In Melbourne, Australia, there is a tiny data center that doesn’t resemble what most people think when they hear the term “data center.” There are no tall server racks humming with fans. There are no floor-to-ceiling blinking indicator lights. Rather, about 120 shoebox-sized devices sit silently, each containing living human brain cells—something more intimate and strange than any silicon chip.
Growing neurons from stem cells and connecting them to electronic hardware is what Cortical Labs, an Australian start-up, is trying to do. It falls somewhere between biology and computer science, between engineering and neuroscience, in a field that had no name five years ago.
| Field | Details |
|---|---|
| Company | Cortical Labs |
| Founded | 2019 |
| Headquarters | Melbourne, Australia |
| Key Product | CL1 — a biological computing system |
| Technology | Lab-grown human neurons on silicon chips (wetware) |
| Key Person | Brett J. Kagan — Chief Scientific Officer & COO |
| Current Operations | Biological computing facilities in Melbourne & Singapore |
| Units Deployed | ~120 CL1 units in a Melbourne data centre |
| Stem Cell Source | Human blood or skin samples |
| Reference Website | Cortical Labs Official Site |
In order for the company’s device, known as CL1, to function, neurons derived from human stem cells are cultivated and placed on chips that have microelectrodes embedded in them. These electrodes essentially transform living tissue into a functional component of a computation by sending electrical signals into the cells and reading their responses back.
The company’s chief scientific officer and chief operating officer, Brett J. Kagan, has put it simply: all you need is a small amount of skin or blood to create an endless supply of cells that can be transformed into neurons. That statement’s simplicity is powerful. It implies something more akin to a supply chain rather than a lab curiosity.
Standardization is what sets this apart from previous neuroscience experiments. Researchers have been cultivating neurons in the lab for decades, so it’s not a novel concept. Cortical Labs asserts that it has eliminated the need for intricate, custom-built setups that previously required months or years of specialized labor.
They claim that this timeline can be condensed into hours or days using their integrated platform. The fact that 120 of these units are reportedly in operation in Melbourne indicates that this is no longer just theoretical, though whether that claim holds up at scale is still up for debate.
If you spend any time examining how much energy contemporary AI systems use, it is difficult to reject Kagan’s deeper argument regarding efficiency. It can take enormous amounts of electricity to train a large language model. Power grids are already being strained by the rate at which data centers are being constructed worldwide. Kagan notes that biology provides solutions to issues that silicon finds difficult. Before a young child recognizes a dog, she may need to see it twice.
For a machine learning model to produce the same result, hundreds of thousands of images may be required, and even then, lighting or angle can cause errors. The biological approach seems to be more intelligent than simply more poetic.
However, Kagan is careful to clarify that this is not a substitute for traditional computing. For quick and accurate mathematical computations, conventional silicon-based systems are still far superior. His vision is more akin to a merger: future systems that combine silicon and biological approaches, each of which addresses the shortcomings of the other. It’s still unclear if that integration will take years or decades, or if it will encounter issues that no one has yet to foreseen. There have been many promising concepts in computing history that have stalled at the brink of viability.
Not all members of the scientific community share this conviction. A flat network of human neurons probably wouldn’t provide significant advantages over silicon alone, according to Alysson R. Muotri, director of the Sanford Stem Cell Education and Integrated Space Stem Cell Orbital Research Center at UC San Diego. He contends that more intricate, three-dimensional brain-like structures known as organoids may have genuine promise, but they are still very much in the experimental stage and far from being deployable.
Perhaps Cortical Labs is creating something truly fundamental. They might also be a single step in a much longer journey, significant but not yet revolutionary as some coverage suggests.
One thing that is difficult to overlook when observing this area is the timing. AI systems are encountering real-world constraints, not theoretical ones, but financial and physical ones. According to research, the demand for computing power at MIT is approximately five times higher than what the university can supply.
Businesses are investing billions in infrastructure simply to stay up with the current state of the industry, let alone its future. The concept of a computing substrate that is adaptable, energy-efficient, and literally derived from human biology lands differently in that setting than it might have in a more tranquil one.
It is said that the CL1 is about the size of a shoebox. Somehow, that detail is important. Rooms are filled with supercomputers. Buildings are filled with cloud infrastructure. It seems genuinely strange to think that something this small, powered by nutrient-rich liquid and living cells, could eventually coexist with silicon in serious computing facilities. If Cortical Labs is even partially correct, this could be truly significant.
According to the company, it is already building facilities in Singapore and Melbourne, working toward a model that would allow users to access biological computing remotely in a manner similar to how they currently access cloud servers.
It’s really unclear what will happen next, and anyone who tells you otherwise is probably trying to sell you something. However, Melbourne’s neurons are firing. They are reading the signals. Additionally, a small group in Australia appears to think that the gap between biology and technology has narrowed, and they now have some supporting data.
