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𝗪𝗵𝗮𝘁 𝗖𝗮𝗿 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀 𝗖𝗮𝗻 𝗧𝗲𝗮𝗰𝗵 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻

  • Writer: Benjamin
    Benjamin
  • 4 days ago
  • 4 min read

Updated: 12 hours ago

What Car Factories Can Teach About AI Consolidation

A young engineer steps through the doors of Ford’s Highland Park plant in 1913, and the first thing that hits him is the vibration in his chest. The factory doesn’t sound busy; it feels alive. The air is thick with the smell of oil and hot metal, the clank of tools, the scrape of chassis being dragged along a rope-driven line. He watches bare frames glide past rows of workers, each rooted to a single spot, each repeating one motion with startling speed. Parts spill from chutes and bins on cue as one man connects the driveshaft, the next lowers an engine with a chain hoist, another snaps on wheels without ever taking a full step.

 

 

In 1908, more than 250 American automakers fought for buyers with wildly different designs, prices, and quality levels, but only two decades later, 44 remained and the “Big Three” (Ford, GM, and Chrysler) controlled most of the market, thanks to massive factories, standardized parts, and economies of scale that smaller rivals simply could not match.

 

From Henry Ford’s business point of view, the assembly line was an answer to a looming threat. He saw demand for the Model T exploding, rivals multiplying, and a brutal math problem: if he kept building cars the old way, he would either price himself out of the mass market or be outproduced by someone willing to do things differently.

 

He studied other industries that had already embraced flow, such as meatpacking, firearms, and sewing machines, and noticed the same pattern: move the product to the worker, simplify each task, and strip out every wasted step. In his mind, the car had to be cheap enough for the people who built it, which meant cutting working hours from twelve to three and then even lower by using interchangeable parts, a continuously moving line, and a vertically integrated plant that controlled everything from raw steel to final assembly. He was not trying to crush an industry; he was trying to make sure Ford was still standing when the shakeout came.

 

 

In AI today, we are watching a similar gravity at work. Hundreds of startups are launching clever copilots, agents, and niche tools, but the real power is already concentrating in a few companies that can afford the staggering cost of training and running frontier models. Infrastructure spending in the hundreds of billions, access to proprietary data, and control of cloud distribution mean that OpenAI, Anthropic, Google, Microsoft, and Meta can offer capabilities and prices that most others cannot match over time.

 

The next step will be addressing the exorbitant costs of R&D. Training the next generation of models will demand even larger data centers, specialized chips, and research teams that look more like national labs than startups. As those costs rise, only a handful of firms will be able to fund and operate these “core engines,” which naturally pushes the market toward consolidation around a few dominant AI platforms. Over time, everyone else will either build on top of those platforms, get acquired, or quietly disappear as they fail to keep up on performance and price.

 

For startup founders, that future is both a constraint and an opportunity. You likely cannot compete head-on at the model layer; it would be like fighting Ford, GM, and Chrysler on their own assembly lines. The opportunity is to design resilient, human-centered systems on top of those platforms, pairing powerful models with real judgment, empathy, and creativity in specific contexts where outcomes matter most.

 

The most practical moves are to: pick a narrow, painful workflow in a specific industry, plug into the tools customers already use, layer in proprietary or customer-specific data, and design a UX that makes AI feel like a trusted teammate rather than a novelty.


The future is human + tech, so make relationship-building, flexibility, feedback loops, and rapid innovation core to your competitive advantage in a way that the large platforms will never integrate as deeply as you do.


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This format, called Go Wide: A Life Less Curated, serves as an antidote to algorithms and echo chambers by revealing how major historical events impacted the world and might shape what comes next.

 

Do you agree with this prediction? Are there other topics we should explore? Let us know at info@webuildscalegrow.com.


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"What Car Factories Can Teach About AI Consolidation" image by skarletmotion



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