For decades, engineers have been working to create smaller and smaller transistors to fit ever-larger numbers of them onto computer chips, all in the name of greater processing power. Now, in the age of artificial intelligence (AI), that effort has turned into an all-out race.

So how small can a transistor actually get? And is smaller even still the goal?

To understand the answer, first you need to know what a transistor is.

"If you take a computer or your mobile phone and you were to pry it open, you will see that there is a printed circuit board," Suman Datta, a professor of electrical and computer engineering at Georgia Tech, told Live Science.

On that circuit board are small, rectangular objects called chips, and inside each chip is a tiny piece of silicon. "And in that silicon, if you zoom in like a million times … maybe, if you're lucky, you will start seeing these tiny switches or transistors sitting etched into that piece of silicon," Datta said.

Each transistor is effectively a switch that turns on and off in response to jolts of electricity pulsing 4 billion times per second. Modern devices contain billions of transistors, and each one turning on and off in a precise orchestration to move and store 1s and 0s powers everything from Google searches and AI data centers to the device you're using to read these words.

The smaller the transistors are, the more can fit on each chip and the greater the chip's speed and functionality. So how small have they gotten so far?

"Researchers have built proof-of-concept devices in which a single atom controls the flow of electrons, although the rest of the device is still much larger," Anton Persson, an assistant professor at Chalmers University of Technology in Sweden, and Tara Peña, an incoming assistant professor at UCLA, told Live Science via a jointly written email. That means the switching part of a transistor has been made at the smallest scale physically possible, even though a complete device that small doesn't exist yet. New materials and approaches could make these devices even smaller. In a study published in June in the journal Nature Nanotechnology, Persson, Peña and colleagues used two-dimensional semiconductors made of tungsten disulfide, among other materials, to shrink nanoribbon transistors down to a channel width of 25 nanometers ‪—‬ about 0.00025 the width of a human hair.

Two-dimensional semiconductors are electrically active materials that are only one or a few atoms thick, and because electrical current can be controlled more precisely in such a thin layer, they allow for smaller transistors than traditional silicon does.

Over the next decade, they still expect silicon transistors to shrink, but more slowly than they did in the past, they said. "To go considerably smaller, we may eventually need to move beyond silicon to atomically thin materials, such as the two-dimensional semiconductors we study."

A host of considerations

Size isn't the only consideration; cost is important, too. It's enormously expensive up front to design and build a new chip. "We are as R&D-intensive as drug companies that spend billions of dollars developing one drug," Datta said. "It's a very similar business model."

There are also manufacturing factors to consider. "The often much harder step is producing billions of those transistors reliably, at scale and at a price that makes sense commercially," Persson and Peña said. "Something that works once in a laboratory does not necessarily work in a factory."

Another consideration is energy efficiency. "If I'm adding more and more transistors onto that little piece of silicon, and at the individual transistor level, they don't become more energy efficient … then when I go from, let's say, 10 transistors to 20 transistors, my power budget has to be doubled, all things being equal," Datta said.

For example, the latest Nvidia data center GPUs consume 1.4 kilowatts of electricity, and Nvidia's upcoming release is estimated to consume nearly a kilowatt more than that. Half of that is given up as waste heat, Datta said.

"So it's not just smaller, faster, cheaper, but also more energy efficient," Datta said. "You have to work on all the four vectors."

Is smaller still the goal in the age of AI?

Still, there is a limit to the physical space available for additional transistors, which is why engineers are turning to 3D designs — think taller instead of smaller.

"Instead of only making each transistor smaller, the idea is increasingly to stack transistors on top of one another so that more of them fit within the same chip area," Persson and Peña said. "That way, we can still make our electronics considerably more powerful, even if each transistor shrinks only modestly."

The quest for faster, more efficient chips is only getting more urgent with the explosive growth of AI. But smaller transistors and more powerful chips aren't just important for AI data centers; they also mean smaller, potentially cheaper consumer electronics.

"People certainly notice the consequences of smaller transistors," Persson and Peña said. "They can use less power and allow more computing power to fit on the same chip. That can mean faster electronics, longer battery life and lower costs. Historically, that combination has enabled today's smartphones to outperform room-sized supercomputers from decades ago."

So how small can transistors get? "We will do everything to make it as small as possible as long as we can control the physics," Datta said. "And as long as we can, we will find a way to make the economics work."

Albert Einstein gave us the theory of relativity, explained the photoelectric effect and predicted the existence of gravitational waves. He revolutionized our understanding of space, time and gravity.

