Text-to-CAD for manufacturing: can AI output survive a machine shop?

I sent a STEP file to my usual machine shop last month without mentioning it came from an AI. Just said I needed a quick quote on a one-off bracket. Aluminum, nothing exotic. The shop owner called me forty minutes later, which is fast even for him. "Jan, did an intern draw this?" he asked. I told him the truth: it was generated by a text-to-CAD tool. There was a pause. "That explains the internal corners," he said. "Tell the AI about end mills."

That call lasted about fifteen minutes and covered most of what I'm about to write here. The bracket looked fine on screen. It had mounting holes, a reasonable profile, fillets in the right general areas. It exported to STEP without errors. But when a person who has actually made thousands of parts from CAD files looked at it, the problems were immediate and numerous. Sharp internal corners that no cutter can reach. A pocket depth that exceeded the tool length-to-diameter ratio. Walls thin enough to chatter. No consideration of how the part would be held in a vise.

This is the story of text-to-CAD and manufacturing in 2026. The geometry exists. The engineering doesn't.

What a machine shop actually needs from a CAD file#

Before I get into what AI gets wrong, it's worth understanding what a manufacturing-ready model requires. Because the bar isn't just "correct shape." It's a much longer list than most non-manufacturing people realize.

A machineable part needs tool access to every feature. Every internal pocket needs corner radii that match or exceed the radius of the smallest end mill that can reach it. Every hole needs to be achievable with standard drill sizes or boring operations. Wall thickness needs to be sufficient for the material and the cutting forces. Features need to be positioned so the part can be fixtured, meaning clamped in a vise or bolted to a plate, without blocking access to the features being cut.

Beyond that, a manufacturing drawing or model needs tolerances. Not "approximately 10mm." Exactly 10mm +0/-0.05, or 10mm H7, or 10mm with a surface finish of Ra 1.6. These specifications tell the machinist what matters and what doesn't, where to spend time and where good enough is good enough.

Text-to-CAD tools produce none of this. They produce shapes. Shapes without tolerances, without DFM consideration, without any awareness that the geometry will need to interact with cutting tools, fixtures, and physics. The limitations are well documented at this point, but seeing them through the eyes of a machine shop makes them feel more urgent.

The internal corner problem#

This is the one every machinist spots first. It's almost a litmus test for whether a model was created by someone who understands machining.

CNC end mills are round. They have a radius. When an end mill cuts a pocket, the corners of that pocket will have a radius equal to (at minimum) the radius of the cutter. A 6mm end mill leaves 3mm corner radii. A 3mm end mill leaves 1.5mm corner radii. You can go smaller, but smaller cutters are slower, more fragile, and more expensive to run.

Text-to-CAD tools routinely generate pockets and slots with zero-radius internal corners. Perfect 90-degree intersections of two walls. These look fine on screen and are physically impossible to machine without EDM (electrical discharge machining), which costs an order of magnitude more than milling.

Every single AI-generated bracket, enclosure, and housing I've sent to a machine shop has had this problem. Every one. The fix is simple: add internal fillets of at least 1.5mm (for a 3mm cutter) on all internal vertical edges. A human CAD user who has been yelled at by a machinist once does this automatically. The AI has never been yelled at by anyone.

I've tried prompting for it explicitly. "Add 2mm fillets on all internal corners" sometimes works, sometimes doesn't, and sometimes adds fillets on external edges where I didn't want them while missing the internal ones entirely. It's the kind of task that requires understanding why the fillet matters, not just where to put one.

Tool access and pocket depth#

Imagine you need to cut a deep pocket in a block of aluminum. The end mill is a long, thin cylinder spinning at thousands of RPM, plunging into metal. The deeper the pocket relative to the tool's diameter, the more the tool deflects, chatters, and potentially breaks. There's a practical limit, usually around 3 to 4 times the tool diameter for standard operations, beyond which you need special tooling, reduced feeds, or a different approach entirely.

Text-to-CAD tools don't model this constraint. I've gotten parts with 20mm deep pockets that are 5mm wide, requiring a tool aspect ratio that would make any machinist wince. The tool would need to be 4mm diameter or less to fit in the pocket, and at 20mm depth, it would be cutting at 5x its diameter. That's not impossible with modern tooling and careful feeds, but it's slow, risky, and expensive. A part designed by someone who knows machining would either widen the pocket, reduce the depth, or split the feature into multiple operations.

The AI doesn't think about any of this because it doesn't model the manufacturing process. It models geometry. The distance between those two things is where the machine shop quote doubles.

