AI Is Starting to Build Better AI

2026-05-07 · Source: IEEE Spectrum · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

The concept of recursive self-improvement (RSI) in artificial intelligence, where machines enhance their own capabilities without human intervention, is moving from theoretical discussion to practical implementation. While fully autonomous RSI remains a future prospect, current AI systems like large language models (LLMs) and specialized agents are demonstrating significant steps. OpenAI's GPT-5.3-Codex reportedly assisted in its own creation, and Anthropic's Claude Code writes most of its company's code. Google DeepMind's AlphaEvolve uses LLMs for algorithmic discovery, optimizing neural networks and chip designs. Projects like Darwin Gödel Machines and the AI Scientist are developing agents that can modify their own code, run experiments, and even write research papers, indicating a closing loop in AI development. Despite these advances, human oversight remains crucial for goal setting, evaluation, and managing complexity, with some researchers advocating for "co-improvement" between humans and AI.

Key takeaway

For Machine Learning Engineers and Research Scientists developing advanced AI, recognize that current LLMs and specialized agents are already performing significant self-improvement tasks, such as code generation and algorithmic discovery. You should focus on integrating these capabilities while maintaining robust human oversight for goal definition, evaluation, and complexity management. Prioritize "co-improvement" strategies to accelerate development safely, rather than pursuing fully autonomous RSI prematurely, to mitigate risks and ensure beneficial outcomes.

Key insights

AI systems are increasingly demonstrating recursive self-improvement capabilities, though full autonomy remains a future challenge.

Principles

• RSI exists on a spectrum, from human-directed assistance to full autonomy.
• Human-AI "co-improvement" can lead to faster and safer progress.
• Open-ended processes and evolutionary algorithms are key to advanced RSI.

Method

LLMs guide the evolution of solutions, optimize architectures, and automate research loops by generating ideas, running experiments, and evaluating results, often by modifying their own code.

In practice

• Use LLMs for code generation, including self-modification tasks.
• Employ evolutionary algorithms for optimizing neural network architectures.
• Integrate AI agents for automating research tasks like experimentation and paper writing.

Topics

• Recursive Self-Improvement
• Large Language Models
• AutoML
• AI Agents
• Chip Design

Best for: Machine Learning Engineer, Research Scientist, AI Scientist, AI Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by IEEE Spectrum.