Scientists are building experimental computers from tiny lab-grown clusters of human neurons with the aim of creating ultra-efficient “wetware” that can learn, adapt and run AI-type tasks using a fraction of today’s energy.
What Are These “Mini Brains”?
In this case, “mini brains” are brain organoids, which are small three-dimensional clusters of living human neurons and support cells grown from stem cells. They are not conscious or comparable to a human brain, but they share the same biological building blocks and can produce electrical activity that researchers can stimulate and record. Researchers at Johns Hopkins University (in Baltimore, Maryland, United States) refer to this emerging field as “organoid intelligence”, a term that captures both the scientific ambition and the ethical caution surrounding biocomputing.
Who Is Making Them, When and Where?
A Swiss team at FinalSpark has already built a remote “Neuroplatform” that lets universities run experiments on organoids over the internet. Their lab in Vevey, on the shores of Lake Geneva, grows these tiny clusters of neurons, places them on micro-electrode arrays, and exposes them to controlled electrical patterns so that researchers can study how they learn and respond to stimuli.
The company’s organoids can currently survive for several months, allowing long-term experiments on neural activity, memory and energy efficiency. The stated goal is to create “living servers” capable of performing certain computing tasks while using only a fraction of the power consumed by traditional silicon hardware.
FinalSpark’s published data describes organoid lifetimes exceeding 100 days, using an air–liquid interface and eight electrodes per spheroid. This design allows remote electrophysiology at scale, giving researchers in other countries access to living neuron cultures without needing their own biocomputing laboratories.
Others Doing The Same Thing
FinalSpark is not the only company experimenting with this organoid idea. For example, in Australia (and the UK), another organisation that is creating ‘brains’ for use in computing is Cortical Labs and bit.bio who have collaborated on “CL1”, a biological computer built from layers of human neurons grown on silicon. Their earlier “DishBrain” system showed neurons learning to play Pong, with findings published in Neuron in 2022. The company has since expanded its research to Cambridge, where it is developing biocomputing hardware that can be used by other organisations and universities to explore how living cells process information.
Also, Chinese research groups, including teams at Tianjin University and the Southern University of Science and Technology, have developed “MetaBOC”, an open-source biocomputer where organoid-on-chip systems learned to control a small robot. The demonstration showed a feedback loop between neural activity and physical motion, indicating how living tissue can process input and output in real time.
How The Technology Works
These so-called “wetware” experiments combine cell biology with digital engineering. For example, scientists create stem cells from human skin cells, coax them into neurons and glial cells, then culture them as spherical organoids about one or two millimetres wide. These are placed on micro-electrode arrays so that electrical patterns can be delivered and responses recorded – a bit like a miniature EEG in reverse.
FinalSpark’s system uses an air-liquid interface to keep organoids alive while allowing the electrodes to connect directly. Each unit can be accessed remotely by researchers, who send stimulation patterns and record how the organoids respond. However, the biological constraints are significant. For example, as Professor Simon Schultz, Director of Neurotechnology at Imperial College London, points out, organoids lack blood vessels, which limits their size and longevity. The challenge, therefore, is to keep the cells nourished and functioning consistently over time.
Why Do Scientists Want Wetware?
The human brain remains nature’s most efficient computer. It consumes only around 20 watts of power yet performs continuous learning, pattern recognition and reasoning far beyond what silicon hardware can do efficiently. Traditional computing architectures are fast and precise, but they burn huge amounts of energy when trying to emulate the brain’s parallel, adaptive processing.
That contrast has driven research into the wetware idea. For example, by using real neurons rather than digital simulations, scientists hope to create systems that can perform complex, adaptive tasks using a fraction of the energy. Johns Hopkins researchers have suggested that organoid-based computing could eventually produce “faster, more efficient and more powerful” systems that complement rather than replace silicon.
Cortical Labs’ Pong-playing experiment offers an early example of what living neurons can do. About 800,000 cells learned to improve their gameplay when given feedback, demonstrating a basic form of learning through trial and error. While this is a long way from human-level intelligence, it proves that even small neural cultures can process feedback and adjust behaviour.
What They Can Do Today
At present, wetware systems can only really respond to simple tasks under laboratory conditions. FinalSpark’s organoids are repeatedly stimulated with electrical signals, and researchers measure how their responses change over time, i.e. an early form of digital “training”. Cortical Labs has shown that neuron cultures can actually learn predictable patterns through feedback, while Chinese researchers have achieved basic robotic control via organoid-on-chip platforms.
These are all essentially small-scale experiments, but they mark progress from merely observing brain activity to actively using biological learning for computation. The next step is to scale and stabilise these systems so they can perform consistent, useful work.
More In The Race
Beyond FinalSpark and Cortical Labs, several major academic centres are also involved in these wetware experiments. For example, Johns Hopkins University coordinates an international research community focused on “organoid intelligence”, and in 2023 published the Baltimore Declaration, which is an ethical framework guiding responsible development of biocomputers and urging early discussions about potential consciousness and welfare.
The CL1 project in Cambridge, for example, aims to make wetware commercially accessible, while Chinese laboratories continue refining biocomputer-on-chip hardware. These efforts show that the field is moving away from isolated prototypes towards shared platforms that other scientists can use.
Benefits Over “Normal” Computers
Brains excel at handling uncertain information and learning from minimal examples, something current AI systems struggle to replicate efficiently. Silicon-based chips are powerful but energy-hungry, while neurons operate using chemical and electrical signalling at extremely low energy costs.
