AI in Science: The Lab Assistant That Never Sleeps

AI is moving beyond summarizing papers and answering questions. In 2026, it’s actively joining the process of discovery in physics, chemistry, and biology.

The AlphaEvolve breakthrough

Google DeepMind’s AlphaEvolve used the Gemini LLM combined with an evolutionary algorithm to discover new algorithms. Its suggestions are checked, the best are selected, and fed back into the LLM to improve further.

Its first discoveries — more efficient ways to manage data center and TPU power consumption — were significant. Researchers are now pushing the approach further, with open-source versions like OpenEvolve and SinkaEvolve appearing within months.

A lab assistant for every scientist

Microsoft Research’s Peter Lee describes a future where “AI will generate hypotheses, use tools that control scientific experiments, and collaborate with both human and AI research colleagues.”

That means every research scientist could eventually have an AI lab assistant that suggests new experiments and even runs parts of them.

From molecules to medicine

AI-driven tools are already modeling protein structures, simulating metabolic pathways, and designing biological systems. Hybrid approaches in materials design and drug discovery are accelerating timelines that once took years.

The theme is consistency: AI doesn’t replace the scientist — it amplifies exploration, allowing research teams to test far more ideas in far less time.