It’s a provocative image: buying a Bugatti Chiron just to take the dog to the park.
That’s how AI keynote speaker and co-founder and chief data scientist at Trust Insights Christopher S. Penn describes in his popular “Almost Timely Newsletter” what many organizations are doing with AI today. And, while it may sound absurd, it’s not far from the truth.
In a recent discussion, Kurt Muehmel, Head of AI Strategy at Dataiku, sat down with Penn to unpack this analogy and explore why so many AI implementations are falling short — not due to lack of technology, but because of limited thinking. This conversation was inspired by one of Penn’s newsletters where he shared a powerful critique: We’re not getting enough value from AI, not because it doesn’t work, but because we’re aiming too low.
We’re taking this transformative technology, and using it to write blog posts and social media updates — the equivalent of buying a Bugatti Chiron just to take the dog to the park.
-Christopher S. Penn, Co-Founder & Chief Data Scientist, Trust Insights
In this clip from the conversation (embedded below), Muehmel reads that line back to Penn and asks him to expand on the idea. The discussion that follows sets the stage for a bigger question: What does it actually look like to think differently about AI?
Beyond Faster, Toward Fundamentally Different
To illustrate the shift in mindset that’s needed, Penn brings up Lumière’s Law, a concept rooted in the early days of film. When motion pictures first emerged, many people assumed they were just faster photographs — a neat upgrade, but nothing revolutionary. They didn’t yet understand that film could be its own medium for storytelling, one that would evolve into something as complex and compelling as “The Avengers.”
Penn points out that today’s generative AI technologies — like transformers (used for language) and diffusers (used for images) — are predictive and probabilistic. They’re capable of handling complexity, memory, and nuance in a way that traditional tools simply aren't. But we rarely use them that way.
Instead, we’re often deploying AI to automate surface-level tasks — speeding up copywriting, summarizing meeting notes, or writing code snippets. And while these uses have value, they barely scratch the surface of what’s possible.
From Mundane to Moonshot
Penn challenges us to imagine more ambitious use cases — what is known in the industry as moving from the mundane to the moonshot. For example, instead of using a language model to write a blog post, what if we used it to analyze viral genomes and predict how a virus might mutate in response to a vaccine?
That’s not hypothetical. That’s real — and it’s happening. These kinds of use cases combine multiple strengths of AI: not just speed, but also scale, memory, flexibility, and complex reasoning over long sequences. They represent differentiated applications of AI that go beyond the obvious and start to unlock its transformative potential.
The Mindset Shift
The core takeaway? The companies that will win with AI aren’t the ones that adopt it first — they’re the ones that think differently about what it’s for.
As Muehmel notes, we’ve seen this movie before. In the early days of the internet, companies digitized brochures and called it transformation. But the web wasn’t just a faster printing press — it was a new paradigm for business, communication, and culture. The same holds true for AI today.
So as you watch the clip above, we invite you to ask yourself: Are we using AI like a faster tool — or as a fundamentally new way to solve problems?
Because if it’s the former, it might be time to park the Bugatti and reimagine the road ahead.
Stay tuned for the next blog in this series, where Muehmel and Penn unpack the “rest of the car” analogy — meaning, focusing beyond just the engine (the language model) to the rest of the car, as those parts are all needed to actually go anywhere!