Wall thickness and rigidity#

Thin walls vibrate during machining. This causes chatter marks on the surface, dimensional inaccuracy, and sometimes catastrophic failure where the wall simply breaks under cutting forces. The minimum practical wall thickness for aluminum milling is around 1mm for short features and more like 2-3mm for anything tall or unsupported.

I measured wall thickness on fifteen AI-generated parts intended for CNC machining. Six of them had at least one wall thinner than 1.5mm. Two had walls thinner than 1mm. One, a particularly ambitious enclosure, had a 0.6mm wall that would have vibrated like a tuning fork the moment a cutter touched it.

The AI generated these thin walls not because it was trying to be clever about weight reduction, but because it was distributing material based on training data patterns without any understanding of what happens when you try to cut thin features in metal. The result looks like a part on screen. It sounds like a dentist drill in the shop.

Hole specifications: close is not good enough#

Holes are the simplest feature in machining and one of the easiest places for AI-generated geometry to go wrong in ways that matter.

A 6mm hole in a CAD model is not useful manufacturing information by itself. Is it a clearance hole for an M6 bolt (needs to be 6.4mm or 6.6mm)? A close-fit hole (6.1mm)? A bearing bore (H7 tolerance, meaning 6.000 to 6.012mm)? A tapped hole (5mm pilot drill, then M6 tap)? Each of these is a different operation with different tooling, and the CAD model needs to communicate which one the designer intended.

Text-to-CAD tools generate holes at nominal dimensions with no tolerance or fit class specification. A "6mm mounting hole" arrives as exactly 6.000mm in the STEP file, which tells the machinist nothing about intent. Most shops will drill it at 6.0mm +/- 0.1mm because that's their standard, and hope that's what you wanted. If you needed a press-fit or a bearing bore, you're in trouble, and you won't know until assembly.

I've also noticed that AI-generated hole diameters don't always match standard drill sizes. Real engineers design with drill charts in mind. A 6.8mm hole is standard. A 6.73mm hole means someone is guessing. I've gotten fractional hole diameters from AI tools that would require custom boring operations when a standard drill would have been fine.

Draft angles: the mold doesn't care about your demo#

If a part is going to be injection molded, every face that runs parallel to the mold pull direction needs a draft angle, typically 1 to 3 degrees. Without draft, the part sticks in the mold. This is not optional. This is not a nice-to-have. This is physics.

Text-to-CAD tools generate parts with zero draft on every vertical face, every time. I have tested dozens of AI-generated enclosures and housings and have never once seen draft angles applied automatically. The AI doesn't know the part will be molded. It doesn't know what a mold is. It generates a box with perfectly vertical walls because that's what the training data looks like in the CAD file, even though the actual manufactured parts in that training data had draft angles applied.

A tooling engineer I showed some AI output to didn't even open the STEP files. He looked at the renders and said, "No draft anywhere. These would need complete rework before I could even start a mold design." He wasn't being difficult. He was being accurate.

This applies to cast parts too. Casting needs draft for the same reason. If your part will be manufactured by any process that involves removing it from a shaped cavity, the AI-generated geometry is missing a fundamental requirement.

Surface finish: the thing that isn't in the model#

Surface finish is specified as Ra (roughness average) in micrometers or microinches. A freshly machined aluminum surface might be Ra 1.6. A ground surface might be Ra 0.4. A polished surface might be Ra 0.1. These specifications affect function (sealing surfaces need to be smooth), appearance (visible surfaces on a product), and cost (smoother is more expensive).

Text-to-CAD models have no surface finish information. None. The geometry is mathematically smooth in the way that all B-Rep surfaces are smooth, but there's no callout telling the manufacturer which surfaces matter and which don't. Without this information, the shop either applies their default finish everywhere (wasting time on surfaces that don't matter) or calls you to ask (wasting everyone's time).

This is less dramatic than the internal-corner problem but equally real in terms of cost and lead time. A proper manufacturing model communicates intent on every surface. An AI-generated model communicates shape and nothing else.

Sheet metal: not even the right kind of model#

Sheet metal manufacturing has its own CAD methodology. A proper sheet metal part is modeled with bend features, K-factors, relief cuts, and a flat pattern that can be laser-cut from sheet stock and then formed on a press brake.

Every text-to-CAD tool I've tested generates sheet metal parts as solid extrusions. They look like bent sheet metal. They are not bent sheet metal. There's no flat pattern. No K-factor. No bend allowance. The model is a solid block shaped like a folded piece of metal, and converting it to actual sheet metal features in SolidWorks or Fusion 360 is often harder than modeling the part from scratch.

