What if the bottleneck strangling your agent pipelines isn’t the silicon at all, but the software you’ve been running on top of it? That question sits at the heart of what French startup Kog is attempting, and as someone who has spent years profiling inference workloads, I think it’s the right question to ask — even if the answer turns out to be complicated.
Kog is enhancing GPU software to boost AI inference efficiency. Rather than joining the crowded race to design new accelerator chips, the company is optimizing the hardware that already exists, with the goal of improving performance and cutting costs. That’s a contrarian bet in a market where the default response to slow inference has been to buy more GPUs, and it deserves a closer technical look.
The Misconception Kog Wants to Kill
There’s a widely repeated claim in AI engineering circles: GPUs are poorly suited for agentic workflows — the multi-step processes where a model reasons, calls tools, waits for results, and reasons again. Kog believes the industry has this wrong, and that the perceived mismatch is a software problem masquerading as a hardware limitation.
From my vantage point, this argument has real merit. Agentic workloads are structurally different from the batch-oriented serving patterns most inference stacks were built around. An agent doesn’t generate one long completion and stop. It produces short bursts of tokens, pauses for external calls, resumes with new context, and repeats — often dozens of times per task. Standard serving assumptions start to creak under that pattern. But the GPU itself doesn’t care about your workflow shape. It cares about whether the software feeding it keeps its compute units busy. If utilization craters during agent loops, the fault usually lies in scheduling, memory handling, and orchestration layers, not in the accelerator’s architecture.
Why Going Deeper Beats Buying More
As GPU costs rise, companies are looking beyond simply purchasing additional accelerators, and the economics here are straightforward. Every percentage point of utilization you recover from existing hardware is capacity you don’t have to buy, power you don’t have to provision, and rack space you don’t have to find. Software optimization compounds across an entire fleet at once, while new hardware only helps the nodes you can afford to add.
There’s also a research argument for this approach that I find compelling. The inference stack — kernels, memory management, request scheduling, batching logic — has historically received less attention than training infrastructure, because training was where the money and prestige lived. That imbalance left efficiency on the table. A team willing to work at the low levels of the GPU software stack, below where most application engineers ever look, can find gains that no amount of prompt engineering or model swapping will deliver.
The Agent-Shaped Future
What makes Kog’s positioning interesting to me isn’t just the near-term efficiency play. In the longer run, the company hopes to feed its methodology into agent-based pipelines, allowing it to support more chips and more models over time. That’s a meaningful architectural ambition: rather than hand-tuning for one accelerator or one model family, the goal is a methodology that generalizes.
If agentic systems become the dominant deployment pattern — and the industry’s current trajectory suggests they will — then inference infrastructure optimized specifically for that pattern becomes strategically important, not merely convenient. An agent that completes its reasoning loop faster isn’t just cheaper to run. It’s more responsive, more usable, and more viable as a product. Latency in agent pipelines multiplies across every step of the chain, so improvements at the inference layer pay out repeatedly within a single task.
A European Dimension
There’s a geopolitical thread here worth pulling. Europe is actively seeking to build its own capability in both chips and models, and a French company working at the intersection of hardware efficiency and model serving fits naturally into that ambition. Owning the software layer that extracts maximum value from accelerators is a form of infrastructure sovereignty — arguably a more achievable one than competing head-on in semiconductor fabrication.
My Honest Assessment
I’ll offer the caution I’d give any team pursuing deep-stack optimization: this work is hard, unglamorous, and easy to underestimate. Squeezing more from GPUs means fighting driver behavior, memory hierarchies, and scheduling edge cases that shift with every hardware generation. Supporting more chips and models multiplies that surface area. The methodology has to be genuinely general, or the maintenance burden will consume the gains.
But the core thesis is sound. The industry has treated GPU limitations for agentic workloads as a law of physics when it may simply be a symptom of immature software. If Kog can demonstrate that the accepted wisdom was wrong — that the hardware we already own can do substantially more — the implications extend well beyond one startup’s fortunes. Sometimes the most valuable thing a company can do is prove that the problem everyone was solving wasn’t the real problem.
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