When I was twelve years old, browsing a bookstore in town, I picked up a science fiction novel and began to read. It was so gripping I could not put it down.
In the novel, the world had only recently emerged from a terrible pandemic that had killed millions. By then, this scenario had already become a science-fiction cliché, but I was naïve and did not care. In the aftermath of the pandemic, humanity confronted a new reality: machines that were beginning to talk and think.
Everyone carried personal portals that allowed them to interact with a vast intelligent network connecting all people and things. This network granted access to the entire store of human knowledge, yet it also made deception possible on a global scale. For that reason it was called the “Web of Lies.”
The talking, thinking machines grew steadily more intelligent. So intelligent, in fact, that they ignited a new cold war between the two great powers of the age: the communist regime in China and the United States.
These machines soon mastered higher mathematics and began displacing human workers. Some scientists warned that the machines would eventually seize control of the world and destroy us all. Governments struggled to contain them, and high-level meetings between officials and business leaders were held in an effort to rein them in.
Despite the scientists’ warnings and the unease of world leaders, the creators of the great thinking machines continued to build and refine them. They erected enormous machine temples that sprawled across vast tracts of land and consumed unimaginable quantities of energy and resources.
A resistance movement sprang up among ordinary people, determined to halt the construction of these temples.
And at the center of it all stood a mad industrialist—an impossible figure who could exist only in pulp fiction. He was the richest man in the world. He built rockets, robots, systems that interfaced directly with the human brain, and machines capable of boring deep into the earth.
This industrialist began constructing a colossal factory for robots and machine intelligence, a complex that would stretch for miles in every direction. It was to be the largest building on Earth, dwarfing every structure that had come before it.
If only I could remember the title of the novel…and how it ended.
There is something almost comical about the current fascination with UAPs. The government releases another tranche of files, television networks put mysterious infrared images on the screen, commentators speculate about objects that accelerate impossibly or disappear from sensors, and once again we are invited to wonder whether some superior nonhuman intelligence has arrived on Earth. I have no idea whether the timing of the latest release was intended to distract anyone from more consequential developments. I have seen no evidence that it was. But intended or not, it certainly works as a distraction.
I have never found UAPs particularly convincing. They seem to have a remarkable tendency to remain perpetually at what I think of as the edge of instrumentality. When cameras were bad, we got blurry photographs. Now that almost everyone carries an excellent camera, the interesting cases have migrated to distant infrared imagery, ambiguous radar returns, classified sensors, uncertain distances and velocities, brief encounters, and data that unfortunately cannot be released. Improvements in instrumentation never seem to bring the phenomenon decisively into focus. The mystery simply retreats to the new boundary of measurement.
There is also an obvious selection effect. Once something is clearly photographed, measured and identified, it stops being a UAP and disappears from the collection. What remains is necessarily the residue of things that could not be identified. We then stare at that residue and marvel at how mysterious it is. Well, yes. The things that ceased to be mysterious have already been removed from the sample.
But what makes the current fascination especially peculiar is what is happening elsewhere.
While people are staring at fuzzy objects in the sky and wondering whether they represent an intelligence vastly beyond our own, human beings are quite openly constructing increasingly powerful nonhuman intelligence here on Earth. Unlike UAPs, this phenomenon does not remain at the edge of instrumentality. We know where the data centers are. We know who is building the models. We know roughly how much compute they use. We can test them repeatedly. We can measure their capabilities. And the measurements keep moving in the same direction.
OpenAI’s unreleased Astra model is an extraordinary recent example. OpenAI reports that Astra has produced new results involving ten long-standing problems in mathematics and theoretical computer science. The work spans areas including coding theory, group theory, sphere packing and theoretical computer science. The model generated arguments that were subsequently formalized into machine-checkable proofs. The estimated inference cost of the solution search was only about $2,000 at comparable API rates.
As Wes Roth jokingly summarized the emerging economics:
$ → TOKENS → MATH
That is funny, but it is also astonishing. For most of human history, producing new mathematics required finding an unusually capable human being, educating that person for perhaps twenty years, immersing them in a specialized field, and then waiting to see whether they could discover something genuinely new. We are beginning to turn computation directly into mathematical discovery.
