The Biological Computing Co. (TBC), an applied biological computing company that uses real neurons to improve AI models, announced a collaboration with Amazon Web Services (AWS) to deliver the world's first neuron-derived AI video model to market.

Built on an open-source video generation model, TBC's optimized text-to-video model delivers 80% lower inference costs and 5x faster generation, while improving quality compared to the base model. The model is enhanced by a lightweight, proprietary software layer derived from measurements of living neural activity. The software layer adds less than 0.1% to the underlying model and runs entirely on conventional AI infrastructure. No biological hardware or new customer workflow is required.

"Our partnership with AWS takes neuron-derived AI optimization to commercial scale," said Alex Ksendzovsky, CEO and co-founder of TBC. "We're turning discoveries from real neurons into faster, cheaper AI for businesses and creators. More importantly, biology gives us a fundamentally different engine for discovering better optimization strategies over time."

Under the collaboration, TBC and AWS are working across multiple layers of the AI stack.

"Nature solved the computing efficiency problem billions of years ago. TBC's insight is that we can learn from the original computer—the human brain—to make AI faster, more efficient, and more economical," said Jason Bennett, Vice President and Global Head of Startups and Venture Capital at AWS. "By building on the AWS AI stack and leveraging go-to-market channels like AWS Marketplace, TBC can move from discovery to commercial scale at startup speed. This is exactly the kind of bold, forward-leaning innovation we love to support at AWS."

TBC plans to run its optimized model on AWS Trainium, make it deployable through Amazon SageMaker AI, and pursue commercial distribution in AWS Marketplace. This will allow customers to access TBC-optimized models within AWS environments and workflows they already use.

For companies deploying generative AI, inference efficiency directly affects product economics. Lower cost per output allows a platform to serve more users with the same infrastructure. Faster generation helps creators and businesses iterate more quickly. Better quality reduces the number of failed or unusable generations that still consume time and compute.

"Compute is becoming one of the biggest constraints on AI," said Jon Pomeraniec, co-founder and COO of TBC. "We need more infrastructure, but we also need to make every unit of compute dramatically more productive. Lower inference costs mean more companies can afford to build, scale and put powerful AI to work."

This is the first commercial product created through TBC's discovery process. Each biological experiment adds to the company's neural-response data, understanding of biological computation and library of potential algorithms. TBC plans to apply the same process to additional AI models and architectures, followed by applications across other AI workloads.