SUMMARYMIT Technology Review examines the physical limits of computing and the slowdown of traditional chip scaling, from Moore’s Law to power, memory, and communications constraints. It describes how data-center spending is surging, why AI training compute has grown rapidly, and how limits such as Landauer’s bound, Bekenstein’s limit, and the Holevo limit define the space for future systems.
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On the evening of December 29, 1959, at an American Physical Society meeting at Caltech, the physicist Richard Feynman gave a lecture called “There’s Plenty of Room at the Bottom,” on how we might manipulate and control things on an extremely small scale. There is no physical law stopping us from writing the entire Encyclopædia Britannica on the head of a pin, he argued, or from storing every book ever written in something the size of a dust speck—or, ultimately, from arranging matter atom by atom. Biology already did versions of this (a living cell keeps its instructions in a wisp of DNA), so all of this was plainly permitted by the laws of physics. We simply had not built the tools yet.
In other words, there was plenty of room at the bottom. He was right, and for decades we descended into that space almost for free. The transistor—the switch at the heart of computer chips—repeatedly halved in size so that chips doubled in density; a computer that once filled a room now fits inside a watch. But that easy descent has now stalled, and the clearest sign is the buildout of data centers. Six of the biggest hyperscalers, the companies that run the world’s largest such facilities, are on track to spend more than $700 billion this year on computing infrastructure, about twice the record they set in 2025, pouring concrete and copper and silicon into new sites faster than the electrical grid can feed them. That buildout buys performance by brute force, adding more of what we already know how to make. The other kind of progress, the kind that made computer chips better, is the one that is quietly running out.
The trend lines that tracked the dramatic improvements of the 20th-century information age are now bending—or breaking—with the dawn of the 21st century’s intelligence age. Moore’s Law, which plots how densely transistors can be constructed on silicon, has slowed down; Dennard’s Law, which states that power consumption in a given patch of computer chip stays the same even as it gets more and more packed with smaller and smaller transistors, has ended; Koomey’s Law, which traces how much computation we can wring from each joule of energy, has begun to flag; Kryder’s Law, which describes how tightly bits can be packed to build magnetic storage, has slowed to a crawl; and Keck’s Law, which charts the exponential rise in the amount of information a single conventional optical fiber can carry, is flattening.
But these scaling laws are merely observations about systems that we have engineered; they are not fundamental physical laws. The physical limits of computing and communications—dictated by quantum mechanics, general relativity, and information theory—lie far below anything in a data center today. The scaling laws chart the descent behind us. The fundamental physical limits chart the room that remains.
Some of these limits may be far out of reach in practice. But they are still worth exploring, because the work pays twice: once in technology development and once in fundamental science. A proof-of-concept system that approaches a limit is at once a technology that forces stalled paradigms to give way and an experiment that deepens our understanding of the physical laws themselves.
The descent
During his 1959 lecture, Feynman offered two prizes of $1,000 each out of his own pocket. One would go to the first person to build a working electric motor smaller than a 0.4-millimeter cube. He expected the challenge to force someone to invent a genuinely new microtechnology. Instead, about a year later, a Caltech-trained engineer named William McLellan turned up with the motor built and running. The quarter-milligram machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watchmaker’s lathe, under a microscope. Feynman, by his own account, was only slightly disappointed. The prize had proved his point in the least interesting way imaginable. The room really was down there, but reaching this first milestone had taken no new technology at all—just an unusually patient man with steady hands.
The second prize aged differently, and more instructively. It was offered to whoever could shrink a page of text by a factor of 25,000 in each direction, small enough for the whole Britannica to fit on a pinhead. That one stood unclaimed for a quarter of a century. It was finally won in 1985 by Tom Newman, a Stanford graduate student who used an electron-beam lithography machine—an ancestor of pattern-making tools used in chipmaking today—to transcribe the opening page of A Tale of Two Cities at the required scale.
The descent was already well underway, however. The year before Feynman’s talk, Jack Kilby, newly hired at Texas Instruments and with no vacation yet to take, spent that July nearly alone in the lab while his colleagues enjoyed time off, and by that September he had wired together a transistor, a capacitor, and three resistors, all made from a single sliver of germanium. This proved that an entire circuit could be carved from one block of semiconductor.
The machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watchmaker’s lathe, under a microscope.
The idea took off, and the number of components on a chip kept doubling on a schedule so regular that Gordon Moore, in a 1965 trade-magazine article, simply drew a line through the trend and extended it into the future. That line became Moore’s Law.
Moore, though, never claimed transistors could shrink forever. He observed that at any given moment there is a certain number of components that makes each one of them as cheap as possible to manufacture. In 1965, that number was 50. Put in too few transistors and you waste the chip; cram in too many and manufacturing errors multiply and yields collapse. It was this cost-minimizing number that was doubling at a steady pace.