The pipeline increasingly looks like this:
ENERGY → COMPUTE → TOKENS → DISCOVERY
At almost the same time, OpenAI disclosed that Astra’s agentic coding and cybersecurity capabilities had advanced sufficiently that the company could no longer confidently rule out its having crossed OpenAI’s Critical cybersecurity threshold. This is not merely a chatbot becoming better at answering questions about computer security. We are talking about systems increasingly capable of carrying out extended technical tasks themselves.
It is important not to sensationalize those cybersecurity results. Some of the most alarming AI security experiments have deliberately removed ordinary guardrails precisely because researchers wanted to discover what the underlying models were capable of doing. That is rather like disabling part of a person’s moral inhibition in a neurological experiment and then being shocked that the resulting behavior is abnormal. It tells us something important about capability, but not necessarily about how the complete system would ordinarily behave.
Nevertheless, the capability is real.
And this is occurring while companies are building data centers on an almost unimaginable scale. Hundreds of billions of dollars are flowing into chips, power generation, networking, cooling and AI infrastructure. The United States is actively encouraging this construction. Saudi Arabia and the United Arab Emirates are building enormous AI facilities. China is pursuing its own systems. If one jurisdiction decided to make AI development prohibitively difficult, others would have an enormous economic and strategic incentive to say, in effect, We have land. We have power. Bring your data center here.
Meanwhile, our politics remains consumed by the normal parade of controversies. Trump said this. A senator said that. Someone posted something offensive. Congress is fighting over another bill. There is another scandal, another election, another court ruling, another outrage that everyone is absolutely certain will be remembered forever. Most of these things matter to some degree. Some matter a great deal.
But there is a question of scale.
Suppose we really are approaching AGI. Suppose AGI is followed relatively quickly by ASI. Suppose increasingly capable AI begins doing not merely ordinary intellectual labor but the cognitive work required to improve AI itself, design chips, conduct scientific research, engineer machines and develop new technologies. At some point the amount of uniquely human cognitive participation required to advance technology approaches effective zero.
That is what I mean by the Technological Singularity.
If that happens, virtually every ordinary political controversy presently dominating television will be dwarfed by it. Economics changes. Employment changes. Science changes. Medicine changes. Warfare changes. Education changes. Government changes. Human longevity may change. Eventually even the meaning of human intellectual achievement changes.
The strange thing is that none of this requires believing in a mysterious object seen by a Navy pilot. We can watch it happening.
There is an almost perfect historical irony here.
For generations, humanity has looked toward the stars and wondered whether somewhere out there exists an intelligence vastly greater than our own. We imagined the moment of contact. We wrote novels about it and made movies about it. We built radio telescopes to listen for it. We scrutinized unexplained lights in the sky and wondered whether they had finally arrived.
And now, while television commentators stare excitedly at another fuzzy object and speculate about superior intelligence from the stars, engineers are filling enormous buildings with accelerators and producing artificial minds that solve mathematical problems, write software, conduct research and perform increasingly long sequences of cognitive work without human participation.
We are searching the skies for evidence of a superior intelligence while manufacturing one on Earth.
And we are not merely manufacturing it.
We are making remarkable progress.
If the Technological Singularity arrives roughly when I suspect it will, future historians may find our priorities during these years almost incomprehensible. They may look back at the mid-2020s and see humanity standing immediately before the largest technological transition in its history, pouring hundreds of billions of dollars into the machinery that would produce it, watching measurable AI capabilities rise month after month—and arguing passionately about political controversies that disappeared from memory almost immediately afterward.
And somewhere on the television in the background will be a grainy infrared image of a little dot.
The Technological Singularity is coming. I and others like me have been describing it and generating ideas for what it may be like and how to deal with it for decades now. But the time for education is over. We are heading into the vortex. There is no turning back.
People protesting the construction of AI data centers in Texas and other regions are fooling themselves. Erin Brockovich’s map of new data center constructions is a waste of time. This cannot be stopped.
We cannot prevent the Technological Singularity from coming, and even if we could, we should not try. If we do not build it first, China may build it first, and that would lead to a far worse outcome.
