How AI is changing software development, minus the hype.
AI coding agents now write, test and document a large share of routine code in well-run teams. That changes what software costs, how long it takes and who you need to build it. It also creates new ways to get things badly wrong. Here is a practitioner view for anyone buying or building software.
AI is shifting engineering effort from typing code to directing and reviewing it.
In teams that have genuinely adapted, engineers describe the change they want, AI agents draft the implementation, tests and documentation, and the engineer reviews, corrects and integrates the result. Several agents can work on separate tasks in parallel, which is why tooling for running agents side by side has appeared, including perchd, an open-source CLI FusionOne built for this workflow.
The practical effect is that routine work, such as standard screens, integrations, data migrations and test suites, typically takes fewer engineer-hours than it did a few years ago. Feedback loops are shorter, because a working version of an idea can exist the same day it is discussed.
What has not sped up nearly as much is the hard part: understanding the problem, making architecture decisions, handling security and unusual edge cases, and agreeing what the product should do. Those now make up a larger share of the work, which is why senior judgement has become more valuable, not less.
Traditional, AI-assisted and AI-native development compared
Most teams today sit somewhere on this spectrum. The label a vendor uses matters less than how the work is actually done.
| Traditional team | AI-assisted team | AI-native team | |
|---|---|---|---|
| How AI is used | Little or not at all | Autocomplete and chat for individual developers | Agents do most routine implementation, engineers direct and review |
| Team shape | Larger, with many mid-level implementers | Similar size, somewhat more output | Smaller and more senior |
| Timeline for routine features | Baseline | Moderately shorter | Typically much shorter |
| Where cost goes | Mostly writing code | Mostly writing code, some review | Mostly design, review, testing and integration |
| Main risk | Slow, expensive delivery | AI output accepted without enough scrutiny | Review becoming the bottleneck if seniors are stretched |
Gains vary a great deal by project. Novel algorithms, complex legacy systems and heavily regulated work benefit less than typical business applications.
The knock-on effects for cost, teams and hiring
Cost
The same scope often needs fewer engineer-hours, so builds that were marginal become viable. Hourly rates do not fall, but the hours should.
Timelines
First working versions arrive sooner and iteration is faster. Decision-making, approvals and user testing become the pace-setting steps instead.
Team shape
Smaller teams of experienced engineers can cover more ground. The traditional pyramid of many juniors under a few seniors is flattening.
Hiring
Strong candidates now show how they break down problems for AI, verify its output and catch its mistakes. Interviews that ban AI test the wrong skill.
Quality control
Code volume rises faster than human attention. Automated tests, static analysis and disciplined review matter more than they did before.
Legacy systems
AI is particularly useful for reading and explaining old code, which makes modernisation projects less daunting than they used to be.
Questions to ask a software vendor about AI
If you commission software, these questions separate teams that have adapted from teams using AI as a sales slogan.
How does AI fit into your day-to-day delivery process?
Look for specifics: which tasks agents handle, and where engineers step in.
How is AI-generated code reviewed before it reaches our product?
Every change should be reviewed by an engineer who can explain it, backed by automated tests.
How does AI show up in your estimate?
A quote built on pre-AI effort assumptions suggests the efficiency is not reaching you.
What happens to our code and data inside AI tools?
Ask which tools are used, under what data terms, and whether proprietary code is used for training.
Who owns the IP in AI-assisted work?
The contract should assign all code to you and address licensing of any generated material.
Who is senior and accountable on our project?
A small AI-native team only works if experienced people are genuinely hands-on.
Faster code makes clear thinking about what to build more important, not less.
When features were expensive, scope was naturally limited by budget. Now that a team can produce working code quickly, it is easy to build too much, too soon, and end up with a large product nobody fully understands. The discipline of cutting scope, testing assumptions with users and saying no has become one of the most valuable skills in a software project.
For business owners, the opportunity is real: custom software, internal tools and integrations that were out of reach are increasingly affordable. The way to capture it is to pair AI-native delivery with experienced people who own the decisions and the quality.
Questions we often hear
Will AI replace software developers?
AI is replacing much of the routine typing of code, not the need for people who understand problems, design systems and take responsibility for quality. Demand is shifting towards engineers who can direct AI effectively and judge its output. Teams are becoming smaller and more senior rather than disappearing.
Does AI make software development cheaper?
Often, yes, for typical business applications. AI-native teams usually need fewer engineer-hours for routine features, tests and integrations, so the same scope costs less. The saving is smaller for novel or highly regulated work, and it only reaches buyers if vendors price by value delivered rather than by pre-AI effort.
What is AI-native software development?
AI-native development means the whole delivery process is designed around AI, not just individual developers using an assistant. Engineers break work into tasks for AI agents, often run several in parallel, and spend their own time on architecture, review, testing and product decisions.
Is AI-generated code safe to use in production?
It can be, with the same controls you would apply to any code: review by an experienced engineer, automated tests, security scanning and dependency checks. Unreviewed AI-generated code is risky because it can include subtle security flaws, invented dependencies or logic that nobody on the team understands.
How should I evaluate a development agency that says it uses AI?
Ask how AI is used in practice, how generated code is reviewed, how it affects their estimate, what happens to your data in their tools and who is accountable on your project. Good answers are specific and consistent; vague claims about speed without a review process are a warning sign.
Related reading and tools
Planning software in the AI era?
Tell us what you want to build or what a vendor has proposed. We will give you a realistic view of what AI-native delivery could change for the cost, timeline and team, and where caution is needed.
Working with companies globally · Response within 24 hours