The popular conception of AGI as a pre-trained system that knows everything is flawed. A more realistic and powerful goal is an AI with a human-like ability for continual learning. This system wouldn't be deployed as a finished product, but as a 'super-intelligent 15-year-old' that learns and adapts to specific roles.

The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.

Google AI leader Jeff Dean highlighted "continual learning"—a model's ability to learn from new inputs post-training—as a key step toward AGI. That leaders are discussing it publicly suggests a breakthrough is near, which could rapidly accelerate AI capabilities and lead to a "fast takeoff" scenario.

The concept that AIs can build better AIs, creating an accelerating feedback loop, is no longer theoretical. Leaders from Anthropic, OpenAI, and Google DeepMind have publicly confirmed they are actively using current AI models to develop the next generation, making RSI a practical engineering pursuit.

The popular concept of AGI as a static, all-knowing entity is flawed. A more realistic and powerful model is one analogous to a 'super intelligent 15-year-old'—a system with a foundational capacity for rapid, continual learning. Deployment would involve this AI learning on the job, not arriving with complete knowledge.

Instead of just expanding context windows, the next architectural shift is toward models that learn to manage their own context. Inspired by Recursive Language Models (RLMs), these agents will actively retrieve, transform, and store information in a persistent state, enabling more effective long-horizon reasoning.

Dario Amodei argues that the current AI paradigm—combining broad generalization from pre-training/RL with vast in-context learning—is likely powerful enough to create trillions of dollars in value. He posits that solving "continual learning," where a model learns permanently on the job, is a desirable but potentially non-essential next step.

Demis Hassabis argues that current LLMs are limited by their "goldfish brain"—they can't permanently learn from new interactions. He identifies solving this "continual learning" problem, where the model itself evolves over time, as one of the critical innovations needed to move from current systems to true AGI.

The key to continual learning is not just a longer context window, but a new architecture with a spectrum of memory types. "Nested learning" proposes a model with different layers that update at different frequencies—from transient working memory to persistent core knowledge—mimicking how humans learn without catastrophic forgetting.