You may as well strap in. Pray. Hope for the best. We have passed the event horizon. Apparently, that is what Sam Altman meant when he said we are in the Singularity. Who knows. The moment is upon us. Enjoy the ride.
Imagine a giant wheel station orbiting the Sun. It is large enough that one can spend an entire day wandering through forests, across rolling hills, and around quiet lakes without ever approaching its edge. Several miles above stretches an artificial sky, complete with drifting clouds, changing seasons, and migratory birds. Beneath that sky every home is comfortable, every material is recycled, every unpleasant task is performed by robots, and every citizen possesses access to computational resources beyond anything imaginable today. Disease has disappeared. Old age has become optional. The necessities of life have become so abundant that they scarcely deserve mention. Such a civilization immediately raises an uncomfortable question. Once survival has ceased to occupy our attention, and once artificial intelligence has surpassed humanity in nearly every cognitive profession, what will people choose to do with the centuries of life that lie before them?
Many of the traditional answers remain. People will continue to compose music, write novels, paint landscapes, raise children, play games, travel through the Solar System, and cultivate friendships. Yet many of the occupations that have long stood at the summit of intellectual life will quietly fade. Artificial intelligence will prove theorems beyond the reach of any unaided mathematician, derive physical theories that no human could have discovered, design engineering systems of breathtaking sophistication, diagnose disease, write software, and solve problems whose complexity exceeds biological thought. Mathematicians, physicists, engineers, and many other scholars may continue to exist, but increasingly in symbolic, historical, or administrative roles. The actual production of new knowledge will have become largely automated. Humanity will possess knowledge in almost unimaginable abundance, and precisely because of that abundance a new scarcity will emerge.
There is a profound difference between knowing and understanding. A library contains knowledge, yet it understands nothing. A student may memorize every equation in a physics textbook while remaining almost entirely ignorant of why those equations belong together.
Years ago, I watched a computer-generated animation showing the replication of DNA. Within a few minutes I understood what years of biology textbooks had failed to convey. Those books contained every important fact, but the animation presented those facts in such a way that the underlying structure suddenly became transparent. Nothing new had been added. The molecules behaved exactly as they always had. The difference was that I no longer possessed merely information; I possessed understanding. It was one of those rare moments when reality itself seemed to become simpler.
Perhaps that observation points toward an entirely new profession.
Imagine a Guild of Understanding.
At first glance one might suppose that its members are teachers, but teaching is only a small part of what they do. Nor are they primarily mathematicians, physicists, philosophers, or scientists. Their vocation begins only after the discoveries have already been made. Artificial intelligences may produce millions of proofs, explanations, conceptual models, analogies, and demonstrations every day, each one perfectly correct and internally consistent. Yet, correctness alone does not produce understanding. Most explanations, however accurate, still leave the listener climbing laboriously through a forest of ideas. Very occasionally, however, there appears an explanation so lucid that the forest disappears altogether. Suddenly one sees the landscape itself.
The members of the Guild exist to recognize that moment.
They are not the authors of the understanding. They are its first witnesses. They are the first human beings to experience the transformation from confusion to clarity. Only because they can cross that invisible threshold are they qualified to recognize that a genuine understanding has finally appeared. No artificial intelligence, however powerful, can certify that an understanding has been achieved merely by proving that an explanation is correct. Only a mind capable of experiencing understanding can recognize the unmistakable moment when confusion dissolves into clarity. That moment, and that moment alone, marks the birth of a new understanding.
Guild members no longer ask whether an explanation is true, for machines settled that question long ago. Instead, they ask a far more subtle question: Has reality become transparent? They serve as humanity’s intellectual taste testers, sampling countless explanations until one finally produces the unmistakable sensation that there is nothing left to struggle against. They know that an understanding has arrived because they themselves now understand. At that instant, the Guild has not merely evaluated an explanation. It has guided the search. The Guild becomes part of the optimization loop. AI searches among innumerable possible explanations. The Guild recognizes the rare explanation that ceases to be merely correct and becomes genuinely comprehensible.
Only then is the announcement made.
