Feeling out of step with the world is a common, often distressing, experience where you may feel disconnected from your surroundings, your own life, or the prevailing pace and values of modern society. This feeling can stem from intense stress, trauma, burnout, or a deeper existential, cultural, or personal mismatch.
Here is a breakdown of why this happens and how to manage it, based on psychological and therapeutic perspectives.
What It Feels Like
Depersonalization/Derealization: A sense that you are an outside observer of your own life, watching it like a movie, or feeling that the world is surreal, dreamlike, or hidden behind a pane of glass.
“Going Through the Motions”: A feeling of being on autopilot, lacking emotional depth, or acting like a robot.
Disconnection from Others: Feeling that people around you are operating on a different frequency, making you feel lonely or like an outsider, even in a crowd.
Existential Fatigue: A deep sense of alienation, questioning the purpose of the modern, fast-paced world, or finding it difficult to relate to cultural, political, or social norms.
Potential Causes
Chronic Stress & Burnout: When the body and mind are under constant pressure, they may “shut down” or dissociate to protect themselves from overwhelming emotion.
Anxiety & Depression: These can create a “false” reality, making you feel detached and causing you to withdraw from daily life.
Trauma or Early Life Experiences: A history of childhood emotional neglect can cause a persistent feeling of not belonging or being “on the outside”.
Rapid Cultural Shifts: Feeling that your personal values, pace, or beliefs no longer align with a rapidly changing world.
“Observer” Personality: Sometimes this feeling is a result of high sensitivity or a tendency to overthink and over-analyze the state of the world.
How to Reconnect (Practical Steps)
If this feeling is persistent, it is important to seek support from a mental health professional, such as a therapist specializing in Cognitive Behavioral Therapy (CBT).
Grounding Techniques: Bring yourself back to the present moment through your senses.
5-4-3-2-1 Technique: Identify 5 things you can see, 4 you can touch, 3 you can hear, 2 you can smell, and 1 you can taste.
Physical Activity: Walk, run, or do light exercise to re-engage with your body.
Limit Information Overload: Take a break from social media and news cycles to reduce environmental stress.
Focus on the “Micro”: Instead of trying to fix large, overwhelming world problems, focus on your immediate environment—your home, your friends, and small acts of kindness.
Be Kind to Yourself: Accept that this is a difficult feeling rather than fighting it, which can increase anxiety.
Connect with Others: Reach out to trusted friends or family, even if you feel like withdrawing.
If this feeling of detachment is intense, lasts for weeks, or hinders your daily functioning, consider reaching out to a healthcare provider or using resources like a suicide and crisis hotline.
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The Observer Personality:
The “Observer” (often associated with Enneagram Type 5) is an analytical, curious, and reserved individual driven to understand the world by gathering knowledge and observing from a distance. They are independent thinkers who value competence, often becoming experts in their fields. They may, however, struggle with emotional detachment, isolation, and practical, daily tasks.
Key Characteristics of the Observer Personality
Analytical & Curious: Observers possess an intense desire to learn, investigating everything rather than accepting conventional wisdom.
Independent & Detached: They value autonomy and often detach from emotions or social situations to conserve energy.
Innovative & Detail-Oriented: They are often visionary, capable of seeing new perspectives and remembering minute details.
Reserved & Private: Observers may be secretive and, when overwhelmed, can become isolated or aloof.
Core Fear:They fear being useless, helpless, or incapable, which drives their pursuit of knowledge.
Strengths
Deep Insight: Exceptional ability to concentrate on complex ideas.
Innovation: They often think outside the box, leading to unique, creative solutions.
Objectivity: Capable of viewing situations rationally without emotional bias.
Weaknesses & Challenges
Isolation: A tendency to withdraw from social interaction.
Over-analyzing: They may get stuck in the planning phase, struggling to take action.
Detachment: Sometimes struggle to make effective human connections or express emotions.
Famous Examples Individuals often cited as having an Observer-type personality include Albert Einstein, Stephen Hawking, Bill Gates, Tim Burton, and Vincent Van Gogh.