For four decades the descent came with another gift: Dennard scaling, so named for the IBM engineer Robert Dennard, who spelled it out in 1974. Each time transistors shrank, they also switched faster and drew proportionally less power, so every generation was an automatic win. Around 2004, though, the gift ran out. Chips could no longer shed the heat of running every transistor flat out; clock speeds, which had raced past three gigahertz, stopped climbing, and Intel canceled the four-gigahertz processor it had promised. The industry pivoted to packing multiple processing cores onto a chip rather than making one core faster, and the texture of progress changed.
Progress is now bought mostly by scaling up the system—more chips wired together, more memory feeding them, more power and cooling around them—rather than by shrinking the transistor. This is most visible in AI, where training compute, the total count of arithmetic operations spent to train a model, has doubled roughly every six months since 2010, far faster than the pace of Moore’s Law allows. Little of that progress is due to shrinking. Computer chips still get better, and transistors still improve and get more densely packed, but each new generation arrives more slowly and pays back less in speed and power than the one before it. Nothing in that slowdown is a law of physics, though. The components of computing still sit far above their fundamental limits.
The ultimate computer
Before we get to those limits, it is worth measuring how far the descent has already gone. In the nearly eight decades since the first transistor, the density of transistors on a chip has grown by 10 orders of magnitude—a 1 followed by 10 zeros, or a factor of 10 billion. Now more than 100 million transistors fit in a square millimeter (100 billion or more to a chip). Computational speed has grown even more, by 12 orders of magnitude, or a factor of a trillion; energy per operation has fallen 17 orders of magnitude, from about 100 joules for an early vacuum-tube machine down to a femtojoule—one-quadrillionth of a joule—for a single switch of a logic gate.
These are not small improvements. But the floor, for computing performance, lies lower still. How much lower could we go? Around the turn of the millennium, the MIT physicist Seth Lloyd took the question to its extreme: Imagine an ordinary lump of matter, a kilogram of it—about the mass of a laptop—packed into a liter of space. How much computing could the laws of physics, pushed to their absolute limit, ever wring from that kilogram? In a 2000 paper in Nature titled “Ultimate physical limits to computation,” he worked out the answer and called the imaginary machine the ultimate laptop.
The speed such a machine can achieve is governed by its energy. A kilogram of matter, according to E = mc2, holds about 9 ×1016 joules, which is roughly what the entire United States draws from its electrical grid every two days. Spend all of that on computation, and the ultimate laptop runs at roughly 5 ×1050 operations per second, some 35 orders of magnitude faster than a leading chip today.
The catch is what running that quickly would look like. Concentrating a kilogram’s rest energy into pure computation would turn the machine into a plasma of about a billion degrees, a state closer to that of the universe in the first minutes after the Big Bang than anything we would recognize as a laptop.
Such a machine is, to put it mildly, a very long way off. The next limit, which governs how much energy a computation must waste, is much closer, and companies are already pushing toward it. In 1961, Rolf Landauer, a physicist at IBM, showed that information and thermodynamics are bound together: Erasing a single bit must release a minimum quantity of heat, about 2.9 ×10-21 joules at room temperature. The figure is small, yet a chip today spends several hundred thousand times more than that on every switch.
Memory density obeys a limit of its own, and this one reaches all the way to gravity. How many bits can a region of space hold? In 1981, the physicist Jacob Bekenstein, while working out the thermodynamics of black holes, determined the amount: The information inside any bounded region is limited by its size and the energy it contains. For a region the size of Lloyd’s ultimate laptop, that comes to about 1042 bits, some 1036 to a cubic millimeter—nearly 30 orders of magnitude beyond what a device that size can store today.
The last limit is on communication—how fast information can be moved. Every wire and every fiber ultimately carries its signal on quanta, the indivisible packets (photons in the case of light) in which energy travels. In 1973, the Soviet mathematician Alexander Holevo determined the ultimate limit on how much information a stream of quanta can convey. A watt of light delivers only so many photons each second, and quantum mechanics fixes how many distinguishable ways they can arrive—how many messages a receiver could ever tell apart. A single channel carrying one watt, at the Holevo limit, could deliver on the order of 1017 bits per second. Today’s optical links, or interconnects, deliver about one-millionth that rate for the same power. But the gap is narrowing quickly inside data centers, where each generation of interconnects is asked to move more bits on less power, spending less energy on the electronics and using light to cover more of the distance.
Four limits, then, mark the true bottom, and they derive from three of the most fundamental theories we have. Quantum mechanics sets how fast a machine can compute. General relativity caps how much it can remember. And information theory limits both how fast it can communicate and how little energy an irreversible step can cost. There is no road map to reaching these limits, and they hold whether or not anyone is trying. Measured against them, the descent has barely begun.
