The Human Brain vs. Artificial Intelligence – Understanding the Difference

Today we are going to deviate from our main topic, which is consciousness, and focus on the hardware. We are going to talk about the processing power of the human brain.

One of the reasons for doing this is that there is so much discussion about AI, its capabilities, and now the challenges associated with data centers and the energy required to run them, that we often lose perspective of what we humans are and the complexity of the biological system that supports our consciousness.

The brain’s power comes from approximately 86 billion neurons and an estimated 100 trillion synapses, all working together. Estimates of total synaptic events range from approximately 10^15 to 10^18 per second, although these biological events cannot be directly equated with computer operations. Models that simulate increasingly detailed biological processes produce much higher computational estimates, reaching 10^25 or more operations per second.

Neurons typically fire between 1 and 100 times per second, with some reaching a few hundred. A modern processor, by comparison, operates at billions of cycles per second. Signals travel along nerves at speeds ranging from approximately 1 to 120 meters per second or roughly 268 miles per hour, depending on the type of nerve fiber.

And the most incredible thing is that the brain does all this using approximately 20 watts of power.

A study by Zheng and Meister, titled “The Unbearable Slowness of Being,” published in 2025, estimated that human behavioral information throughput operates at approximately 10 bits per second. This includes activities such as typing, speaking, reading and decision-making.

Meanwhile, our sensory systems gather information at an estimated rate of approximately one billion bits per second. The retina alone sends information to the brain at a rate of millions of bits per second.
This means there is an enormous reduction between the information our senses collect and the information that becomes available to conscious awareness. The difference is remarkable, and the mechanism behind this reduction is still not fully understood.

Heartbeat, breathing, digestion, temperature regulation and hormone control are handled mostly by the brainstem, hypothalamus and autonomic nervous system. These functions operate continuously and unconsciously. Although their information throughput is comparatively low, they are critical to our survival. Much of the brain’s energy is devoted to this continuous maintenance and internal activity, rather than conscious thinking.

The best way to summarize this is that the brain is slow in its individual components, enormously active as a whole, and extremely narrow at the conscious end. Vast amounts of processing take place underneath awareness, while only a small stream reaches our conscious experience.

Comparing biological processing with computer processing
The following figures provide an idea of the computational resources required to simulate the brain at different levels of biological detail.

FLOPS stands for floating-point operations per second. It measures how many arithmetic calculations a computer can perform each second using floating-point numbers, such as 3.14 × 2.7.

The scale increases in powers of 1,000:

Megaflops: 10^6 per second.
Gigaflops: 10^9 per second (modern phones and laptops can reach hundreds of gigaflops in certain workloads).
Teraflops: 10^12 per second (a good gaming graphics card can deliver tens of teraflops).
Petaflops: 10^15 per second.
Exaflops: 10^18 per second (the scale reached by the largest supercomputers).

It is important to understand that FLOPS and biological synaptic events are different measurements. The following are estimates of the computational resources needed to reproduce the brain at different levels of biological detail. They are not direct measurements of the brain’s processing speed, and the estimates depend on the assumptions and methods used.

Different levels of brain simulation

Functional or abstract model
Neurons are represented as simple computational units, with the objective of reproducing behavior or overall functionality.
Estimated computational requirement: approximately 10^14 to 10^17 FLOPS.
This is within the reach of a single powerful AI server or a small computing cluster, depending on the model.

Spiking neural network
Each neuron is represented as an individual unit that communicates through electrical spikes and synaptic connections.
Estimated computational requirement: approximately 10^18 FLOPS.
This is roughly the territory of the largest exascale supercomputers.

Detailed single-neuron biophysics
This level attempts to reproduce the internal behavior of neurons, including dendrites, ion channels and different cellular compartments.
Estimated computational requirement: approximately 10^21 to 10^23 FLOPS.
This would require approximately 1,000 to 100,000 times the computational capacity of the largest exascale machines.

Molecular detail
This is the most complex level, attempting to reproduce proteins, metabolism and molecular signaling processes.
Estimated computational requirement: approximately 10^25 to 10^27 or more FLOPS.
This remains far beyond current computational capabilities.

These tiers draw largely on the whole-brain emulation roadmap developed by Sandberg and Bostrom in 2008, together with other computational estimates, including analyses by Joe Carlsmith. They should be understood as modeling scenarios rather than definitive hardware requirements.

