Experts Discuss the Future of Bioengineering: AI-Driven Autonomous Labs

Artificial intelligence (AI) is expected to play a pivotal role in the future of the bioindustry by designing experiments and utilizing robots to conduct them in 'autonomous laboratories.' However, securing high-quality experimental data and establishing international safety measures to prevent technological misuse remain significant challenges.

On October 1, Professor Huimin Zhao from the University of Illinois Urbana-Champaign and Professor Shoji Takeuchi from the University of Tokyo discussed the future of AI and biotechnology integration during a press conference at the '2026 Korean Society for Biotechnology Fall Conference and International Symposium' held at the Jeju International Convention Center.
 Professor Zhao, an expert in synthetic biology and enzyme engineering, emphasized that AI is significantly lowering the barriers to entry for biofoundries, which automate the entire process of bio research from gene design to production and testing.

"In the past, using a biofoundry required at least several months of training, and my lab students were no exception," Zhao explained. "Now, researchers can design experimental workflows by conversing with AI in natural language, which also informs them about what the AI can perform in the biofoundry." He added that the next step involves integrating AI directly with robots, termed 'physical AI.'

Zhao also predicted substantial industrial impact, stating, "Enzymes and microorganisms are already widely used in chemicals and detergents, but the difficulty in designing and improving biological systems has limited their application. By dramatically reducing development cycles through AI-based biofoundries, we can rapidly develop protein-based pharmaceuticals like antibodies."

He noted that Korea has the resources to expand biofoundries, thanks to government support. "Now, we need to prioritize how to integrate AI into biofoundries, which presents an opportunity for Korea," he emphasized.

Zhao identified 'data' as the primary obstacle in AI drug and bio research. He stated, "Among the key elements of AI—algorithms, computing, and data—data is the most significant limiting factor in scientific research. Literature data often omits failed results and may not be reproducible, making it difficult to use for AI training." He proposed that standardizing data produced by robots could allow for the integration of data from various institutions, presenting autonomous laboratories as a solution.

He also cautioned against the misuse of technology, stating, "Regulations should not be so strict as to hinder progress, but government oversight is essential. Unlike AI in computers, autonomous laboratories conduct biological experiments in the physical world, which can introduce additional safety issues." Zhao suggested that 'international consensus is necessary.'

Concerns have been raised about AI potentially replacing researchers as it rapidly advances. In response, Zhao remarked, "Students in molecular biology currently spend about half their time on repetitive tasks like plasmid construction. These tasks can be delegated to robots, allowing researchers to tackle more complex problems that were previously beyond their reach," dismissing the fears.
 Professor Shoji Takeuchi, a pioneer in the field of biohybrid robots, presented research achievements and challenges related to using living muscle during the conference.

He identified the lifespan of muscle tissue as a major challenge. "We have secured data showing that muscles can operate for about 100 days," Takeuchi said. "To extend their lifespan, we need to implement a 'turnover' system that allows damaged muscle fibers to regenerate like real muscle."

He also highlighted potential medical applications, stating, "A biohybrid hand powered by living muscle can connect to a patient's nerves or muscles, allowing it to operate without batteries or electric motors, making it lightweight and beneficial for patients." He added that three-dimensional tissues could also be utilized in regenerative medicine and drug testing models, although these ideas are still in the research phase.

In the sensor field, he introduced technology that mimics a dog's nose. "Detection dogs can only concentrate for 5 to 10 minutes," Takeuchi noted. "By incorporating a dog's olfactory capabilities into robots, we can enable long-term detection and apply it to early disease diagnosis."

Takeuchi proposed 'living AI' as the next step in the integration of AI and biotechnology. He stated, "Current physical AI has advanced to the point of making humanoids dance and run, but we still do not know how to control living muscles and nervous systems. The next generation of physical AI must understand and control biological components."

The symposium, themed 'AI STAR (AI Integrated Sustainable Transformations for Advanced Robust Biotechnology),' runs until October 2 and features over 2,000 biotechnology experts from around the world presenting more than 900 research findings across 33 specialized sessions.