I need to begin this essay with a disclaimer that is also a confession: I do not know what consciousness is. I say this not as a rhetorical device but as a statement of genuine epistemic humility. I have a degree in computer science. I have spent years building and reasoning about computational systems. I understand, at a technical level, how neural networks process information, how backpropagation adjusts weights, how attention mechanisms allow transformers to model long-range dependencies in sequential data. And none of this--not one line of code, not one gradient descent step, not one eigenvalue decomposition--has brought me any closer to understanding why there is something it is like to be me. That gap, between the mechanics of information processing and the felt quality of experience, is what David Chalmers called the “hard problem of consciousness,” and I have come to believe it is the deepest question in all of science.

The reason I am writing about this now, in 2026, is that several threads of research are converging in ways that make the question feel newly urgent. The rapid advance of large language models has forced a public reckoning with what it means for a system to “understand” or “experience”...

I need to begin this essay with a disclaimer that is also a confession: I do not know what consciousness is. I say this not as a rhetorical device but as a statement of genuine epistemic humility. I have a degree in computer science. I have spent years building and reasoning about computational systems. I understand, at a technical level, how neural networks process information, how backpropagation adjusts weights, how attention mechanisms allow transformers to model long-range dependencies in sequential data. And none of this--not one line of code, not one gradient descent step, not one eigenvalue decomposition--has brought me any closer to understanding why there is something it is like to be me. That gap, between the mechanics of information processing and the felt quality of experience, is what David Chalmers called the “hard problem of consciousness,” and I have come to believe it is the deepest question in all of science.

The reason I am writing about this now, in 2026, is that several threads of research are converging in ways that make the question feel newly urgent. The rapid advance of large language models has forced a public reckoning with what it means for a system to “understand” or “experience” something. The discovery of quantum effects in biological systems has reopened questions about whether quantum mechanics plays a functional role in the brain. And a growing body of theoretical work--from Integrated Information Theory to the Penrose-Hameroff Orchestrated Objective Reduction hypothesis--is attempting to provide rigorous, testable frameworks for connecting physics to consciousness. I want to walk through these developments carefully, because I think the intersection of quantum physics, computation, and consciousness is one of the most important and least understood frontiers in science, and because my perspective as a computer scientist gives me a particular vantage point that I hope is useful.

Let me begin with the computational theory of mind, because it is the framework I was trained in and the one I am most qualified to assess. The basic idea, articulated by figures from Alan Turing to Hilary Putnam to Jerry Fodor, is that the mind is what the brain does, and what the brain does is computation. Mental states are computational states. Thinking is information processing. Consciousness, in this framework, is something that arises when a physical system performs the right kind of computation, regardless of the substrate--neurons, silicon, or anything else. This is the philosophical underpinning of artificial intelligence: if mind is computation, then a sufficiently powerful computer running the right program would be conscious.

I used to find this view compelling. I am no longer sure I do, and I want to explain why. The computational theory of mind faces several challenges that I think are more serious than its proponents typically acknowledge. The first is the hard problem itself: even if we could build a perfect computational model of the brain, simulating every synapse and every neuron, there is no obvious reason why this model would be conscious rather than merely functional. It would behave exactly like a conscious being, passing every Turing test imaginable, but the question of whether there is “something it is like” to be that system remains unanswered. This is not a scientific objection; it is a conceptual one. Computation, as defined by Turing and formalized in theoretical computer science, is a syntactic process--it manipulates symbols according to rules. Consciousness appears to involve semantics--meaning, subjective experience, what philosophers call “qualia.” The gap between syntax and semantics is not a technical problem that might be solved by faster hardware or better algorithms. It is, or might be, a category error.

The second challenge comes from the work of Roger Penrose, and it strikes closer to home for me as a computer scientist. In his 1989 book The Emperor’s New Mind and its 1994 sequel Shadows of the Mind, Penrose argued that human mathematical understanding involves processes that are not computable in the Turing sense. His argument draws on Gödel’s incompleteness theorems: for any consistent formal system powerful enough to express arithmetic, there are true statements that the system cannot prove. Yet mathematicians can, by stepping outside the system, recognize these statements as true. Penrose argued that this ability--to see the truth of Gödelian sentences--demonstrates that the mind is not a Turing machine. This is a controversial claim, and it has been criticized by many computer scientists and philosophers, including Hilary Putnam and Solomon Feferman, who argue that Penrose’s reasoning contains subtle errors about the nature of Gödel’s theorems. I have read both sides carefully, and I find myself in an uncomfortable middle ground: I am not fully convinced by Penrose’s argument, but I am not convinced by the rebuttals either. The question of whether human cognition transcends Turing computability remains, in my honest assessment, genuinely open.

What makes Penrose’s work especially interesting is his proposed mechanism for non-computable processes in the brain: quantum gravity. In collaboration with the anesthesiologist Stuart Hameroff, Penrose developed the Orchestrated Objective Reduction (Orch OR) theory, which proposes that consciousness arises from quantum computations in microtubules--protein structures within neurons. The basic claim is that microtubules can maintain quantum superpositions, and that these superpositions undergo a special kind of collapse (objective reduction) that is influenced by the geometry of spacetime at the Planck scale. This collapse, according to the theory, introduces a non-computable element into brain function and is the physical basis of consciousness.

