Beyond Chatbots: AI Evolves Itself

Recursive Self-Improvement (RSI) is the hottest topic in AI right now — I see quite a few startups building on this. We are finally seeing AI start to design, code, and train the next generation of AI all by itself.

1. Search Skill is the Hidden Bottleneck

We usually think an agent’s performance depends entirely on its reasoning power. But in reality, a huge chunk of its capability comes from its basic search skill. To solve any complex problem, an AI cannot just guess the answer; it has to break the problem down into steps and systematically search for the right information first.

This is even more critical during post-training self-improvement. To fix its own weaknesses, the AI must rely on its search ability to find the exact data, code, and test cases it needs. In fact, many times when an AI “solves” a hard problem, it probably just searched and found a similar solution rather than truly understanding it.

This aligns with Yann LeCun’s argument that LLMs are not true AGI because they rely too much on retrieval rather than genuine reasoning. However, this limitation is exactly why I believe reinforcement learning is one approach to solving the hard problem of true comprehension. Unlike retrieval, RL forces an agent to build an internal logic of cause and effect, driving it to reason through novel scenarios rather than just mimicking past solutions.

2. Coding is the Engine of AI Evolution

Why is AI evolving exponentially right now? The answer is coding. At the end of the day, training a model is just a series of coding tasks: data pipeline cleaning, infrastructure building, and algorithm writing.

Once an AI masters coding, it naturally gains the ability to write scripts to optimize and train itself. In short, top-tier coding capability is no longer just a feature — it is the foundational infrastructure required for any advanced model to evolve.

3. The Threat of “Recursive Drift”

When an AI starts generating its own data to train itself, it runs into a massive bottleneck called “recursive drift.” Think of it like a game of telephone: if the first-generation AI makes a tiny logical error, that mistake multiplies generation after generation. Eventually, the model will drift completely.

The future of AI is no longer about building better chatbots; it is about building autonomous digital scientists that can solve the world’s hardest problems.