By Jin Hyung Lee

Artificial intelligence continues to command global attention, with algorithms rapidly advancing in speed, precision, and capability. This technology is tackling tasks once considered uniquely human, from medical diagnostics to creative expression and language generation.

Yet behind the headlines, another revolution is taking shape. This one is grounded not in machine learning, but in the intricate workings of the human brain.

By decoding how the brain performs complex tasks with minimal energy, researchers could pave the way for AI systems that align with human needs, collaborating instead of competing for limited resources.

The implications for future technologies are profound. Let’s talk about them!

Brain science could unlock better lives for millions
Around the world, hundreds of millions of people suffer from brain-related disorders—Alzheimer’s, Parkinson’s, epilepsy, depression, and countless others.

The economic toll is staggering: more than $1.32 trillion every year, with projections climbing sharply as populations age. Even that number doesn’t capture the daily human costs—families struggling to care for loved ones, patients losing independence, and healthcare systems buckling under the pressure.

The good news? We can (and will) do better.

Understanding the brain—how it forms memories, processes emotion, and recovers from injury—is the foundation for earlier diagnoses, better treatments, and potentially even cures.
When we invest in world-changing brain research, we invest in human dignity, longevity, and quality of life on a global scale.

The brain also holds the blueprint for smarter, greener AI
The same research that helps us treat disease also holds the secret to building a new kind of artificial intelligence—one that’s not only powerful, but energy-efficient, sustainable, and more aligned with how we think.

The human brain runs on just 20 watts of power (less than a lightbulb), yet it can process information, adapt, learn, and feel.

Today’s AI, in contrast, requires entire data centers and consumes energy on a scale that’s both environmentally and economically unsustainable.

As we hit the limits of current silicon-based computing, neuromorphic engineering—designing systems that mimic the brain’s architecture—is emerging as a viable path forward. Think chips that learn through connections like neurons and store memory where it’s processed (like synapses), slashing energy needs and unlocking new kinds of machine intelligence.

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