Why Building Self‑Improving AI Needs a Full‑Scale Engineering Roadmap, Not a Switch
Monte Carlo’s new essay shows that true agentic reinforcement‑learning systems demand more than a clever algorithm—they require concrete infrastructure blueprints, rigorous monitoring, and ongoing human labor to manage data pipelines and safety checks. Without that engineered backbone, self‑improvement remains brittle, limiting scalability and trust.