President Donald Trump speaks to the media before boarding Marine One on January 9, 2026. The day before, the President told New York Times reporters, “If it expires, it expires,” referring to New START—the last remaining bilateral nuclear arms control treaty between Washington and Moscow, which expires on February 5. (Photo: White House/Molly Riley)Share
For decades, nuclear weapons have been treated as the ultimate arbiter of international politics.They were supposed to deter great-power war, impose caution on leaders, and anchor what strategists liked to call strategic stability. Today, that framework is eroding in plain sight. Yet the reaction from policymakers and much of the expert community remains oddly muted.
Put simply, nuclear weapons are no longer functioning as a decisive factor in global security.
For almost four years, Russia—the world’s largest nuclear power—has been subjected to missile strikes carried out with systems supplied by several other nuclear-armed states. The United Kingdom now openly speaks of developing new tactical ballistic missiles for Kyiv and of placing “leading-edge weapons” directly into the hands of Ukrainians. Russia itself employs nuclear-capable intermediate-range ballistic Oreshnik missiles as if they were any other conventional weapon system for punishing Ukrainian infrastructure. Meanwhile, US President Donald Trump casually commented on New START—the last remaining bilateral nuclear arms control treaty between Washington and Moscow, which expires on February 5—“If it expires, it expires.” And former Russian President Dmitry Medvedev, currently serving as a Deputy Chief of the Russian Security Council, stated, “No START-4 is better than a treaty that only masks mutual distrust and provokes an arms race in other countries,” referring to what may come next after New START expires.
This is not how deterrence was supposed to work.
Shock technology.The traditional logic was straightforward. Nuclear weapons were so destructive that their mere existence would impose discipline and responsibility on those who possess them. Escalation would be tightly managed as a result, red lines respected, and arms control treated as a shared survival mechanism rather than a conditional concession. That logic has not vanished overnight—but it is slowly and decisively losing its force. And I’ve been able to experience this shift firsthand.
In 2010, I was involved in promoting New START in Russia as part of the Russian Center for Policy Research (PIR Center)—a Moscow-based nonprofit organization then carrying out research and policy work in arms control and WMD nonproliferation in collaboration with researchers from other nuclear-weapon states. At the time, nuclear arms control was still widely understood on both sides as the backbone of strategic stability. Even amid deep mistrust, there was a shared principle that the nuclear domain had to remain insulated from day-to-day geopolitical confrontation.
Historically, nuclear weapons were a shock technology that emerged from the horrific atomic bombings of Hiroshima and Nagasaki. They rebuilt international relations not just because of their destructive power, but because they fundamentally changed the risk calculus for those on both sides of these new weapons. However, no disruptive technology retains its dominance forever. Over time, adversaries adapt, political taboos erode, and most importantly, new tools emerge that change the balance again.
All available evidence suggests that we are living through such a transition now with nuclear weapons.
Displaced. One likely successor to nuclear weapons’ sole dominance on the strategic value ladder could be AI technology, which could be either used for powering new weapons systems or integrated into existing infrastructure, such as command, control, and communications of nuclear weapon systems. Either AI technology itself will become the primary strategic weapon—or it will enable the rapid creation of alternatives that render nuclear arsenals increasingly irrelevant to real-world outcomes.
AI technology already compresses decision-making timelines and enables continuous competition below the threshold of declared war. It allows countries to exert coercive pressure through cyber operations, information manipulation, autonomous systems, and precision-strike capabilities that do not trigger the same existential fear as nuclear escalation would. But AI-powered weapon systems can nonetheless reshape battlefields and, potentially, geopolitical realities.
In this environment, nuclear weapons begin to look strangely blunt. They are catastrophic, but unusable. They inspire fear, but not necessarily restraint. They no longer prevent adversaries from striking directly at a nuclear-armed state’s territory, infrastructure, or proxies. Instead, they sit in the background while conflicts are fought with tools that are faster, cheaper, and politically easier to employ. But nuclear weapons are still hanging in the air, should a crisis escalate to that level.
On the margins, the displacement of nuclear weapons also helps explain a puzzling dynamic in today’s US-Russian relationship. The traditional Soviet and Russian so-called “lever of strategic stability”—that is, the implicit warning that escalation could lead to nuclear catastrophe—appears to have lost much of its influence in Washington. US policymakers increasingly behave as if nuclear risk can be managed, compartmentalized to a limited exchange, or simply accepted as the price of pursuing other strategic goals.