The room that remains
The biggest of the gaps is in computing speed: There are 35 orders of magnitude between the technology’s current state and its limit. That’s equivalent to the difference between a nanosecond and 200 million times the age of the universe. It is also the least bridgeable gap of the four. To close it you would have to find a completely different way to build a computer, one that could compute with the entire energy contained in a lump of matter.
Quantum computers could get us part of the way there if they could convert every degree of freedom in a lump of matter—every atom, and every way it can move or spin—into a computing element that we could reliably manipulate. In practice, current quantum computers go the other way, spending a lot of matter to create each computing element. Trillions of atoms go into each quantum bit, or qubit, the quantum counterpart of the bit in an ordinary computer. Because every qubit is fragile, dozens to thousands of them must be woven together to make a single one reliable enough for useful computation.
Energy is where the floor is nearest. Today’s chips operate less than six orders of magnitude above the Landauer minimum. Conventional computing logic can close only part of that gap. A reliable switch has to be flipped by an amount of energy large enough that thermal fluctuations never flip it by accident, and that margin pins the transistor a hundred to a few thousand times above Landauer no matter how it is refined.
But there are ideas for getting that energy lower by changing the physics of the computation. In 1973, IBM’s Charles Bennett showed that any computation can be arranged to throw nothing away. Such a reversible computation pays no erasure toll at all, because every step can be run backward to recover what went in.
This approach is called adiabatic or reversible logic, and the first commercial attempts are now underway. For example, Vaire Computing, a startup founded to commercialize reversible logic, tested its first chip in 2025; the chip has resonant circuits that recover part of the switching energy instead of venting it as heat.
Narrowing the memory density gap, meanwhile, would mean building devices that use all three dimensions at molecular scale. The most concrete example is DNA, which stores information at the scale of its own atoms, and which biology has used as a storage medium for 4 billion years. Feynman, being conservative, guessed that a single bit would require a cube of 125 atoms. DNA holds it in about 30. What keeps DNA storage out of the data center is not its density but the cost and speed of writing it, still many orders of magnitude from anything practical. The library in a dust speck that Feynman imagined is permitted by physics and held back only by fabrication.
Communication sits about six orders of magnitude—just a factor of a million—from the Holevo limit, which is the threshold the industry will likely meet first. Interconnects are multiplying faster than the grid can power them, so the industry is moving to transmitters and receivers that use less energy per bit. Nvidia and Broadcom now ship optical interconnects built right against the silicon, so a signal leaves the chip package already on photons. Quantum computers, by comparison, are beginning to rely on interconnects that encode information on quantum signals, usually in the form of single photons. As classical interconnects spend fewer resources on each bit, they descend toward the regime where these quantum interconnects already operate. The Holevo limit is where the two will meet.
Between the floor and the ceiling
It is fair to object that a gap of 35 orders of magnitude is a mirage—that a limit so remote says nothing about what any real machine will ever do, and that pointing to it is like navigating by the horizon. The objection has teeth. Reversible logic, quantum computing, and DNA storage have each been perpetually 10 years away for 40 years. And it will likely never be practical to build a computer that uses all the energy in a lump of matter.
But these gaps are invitations. There is a ceiling, set by how much we can build: the number of devices we can fabricate; the power we can generate to run them; and the capital, land, and cooling water we can assemble into a single system. And there is a floor, set by how much a single joule, a single second, and a single gram of matter can be made to compute, store, or transmit. We can progress by building more or by approaching the floor, which means making each device do more with less.
These gaps are invitations. We can progress by building more or by approaching the floor, which means making each device do more with less.
For most of the last century, we proceeded down both paths together. We built more computers, and the computers themselves grew smaller and better. Now we mostly build more, and the ceiling is coming into view. A power grid can carry only so many gigawatts, and a region can spare only so much land and water. Yet every road map for what comes next is drawn from the same starting point: the machines we already have. Build more of them and you climb toward the ceiling; build better ones and you descend a little further, but only as far as the transistor’s own floor. There is a third direction. Start with a physics experiment operating at the floor and build up.
It has been done before. The transistor was a physics experiment before it was an industry; the laser was a laboratory curiosity before it carried the world’s conversations under the ocean; giant magnetoresistance went from physics discovery to the read head of every disk drive within a decade. In each case, the physics experiment came first and showed what was possible, and engineers then turned it into a machine that can be scaled up. Building the experiments that operate at these fundamental limits, and then scaling them up without losing that performance, is the work that remains. The next machines will not be reached by descent; they will be built from the bottom up. There is still plenty of room at the bottom.
Volkan Gurses is a postdoctoral researcher at MIT and a visiting researcher at Nvidia. He received his PhD in electrical engineering from Caltech. He studies the fundamental physical limits of information technologies and builds integrated circuits and systems that approach those limits.