When we say that a brain simulation at the spiking-network level could require approximately 10^18 FLOPS, we are talking about roughly one exaflop, the territory of the largest supercomputers on Earth.
A supercomputer operating at the exascale level can consume approximately 20 to 30 megawatts of electricity. That is roughly one million times more power than the brain’s 20 watts.
This comparison does not mean that the supercomputer and the brain perform identical tasks. It illustrates the enormous difference in energy requirements between biological processing and large-scale digital simulation.

Put another way, the brain performs its extraordinary biological and cognitive functions using roughly the same amount of energy as a dim light bulb, while also controlling the entire body.
We are still far from creating hardware capable of reproducing all the biological processes of a human brain. Nevertheless, even today, we do not have a complete understanding of how the brain works.

A study by Beniaguev et al. (2021) demonstrated that reproducing the behavior of a single realistic cortical pyramidal neuron required a deep neural network with five to eight layers. This illustrates how much more complex an individual biological neuron can be than the simplified artificial neuron commonly used in conventional neural networks.

The brain also contains approximately 100 trillion synapses. Storing information about these connections could require hundreds of terabytes to petabytes, depending on how their structure and state are represented. This estimate does not include all the additional biological details required to reproduce their behavior.

Why is the brain so efficient compared to AI?
Because the brain uses fundamentally different methods to process information.

Analog, local computation. Memory and processing are closely integrated at the synapse. Conventional computers constantly move data between separate memory and processing units, and this movement represents a significant energy cost.

Sparse activity. At any given moment, only a fraction of neurons are firing. Much of the brain remains relatively quiet, avoiding the need to expend energy continuously across all its components.

Event-driven signaling. Neurons communicate through brief electrical spikes when something needs to be signaled, rather than operating on a constant clock.

Massive parallelism at low speed. Individual biological components operate relatively slowly, often at tens of spikes per second, but billions of them work simultaneously.

Tolerance for noise. Neural circuits operate with imprecise and noisy components rather than requiring the exact digital arithmetic used by conventional computers.

This difference is one of the motivations behind neuromorphic computing: chips designed to imitate some of the brain’s event-driven, spiking and energy-efficient processing mechanisms.
Examples include Intel’s Loihi and IBM’s NorthPole. These systems have demonstrated substantial energy savings for certain tasks, although they remain far from reproducing the brain’s general capabilities.

Part of the brain’s efficiency comes from the fact that it does not process everything equally. It predicts, filters and prioritizes information, allowing most of its activity to take place below conscious awareness.
The brain does not need to bring all the information it processes into conscious awareness. It continuously evaluates and prioritizes information, allowing us to interact with our environment without having to consciously manage every biological and sensory process.
Conscious awareness is therefore not a reflection of everything the brain processes, but a selective interface with that processing.

There are also several mechanisms beyond conventional synaptic communication that contribute to the brain’s operation:

Neuromodulators: Dopamine, serotonin, noradrenaline and acetylcholine alter how entire neural circuits behave, influencing attention, mood, learning and the strength of neural responses rather than simply carrying a specific message.

Hormones and volume transmission: Chemicals diffuse through tissue and the bloodstream, affecting many cells over longer periods.

Glial cells: Astrocytes communicate through calcium waves and appear to modulate synapses. Their precise contribution to information processing is still being investigated.

Electrical synapses (gap junctions): These provide direct and very fast connections between certain neurons, helping synchronize their activity.

Ephaptic coupling: The electric fields generated by neighboring neurons may influence one another without requiring a conventional synaptic connection. This remains an active area of research.

These mechanisms illustrate that the brain is not simply a network of neurons exchanging electrical signals. It is a complex biological system involving electrical, chemical and metabolic processes that interact continuously.

What does this mean for us?
If you think that AI has incredible power for processing information, take a step back and consider what we humans already possess.

The real question is not whether AI can eventually match or surpass certain human capabilities. It is whether we, as human beings, are making proper use of the extraordinary biological capabilities we already have. Understanding how our brain works is one of the first steps toward making better use of its natural capabilities, further developing our consciousness and exploring dimensions of existence that go beyond information processing.

We have spent enormous resources developing machines capable of processing information, while we still have much to learn about the biological system that makes our own experience possible.

Perhaps it is time to pay a little more attention to the most sophisticated piece of hardware we will ever operate: ourselves.

The article in images

https://elnuevocamino.com/wp-content/uploads/2026/11/The_Ultimate_Hardware.pdf