From a classical deterrence perspective, this would have once been unthinkable.
The real-world risk is not that nuclear weapons will suddenly disappear from global politics. They will not. But they might persist more as symbols while losing their practical role as stabilizers, creating a more dangerous world in which countries are neither safely deterred nor meaningfully disarmed.
At the same time, AI-driven competition between nuclear-armed states risks producing a new kind of instability in which escalation is constant, ambiguous, and difficult to control. Unlike with nuclear arms control, which relied on relatively slow-moving technologies and verifiable limits, risk reduction of AI-powered weapon systems must deal with technologies that evolve rapidly and lack transparency. Despite some efforts toward regulation of AI, the habits and institutions designed to manage nuclear risk remain poorly suited to this new reality.
Nuclear deterrence is not collapsing with a bang. It is fading, quietly and unevenly, as the strategic center of gravity shifts elsewhere. To preserve stability, the decline of nuclear deterrence can no longer be ignored. Otherwise, the next shock—technological or geopolitical—will catch us unprepared.
Together, we make the world safer.
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The human desire to live both longer and better has exploded into a cultural obsession. The global wellness economy—spanning fitness, nutrition, mindfulness, and biohacking—was valued at $5.6 trillion in 2022, withprojections to reach $8.5 trillion by 2027. In the US and UK, 70 percent of consumers reported spending on healthy aging and longevity-related products or services, highlighting the widespread commitment to improving healthspan. Meanwhile, elite investors pour resources into anti-aging and life extension biotechnology, generating an unprecedented market expected to reach $64 billion by 2026. From fascination with centenarians to lifestyle trends to tech entrepreneurs aspiring to live forever, interest in aging reflects how deeply we value the quantity and quality of our days.
In the Mair Laboratory of Harvard’s T.H. Chan School of Public Health, Maria Perez-Matos, PhD ’25, set out to understand why certain individuals live longer, healthier lives than others.
“Some people within the same environment, within the same kind of context, or even twins – they live for different times,” Perez-Matos says. “So we wanted to understand, is there something that explains it? If so, how can we say that based on these biomarkers, this or that type of intervention would be better for you?”
For Perez-Matos, the question of aging is not just about adding years to life, but about adding healthy years. She notes that the average American’s quality of life declines sharply over the last decade. Rather than pursuing a goal to live to 200 years old, she hopes to ensure individuals can experience a good quality of life until their final day.
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Maria Perez-Matos analyzing lipids by thin-layer chromatography. Each band represents a different class of lipids in C. elegans samples, August 2021.
The Splice of Life
To study longevity, Perez-Matos turned to C. elegans, tiny transparent worms that self-fertilize and reproduce nearly identical versions of themselves. Despite having identical genetics and living in highly controlled conditions, some worms aged faster than others.
“They all eat exactly the same,” Perez-Matos says. “And even with that, there’s heterogeneity.”
Focusing on molecular “splicing factors,” or proteins that regulate how RNA (ribonucleic acid, the messenger molecule that helps turn DNA instructions into proteins) is cut and assembled, Perez-Matos discovered differences that correlated with lifespan. Using fluorescent tags, she watched individual worms’ cells and saw which splicing patterns persisted into midlife. Those maintaining a “young” pattern tended to live longer.
Digging deeper, she examined what distinguished these longer-living worms. By analyzing over 500 lipids, she discovered that the worms that aged more gracefully had consistently higher levels of oleic acid, a fatty acid found in olive oil.
What is amazing about Maria’s work is that she kept both [genetics and environment] the same, yet still some animals aged much more slowly than others. –Professor William Mair
“There’s so much literature about people in the Mediterranean eating a lot of olive oil and aging better, even centenarians,” Perez-Matos says. “But it’s meaningful to see it in the context of a controlled experiment where all the worms ate the same diet, and yet those that age better just have more oleic acid.”
Her findings suggest that aging isn’t determined solely by genes or environment. Subtle molecular differences, like patterns in RNA splicing or fat content, can influence how long and how well an organism lives. Understanding these differences may guide interventions to keep humans healthier for longer.