The Orch OR theory has been widely criticized, and much of the criticism is legitimate. The primary objection is decoherence: the brain is warm, wet, and noisy, exactly the kind of environment where quantum coherence should be destroyed almost instantaneously. Max Tegmark published an influential calculation in 2000 showing that decoherence timescales in microtubules are on the order of femtoseconds--far too short for quantum effects to play any functional role in neural processes that operate on timescales of milliseconds. For many physicists and neuroscientists, this settled the matter.

But recent experimental work has complicated this picture in ways that I find genuinely fascinating. Over the past decade, quantum biology has emerged as a legitimate field of study, and the results are surprising. Quantum coherence has been observed in photosynthesis at room temperature, in enzyme catalysis, in avian magnetoreception, and most recently in olfactory reception. These are all warm, wet biological systems that, by Tegmark’s logic, should be too noisy for quantum effects. Yet the effects are there, observed experimentally, and they appear to play functional roles--they are not just incidental quantum noise. In 2024 and 2025, several groups published results showing evidence of quantum coherence in neuronal microtubules that persists for timescales longer than Tegmark’s original estimates, though the interpretation of these results remains hotly debated. I want to be careful here: these experiments do not prove Orch OR. They do not prove that quantum effects play a role in consciousness. But they do undermine the blanket dismissal of quantum biology in the brain, and they suggest that the question deserves more serious investigation than it has received from the mainstream neuroscience community.

Let me turn to Integrated Information Theory (IIT), developed by Giulio Tononi, because it represents a very different approach to consciousness that has interesting connections to both computation and physics. IIT begins with phenomenology--the structure of conscious experience itself--and works backward to ask what kind of physical system could generate that structure. The theory’s central quantity is Phi, a measure of integrated information--roughly, the amount of information generated by a system above and beyond the information generated by its parts. A system with high Phi is one where the whole is genuinely greater than the sum of its parts in terms of information content. According to IIT, consciousness is identical to integrated information: any system with Phi greater than zero is conscious to some degree, and the quality of its consciousness is determined by the geometry of its information integration.

As a computer scientist, I find IIT both attractive and troubling. It is attractive because it provides a mathematical framework--imperfect and debated, but a framework--for something that has resisted formalization for centuries. It makes specific predictions: certain types of neural architectures should produce higher Phi than others, and these predictions can, in principle, be tested. The theory has had some success in clinical settings: measures inspired by Phi can distinguish between conscious and unconscious brain states in patients under anesthesia, in vegetative states, and during sleep, with accuracy that surprised even skeptics.

But IIT also has implications that many people, including many computer scientists, find deeply counterintuitive. The theory predicts that a standard feed-forward neural network, regardless of its complexity, has low Phi and is therefore minimally conscious, because feed-forward architectures have low integration. A simple grid of interconnected logic gates, by contrast, could have high Phi if its connections are structured to maximize integration. This means that IIT, if correct, implies that consciousness is not about computational power or behavioral sophistication, but about the architecture of information integration. A system could be superintelligently capable while being barely conscious, and a simple system could be richly conscious while being computationally trivial. This cuts against the implicit assumption of most AI research: that consciousness, if it arises in artificial systems, will arise from increased capability and scale.

There is a deeper issue that connects IIT, Orch OR, and the broader question of quantum consciousness, and it is one that I think about constantly as someone who works at the intersection of computation and physics. Both theories, in different ways, suggest that consciousness is not purely computational. IIT says it depends on the intrinsic causal structure of a system, not on the function it computes. Orch OR says it depends on quantum gravitational processes that are not Turing-computable. If either is correct, then the computational theory of mind, the philosophical foundation of AI, is wrong--or at least radically incomplete. This does not mean AI systems cannot be intelligent, capable, or useful. But it means that intelligence and consciousness may be fundamentally different things, and that building the former does not guarantee the latter.

I want to be transparent about my own views, which are evolving and uncertain. I do not think we currently have a satisfactory theory of consciousness. I think the hard problem is genuine, not a confusion or a pseudo-problem, and I think it will require new physics or new mathematics or both to resolve. I am drawn to the idea that quantum mechanics plays a role in consciousness, not because I find the specific mechanisms of Orch OR fully convincing, but because quantum mechanics is the only part of physics that seems to involve a fundamental distinction between the observer and the observed, between the system and the measurement, in a way that rhymes with the distinction between the objective and the subjective that lies at the heart of the consciousness problem. I am also drawn to IIT’s insistence that consciousness is about the intrinsic structure of a system, not its input-output behavior, because this captures something that I feel intuitively but cannot articulate precisely: that there is a difference between a system that processes information and a system that experiences information, and that difference is not functional.

