AI agents like OpenClaw learn via "skills"—pre-written text instructions. While functional, this method is described as "janky" and a workaround. It exposes a core weakness of current AI: the lack of true continual learning. This limitation is so profound that new startups are rethinking AI architecture from scratch to solve it.
Related Insights
Even with vast training data, current AI models are far less sample-efficient than humans. This limits their ability to adapt and learn new skills on the fly. They resemble a perpetual new hire who can access information but lacks the deep, instinctual learning that comes from experience and weight updates.
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.
Many AI projects fail to reach production because of reliability issues. The vision for continual learning is to deploy agents that are 'good enough,' then use RL to correct behavior based on real-world errors, much like training a human. This solves the final-mile reliability problem and could unlock a vast market.
A key capability is creating skills that continuously search the web, Reddit, and X for the latest techniques on a topic. The agent then incorporates this new knowledge to improve its future outputs and stay current.
The current focus on pre-training AI with specific tool fluencies overlooks the crucial need for on-the-job, context-specific learning. Humans excel because they don't need pre-rehearsal for every task. This gap indicates AGI is further away than some believe, as true intelligence requires self-directed, continuous learning in novel environments.
The next evolution for AI agents is recursive learning: programming them to run tasks on a schedule to update their own knowledge. For example, an agent could study the latest YouTube thumbnail trends daily to improve its own thumbnail generation skill.
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.
A major flaw in current AI is that models are frozen after training and don't learn from new interactions. "Nested Learning," a new technique from Google, offers a path for models to continually update, mimicking a key aspect of human intelligence and overcoming this static limitation.
A significant hurdle for AI, especially in replacing tasks like RPA, is that models are trained and then "frozen." They don't continuously learn from new interactions post-deployment. This makes them less adaptable than a true learning system.
The perceived need for a new "continual learning" architecture is overstated. Current models can already achieve this functionally by building their own tools and apps based on new information. This reframes the challenge from a fundamental research problem to a practical prompt engineering and application design issue.
