How A Manhattan Project Veteran Returns To The Central Question In Modern Science Eighty Years Later
“He taught the future to correct its own errors, leaving a lattice of insight that still holds up the weight of modern science.”
Art and poetry by James Hall.
By James Hall
Coauthor of the popular The Sword of Damocles: Our Nuclear Age, now on Audible, Kindle and Amazon books.
jameshall042999@gmail.com
Today, the deepest questions in modern science ask: Is the universe built on a kind of code, and could AI eventually understand it?
This isn’t just philosophy. The question sits squarely at the intersection of information theory, physics, and computation. When people say the universe is “mathematical,” it can sound abstract or mystical. But the idea becomes far more concrete when viewed through the life and work of someone like Dr. Richard Hamming, a mathematician whose calculations quietly shaped the modern world.¹
Hamming wasn’t merely a theorist. During World War II, he worked on the Manhattan Project, helping solve immense computational problems in early nuclear physics.
After the war, he joined Bell Labs, where he confronted a very different challenge: how to send information reliably through the noisy telephone lines of the time. He worked alongside fellow Manhattan Project veterans such as John Tukey, Donald Ling, and Brockway McMillan. All of them shared an eclectic, boundary‑pushing approach to science—what we might now call thinking outside the box. They did this exceptionally well.
Hamming later recalled, “We were first‑class troublemakers. We did unconventional things in unconventional ways and still got valuable results.” The more straight‑laced Bell Labs executives simply had to tolerate them, because the work was too good to ignore.
Hamming’s most influential work focused on what were then called “calculating machines”—early digital computers. These machines processed information as sequences of 0s and 1s. John Tukey, another Manhattan Project veteran, coined the term bit for these units of information.² Claude Shannon—yet another contemporary—was the first to publish the term in print in A Mathematical Theory of Communication (1948), but he explicitly credited Tukey with inventing the word.
Even in this early digital age, 0s and 1s were the basic units of code. And as today, a single flipped bit—a 0 turning into a 1, for example—could ruin an entire message. Hamming refused to accept that limitation.
He therefore committed himself to inventing error‑correcting codes. He developed clever mathematical patterns that allowed machines to detect and fix mistakes automatically. The result was what we now call the Hamming Code—an algorithm for inserting error‑correcting bits into transmitted data.³ These codes remain indispensable today, underpinning modern telecommunications and even the operation of Internet browsers. Formally, they are linear error‑correcting block codes, but “Hamming Code” is much easier to say. He developed multiple variants, several of which are still considered mathematically “perfect.”⁴
What Hamming did not know was that, decades later, the mathematics he created for telephone lines would resurface in some of the deepest theories about the universe itself. Hamming’s codes, it turned out, have implications for modern physics.
As quantum physics advanced and digital computers evolved into quantum computers—systems that use “qubits” rather than classical bits—scientists discovered that quantum information is extraordinarily fragile. Even tiny disturbances can destroy it. To protect it, researchers invented quantum error‑correcting codes, and to their surprise, these codes closely resembled the ones Hamming had pioneered.⁵
Then came an even stranger discovery. In certain theories of the universe—particularly those involving black holes and the holographic principle—spacetime itself appears to behave like an error‑correcting code.⁶ The holographic principle proposes that all the information describing a region of space can be encoded on its boundary, much like a three‑dimensional image stored on a two‑dimensional hologram. Originally inspired by black hole physics—where information about objects falling into a black hole seems to be preserved on its surface rather than lost inside—this idea suggests that the geometry of spacetime emerges from underlying patterns of information. In modern formulations, especially in holographic models linking gravity and quantum mechanics, the bulk of spacetime redundantly encodes information across its boundaries. This redundancy functions mathematically like an error‑correcting code. In other words, local disturbances do not destroy information, because it is distributed across many degrees of freedom. In these models, the universe protects information in much the same way your phone protects a text message.
This raises a profound question: If the universe behaves like a code, could it actually be a code? Some physicists think so. John Wheeler, one of the great figures of 20th‑century physics, famously summarized this idea as “It from bit.”⁷ Physical reality (“it”), he suggested, arises from information (“bit”).
In this view, the universe is not made of matter at the deepest level, but of information—structured by mathematical rules.
