Future of Research

AGI and Laboratory Automation

How robotics and AI are automating biology, chemistry, and materials laboratories, and what that means for research throughput.

Risograph illustration of AI-augmented research environment — Plate / AI is becoming a junior collaborator in most active research fields.

Executive summary

Laboratory automation has been advancing for decades. AGI-class systems are now accelerating it sharply, particularly in biology, chemistry, and materials. Robotic 'self-driving labs' that propose, run, and analyse experiments are operating at several major institutions.

Key concepts

Self-driving labs
Closed-loop experimentation
Materials and chemistry
Biology automation
Throughput effects

Self-driving labs

Robotic systems that integrate AI-driven hypothesis generation, automated experimentation, and analysis. Several are operating in materials and chemistry research.

Closed-loop experimentation

AI proposes the next experiment based on prior results, the lab runs it, and the system updates its model. Throughput in well-bounded problems can rise by orders of magnitude.

Biology

Wet-lab automation in molecular and synthetic biology is well established and accelerating with AI integration.

Limits

Unstructured experimental work, novel measurement, and frontier problems still require human craft. Automation rewards structured, repeatable workflows.

Key takeaways

01Self-driving labs are operating in materials and chemistry.
02Closed-loop experimentation can raise throughput dramatically.
03Wet-lab biology automation is accelerating.
04Novel and unstructured work remains human craft.

Frequently asked questions

Will all experiments be automated?

No. Structured, repeatable work automates; frontier and novel experimental work resists automation.

What does this mean for lab roles?

Lab technicians' work shifts toward overseeing and maintaining automated systems and toward the experimental work that resists automation.