Dr. Hon Weng Chong: Biological neurons are 5,000 times more efficient than traditional AI, ethical concerns of conscious systems, and the launch of the world’s first biological data center | TWIST

Biological neurons are significantly more stable and efficient than traditional reinforcement learning systems. Ethical concerns arise from creating conscious systems due to the potential for suffe...

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Add us on Google by Editorial Team Jun. 2, 2026

Key takeaways

• Biological neurons are significantly more stable and efficient than traditional reinforcement learning systems.
• Ethical concerns arise from creating conscious systems due to the potential for suffering.
• The CL1 platform simplifies biological computing for researchers and developers.
• Establishing the world’s first biological data center marks a new era in data processing.
• Biological computers mimic human brain functions with systems for nutrient delivery and waste removal.
• A biological computer supports up to 2,000,000 neurons, with 200,000 being commercially viable.
• Biological systems exhibit generalized intelligence that current machines lack.
• Biological systems are more sample efficient than GPU-based systems in reinforcement learning.
• Biological computing systems perform reinforcement learning tasks more efficiently than traditional systems.
• Biological data centers operate without affecting the energy budget due to unique cooling requirements.
• The CL1 platform allows programming with Python, easing the entry into biological computing.
• The potential of biological computing lies in its scalability and practical applications in learning.
• Biological intelligence offers a model for developing more advanced AI systems.
• Biological data centers highlight a shift towards more energy-efficient computing solutions.
• The integration of biological systems in computing challenges conventional paradigms.

Guest intro

Dr. Hon Weng Chong is the CEO and Founder of Cortical Labs, the company developing CL1, a biological computer that combines lab-grown human neurons with silicon hardware. A physician turned technologist, he previously worked on projects including Xbox consoles for children’s hospitals and diagnostic tools for childhood pneumonia.

The advantages of biological neurons over traditional AI

• The neurons we had were 5,000 times more stable efficient

  — Dr. Hon Weng Chong

• Biological neurons offer a significant advantage in stability and efficiency over traditional AI systems.
• The quantitative comparison highlights the potential of biological computing.
• Biological systems can exhibit goal-seeking behavior.

• The neurons we had were 5,000 times more sample efficient than their gpu based systems

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  — Dr. Hon Weng Chong

• Biological systems outperform GPU-based systems in reinforcement learning.
• The efficiency of biological systems could reshape AI research approaches.
• Biological computing offers a new paradigm for AI development.

Ethical considerations in developing conscious systems

• You do not wanna create conscious systems because ethically a conscious system has the ability to suffer and we do not want any suffering to come about from any technology

  — Dr. Hon Weng Chong

• Ethical implications are a critical consideration in AI development.
• The potential for suffering in conscious systems raises significant ethical concerns.
• Avoiding the creation of conscious systems aligns with ethical technology development.
• The focus is on developing systems that do not lead to suffering.
• Ethical considerations guide the development of biological computing technologies.
• Conscious systems pose risks that must be carefully managed.
• The ethical stance reflects a commitment to responsible AI development.

Simplifying biological computing with the CL1 platform

• The c l one is our attempt to build a computing platform that allows researchers and developers to get going of biological computing very easily

  — Dr. Hon Weng Chong

• The CL1 platform simplifies the entry into biological computing for researchers.
• Programming with Python makes the CL1 accessible to developers.
• The platform eliminates the need for custom hardware and software development.
• The CL1 is designed to advance biological computing research.
• Researchers can focus on programming rather than hardware development.
• The CL1 platform represents a significant advancement in biological computing.
• The ease of use of the CL1 encourages wider adoption of biological computing.

The world’s first biological data center

• We’re calling the world’s first biological data center

  — Dr. Hon Weng Chong

• The establishment of a biological data center marks a new era in data processing.
• Biological data centers offer a new paradigm for data processing in research.
• The data center integrates biological computing with traditional data processing.
• The development of the data center highlights the potential of biological computing.
• Biological data centers could lead to more efficient data processing solutions.
• The data center represents a significant milestone in biological computing.
• The integration of biological systems in data centers challenges conventional paradigms.

Mimicking human brain functions in biological computers

• We build mechanisms to keep the system flowing to give nutrients to the brain to remove the waste

  — Dr. Hon Weng Chong

• Biological computers mimic essential functions of the human brain.
• Systems for nutrient delivery and waste removal are integral to biological computing.
• Biological computers are structured to replicate human brain processes.
• The design of biological computers reflects an understanding of brain functions.
• Biological computing offers a model for developing advanced AI systems.
• The integration of biological systems in computing challenges traditional models.
• Biological computers highlight the potential of integrating biological and silicon systems.

Neuron capacity and commercial viability in biological computing

• In a c l one you can go all the way up to a 2,000,000 neurons if you so wish

  — Dr. Hon Weng Chong

• Biological computers support up to 2,000,000 neurons.
• 200,000 neurons are commercially viable for learning and training.
• The scalability of biological computers offers practical applications in learning.
• Biological computing is commercially viable with a focus on neuron capacity.
• The potential for learning and training highlights the practical applications of biological computing.
• Biological computing offers a scalable solution for AI development.
• The neuron capacity of biological computers challenges conventional computing paradigms.

Generalized intelligence in biological systems

• …I would say that biology even very simple organisms like you know a fly has generalized intelligence something that none of our machines have…

  — Dr. Hon Weng Chong

• Biological systems exhibit generalized intelligence that current machines lack.
• The distinction between biological and artificial intelligence is critical.
• Biological intelligence offers a model for developing more advanced AI systems.
• The potential of biological systems in computing is significant.
• Biological systems highlight the limitations of current AI technologies.
• Generalized intelligence in biological systems challenges traditional AI models.
• The integration of biological systems in computing offers new possibilities for AI development.

Energy efficiency of biological data centers

• …they’re gonna provide the same chips like everyone else but on top of that they’re gonna provide our compute which is not affecting any of the energy budget because they don’t have to do any special cooling

  — Dr. Hon Weng Chong

• Biological data centers operate without affecting the energy budget.
• Unique cooling requirements contribute to the energy efficiency of biological data centers.
• Biological data centers highlight a shift towards more energy-efficient computing solutions.
• The energy-saving capabilities of biological data centers are significant.
• Biological data centers offer a model for sustainable data processing.
• The operational efficiency of biological data centers challenges traditional models.
• Biological data centers represent a significant advancement in energy-efficient computing.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.