On September 23, 1949, Harry Truman walked to a podium and delivered a 37-word statement notifying the American people that the Soviet Union had detonated an atomic bomb on the Kazakh steppe. Omitted was the fact that the Soviet Union was also building a fleet of long-range bombers capable of dropping nuclear weapons on the United States across the Arctic.
The Pentagon lacked the ability to track these bombers in real time, a gap that created the possibility of a “nuclear Pearl Harbor,” a sudden Soviet atomic strike on US military bases and cities with little or no warning. The shock forced a scientific mobilization that permanently changed our view of what computers could and should be used for.
Before the Cold War, primitive computers were largely viewed as high-speed calculators. During World War II, this brute-force calculation had proved valuable, as machines like the British Colossus chewed through mountains of intercepted communications to break German military encryption. Yet these wartime breakthroughs operated strictly after the fact, relying on recorded data processed in isolated batches. The problem of untrackable Soviet bombers demanded a radically new approach: a system that could ingest continuous radar data, process it instantly, and visually represent this data on a screen for human decision makers. In solving this problem, scientists would convert computers from tools that solved equations into devices that managed reality as it happened.
In 1951, the Massachusetts Institute of Technology was awarded the primary contract to develop these systems, leading to the formation of Lincoln Laboratory, a research and development center focused on air defense. The focal point of the MIT effort was Project Whirlwind, whose original goal in 1944 was to develop a computerized naval flight simulator. Lincoln Laboratory made Whirlwind its computational core and the prototype for the Semi-Automatic Ground Environment, or SAGE, which became the Air Force’s massive computerized air defense system.
The mainframe systems built by the SAGE team became the ultimate hardware sandbox of the era. By 1958, they had constructed the TX-2, a powerful computer designed specifically to handle advanced graphics and continuous data streams. It was the TX-2 that would host Sketchpad, the world’s first interactive computer graphics program and the direct ancestor of all computer-aided design (CAD) programs. Without CAD, designing high-end industrial products such as microchips or fighter jets would be impossible. No team of draftsmen could draw a billion transistors on paper.
Yet despite its foundational role in modern manufacturing, the core mechanics of this software have not fundamentally changed since 1988. That was the year the aptly named Parametric Technology Corporation released the first commercial CAD program featuring parametric modeling. Parametric modeling lets an engineer define the relationships between the parts of a design, so that changing one element automatically updates everything that depended on it. CAD systems remain stuck at the level of capturing geometric intent rather than functional design intent. They can generate a geometric model, but cannot reason about whether an engineering design is correct, optimal, or even physically coherent.
This stagnation matters because CAD sets the clock speed of all physical innovation downstream of it. Design iteration for advanced manufacturing is only as fast as an engineer can encode shapes, run them through multiple separate analysis tools, interpret the results, and manually re-encode.
Some believe AI represents a potential path out of this stagnation. A “Claude for engineers” could enable the pace of iteration in the physical world to match the speed of software. Jeff Bezos has recently come out of retirement to develop precisely this technology. In November 2025, Bezos announced that he would become co-CEO of Prometheus, a San Francisco startup building what he describes as “a very, very modern version of CAD.” But Bezos is not alone in this ambition. The Chinese Communist Party has identified industrial design software as a critical “chokepoint” technology it must circumvent, and AI could enable it to leapfrog its Western rivals and transform its ability to iterate and optimize the designs for complex machines.
The future may belong to whoever can build the machines that can design machines.
