This AI Could Think Like Legendary Car Builders

With the popularity of OpenAI, billions of people have discovered how AI can allow us to explore ideas and create content or images that can go as far as our own imagination. 

If you can dream it, you can create it. 

As the founder of an AI platform for car enthusiasts, our users can ask “Mia” (our AI assistant) to customize their cars to visualize what their dream vehicle would look like. But what if the next frontier of AI could go beyond the boundaries of your imagination? What if we could tap into the know-how and vision of those with exceptional creativity and experience?

In the realm of artificial intelligence, LLMs (Large Language Models) are a subset of machine learning known as deep learning, using algorithms trained on large data sets to recognize complex patterns. LLMs learn by being trained on massive amounts of text. Research from Pragma predicts that the global LLM market will grow from $1.6 billion in 2023 to $260 billion in 2030. 

While LLMs are the backbone of AI as we know it today, we haven’t even unleashed one percent of their potential. Moving from training LLMs to deliver knowledge to creating tools that deliver wisdom will be a tipping point that could transform AI as we know it. 

LLMs can help us seize and preserve the knowledge of living legends.

In the world of cars, legends aren’t just born on the track—they are forged in garages, dyno rooms, and midnight wrench sessions. Whether it’s Smokey Yunick pushing the boundaries of NASCAR rules, the late greats of Japanese tuning who made the Supra and RX‑7 into icons, or Wisconsin‑based Ringbrothers—whose resto-mods marry timeless design with modern performance and have become the poster model of what a true vehicle build can be—these experts weren’t just mechanics and fabricators—they were alchemists of performance and design.

And yet, their knowledge—their intuition for a misfire, the sixth sense for chassis balance, the hacks for squeezing an extra 10 horsepower from a factory engine—often lived only in their heads, shared through mentorship, forum threads, or, if you were lucky, over beers at the track. 

But what happens when those legends retire? When the old forums go dark? When the know-how behind making a 1995 Miata outrun a modern hot hatch fade with time?

This is where Large Language Models (LLMs) and intelligent agents can do something extraordinary: not replace the legends, but preserve them. By ingesting decades of service manuals, forum wisdom, tech articles, dyno charts, and yes, even the handwritten notes left on shop whiteboards, LLMs can begin to build a living, searchable brain of aftermarket mastery. Pair that with agent-based systems that can reason, suggest workflows, and simulate diagnostic thinking, and you get more than just data—you get a digital pit crew trained by the greats.

Imagine a new enthusiast asking, “What’s the best cam setup for a naturally aspirated LS3 on a road course?” and getting a response not just from one source but from the collective wisdom of hundreds of builders, racers, and tuners—instantly.

We’ve always had legends. Now we can make their knowledge immortal.

I believe the next frontier for AI will be to tap into knowledge, capturing how experts think and rationalize. There are billions of unstructured opinions from thought leaders across the web. 

LLMs can institutionalize all this unstructured knowledge across many fields. For my own business, I think about this in terms of seizing the know-how, experience, and vision from car-building legends who have seen thousands of repairs, upgrades, and modifications. They have overcome millions of issues, and their know-how is invaluable. 

However, this doesn’t only apply to cars, bikes, trucks, and UTVs. Think about a person looking to build their dream house with the help of an AI assistant. They might have an idea of how they’d like it to look and which type of door and windows they like—even their preferred layout. 

But what if they could tap into AI to design it with the aesthetic sensitivity of Norman Foster or the energy-efficient mindset of Elon Musk? 

4 Key Takeaways: the big AI shift from knowledge to wisdom

1. AI’s Expanding Role in Creativity: AI, particularly Large Language Models (LLMs), is not just about generating content based on imagination; it holds the potential to harness and preserve the wisdom of experts, transforming how we engage with creativity and knowledge.
2. Preserving Expert Knowledge: Legendary figures in the automotive world, such as Smokey Yunick and the Ringbrothers, have accumulated decades of expertise that AI can capture, creating a lasting digital repository of their wisdom. This would allow future enthusiasts to tap into the knowledge of masters even after they retire.
3. From Data to Wisdom: The next evolution of AI is moving from simply delivering knowledge to offering wisdom. By integrating AI with experience and insight from seasoned experts, we can create tools that offer practical, actionable advice, much like having a digital pit crew that understands every nuance of car building and modification.
4. The Future of AI: The future of AI might not just be about visualizing what we want, but also about visualizing what experts and visionaries would design for us. AI’s true potential lies in connecting us with the collective intelligence and creativity of those who have shaped industries and cultures.

In a nutshell, the future of AI might not be just about visualizing our dreams; it might be about visualizing what geniuses would have dreamt for us.