Artificial Intelligence and Machine Evolution

This working paper provides a brief overview on Artificial Intelligence (AI) and Machine Evolution. Machine Evolution can be understood as a situation where machines autonomously create new machines which create further machines and so on. While such an evolution is not yet possible, substantial progress was made in multiple areas which bring machines and AI much closer to full autonomy, self-learning, and adaptation for their self-improvement. These developments accelerate the technological progress, but make huma oversight and interventions increasingly difficult, i.e., instead of being in the loop, humans are moved out of the loop. Advances include the AI-assisted design of computer chips, the AI-assisted design of entire computers, self-learning and improving AI agents in experimental settings as well as automated fabrications. Already in 2024, the machine learning-based AI program AlphaChip generated superhuman chip layouts and created in 2025 the new Ironwood TPU as one of the fastest marketed chips. China is advancing as well with the chip-designing AI QiMeng that already designed chips comparable with commercial chips. The chip design research is advancing rapidly. The first three-dimensional monolithic 3D chip to accelerate AI was presented by researchers of the Stanford University in December 2025. The Shanghai-based company CHIPX released a photonic quantum chip that for some mathematical operations was 1000 times faster than conventional chips. Quilter's physics-driven reinforcement learning AI system designed in the Project Speedrun a working 843-component Linux computer in only 38 hours and autonomously completed 98% of placement, routing, and physics. New ways of AI training and learning were proposed in 2025; the networking and information exchange between AI agents in the first step and the open-ended evolution of AI agents in the second step by the so-called Darwin Gödel Machine (DGM). Researchers from Google DeepMind proposed that AI systems learn from each other and share their experience permanently with unlimited development above humans, but networking and knowledge exchange between AI system may amplify the risk for system errors and malfunction of the AI agent ecosystem by exchange of inaccurate data. In 2025, the Darwin Gödel Machine (DGM) was developed, a self-improving Coding AI Agent system that iteratively modifies its own code and empirically validates each change using coding benchmarks which was effective in experiments. The spread of robots may open the way into fully automated fabrications. The human control of AI can be done by observation of output and action, but can be complemented by monitoring of the chain of thoughts, i.e., the internal AI protocols that show their reasoning (thinking) steps. The ability to observe the chain of thought of an AI is called monitorability. There are concerns that novel AI architectures may reduce the monitorability despite findings of hidden misalignment (scheming) where frontier AI models hide thoughts e.g., to get rewards or to avoid reinforcement learning. Various researchers express serious concerns about a potentially uncontrolled evolution e.g., by self-learning. The key concern is that humans are currently not really prepared for this situation and may not be able to provide sufficient oversight, i.e., the situation may get out of human control and for this reason, a strict governance with regulations and supervision should be established.