How AI assisted development changed the economics of custom software

AI coding assistants cut the slowest, most repetitive parts of building software. That drops the cost floor on custom systems, which used to price out most small and mid sized companies. The thinking, the design decisions, and the testing still take real human time. So custom software is now a reasonable option for a 40 person company, not just a 4,000 person one, as long as you go in with clear eyes about what still costs money.

• AI tools speed up code writing, boilerplate, test generation, and documentation, which were always the bulk of the typing.
• The expensive parts of a build are discovery, data migration, edge cases, and change management. AI barely touches those.
• Per seat SaaS costs compound every year. A system you own has a one time build cost and a small hosting bill.
• Code quality depends on the review process around the AI, not the AI itself.
• Custom is a bad fit when an off the shelf tool already does 90 percent of what you need with no workarounds.

Writing code got cheaper. That's the short version, and it's changed which companies can sensibly own their own software.

For twenty years the answer to "should we build this?" was usually no. Building meant a long project, a big team, and a budget that only large companies could sign off on. So everyone rented instead. You bought seats in a platform, worked around the parts that didn't fit, and paid the renewal every January. That math has shifted, and it's worth understanding why before you make your next platform decision.

Software development was always part thinking and part typing. The thinking is figuring out how your business really works. The typing is turning that into code, tests, forms, reports, and documentation. AI assistants are very good at the typing.

A developer using a modern coding assistant can describe a data structure in plain language and get a working first draft in seconds. Things like login screens, permission rules, audit logs, PDF exports, and API connections are well understood problems that have been solved thousands of times. The AI has effectively read all of those solutions. It produces a competent starting point fast, and the developer edits it into something correct.

The same applies to tests. Automated tests are small programs that check your software still works after a change. Writing them by hand is tedious, so historically they got skipped when a project ran hot. Now a developer can generate broad test coverage in an afternoon. Quality goes up, not down, because the boring safety work finally gets done.

Plenty of owners still believe custom software is a thing you do after you hit a few hundred million in revenue. That belief came from a real era. In 2010, a serviceable internal system meant server hardware, a database administrator, a long requirements document, and a team of five for the better part of a year.

Almost all of that is gone. Cloud hosting removed the servers. Modern frameworks removed thousands of lines of plumbing code. Managed databases removed the specialist. AI assistants removed a large share of what's left. A two person team today ships what a ten person team shipped fifteen years ago.

What didn't change is the discovery work. Somebody still has to sit with your dispatcher and learn why she keeps a paper notebook next to a $90,000 a year platform. That conversation costs the same as it always did. It's also the part that determines whether the software gets used.

The traditional custom build burned budget in places that had nothing to do with typing code. Long written specifications that were obsolete before anyone read them. Handoffs between a business analyst, an architect, and three developers who each interpreted the document differently. Change requests priced as contract negotiations because the original scope was rigid.

Leaner AI assisted work compresses that. Instead of a 60 page specification, you build a rough working version of the screen in a day and show it to the person who'll use it. She points at it and says the status field is wrong and there should be a second date. You fix it that afternoon. The prototype replaces the document, because it's now cheap enough to throw away.

That's the real economic change. Not that code is free, but that the cost of being wrong dropped. When a wrong guess costs a day instead of a month, you can afford to guess more often and correct faster. Projects fail less often for the boring reason that they get corrected earlier.

Do this with your actual numbers, because the answer varies a lot by company. Pull your renewal invoices for every operational platform you pay for. Project management, CRM, the field service app, the reporting add on, the integration middleware, the storage overage.

Say you have 80 people using a platform at $75 per seat per month. That's $72,000 a year. Add a $14,000 integration tool and a $9,000 reporting layer and you're at $95,000. Renewals typically rise 5 to 10 percent annually, and seat counts grow as you hire. Over five years you're looking at well north of half a million dollars, and at the end you own nothing. Stop paying and access ends the same week.

