The Inevitable Shift to AI-Native Execution
The Scalability Wall
The current paradigm of AI infrastructure is hitting a hard limit. We call it the "Human Compiler Army" bottleneck.
As model architectures grow more complex and hardware options proliferate (NVIDIA, AMD, Google TPU, Groq, Cerebras), the complexity of mapping software to hardware is increasing exponentially. Today, this mapping is done manually by elite teams of performance engineers who hand-tune kernels for specific devices.
This approach is fundamentally unscalable.
The Bottleneck: Human Cognitive Bandwidth
- Complexity Explosion: Optimizing a single kernel for a single architecture takes weeks. Optimizing an entire model for a heterogeneous cluster is a multi-month project.
- Vendor Lock-in: Because optimization is so expensive, companies optimize for one platform (usually CUDA) and get stuck there.
- Lost Efficiency: Most hardware runs at 30-40% utilization because the software stack cannot dynamically adapt to the underlying silicon.
The Solution: Autonomous Epistemic Systems
General Diffusion is building the Autonomous Epistemic System for compute.
Instead of human engineers manually writing kernels, we are building AI agents that understand the physics of silicon. These agents:
- Profile Hardware: Automatically learn the performance characteristics (bandwidth, latency, thermal limits) of any new chip.
- Partition Graphs: Intelligently split model graphs across different types of hardware to maximize throughput.
- Generate Code: Write mathematically perfect, hardware-native code (Triton, Mojo, CUDA) in real-time.
The Path to ASI
We believe that AI-Native Execution is a structural prerequisite for Artificial Superintelligence (ASI).
ASI will not run on a single static cluster. It will require a fluid, planetary-scale compute substrate where workloads flow like water to the most efficient available hardware.
By removing the human from the optimization loop, we unlock the true potential of heterogeneous compute. We turn a manual, brittle process into an autonomous, self-improving system.
This is the inevitable shift. And we are building the engine to drive it.