AI coding sits in a strange and rare position. It is purely ephemeral. It does not exist unless it is working. Unlike cloud infrastructure that has permanent IP addresses, DNS records, persistent volumes, and state that takes months to migrate, AI coding has essentially zero switching costs. The agent runs, produces a PR, and disappears. Tomorrow you can use a different agent and nothing breaks.
That is an unusual condition for infrastructure, and it creates an opportunity that does not normally exist — a real middleware layer that can adapt and optimize as the underlying models improve. In cloud, AWS captured the middleware position because the switching cost wall was too tall to climb. In AI coding, the wall is not there. The customer can swap the underlying model on Tuesday and nobody notices.
The big AI companies will build amazing coding agents. They are pouring billions into it. But will Anthropic build a tool that fairly compares Claude against GPT-4 and routes work to whoever performs better for your codebase? Probably not — and not because they are bad actors. Because that is not their job. Their job is to make Claude win. Fair comparison is the customer's job, and the customer needs a vendor whose only loyalty is to outcomes.
That is the opportunity. A horizontal platform that serves the customer's best interests rather than promoting any single model. One that benefits when the underlying models get better, regardless of which lab produced them. One that can credibly say: we picked Grok for this task because Grok actually won this task.
This is the same structural argument I made in the multi-agent platform play. The lab does not optimize for buyer truth. The middleware does. The window to build this is small — once one player establishes the routing layer at scale, it becomes very hard to dislodge.
— Ry
Sources
Related Essays
The Multi-Agent Platform Play
Nobody knows which AI coding agent actually works best because no platform runs them side-by-side. That is the opening for a horizontal layer that serves the customer, not the model.
The Road Ahead for AI Coding
We are still early. The trajectory is clear. The companies that build platforms — not just use tools — define the next decade of software development.
Democratizing Software Engineering
AI coding is not about replacing developers. It is about expanding who can request software work in the first place — and 10x-ing total demand.
Key takeaways
- AI coding is ephemeral — it does not exist unless it is working. Zero switching costs.
- Cloud locked customers in with persistent IPs, DNS, and state. AI coding has none of that.
- The lab will not build the fair comparison tool. That is the entire opportunity for middleware.
FAQ
Why does ephemerality matter so much?
Because lock-in usually comes from persistent state — your IPs, your databases, your DNS records. AI coding has none of that. The agent runs, produces a PR, and disappears. Switching providers is a config change, not a migration. That is unprecedented for infrastructure.
What is the middleware layer doing exactly?
Adapting and optimizing as the underlying models improve. Routing work across providers. Reporting back which agent actually delivered for which task. Acting on the customer's behalf, not on any one lab's behalf.