In December 1947, three physicists at Bell Telephone Laboratories—John Bardeen, William Shockley, and Walter Brattain—built a compact electronic device using thin gold wires and a piece of germanium, a material known as a semiconductor. Their invention, later named the transistor (for which they were awarded the Nobel Prize in 1956), could amplify and switch electrical signals, marking a dramatic departure from the bulky and fragile vacuum tubes that had powered electronics until then.
Its inventors weren’t chasing a specific product. They were asking fundamental questions about how electrons behave in semiconductors, experimenting with surface states and electron mobility in germanium crystals. Over months of trial and refinement, they combined theoretical insights from quantum mechanics with hands-on experimentation in solid-state physics—work many might have dismissed as too basic, academic, or unprofitable.
Their efforts culminated in a moment that now marks the dawn of the information age. Transistors don’t usually get the credit they deserve, yet they are the bedrock of every smartphone, computer, satellite, MRI scanner, GPS system, and artificial-intelligence platform we use today. With their ability to modulate (and route) electrical current at astonishing speeds, transistors make modern and future computing and electronics possible.
This breakthrough did not emerge from a business plan or product pitch. It arose from open-ended, curiosity-driven research and enabling development, supported by an institution that saw value in exploring the unknown. It took years of trial and error, collaborations across disciplines, and a deep belief that understanding nature—even without a guaranteed payoff—was worth the effort.
After the first successful demonstration in late 1947, the invention of the transistor remained confidential while Bell Labs filed patent applications and continued development. It was publicly announced at a press conference on June 30, 1948, in New York City. The scientific explanation followed in a seminal paper published in the journal Physical Review.
How do they work? At their core, transistors are made of semiconductors—materials like germanium and, later, silicon—that can either conduct or resist electricity depending on subtle manipulations of their structure and charge. In a typical transistor, a small voltage applied to one part of the device (the gate) either allows or blocks the electric current flowing through another part (the channel). It’s this simple control mechanism, scaled up billions of times, that lets your phone run apps, your laptop render images, and your search engine return answers in milliseconds.
Though early devices used germanium, researchers soon discovered that silicon—more thermally stable, moisture resistant, and far more abundant—was better suited for industrial production. By the late 1950s, the transition to silicon was underway, making possible the development of integrated circuits and, eventually, the microprocessors that power today’s digital world.
A modern chip the size of a human fingernail now contains tens of billions of silicon transistors, each measured in nanometers—smaller than many viruses. These tiny switches turn on and off billions of times per second, controlling the flow of electrical signals involved in computation, data storage, audio and visual processing, and artificial intelligence. They form the fundamental infrastructure behind nearly every digital device in use today.
The global semiconductor industry is now worth over half a trillion dollars. Devices that began as experimental prototypes in a physics lab now underpin economies, national security, health care, education, and global communication. But the transistor’s origin story carries a deeper lesson—one we risk forgetting.
Much of the fundamental understanding that moved transistor technology forward came from federally funded university research. Nearly a quarter of transistor research at Bell Labs in the 1950s was supported by the federal government. Much of the rest was subsidized by revenue from AT&T’s monopoly on the US phone system, which flowed into industrial R&D.
Inspired by the 1945 report “Science: The Endless Frontier,” authored by Vannevar Bush at the request of President Truman, the US government began a long-standing tradition of investing in basic research. These investments have paid steady dividends across many scientific domains—from nuclear energy to lasers, and from medical technologies to artificial intelligence. Trained in fundamental research, generations of students have emerged from university labs with the knowledge and skills necessary to push existing technology beyond its known capabilities.
And yet, funding for basic science—and for the education of those who can pursue it—is under increasing pressure. The new White House’s proposed federal budget includes deep cuts to the Department of Energy and the National Science Foundation (though Congress may deviate from those recommendations). Already, the National Institutes of Health has canceled or paused more than $1.9 billion in grants, while NSF STEM education programs suffered more than $700 million in terminations.
These losses have forced some universities to freeze graduate student admissions, cancel internships, and scale back summer research opportunities—making it harder for young people to pursue scientific and engineering careers. In an age dominated by short-term metrics and rapid returns, it can be difficult to justify research whose applications may not materialize for decades. But those are precisely the kinds of efforts we must support if we want to secure our technological future.
Consider John McCarthy, the mathematician and computer scientist who coined the term “artificial intelligence.” In the late 1950s, while at MIT, he led one of the first AI groups and developed Lisp, a programming language still used today in scientific computing and AI applications. At the time, practical AI seemed far off. But that early foundational work laid the groundwork for today’s AI-driven world.
After the initial enthusiasm of the 1950s through the ’70s, interest in neural networks—a leading AI architecture today inspired by the human brain—declined during the so-called “AI winters” of the late 1990s and early 2000s. Limited data, inadequate computational power, and theoretical gaps made it hard for the field to progress. Still, researchers like Geoffrey Hinton and John Hopfield pressed on. Hopfield, now a 2024 Nobel laureate in physics, first introduced his groundbreaking neural network model in 1982, in a paper published in Proceedings of the National Academy of Sciences of the USA. His work revealed the deep connections between collective computation and the behavior of disordered magnetic systems. Together with the work of colleagues including Hinton, who was awarded the Nobel the same year, this foundational research seeded the explosion of deep-learning technologies we see today.
One reason neural networks now flourish is the graphics processing unit, or GPU—originally designed for gaming but now essential for the matrix-heavy operations of AI. These chips themselves rely on decades of fundamental research in materials science and solid-state physics: high-dielectric materials, strained silicon alloys, and other advances making it possible to produce the most efficient transistors possible. We are now entering another frontier, exploring memristors, phase-changing and 2D materials, and spintronic devices.
If you’re reading this on a phone or laptop, you’re holding the result of a gamble someone once made on curiosity. That same curiosity is still alive in university and research labs today—in often unglamorous, sometimes obscure work quietly laying the groundwork for revolutions that will infiltrate some of the most essential aspects of our lives 50 years from now. At the leading physics journal where I am editor, my collaborators and I see the painstaking work and dedication behind every paper we handle. Our modern economy—with giants like Nvidia, Microsoft, Apple, Amazon, and Alphabet—would be unimaginable without the humble transistor and the passion for knowledge fueling the relentless curiosity of scientists like those who made it possible.
The next transistor may not look like a switch at all. It might emerge from new kinds of materials (such as quantum, hybrid organic-inorganic, or hierarchical types) or from tools we haven’t yet imagined. But it will need the same ingredients: solid fundamental knowledge, resources, and freedom to pursue open questions driven by curiosity, collaboration—and most importantly, financial support from someone who believes it’s worth the risk.
Julia R. Greer is a materials scientist at the California Institute of Technology. She is a judge for MIT Technology Review’s Innovators Under 35 and a former honoree (in 2008).
