AGI Is an Engineering Problem

Summary

We’ve reached an inflection point in AI development. The scaling laws that once promised ever-more-capable models are showing diminishing returns. GPT-5, Claude, and Gemini represent remarkable achievements, but they’re hitting asymptotes that brute-force scaling can’t solve. 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. Let me be blunt: AGI is an engineering problem, not a model training problem. The Plateauing Reality The current generation of large language models has hit a wall that’s become increasingly obvious to anyone working with them daily. 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. We’ve seen this movie before. Every technology wave follows the same trajectory: initial breakthrough, rapid scaling, then increasing marginal costs for decreasing marginal gains. The semiconductor industry hit this wall in the early 2000s when clock speed scaling became impossible. The solution then wasn’t to brute-force faster processors—it was to fundamentally rethink the architecture with multi-core designs. AI is at the same inflection point. We need to stop asking “how do we make the model bigger?” and start asking “how do we make the system smarter?” The Systems Approach to AGI 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. Each system has evolved specific purposes, and they operate asynchronously with complex feedback loops between them. True AGI requires us to engineer similar systems. Here...