Hardware maker FutureBit has introduced HashFly, a web-based experiment that simulates activity across 2,914 neural pathways derived from MaleCNS v1.0, a digital wiring diagram of an adult male fruit fly’s brain and central nerve cord. The demonstration performs simplified Bitcoin hashing calculations on conventional computer hardware rather than specialized mining chips. While the system is not competitive with current ASIC miners in terms of hash rate or difficulty, FutureBit stated it is working to simulate all neurons in the dataset with SHA-256 and plans to publish its findings. The company noted on X that if scaled on real organic neurons, such a system could theoretically hash at approximately one watt per terahash, which would be ten times more efficient than leading three-nanometer silicon ASICs.
In addition to HashFly, a separate project called FlyMiner utilizes a digital map containing 139,255 fruit fly neurons and 16.8 million connections to control a standard Bitcoin mining program. This system triggers tests for possible solutions when simulated signals from movement-control neurons reach a set threshold, operating at speeds up to 700,000 attempts per second via solo mining pool CKPool. For context, FutureBit’s five-inch Apollo III ASIC miner reaches 18 terahashes per second, highlighting the vast performance gap between biological simulations and industrial hardware. These experiments follow other unconventional mining efforts, including a 1989 Nintendo Game Boy converted into a miner producing about 0.8 hashes per second in March 2021, and Nodal Power raising $13 million in August 2023 to mine Bitcoin using electricity generated from landfill methane.
The HashFly and FlyMiner projects illustrate a growing curiosity within the cryptocurrency sector regarding bio-inspired computing architectures, even though they remain purely theoretical demonstrations rather than viable commercial alternatives. By leveraging existing open-source neurobiological data, such as the MaleCNS v1.0 diagram, these experiments highlight how developers are exploring non-traditional computational models to address the energy efficiency challenges inherent in Proof-of-Work consensus mechanisms. The specific claim that organic neurons could achieve ten times the efficiency of advanced silicon ASICs suggests that biological systems may offer superior power-to-performance ratios, although this remains unproven outside of simulation environments running on conventional hardware.
From an operational risk and market structure perspective, these developments underscore the continued dominance of specialized silicon infrastructure in Bitcoin mining while signaling potential long-term disruptions in hardware design philosophy. Current industrial miners rely on extreme specialization and high energy consumption to maintain network security, creating a barrier to entry that favors large-scale operations. If future research validates the efficiency claims associated with organic or bio-mimetic computing, it could eventually pressure the industry to reconsider the sustainability metrics of mining hardware. However, given the current disparity between the 700,000 attempts per second achieved by FlyMiner and the 18 terahashes per second of standard ASICs, immediate impacts on network hash rate distribution or energy consumption patterns are negligible.
