How much faster is an AI-native team, really?
Describe a project by size, type of work, codebase and compliance needs. The calculator compares a traditional team, an AI-assisted team and an AI-native team, shows which kinds of work AI actually speeds up, and is candid about where it does not.
Describe the project, compare three teams
Start from a preset, then shape the work mix. Each type of work has its own conservative AI assumption.
- Traditional team16 wks
- AI-assisted team15 wks
- AI-native team13 wks
- Traditional team$185,440
- AI-assisted team$168,730
- AI-native team$174,760
- Boilerplate and CRUD3.2 wks
- Integrations and APIs1.1 wks
- Novel business logic0.6 wks
- UI and front end1.5 wks
- Testing1.5 wks
- Documentation0.6 wks
- Most of the time saved comes from two work types: Boilerplate and CRUD (3.2 wks) and UI and front end (1.5 wks).
- Even with a 15% higher rate and AI tooling of $2,400, the AI-native team costs about $11,000 less than a traditional team under these assumptions.
- An AI-assisted team takes 15 wks and costs $168,730: cheaper than the AI-native team here because it pays no rate premium, but it finishes later.
- Reviewing AI-generated work and fixing what slips through absorbs about 2.9 wks of the 12 wks that AI saves on writing code.
- Discovery, stakeholder decisions and meetings are held constant for every model. Faster code does not speed up slow decisions.
Indicative assumptions, not measured results. AI-native effort multipliers range from 0.5 for documentation to 0.88 for novel logic, reduced further by codebase and compliance. Engineer-weeks are 40 hours.
Indicative only. We will send the inputs above with your message so a senior engineer can sanity-check them.
Split the work, apply modest multipliers, then add back the overheads.
Start from a traditional estimate
Enter engineer-weeks, or story points with your velocity. This is the effort a team without AI tooling would expect.
Divide it by type of work
Boilerplate, integrations, novel logic, UI, testing and documentation each get their own AI multiplier, because AI helps some far more than others.
Discount for context
Legacy code and regulated domains keep less of the saving. Review of AI-generated code and rework from missed defects are added back.
Convert to weeks and cost
Overheads for coordination, compliance and understanding existing code are added, then effort is spread across the team and priced at your rate.
AI software development speed is uneven, and that is the point.
AI-assisted teams use autocomplete and chat inside the editor. It helps individual engineers with routine code and explanations, but the workflow around them stays the same, so the gains are modest. AI-native teams change the workflow itself: engineers break work into well-specified tasks, run several coding agents in parallel, and spend their own time on design, review and the problems that need judgement.
That second approach is how FusionOne works. We built and open-sourced perchd, a command-line tool for developers running multiple AI coding agents side by side, because running them in parallel is where much of the time goes. Even so, the multipliers in this calculator are deliberately conservative. They represent typical gains on well-suited work with senior review, not best-case demonstrations.
The biggest effort reductions tend to come from boilerplate, CRUD screens, tests and documentation. Integrations benefit when APIs are well documented. Novel business logic benefits least, because the hard part is deciding what the software should do, and no model can make that decision for you.
When AI-native development will not save you much
If several of these describe your project, expect the AI-native timeline to sit much closer to the traditional one than the headline numbers suggest.
The work is mostly new, unsolved logic
Pricing engines, matching algorithms and domain rules take thinking time, and AI does not shorten that much.
The codebase has no tests
Without tests, every generated change needs careful manual verification, which eats into the time saved.
Decisions are slow
If requirements wait weeks for sign-off, faster code just means the team waits sooner.
Nobody senior reviews the output
Skipping review looks faster until security issues and subtle defects reach production.
Heavy regulation
Audit trails, validation and approvals take the same calendar time no matter who writes the code.
Data cannot leave your environment
Strict privacy or IP constraints can limit which AI tools are allowed, and self-hosted options need setting up first.
Questions we often hear
How much faster is AI-assisted software development?
It depends heavily on the work. Teams typically see meaningful savings on boilerplate, tests and documentation and much smaller ones on novel logic. Across a whole project, coordination, decisions and review dilute the gain, which is why realistic project-level improvements are far smaller than the demos you see online.
What is the difference between AI-assisted and AI-native development?
AI-assisted development adds AI tools to an unchanged workflow, such as autocomplete and chat in the editor. AI-native development redesigns the workflow around AI: specified tasks, coding agents working in parallel, AI-generated tests and documentation, and senior engineers accountable for architecture and review.
Does AI-native development cost less?
Often, but not always. AI-native teams tend to be more senior and may charge higher rates, and agent tooling has a cost. The calculator lets you set a rate premium and tooling budget, so you can see whether the effort saved outweighs them for your project.
Is AI-generated code safe to use in production?
It can be, with the same discipline as any other code: senior review, automated tests, security scanning and clear ownership. The risks come from skipping those steps, not from AI itself. That is why the calculator adds review time and counts rework when review is light.
How should I measure AI productivity in my own team?
Compare like with like over several weeks: cycle time from ticket to production, review turnaround, escaped defects and rework, on similar kinds of work before and after changing how the team uses AI. Lines of code and self-reported speed are poor measures, because generated code is cheap and the real cost sits in review and maintenance.
Does AI help with legacy systems?
AI is useful for reading, explaining and documenting unfamiliar legacy code, which shortens the time engineers spend understanding it. Generating changes in a legacy system is riskier, especially without tests, so the time saved on writing code is smaller than on a modern codebase.
Related reading and tools
Want a realistic AI-native plan for your project?
Send your calculator results and a short description of the work. A senior engineer will tell you where AI-native delivery would genuinely save time, where it would not, and what a sensible plan looks like.
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