Cheaper code is making custom software economically viable again

For years, custom software had to solve a very large problem to absorb the cost of a development team. Repetitive work therefore stayed in spreadsheets, email or one person's memory — not because it worked well, but because automating it looked too expensive.

When code costs less, the break-even point falls

AI agents can sharply reduce the time required to implement, test and evolve an application. They do not make architecture, security or business understanding free. They change the economics: a local improvement repeated thousands of times can now justify a focused tool.

The return often sits closest to frontline work

The strongest candidates are not always the most visible programmes. They are often teams retyping the same information, reconciling several sources by hand, correcting recurring errors or waiting for one key person before a decision can move.

A useful estimate starts with reality: frequency × time saved × cost of work, then adds errors prevented, lead time reduced and revenue unlocked. That gain is compared with the cost of building, operating and maintaining the system.

Cheaper code does not make every idea good

An unstable, rare or poorly understood process can still become bad software. A standard product remains preferable when it fits. Custom software becomes compelling when the process is frequent, specific to the business, measurable and slowed by hand-offs between tools or people.

Reduce the risk of building the wrong thing too

That is the purpose of Reverse SDLC: start with real work, reconstruct first principles, let domain experts complete the specifications, then prototype with users. Agents accelerate an MVP whose intent and constraints have already been challenged.

The real shift is not merely that code is cheaper. It is that more operational problems can finally receive a proportionate solution — with one person accountable from discovery through production.