For decades, artificial intelligence has borrowed the language of biology.

Neural networks.
Learning.
Memory.
Attention.

But beneath all those biological words lived silicon, mathematics and software.

Now somebody has opened a much stranger door.

What happens when the neural network is no longer artificial?

🐰 Follow the White Rabbit.

Cortical Labs has developed the CL1, which the company calls the first code-deployable biological computer.

Inside it are not simulated neurons.

They are living neurons.

The cells grow across a silicon interface capable of sending electrical signals into the neural network and recording the signals that come back.

They are kept alive by an internal life-support system.

Software provides stimuli.

The neurons respond.

Those responses affect their simulated environment.

Then the environment changes again.

Input → biological response → feedback → adaptation.

That loop should sound familiar.

It is one of the foundations of learning.

Except this time, part of the computer is alive.

Before Hatta loses his hat completely:

🎩 This is not a tiny human brain trapped inside a box.

Cultured neural networks and brain organoids are extraordinarily simplified biological systems.

There is presently no good scientific basis for declaring that these systems possess humanlike consciousness, personality, emotions or self-awareness.

That distinction matters enormously.

Wonder without skepticism becomes mythology remarkably quickly.

But skepticism without wonder can make us walk straight past a very peculiar doorway.

And this doorway is real.

Traditional AI takes inspiration from brains while running on hardware that works very differently from them.

Biocomputing asks another question:

Instead of spending enormous resources making silicon behave more like neurons, why not compute with neurons?

Biological neurons have several remarkable properties.

They reorganize connections.

They respond dynamically to experience.

They function in massively interconnected networks.

And biology performs sophisticated information processing on astonishingly little energy.

A human brain operates on roughly the power of a dim household light bulb.

That does not mean biological computers are about to replace data centers.

Today’s systems are tiny, experimental and difficult to maintain.

But the efficiency gap is one reason researchers are fascinated.

Cortical Labs grows neurons across an electrode array.

Other researchers are investigating brain organoids: three-dimensional clusters of neural tissue grown from stem cells.

That emerging field has acquired a deliciously provocative name:

Researchers at Johns Hopkins and elsewhere are investigating whether organoids can perform useful information-processing and learning tasks.

In March 2026, a Johns Hopkins team received a five-year, $15 million NIH award for a project called DROIDp: the Drug Research Organoid Intelligence Development Platform.

Its immediate purpose is not creating biological supercomputers.

It is considerably more practical.

Researchers hope organoids combined with advanced sensors and AI analysis can help study neurological disease, evaluate drugs and potentially reduce reliance on animal testing.

Alzheimer’s disease is already among the targets being explored.

So the first major revolution from biological computing might not be a computer that beats your laptop.

It might be a laboratory system that understands human disease better because part of the experiment is actually made of human cells.

Suppose biological computing improves.

Suppose a culture learns.

Suppose it remembers.

Suppose increasingly complex neural structures respond differently because of previous experiences.

At what point do our ethical obligations change?

Notice what we are not asking.

We are not saying:

That would leap far beyond the evidence.

The more careful question is:

Scientists and bioethicists are already discussing exactly that problem.

That may be one of the strangest features of this technology.

The ethical conversation cannot safely wait until somebody proves consciousness.

Because by then, if consciousness were possible, the experiment would already be running.

For seventy years we have largely imagined computing as a progression:

Vacuum tubes.

Transistors.

Microprocessors.

Silicon.

Artificial neural networks.

AI.

But evolution constructed information-processing systems billions of years before humans constructed computers.

Cells sense.

Networks adapt.

Brains predict.

Organisms learn.

So perhaps there are actually two technological roads approaching each other.

One begins with machines and attempts to reproduce intelligence.

The other begins with biology and asks whether intelligence can become technology.

And somewhere ahead...

the roads may meet.

Here is the question I cannot quite leave behind:

We have spent decades asking whether a sufficiently sophisticated machine could become more alive.

Biocomputing turns the telescope around.

🎩 Hatta is staring suspiciously at the motherboard.

The motherboard may be staring back.

Not yet.

Probably.

But this is a Rabbit Hole.

We keep the lantern lit.

Sources & further exploration:
Cortical Labs, CL1 biological computing platform
Johns Hopkins University, Organoid Intelligence research
Johns Hopkins Berman Institute of Bioethics, DROIDp / NIH Complement-ARIE program
Nature Reviews Bioengineering, “Biocomputing with organoid intelligence”

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