How the Marginal Cost of New Software Approaches Zero with AI Development

Key Takeaways

Software development has never behaved like a commodity.
In traditional software development, cost scales approximately linearly with scope.
Every project under CEM produces two outputs.
The marginal cost of new software approaching zero changes the calculus of portfolio strategy entirely.

Published: February 2026 | Stealth Labz | Search Intent: Informational Keywords: marginal cost software development, AI software development economies of scale, decreasing software build costs

The Setup

Software development has never behaved like a commodity. Every new product required a new team, a new timeline, a new budget. The cost of building product number five was essentially the same as building product number one. There were no economies of scale. No learning curve discount. Each project started fresh -- fresh codebase, fresh architecture decisions, fresh integration work.

Benedict Evans articulated the shift underway in his analysis "AI and the Marginal Cost of Intelligence": when the cost of generating a unit of intellectual output drops toward zero, entire industries restructure around that reality. Photography did it when digital replaced film. Publishing did it when the internet replaced printing. Software development is doing it now -- not because AI writes all the code, but because AI-assisted execution fundamentally changes what one person can produce, and compounding execution changes what each successive project costs.

The conventional model treats each software build as an independent investment. SaaS Capital's operating benchmarks show that median SaaS companies spend 20-30% of revenue on R&D, with engineering headcount as the primary cost driver. Bessemer's Cloud Index tracks a median engineering efficiency ratio that has barely moved in a decade -- more engineers produce more software, roughly linearly. There is no natural mechanism in the traditional model for the cost of the next product to be materially less than the cost of the last one.

What the Data Shows

The External Benchmark

In traditional software development, cost scales approximately linearly with scope. Two products cost roughly twice what one product costs. Ten products cost roughly ten times what one costs. This is confirmed by every industry benchmark: Bessemer's Cloud Index shows engineering costs growing proportionally with product scope, and SaaS Capital's data shows no meaningful improvement in cost-per-feature as companies scale engineering teams. The marginal cost of a new product, in the traditional model, is approximately equal to the average cost of the products that came before it.

The Internal Curve

The CEM portfolio tells a different story. External support cost per project, tracked across nine sequential builds:

Project 1: $7,995
Project 2: $4,080
Project 3: $4,005
Project 4: $1,680
Project 6: $330
Project 7: $330
Project 8: $90
Project 9: $0

That is not a gradual decline. It is a collapse. The ninth production system -- a fully functional product shipped to production -- cost $0 in external support. The operator built it entirely from existing infrastructure with AI assistance and no contractor involvement.

The Compounding Math

By the late portfolio, 95%+ of infrastructure components for each new project came from foundation -- the reusable architecture accumulated across all prior builds. New projects required only the logic that made them different from what already existed, typically 5-20% of total functionality. The remaining 80-95% was inherited.

In business terms: without a compounding foundation, building five products costs 5x. With a mature foundation, building five products costs approximately 2x. Building ten products without foundation costs 10x. With foundation: approximately 3x.

The build time followed the same curve. Days to ship a functional product:

Project 1: 24 days
Project 5: 11 days
Project 8: 9 days
Project 9: 5 days

Same operator. Same tools. The system got faster because each build deposited reusable patterns -- authentication, database schemas, admin interfaces, API structures, deployment configurations -- into a shared foundation that subsequent projects drew from.

Cross-Vertical and Cross-Geography Transfer

The foundation did not just compound within a product family. It compounded across verticals and geographies. Insurance patterns from PRJ-08/PRJ-09/PRJ-10/PRJ-11 transferred to the operations platform (PRJ-01). E-commerce checkout patterns from PRJ-06 transferred to insurance quoting. South African geography patterns from PRJ-05 transferred to the US-based PRJ-07. When PRJ-07 shipped in 16 days at $330, it drew from the insurance cluster's vertical patterns, the US products' geography patterns, and the flagship platform's admin patterns simultaneously.

The foundation compounds across every axis at the same time: vertical, geography, product family, and infrastructure layer.

How It Works

Every project under CEM produces two outputs. The first is the product -- the features, interfaces, and business logic that the customer interacts with. The second is the foundation -- the reusable infrastructure that the next project inherits.

Take authentication as a concrete example. PRJ-08 (the first insurance product) built authentication from scratch: multi-tenant roles, admin permissions, partner access. Cost: days of work plus external support. PRJ-10 inherited that authentication system and added insurance-specific role permissions. Cost: hours, not days. PRJ-05 inherited authentication plus insurance roles and customized for a different geography. Cost: minimal. By PRJ-04, authentication deployed in minutes. Cost: effectively zero.

This pattern repeated across every reusable component -- database schemas, admin UIs, API structures, deployment configurations. Each one followed the same curve: expensive the first time, nearly free by the tenth.

The AI tooling accelerates this transfer. With AI-assisted development, the operator does not manually copy and adapt patterns. The AI understands the existing codebase, identifies applicable patterns, and applies them to the new context. What would have been a manual refactoring exercise becomes a prompted generation against a known foundation. This is why the cost collapse is exponential rather than linear -- each project makes the AI more effective at generating the next project, because the foundation it draws from is deeper.

What This Means for Multi-Product Operators

The marginal cost of new software approaching zero changes the calculus of portfolio strategy entirely. Under the traditional model, every additional product is a capital allocation decision: is this product worth $200,000-$500,000 to build? Under a compounding model, the question becomes: does this product fit the existing foundation, and does it contribute patterns that make future products cheaper?

The CEM portfolio went from $7,995 for the first product to $0 for the ninth -- a 100% cost reduction across nine builds. The total portfolio cost of $67,895 produced $795,000-$2,900,000 in market replacement value. Ten systems. Seven verticals. Two geographies. 596,903 lines of production code.

For decision-makers evaluating multi-product strategies, the data indicates that the first product is an investment not just in a product, but in a foundation that makes products two through ten dramatically cheaper. The operator who builds early compounds longest. The operator who waits pays full price for every product, every time.

References

Evans, B. (2024). "AI and the Marginal Cost of Intelligence." Analysis of AI's impact on production economics.
SaaS Capital (2024). "Operating Benchmarks." SaaS company R&D and engineering spending data.
Bessemer Venture Partners (2024). "Cloud Index." Engineering efficiency metrics for cloud companies.
Keating, M.G. (2026). "Case Study: The Cost Inversion." Stealth Labz. Read case study
Keating, M.G. (2026). "Case Study: The Foundation Effect." Stealth Labz. Read case study