A sheet metal shop that received one of these files would have to reverse-engineer the designer's intent, create a proper flat pattern, and hope the bend radii work for the material and tooling they have. That's not a manufacturing-ready deliverable. That's a puzzle.

What the AI actually produces versus what manufacturing needs#

Here's a table that summarizes the gap, because after enough paragraphs of bad news, a clean comparison helps:

What manufacturing needs: tool-access-aware geometry with appropriate internal radii, toleranced dimensions, surface finish callouts, draft angles for molded parts, flat patterns for sheet metal, feature relationships that capture design intent, and fixturing consideration.

What text-to-CAD produces: a nominally dimensioned 3D solid with no tolerances, no surface finish, no draft, no flat pattern, no feature relationships, sharp internal corners, and no awareness of how the part will be held or cut.

The gap between these two lists is not a software version away from being closed. It represents fundamental manufacturing knowledge that current AI training data doesn't encode.

The rework time equation#

People ask me whether text-to-CAD saves time for manufactured parts. The honest answer is: it depends on how you count.

If you're starting from zero and need a rough concept of a bracket to discuss with your team, yes, generating a shape in thirty seconds beats spending fifteen minutes in Fusion 360. You get something to react to quickly, and that has value in the early design phase.

If you need a part that will actually be machined, the math gets ugly. The AI saves you maybe ten to fifteen minutes of initial geometry creation. But then you spend thirty to sixty minutes fixing the model: adding proper fillets, correcting hole sizes to standard dimensions, adjusting wall thickness, adding tolerances, adding surface finish callouts, checking tool access, and rebuilding the feature tree so the part is parametrically editable. The rework for real manufacturing often takes longer than the generation saved.

For a one-off prototype bracket, this might still be a net positive. For a production part that will go through design reviews, DFM checks, and tolerance stack analysis, the AI-generated starting point barely moves the needle. The engineering is the hard part, and the AI doesn't do any of it.

What I actually use text-to-CAD for in manufacturing contexts#

Despite everything I've said, I do use text-to-CAD in my manufacturing workflow. Just not for what the marketing suggests.

I use it for generating rough concept geometry during early design discussions. When a client describes what they need and I want to show them a shape within the meeting instead of sending something next day, a quick AI-generated model on screen gets the conversation moving. Everyone understands it's a placeholder. It's visual communication, not engineering output.

I use it for generating fixture and jig concepts. These are often simple geometry that will be 3D printed or quickly machined with loose tolerances. The AI gets me 80% of the way to a usable fixture in seconds, and the remaining 20% is adjusting a few dimensions in Fusion.

I use it for exploring form factors before committing to detailed design. If I'm not sure whether a housing should be 80mm wide or 100mm wide, generating both options quickly and looking at them in the context of the assembly is faster than modeling each one.

What I don't use it for: anything that goes to a machine shop without me reworking it first. Anything with tolerances that matter. Anything that will be injection molded, cast, or formed. Any part that interacts with other parts in an assembly where dimensional relationships are critical.

Where this might improve#

The most likely near-term improvement is DFM validation layers bolted onto text-to-CAD output. Several CAD companies are working on automated DFM checks that flag problems like sharp internal corners, thin walls, and missing draft angles before the user sees the model. This doesn't make the AI smarter about manufacturing, but it catches the worst mistakes.

Training on manufacturing-contextualized data would help more. If the AI learned from parts that included their manufacturing process, material, and tolerance annotations alongside the geometry, the output might start reflecting real constraints. But that data is mostly locked inside company PLM systems and rarely includes the manufacturing context in a machine-readable way.

Process-specific generation modes could also help. Instead of "generate a bracket," imagine "generate a bracket for 3-axis CNC milling in 6061 aluminum." That prompt carries enough context for the AI to apply appropriate constraints, if it had the training data to understand them. We're not there yet.

The honest verdict#

Text-to-CAD output does not survive a machine shop in 2026. Not without significant rework by someone who understands the manufacturing process. The geometry comes out looking like parts but behaving like sketches. It's missing the engineering layer that separates a shape from a specification.

For early concept work and quick visualization, text-to-CAD is a useful speed boost. For prototyping on FDM printers, it's workable. For anything going through a manufacturing process that involves tooling, fixturing, or tolerances measured in hundredths of a millimeter, the human engineer is still doing all the hard work, and the AI is providing a slightly head-started shape to work from.

My machinist's advice was simpler than anything I've written here. "If the AI can learn about internal corner radii," he said, "that alone would cut my callback rate in half." He's not wrong. And the fact that we're still talking about something that basic tells you exactly where this technology sits relative to manufacturing reality. It's early. It's useful at the margins. It's nowhere near the shop floor.