AGI Is an Engineering Problem
12 hours ago
- #Systems Engineering
- #AI Development
- #AGI
- AI development has reached an inflection point where scaling laws show diminishing returns.
- Current large language models (LLMs) like GPT-5, Claude, and Gemini are hitting performance asymptotes.
- AGI requires engineered systems combining models, memory, context, and deterministic workflows.
- LLMs lack persistent memory, coherent context across sessions, and reliable multi-step reasoning.
- The solution is not bigger models but smarter systems, similar to multi-core processor designs.
- AGI needs specialized systems for context management, memory, deterministic workflows, and modular models.
- Context management must handle retrieval, world models, domain bridging, and uncertainty quantification.
- Memory systems should update beliefs, consolidate information, forget irrelevant details, and track reliability.
- Deterministic workflows should incorporate probabilistic components with validation and rollback capabilities.
- Specialized models should be used modularly, routing tasks to domain-optimized components.
- AGI is a distributed systems problem requiring fault-tolerant pipelines, monitoring, and scalable infrastructure.
- A roadmap includes foundation, capability, and emergence layers for building AGI systems.
- The future of AGI lies in architectural engineering, not just algorithmic breakthroughs.