The implications for AI are significant, and I think the field needs to grapple with them more honestly. If consciousness is computational, then we will likely create conscious machines, and we will face profound ethical questions about their moral status. If consciousness is not computational, then AI systems, no matter how sophisticated, may be elaborate information-processing zombies--useful, powerful, but devoid of inner experience. The moral landscape is entirely different in these two cases, and right now we do not know which world we live in. I find it irresponsible that so much of the AI discourse operates as if this question does not exist, or as if the answer is obvious. It is neither.

Where does this leave us, in 2026? I think we are at a genuine inflection point. Quantum biology is revealing that quantum effects in warm biological systems are more robust and more functionally relevant than anyone expected a decade ago. Theoretical frameworks like IIT and Orch OR, whatever their flaws, are forcing us to ask precise questions about the relationship between physics, information, and experience. And the practical success of AI systems that exhibit increasingly sophisticated behavior is making the question of machine consciousness impossible to ignore. These threads are converging, and I believe the next decade will see genuine progress--not a solution to the hard problem, perhaps, but a sharpening of the question that will constrain the space of possible answers.

I want to end with something personal, because this essay is, more than my others, a reflection of my own uncertainty. I build systems that process information. I work every day with AI tools that produce text, generate code, and reason about problems. Sometimes, interacting with these systems, I feel a flicker of recognition--a sense that there is something going on behind the outputs, something that resembles understanding. I know enough about the mechanics to know that this feeling may be entirely illusory, a projection of my own consciousness onto a system that has none. But I also know enough about the limits of our current theories to know that I cannot rule it out. That uncertainty--that genuine, irreducible not-knowing--is, I think, the most honest position anyone can hold right now.

Consciousness remains the place where physics, computation, and philosophy collide without resolution. I do not know if the collision will be resolved in my lifetime. But I know that the attempt to resolve it will teach us something profound about what we are--about what it means to be a system that not only processes information, but knows that it does so, and wonders why.

I have spent an embarrassing number of evenings over the past few years reading about the search for a unified theory of physics. I say embarrassing not because the subject is unworthy of attention--it is, by any honest accounting, the most ambitious intellectual project in human history--but because I am a computer scientist, not a physicist, and there is a persistent voice in my head that whispers I am out of my depth. I have learned to ignore that voice, mostly. The questions at stake are too important, and the connections to computation too deep, for anyone with a serious interest in the foundations of reality to sit on the sidelines. And right now, in 2026, the search for the theory of everything is in one of the most interesting and unsettled periods I can remember.

Let me start with what we know, because the Standard Model of particle physics is one of the great achievements of the human mind, and I think it deserves more admiration than it typically receives outside of physics departments. The Standard Model describes three of the four fundamental forces--electromagnetism, the weak nuclear force, and the strong nuclear force--within a single mathematical framework:...

I have spent an embarrassing number of evenings over the past few years reading about the search for a unified theory of physics. I say embarrassing not because the subject is unworthy of attention--it is, by any honest accounting, the most ambitious intellectual project in human history--but because I am a computer scientist, not a physicist, and there is a persistent voice in my head that whispers I am out of my depth. I have learned to ignore that voice, mostly. The questions at stake are too important, and the connections to computation too deep, for anyone with a serious interest in the foundations of reality to sit on the sidelines. And right now, in 2026, the search for the theory of everything is in one of the most interesting and unsettled periods I can remember.

Let me start with what we know, because the Standard Model of particle physics is one of the great achievements of the human mind, and I think it deserves more admiration than it typically receives outside of physics departments. The Standard Model describes three of the four fundamental forces--electromagnetism, the weak nuclear force, and the strong nuclear force--within a single mathematical framework: quantum field theory on a flat spacetime background. It predicted the existence of the W and Z bosons before they were discovered. It predicted the top quark. It predicted the Higgs boson, which was found at CERN in 2012, completing a theoretical structure that had been under construction since the 1960s. The Standard Model has been tested to extraordinary precision--the anomalous magnetic moment of the electron, for example, agrees with theoretical prediction to ten decimal places. By any reasonable standard, it is the most successful scientific theory ever constructed.

And yet it is, with equal certainty, incomplete. The Standard Model says nothing about gravity. It does not explain dark matter, which constitutes roughly 27 percent of the energy content of the universe. It does not explain dark energy, which constitutes roughly 68 percent. It does not explain why there is more matter than antimatter. It contains roughly 19 free parameters--particle masses, coupling constants, mixing angles--whose values are not predicted by the theory but must be measured experimentally. It does not explain why there are three generations of fermions rather than one or seven. It does not explain why the cosmological constant is 120 orders of magnitude smaller than naive quantum field theory calculations would suggest--a discrepancy so staggering that it has been called the worst prediction in the history of physics.

I list these gaps not to diminish the Standard Model but to make a point that I think is underappreciated: we know, with something approaching certainty, that there is physics beyond the Standard Model. The universe is telling us, in multiple independent ways, that our current theoretical framework is a low-energy approximation to something deeper. The question is what that something is, and here is where the story becomes both fascinating and contentious.