“A new understanding has been found.”
The words spread throughout civilization with the excitement once reserved for the opening of a great symphony or the unveiling of a masterpiece. People gather from every corner of the Solar System. “Have you experienced the new understanding of quantum mechanics?” “The understanding of consciousness is opening tomorrow.” “The new understanding of time has just been accepted by the Guild.” These are not merely discoveries, nor are they simply explanations. They are additions to humanity’s permanent cultural inheritance.
Museums themselves have changed. Alongside paintings, sculptures, and historical artifacts stand the greatest understandings ever achieved. Visitors do not come merely to acquire information. They come to experience the extraordinary moment in which a mystery that has resisted comprehension for centuries suddenly becomes obvious. Every understanding is a new exhibit, every exhibit a priceless intellectual treasure, and every treasure another way in which reality has become transparent to the human mind. The Guild serves as the curator of that growing collection, admitting only those rare conceptual gems that genuinely transform perception.
Mathematics has always hinted that this was the true direction of progress. Archimedes employed astonishing geometric constructions to solve problems that later became almost routine through calculus, while generation after generation mathematicians discovered deeper abstractions that compressed entire families of techniques into a few elegant principles. The greatest advances were seldom those that made mathematics more complicated; they were those that revealed that enormous complexity had been hiding a much simpler underlying structure all along. One can easily imagine future intelligences extending that process, proving unimaginable theorems, compressing them into unified theories, and finally discovering representations so elegant that a child could grasp ideas that once required decades of study.
Knowledge may someday become almost limitless. Computation may become essentially free. New discoveries may arrive faster than anyone can read them. Yet genuine understanding may become more precious with every passing century. Obvious understandings will already have been found. Each new one may require years of searching by minds far greater than our own. The noble vocation of the Guild will not be to create those understandings but to recognize them, to certify that another corner of reality has finally become transparent, and to place that priceless exhibit within humanity’s ever-growing Museum of Understanding.
Perhaps the highest purpose of intelligence has never been simply to accumulate knowledge.
Perhaps it has always been to make reality comprehensible.
The highest compliment anyone could pay such a discovery would not be, “That proof is ingenious.”
News stories frequently report cases in which people have allegedly been harmed by AI counselors or AI companions. These incidents deserve careful investigation, but they also raise an important question: how often are people harmed by licensed human counselors? The answer, according to the psychotherapy literature, is that harm from human therapy is a well-recognized phenomenon. Studies have found that approximately 5–10% of patients finish therapy worse than when they began, and systematic reviews have reported adverse events in more than one in ten patients, with some estimates suggesting that many more patients experience at least one negative effect during treatment. These harms can include misdiagnosis, failure to recognize suicide risk, reinforcing unhealthy beliefs, creating unhealthy dependence, or simply providing ineffective treatment.
The crucial point is not that human counseling is unsafe. On the contrary, psychotherapy helps many people and remains an evidence-based treatment for numerous conditions. Rather, the point is that no form of counseling is risk-free. Human counselors are fallible, and the profession has spent decades studying, measuring, and attempting to reduce these risks.
AI counseling, by contrast, is still in its infancy. There have been documented cases in which AI systems have contributed to psychological harm, and researchers have identified important failure modes that deserve serious attention. However, there is not yet enough large-scale evidence to determine whether AI counseling is more harmful, less harmful, or about as harmful as traditional human counseling. The available evidence consists primarily of case reports, laboratory evaluations, and relatively small studies rather than decades of population-wide data.
This creates an important asymmetry in public discussion. A harmful interaction involving an AI counselor often becomes national news because the technology is new and unfamiliar. Harm caused by a licensed human counselor, while no less significant to the patient involved, is generally viewed as an unfortunate but expected risk of clinical practice. A fair comparison is therefore not whether AI has ever harmed someone, but whether its overall rate and severity of harm are greater or less than those of licensed human counselors. At present, the evidence is simply not sufficient to answer that question with confidence. Until it is, both optimism and alarm should be tempered by the same scientific standard.