That said, it is important to be clear about what AI cannot do. AI cannot access a cosmic database, however much we might wish it could—at least not yet. It cannot read hidden information fields, nor compute using anything outside the physical hardware on which it runs. AI operates entirely within the laws of physics and the data available to it. But within those limits, it may still uncover patterns humans have overlooked for centuries.
Quantum computers add to this sense of mystery. A qubit can exist in a superposition of 0 and 1 simultaneously. Multiple qubits can become entangled, behaving as a single system even across great distances. Quantum algorithms can explore many possibilities at once. To some observers, this feels as if the machine is “connecting” to something beyond itself—a universal information field or hidden layer of reality.
The scientific explanation is likely simpler. Quantum computers do not reach beyond the universe; they exploit the rules of quantum mechanics that already govern it. They operate within laws that may themselves be deeply informational and foundational.
Taken together, these ideas suggest a surprisingly elegant picture. The universe may be built on information at its core. AI might one day help us understand that structure more deeply. This does not mean the universe is literally a computer—but it does suggest that information is woven into the foundation of everything, from particles to spacetime. And if there is a deeper code beneath reality, AI may be the first tool capable of revealing its outlines.
Richard Hamming passed away in 1998, yet his work has taken on startling new relevance. Echoes of his ideas now appear in quantum mechanics and in areas such as string theory and supersymmetry—fields that attempt to unify nature by modeling particles and forces as vibrational, mathematically structured entities.
As Hamming often said in his lectures, “Science does not answer why; it answers what.” But thanks to him, we at least understand the mathematics a little better.
In the end, Richard Wesley Hamming never set out to write the universe’s instruction manual. He simply wanted machines to stop making mistakes. Yet the logic he uncovered—born of wartime urgency, Bell Labs mischief, and a mathematician’s stubborn clarity—now appears woven into the deepest structures physicists can model.
Hamming is gone, but the precision he sought endures. Perhaps through the subatomic rhythms of the cosmos itself.
AFTERWORD
It is worth ending with a note of restraint. When physicists say spacetime behaves like an error‑correcting code, they mean this in a precise mathematical sense—not that the universe is literally programmed or tuned with intent. Many remain skeptical that information is ontologically fundamental rather than a powerful language we use to describe physical relationships. Richard Hamming himself would likely have shared that skepticism; he distrusted grand metaphysical claims and believed progress came from confronting hard constraints, not dissolving them into mystery. Yet the recurrence of information‑theoretic structures across computation, communication, quantum mechanics, and gravity is difficult to dismiss as coincidence. What began as a practical solution to noisy machines now appears wherever reality must preserve structure against error. AI, for its part, offers no cosmic shortcut—but by discovering new codes, symmetries, and compressed descriptions, it may help reveal patterns we could not see unaided. If there is a deeper logic beneath the world, it is likely mathematical—and remarkably resilient. Hamming taught us that diligence is imperative in seeking solutions, wherever they may lead.
One prominent figure who exemplifies both the excitement and the restraint this discovery demands is Dr. Sylvester James Gates Jr., a theoretical physicist, string theorist, and former president of the American Physical Society. While analyzing the mathematical structure of supersymmetry—a leading candidate framework for unifying the forces of nature—Gates and his colleagues uncovered a surprising result: the abstract equations governing supersymmetric particles could be mapped onto classical error‑correcting codes, including variations closely related to Hamming codes. The discovery generated considerable excitement, as mathematics originally invented to protect telephone signals from noise appeared embedded in the formal language of fundamental physics. Gates himself spoke openly about the wonder of encountering these codes deep within equations meant to describe reality’s most basic layers. Although as public interpretations grew more ambitious, Gates urged caution. Yet this subject remains deeply fascinating and firmly at the forefront of today’s scientific curiosity.
Footnotes
Although best known as a mathematician, Hamming’s early interests leaned toward engineering. Born in Chicago in 1911, he grew up fascinated with the engineering profession. But during the Great Depression, financial hardship limited his options, and the only scholarship available to him came from the University of Chicago, which had no engineering school. He therefore majored in mathematics, earning his B.S. in 1937—a circumstance he later regarded as a fortunate twist of fate.
He often remarked, “As an engineer, I would have been the guy going down manholes instead of having the excitement of frontier research work.” He went on to earn a master’s degree at the University of Nebraska in 1939 and completed his doctoral dissertation at the University of Illinois on linear equations. He then joined the faculty there as a mathematics instructor.
