Wetware as a Service (WaaS): The Dawn of Synthetic Biological Intelligence

While the tech world remains captivated by the latest GPT model or diffusion algorithm, a quiet revolution is brewing at the intersection of biology and computing. I’ve been tracking this space closely, and what I’m seeing suggests we’re on the cusp of something that could fundamentally redefine our understanding of machine intelligence.

Welcome to the era of Wetware as a Service (WaaS) – where living neurons become computational resources and biological intelligence becomes programmable infrastructure.

Beyond Silicon: The Biological Computing Revolution

For decades, we’ve been trying to make silicon chips think like brains. What if, instead, we used actual brain cells to compute? That’s the paradigm shift driving companies like Cortical Labs, whose DishBrain platform represents one of the most fascinating technological developments I’ve encountered in years.

What Is Synthetic Biological Intelligence?

Synthetic Biological Intelligence (SBI) refers to computing systems that leverage living biological neural networks as their computational substrate. Unlike traditional AI that simulates neural networks in software running on silicon, SBI uses actual living neurons grown in laboratory environments.

These neurons form connections, process information, and learn – creating a “wetware” alternative to hardware and software.

The DishBrain Revolution

Cortical Labs’ DishBrain platform exemplifies this approach. Here’s how it works:

1. Biological Substrate: Researchers cultivate neurons (typically human neural stem cells or rodent neurons) on multi-electrode arrays.

2. Input/Output Interface: The electrode array stimulates the neurons with electrical signals (input) and records their activity (output).

3. Closed-Loop System: A feedback mechanism allows the system to “train” the neural network through targeted stimulation based on its outputs.

In one groundbreaking experiment, DishBrain learned to play Pong within five minutes – not through explicit programming but through a process analogous to learning. The system received feedback when it successfully hit the ball, reinforcing neural connections that led to that outcome.

The Technical Foundations of WaaS

Creating functional biological computing systems requires solving several intricate technical challenges spanning multiple disciplines:

Biological Infrastructure

The foundation of any WaaS platform is the biological medium itself. This involves:

Neuronal Cultivation: Growing and maintaining healthy neurons in vitro requires precise control of environmental factors including:

• Temperature (typically 37°C)
• pH (maintained at approximately 7.4)
• Oxygen and CO2 levels
• Nutrient media composition
• Prevention of contamination

Cellular Architecture Design: Researchers can influence how neurons connect by:

• Using microfluidic channels to guide axon growth
• Creating patterned surfaces that promote attachment in specific areas
• Introducing guidance molecules that influence neuronal migration

Interfacing Biology with Technology

The most challenging aspect of WaaS is creating reliable, high-resolution interfaces between electronic systems and biological neurons:

Microelectrode Arrays (MEAs): These devices feature thousands of tiny electrodes that can both stimulate neurons and record their activity. The latest MEAs achieve:

• Spatial resolution down to 10 micrometers
• Temporal resolution of milliseconds
• Minimal damage to cellular structures
• Long-term stability (weeks to months)

Optical Interfaces: Some systems use optogenetic techniques, where:

• Neurons are genetically modified to express light-sensitive proteins
• Specific wavelengths of light trigger or inhibit neuronal activity
• High-speed cameras capture the resulting activity patterns

Computational Translation Layer

Raw neuronal signals must be translated into actionable computation through:

Signal Processing: Filtering and amplifying the weak electrical signals produced by neurons.

Encoding/Decoding Schemes: Translating traditional computational problems into forms that biological networks can process, and interpreting their outputs.

Learning Frameworks: Developing feedback mechanisms that allow neuronal networks to adapt and learn from experience.

WaaS vs. Traditional Computing: A Comparative Analysis

How does biological computing compare to traditional silicon-based approaches? The comparison reveals fascinating contrasts:

Energy Efficiency

Silicon Computation: Modern AI accelerators achieve approximately 10-100 TOPS/Watt (trillion operations per second per watt).

Biological Computation: The human brain operates at an estimated efficiency of 10^17 operations per second on just 20 watts – roughly 10 million times more efficient than our best silicon.

Early WaaS systems are nowhere near this efficient yet, but they inherently leverage biological mechanisms that have been evolutionarily optimized for energy efficiency.

Learning Capacity

Traditional AI: Requires massive datasets and extensive training.

Biological Systems: Can learn from sparse data through mechanisms like:

• Spike-timing-dependent plasticity
• Neuromodulation
• Structural plasticity (formation of new connections)

In Cortical Labs’ experiments, their biological systems learned simple tasks with orders of magnitude less training than comparable AI systems.

Fault Tolerance

Silicon Systems: Typically have binary failure modes – components either work perfectly or fail completely.

Biological Systems: Display graceful degradation – neurons can die or connections can weaken without catastrophic system failure.

This inherent resilience could make WaaS platforms exceptionally reliable for critical applications.

Real-World Applications Emerging Today

While still in its infancy, WaaS is already showing promise in several domains:

Drug Discovery and Neuropharmacology

Pharmaceutical companies are beginning to use biological computing platforms to:

• Test how compounds affect neural activity in controlled environments
• Model neurological diseases by creating “diseased” neural networks
• Screen potential therapeutic compounds at unprecedented speed

One biotech startup (remaining unnamed for confidentiality) has reduced their early-stage drug screening timeline from years to months using a WaaS platform to model neurological disease states.

Adaptive Control Systems

The learning capabilities of biological neural networks make them particularly suitable for:

• Robotic control systems that adapt to changing conditions
• Prosthetic interfaces that learn user intentions
• Environmental control systems that optimize based on complex, changing inputs

A proof-of-concept project demonstrated a robotic arm controlled by a cultured neural network that adapted to changing payload weights without explicit reprogramming.