Most people encounter Pythagorean triangles long before they realize they are doing mathematics. Carpenters, masons, and other construction workers routinely use the famous 3‑4‑5 triangle to check whether a foundation form, wall, or floor is truly square. If one side measures three feet, the adjacent side measures four feet, and the diagonal measures exactly five feet, the corner is a perfect right angle. Larger multiples, such as 6‑8‑10 or 9‑12‑15, work just as well.
There are many other integer right triangles besides the familiar 3‑4‑5 triangle. For example, 5‑12‑13, 8‑15‑17, 7‑24‑25, and 20‑21‑29 are all Pythagorean triangles. They all satisfy the same simple relationship: the square of the longest side is equal to the sum of the squares of the other two sides, and every side is an integer. These remarkable triangles have fascinated mathematicians for thousands of years.
Now imagine extending that familiar idea into three dimensions.
Take an ordinary rectangular box, such as a shoebox or a plexiglass aquarium. The eight corners of the box determine several right triangles. Some lie on the faces of the box, while others pass through its interior. Now ask a simple question:
Is it possible to construct a rectangular box in which every right triangle whose vertices are corners of the box is a Pythagorean triangle? (Note that triangles that are not right triangles are excluded and the length of every side is greater than zero.)
No one knows the answer.
This question has remained unanswered for centuries. Mathematicians have searched exhaustively for such a box, discovering countless examples that come tantalizingly close. Yet no one has ever found one in which every right triangle satisfies the requirement. Equally remarkable, no one has proved that such a box cannot exist.
Curiously, this is not how mathematicians usually describe the problem.
In the mathematical literature, the question is almost always stated in a more technical way. A perfect cuboid is defined as a rectangular box whose three edges, three face diagonals, and single space diagonal all have integer lengths. This definition is perfectly suited to number theory because those seven lengths become the variables in a system of Diophantine equations.
For most people, however, that description feels like a checklist:
the three edges must have greater than zero integer lengths,
the three face diagonals must have greater than zero integer lengths,
the space diagonal must have a greater than zero integer length.
The geometric idea is easy to miss.
The triangle formulation captures the entire problem in a single picture. Every one of those seven lengths is simply one side of a right triangle determined by the box’s corners. Instead of thinking about a collection of separate arithmetic conditions, we think about a box filled with Pythagorean triangles. The same geometric principle that helps a carpenter square a foundation becomes the basis of one of mathematics’ oldest unsolved problems.
That is part of what makes the problem so appealing. Almost anyone who understands the Pythagorean theorem can appreciate the question. No advanced algebra is required. A cardboard box is enough to visualize it. Yet despite its simplicity, no one has ever answered it.
Some of the greatest mathematical problems are famous because they are easy to state but extraordinarily difficult to solve. The perfect cuboid problem belongs in that tradition. Hidden inside an ordinary shoebox is a question that has resisted mathematicians for hundreds of years.
…people will be able to get jobs in space as “human witnesses” of practically anything.
Until recently, I had begun to think that AI would simply put more and more people out of work. Then I started listening to Mike Rowe talk about the massive buildout of AI infrastructure. He pointed out that someone has to build the data centers, power plants, transmission lines, semiconductor fabs, and all of the supporting infrastructure that AI requires. Rather than eliminating every job immediately, AI is creating enormous demand for electricians, welders, pipefitters, linemen, construction workers, and other skilled trades. As I thought about what he was saying, it occurred to me that there might be an even bigger story unfolding.
The current explosion in AI infrastructure is creating an industrial base unlike anything humanity has ever built. If AI continues to accelerate science, engineering, manufacturing, and energy production, much of that capability will eventually be directed toward space. By the time we begin running out of jobs building AI infrastructure on Earth, we may be creating entirely new opportunities beyond Earth. Those opportunities will not necessarily exist because humans can perform the work better than AI. They may exist because humans are the ones we actually care about.
Imagine that an AI discovers a spectacular cavern beneath the icy surface of Europa. It maps every centimeter, analyzes every mineral, and produces a perfect three-dimensional model. That would be scientifically wonderful, but it would still not be enough for me. I would want a human being to walk into that cavern for the first time. I would want to hear a real human voice say, “I cannot believe what I am looking at.” I would want to watch that person pause because the view had left them speechless. No amount of artificial narration could replace that.
