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AGI is an engineering problem, not a model training problem
LLM models are plateauing, but true AGI isn't about scaling the next breakthrough model—it's about engineering the right context, memory, and workflow systems. AGI is fundamentally a systems engineering problem, not a model training problem.
They’re impressive pattern matchers and text generators, but they remain fundamentally limited by their inability to maintain coherent context across sessions, their lack of persistent memory, and their stochastic nature that makes them unreliable for complex multi-step reasoning. The human brain isn’t a single neural net—it’s a collection of specialized systems working in concert: memory formation, context management, logical reasoning, spatial navigation, language processing. The path to AGI isn’t through training a bigger transformer—it’s through building distributed systems that can orchestrate hundreds of specialized models, maintain coherent context across sessions, execute deterministic workflows around probabilistic components, and provide fault-tolerant operation at production scale.
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