Biocomputing, AI, and medicine converge in the CL1, the world’s first commercial biological computer powered by human neurons. Discover how this disruptive technology could revolutionize artificial intelligence, reduce animal testing, and pave the way for new forms of conscious computing.
“A graphic representation of data abstracted from banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding…” — William Gibson, Neuromancer
Maurício Pinheiro
Keywords: CL1, Biocomputing, Computer with human neurons, Biological artificial intelligence, Biological computer, What is biocomputing, How CL1 works, Human neurons in computers, Cortical Labs CL1, DishBrain, Biological operating system, CL1 biological computer price, AI with human cells, Artificial brain, Future of artificial intelligence, Synthetic biology and computing, Lab-grown neurons, Reinforcement learning with living neurons, Biocomputing applications in medicine, Difference between traditional AI and biocomputing, Human stem cells, Biological neural networks, Chip with living neurons, Adaptive learning, Ethics in artificial intelligence, Artificial consciousness, Energy efficiency in AI, Pharmaceutical testing with neurons, Synthetic intelligence, Brain-inspired computing
Imagine a living brain growing atop a silicon chip.
Now imagine that this brain can learn, respond to stimuli, interact with digital systems, and—perhaps soon—think.
This is not science fiction. In 2025, the Australian startup Cortical Labs introduced the CL1 to the world: the first commercial computer made with human neurons cultivated in a laboratory.
A piece of technology that ushers in a new era—biocomputing.
The CL1’s concept is as bold as it is simple. It brings together roughly 800,000 living human neurons over a mesh of microelectrodes capable of sending and receiving electrical signals. All of this happens inside a life-support capsule, where temperature, nutrients, and gases are precisely controlled. The result is a hybrid organism—part machine, part biology—that can already be purchased by laboratories and institutions for about $35,000. And perhaps most intriguingly, it is also available via the cloud, as a subscription-based brain.
To grasp the impact of this invention, one must first understand what biocomputing actually is. It is an emerging field that merges biology, engineering, physics, and computer science in an effort to replace traditional silicon transistors with living elements—such as proteins, DNA, or, as in the case of the CL1, neurons. While classical computers operate on binary logic (zeros and ones), biological systems process information in an adaptive, distributed, and remarkably efficient way. Nature, after all, took billions of years to perfect the human brain—so why not leverage that legacy?
The idea of computing with life is not entirely new. In 1994, scientist Leonard Adleman demonstrated how DNA could be used to solve complex mathematical problems. Years later, researchers at Stanford built logical circuits using RNA. But it wasn’t until 2022 that the concept took on more concrete form with the DishBrain project: a small cluster of neurons that learned to play Pong, the classic 1970s game, in real time, using only electrical stimuli as reward and punishment (reinforcement learning).
The CL1 is the evolution of that experiment. What was once a laboratory curiosity has now become a functional, scalable, and marketable platform. Its neurons are grown from human stem cells and arranged on a matrix of electrodes that serve as the input and output gates for electrical signals. Each pulse sent or received triggers a measurable response. In a short time, the system begins to form patterns, adapt, and even anticipate events—in other words, it begins to learn.
This process is made possible by biOS, the operating system developed by Cortical Labs, which translates digital commands into biological language and vice versa. It’s through this system that scientists can “program” the living neurons. In the DishBrain experiment, for example, the neurons learned to play Pong because they received pleasant stimuli when hitting the ball and unpleasant signals when missing it. Within minutes, the neurons began to understand the game, adjust their behavior, and improve their performance.
Perhaps the most surprising aspect is that all this occurs with an almost negligible energy cost. While large AI models—like those powering virtual assistants and automatic translators—consume megawatts in massive data centers, the CL1 runs on less than a thousand watts per year. This energy efficiency is one of biocomputing’s most enticing promises: understanding how the brain does so much with so little.
But the CL1’s potential goes beyond AI. In medicine, it could serve as a testing platform for neurological drugs, simulating the behavior of real human networks without the need for lab animals. It could also aid in studying degenerative diseases such as Alzheimer’s and Parkinson’s, allowing researchers to observe drug impacts directly on human neurons in real time.
However, like any disruptive technology, the CL1 raises profound dilemmas. The cultured neurons are alive. They respond to stimuli. They learn. Are they conscious? Do they suffer? For now, the answer is no—experts affirm they are sentient but lack consciousness, memory, or any subjective experience. Even so, it’s impossible to avoid the ethical questions this frontier begins to touch.
Another practical limitation is the lifespan of neural cultures: around six months. After that, the neurons lose functionality and must be replaced. Moreover, there is still no effective way to transfer memory from one generation of neurons to another. Each new CL1 begins as a tabula rasa, which poses challenges for long-term projects.
Despite these hurdles, the future of biocomputing seems inevitable. Cortical Labs has already announced plans to increase neuron counts, develop more complex networks, and offer remote access through the Cortical Cloud—a kind of “brain-as-a-service.” Other companies are following a similar path, experimenting with neural interfaces for robots, drones, and adaptive systems that can learn with less data and respond with greater intelligence.
Ultimately, the CL1 is not just a machine. It is a symbol—a sign that the boundary between biology and technology may be dissolving. If artificial intelligence sought to imitate the brain, biocomputing is taking a step further: it is incorporating it.
We live in an age where life becomes code, and code becomes alive. The CL1 is only the first chapter of a story still being written—perhaps by brains not yet born, but already beginning to think.
References:
- Adleman, Leonard M. Molecular Computation of Solutions to Combinatorial Problems. Science, vol. 266, no. 5187, 1994, pp. 1021–1024.
- Goldwag, J., Wang, G. DishBrain plays Pong and promises more. Nature Machine Intelligence 5, 568–569 (2023).
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