Blake Courter

Thanks Aditya for hosting on your ongoing series Breaking the Bottleneck.

Geometry as Code: Blake Courter’s Blueprint for Implicit CAD

“With implicits, construction is transparent to the user; the kernel’s job is to render and convert back to B-reps, so the modeling tech can live in user space and geometry becomes open-source code.”

It was a pleasure to speak at nTop’s nCDS event in LA:

As Gradient Control Laboratories has become fully consumed with projects, we’ve started to consider how we might broaden our impact. With the future of engineering software in mind, we’ve joined the Speculative Technologies “Brains” accelerator program for feedback on some of our more ambitious thoughts.

Speculative Technologies Newsletter: Meet the 2025 Brains Fellows

Am thrilled to become part of such an inspiring peer group and mentor team.

At Gradient Control Laboratories (GCL), we have the privilege of seeing patterns emerging among the most innovative engineering software startups. Last year, we tracked the rise of differentiable engineering as the first differentiable CAD and CAE APIs appeared. Now, as we wire AI agents into PLM and BIM architectures, we’re ready to share our expectations for 2025 and beyond.

This post originated from conversations with Luke Church, GCL and Brad Rothenberg, nTop. It now includes significant contributions and feedback from: Mark Burhop, Sciath aiM (from whom we anticipate a nuanced paper on this topic); Jacomo Corbo, PhysicsX; Kiegan Lenihan, xNilio; Peter Harman, Infinitive; Saigopal Nelaturi, C-Infinity; Hugo Nordell, Encube; Alex Huckstepp, Uptool; Neel Goldy Kumar, Intact Solutions; Blake Reeves, Pasteur Labs; Andy Fine, Fine Physics; Kyle Bernhardt, Collectiv; and Claude 3.5 Sonnet.

Executive Summary

The future of AI in engineering won’t arrive as a single superintelligent design system. Instead, 2025 will see the rise of specialized AI agents that work alongside engineers throughout the product lifecycle — simulating assemblies, automating documentation, optimizing components, and configuring supply chains. These agents, operating both within existing tools and through new platforms, represent a fundamental shift in how we develop products, one that promises to dramatically accelerate and enhance the engineering process. Success will require solving key technical challenges around security and agent coordination. GCL is convening industry leaders this spring to tackle these challenges together.

Vision

The engineering industry’s vision for AI-powered design tools seems to mirror science fiction, from Star Trek’s Holodeck to Tony Stark’s workshop. The narrative follows three steps:

1. An engineer declares intent, describing a design objective, its constraints, and performance goals;
2. The computer synthesizes a complete design proposal, from geometry to materials to manufacturing; and
3. Through rapid iteration and feedback, the engineer and AI converge on an optimal solution.

The crew of the Enterprise collaborates with The Computer to reconstruct a medical table in the Holodeck. Although this episode of TNG jumped the shark, this scene influenced me greatly.

Had the pleasure to sit down with Em and Pete to discuss the future of CAD, with a focus on implicit modeling.

https://t.co/EJY8av93Cp

— emm0sh (@emm0sh) September 19, 2024

Executive summary

• By including differentiable, parametric models in engineering processes, engineering software can better interoperate between human and artificial designers.
• Existing CAD, CAM, and CAE tools can speak this language by adding differential interoperability to their APIs.
• We provide a visual introduction to differential engineering using a cantilevered beam.
• By examining the derivative of a rotation, we briefly unlock some deep math beauty and an application of Unit Gradient Fields (UGFs).
• Differentiable engineering scales to product-level systems engineering.

✏️ Math advisory: this post assumes you’re okay with derivatives, the chain rule from basic calculus, and a little vector math. We will introduce intuitive visual tools to illustrate such concepts in design engineering. While I feel compelled to show the work, you can probably skim and glean the concepts from the illustrations.

👥 Lots of credit: These ideas came from discussions with many people, including:

• Sandilya (Sandy) Kambampati, Intact Solutions
• Luke Church, Gradient Control Laboratories
• Trevor Laughlin, nTop
• Jon Hiller, PTC
• Peter Harman, Infinitive

Introduction

Today, we practice three paradigms of computer-aided design (“CAD”), manufacturing (“CAM”), and engineering (“CAE”):

• One-off design, where the focus is producing individual parts or products;
• Parametric generative design, where the result is recipe to produce variants of similar parts or products; and
• Computational generative design, where the final geometry is guided by simulation, often iteratively and with spatially-varying parameters.

As each of these generations has built on earlier technology, the emerging generation of engineering software powered by artificial intelligence and machine learning algorithms (“AI/ML”) is being trained on existing empirical, simulated, and textbook knowledge. However, while this new generation of tools promises ease-of-use, more accurate results, and orders of magnitude faster performance, it does not yet offer a meaningful shift in interaction paradigm. As these new tools become increasingly sophisticated, will new interaction paradigms emerge? Will we realize the sci-fi vision of product-level generative co-designers?

Let’s examine how AI and ML can blend with today’s optimization tech to expand engineers’ navigable design space. As generative design scales to the subsystem and product level, we’ll demonstrate how to delegate tasks to AI and ML without the meaning becoming hidden in a nonintuitive latent spaces, as with LLMs and generative art. We’ll focus on the role of a designer, human or automated, expressed in the language of optimization and machine learning: a differentiable approach to design engineering.

Abstracting the design engineer

Let’s propose a model for a design engineer, human or automated, which we’ll call “Mechanical Design Automation (MDA)”:

Here’s just the map!

There was a time when I could keep track of all the engineering software companies. We had a few big CAD and CAE vendors, a handful of smaller companies defying VC pressure, and a CAM company for every manufacturing market. 3D printers were things that our resellers lugged around but didn’t really work. Life was simple. I could keep it all in my head.

Over on the LatticeRobot blog, a long post about our approach to LatticeRobot’s parameterization.

The LatticeRobot Unit Cell Parameter System

In addition, it’s worth mentioning that these parameterizations are build on top of Gradient Control Laboratories’ high level implicit scripting language, GCL Script (“GCLS”), provides a novel, high level API to implicits and powers LatticeRobot’s CodeRep output. Please be in touch if you’d like to learn more.