In a landmark moment for artificial intelligence and biotechnology, Australian company Cortical Labs has commercially launched the world’s first “biological computer.” This cutting-edge technology, named CL1, seamlessly integrates human brain cells with silicon hardware, creating a dynamic and adaptive neural network.
The breakthrough system, unveiled in Barcelona on March 2, 2025, introduces what Cortical Labs terms Synthetic Biological Intelligence (SBI). The company claims the technology is significantly more energy-efficient, sustainable, and adaptable than conventional AI, potentially surpassing the capabilities of silicon-based processors used in large language models like ChatGPT.
A vision realized after years of research
“Today is the culmination of a vision that has powered Cortical Labs for almost six years,” said Dr. Hon Weng Chong, Cortical Labs’ founder and CEO. “We’ve enjoyed a series of critical breakthroughs in recent years, most notably our research in the journal Neuron, where cultures were embedded in a simulated game-world, mimicking the arcade game Pong.”
Dr. Chong emphasized that CL1 is designed to democratize access to this advanced technology. “Our long-term mission has been to make this technology accessible to researchers without requiring specialized hardware and software. The CL1 is the realization of that mission.”
Bringing ‘wetware-as-a-service’ to the market
One of CL1’s most striking features is its accessibility. Cortical Labs is offering the biocomputer both for outright purchase and through a cloud-based subscription model called ‘Wetware-as-a-Service’ (WaaS). This allows researchers and companies worldwide to tap into SBI technology remotely, eliminating the need for expensive in-house biocomputing setups.
“This platform will enable researchers, innovators, and big-thinkers around the world to turn the CL1’s potential into tangible, real-world impact,” Dr. Chong added. “We’ll provide the platform and support for them to invest in R&D and drive new breakthroughs.”
A game-changer for science, medicine, and AI
The CL1’s potential applications are vast, spanning drug discovery, clinical testing, and the development of robotic intelligence. In 2022, Cortical Labs made headlines by training a self-adapting neural network—consisting of 800,000 human and mouse neurons—to play Pong. Since then, the company has significantly refined its technology.
“We almost view it as a different form of life,” said Brett Kagan, Chief Scientific Officer at Cortical Labs. “We think of it as a mechanical and engineering approach to intelligence. We’re using the substrate of intelligence—biological neurons—but assembling them in a new way.”
A new era of AI and biological computing
The CL1 system relies on lab-grown neurons placed on a planar electrode array—a structured network of 59 electrodes that allow for real-time activation and control of the neural system. The SBI ‘brain’ is housed within a compact, self-sustaining life-support unit, connected to an advanced software interface. “In essence, it’s like a body in a box,” Kagan explained. “It has filtration for waves, a media storage system, circulation pumps, gas mixing, and precise temperature control.”
The company is constructing the world’s first biological neural network server stack, comprising 30 CL1 units. This system is expected to go online in the coming months, with four full stacks available for commercial use before the end of the year. Each unit is expected to cost around $35,000—considerably less than comparable high-end biotech solutions, which typically exceed $80,000.
Advancing disease research and drug development
One of the CL1’s most promising applications is in medical research, particularly for neurological diseases such as epilepsy and Alzheimer’s. The technology could provide unprecedented insights into the complexities of the human brain, allowing for more precise drug testing and disease modeling.
“The large majority of drugs for neurological and psychiatric diseases that enter clinical trials fail,” Kagan noted. “With CL1, we hope to improve the accuracy of preclinical testing, reducing reliance on animal models and increasing ethical research alternatives.”
Ethical considerations and future challenges
The development of SBI technology raises profound ethical questions, particularly concerning consciousness and sentience in artificially grown neural networks. Cortical Labs has emphasized its commitment to ethical guidelines, working closely with regulatory bodies to ensure responsible use of biological computing.
“There are numerous regulatory approvals required, based on location and specific use cases,” the company stated. “Compliance with these regulations is essential to ensure responsible and ethical use of biological computing technologies.”
Despite its groundbreaking potential, Cortical Labs faces challenges in securing funding and establishing itself within existing technology sectors. “We don’t fit into a box,” Kagan admitted. “We’re a technology that crosses multiple boundaries—AI, medicine, robotics. We can do all of them, but we’re not solely any one of them.”
A glimpse into the future
Looking ahead, the company is working on an even more ambitious goal: the development of what it calls the ‘Minimal Viable Brain’ (MVB). This research aims to determine the smallest functional neural network capable of exhibiting true cognitive behavior.
“The smallest working brains we know of have around 301 neurons,” Kagan explained. “But is that truly the minimal viable brain? Or could intelligence emerge from a network of just 30 specialized neurons? We don’t know the answer yet, but this technology will allow us to explore that question.”
With CL1 set for wide availability in the latter half of 2025, Cortical Labs is at the forefront of a paradigm shift in artificial intelligence and neuroscience. The implications of biological computing could be far-reaching, influencing not only technology and medicine but also how we fundamentally understand intelligence itself.
The dawn of synthetic biological intelligence has arrived, and the world is watching closely.
