RR: I certainly remember Dad and his slide rule tutorials! I think you also told me that Generative AI alone is estimated to raise global GDP by $7 trillion and lift productivity growth by 1.5 percent over a ten-year period, assuming it’s widely adopted. That’s pretty staggering. Can you connect the dots on that a bit and perhaps give us some examples of how AI implementations are directly responsible for economic growth.

JR: Well, that’s right. But no one really knows, of course. That $7 trillion number is an estimate made by the 2023 Stanford Emerging Technology Review. Just a couple of short comments on this. Since the launch of ChatGPT in the fall 2022, we’ve been in a steep, almost vertical take-up curve. This is normal and predictable with new technologies – it’s called the “hype cycle,” where there is a lot of noise (hype) around the introduction. As the hype curve reaches the top there’s a plateau. If advancements and new applications prove out, the curve starts over, and growth continues. That’s what many believe will happen with Generative AI. We are definitely in that steep vertical climb phase now and new uses for it are being discovered every day. It looks transformative. Especially, or at least in terms of efficiency – doing things much, much faster and with that, wholly new things, period. Like the discovery of new drugs (for example, the mRNA vaccine during Covid) and new diagnostics (for example, using AI to analyze vast databases of MRIs for cancer diagnosis and treatment; and doing it so fast that a whole medical school of doctors could not achieve the same thing in 100 lifetimes). The associated projections of economic growth are speculative. But as long as the negative downsides don’t derail things, downsides such as intellectual property issues, bias, and bad actors, the potential uses of GenAI are so vast that its contribution to economic growth will be enormous.

There are other issues that could constrain the growth, however. Not with its take-up but with its requirements. AI requires an array of special material natural resources, which requires exploration, extraction, shipment, and refinement. This raises some ecological concerns. AI also requires huge amounts of datacenters and electricity to power them – in 2022 data centers made up about 2.5% of electricity demand in the U.S. Some predict this will rise to 20% by 2030, and that AI will account for three-quarters of the demand; this will demand strengthening power grids around the world. Then there’s the human cost concern. The extraction of minerals and the manufacture and assembly of AI driven devices typically uses low-cost labor to make the growth cost effective. There are serious ethical concerns surrounding this issue. All of these issues show a kind of underbelly to AI. It is not clear whether they will constrain AI growth.

RR: It’s all incredibly fascinating and at times, hard to even comprehend where it all may take us as a species, let alone in various business sectors. But because we’re an aviation magazine focused on jet aircraft interiors, might you give us some examples of how our industry sector is already benefitting from AI and perhaps pontificate on where it may ultimately take us?

JR: Okay. I’ll tell you what I know, which is limited and of course fluid. As I’m sure you and your readers know, aviation as a whole has been using AI in various ways for some time now. For example, in the defense and the commercial aviation industries with the development and use of drones and with pilotless planes. It is also being used a lot in airline maintenance (predictive maintenance) and project management in aircraft manufacturing, like at Boeing and Airbus and increasingly in modification centers for defense, commercial, and private airplane construction and rehab. In and through these, logistics and supply-chain management runs through them all, for example for parts and inventory management. Also, AI is used in simulations for airline traffic control systems and flight simulators for pilot training. All of these applications utilize the machine learning components of AI, where data is developed and maintained to allow for rapid analysis and pattern recognition suggestions for design and development, including in avionics.

I have also come across a few articles about AI in the aircraft interiors space. Some that relate to visual scenario-based concept design using Generative AI – including hard surface finishes, fabric, carpet textures, and color palette scenarios, to name a few. Most are not great at this point. They often take more time to correct or refine than the effort is worth. But as they come along, I imagine there will need to be trained specialists to use them, as in the cases of CAD, Photoshop, 3D Max, etc. There are also the new AI-assisted project tools, such as those used in the aircraft manufacturing space. These will likely be applied more and more to the interiors space, if not already. Other areas where I can imagine AI might be applied include increased collaboration between interior designers and the engineering and certification teams. It all depends on the data that can be garnered to build machine learning use cases. I can foresee, for example, that if the data of enough finished interiors (the data) could be built into an AI machine learning and Generative AI tool, a whole interior, top-to-bottom, could be built and generated with all the necessary specs and certification requirements. It’s also reasonable to think that AI generated detail design analyses for use in leading up to what you call PDR and CDR, could be widely utilized at some point. That might be a stretch. But it’s certainly not out of the question.

It's important that designers begin to imagine and look for new tools that use AI. It could transform the space. At minimum, embracing AI will help maintain competitiveness and possibly create competitive advantage.