The AI industry’s hardware obsession is mostly a distraction. Everyone talks about chip shortages, next-gen accelerators, and custom silicon as if the only path to better AI performance runs through a fab in Taiwan. But a French startup called Kog is making a bet I’ve been waiting for someone to make: the GPUs you already own are being wasted, and the fix is software, not more shopping.
I review AI tooling for a living, and I can tell you that the gap between what hardware can theoretically do and what most inference stacks actually extract from it is one of the most under-discussed problems in this space. Kog is going straight at it.
What Kog Is Actually Doing
The pitch is refreshingly unglamorous. Kog is enhancing GPU software to boost AI inference efficiency. No new chip. No exotic architecture. No promise of a hardware breakthrough three years out. The company focuses on optimizing existing hardware, with the goal of improving performance and cutting costs on the accelerators companies already have racked and running.
As GPU costs keep climbing, that framing matters. The default industry response to a performance ceiling has been “buy more accelerators.” Kog’s response is “use the ones you have properly.” From a buyer’s perspective — and that’s the lens I always apply — the second option is the one your finance team will actually thank you for.
The Contrarian Bet on Agentic Workflows
Here’s where it gets interesting. There’s a widely repeated claim floating around that GPUs are poorly suited for agentic workflows — the multi-step AI processes where a model plans, calls tools, checks its own work, and iterates. Kog thinks that’s a misconception.
That’s a genuinely bold position. The conventional wisdom has been hardening into accepted truth: agents are chatty, iterative, and latency-sensitive, so GPUs built for big parallel batches supposedly struggle with them. If Kog is right that the problem lives in the software layer rather than the silicon, a lot of purchasing decisions being made right now deserve a second look.
In the longer run, Kog hopes to feed its methodology into agent-based pipelines that will let it support more chips and models. That’s the part I’ll be watching. A clever optimization that works on one chip family is a nice demo. A methodology that generalizes across hardware and models is an actual product category.
Why the European Angle Matters
There’s also a strategic dimension here. Europe is trying to build its own capability on both chips and models, and Kog sits at an interesting intersection of that effort. A European software layer that makes existing hardware go further is arguably more achievable in the near term than a European answer to Nvidia. You don’t need to win the fab race if you can win the efficiency race.
And efficiency has a direct commercial logic. Squeezing more inference out of the same hardware means serving more requests per dollar — which, as Kog’s Delalleau has pointed out, would mean more revenue. Simple math, but the kind that tends to survive contact with reality better than moonshot roadmaps do.
My Honest Take
I’ll be upfront about what we don’t know yet. The public details are thin. There are no independent benchmarks I can point you to, no published numbers I can stress-test, and no way for me to tell you today whether Kog’s optimizations deliver in production the way the thesis suggests. As a reviewer, that’s my standard caveat: a compelling premise is not a verified product.
But the premise itself is one I strongly endorse. Some of the biggest real-world wins I’ve seen in AI deployments came not from newer hardware but from better software — smarter scheduling, tighter kernels, less waste between the model and the metal. The inference layer is where theoretical FLOPS go to die, and any company treating that as the main problem rather than an afterthought is pointed in the right direction.
So here’s my scorecard for now:
- Thesis: Strong. Software-side inference optimization is underinvested relative to hardware spending.
- Differentiation: Promising. Challenging the “GPUs can’t do agents” assumption is a real position, not marketing fog.
- Proof: Pending. I want benchmarks, supported chip lists, and customer results before I call this one.
If Kog delivers on the agent-pipeline vision and broadens its chip and model support, it could become one of those quiet infrastructure companies that everyone depends on and nobody talks about. That’s usually where the durable value in this industry lives. For now, put Kog on your watch list — and maybe pause before signing that next GPU purchase order.
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