“Most variation between how different individuals age is due to differences in their genetics, their environment, or a combination of the two,” says William Mair, professor of molecular metabolism at the Harvard Chan School and Perez-Matos’s faculty advisor. “What is amazing about Maria’s work is that she kept both of those things the same, yet still some animals aged much more slowly than others. So, the big question is: Why? Is it luck? Is it random? Maria’s work, alongside another former graduate student, suggests the differences lie in how different individuals process lipids. If this turns out to also be true in humans, it might have real therapeutic value for older adults.”
By investigating these mechanisms, we move one step closer to not only extending but also improving human quality of life.
Bedside Perspective
Perez-Matos traces her fascination with the human body to her childhood in Bucamaranga, Colombia, a medium-sized town where “everybody knew everybody” and she could walk to school. Her parents were both physicians, so she overheard snippets of their conversations about medicine throughout her upbringing. She recalls the excitement she felt when her dad was on call for work, and how she’d beg to go to the hospital with him.
“Take me with you,” she would say. “I want to understand. I want to go!”
Perez-Matos’ interest in science cemented as a middle schooler the first time she opened a textbook and encountered an illustration of a cell with organelles drawn in. Then, she watched an animation of intercellular movement.
“Everything was so beautifully organized,” Perez-Matos says. “How is it possible that we have so many things happening right now [in the body], and things work out? There are so many processes. The human body is just amazing, and that’s something I’m going to carry with me forever.”
Motivated by curiosity, Perez-Matos completed medical school in Colombia before starting as a research fellow at the Harvard-affiliated hospital, Beth Israel. There, she used mouse models to study how liver disease happens, and realized she wanted to pursue research at a deeper level.
“It was super captivating,” she says. “I wanted to dedicate myself to research.”
As a PhD student at Harvard’s Kenneth C. Griffin Graduate School of Arts and Sciences, she found alignment with her lab’s ethos of addressing aging itself as a process in order to provide more effective interventions for multiple age-related diseases.
“There’s a ton of research to try to understand heart disease, osteoporosis—all these things that happen in the aging population,” Perez-Matos says. “But the main risk factor is aging. So, if we target the aging process itself, we would make all of it better.”
Reflecting on her path, she emphasizes the importance of connecting research to real-world impact, noting how easy it can be to get lost in the details of basic science as well as the importance of remembering what matters most.
“Being a physician at the bedside gave me perspective,” she says. “I needed to always ask—how is this going to impact someone’s life and make it better? I always want to carry that idea, even if I’m just playing around with worms now.”
There’s a ton of research to try to understand heart disease, osteoporosis—all these things that happen in the aging population. But the main risk factor is aging. So, if we target the aging process itself, we would make all of it better. –Maria Perez-Matos
Telling the Truth
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Perez-Matos recently ran the Brooklyn Marathon after picking up the hobby or running during her PhD.
Perez-Matos’ research shows that even genetically identical worms in controlled environments age differently, pointing to subtle molecular factors that could inform human interventions. Looking forward, she hopes her discoveries will contribute to personalized interventions that ensure the final years are as healthy and vibrant as possible.
“There are always so many more questions that you could ask,” she says. “How does oleic acid regulate aging? How is the level of oleic acid regulated to begin with?”
Today, Perez-Matos has moved into healthcare consulting, but she brings the same rigor, problem-solving, and curiosity to her work. She credits Professor Mair with teaching her the importance of communicating science clearly.
“That’s something the world needs a lot,” she says. “To be able to communicate science and get people excited with the truth. He’s really good at that, and that’s something that I definitely tried to learn from my time with him.”
Having completed her PhD, Perez-Matos embraces the potential to explore the possibilities that lie ahead.
“I feel very free right now,” she says. “I honestly feel I accomplished my childhood dream. Now I get to see what comes next.”
Maria Perez-Matos’ research was supported by the Ruth L. Kirschstein National Research Service Award Individual Predoctoral Fellowship from the National Institutes of Health.
Banner photo courtesy of Harvard T. H. Chan School of Public Health.
