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Brain-as-a-service set to start before 2026, powered by human brain cells fused with silicon hardware

openai24 by openai24
March 9, 2025
Brain-as-a-service set to start before 2026, powered by human brain cells fused with silicon hardware
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  • Cortical Labs has built the first deployable biological computer, priced at $35,000
  • The CL1 integrates living neurons with silicon for real-time computation
  • The next step will be to build a biological neural network server stack

Despite the unquestionably impressive advancements we’ve witnessed in recent years, AI is still lagging far behind human intelligence. While it can process vast amounts of data, recognize patterns, and generate responses at speed, it lacks true understanding and reasoning, and although it’s getting better, the issue of hallucinations – when the AI makes stuff up – remains a problem.

Two years ago, researchers from Johns Hopkins University in Australia, together with scientists at Cortical Labs in Melbourne, suggested that the answer to real, less artificial AI was organoids – computers built with human brain cells. Fast forward to today, and Cortical Labs has turned the theory into reality with the production of the world’s first commercialized biological computer.

The CL1, which will be manufactured to order but is available for purchase online (the option to buy time on the chips will also be offered), is a Synthetic Biological Intelligence (SBI).

Connecting directly to neurons

“Real neurons are cultivated inside a nutrient-rich solution, supplying them with everything they need to be healthy. They grow across a silicon chip, which sends and receives electrical impulses into the neural structure,” the company says.

The world the neurons exist in is created by Cortical Labs’ Biological Intelligence Operating System (biOS) and “runs a simulated world and sends information directly to the neurons about their environment. As the neurons react, their impulses affect their simulated world. We bring these neurons to life, and integrate them into the biOS with a mixture of hard silicon and soft tissue. You get to connect directly to these neurons.”

By deploying code directly to the real neurons, the company claims the CL1 can solve today’s most difficult challenges, “The neuron is self-programming, infinitely flexible, and the result of four billion years of evolution. What digital AI models spend tremendous resources trying to emulate, we begin with.”

“Today is the culmination of a vision that has powered Cortical Labs for almost six years,” noted Dr. Hon Weng Chong, Founder and CEO of Cortical Labs. “However, our long-term mission has been to democratize this technology, making it accessible to researchers without specialized hardware and software. The CL1 is the realization of that mission. While today’s announcement is incredibly exciting, it’s the foundation for the next stage of innovation. The real impact and the real implications will come from every researcher, academic, or innovator that builds on top of it.”

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A report from New Atlasclaims Cortical is constructing a “first-of-its-kind biological neural network server stack, housing 30 individual units that each contain the cells on their electrode array, which is expected to go online in the coming months.” The site reports the company is aiming to have four stacks available for commercial use via a cloud system by the end of 2025.

As for pricing, the CL1 will be surprisingly affordable. “The units themselves are expected to have a price tag of around US$35,000, to start with (anything close to this kind of tech is currently priced at €80,000, or nearly US$85,000),” New Atlas adds.

For context, Apple’s “best failure” the Lisa, which paved the way for the Macintosh and even Microsoft Windows, sold for $9,995.00 in January 1983 which, adjusting for inflation, works out to a comparable $32,500 today. Will the CL1 prove be as important to computing’s future as the Lisa was? It’s impossible to say, but for now its impact will largely depend on scalability, practical applications, and how well it integrates into existing AI and computing systems.

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