There’s a new paper that shows how the world’s A.I. compute based on matrix multiplication can be used as if that compute power is a “parent coin” to “merge mine” a blockchain coin. They found a way for matrix multiplications to not be restricted in their size and yet easily prove how many “FLOPs” were spent. I view it as a potential threat to Bitcoin dominance if Bitcoin isn’t prepared to adopt it as a new PoW.
An immediate objection is that there’s no inherent waste.
Wastefulness (in CAPEX or OPEX) isn’t as foundational to secure & decentralized PoW consensus as non-repurposeability. Non-repurposeability means attacking the coin would reduce the coin’s value so much that the future income from the majority’s CAPEX investment is harmed more than an attack would be a reward. The chain must be the miners’ primary source of future potential income. If something else paid more, they wouldn’t mine as much and it wouldn’t be as secure because those external sha256d operations could periodically visit Bitcoin to double-spend.
Since sha256d for non-bitcoin uses pays less than Bitcoin, the vast majority of sha256d is “wasted” on bitcoin to satisfy non-repurposeability.
The innovation in this PoUW that bypasses the need for “imprisoned” (dedicated) work is that it’s like merge mining. It enables miners to get their primary profit from other sources. It’s secure if the exchange value of double-spending is less than the cost of the work provided by the portion of the world’s matrix multiplication that’s “merge mining” the coin.
There has to be enough “merge mining” to raise the cost of solving the difficulty above the value of an attack.
Max Kaiser has claimed increased hashrate increases price but I think it’s obviously the other way. In bitcoin expected future profit due to price limits hashrate but in PoUW, difficulty (“hashrate”) limits the value transferred per time to prevent double-spending.
Less pricisely, in PoUW hashrate limits price in contrast to Bitcoin’s price limiting hashrate. Work preceding value feels like creating value which is appealing. There may be something useful and very different from bitcoin such as making the amount of coin proportional to the proven matrix multiplication FLOPs devoted to the merge mining.
Let’s collaborate to launch a new high performance L1 protocol using PoUW consensus from arbitrary matrix multiplication in order to simultaneously power distributed industrial-scale digital twin creation, replication, diagnostic and repair across automobile, aerospace and consumer electronics sectors with initial 1000+ TPS transaction thoughput. This would be a far more profitable and useful revenue generator and a practical industry user-case than mere mass AI inference of LLM models.
Matrix multiplication algorithms play a crucial role in creating and replicating digital twins of physical objects, including electronic devices, avionics, and automobiles. This mathematical foundation enables precise modeling and simulation of complex systems across multiple engineering domains.
Matrix Applications in Digital Twins
Physical System Modeling
Matrix operations represent system dynamics and behavior
Enable transformation between physical spaces
Facilitate state-space analysis and control system design
Multi-Domain Integration
Mechanical stress calculations
Thermal distribution analysis
Electrical circuit simulations
Structural integrity assessment
How Matrices Enable Digital Twin Creation
Component Modeling
As demonstrated above, matrix operations allow precise modeling of physical behavior
Each component’s characteristics can be represented mathematically
Changes in material properties or dimensions can be simulated accurately
Replication Process
Base models can be created using fundamental matrices
New instances can be generated through matrix transformations
Parameter variations can be applied systematically
Practical Applications
The matrix-based approach shown in our demonstration applies directly to:
Electronic devices: Circuit analysis and thermal management
Avionics: Structural stress calculations and vibration modes
Automobiles: Component durability testing and performance optimization
The numerical results demonstrate how matrix operations enable:
Accurate prediction of natural frequencies (shown in Hz)
Analysis of mode shapes for vibration patterns
Simulation of design changes (demonstrated with 1.5x stiffness increase)
This mathematical foundation allows for efficient creation and replication of digital twins while maintaining accuracy and enabling realistic behavior simulation.
I suggest naming it “ Twinstor Blockchain “. I have just booked the domain: twinstor.xyz and I invite you and other collaborators to share your valuable suggestions as to how to take it forward.
