How Living Computers Could Change Everything

Author: Matt Ferrell

It’s no secret that tech companies are racing to build “artificial general intelligence,” or AI that can match a human brain without needing a lifeline. But here’s the kicker: our brains already have the home field advantage. They do the same heavy lifting with just a fraction of the resources. Whether it’s energy, water, land, components, or, you know… money… human brains are just way cheaper.

Scientists figured this out long before we were arguing with ChatGPT about sandwich recipes. And now, with the AI race heating up faster than a server farm in August, biotechnologists are asking: Why build AI like a brain when you could just use the real thing?

Right now, you can either buy a human brain cell-based computer… or rent time on a remote one. Yep, even brainpower’s got a subscription plan these days. So what can these living computers actually do? How do they work? And, most importantly, should we be freaking out a little bit?

Why?

Got a spare $35,000 and a lab coat lying around? You could be the proud owner of the CL1, which is a biocomputer from Australia’s Cortical Labs. Unlike your regular computer running a BIOS (all caps), the CL1 runs on a biOS — “Biological Intelligence Operating System.” Because instead of just mimicking a brain… it literally uses one. It’s packed with living human neurons, which are cells that react, learn, and adapt inside a simulated world.¹ It’s like a real brain, minus the existential dread. (Probably.)

And Cortical Labs isn’t the only one making living computers. FinalSpark over in Switzerland is also training human neurons in the form of brain organoids. These are tiny clusters of living brain cells you can rent for research. I even talked to a few researchers using these systems, and trust me, it’s as weird and fascinating as it sounds.

But before we get too deep into the how, let’s ask the obvious: Why? Why go through all the trouble of growing brain cells when we have perfectly good silicon computers. According to Cortical Labs’ CEO Hon Weng Chong, it’s simple:

“Everyone is racing to build AGI, but the only true AGI we know of is biological intelligence, human intelligence.”²

But there’s a bigger, messier reason too: sustainability.³⁴ Generative AI eats everything — more energy, more water, more land, more chips.⁵ It’s a runaway resource hog, and it’s only getting worse. It’s something I dug into recently in another video if you want the full breakdown.

Tech companies aren’t slowing down, they’re scaling up.⁶ However, you can only expand so far before you start stepping on toes. Data centers aren’t just hogging electricity. They’re draining water, eating up land, and putting stress on local communities.^7891011 In early 2024, OpenAI CEO Sam Altman put it bluntly:

“We do need way more energy in the world than I think we thought we needed before…And I think we still don’t appreciate the energy needs of this technology. The good news, to the degree there’s good news, is there’s no way to get there without a breakthrough.”¹² -Sam Altman

FinalSpark’s Fred Jordan thinks he’s found it. Living neurons. If we can train biocomputers like traditional AI, Jordan says we could slash AI’s energy use by thousands of times — making its carbon footprint almost invisible.⁴¹³ And to really see why that matters, let’s talk numbers.

Meet the Frontier: America’s first exascale supercomputer.¹⁴ It cranks out 1.1 exaFLOPS — that’s a quintillion calculations per second — while weighing more than 266 metric tons, stretching across 74 cabinets, and costing $600 million to build.¹⁴¹⁵¹⁶ Now compare that to your brain:

• 1.3 kilograms (3 pounds).
• 20 watts of power usage.
• 1 exaFLOP of raw performance.
• And a price tag that just says: “not applicable.”¹⁵¹⁷

Seriously — David Byrne was right: we’re makin’ flippy floppy.

And Cortical Labs gets it. Their motto?

“What digital AI models spend tremendous resources trying to emulate, we begin with.”

Forget AGI. They’re chasing SBI, or Synthetic Biological Intelligence.¹⁸¹⁹

How?

