalOS makes AI work improvable, governable, provable, and institutionally usable. https://
github.com/MontrealAI/pro
of-gradient
… #GoalOS #MontrealAI -
QUEBEC.AI launches Sovereign AI: Capability under control, building, governing, securing, benefiting.
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http://
QUEBEC.AI launches Sovereign AI: AI‑First sovereignty = capability under control. Data, infrastructure, agents, proof, governance. Quebec enters AI‑First sovereignty: build, govern, secure, benefit. http://
quebecartificialintelligence.com/sovereign-ai #AIFirst #QuebecAI -
Learned vs. Inherited Capabilities: Distillation vs. Ground-Up Intelligence
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The phrase “capabilities should be learned, not inherited” is doing a lot of work here. It draws a clear line between imitating intelligence through distillation and building the internal machinery to generate, evaluate, and improve capabilities from the ground up. That
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Microsoft AI’s MAI-Thinking-1: A Hill-Climbing Machine for Frontier Models
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AI progress is not a model. It is a machine that keeps improving models. That is the core idea behind Microsoft AI’s new technical report: MAI-Thinking-1: Building a Hill-Climbing Machine This is not just a model release. It is a blueprint for turning frontier model
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Token-level SSL vs Latent Prediction for Recursive Hierarchy Learning
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The key distinction: Token-level SSL asks the model to recover hierarchy through the leaves. Latent prediction lets the model climb the hierarchy recursively. Once one abstraction level is learned, it becomes supervision for the next.
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Paper proposes sleep-like memory consolidation for LMs
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Language models may not need longer context. They may need sleep. A fascinating new paper by Sangyun Lee, Sean McLeish, Tom Goldstein, and Giulia Fanti proposes one of the most biologically resonant ideas in long-context AI: sleep-like memory consolidation. The problem is
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Semantic structure vs. function: A caution for mechanistic interpretability
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The line that stayed with me: semantic structure may be useful for function without being driven by function. That is a powerful caution for mechanistic interpretability. Some beautiful structures inside models may be less like “designed concepts” and more like the linear