SemiAnalysis analyst Jordan Nanos stated that OpenAI engineers are not clear about what each line of their own kernel code does while scrolling through it, but this does not affect the results, as the AI can write, test, and run high-performance kernels.

He referred to this code as "not garbage because it runs," while also saying it is "essentially AI-generated assembly thrown into Gluon." Gluon is the low-level kernel language built by OpenAI on top of Triton, following a single program multiple data model and exposing hardware-near abstractions.

Nanos mentioned that engineers looking at the code understand hardware and system concepts but cannot clearly explain what the segment prepared for DeepSeek's MLA kernel is actually doing. They can point to each line, but the response is negative. Testing and correctness are handled by models and verification scripts.

SemiAnalysis's written materials state that OpenAI wrote the Jalapeño kernel using an internally scaled-up Codex, with some manually tuned kernels extending to about three thousand lines, accompanied by correctness checks and custom sanitizers. Initially, humans were involved, but later it shifted to more automation. The internal inference engine is named Teacup. Before using InferenceX for benchmarking DeepSeek, the company did not even have its own MLA kernel implementation.

Gluon creates layouts as first-class abstractions, mapping hardware resources to tensor elements and providing provable layout transformations. For NVIDIA GPUs, it can directly access instructions like tensor memory acceleration, matrix multiply-add, and memory barriers. Kernel optimization is described as a verifiable reinforcement learning loop: assigning tasks, conducting tests, and allowing models to iterate data movement and computation units.

In market mechanisms, the buyers are those looking to squeeze more tokens per watt from inference loads, while the sellers are the human-readable kernel crafts. The events are driven by self-developed chips and open-source model benchmarks, with funding shifting from manually tuned kernels in the CUDA ecosystem to "model-written kernels and testing closures"; benefiting are laboratories that can hand over hardware semantics to models in a closed loop, while those relying on a moat built on a few kernel engineers writing assembly face pressure.

Source: Public Information