The Wedge and the Moat
It is tempting to describe China’s manufacturing advantage and India’s services advantage as stories of cheap labour. That description misses the central lesson of both. Lower cost was the opening. The enduring advantage came from the production system built around it.
China did not become the world’s manufacturing centre merely because wages were lower; plenty of places had low wages. It built dense supplier networks, specialised industrial clusters, tooling expertise, logistics infrastructure, quality systems and a culture of rapid, cumulative cost reduction. A designer could move from prototype to components, assembly, packaging and shipment through an ecosystem that grew more capable as more production entered it. The advantage compounded even as wages rose — which is how you know the wages were never the point. Western manufacturers learned to speak of “the China price” as a noun: not a cheap factory quote, but an integrated production capability that established players could not match without dismantling their own economics.
India’s information-technology revolution followed the same arc. Lower-cost engineering talent was the wedge. The lasting invention was the delivery machine: work decomposed across locations, offshore and onsite coordination, codified processes audited to maturity levels the clients themselves could not pass, talent pyramids, training engines that turned hundreds of thousands of graduates a year into deployable engineers. Global corporations did not send work to India because Indians were inexpensive. They sent it because Indian firms had industrialised the process of delivering technology services reliably at scale. The wage gap opened the door; the delivery machine kept it open for three decades.
The pattern, stated once: in every affordability revolution, the cost input is the wedge and the production system is the moat. Cheap labour won China the first order; the factory system won it the next ten thousand. Cheap engineers won India the first contract; the delivery machine built an industry.
Three revolutions, one pattern: the cost input opens the door; the production system keeps it open.
Now apply the pattern to AI — and confront the obvious objection directly. If AI makes software cheap for one entrant, will it not make software cheap for everyone? It will make code cheaper for everyone. It will not automatically give anyone a foundry. The same objection could have been raised against China and India: industrial equipment was purchasable anywhere, and talent was mobile. Yet particular regions and companies built compounding advantages by organising those inputs better than anyone else. AI-generated code is the ore, not the product. Whoever merely uses the coding models holds a discount that expires the moment competitors log in. Whoever industrialises them builds the third great production system.
What does industrialising look like? A foundry does not create every component from first principles; it combines common machinery, standard processes and reusable materials to produce many finished products. In software terms: identity and permissions built once; workflow engines, connectors, data management, notifications, billing, audit, analytics, backup and recovery built once — so that each new product is mostly a domain model, a set of screens or conversational flows, and configuration on shared foundations. The first product may take substantial human effort. The fifth should reuse the machinery of the first four. The twentieth should inherit a library of proven patterns and quality checks. A conventional software company organises itself around one product that gradually expands; a foundry organises itself around the repeated act of production. It is the difference between building a machine and building the machine that makes machines. If every product requires an entirely new architecture, it is not a foundry. It is traditional IT services using AI-generated code.
Industrialising quality, not just speed
This is also why “AI makes coding cheaper” is an incomplete argument — and where the naive version of this thesis dies. If every application is independently generated, independently tested and independently supported, the apparent productivity gain disappears into inconsistency, technical debt and maintenance. Rapidly generated code produces fragile products when nobody understands their architecture or verifies their behaviour. A flood of brittle, insecure AI-built software would raise customer costs, not lower them.
Manufacturing did not become dependable because machines produced parts quickly. It became dependable through specifications, tolerances, inspection, statistical quality control and traceability. The software foundry needs the equivalents: machine-readable specifications that agents can execute well; automated tests as the default, not the afterthought; security policies and architecture rules enforced by the production system itself; versioned components; behavioural monitoring; failure replay and rollback; human approval reserved for high-risk changes. The foundry must industrialise quality as aggressively as it industrialises speed. The objective is not maximum code generation. It is maximum reliable utility per unit of cost.
Do this well, and something larger happens than one company’s cost advantage. A new reference price forms — a foundry price — just as previous production systems established new reference prices for goods and services. Once customers know that focused, reliable software can be delivered for a fraction of the traditional price, the old price becomes harder to defend everywhere. That is how a productivity tool becomes a market revolution.