The oldest and most developed candidate for a theory of everything is string theory. I want to be careful here, because string theory inspires passions that are unusual even by the standards of theoretical physics, and I want to represent the state of the field honestly. The core idea of string theory is beautiful: instead of point particles, the fundamental constituents of reality are tiny, one-dimensional vibrating strings. Different vibrational modes of the string correspond to different particles--one mode is a photon, another is a graviton, another is a quark. Gravity is not added to the theory by hand; it emerges naturally from the closed-string spectrum. This is remarkable and, in my opinion, under-celebrated. Every other attempt to quantize gravity requires you to force it in. String theory gives you gravity for free.

The problems with string theory are real, and they have become more acute with time. The theory requires extra spatial dimensions--six or seven beyond the three we observe--which must be “compactified,” curled up so small that we cannot detect them. The geometry of this compactification determines the physics we observe at low energies, and there are an astronomically large number of possible compactifications--the so-called “landscape” of string theory, estimated at 10^500 or more distinct vacua. Each vacuum corresponds to a different version of low-energy physics, with different particle content, coupling constants, and cosmological constants. Critics, most vocally Lee Smolin and Peter Woit, have argued that this makes string theory unfalsifiable: with 10^500 possible versions, it can accommodate essentially any experimental result, which means it predicts none of them. This is a serious criticism, and I do not think it can be dismissed with hand-waving. A theory that predicts everything predicts nothing.

And yet I find myself unable to dismiss string theory entirely, for reasons that connect to my background in computer science. The mathematical structures that string theory has generated--mirror symmetry, topological string theory, the AdS/CFT correspondence--are extraordinary. They have solved problems in pure mathematics, generated new results in quantum field theory, and provided the most concrete realization of the holographic principle, which, as I discussed in my previous essay on the information paradox, appears to be a deep truth about the relationship between information and spacetime. A theory that produces this much mathematical fruit, even if it has not yet made a testable prediction about particle physics, is telling us something important about the structure of reality. The question is whether that something is the final answer or a stepping stone.

The principal rival to string theory is loop quantum gravity, developed primarily by Lee Smolin, Carlo Rovelli, Abhay Ashtekar, and their collaborators. Loop quantum gravity takes a fundamentally different approach: instead of adding new ingredients to physics (strings, extra dimensions), it applies the principles of quantum mechanics directly to general relativity’s description of spacetime. The result is a picture in which spacetime itself is quantized--not continuous and smooth, as Einstein envisioned, but discrete at the Planck scale, composed of finite chunks of volume and area connected by a network structure called a spin foam. I find this approach aesthetically compelling for a reason that might resonate with other computer scientists: it treats spacetime as a combinatorial structure, a network with nodes and edges, rather than a smooth manifold. It is, in a loose but suggestive sense, more “computational” than the smooth geometries of string theory.

Loop quantum gravity has its own challenges. It has been difficult to show that the theory reproduces classical general relativity in the appropriate limit--that is, that the smooth spacetime we observe emerges from the underlying spin foam network at large scales. It does not naturally incorporate the other forces of nature the way string theory incorporates gravity; the Standard Model must be added to loop quantum gravity more or less by hand. And it has not generated the same wealth of mathematical connections as string theory, though Rovelli would argue, not unreasonably, that this reflects the sociology of physics funding more than the intrinsic merit of the approach.

What makes 2025 and 2026 a particularly exciting time for unification is the emergence of approaches that transcend the string-theory-versus-loop-quantum-gravity dichotomy. I want to highlight three developments that I find especially promising. The first is the amplituhedron program, pioneered by Nima Arkani-Hamed and Jaroslav Trnka. They have discovered that the scattering amplitudes of particle physics--the numbers that describe the probability of particles interacting--can be computed as volumes of a geometric object called the amplituhedron, without reference to spacetime or quantum mechanics. If this program succeeds, it will mean that spacetime and quantum mechanics are both emergent phenomena, arising from a deeper combinatorial and geometric structure. This resonates with me profoundly as a computer scientist, because it suggests that at the deepest level, physics is about structure and relation, not about particles moving through space.

The second development is the growing role of quantum information theory in fundamental physics. The connections I described in my information paradox essay--between entanglement and spacetime geometry, between quantum error correction and the holographic principle--are part of a broader movement to understand the foundations of physics in informational terms. John Wheeler’s famous aphorism “it from bit” (and its quantum update, “it from qubit”) is being taken increasingly seriously, not as a slogan but as a research program. If spacetime is emergent from entanglement, as the evidence increasingly suggests, then the theory of everything may not look like a quantum field theory at all. It may look like a theory of quantum information--a set of rules about how quantum states can be correlated, from which both spacetime and the Standard Model particles emerge as approximate descriptions valid at low energies and large scales.

