generative design and dynamo blog by weeklydynamo Newsletter Source

A long-form project note about translating a pathfinding concept into a Dynamo and Generative Design workflow, including CAD geometry, Revit Generic Model caching, Data.Remember, scalar GD outputs, and the failures that shaped the final structure. From Concept to Dynamo: The Trial-and-Error Behind a Generative Design Workflow Building the concept is one task. Moving it into a real .dyn workflow is another. A few days ago I published Automation Lab: Runnable Pathfinding Experiment . That post introduced a small browser-based experiment: place start points, end points, and obstacles on a canvas, then generate route options around blocked areas. On the web, the idea is easy to understand. A point is a point. An obstacle is a drawn shape. A route is a line. The coordinate system is controlled, the geometry types are clean, and every object is created by the application itself. That makes the browser experiment useful as a concept layer. It can show the intent quickly: generate a ro...

Generative design is often described as a way for software or AI to create design options automatically. In practice, the more important point is not automation itself. The real value comes from defining design variables, evaluation criteria, and a search strategy so that many alternatives can be generated, compared, and improved. For AEC workflows, this matters because design decisions are rarely based on a single criterion. A building option may need to balance area efficiency, daylight, cost, constructability, code constraints, structural logic, and documentation requirements. Generative design tools help explore that decision space, but each tool is built around a different workflow and level of control. The Basic Structure of Generative Design Most generative design workflows can be understood through three components: a generator, an evaluator, and a solver. Component Role Generator Creates design alternatives from input variables and constraints. Evaluator Measur...

From Generative Design Optimization to Quantity Takeoff Automation: Building a Scalable AEC Process System In many AEC workflows, Generative Design optimization and Quantity Takeoff automation are treated as separate topics. One belongs to the front end of design exploration.   The other belongs to the back end of documentation, estimation, or reporting. But in real projects, especially repetitive and high-complexity projects, that separation is too artificial. The real opportunity is not to optimize a layout in isolation, and not to extract quantities only after the design is already fixed. The deeper opportunity is to build a connected system where: - spatial logic - room classification - layout rules - object libraries - placement automation - parameter logic - quantity extraction are designed as one continuous workflow. That is the difference between isolated automation and scalable process architecture. The real problem: many AEC automations stop at one layer This is a re...

Generative Design in AEC Can Become a Synthetic Data Factory For years, Generative Design in AEC has been introduced through a familiar promise: generate many options quickly, compare them, and select the best one. That promise is still valid.   But I think it is too small. The bigger opportunity is not just that Generative Design can make alternatives. It is that Generative Design can produce **structured, high-purity, logically consistent data** at scale. And once we see that clearly, a new role emerges: **Generative Design can become a Synthetic Data Factory.** That changes everything. Because one of the biggest bottlenecks in AI for AEC is not model architecture. It is not GPU access. It is not even the lack of interest from the industry. The bottleneck is data. More specifically, it is the lack of domain-specific, logically clean, reusable training data that reflects real engineering intent. That is where Generative Design becomes far more valuable than most current narra...

WeeklyDynamo Notes:  What I’m Tracking in AEC Automation, BIM, and AI Lately, I have been thinking less about isolated tools and more about how the AEC workflow itself is changing. That shift matters. For a long time, many conversations in our field were separated into categories: - BIM - automation - Generative Design - digital twin - AI - quantity takeoff - data management Each topic had its own language, its own examples, and often its own audience. But in real projects, they do not exist as separate islands. They increasingly behave as parts of one connected system. That is what I have been trying to track through WeeklyDynamo. This blog is not only a place to post isolated technical notes. It is also where I want to document the structural changes happening across AEC workflows: how decisions are made, how information moves, where automation creates leverage, and where AI actually fits. So for this note, I want to briefly organize the themes I have been following most closely....

AI is now being inserted into almost every conversation about AEC automation. But in many cases, the question is still too vague. The industry keeps asking: “How can we use AI?” A better question would be: **Where exactly should AI sit inside the automation process?** That difference matters because automation is not a single action. It is a chain of decisions, transformations, validations, and outputs. Some parts of that chain benefit greatly from probabilistic inference. Other parts require deterministic control. If AI is placed in the wrong layer, the workflow becomes unstable. If it is placed in the right layer, it can create real leverage. This is the core issue. The value of AI in AEC does not come from making everything intelligent.   It comes from assigning intelligence to the right layer of the system. In my view, that is where the conversation needs to mature. AI should not be treated as a blanket replacement for automation.   It should be treated as one co...