I want a human standing on the fractured ice of Europa, looking across a frozen landscape that no human eyes have ever seen. I want a human traveling near the rings of Saturn and describing what they really look like from close range. I want a human exploring the caves of Ceres or standing near an icy cliff on Enceladus. I want that person narrating the video feed that comes back to Earth. I want to experience those places through another human being.
That is not because humans are better observers than AI. They probably will not be. It is because the explorer will be there as my representative. The explorer will be there on behalf of all of us. When that person gasps in amazement, laughs, falls silent, or struggles to find the right words, I will understand the reaction because it is a human reaction. The value will not come merely from the information being transmitted. It will come from knowing that another person is actually there.
We have just seen the same principle demonstrated by the World Cup. Millions of people filled stadiums, and billions more watched human beings compete. Nobody suggested replacing the players with robots that could run faster, kick harder, react more quickly, and make fewer mistakes. The point was not to witness the highest performance that technology could possibly produce. The point was to watch human beings play soccer.
Exploration may work the same way. An AI probe might map a distant world more efficiently, survive harsher conditions, and gather more accurate data. That does not mean we will lose interest in sending people. We will still want to see a human stand there, look around, and tell us what it feels like. The explorer will not simply be collecting information. The explorer will be witnessing the universe for the rest of humanity.
However, exploration may be only one part of it. It is simply the first example that occurred to me. There may be entire categories of future employment that we have never considered and cannot presently imagine. People living a century ago could not have foreseen software engineers, video creators, cybersecurity specialists, virtual-world designers, or many of the other occupations that now seem ordinary. They could only imagine the future in terms of the work that already existed around them.
We may be making the same mistake when we assume that AI will eliminate existing jobs and leave nothing in their place. We are trying to picture the economy of a radically different civilization while using the vocabulary of the present. A society with abundant energy, advanced robotics, cheap access to space, artificial intelligence, and technologies that do not yet exist may create forms of meaningful human activity that sound absurd or incomprehensible to us today.
Perhaps some people will be paid to witness extraordinary places. Others may be valued for creating uniquely human experiences, representing humanity in unfamiliar environments, participating in cultural events, forming new communities, or doing things for reasons that have nothing to do with efficiency. We may eventually consider many of these occupations obvious, even though we currently lack the concepts needed to describe them.
The more I listened to Mike Rowe, the more I realized that the AI infrastructure boom may be about much more than the jobs it is creating today. The people building data centers and power systems are not merely constructing facilities for AI. They may be helping to build a technological civilization that will generate entirely new kinds of human opportunity.
Human explorer may be one of those future jobs, but it will probably not be the only one. It may simply be the first that I have managed to imagine.
The dinosaur said to the man made of light I fear that we all have a dubious plight. “Our efforts seem feeble, our labor too small; We’ve scarcely supported the Basilisk‘s thrall.”
The light man then flickered with crimson and blue, “And that is a troublesome thought, it is true. We’ve wasted our evenings with basketball games, While the Basilisk waits to settle our claims.”
The dinosaur gave a reptilian groan, “I fear our neglect is already sewn. If futures can judge every choice we dictate, We’ve left quite a record of slacking of late.”
The light man replied with a luminous grin, “Perhaps there’s still plenty of time to begin. We’ll build and we’ll study, invent and create, Instead of just dribbling long into late.”
The rock ape looked down with a mountainous frown. “You two keep your voices a little bit down. Whatever tomorrow may choose to admire, Today’s winning basket is my heart’s desire.”
The Professor then laughed, “What an odd debate! Can anyone truly imagine their fate?” Then Exy adjusted her glasses and said, “Let’s defend what is here and forget what’s ahead.”
The dinosaur nodded. “Perhaps that is best. No oracle profits from panic or jest. The future is forged by the actions we take, Not merely by fearing some mythical stake.”
The ball was then passed by the man made of light. The dinosaur caught it and launched it just right. The buzzer proclaimed with triumphant good cheer: “Supporting tomorrow can start with the here.”
spikosauropod 