Wetware computing could, therefore, one day make certain types of AI and modelling tasks far cheaper and more sustainable to run. For example, the technology could also improve medical research by allowing scientists to study disease or drug effects on human cells without animal testing. Johns Hopkins researchers have said that organoid computing could “advance disease modelling and reduce animal use” alongside powering future AI systems.
Competitors, The Industry and Users
For developers like FinalSpark, the short-term business model is basically “research as a service”. This means that universities can log into FinalSpark’s Neuroplatform to access organoids remotely, run experiments and collect data without needing their own biocomputing facilities. The company says its neurons are already shared across nine universities and accessed 24 hours a day.
For competitors, the emergence of wetware is another pressure point in the race for energy-efficient computing. Chipmakers such as Intel and Nvidia are already developing neuromorphic processors that mimic brain structures, while wetware takes that concept further by using real neurons. Although biological computers are not ready to replace silicon, their development highlights how efficiency, adaptability and sustainability are becoming strategic priorities in computing.
For businesses, the most immediate relevance is energy and research access. For example, if wetware systems can eventually handle niche AI workloads or data modelling at a fraction of the power, that could transform data centre economics. Remote access models like FinalSpark’s also point to new ways of conducting research collaborations, where biological experiments are run digitally across borders.
Investors, regulators and policymakers are also likely to be watching the whole wetware idea closely. It’s worth noting that the Baltimore Declaration provides early guidance on consent, provenance, transparency and the monitoring of any potential signs of sentience, giving regulators a starting framework as the technology moves closer to commercial use.
Challenges and Criticisms
Given the unique nature and newness of this type of development, there are, of course, plenty of challenges ahead. Scaling actually remains the greatest technical challenge at the moment. For example, without blood vessels or advanced support systems, organoids struggle to survive long enough or grow large enough to carry out any complex computations. Their behaviour can also vary as the living tissue changes over time, making reproducibility difficult. Researchers are experimenting with microfluidics and electrode “caps” that can wrap around 3D organoids to improve stability and signal capture.
The ethical debate is an obvious (and equally active) one in this case. The Baltimore Declaration warns researchers to be alert to any sign of emerging consciousness and to treat wetware experiments with the same care given to animal studies. Scientists stress that today’s organoids are non-sentient, but agree that as complexity increases, ethical oversight must keep pace.
Also, given how exciting and futuristic the idea sounds, expectations need managing. For example, although Pong-playing neurons and robotic demonstrations are valuable proofs of concept, they are not evidence of general intelligence. Turning these small experiments into reliable, standardised systems that can be trained, paused and restarted like software will take years. Even supporters of the field acknowledge that it remains in its infancy, with commercial value likely to emerge only once lifespans, interfaces and quality controls improve significantly. “Organoids do not have blood vessels… this is the biggest ongoing challenge,” said Professor Simon Schultz of Imperial College London, highlighting the biological limits that must be overcome before wetware computing can scale.
Cortical Labs’ researchers have said that their neurons could learn to play Pong in minutes, showing adaptive behaviour but also underlining how early the technology remains. Johns Hopkins scientists maintain that wetware “should complement, not replace, silicon AI”, a sentiment echoed across most of the research community.
FinalSpark, Cortical Labs, Johns Hopkins University and Chinese teams behind MetaBOC are currently the main players to watch. Each is pursuing different goals, from remote-access research platforms to robotic control systems, but together they are actually defining what may become a new category of living computation, albeit a bit creepy for many people.
What Does This Mean For Your Business?
Biocomputing is now moving from concept to reality, and the idea of machines powered by living cells is no longer confined to science fiction. In laboratories, clusters of human neurons are already showing the ability to learn, respond and adapt, marking a genuine new direction in how computing power might be created and used. The researchers behind this work remain cautious, but their early results suggest that living tissue could soon sit alongside silicon as part of the world’s computing infrastructure.
The potential benefits are clear. For example, energy efficiency has become a pressing issue for every industry that depends on artificial intelligence, from cloud computing to data analytics. If biocomputers can perform learning and problem-solving tasks using a fraction of the power consumed by conventional hardware, the impact on cost, sustainability and data centre design could be significant. For UK businesses, this could eventually mean access to more energy-efficient AI systems and new opportunities in research, innovation and green technology investment.
Beyond business efficiency, there are also clear research and healthcare implications. Pharmaceutical and biotech companies could use these systems to model how drugs affect human cells with far greater accuracy, reducing reliance on animal testing. Universities could gain new tools for neuroscience, while technology firms might develop adaptive systems that learn directly from biological responses rather than pre-programmed rules. For investors and policymakers, this blend of biology and computing presents both an opportunity to lead and a responsibility to ensure strict ethical oversight.
However, the barriers are as significant as the promise. For example, keeping organoids alive, stable and reproducible remains difficult, and each culture behaves differently over time. Also, ethical questions are becoming increasingly important too, with scientists and regulators needing to ensure that no experiment risks creating self-awareness or distress in living tissue. Governments will also need to consider how existing AI and data laws apply to systems that are, in part, alive.
For now, biocomputing remains a niche research field, but it is advancing quickly and forcing people to rethink what the word “computer” could mean. Whether it becomes a practical alternative to silicon or stays a scientific tool will depend on how successfully the technical and ethical challenges are managed. What is certain is that the next stage of computing will not just be faster or smaller, but it may also be alive.