Biological Computing
Biological computing using living neurons has crossed from science fiction into commercial reality. The first biological computers are now available for purchase, using lab-grown human brain cells to process information with extraordinary energy efficiency. These systems represent a fundamental shift from traditional silicon-based computing toward hybrid biological-digital architectures that leverage billions of years of neural evolution.
This emerging field, termed Organoid Intelligence, harnesses the computational power of actual brain tissue grown from human stem cells. Unlike neuromorphic chips that simply mimic neural behavior, biological computing employs real neurons that form functional networks, learn through experience and process information through natural synaptic connections.
Living neurons create programmable biological computers
Biological computing systems work by growing brain organoids from human induced pluripotent stem cells in laboratory conditions. These three-dimensional neural cultures, typically 0.5 to 5 millimeters in diameter, contain between 10,000 and 800,000 living neurons that spontaneously develop electrical activity and complex oscillatory behavior similar to human brain tissue.
The technical architecture integrates these living networks with multi-electrode arrays containing 8 to 384 electrodes that both stimulate and record neural activity. Advanced microfluidic life support systems maintain organoid viability for up to 100 days, a significant improvement from initial lifespans measured in hours. Communication occurs through electrical stimulation and chemical signals using neurotransmitters like dopamine and serotonin.
The systems leverage natural neural plasticity mechanisms for learning. Correct responses receive predictable electrical patterns while errors trigger chaotic stimulation, mimicking reward-based learning in biological brains. Information storage occurs through structural and functional changes in neural networks, enabling pattern recognition and memory formation through synaptic plasticity.
Energy efficiency represents the most compelling advantage. The human brain processes information using just 20 watts of power, while an equivalent supercomputer would consume 21 megawatts. Training GPT-3 required approximately 1,300 megawatt-hours of electricity, equivalent to the annual consumption of 800 European households.
Commercial Systems Launches
The field achieved significant milestones in 2024 and 2025 with multiple commercial deployments and research breakthroughs demonstrating practical applications.
Cortical Labs launched the CL1 in March 2025, marking the world's first commercial biological computer. Priced at $35,000 per unit, the system contains 800,000 lab-grown human neurons with integrated life support maintaining cell viability for six months. The platform includes sub-millisecond feedback loops for real-time processing and cloud services available at $300 per week per unit.
FinalSpark introduced the Neuroplatform in 2024, offering remote access to 16 human brain organoids for $500 per month. This Swiss platform enables 24/7 streaming of neural activity with integrated Python scripting for experiment control. The system has attracted 34 universities worldwide and generated over 18 terabytes of neural activity data.
Research breakthroughs provided proof-of-concept demonstrations. In 2022, scientists at Cortical Labs successfully taught 800,000 neurons to play Pong within five minutes, demonstrating goal-directed behavior and real-time adaptation. This achievement, published in the journal Neuron, established synthetic biological intelligence as a viable computational approach.
Johns Hopkins University researchers led the establishment of Organoid Intelligence as an official scientific discipline through the Baltimore Declaration and foundational publications in major journals. Their work developed 3D shell microelectrode arrays for high-resolution recording and demonstrated integration of artificial intelligence with biological neural networks.
Key Startups
Three primary startups are driving commercial development of biological computing technologies, each focusing on different applications and approaches.
Cortical Labs, founded in 2019 as a spinout from Monash University, leads commercial biological computing development. The Australian company, backed by CIA venture fund In-Q-Tel and Horizons Ventures, pioneered the DishBrain technology that learned Pong. CEO Dr. Hon Weng Chong and Chief Scientific Officer Brett Kagan have positioned the company at the forefront of wetware-as-a-service offerings.
FinalSpark, established in 2014 by Dr. Fred Jordan and Dr. Martin Kutter, developed the first remote-access biological computing platform. The Swiss company, currently seeking $50 million in Series A funding, has created a unique dopamine-based reward system for training organoids and extended operational lifespans from hours to over 100 days.
Koniku, founded in 2015 by Nigerian neuroscientist Dr. Oshiorenoya Agabi, focuses on biological detection systems. With $1.8 million in funding and partnerships with Airbus, the company developed Konikore processors that combine biological neurons with silicon for detecting explosives, drugs, and diseases. Their systems achieve parts-per-billion sensitivity with response times under 10 seconds.
Major research institutions also contribute significant developments. Johns Hopkins University established the Organoid Intelligence field through foundational research, while Tianjin University in China developed MetaBOC systems enabling brain organoids to control robots with 78% accuracy in speaker recognition tasks.
Early adoption shows promise across multiple sectors
Biological computing has moved beyond laboratory demonstrations into practical applications across healthcare, research, and technology sectors, though adoption remains in early stages.
Academic institutions represent the primary current adopters, with 34 universities accessing FinalSpark's platform for neuromorphic research and cognitive behavior studies. Universities including Michigan, Free University of Berlin, and Lancaster University Leipzig use these systems for pattern recognition research and neural network development.
Pharmaceutical companies are exploring biological computing for drug discovery and neurological disorder research. The technology enables disease modeling for conditions like Alzheimer's, epilepsy, and Parkinson's disease using human-relevant neural responses. This approach offers alternatives to animal testing while providing more accurate data for pharmaceutical development.
Healthcare applications include medical device development, brain-computer interface research, and personalized medicine approaches. The systems enable real-time monitoring of drug effects on neural networks and enhanced understanding of brain disease mechanisms.
Current limitations present significant adoption barriers. Biological components survive only 100 to 180 days on average, requiring regular replacement. Systems remain limited to thousands or hundreds of thousands of neurons, far below brain complexity levels. High costs, with CL1 units priced at $35,000, limit accessibility for many potential users.
Regulatory and ethical concerns constrain adoption. Ongoing debates about potential consciousness in biological computing systems require new ethical frameworks governing the use of human neural tissue. Using biological neural networks opens up many hairy ethical concerns and has an eerie similarity to the plot of The Matrix. Before we understand the emergence of (self-)cosciousness, we should not rush the adoption of this bio-technology as it may be an inherently evil application.
Conclusion
Biological computing using living neurons has transitioned from theoretical research to commercial reality, offering a sustainable path toward more intelligent, adaptive, and energy-efficient computing systems. While technical challenges around lifespan, scalability, and standardization remain significant, the field demonstrates extraordinary potential for revolutionizing drug discovery, neuroscience research, and artificial intelligence applications.
The convergence of stem cell technology, advanced electrode interfaces and artificial intelligence has created a new computational paradigm that leverages natural neural evolution. As these systems mature and scale, they promise to address the energy crisis in computing while unlocking new forms of machine intelligence that complement traditional silicon-based approaches. The first commercial biological computers mark the beginning of a transformation that could fundamentally reshape how we approach computation in the coming decades, though the ethics of it all remains unclear at best and evil at worst.
References
- https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1017235/full
- https://pure.johnshopkins.edu/en/publications/first-organoid-intelligence-oi-workshop-to-form-an-oi-community
- https://spectrum.ieee.org/biological-computer-for-sale
- https://www.scientificamerican.com/article/these-living-computers-are-made-from-human-neurons/