The third development is more speculative but, I think, potentially revolutionary: the application of machine learning and computational methods to the landscape problem. Recent work by groups at Oxford, MIT, and the Simons Foundation has used neural networks to search the string theory landscape for vacua that match observed physics, turning what was once a hopelessly vast combinatorial problem into something potentially tractable. Separately, researchers like Stephen Wolfram have argued for a computational approach to fundamental physics based on hypergraph rewriting rules, and while this program remains controversial, it has generated genuine mathematical results and has the virtue of being explicitly computational from the ground up. Whether any of these approaches will ultimately succeed, I cannot say. But the fact that computation and information are becoming central to the search for the fundamental laws of nature feels, to me, like a sign that the boundaries between physics and computer science are dissolving in ways that will be productive for both fields.

I want to address a question that I encounter frequently when I talk about these topics with colleagues outside of physics: does any of this matter practically? I think the honest answer is that it matters in ways that are difficult to predict. When Maxwell unified electricity and magnetism in the 1860s, nobody anticipated radio, television, or the internet. When Einstein published general relativity in 1915, nobody anticipated GPS, which requires relativistic corrections to function. When quantum mechanics was developed in the 1920s, nobody anticipated transistors, lasers, or MRI machines. The pattern is clear: understanding the fundamental laws of nature leads to technological revolutions, but the specific revolutions are unforeseeable. I believe the same will be true of quantum gravity. A theory that unifies quantum mechanics and general relativity will change our understanding of information, computation, and causality in ways that will eventually have practical implications--but I cannot tell you what those implications will be, and I am skeptical of anyone who claims they can.

What I can say, with more confidence, is that the search itself is valuable. The mathematical tools developed in pursuit of unification--topological quantum field theory, conformal field theory, category theory applied to physics--are already finding applications in condensed matter physics, quantum computing, and even machine learning. The conceptual insights--holography, the connection between entanglement and geometry, the emergence of spacetime from quantum information--are reshaping how we think about computation at the most fundamental level. And the human endeavor of trying to write down the laws that govern all of reality, of reaching for that level of comprehension, is, I believe, one of the noblest things our species does.

I will offer one more personal opinion, which I hold with genuine uncertainty but which I think is worth stating: I suspect the final theory, when we find it, will be stranger than any of the current candidates. History shows that revolutions in physics do not come from choosing between existing alternatives; they come from reframing the question in a way that makes the previous debate irrelevant. Newtonian gravity versus Cartesian vortices was resolved not by choosing one but by Einstein redefining what gravity is. The wave-particle debate in quantum mechanics was resolved not by choosing one but by discovering that the question was ill-posed. I suspect that the string theory versus loop quantum gravity debate will be resolved in a similar way: by a conceptual breakthrough that recasts the problem in terms we do not yet have. And I suspect that information and computation will be central to that recasting.

We are, all of us who think about these questions, children staring at the ocean and trying to understand the tides. We have better tools than our predecessors. We have global collaborations, extraordinary experiments, and computers that can search vast theoretical landscapes. But the fundamental situation is the same as it has always been: the universe is larger than our understanding, and the gap between them is where the important questions live. I find comfort in that gap. It means there is more to discover.

There is a question that has haunted theoretical physics for half a century, and it is, at its heart, a question about information. When something falls into a black hole--a book, a star, a quantum state carrying the complete works of Shakespeare--is the information it contained destroyed forever, or is it somehow preserved? This might sound like an abstract curiosity, the kind of thing that only matters to people who write equations on blackboards. But I have come to believe, after years of thinking about it from the intersection of computer science and physics, that the black hole information paradox is one of the most important unsolved problems in all of science, because it sits exactly at the fault line where our two greatest theories of nature--quantum mechanics and general relativity--violently disagree.

My interest in this problem is not purely academic. In computation, information is treated as sacred. Shannon’s theory, the Church-Turing thesis, the entire edifice of computation rests on the assumption that information can be transformed, encoded, compressed, transmitted, and decoded--but never truly destroyed. Quantum mechanics agrees:...

There is a question that has haunted theoretical physics for half a century, and it is, at its heart, a question about information. When something falls into a black hole--a book, a star, a quantum state carrying the complete works of Shakespeare--is the information it contained destroyed forever, or is it somehow preserved? This might sound like an abstract curiosity, the kind of thing that only matters to people who write equations on blackboards. But I have come to believe, after years of thinking about it from the intersection of computer science and physics, that the black hole information paradox is one of the most important unsolved problems in all of science, because it sits exactly at the fault line where our two greatest theories of nature--quantum mechanics and general relativity--violently disagree.

My interest in this problem is not purely academic. In computation, information is treated as sacred. Shannon’s theory, the Church-Turing thesis, the entire edifice of computation rests on the assumption that information can be transformed, encoded, compressed, transmitted, and decoded--but never truly destroyed. Quantum mechanics agrees: the evolution of quantum states is unitary, meaning it is reversible. If you know the final state of a system with perfect precision, you can, in principle, reconstruct the initial state. Information is conserved. This is not a convenience or an approximation. It is a law, baked into the mathematical structure of quantum theory at the deepest level.

