People keep asking some version of this: if there are dozens of models that all do roughly the same thing, why would the model itself be the thing that matters? I think the honest answer is that it mostly isn't anymore, and the shift shows in how the costs and the competitive picture are moving.
TechPolicy.press's essay "Taking AI Commoditization Seriously" by Trent Kannegieter, from March 2025, makes the core point: similar capabilities from a host of vendors increase competition and decrease prices. Once that happens, value moves up and down the stack instead of sitting in the model layer, into the applications built on top and into the hardware and tooling underneath. The model stops being the differentiated asset.
The other half is context. There's a thread from a developer who built a workspace tool because his starting frustration wasn't that models were weak, but that the knowledge needed to do useful work was scattered across a dozen apps. Sequoia Capital put a name on the direction in their "Act Two" essay: the moat they originally predicted, more usage leading to a better model, turned out to be shaky, and what actually holds is the workflow and the user network around the model. Access to a good model becomes table stakes. Accumulated context becomes the compound interest.
That matches what I see running models locally. The marginal difference between one decent open model and the next is narrowing a lot faster than the difference between having your data and workflow wired into a system and not having it. The frustrating part of using AI day to day is rarely the model now. It's that it can't see what you're actually working on.
Where I'd push back on the strong version: commoditization isn't settled yet at the frontier tier. Kannegieter is explicit that it's a possibility on the way, not a finished event, and he frames the whole piece as preparation for a world that hasn't fully arrived. A commoditized model layer also has a downside nobody's solved: it disempowers the safety teams at today's big labs, because a lab's constraints only bind when everyone actually runs its model.
What does start to sound right is that the model is becoming the least interesting part of the stack for most people. Do you agree, or is there still a capability gap you rely on?
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