Biocomputing sounds promising for efficiency, but what actually is a biocomputer? In 2024, Hon Weng Chong called the CL1 a “body in a box.”²⁰ And no, it’s not a horror movie prop floating in a glass jar. The neurons inside are actually stem cells — or rather, they were stem cells, before scientists reprogrammed them.¹³⁴

Here’s the basic rundown. The human body has a few types of stem cells. Embryonic stem cells exist during early development, while adults have stem cells in places like the skin and bone marrow, producing new cells on demand.²¹ Once these stem cells specialize into a particular role (say, skin cells) they normally stay that way. That ability to specialize into anything is called pluripotency.²²

For a long time, researchers thought mature human cells couldn’t revert to their original state. Then in 2012, Kyoto University’s Shinya Yamanaka won a Nobel Prize for proving otherwise. By flipping a specific set of genes, he turned ordinary mouse skin cells back into pluripotent stem cells, ready to become anything again — a complete biological reset.²³¹³ And yes, it works for humans too.

That’s exactly how Cortical Labs and FinalSpark grow their neurons. The process kicks off with dedifferentiation:

• Researchers sample blood or skin cells from adult volunteers.
• They reprogram them into induced pluripotent stem cells — iPSCs.¹⁸²⁴²⁰
• Then, through weeks of careful incubation and gene tweaking (sadly, no epic training montage), the cells slowly transform.

Finally, researchers differentiate the iPSCs again, this time steering them toward becoming neural progenitor cells. Basically, baby neurons-in-training that, if they pass the tests, move on to the next stage.¹⁸²⁰

As Cortical Labs explains it, once the cells are about ready to turn into neurons, the researchers move them onto a multi-electrode array (MEA) chip. The cells attach to the chip, allowing electrical signals to be both sent to them and received from them. For a few months, they continue to develop, and then bam, human neurons, merged with silicon.¹⁸²⁵ It doesn’t get much more sci-fi than that.

The whole point of this process? To build brain organoids, which are miniature 3D tissue cultures modeled after real organs. Researchers use organoids to study how organs like kidneys, lungs, and yes, brains develop and function.²⁶ Think of them like those plastic models you saw in biology class… except these ones are alive. They eat, they grow, they create waste … and eventually, they die.¹⁸²⁰

Now, to be clear: these aren’t fully formed human brains. Not even close. There’s a reason they’re called organoids and not organs. They’re organ-like.²⁷ Organoids are tiny, limited to specific brain regions, and cap out around 5 million cells (about the size of half a centimeter).²⁸ By comparison, your brain has around 86 billion neurons, plus another 85 billion non-neuronal cells.²⁹³⁰ So yeah. Tiny.

When it comes to synthetic neurons, there’s a lot more nuance than I can fit here. Cortical Labs actually breaks it down step-by-step on their YouTube channel if you want to nerd out. The short version? It takes months of careful work.¹⁸²⁵ FinalSpark’s timeline clocks in at about four months to create a single brain organoid.³¹

And just as the CL1 hit the market, MIT researchers announced something wild. They figured out how to skip the stem cell stage altogether by generating neurons directly from skin cells.³² If it scales, this shortcut could make neuron production way faster and cheaper for biocomputing.

In the end, the goal is to tap brain organoids as an alternative to AI, or what some are calling Organic Intelligence, or OI.¹⁵ Now, you might be thinking: What about the ethics of growing mini-brains in the lab? Good question. And it’s something researchers across biotech, neuroscience, and philosophy are struggling to answer, too.¹⁹³³³⁴ But before we wander too far down that rabbit hole… Let’s first talk about what you can actually do with these things.

What?

Outside of building SBI, OI, biocomputing, wetware (whatever you want to call it), brain organoids are already making waves in medical research. Scientists are using them to model diseases, test new drugs, explore gene therapies, and push the boundaries of personalized medicine.²⁸³⁵ And someday, advances here could even help reduce the need for animal testing.²⁸¹⁷

As for biocomputers like the CL1? Cortical Labs and FinalSpark are betting big on them as a greener alternative to today’s resource-hungry AI. (Or at least, that’s their pitch.)