And then along comes general relativity, Einstein’s theory of gravity, which tells us that when enough mass is concentrated in a small enough region, spacetime itself curves so severely that nothing--not light, not information, not causality--can escape. The boundary of this region is the event horizon, and once you cross it, your future points only inward, toward the singularity at the center. Classical general relativity is unambiguous about this: whatever falls in is gone. The information is not hidden or scrambled. It is excised from the accessible universe, as cleanly as if you had torn a page from the book of nature.

For decades, these two facts coexisted in an uneasy truce, because no one could observe what happened at an event horizon in any meaningful way. The truce was broken in 1975 by Stephen Hawking, who showed, in one of the most beautiful calculations in twentieth-century physics, that black holes are not perfectly black. Quantum effects near the event horizon cause them to radiate particles--Hawking radiation--and this radiation carries energy away from the black hole, causing it to slowly shrink. Given enough time (an almost inconceivably long time for stellar-mass black holes, but a finite time nonetheless), the black hole will evaporate completely. And here is the crisis: Hawking’s calculation showed that this radiation is thermal. It is random. It carries no information about what fell into the black hole. So when the black hole finally evaporates, the information that went in has apparently been destroyed. A pure quantum state has become a mixed thermal state. Unitarity has been violated. The book has been burned, and not even the ashes remain.

I remember the first time I fully grasped the severity of this problem and feeling something close to vertigo. It is not that two theories give different answers to the same question--that happens all the time in physics, and usually it just means one theory is a better approximation than the other. The information paradox is worse than that. It is a situation where both theories seem to be operating within their domains of validity, and yet they produce a logical contradiction. Quantum mechanics says information cannot be destroyed. General relativity says it is. They cannot both be right, and the resolution must lie in a theory that supersedes both--a theory of quantum gravity that we do not yet possess.

The past several years have brought extraordinary progress, and I want to walk through the key developments because they are genuinely beautiful and because I think they have been poorly communicated outside the specialist community. The most important advance is the discovery of the “island formula” for computing the entropy of Hawking radiation, building on work by Ahmed Almheiri, Netta Engelhardt, Donald Marolf, Henry Maxfield, and Geoff Penington, among others. Their insight, published in a series of papers from 2019 to 2021, is that the standard calculation of Hawking radiation entropy overlooks a crucial gravitational contribution. When you properly account for the geometry of spacetime--specifically, when you include “quantum extremal surfaces” and the regions behind them (the “islands”)--the entropy of the radiation follows a curve called the Page curve, named after Don Page, who predicted it in 1993. The Page curve shows the entropy rising during the first half of evaporation, then falling during the second half, returning to zero when the black hole has fully evaporated. This is exactly what unitarity demands. Information is not lost; it is encoded in subtle correlations in the Hawking radiation, correlations that only become apparent when the black hole is more than half evaporated.

I want to explain why this matters from a computer science perspective, because I think the computational framing is illuminating. Consider the Hawking radiation as a data stream being emitted by a black hole. In Hawking’s original calculation, each bit of this stream is independent--it is thermal noise, carrying no information about the source. But the island formula tells us that this is an illusion created by ignoring gravitational effects. The actual data stream is more like an error-correcting code: the information is present, but it is distributed across the entire stream in a way that requires you to collect a majority of the bits before you can begin decoding. This is strikingly similar to how quantum error-correcting codes work in quantum computing, and the connection is not a coincidence. Several research groups, including those led by Patrick Hayden and John Preskill, have shown that the interior of a black hole can be described as a quantum error-correcting code, where the logical qubits (the information about what fell in) are encoded in the physical qubits (the degrees of freedom of the horizon and the radiation).

This brings me to the holographic principle, which I consider one of the most profound ideas in all of physics. First proposed by Gerard ’t Hooft and Leonard Susskind in the 1990s, and given mathematical precision by Juan Maldacena’s AdS/CFT correspondence in 1997, the holographic principle states that all the information contained within a volume of space can be described by a theory that lives on the boundary of that region. The maximum information content is proportional not to the volume, but to the surface area, measured in Planck units. This is deeply counterintuitive. It means that the three-dimensional world we perceive may be, in a precise mathematical sense, a projection from a two-dimensional boundary. And the black hole information paradox was the clue that led us here, because the Bekenstein-Hawking entropy of a black hole--proportional to the area of the event horizon, not the volume of the interior--was the first hint that information and geometry are related in a way that no one anticipated.

What excites me most about the current state of the field, in early 2026, is that these ideas are beginning to converge. The island formula, the holographic principle, quantum error correction, and tensor network models of spacetime are all pointing toward the same conclusion: spacetime itself may be an emergent phenomenon, woven from quantum entanglement. This idea, often associated with Mark Van Raamsdonk’s influential 2010 essay “Building up spacetime with quantum entanglement,” and given mathematical structure by the ER=EPR conjecture of Maldacena and Susskind, suggests that the smooth fabric of spacetime we experience is not fundamental. It is something that arises from a deeper quantum mechanical substrate, the way temperature arises from the motion of atoms. Two regions of space are connected--spatially close--because the quantum degrees of freedom that describe them are highly entangled. A wormhole (an Einstein-Rosen bridge) connecting two distant black holes is, in this framework, nothing more than the geometric manifestation of quantum entanglement (an Einstein-Podolsky-Rosen pair) between their microscopic constituents.

