AGI Requires Systems Engineering, Not Just Model Scaling
By
vincirufus
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Summary
The article argues that artificial general intelligence (AGI) is fundamentally an engineering challenge rather than a model training problem. It contends that current large language models like GPT-5, Claude, and Gemini are hitting diminishing returns through brute-force scaling, and that true AGI will require engineered systems combining models, memory, context, and deterministic workflows to create capabilities greater than individual components.
Key quotes
· 3 pulledAGI is an engineering problem, not a model training problem.
The path to artificial general intelligence isn't through training ever-larger language models—it's through building engineered systems that combine models, memory, context, and deterministic workflows into something greater than their parts.
We've reached an inflection point in AI development. The scaling laws that once promised ever-more-capable models are showing diminishing returns.
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