DeepSeek's Wenfeng and AI Leaders Agree: Continuous Learning Is the Key Gap to AGI

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DeepSeek founder Liang Wenfeng, in a rare investor briefing, identified continuous learning — not larger models or new modalities — as the critical missing capability on the path to AGI. He argued that solving this problem would lead to a gradual singularity and eventually embodied intelligence, a view echoed by figures at OpenAI, Microsoft, and Andrej Karpathy. Experts contend that storing learned knowledge inside model weights is fundamentally unworkable due to economics, opacity, and vendor lock-in, meaning each new session effectively resets to zero. The emerging alternative is storing agent memory in durable, portable, model-agnostic external files — typically plain markdown managed via git — which remain intact even when the underlying model or runtime changes. Open-source projects like Tolaria exemplify this files-first approach, though developers note that effective continuous learning also requires a sophisticated retrieval runtime, not just a folder of notes.

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