Biological Computers Are Real Now. I Tried One

2026-06-12 · Source: Siraj Raval · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Novice, medium

Summary

Cortical Labs has commercialized biological computers, utilizing 800,000 live human brain cells grown on silicon chips. These "Dishbrain" systems, first demonstrated playing Pong in 2022, learn and adapt through real-time electrical feedback, unlike traditional silicon AI. The company launched its CL1 commercial biocomputer in 2025, with 115 units initially priced at \$35,000 each, and now offers "wetware as a service" for \$300 per week. These systems consume significantly less energy, roughly equivalent to an LED, compared to silicon AI. Developers can access this technology today via a Python SDK, which includes a local simulator and allows deployment to real biological hardware in Melbourne, enabling experimentation with neuron adaptation and learning patterns.

Key takeaway

For AI Engineers exploring novel computing paradigms, you should investigate Cortical Labs' biological computing SDK. This technology represents a distinct substrate with significant energy efficiency advantages over silicon AI. By using the free simulator or cloud access, you can experiment with real-time neuron adaptation and contribute to early ethical discussions, shaping the future of this emerging field.

Key insights

Biological computers, using cultured human neurons, offer a new, energy-efficient computing substrate capable of real-time adaptive learning.

Principles

• Neurons learn via electrical feedback, not static data.
• Biological computing offers dramatic energy efficiency.
• Ethics discussions must precede widespread adoption.

Method

The system uses a multi-electrode array to record neuron spikes, compute responses, and stimulate channels in a closed loop, causing neurons to adapt.

In practice

• Install the CL SDK to access a local simulator.
• Open a connection to record neuron activity.
• Implement closed-loop stimulation patterns.

Topics

• Biological Computing
• Cortical Labs
• Neuron Networks
• Wetware as a Service
• AI Ethics
• Multi-Electrode Arrays
• Python SDK

Best for: AI Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Siraj Raval.