An overview of a laboratory equipped with robot arms and automated equipment. Autonomous laboratories are moving away from structures where humans perform all experiments directly, evolving towards a loop connecting AI's suggestions, robots' repetitive execution, and human interpretation and verification. [Photo=Argonne National Laboratory]

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For a long time, science has progressed through human hands. Researchers would read papers, formulate hypotheses, operate equipment, record failures, and move to the next step.

This process was slow, but it was also why science gained trust. It was relatively clear what was attempted, why, where failures occurred, and who made which decisions.

Science was not just an accumulation of results but a work built upon a record of failures and revisions.

However, the landscape of laboratories is changing.

Now, a reality is emerging where AI reads vast numbers of papers and experimental data to suggest candidate materials or reaction conditions, automated equipment and robots perform experiments, and then AI analyzes the results to determine the next experiment.

Academia and industry refer to this as an 'autonomous laboratory' or 'self-driving lab'. Recent research describes autonomous laboratories not as simple repetitive automation, but as experimental systems where design, execution, and interpretation are connected in a closed loop.

The core is not simple automation.

It lies in the fact that systems are beginning to suggest and select "what the next experiment should be," going beyond machines rapidly repeating sequences pre-determined by humans.

Today, science is facing problems where papers and data are exploding, and the number of possibilities exceeds human intuition. In fields with many variables like battery materials, drug candidates, and semiconductor processes, it is difficult for humans to verify all combinations directly.

Ultimately, what distinguishes research productivity is the ability to decide which candidates to test first, which failures to discard quickly, and which possibilities to postpone, rather than the repetition of manual tasks.

Autonomous laboratories have emerged to reduce this exploration cost.

Actual application cases are already clear. One of the most symbolic scenes is in the field of new materials.

A paper published in Nature in 2023 by A-Lab linked computational databases, literature-based synthesis rules, machine learning, and robotic experiments into a single autonomous loop.

This system operated continuously for 17 days, successfully synthesizing 36 out of 57 target materials, and reflecting the results of failed experiments in subsequent choices.

What's important is not the exaggeration that 'machines did science alone,' but the fact that the planning, execution, and some interpretation of experiments have begun to be connected within a single system.

In the battery field, the change in speed is even more evident.

Argonne National Laboratory in the United States announced in January 2026 that "using AI and robotics, over 6,000 battery chemistry experiments were performed over five months."

Battery research involves too many variables such as composition, electrolyte, additives, temperature, and charge/discharge conditions, making it difficult for humans to verify all combinations directly. In this situation, autonomous laboratories do not replace humans but change the speed of research by narrowing down the possibilities and prioritizing promising pathways.

The researcher's time shifts from repetitive manual tasks to selecting questions.

The fields of life sciences and chemistry are already moving in the same direction.

A 2025 Scientific Reports paper presented a closed-loop bio-autonomous laboratory system combining robotics and AI, leading from culturing, pre-processing, measurement, analysis, to hypothesis formation.

In the same vein, Coscientist, published in Nature in 2023, demonstrated its ability to support the design and execution of chemical experiments by connecting literature search, hardware document exploration, code execution, cloud laboratory commands, and experimental data analysis, all based on large language models.

Although the fields differ, the structure is the same: experiments are shifting to a cyclical system of 'reading-selecting-executing-interpreting-re-selecting'.

Therefore, the true meaning of an autonomous laboratory is closer to a 'laboratory that fails faster and corrects faster' rather than a 'laboratory that doesn't make mistakes'.

In science, failure is not something to be eliminated but a cost for moving to the next stage. Autonomous laboratories reduce that cost.

This is because machines can repeat the same experiment under consistent conditions without fatigue and can immediately reflect failed results in the next selection.

In this respect, autonomous laboratories are a technology that changes the speed of science, but at the same time, they are also a technology that more acutely reveals the structure of responsibility in science.

However, the risks also increase precisely at that point.

Machines faithfully execute the goals set by humans. If the goals are wrong, they can rush in the wrong direction faster, and if the data is biased, they can repeat that bias more precisely.

If the premises of the analysis model are incorrect, the laboratory can become a device that amplifies errors massively rather than one that reduces them. Speed may appear as a symbol of progress, but speed without interpretation becomes an amplifier of errors.

Autonomous laboratories are not devices that lessen human responsibility but rather devices that more clearly reveal the accuracy of human judgment.

Indeed, debates surrounding autonomous laboratories have already begun.

A-Lab was received as a highly symbolic achievement, but subsequently, issues were raised in academia regarding "what should be considered novel" and "to what extent should it be called a discovery."

C&EN reported in January 2026 that Nature issued a correction to the paper, and the expression that previously gave the impression of "completely new materials" was revised.

The core issue is ultimately not "did the machine move?" but "what should be considered a discovery, and whose judgment should be the final standard?"

Technology can increase the speed of experimentation, but it cannot take over the task of defining the meaning of science.

The issue of reproducibility also becomes more important.

Autonomous laboratories have the potential to improve experimental reproducibility through consistent conditions and automated procedures. However, at the same time, they can also make the objects of verification more complex.

The results can vary significantly if equipment settings, data preprocessing methods, algorithm selection, or objective function design are slightly different.

Future verification will require asking not only "was the same experiment performed?" but also "was the same decision-making process followed?" This marks an era where not just experimental reproducibility but also selection reproducibility becomes an issue.

In this regard, scientists in the era of autonomous laboratories must be supervisors and record-keepers, not just researchers.

They must document what data the system used for its proposals, how failed experiments were reflected in the next steps, and at what points humans intervened to modify the direction. As the experimental process moves to machines, the level of documentation must become even higher.

This is because, as humans use their hands less, the traces of their judgment and intervention must be recorded more precisely.

This is why recent autonomous laboratory research repeatedly emphasizes human goal setting and supervision structures.

Ultimately, autonomous laboratories signify not the end of scientists but their redefinition.

It is highly probable that future scientists will no longer exclusively mean those who conduct all experiments with their own hands. Those who design goals, supervise systems, interpret the meaning of results, and read the context missed by machines may become more important.

Even if machines handle repetitive manual tasks, as long as humans are the ones who decide what to ask and what to believe, the role of the scientist will not disappear. However, that role will undoubtedly shift.

 The history of civilization has always moved in this way.

The steam engine changed human muscles, and computers changed human calculations. Autonomous laboratories are now changing human experimentation.

However, as tools become more sophisticated, what becomes more important is not the tools themselves but the standards.

What shall we discover? What risks shall we take? At what level shall we stop, and at what point shall we demand verification again?

If humans are still the ones who answer these questions, the master of science will not change even in the era of autonomous laboratories. What changes is the scientist's job.

It shifts from those who directly conduct experiments to those who design questions, verify results, and retain responsibility until the end.

Responsibility cannot be automated, especially in an era where experiments are automated. The essence of autonomous laboratories lies precisely on that paradox.

※ This article has undergone verification for factual accuracy, logical consistency, and source integrity in accordance with the ALO system (patent pending).