So, what can you actually do with a biocomputer right now? Well, in the world of computing, milestones usually come dressed up as games. Alan Turing’s “imitation game” inspired the Turing Test.³⁶³⁷ IBM’s Deep Blue made headlines by beating chess champion Garry Kasparov in 1997.³⁸ Then DeepMind’s AlphaGo took down Go master Lee Se-dol in 2016.³

And now? Meet DishBrain, Cortical Labs’ tiny, living player. It doesn’t stand tall like Deep Blue — which its creator famously compared to “an office refrigerator” back in 1995.³⁹ It doesn’t need 1,202 CPUs like AlphaGo did.⁴⁰ And it definitely doesn’t hog 7,300 square feet like Frontier, the world’s first exascale supercomputer.¹⁵

DishBrain fits inside… a Petri dish.

By 2022, it could play Pong.⁴¹⁴²

Here’s the Breakout breakdown. During DishBrain’s development, Cortical Labs studied human and mouse neurons grown on MEA chips. The idea? Based on the Free Energy Principle, which says intelligent systems prefer predictability, they trained the neurons to learn how to play Pong using electrical feedback.⁴¹⁴³⁴⁴

The setup actually mirrors how we interpret the world: We get sensory input → our brains translate it into electrical signals → we respond. DishBrain’s neurons did the same thing inside a simulated game world.⁴³ When the neurons missed the ball, they got hit with random, unpredictable signals: 4 seconds of a 150 mV voltage at 5 Hz.²⁰ Not exactly emotional punishment, but more like getting a foul called and hearing static instead of a whistle.

When they successfully intercepted the ball? They got a reward: a clean, smooth sine wave at 100 Hz for 100 milliseconds.⁴¹²⁰ Over time, predictable feedback helped the neurons get better at playing. And the more sensory input they had, the better they performed. When there was no feedback at all? Performance flatlined.

Cortical Labs argues this shows DishBrain wasn’t just reacting, it was learning.⁴¹ Not well enough to beat you at Pong yet… but still, pretty wild.

Like a lot of biocomputing, the theoretical neuroscience behind Cortical Labs’ work is a rabbit hole way too deep for one video.⁴⁴ But if you want a full biotech deep dive someday, let me know!

For now, let’s put down the journal articles and talk about something more hands-on. Despite the CL1’s $35,000 price tag (and the fact you need to be a legit researcher to buy one) interacting with biocomputers isn’t as locked down as you might think.⁴⁵ For starters, the CL1’s API documentation is publicly available on GitHub. Plus, both FinalSpark and Cortical Labs run public Discord servers where you can ask questions, swap notes, and geek out with others.⁴⁶⁴⁷

And if you don’t have a lab budget? Both companies are offering subscription services to rent remote access to their living neural networks. There are some caveats, though:

• FinalSpark’s platform is live but subject to approval.
• Cortical Labs’ rental service is still gearing up.

Right now, FinalSpark lists 10 universities as official users of its “neuroplatform.”⁴⁸⁴⁹⁴

University Collaborations

I had the chance to talk to two research teams using FinalSpark’s system — which lets them work with brain organoids remotely.

First up: Dr. Kyle Wedgwood and research intern Wiktor Wiejak at the University of Exeter in England. Wedgwood’s a mathematician specializing in neuroscience, and he’s using FinalSpark’s organoids to explore what he calls “the fundamentals” of how neurons work:

“Here it’s really trying to ask the question about what can we import from sort of mathematical descriptions of things like neuronal networks and use them to understand how neuronal networks work, how cells communicate with each other, but also how can we exert some sort of control or some modulation in a sort of targeted way on you neuronal networks.” -Dr. Kyle Wedgwood

When I asked what “training” neurons looks like, Wedgwood pointed right back to Cortical Labs’ Pong study:

“Over time, if you stimulate these networks, they respond in a way by effectively strengthening and weakening different kind of connections between neurons. So broadly, this is called synaptic plasticity, and it’s one of the fundamental ways that brains learn, remember, acquire new skills, all this kind of stuff. …Obviously the neural network did not become really, really good at playing Pong right? It just got better than it was in the beginning. But it did show that actually you can study kind of learning in these systems.” -Dr. Kyle Wedgwood

Then, I sat down with Dr. Tjeerd Olde Scheper from the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute at Oxford Brookes University. As a computational neuroscientist, Scheper’s digging into how biological systems store and represent information — and whether they might someday outperform our traditional computers:

