Here is a simple model for how we make “things” (toasters, iPhones, buildings, software):

• Collect and Synthesize requirements
• Create Conceptual & Detailed Design
• Make the “thing”

Generative AI is changing “the making of” in three ways

1. (Semi) automating the synthesis of requirements
2. Automating the translation of requirements into designs
3. Automating the actual production

Let’s unpack

1. Automating the synthesis of requirements

Today the lead designer/product manager synthesises data into specific requirements. Getting to a good design often requires vast amounts of input data. And the best designers do a great job at combining new research with prior knowledge.

But AI algorithms can make a big impact here.

For instance, AI can sift through reams of data (e.g. thousands of user tickets) to spot trends. This can help organise requirements and empower designers and PMs. E.g. – leaving them to focus on higher-level prioritisation.

2. Automating the translation of requirements into designs

Historically, humans have translated requirements into designs/products/code.

With generative AI, computers can create new designs with natural language as input. And they can also create output based on other inputs – e.g. a visual design. For instance, a new workflow could be like this:

• the designer describes requirements in English
• the AI translates the requirements into screen designs
• the AI then translates the screens into the necessary back-end architecture

A generative AI model can theoretically use anything as input. This can accelerate the design process to whole new levels.

3. Automating the actual production

Today, human specialists translate designs into a production process. Shop drawings for architects. Machine parameters for factories. Some things are automated, but there is so much more that can be done.

And once we automate this third step, we will have transformed the way we make things.

Widget, meet your “AI Maker”.

Generative AI will serve to reduce the product development lifecycle. That reduces costs. But it will also lead to better outcomes. Why? Because designers will be able to explore a much wider design space at a lower cost.

We are already seeing a lot of automation across the manufacturing life-cycle. But on a grand scale, we are only getting started.

My prediction is as follows: every industry that makes something will be nearly unrecognisable within a decade. Be it buildings, products or software.

As always, this presents an opportunity. But it is also a big shift to existing industry and socio-economic structures.

Let’s embrace this wave of opportunity. And let’s also prepare society to support the transition in a sensible way.