VIDEO REVIEW · WHY SO HARD
Why Is AI CAM So Hard? A CloudNC Co-Founder Explains the Technical Essence in 3 Minutes
01What this video is about
CloudNC is a UK-based AI CAM developer that runs its own precision machine shop, raised a US$45 million Series B led by Autodesk, and whose CAM Assist has been adopted by thousands of shops worldwide (for the full background, see The TITANS of CNC Hands-On Test and the Thousand-Shop CAM Assist Adoption Case). In April 2025, co-founder Chris Emery uploaded this under-3-minute short to the official channel, its title the very question on many people's minds: "Why is applying AI to CNC machining so difficult?!"[1]
The video's description points to two things: one, showing how CAM Assist "generates machining strategies," and two, emphasizing how this route differs from other approaches on the market — particularly feature-based machining[1]. When a developer explains in their own words where their technology is hard, this kind of video is worth studying more closely than marketing material: what it reveals is the field's real boundaries.
02The video's core argument: generating strategies vs feature-based machining
Traditional feature-based machining takes the "rule library" route: the system recognizes a hole and applies a drilling cycle, recognizes a slot and applies a slot-milling template. The advantage is predictability; the drawback is that a rule library can never keep up with the combinatorial explosion of real parts — once features overlap or geometry goes beyond what's defined, the rules can't catch it.
This is exactly what Emery contrasts in the video: CAM Assist's approach isn't a table lookup, but generating a complete machining strategy — facing a part and answering the overall question of "in what order, with what tools, and by what method to make it"[1]. It also explains why the company runs its own machine shop: the quality of a strategy ultimately has to be verified on a real machine.
03Theory breakdown one: cutting is a physics problem, not just a geometry problem
AI's success in images and language is, at its core, pattern recognition; but CNC machining adds a hard constraint — physics. Cutting forces, tool deflection, machine vibration, and chatter mean that a toolpath being "geometrically correct" and "physically feasible" are two different things. The classic textbook on cutting mechanics spends an entire book explaining that the combination of feed, spindle speed, and depth of cut is governed by the boundaries of mechanics and stability — go beyond the boundary and you get broken tools, chatter marks, and scrap[2].
This is precisely the theoretical basis for "you can't trust an AI-produced program directly": a model can learn common strategies from data, but every output still has to be checked against physical reality. The practical means of checking are cutting simulation and parameter boundaries — verify in a virtual environment first, then go to the real machine (see also How Do You Set Cutting Parameters?).
04Theory breakdown two: process planning has no unique solution, and data is scarce
The second layer of difficulty lies in the very nature of process planning (CAPP). Academic reviews long ago pointed out that the same part can have multiple machining plans that are all "correct," and the optimal solution depends on the machine, tools, workholding, batch size, and shop conventions — this kind of "no unique solution, multiple objectives" problem is far harder than a recognition task with a standard answer[3].
The third layer is data: machine-learning research in manufacturing broadly faces the challenges of small data volumes, expensive labeling, and heavy dependence on each shop's context[4]. The internet has an infinite supply of cat photos to train image models, but no infinite supply of "CAM projects with complete machining results attached." That's why companies capable of generating and validating their own data (running their own shops, or going deep in a specific scenario) have a particular advantage in this field.
05What "knowing where the difficulty lies" means for tool selection
Understanding the difficulty isn't academic curiosity — it hands you a verification checklist directly. When evaluating any AI CAM / AI CNC tool, you can turn these three difficulties into questions:
| Difficulty | The corresponding verification question |
|---|---|
| Physical constraints | Has the generated program gone through cutting simulation? Do the parameters fall within the boundaries of your machine and tools? |
| No unique solution | Can the strategy fit "your shop's" tool library, controller, and conventions, rather than a generic demo part? |
| Data and verification | Does the output have an independent verification mechanism (simulation, dimensional cross-checking), or does it rely on the human eye alone? |
BestAI CAM's workflow is designed to follow exactly this checklist: after AI builds a 3D model from the 2D drawing, it generates G-code based on the in-house tool library, controller, and travel/spindle-speed limits, forces it through 3D cutting simulation, then has an independent AI cross-check the dimensions against the original drawing, with tolerances, datums, and special process methods getting a professional's final sign-off (for the full flow, see The Complete Guide to CNC Automated Programming). The hard part doesn't disappear, but it can be broken into a series of verifiable steps — which is what we believe an honest AI CAM should look like.
06FAQ
Why has AI advanced so fast in other fields but slowly in CNC programming?
Three reasons rooted in theory: first, cutting is constrained by physics — being geometrically correct doesn't mean it's physically feasible, and the boundaries of mechanics and vibration must be respected; second, process planning has no unique solution — the best plan depends on the machine, tools, workholding, and shop conventions; third, manufacturing data is scarce and expensive to label, unlike images or language where there's a vast trove of public data to train on.
What's the difference between feature-based machining and AI-generated strategies?
Feature-based machining relies on a predefined rule library: recognize a feature and apply the matching template — predictable, but unable to catch overlapping features and geometry beyond what's defined. CloudNC's route in the video is to have AI generate an overall machining strategy — answering the complete question of "in what order, with what tools, and how to make it" — and then verify it with people and simulation.
Given that it's this hard, can AI CAM actually be used right now?
Yes, but it has to be used the right way: treat AI as an accelerator that does most of the groundwork, paired with verification gates you can't skip — cutting simulation, parameter boundaries, dimensional cross-checking, and human oversight. Thousands of shops worldwide already use it this way in daily workflows. The existence of the difficulty isn't a reason not to use it, but a reason it "must be used with verification."
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07References
- CloudNC (YouTube). Why is applying AI to CNC machining so difficult?! (published 2025-04-15, presented by co-founder Chris Emery). youtube.com/watch?v=2hauE_mIWmg
- Altintas, Y. (2012). Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design (2nd ed.). Cambridge University Press.
- Xu, X., Wang, L., & Newman, S. T. (2011). Computer-aided process planning — A critical review of recent developments and future trends. International Journal of Computer Integrated Manufacturing, 24(1), 1–31.
- Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45.