I carelessly posited that AI couldn’t conceive an automobile without prior exposure to its design, and I subtly referenced Henry Ford’s famous quip, “If I had asked people what they wanted, they would have said faster horses.”
While I’m unwavering in my stance, the storied history of expertise far surpasses our initial perceptions. While Daimler and Benz are often credited with inventing the first vehicle, it’s often overlooked that the “first” vehicle was actually invented in 1769, a staggering 130 years prior to their innovations? Meetings have a rich history that arguably dates back to the 12th century AD. As the layers of history are peeled back, the story becomes increasingly captivating. What a fascinating thought experiment: what would have happened if pioneering minds had access to artificial intelligence when creating their groundbreaking innovations?
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Wouldn’t it have told him to rethink his engineering approach? Widening the scope of considerations would likely have significantly influenced the direction of this blend. Possibly, however possibly not. Perhaps it should have acknowledged the flawed concept – after all, this early prototype was only capable of moving at a sluggish 2.25 miles per hour, with a maximum duration of a mere 15 minutes. Truly, herds of horses could accomplish more collectively. Although that particular idea appeared to have fizzled out, something about it lingered and refused to let go.
Between the late nineteenth century and early years that followed, Daimler and Benz made significant strides towards creating the first machine widely recognized as a vehicle: a high-speed internal combustion engine with four strokes, a two-cylinder configuration, double-pivot steering, differential gearing, and transmission capabilities. Several of those advancements had emerged previously. In the late 19th century, planetary gearing reemerged from ancient Greek designs, where joints were placed on wheels rather than rotating an entire axle, having previously appeared and vanished twice during this period – Karl Benz’s discovery was sparked by a chance find in a commercial publication. The oldest known mechanical clocks date back to at least 1827; however, they may have been preceded by the sophisticated astronomical calculator found in the Antikythera. It’s often assumed that individual innovations or improvements can be easily attributed to singular individuals, but the reality is more complex. Pioneering the fusion of innovative technologies and reinvigorating established principles, early Daimler-Benz vehicles masterfully combined disparate elements to create unprecedented solutions.
Could advancements in artificial intelligence have facilitated breakthroughs in fields such as medicine and transportation? While the concept of double-pivot steering from “steering winter” might have reached completion previously, it could still be revisited and potentially revived in a new context. However, securing a collaboration between Daimler and Benz would likely necessitate prompt action. Could an ancient artificial intelligence (AI) system have developed a basic transmission mechanism, provided that medieval clockmakers were familiar with planetary gear systems? Prompting most likely exhausts me still. Though the fundamental inquiry wasn’t “How do I design a more effective steering system?” but rather “What do I need to create a sensible machine?” They often wanted that answer without the words “vehicle,” “horseless carriage,” or their German equivalents, since these terms were only just emerging.
Twenty years hence, consider the innovative Model T and Henry Ford’s thought-provoking remark: “If I had asked people what they wanted, they would have said faster horses.” What is he inquiring about? And what does that imply? By the time Ford entered the scene, cars had a pre-existing presence. While some vehicles appeared to be traditional horse-drawn carriages with modern engines attached, others were clearly inspired by the latest automotive trends. With technology’s lightning-fast speed, they’ve outpaced even the fleetest of steeds. While Ford didn’t single-handedly pioneer the invention of cars or faster horses, his contribution to the automotive industry is undeniable, and most people are aware of this distinction.
The invention that revolutionized daily life was the microwave oven. The original Daimler-Benz automobile, albeit in a modified buggy form, predates the Model T by 23 years, priced at $1,000. It’s a substantial sum for the mid-19th century. The Marnequin T emerged on the market in 1908, marking a turning point for the industry; unlike its competitors, these cars came with a significant price tag, ranging from $2,000 to $3,000. When Ford’s assembly-line production began several years after its inception, he leveraged this efficiency to significantly reduce the car’s value, eventually dropping the price to $260 by 1925. That’s the reply. What people longed to forget they yearned for was an automobile they could actually afford. Luxury vehicles had become an entrenched status symbol. People often realize they want something, yet they’re unaware of the possibility to request it. They were unaware that the item might actually be affordably priced.