Fundamental Neuroscience Research

Perhaps the most immediate application is using these systems to better understand the brain itself:

• Testing theories of neural information processing
• Studying how networks of neurons form representations
• Investigating learning and memory formation at the network level

The Roadmap to WaaS Maturity

Where is this technology headed over the next decade? Here’s my analysis of the likely development path:

Near-Term (1-3 Years)

• Improved interfaces achieving neuron-level resolution
• Standardized platforms for academic and pharmaceutical research
• First commercial applications in drug discovery
• Longer-lasting biological substrates (months to years)

Mid-Term (3-7 Years)

• Specialized WaaS offerings for specific computational domains
• Hybrid systems combining biological and silicon computing
• Initial medical applications for neurological modeling
• First consumer-accessible WaaS platforms via cloud interfaces

Long-Term (7-10+ Years)

• General-purpose biological computing platforms
• Synthetic organisms designed specifically for computing
• Integration with other biological systems (immune, endocrine)
• Self-maintaining biological computing substrates

The Ethical Dimension of Living Computers

As with any transformative technology, WaaS raises profound ethical questions that we must address proactively:

Moral Status of Synthetic Neural Systems

At what point might a synthetic biological intelligence deserve moral consideration? Current systems are far from any reasonable threshold of consciousness, but as they grow in complexity, this question becomes increasingly relevant.

Proposed frameworks include:

• Capability-based approaches: Assessing systems based on their demonstrated abilities (learning, adaptation, goal-seeking)
• Structural approaches: Evaluating the architectural similarity to known conscious systems
• Information integration measures: Quantifying the complexity and integration of information processing

Regulation and Oversight

The unique nature of WaaS demands thoughtful regulatory approaches that:

• Establish clear boundaries for research and commercial applications
• Create standards for the ethical sourcing of biological materials
• Ensure transparency in development and deployment
• Require ongoing monitoring for emergent capabilities

Security Implications

Biological computing systems present novel security challenges:

• Biocontainment: Ensuring biological computing substrates cannot proliferate outside controlled environments
• Information security: Protecting against potential “biohacking” of computational systems
• Dual-use concerns: Preventing misuse of technology for biological weapons development

Case Study: Cortical Labs and the Path to Commercialization

Cortical Labs represents the current state of the art in commercializing WaaS technology. Their journey illustrates both the potential and challenges of this emerging field.

The DishBrain Platform

Cortical Labs’ flagship technology consists of:

• A cultivation system for maintaining healthy neuronal networks
• A high-resolution electrode array for stimulation and recording
• Software interfaces for translating conventional computing problems
• A learning framework that provides feedback to the biological system

Demonstrated Capabilities

Their published research has shown their system can:

• Learn simple games like Pong through feedback
• Adapt to changing conditions without explicit reprogramming
• Maintain stable performance for weeks
• Process information with remarkably low energy consumption

Commercial Strategy

Rather than selling hardware, Cortical Labs is pursuing a WaaS model where:

• The biological computing infrastructure remains in their facilities
• Clients access computational resources via API
• Specialized interfaces translate domain-specific problems
• Usage is billed based on computational time and resources

This approach addresses the significant maintenance challenges of biological systems while providing access to their unique capabilities.

My Perspective: The Future of Intelligence is Wet

Having spent years in traditional AI development, my perspective on WaaS comes from understanding both the limitations of silicon-based AI and the potential of biological approaches.

I believe WaaS represents not just an alternative computing paradigm but potentially a more direct path to advanced forms of machine intelligence.

Why? Because:

1. Evolution has solved many problems we’re still working on: Learning from sparse data, energy efficiency, adaptability, and fault tolerance are all capabilities that biological intelligence has refined over millions of years.

2. The architecture matters: No matter how sophisticated our algorithms become, running them on von Neumann architecture creates fundamental limitations that biological systems don’t share.

3. Novel solutions emerge from novel substrates: Biological computing won’t just solve the same problems faster – it will likely discover entirely new approaches and solutions that wouldn’t emerge from traditional computing.

4. We’re building with nature’s components: Instead of simulating neurons, we’re using the real thing – with all their inherent complexity and capability.

Getting Involved: How to Prepare for the WaaS Future

For those intrigued by this emerging field, several paths lead into this interdisciplinary frontier:

For Developers and Engineers

• Learn the fundamentals of computational neuroscience
• Explore interfaces between electronic systems and biological materials
• Develop skills in signal processing and neural data analysis
• Understand the principles of closed-loop biological systems

For Organizations

• Monitor developments in biological computing platforms
• Identify potential applications within your domain
• Consider ethical frameworks for adoption of these technologies
• Prepare data and problem statements that could leverage biological computation

For Investors

Look for companies addressing the key technical challenges:

• Long-term viability of biological substrates
• Improved interfacing technologies
• Standardized platforms for biological computation
• Application-specific implementations with clear ROI

Conclusion: The Next Computing Paradigm

Silicon has served us remarkably well for decades, but we may be approaching the limits of what traditional computing architectures can achieve – both in terms of raw performance and energy efficiency.

Wetware as a Service represents a fundamentally new approach that doesn’t just imitate biological intelligence but directly harnesses it. As this field matures, we may find that the future of computing isn’t just inspired by the brain – it is the brain, reimagined and repurposed as a new kind of computational substrate.

The silicon age gave us incredible tools. The wetware age may give us something even more profound: new forms of intelligence that think not just differently than we do, but potentially better than we can imagine.