Perspective
Watch an AI code generator work and it's genuinely impressive. Describe an app, get a codebase. Thirty seconds, thousands of lines.
Now ask the uncomfortable question: who reviews those lines?
Nobody. That's the point of the product - you didn't want to deal with code, so nobody involved can deal with code. The AI wrote it, the founder can't read it, and no engineer will ever audit it. Thousands of unreviewed lines are now running your business.
A founder who asks for "a CRM with lead scoring" doesn't want code. They want a system: leads stored somewhere safe, a score computed a certain way, the right person alerted at the right time. Code is one possible representation of that system - and for this audience, the worst one.
Code can express anything, which is exactly the problem. Every generation is a fresh improvisation. Ask twice, get two different codebases with two different sets of bugs. Security exists if the model happened to include it that day - and the public record on that is not good. In 2025, one popular AI builder's generated apps exposed user data across 170+ sites because row-level security wasn't consistently generated. Real production databases have been deleted by AI agents mid-session.
There's an old principle in engineering: the power of a tool should match the precision of its operator. Nobody hands an unreviewed intern root access on day one - not because the intern is stupid, but because the blast radius is wrong.
The same logic applies to LLMs. Their judgment is remarkable; their consistency is not. So give them a job where judgment matters and consistency doesn't: understanding what a human wants. Then hand the result to machinery that is boring, deterministic, and identical every time.
That's generating a system instead of code:
Constraining generation doesn't mean banning AI from the product. Inside a running system, AI steps do real work: scoring leads, triaging inboxes, extracting invoice data, drafting replies. But each step runs inside a frame - cost caps, retries, confidence thresholds, and human approval on anything consequential. AI supplies judgment at the edges. It never freelances in the middle.
When you evaluate an AI builder, ignore the demo. Demos are the one thing every generator is good at. Ask instead: what exactly does the AI produce, and who can verify it?
If the answer is "code, and effectively no one" - you're not buying software, you're buying a liability with a nice UI.
If the answer is "a definition you can read, compiled by machinery that never improvises" - that's a system. That's the version of this technology a business can actually run on.
Chromoly is built the second way. The full pipeline - prompt to spec to compiler to runtime - is documented openly on the architecture page.
Describe a system. Read what the AI understood. Approve what actually ships.
Start free