The Biological Computing Company (TBC) is building an organic computing platform that integrates living neurons with modern AI. Founded by neurosurgeons Dr. Alex Ksendzovsky and Dr. Jon Pomeraniec, TBC grows cortical neurons on high-density multi-electrode arrays — cultures containing 100,000 to 500,000 living neurons — and uses them as a computational substrate to generate richer data representations for downstream AI models.

TBC is not building a biological computer that replaces silicon. Their approach uses biology as an experimental platform to extract computational principles and structured representations that improve existing digital AI systems. Real-world data is encoded into electrical stimulation patterns, passed through the living neural network, and the resulting high-dimensional neural responses are decoded into software adapters that ship as pure digital products.

TBC operates within a reservoir computing framework: the biological neural network acts as a fixed, nonlinear transformation that projects inputs into a higher-dimensional feature space. No training signal is provided to the neurons, all learning happens in the downstream digital model. The biological network’s intrinsic recurrent dynamics spread and transform input signals through thousands of synaptic connections, producing spatiotemporal response patterns that capture structure conventional encoders can’t learn.

TBC has published a four-part technical blog series documenting results across perception, representation learning, generative video, and algorithm discovery:

TBC raised a $52M seed round led by Primary Ventures (the same firm that wrote the first check into Etched.) TBC is planning to ship their next product soon, targeting foundational model labs and hyperscalers across computer vision, generative video, and world model applications.

Dr. Alex Ksendzovsky is the CEO and co-founder of TBC. A trained neurosurgeon from the University of Maryland and former NIH researcher, Alex first saw a robot powered by a single neuron as an undergraduate studying philosophy of mind, going on to spend two decades working to harness biological neural computation. He and co-founder Jon Pomeraniec transitioned from neurosurgery and academic research to build TBC out of their Mission Bay lab in San Francisco, now with a team of 23 spanning computer vision, computational physics, and neuroscience.

TBC’s line of research builds on over two decades of published work from independent groups demonstrating that cultured neurons on electrode arrays can process information, learn, and exhibit goal-directed behavior. DARPA has recognized this trajectory: its O-CIRCUIT program (May 2026) is funding the development of biological processing units for edge AI inference. Selected references spanning this literature:

1. Kagan, B. J. et al. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 110, 3952–3969 (2022). doi:10.1016/j.neuron.2022.09.001
2. Robbins, A. et al. Goal-directed learning in cortical organoids. Cell Reports 45 (2026).
3. Rountree, C. et al. Long-term potentiation and closed-loop learning in paired brain organoids for CNS drug discovery. bioRxiv (2026). doi:10.1101/2025.07.03.663054
4. Bakkum, D. J., Chao, Z. C. & Potter, S. M. Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task. J. Neural Eng. 5, 310–323 (2008).
5. Chao, Z. C., Bakkum, D. J., Wagenaar, D. A. & Potter, S. M. Effects of random external background stimulation on network synaptic stability after tetanization. Neuroinformatics 3, 263–280 (2005).
6. Ciampi, L. et al. Neuro-inspired visual pattern recognition via biological reservoir computing. arXiv:2602.05737 (2026). arxiv.org/abs/2602.05737
7. Iannello, L. et al. From neurons to computation: biological reservoirs for pattern recognition. arXiv:2510.05637 (2025). arxiv.org/abs/2510.05637