Comment by Barathkanna

As someone building AI infrastructure, this deflation idea shows up very differently for us. Application founders can afford to say they will wait for the next model, but infrastructure founders cannot. The value we create is not in shipping features faster. It is in absorbing the volatility that sits underneath the entire AI stack.

Models keep getting cheaper and more capable every few months. However, the underlying compute economics do not deflate at the same rate. GPU provisioning, inference orchestration, bandwidth constraints, latency guarantees, regulatory requirements, and failure handling do not become magically simple because a new model improved its reasoning. In reality, each improvement on the model side increases pressure on the infrastructure side. Bigger context windows, heavier memory footprints, more parallel requests, and more complex agentic workflows all increase the operational burden.

For infrastructure teams, waiting does not help. The surface area of what needs to be built only grows. You cannot delay autoscaling, observability, scheduling, routing, or privacy guarantees. Applications will always demand more from the infrastructure, and they will expect it to feel like a commodity.

My view is that technical deflation applies much more to application startups than to infrastructure startups. App founders can benefit from waiting. Infra founders have to build now because every model improvement instantly becomes a new expectation that the infra must support. The baseline keeps rising.

The real moat in the next era is not the speed of feature development. It is the ability of your infrastructure to absorb the increasing chaos of more capable models while keeping the experience simple and predictable for the user