Virtual Concepts: A New Era of Design

How Generative AI and Rapid Iteration can transform the Way We Create Cars

I often think about the process of designing a car. As an engineer, I’m comfortable diagnosing problems and implementing fixes, but I’m not equipped for the initial, more artistic phases of vehicle design.

However, perfectly formed lines aren’t what design is all about. Concepts are linked to personas, market requirements, and technology trends. It must fit within a certain envelope, achieve a target drag coefficient, and have a specific wheelbase.

The vehicle design exists within certain constraints, living at the intersection of engineering feasibility and creative flair.

Using these constraints, I could use my drafting skills to lay out the wheels, box off the area for the trunk, and mark out the overall length and height of the vehicle. However, as soon as I draw one artistically inspired line, my technical drawing would look like it had been graffitied by a two-year-old.

I simply haven’t done the reps of drawing vehicle concepts over and again, learning about perspective, shading, etc., to be able to put my imagination on paper.

Yet authors often use vivid, precise language to describe an object’s texture, contrasting colors, and how a character reacts to these sensory details—bringing everything to life in the reader’s mind. So, I wondered, why not feed a detailed prompt into ChatGPT to see how it interprets my vision?

I thought about what I desire for my next car and came up with the following prompt:

“Design an understated electric sedan for a 45-year-old professional male who values minimalist design, with some distinctive features, such as narrow angular headlights or sharp feature lines on the door. It should be able to corner well with an aggressive rake angle and sit low to the ground and have low-profile 22-inch alloy wheels. The ideal body color would be white, and the overall design should communicate refinement and performance. Produce an image that is photorealistic, studio lighting, ultra-detailed, high resolution.”

Now did ChatGPT come up with a mock-Tesla because that was what I was describing or because out of all the electric sedans the model was trained on, the majority were Teslas? There is an element of creativity missing.

So, could an AI be as creative as Frank Gehry playing with cardboard to develop his iconic building concepts? Not on its own, and not without an ongoing interaction with the designer, through sketches, sharing ideas and iterations.

But it is a useful tool, one which could rapidly increase the speed of product development, holding requirements in mind as the design morphs into the end product.

I remember once working with a scooter company being amazed at how quickly they iterated and launched new vehicles, more akin to the development timelines of a cell phone than a car. Designers rapidly responding to changing tastes, altering the shape and color of the scooter and having it on the market within six months.

In a traditional automotive company, product development would begin with concepts, and some of those concepts would be chosen to be made into scaled clays and a further subset into full-size versions.

It is not until the clay has been reviewed, observed under different lighting, lines being subtly altered in response to wind tunnel results, and had initial feasibility studies done for crash and manufacturability that it is frozen, and engineering can be in a period of twelve months or longer.

This is a huge barrier to entry for a start-up.

But a designer working one-to-one with a client can drastically reduce that barrier. Conversing to understand their needs and wants whilst simultaneously interacting with the virtual design, feeding in additional prompts, requirement, or directly altering the feature line of a door.

At a broader scale, the same principles could apply to an agency building a brand in conjunction with the product, gathering customer data and then A/B testing the generated designs. It opens new ways in which a product can be developed, with faster feedback loops and lower cost.

Then what’s next? There is still a huge amount of capital investment in engineering the design, validating it, manufacturing it.

But maybe not?

There is no reason your design can’t be generated into a three-dimensional model and broken down into parts, components selected and built. Leaving you to focus on adding value to the customer, building the brand and exciting the market.

How design tools develop in conjunction with emerging AI trends will blur the line between the sequential process of design and manufacturing, leaving many of us wondering how we can respond to these changes?