Kog is going deeper to squeeze more inference out of GPUs

The GPU obituary was premature. While the industry chases bigger chips and exotic accelerators, Kog is betting on a dirtier truth: we’ve barely scratched the silicon we already own. Their claim — that GPUs aren’t inherently bad at agentic workflows, we just deploy them lazily — is the kind of contrarian, compute-first thinking this space needs. This matters because agentic AI isn’t a training problem; it’s an inference gauntlet. Every tool call, every loop, every token burned in a reasoning chain multiplies cost. If Kog can squeeze more throughput from existing hardware by going deeper into the stack — kernel-level, memory-aware, latency-hungry — they’re not just optimizing code. They’re rewriting the economics of autonomous agents. It also signals a healthy shift: software-defined silicon is back on the menu, and the datacenter itself is the product. A French startup cracking the inference ceiling also reminds us that AI compute isn't a Silicon Valley monopoly. The edge can come from elsewhere. So before we mourn the GPU, let’s see if the real bottleneck was always our imagination — not the die. ```json { "key_insight": "Squeezing more inference from existing GPUs may matter more than chasing new hardware, especially as agentic workloads shift the cost curve from training to reasoning.", "confidence": 0 } ```