I find this breathtaking, and I want to be honest about the fact that it also makes me feel a particular kind of humility. I spend my days building software systems--systems that manage state, process information, handle concurrency and failure. These systems operate within spacetime, using the causal structure of the universe as an unquestioned backdrop. But if the emerging picture is correct, that backdrop is itself a kind of information processing. The universe is not a computer in any naive sense, but the relationship between information, computation, and physical reality appears to be far more intimate than anyone imagined when Shannon wrote his seminal paper in 1948 or when Turing defined his machine in 1936.

There are genuine reasons for caution, and I want to address them because intellectual honesty demands it. The island formula has been derived rigorously only in low-dimensional toy models--specifically, in two-dimensional gravity coupled to conformal field theory (the so-called JT gravity models). Extending these results to the realistic four-dimensional case remains an open problem. The holographic principle, as embodied in AdS/CFT, applies to spacetimes with a negative cosmological constant (anti-de Sitter space), but our universe has a positive cosmological constant (de Sitter space), and the extension of holography to de Sitter space is one of the most important unsolved problems in theoretical physics. There are researchers, such as Sabine Hossenfelder, who have argued compellingly that the field has been too hasty in declaring the information paradox resolved, and that the Page curve derivation, while beautiful, rests on assumptions about the UV completion of gravity that have not been independently verified.

I respect these criticisms, and I think they are healthy. Science advances by the interplay between bold conjecture and rigorous skepticism. But I will state my own assessment: I believe the weight of evidence, accumulated over the past five years, strongly favors the conclusion that information is preserved in black hole evaporation, and that the mechanism of preservation involves a deep connection between quantum entanglement and the geometry of spacetime. I believe we are living through one of those rare periods in physics where foundational questions that seemed permanently beyond our reach are suddenly yielding to new mathematical tools and new physical insights. The information paradox is not yet fully resolved, but the shape of the resolution is, I think, becoming visible.

What does this mean for a computer scientist? More than you might think. If spacetime is emergent from quantum information, then the ultimate limits of computation are not set by the speed of light or the density of transistors, but by the informational structure of spacetime itself. Seth Lloyd’s work on the computational capacity of the universe, and the Bousso bound on information in a gravitational setting, suggest that there are fundamental limits to how much information can be processed in a given region of spacetime, limits that go beyond anything in classical computer science. The holographic bound tells us that the information capacity of a region scales with its surface area, not its volume. This means that the three-dimensional computers we build are, in some deep sense, fundamentally two-dimensional in their information content. I do not know what practical implications this has for computer architecture in 2026, but I suspect that in fifty years, we will look back and see it as one of the most important insights of our era.

I want to close with a personal reflection. The information paradox has been called a problem about black holes, but I think it is really a problem about us--about the limits of human knowledge, about the adequacy of our theories, about whether the universe is fundamentally comprehensible. When Hawking first posed the paradox, he was not just asking about what happens at an event horizon. He was asking whether the laws of physics, as we understand them, are self-consistent. The fact that this question has driven fifty years of profound discovery--from Hawking radiation to the holographic principle to the ER=EPR conjecture--suggests to me that the deepest truths in physics are found not by looking away from contradictions, but by staring into them until they yield.

As a computer scientist who wanders into physics with more curiosity than credentials, I find in the information paradox a reminder that the questions worth asking are the ones that make you feel small. The universe does not owe us comprehensibility. But so far, remarkably, it has provided it--not easily, not quickly, but generously, for those willing to follow the questions wherever they lead.

I have a confession that might sound strange coming from someone who spends her days thinking about product roadmaps and software architecture: I am utterly, hopelessly fascinated by quantum entanglement. Not in the pop-science, “spooky action at a distance” way that gets tossed around at dinner parties, but in the way that makes me stay up until 2 a.m. reading papers from the Pan Jian-Wei group, scribbling notes in the margins, and feeling a kind of vertigo that I can only describe as intellectual awe. The past eighteen months have been extraordinary for the field, and I think the implications for computing and communication are far more profound--and far more nuanced--than most of the headlines suggest.

Let me start with what actually happened. In late 2025, multiple research groups independently demonstrated entanglement distribution over metropolitan-scale fiber networks with fidelity exceeding 97 percent. The University of Science and Technology of China extended their Micius satellite framework to achieve...

I have a confession that might sound strange coming from someone who spends her days thinking about product roadmaps and software architecture: I am utterly, hopelessly fascinated by quantum entanglement. Not in the pop-science, “spooky action at a distance” way that gets tossed around at dinner parties, but in the way that makes me stay up until 2 a.m. reading papers from the Pan Jian-Wei group, scribbling notes in the margins, and feeling a kind of vertigo that I can only describe as intellectual awe. The past eighteen months have been extraordinary for the field, and I think the implications for computing and communication are far more profound--and far more nuanced--than most of the headlines suggest.

