The cost of building software has fallen. Not incrementally, but substantially, and the change has happened fast enough that many organisations have not yet adjusted their assumptions.
The Numbers Have Changed
Work that used to take a year now takes three to four months. Work that used to take three months now takes three to four weeks. Prototypes that validate whether a solution is worth pursuing can be in hand in days rather than months.
These are not projections. They reflect what AI-native engineering teams are delivering now, across a range of industries and problem types.
The mechanism behind this is not simply that engineers write code faster, though they do. A large proportion of the implementation work that consumed engineering time — boilerplate, integrations, test scaffolding, documentation — can now be handled by AI tooling. Experienced engineers using these tools spend more of their time on work that requires judgment, and less on work that is mechanical. The leverage created by that shift is significant.
What This Changes Strategically
For organisations thinking about strategy and investment, this changes a number of calculations.
The threshold for what is worth building has shifted. Custom software has historically been expensive enough that many workflow problems, product gaps and internal inefficiencies did not justify the investment. Problems that were previously addressed with workarounds or off-the-shelf tools that were "close enough" may now warrant a purpose-built solution at a cost and timeline that is commercially viable.
The value of early validation has increased. When a prototype takes days rather than months, the risk profile of exploring an idea changes. Organisations can test whether a proposed solution actually works for real users before committing to a full build. The cost of being wrong early is much lower than it was.
The case for owning your own technology is stronger. SaaS products are built for the median customer. Custom software is built for your specific workflow, your data model and your integration requirements. When the cost of custom was high, the tradeoff often favoured SaaS. As that cost comes down, so does the argument for depending on someone else's platform.
The Counterpoint
Lower cost does not mean lower risk. Moving faster creates new exposures if the right engineering discipline is not in place. The savings available through AI-native delivery are real, but they depend on:
- Teams that know how to use these tools well
- Organisations investing in the right foundations
- Leadership that understands the difference between fast and reckless
Understanding the new economics of software development is not just useful context for technology leaders. It is relevant to anyone making decisions about where to invest, what to build and what competitive position the organisation wants to hold.