The human brain: 86 billion neurons firing at ~1000 Hz, connected by 100 trillion synapses, running on just 20 watts. Meanwhile, our most advanced AI requires data centers consuming megawatts. This explorer lets you understand the gap—and what it would take to close it.
Human Brain
Processing Units
86billion neurons
Connections
100trillion synapses
Clock Speed
~1000Hz per neuron
Power Draw
~20watts
Parallelism
86Bsimultaneous
Memory
~2.5petabytes (est.)
NVIDIA H100 GPU
Processing Units
16,896CUDA cores
Transistors
80billion
Clock Speed
~2GHz (2 billion Hz)
Power Draw
700watts (TDP)
Parallelism
16.9Ksimultaneous
Memory
80GB HBM3
Live Neural Activity Simulation
Simulated Neurons
200
Firing Rate
0
Scale Factor
1 : 430,000,000
Real Brain Equivalent
86 billion firing/sec
This Simulation
0 ops/sec
To Match Brain
∞× faster needed
Build Your Brain-Scale AI System
How efficiently can silicon simulate biological neurons? (Very uncertain)
Required Hardware
GPUs Required
50,840
Total Power
35.6 MW
vs Brain Efficiency
1,780,000× less efficient
Hardware Cost
$1.53 billion
Annual Power Cost
$31.2 million/year
Data Center Size
~50,000 sq ft
Cooling Required
~10,000 tons AC
4.3×1018
ops/watt/sec
Brain Efficiency
2.9×1012
ops/watt/sec
H100 Efficiency (FP16)
When Will We Reach Brain-Scale?
2012AlexNet
2017Transformer
2020GPT-3
2024GPT-4
202710T params?
2030100T params?
203X?Brain-Scale
Parameter Count ≠ Neurons
GPT-4's ~1.8T parameters ≠ 1.8T neurons. Parameters are static weights; neurons are dynamic processors. Different paradigms.
Huang's Law
GPU performance doubles every ~2 years. At this rate, matching brain's parallel scale needs ~25 more years of scaling.
Algorithmic Efficiency
Training compute efficiency improves ~2× annually. Better algorithms may matter more than raw hardware.
The Efficiency Gap
Brain: 20W. Brain-scale AI: ~35MW. Closing this 1,750,000× gap may require neuromorphic computing or new physics.
🔬 Key Research Insights
Why 1000 Hz Matters
Individual neurons fire slowly (~1000 Hz max), yet the brain outperforms GHz computers on many tasks. The secret: 86 billion units operating simultaneously. Parallelism beats clock speed.
The Connection Density
Each neuron connects to ~1,000-10,000 others. Total: 100 trillion synapses. This is the brain's "weights"—but they're analog, stochastic, and dynamically updating.
Sparse Activation
Only ~1-4% of neurons fire at any moment. The brain achieves efficiency through sparsity. Modern AI (MoE models) is beginning to learn this trick.
The 20 Watt Miracle
Your brain runs on roughly the power of a dim light bulb. Equivalent silicon computation requires megawatts. Evolution optimized for efficiency; we optimized for speed.
🤔 Open Questions
Is Scale Sufficient?
Will simply scaling parameters and compute achieve AGI? Or are we missing fundamental architectural insights that biological neural networks embody?
What's the Minimum?
What's the minimum parallelism required for general intelligence? Insects have ~1M neurons and show remarkable capabilities. Where's the threshold?
Neuromorphic Future?
Intel's Loihi, IBM's TrueNorth attempt brain-like computing. Can spiking neural networks close the efficiency gap? Early results: 1000× more efficient for some tasks.
The Hard Problem
Even at brain-scale compute, would silicon be conscious? The architecture may matter more than the scale. We're building jets, not birds.