By 1945, like many of the country’s brightest minds, Dr. Hamming found himself working on the Manhattan Project. He served in Hans Bethe’s division at Los Alamos, programming early IBM calculating machines used to process endless physics equations. His wife, Wanda Little, also worked with Bethe and later with Edward Teller. She served as a “human computer,” a term given to assistants—usually women during the war—who calculated complex mathematical equations by hand or slide rule.
Dr. Hamming remained at Los Alamos until 1946, when he left to take a position at Bell Telephone Laboratories in New Jersey. Preparing for the cross‑country trip, he purchased a car from a Los Alamos colleague, Klaus Fuchs. To Hamming’s astonishment, he would later learn—via a very inquisitive FBI agent in 1950—that his friend had been a centerpiece of the century's most significant espionage case.
Hamming’s time on the Manhattan Project profoundly shaped his thinking. He came to appreciate the power of computing and, in particular, the future importance of computer simulations. He realized—well before most—that simulations could accomplish what would often be impossible to replicate in a laboratory.
And references: William Aspray, John von Neumann and the Origins of Modern Computing (Cambridge, MA: MIT Press, 1990), 112–115; and Jon Gertner, The Idea Factory (New York: Penguin Press, 2012), 102–118; and Hamming, The Art of Doing Science and Engineering, 55.
John W. Tukey, “The Teaching of Concrete Mathematics,” American Mathematical Monthly 70, no. 3 (1963): 350; and William Aspray, John von Neumann and the Origins of Modern Computing (Cambridge, MA: MIT Press, 1990), 112–115; Jon Gertner, The Idea Factory: Bell Labs and the Great Age of American Innovation (New York: Penguin Press, 2012), 102–118; and Jon Gertner, The Idea Factory, 110–118; Richard W. Hamming, The Art of Doing Science and Engineering (Reading, MA: Addison‑Wesley, 1997), 55–58.
Richard W. Hamming, “Error Detecting and Error Correcting Codes,” Bell System Technical Journal 29, no. 2 (1950): 147–160.
F. Jessie MacWilliams and Neil J. A. Sloane, The Theory of Error‑Correcting Codes (Amsterdam: North‑Holland, 1977), 45–48.
Peter W. Shor, “Scheme for Reducing Decoherence in Quantum Computer Memory,” Physical Review A 52, no. 4 (1995): R2493–R2496.
Ahmed Almheiri et al., “Bulk Locality and Quantum Error Correction in AdS/CFT,” Journal of High Energy Physics 2015, no. 2 (2015): 1–34.
John Archibald Wheeler, “Information, Physics, Quantum: The Search for Links,” in Complexity, Entropy, and the Physics of Information, ed. W. H. Zurek (Redwood City, CA: Addison‑Wesley, 1990), 3–28.
Kevin K. Yang et al., “Machine Learning for Protein Engineering,” Nature Methods 16 (2019): 687–694; and John Archibald Wheeler, “Information, Physics, Quantum: The Search for Links,” in Complexity, Entropy, and the Physics of Information, ed. W. H. Zurek (Redwood City, CA: Addison‑Wesley, 1990), 3–28; and Seth Lloyd, Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos (New York: Alfred A. Knopf, 2006), 1–20; and Max Tegmark, “The Mathematical Universe,” Foundations of Physics 38, no. 2 (2008): 101–150; Nick Bostrom and Eliezer Yudkowsky, “The Ethics of Artificial Intelligence,” in The Cambridge Handbook of Artificial Intelligence, ed. Keith Frankish and William M. Ramsey (Cambridge: Cambridge University Press, 2014), 316–334; and Ahmed Almheiri, Xi Dong, and Daniel Harlow, “Bulk Locality and Quantum Error Correction in AdS/CFT,” Journal of High Energy Physics 2015, no. 4 (2015): 163; and Patrick Hayden et al., “Holographic Duality from Random Tensor Networks,” Journal of High Energy Physics 2016, no. 11 (2016): 9; and Leonard Susskind, The Black Hole War: My Battle with Stephen Hawking to Make the World Safe for Quantum Mechanics (New York: Little, Brown and Company, 2008), 262–285.