“Each cell, individual cell, solves a huge number of computational tasks every moment in time…We have a lot of knowledge about the biochemistry, the molecules involved, the structure of a lot of those things as well. That’s getting more apparent as well, but how they actually interact with each other, how they come combine with each other to create this complex, system and do that to solve complex problems is, is still quite of, you know, quite fairly much unclear.“
“So from my point of view is, if we have a better understanding how each of those components work by letting each part of this component decide for itself what its best behavior should be —and, I mean behave in a dynamic sense, so how it changes over time.” -Dr. Tjeerd Olde Scheper

So, there’s a glimpse of how researchers are already putting biocomputers to work. But here’s the fun part: You don’t need a PhD, or a grant, to get involved. If FinalSpark likes your project enough, you could potentially work with them for free.⁴⁸ They also host a 24/7 livestream of some of their neurons online.⁵⁰ Yes… you can literally watch a Petri dish. Could be better than some screensavers.

Looking for something a little flashier? FinalSpark also offers “the butterfly,” a 3D simulation showing about 10,000 neurons reacting to sensory input in real time. Basically, it’s like controlling a virtual RC car (except your remote is a clump of neurons). And don’t worry: heavy emphasis on virtual. It’s just visuals.⁵¹⁵²

Of course, FinalSpark co-founder Fred Jordan is quick to remind everyone: This tech is very early days. It’s not going to replace your phone or laptop anytime soon … and it might never. As Jordan’s joked, running Windows on a brain organoid would be … unrealistic. No word yet on whether it could run DOOM, though. Still, Jordan’s point is worth remembering: The inventors of the semiconductor had no idea what the world would eventually build on top of it.¹³

The Bigger Questions

That’s exactly the scary part, isn’t it? All the what ifs. I’m guessing you don’t need me to invent hypotheticals … you’ve probably already thought of a few. The biggest one? How would we even know if a brain organoid achieved consciousness? And if it did… what then?

Well, I’ve got good news and bad news.

• Bad news: We have no idea what we’re doing.
• Good news: We have no idea what we’re doing.

We don’t even have a solid definition for human consciousness yet.³⁴ Cortical Labs is actually running a public survey to tackle this exact problem.⁵³ Without a shared language, it’s tough to even describe biotech research properly. Especially when terms like “sentience,” “consciousness,” and “thinking” trigger emotional reactions.¹⁷⁵⁴ Who gets to decide what counts as thinking? What’s the threshold for sentience? Here’s your chance to toss in your two cents.

That said, and I really can’t emphasize this enough, we’re nowhere close to needing any emergency shutdown buttons. Brain organoids, even at their best today, are tiny compared to real brains. Their networks aren’t even on the same playing field as a bird, a mouse, a snake… or even an insect.⁵⁵⁵⁶ No disrespect to the humble fruit fly.

As Dr. Scheper put it:

“We have made progress in trying to understand what we can and cannot do…at the moment, it’s not something that we can make them push to do anything that is even remotely related to what intact brains or even slices or parts of brain can do, because those have been trained and accommodating.” -Dr. Tjeerd Olde Scheper

When?

So, where does biocomputing land on NASA’s Technology Readiness Scale? Short answer: still in the lab. Biocomputers like the CL1 and FinalSpark’s neuroplatform are under active development. Researchers using them are basically beta testers and giving feedback, suggesting improvements, and helping the developers build in real time.⁴ It’s a true collaboration between scientists and startups.

Zooming out even further, biocomputing as a whole is still barely scratching the surface. It’s an emerging science, a little like quantum computing… or the quietly growing comeback of analog computing. Right now, we’re standing at the top of the canyon, admiring the view. But what’s waiting down at the bottom? No one knows yet. And hopefully not anything with too many mouths that can’t scream.

Biocomputing, and biotech more broadly, is way too big to cover fully in one sitting. If this video fired up your neurons, I’ll be publishing some of the full interviews over on Still TBD. We go much deeper into the strengths, challenges, and wild possibilities of brain organoids. And honestly? There’s still so much more out there to explore.