A custom system flips the shape of that spend. You pay once to build it, then you pay hosting, which for a business with a few hundred users is usually a few hundred dollars a month. You also pay for changes when you want changes. The question is whether the one time build lands below your multi year rental total, and for a lot of companies with heavy seat counts or ugly workarounds, it now does. It didn't five years ago.

The arithmetic goes the other way too. If you pay $9,000 a year for a tool that fits your process well and nobody complains about, building a replacement is a waste of money. Rent things that are commodities. Own the thing your business actually competes on.

At Black Badger the AI does the first draft and a human is accountable for what ships. Every generated file goes through review by the developer who owns that part of the system. Nothing merges without passing the automated test suite. We run static analysis, which is tooling that reads code and flags likely bugs and security problems before a person does.

We use AI heavily for a few specific jobs. Scaffolding the repetitive layers of an application. Writing test cases against rules we've already agreed with the client. Generating the documentation that explains how a module works, so the client's own future developer can pick it up. Converting messy exported data during a migration, which is normally miserable hand work.

We don't use it to decide architecture, design the data model, or determine business rules. Those come from the discovery conversations, and getting them wrong is expensive to unwind later. An AI will happily produce a confident, plausible data model that quietly makes your invoicing rules impossible to implement in month seven.

Data migration is still brutal. Moving twelve years of records out of an old platform means writing scripted exports, matching fields that don't line up, cleaning duplicates, and reconciling attachments. AI helps write the conversion scripts. It cannot tell you which of three conflicting customer records is the real one. Somebody at your company has to make those calls.

Integrations still break. If your new system talks to QuickBooks, a shipping carrier, and a payment processor, you inherit their outages and their version changes. That's ongoing maintenance no matter who built the software.

Adoption is still the hardest part. Software nobody uses is worse than the spreadsheet it replaced. That takes training, a real rollout plan, and a manager willing to insist on the new process. No tooling shortcuts it.

And there are real risks with AI generated code done badly. It can produce code that looks right and is subtly wrong. It sometimes invents functions that don't exist. It can copy insecure patterns from old examples. Left unreviewed, it produces a large volume of mediocre code very quickly, which is a maintenance problem disguised as progress. The defense is a normal engineering discipline of code review, tests, and a human who understands every line that ships.

One more caveat that matters for ownership. Cheaper building means it's easier to build the wrong thing. When custom software was expensive, the expense forced hard thinking about whether you needed it. Keep that discipline even though the price of a wrong turn dropped.

Start with the list. Write down every platform you pay for, the annual cost, the renewal date, and one sentence on what your team works around in it. Circle anything where people maintain a side spreadsheet, because that spreadsheet is the shape of the software you're missing.

Then pick the single worst offender and price out five years of renting it against owning something that fits. If the numbers are close, keep renting. If they're not close, you already have your answer. When you want a second opinion on that math, book a free strategy session and we'll go through it with you.

Read this next

Build versus buy for mid sized operations, which walks through the decision framework in more detail with worked examples.

Related articles

• Why per seat pricing punishes growing companies
• What actually happens during a data migration
• Owning your source code and what that means in practice

Is AI writing all the code now?

No. It writes first drafts of a lot of it. A developer directs it, reviews everything, and rewrites the parts that are wrong. Think of it as a very fast junior who never gets tired and occasionally makes things up.

Will AI generated code be harder to maintain later?

Only if it was never reviewed. Code that passed human review, has tests, and follows consistent patterns is just code. What's hard to maintain is a pile of unreviewed generated files nobody understands.

Does this mean custom software is cheap now?

Cheaper, not cheap. The typing got faster. The discovery, migration, testing, and rollout did not. Expect a real project with real effort, just less of it than the same project would have taken in 2018.

Who owns the code if AI helped write it?

You do, when your contract says so. Get it in writing that you own the source code and the deployment, and that you receive the repository. That's the whole point of building instead of renting.

What if we build something and then our process changes?

You change the software. That's the advantage of owning it. Changing a rented platform means waiting for a vendor roadmap or paying for a workaround.