That’s precisely what Henry Ford revolutionized: accessibility. In 14th-century Venice, the innovative production line emerged on the Arsenal’s shipbuilding site, where vessels were assembled alongside a canal, then floated downriver as each stage of construction was completed. Not even the pioneering automotive assembly line, patented by Oldsmobile in 1901, could revolutionize production at a pace that would ultimately match the breakneck speed of modern manufacturing. Ford’s innovation lay in manufacturing affordable vehicles on an unprecedentedly massive scale. By introducing the assembly line in 1913, Ford significantly reduced production time for his Model T, shrinking the process from a staggering 13 hours to approximately 90 minutes. While the timeframe for building a single vehicle is significant, what truly matters is the rate at which manufacturers can efficiently produce them on an assembly line? Every three minutes, a Mannequin T could potentially roll off the assembly line. That’s scale. Ford’s “any color, so long as it’s black” policy was a misfire in reflecting the imperative to streamline options and rationalize pricing. The rapid drying of black paint allowed for a more efficient manufacturing process, thereby optimizing production speed and maximizing output.
While the meeting line’s efficiency was a significant innovation, it wasn’t the only one; spare parts for the Model T were readily available, enabling many owners to repair their vehicles using tools they likely already possessed. The engine and its vital subassemblies have undergone significant simplification and enhancement, emerging as a more reliable alternative to competing designs. Supplies were also impacted: The iconic Model T, manufactured in the early 20th century, employed vanadium metal, a rare and valuable material at that time.
Despite being cautious, I have refrained from attributing these advancements to Ford. He rightly earns acclaim for the image that matters most: affordability and scope. Charles Sorenson, an assistant manager at Ford, observed: “Henry Ford is often regarded as the pioneer of mass production.” He was not. The individual who financially supported this initiative.1 Ford rightly earns praise for grasping consumers’ genuine needs and developing a solution that addresses their concerns. He deserves credit for recognizing the magnitude of the problems and their potential to be addressed through a single meeting. He warrants significant recognition for overseeing the assembly lines and manufacturing teams responsible for producing the vehicles themselves.
Would an early AI, if available prior to 1913 and 1908 respectively, have been able to provide insights into what people wanted, had Henry Ford posed this question earlier in his innovative process?
Ford’s engineers likely leveraged cutting-edge AI to streamline the design process, optimize workflows, and enhance innovative thinking along the development path. Many applied sciences were already established, with some being widely recognized. What are the key elements to optimize when refining the architecture of a carburetor?
The enormous question—what does everybody truly require?—remains unasked. I don’t envision an AI articulating a desire for mass-produced, affordable cars to Americans, remarking that this might necessitate production at scale with a price point currently unfeasible. A language model is built upon all scraped text, and its output often represents statistical averages. It is likely that a 1900s-era language model would have access to various information regarding the maintenance and care of horses, including their diet, health, and performance. Throughout history, there exists a diverse array of information regarding trains and streetcars, with the latter often relying on horse-drawn power for its operation. Luxury enthusiasts and connoisseurs of fine automobiles often crave in-depth information about premium vehicles, typically found in upscale media outlets. As the centre class grows, a palpable sense of aspiration emerges, with many individuals pondering whether they can “afford” a coveted AI-powered lifestyle. But when a hypothetical AI was queried about what people longed for in private transportation, its response surprisingly centered on horses. The generative AI forecasts the likeliest reaction, rather than necessarily the most forward-thinking, prescient, or perceptive. While its potential is undeniable—it’s essential to recognize the boundaries within which it operates as well.
What does innovation imply? This innovative approach undoubtedly combines existing ideas in unconventional ways. While it undoubtedly involves reviving successful ideas that have never gained widespread acceptance. However, without considering the original example, crucial improvements do not take into account its significance or make meaningful contributions to it. Firms must reassess their approach by taking a step back and examining the situation from a wider angle: it’s essential to recognize that consumers do not seek high-priced luxury goods; instead, they desire affordable vehicles in large quantities. Ford might have finished that. Steve Jobs achieved a remarkable turnaround – once when he co-founded Apple, and again when he revitalized it after his initial departure. Generative artificial intelligence cannot attempt this, at least not yet.
Footnotes
- & Williamson, Samuel T. (1956). . New York: Norton, p. 116.