In fact, he is so famous for his brilliance that "Einstein" is synonymous with "genius." But being a genius doesn't mean you're infallible. So was Einstein wrong about anything?

"For sure, he was wrong about tons of things," Nicolás Yunes, a theoretical physicist at the University of Illinois Urbana-Champaign, told Live Science. "We remember him for the things he was right about, for the most part, because the things he was right about shocked the scientific world and eventually those ripples affected everyone on Earth."

For example, one of his most famous predictions was marred by a mathematical mistake. In 1916, Einstein correctly realized that the acceleration of matter could produce ripples in space-time that we now call gravitational waves. But when he and the physicist Nathan Rosen described these waves using mathematics, they ran into a problem: Any solution to the equations of general relativity that would allow for gravitational waves "blew up," Yunes said. "They had these singularities … these divergences. And solutions that diverge and explode like that, they can't be physical representations of reality."

As a result, Einstein changed his mind and decided that gravitational waves couldn't exist. He wrote up his results and submitted them to the journal Physical Review, which had recently begun sending papers to outside experts for peer review. An anonymous reviewer caught an error in Einstein's math, and when Einstein learned about it, he was so furious that he withdrew the paper and submitted it to a different journal.

But the reviewer had spotted a real error: Einstein's mathematical infinities were a coordinate artifact. Much like Earth's lines of longitude seem to converge to a "singularity" at the North Pole even though nothing unusual is happening there on the ground, Einstein's math could have been fixed by using a different set of coordinates.

Without Einstein's knowledge, the reviewer befriended and demonstrated the error to Einstein's assistant, who explained it to Einstein. Einstein corrected the error and republished the paper with the opposite conclusion, showing that gravitational waves do, in fact, exist.

Calculating "impossible infinities"

Remarkably, Einstein stumbled on the same error twice — at least, it seemed that way. When examining the mathematics around black holes, he once again calculated impossible infinities ‪—‬ this time, at the edge of a black hole. And once again, he concluded that this must mean the object couldn't exist.

"Einstein remained skeptical of the existence of black holes," John D. Norton, a professor in the Department of History and Philosophy of Science at the University of Pittsburgh, told Live Science in an email. "He was resolute in his judgment that there would be a singularity in space-time already at the event horizon of a black hole, which is now merely regarded as marking the point of no return for those falling into a black hole."

But this time, Einstein was not convinced that he made a mistake. Norton argues this wasn't just stubbornness; it reflected a philosophical belief in how Einstein thought mathematics and physics should relate to each other.

"He was unmoved by alternative analyses that found his mathematical infinities to be merely artifacts of the particular mathematical methods he preferred," Norton said.

Einstein and quantum mechanics

Einstein's resistance to quantum mechanics is probably his most famous wrong turn. His central objection was to a phenomenon called quantum entanglement: the idea that two particles can be linked in such a way that measuring one instantly affects the other, no matter the distance between them. In a 1947 letter to his colleague Max Born, he wrote, "I cannot seriously believe in it because the theory cannot be reconciled with the idea that physics should represent a reality in time and space, free from spooky actions at a distance."

Einstein thought this instantaneous phenomenon seemed to violate special relativity, which says that nothing can travel faster than light. As a result, he believed quantum mechanics must be incomplete and that there was some deeper, unknown description of reality that would restore order.

"He died not liking quantum mechanics," Yunes said. It wasn't until 1964, almost a decade after Einstein's death, that John Bell proved entanglement was real.

"Today, a lot of the technology that we have relies on quantum mechanics, and so we know it's correct," Yunes said. "But it's still incompatible with general relativity, with [Einstein's] classical theory."

"It's possible that general relativity is wrong," Yunes said. "It's also possible that quantum mechanics is not the right description when you're talking about systems that are very strongly gravitating and you're looking at Planck-scale-type dynamics," which are incredibly small scales at which quantum effects dominate. For instance, the center of a black hole, which is both compressed to the quantum scale but also sits inside the most intense gravitational field in the universe, is a place where general relativity predicts a singularity that quantum mechanics has no way to describe.

But many of Einstein's mistakes still moved science forward. "General relativity is a good example," Norton said. "Einstein based it on the idea that he was generalizing the principle of relativity to acceleration and on what he soon called Mach's principle. Neither proved compatible with his final general theory of relativity."

Einstein's mistakes might come as a surprise to others, but they didn't to him. Once, when writing a book with his collaborator Leopold Infeld, Infeld told him he was taking special care with it because Einstein's name would appear on it. Einstein laughed and said, "There are incorrect papers under my name, too."

See how much you know about Albert Einstein with our Einstein quiz!