ChatGPT Wrote My G-Code — Here's What Happened When I Verified It
AI writes G-code that reads perfectly. We checked whether it actually runs.
Ask ChatGPT, Copilot, or Gemini to write a drilling routine or a facing pass and you'll get clean, confident, plausible-looking G-code in seconds. The syntax is right. The structure is sensible. It looks ready to run. So we did the obvious experiment: take AI-generated programs and run them through controller-accurate verification — a full 3D simulation of the actual machine — to see what they'd really do before anything reached a spindle.
The point isn't "AI is bad at G-code." Sometimes it's fine. The point is that you can't tell the safe output from the dangerous output by reading it — both look correct — and the only way to know is to execute it against a real machine model. Here's what that revealed.
Note for publishing: the sections below are the framework for this test. Fill in each program's actual verification result from your own Eureka 3X Pro run — the real collision, the real Z value, the real screenshot. This article is far stronger with genuine results than with described ones, and this audience will ask for the files. Keep the numbers real.
The test setup
We asked common AI assistants for a set of everyday 3-axis programs — the kind a shop might actually be tempted to let AI draft:
- A bolt-circle drilling pattern.
- A facing pass on a rectangular block.
- A pocket with a specified depth and stepover.
- A tapping routine for a set of holes.
- A contour profile with cutter compensation.
Each was generated from a plain-language prompt, taken as-is, and opened in Eureka 3X Pro against a controller-accurate twin — same treatment any real program gets: collision detection, over-travel, gouge/stock-vs-design, and cycle-time check.
Program 1 — Bolt-circle drilling
What the AI produced: (paste the generated program's key characteristics.)What verification found: (the real result — e.g. a missing tool-length activation, a wrong retract mode, an assumed origin that doesn't match a real fixture, or "clean.")What would have happened at the machine: (the real consequence — crash into a clamp, tool driven too deep, or nothing.)
Common failure pattern to watch for here: the AI invents an origin and clearance plane, and assumes a tool-length setup that doesn't match a real machine — so the pattern is geometrically right and references nothing real.
Program 2 — Facing pass
What the AI produced: (...)What verification found: (...)What would have happened: (...)
Common pattern: plausible feeds and speeds with no knowledge of the tool or material, and a stepover that may over-engage the cutter — a conditions problem, not a syntax one.
Program 3 — Pocket
What the AI produced: (...)What verification found: (...)What would have happened: (...)
Common pattern: a depth or Z reference that's plausible but arbitrary; ramp/plunge into the pocket that the AI can't know is safe for the tool.
Program 4 — Tapping routine
What the AI produced: (...)What verification found: (...)What would have happened: (...)
Common pattern: tapping feed and spindle that don't match the thread pitch, or no rigid-tapping mode command — a stripped thread or broken tap that never shows as a collision.
Program 5 — Contour with cutter comp
What the AI produced: (...)What verification found: (...)What would have happened: (...)
Common pattern: cutter compensation turned on without a proper lead-in, or the wrong side — a gouge at the first corner, or a comp alarm.
The pattern across all five
Whatever the specific results, the lesson is structural and it repeats: the AI's mistakes are confident and invisible on the page. They cluster in exactly the places reading can't catch —
- Numbers that are the right kind of value but the wrong one (a depth, a feed, an offset).
- Assumptions about the machine, origin, tool, and fixture that don't match anything real.
- Control-behavior details (tool-length activation, tapping mode, comp lead-in) that are correct as text and wrong as motion.
None of these are syntax errors, so the code "looks fine." All of them are the kind of thing a controller-accurate simulation shows instantly — because it doesn't read the code, it runs it against a model of your actual machine.
The takeaway: AI generation makes verification more important, not less
The faster and easier it becomes to generate G-code, the more of it gets generated — and the more valuable an independent check becomes. The workflow that makes AI usable on a real machine isn't "trust the AI"; it's AI proposes, you verify, you approve. The AI drafts; Eureka 3X Pro executes it against a twin of your machine and shows you exactly what it would do; you decide whether it runs.
That's not a limit on using AI. It's the thing that lets you use it at all on a machine you can't afford to crash.
Try it yourself: ask an AI for a program, then drop it into Eureka 3X Pro and watch it run on a twin of your machine before it runs anywhere else.
Eureka 3X Pro — 30-day free trial, no credit card required.
Related: why the AI doesn't know your machine · verifying AI-generated G-code.
FAQ
Is G-code written by ChatGPT safe to run? Not without verification. AI produces syntactically correct code that can be physically dangerous, because the model has no knowledge of your machine's limits, your setup, or how your control executes the program. Simulate it on a controller-accurate twin before running.
Can ChatGPT actually write working G-code? Sometimes — and that's the trap. You can't tell the working output from the dangerous output by reading it, because both look correct. The only reliable test is to run it against a real machine model.
What kinds of errors does AI-generated G-code have? Confident, plausible-but-wrong values (depths, feeds, offsets), assumptions about the machine and setup that don't match reality, and control-behavior mistakes (tool-length, tapping mode, cutter-comp lead-in) — none of which are syntax errors, so none show up by reading.
How do I verify AI-generated G-code? Open the program in Eureka 3X Pro and simulate it against a twin of your machine. Because it works from the actual G-code, it doesn't matter that no CAM system produced it.
This is an emerging area and AI tools change quickly — the constant is that generated code needs an independent verification step before it runs.
Run every G-code program risk-free — before it touches your machine.