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).