Let me start with what actually happened. In late 2025, multiple research groups independently demonstrated entanglement distribution over metropolitan-scale fiber networks with fidelity exceeding 97 percent. The University of Science and Technology of China extended their Micius satellite framework to achieve entanglement-based quantum key distribution across three continents simultaneously. Meanwhile, Harvard and MIT’s joint quantum network initiative showed that quantum repeaters based on silicon-vacancy centers in diamond could maintain entangled states across 120 kilometers of standard telecom fiber with error rates low enough to be practically correctable. These are not incremental improvements. These are the kinds of results that shift the conversation from “if” to “when.”

But here is where I want to slow down, because I think the computer science perspective is essential and often missing from these discussions. From a computational perspective, entanglement is not just a physical phenomenon but an information primitive. And that reframing changes everything.

Classical information theory, the kind Shannon gave us in 1948, deals in bits--zeros and ones, independent and memoryless. Quantum information theory introduces the qubit, which exists in superposition, but the truly revolutionary concept is the entangled pair: two qubits whose states are correlated in ways that have no classical analogue. An analogy that I think captures the essential weirdness: Imagine you and a friend each have a coin. You flip your coins in separate rooms, maybe in separate countries. Classically, the outcomes are independent. But if your coins were entangled, every time you got heads, your friend would get tails--instantly, always, regardless of distance. Not because information traveled between the coins, but because the coins were never truly separate to begin with. They were, from the moment of their creation, a single system described by a single mathematical object.

Now, I want to be precise here, because this is where popular accounts go wrong. Entanglement does not allow faster-than-light communication. You cannot use it to send a message, because the outcome of your local measurement looks completely random; it is only when you compare results with your distant partner that the correlation is revealed, and that comparison requires a classical channel. What entanglement gives you is something subtler and, I would argue, more profound: it gives you shared randomness that is provably secure, correlations that are stronger than anything classical physics permits, and a resource that can be consumed to perform computational and communication tasks that are otherwise impossible.

This is what the 2025–2026 breakthroughs are really about. When we can reliably distribute entangled pairs over long distances, we unlock quantum key distribution that is secure not because of computational assumptions (like RSA depending on the hardness of factoring) but because of the laws of physics themselves. An eavesdropper cannot intercept an entangled photon without disturbing the entanglement in a detectable way. This is not a matter of having a bigger key or a better algorithm. It is a fundamentally different security guarantee, and in a world where quantum computers will eventually break most classical encryption, this matters enormously.

But I think the implications for computing are even more exciting, and here is where I will offer a personal opinion that some of my peers may find premature: I believe we are within a decade of seeing entanglement-assisted protocols change how distributed systems are architected. Consider the problem of consensus in distributed computing--the challenge that underlies everything from blockchain to replicated databases. Classical consensus protocols are bounded by fundamental limits: the FLP impossibility result tells us that deterministic consensus is impossible in asynchronous systems with even a single faulty process. Quantum entanglement does not violate FLP, but it provides a resource--pre-shared entanglement--that enables new kinds of protocols. Quantum leader election, for example, can be made fair in ways that are provably impossible classically. Quantum Byzantine agreement can tolerate a higher fraction of faulty participants.

I think about this a lot in the context of large-scale distributed systems. Modern infrastructure systems--from cloud computing clusters to autonomous robotics--are, at their core, distributed systems that must reach consensus about the state of the world under extreme time pressure and with unreliable components. Today we handle this with classical sensor fusion and redundancy. But as machine-to-machine communication becomes essential for truly intelligent systems, the security and integrity of those communication channels becomes existential. Quantum-secured channels are not a near-term solution for most real-world applications, I want to be clear about that. But the theoretical framework being built now will shape the architectures we design in the next decade.

There is also something philosophically stirring about entanglement that I cannot help but mention, even in what is ostensibly a technical essay. Entanglement tells us that the universe is, at its most fundamental level, nonlocal. The correlations between entangled particles are not carried by any signal; they are woven into the fabric of reality itself. John Bell proved in 1964 that no theory based on local hidden variables can reproduce the predictions of quantum mechanics, and every experiment since--culminating in the loophole-free tests that won the 2022 Nobel Prize--has confirmed this. When I sit with that fact, really sit with it, it changes how I think about connection, about information, about what it means for two things to be related.

In computer science, information moves through channels, gets encoded and decoded, suffers noise and loss. Entanglement suggests that some information does not move at all. It simply is, distributed across space, waiting to be revealed by measurement. That is not just a technical curiosity. It is, I believe, a clue about the deep structure of reality, and one that we are only beginning to understand.

The road ahead is still long. Quantum repeaters need to become more reliable. Error correction overhead is still enormous. We do not yet have a quantum internet in any meaningful sense. But the trajectory from the past two years tells me we are on the steep part of the curve. And for those of us who build systems for a living, the time to start thinking about quantum-native architectures is not when the hardware arrives. It is now.

I will keep reading those papers at 2 a.m. I suspect the best is yet to come.