On the evening of December 29, 1959, at an American Physical Society meeting at Caltech, Richard Feynman gave a lecture called "There's Plenty of Room at the Bottom," arguing that no physical law stops us from writing the entire Encyclopædia Britannica on the head of a pin, storing every book ever written in something the size of a dust speck, or arranging matter atom by atom. Biology already does versions of this - a living cell keeps its instructions in a wisp of DNA - so the laws of physics plainly permit it. We simply hadn't built the tools yet.
He was right, and for decades we descended into that space almost for free. The transistor 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 stalled, and the clearest sign is the buildout of data centers. Six of the biggest hyperscalers 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 chips better, is quietly running out.
The trend lines that tracked the 20th-century information age are bending or breaking with the dawn of the 21st century's intelligence age. Moore's Law, plotting transistor density on silicon, has slowed. Dennard's Law - power consumption stays the same as transistors shrink - has ended. Koomey's Law, tracing computation per joule, has begun to flag. Kryder's Law, on magnetic storage density, has slowed to a crawl. Keck's Law, charting fiber-optic capacity, is flattening.
But these scaling laws are merely observations about engineered systems, not fundamental physical laws. The true limits - 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 limits chart the room that remains. Some may be far out of reach in practice, but they're worth exploring because the work pays twice: once in technology development and once in fundamental science.
During his 1959 lecture, Feynman offered two prizes of $1,000 each out of his own pocket. One went to the first person to build a working electric motor smaller than a 0.4-millimeter cube. He expected it 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 - a quarter-milligram machine, roughly the size of a grain of sand, assembled by hand with a toothpick and a watchmaker's lathe under a microscope. Feynman was only slightly disappointed. The prize proved his point in the least interesting way imaginable: the room 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. 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, finally won in 1985 by Tom Newman, a Stanford graduate student who used an electron-beam lithography machine - an ancestor of today's chipmaking pattern tools - 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 colleagues enjoyed time off. By that September he had wired together a transistor, a capacitor, and three resistors, all from a single sliver of germanium, proving an entire circuit could be carved from one block of semiconductor.
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 never claimed transistors could shrink forever; he observed that at any given moment there's a certain number of components that makes each one as cheap as possible to manufacture. In 1965, that number was 50. Too few transistors waste the chip; too many multiply manufacturing errors and collapse yields. It was this cost-minimizing number that was doubling steadily.
For four decades the descent came with another gift: Dennard scaling, named for IBM engineer Robert Dennard, who spelled it out in 1974. Each time transistors shrank, they switched faster and drew proportionally less power - every generation an automatic win. Around 2004, 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 Moore's Law allows. Little of that progress is due to shrinking. Chips still get better, transistors still improve and get more densely packed, but each new generation arrives more slowly and pays back less in speed and power. Nothing in that slowdown is a law of physics, though; the components of computing still sit far above their fundamental limits.
Before we get to those limits, it's 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 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 lies lower still. Around the turn of the millennium, MIT physicist Seth Lloyd took the question to its extreme: Imagine an ordinary kilogram of matter - 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 × 10^16 joules, 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 × 10^50 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 the universe in the first minutes after the Big Bang than anything we'd 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, 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 10^42 bits, some 10^36 to a cubic millimeter - nearly 30 orders of magnitude beyond what a device that size can store today.
Over the decades, we have pushed the performance of our computers and communications technology closer and closer to fundamental limits. The biggest change has been in the energy required for a single computation, which has dropped by 17 orders of magnitude since the 1940s - a factor of a hundred thousand trillion. But even with that big drop, we're still a factor of several hundred thousand from the fundamental physical limit set by information theory and thermodynamics. The largest gap exists for computational speed: our current chips are some 35 orders of magnitude - a 1 followed by 35 zeros - short of what quantum mechanics allows for a one-kilogram computer.