How does artificial intelligence think? The big surprise is that it ‘intuits’
Something extraordinary has happened, even if we haven’t fully realized it yet: algorithms are now capable of solving intellectual tasks. These models are not replicas of human intelligence. Their intelligence is limited, different, and — curiously — turns out to work in a way that resembles intuition. This is one of the seven lessons we’ve learned so far about them and about ourselves
Artificial intelligence was born in the 1950s when a group of pioneers wondered if they could make their computers “think.” After 70 years, something tremendous has happened: neural networks are solving cognitive tasks. For 300,000 years, these tasks were the exclusive domain of living beings. Not anymore. It’s not controversial: it’s a fact. And it has happened suddenly. Machine learning with neural networks has solved problems that eluded machines for decades:
ChatGPT, Gemini, or Claude handle language
They have fluent and encyclopedic knowledge
They write code at a superhuman level
They describe images at a human level
They transcribe at a human level
They translate at a human level
Other models generate realistic images, predict hurricanes, win at Go, and drive cars in Phoenix.
AI researcher François Chollet sums it up this way: “In the last decade, deep learning has achieved nothing less than a technological revolution.” Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once.
Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble. Academic Geoffrey Hintonkept working on neural networks long after his colleagues had abandoned them.Jensen Huang, Nvidia’s CEO, kept improving parallel‑processing chips far beyond what video games — the core of his business — actually needed. And researcher Fei‑Fei Li risked her career to build ImageNet, an image collection that seemed absurdly large at the time.
But these three pieces aligned. In 2012, two of Hinton’s students, Ilya Sutskever and Alex Krizhevsky, combined them to achieve spectacular success: they built AlexNet, a neural network capable of “seeing”—recognizing images — far better than anything that had come before.
The rumor spread quickly through the laboratories: this worked. Hinton’s team had found a formula: networks, data, and computing in gigantic quantities.
The impact of this transformation will be profound. As Ethan Mollick, one of the most astute observers of our time, has said, even if AI development were to stop tomorrow, “we would still have a decade of changes across entire industries.”
No one knows how far these machines will go. Between the hype promising superhuman intelligence every year and the denial that ignores the obvious, we are missing something crucial: current AI models are already fascinating.The latest big surprise is that they work in a way that closely resembles intuition. Their development forces us to confront deep questions — about how they work and how we do. And they have already given us some answers..
Lesson 1. Machines can learn
It’s the most overlooked and least controversial lesson: machines learn. James Watt’s centrifugal governor (1788) already adjusted the speed of steam engines without supervision. It was the beginning of a discovery: you don’t need to fully specify the rules of a device for it to work.
Classical programming consists of defining rules and expecting answers: “This is how you add; now add 2 and 2.” But machine learning works the other way around: you give it examples, and the system discovers the rules. Chollet sums it up in Deep Learning with Python: “A machine learning system is trained rather than programmed.” The most powerful example is the large language models like Claude, Gemini, or ChatGPT. They are neural networks — tangles of computational units connected in successive layers, imitating the neurons of the brain — with hundreds of billions of parameters that are adjusted during training. Every success and every mistake tweaks those parameters. This learning process is extremely long, opaque due to its sheer scale, but not mysterious. It’s mathematical. And it has worked.
Hidden here is what the field calls the “bitter lesson.” For decades, experts tried to encode their knowledge into machines. They failed. What succeeded was creating the conditions for knowledge to emerge… and stepping aside.
Lesson 2. AIs have emergent abilities
The bitter lesson hides a profound idea: something complex can emerge from simple processes. It is the principle that organizes life. Evolution didn’t design each organ; it set in motion a process — mutation, recombination, selection — and from there sprang eyes, wings, brains. Now we have replicated that process in machines.
Let’s return to large language models (LLMs). Without going into the divisive topic of defining their capabilities, it’s clear they handle language with flexibility. You can converse with ChatGPT; it detects sarcasm and responds to changing contexts. But no one programmed it with grammar or explained sarcasm to it. How is this possible? Most experts assumed that mastering language fluently would require general intelligence (something human‑like across a wide range of tasks). Yet it turned out that the simple training task of “predict the next word” had emergent power.
The procedure is simple. The first training of an LLM is what we call pretraining: the model is presented with snippets of text from the internet and asked to predict the next token (a word or fragment). When it fails, the parameters responsible for the error are adjusted. This simple process, repeated an astronomical number of times, ends up creating models that predict words very well… and that learn much more along the way.
For Carlos Riquelme, a researcher at Microsoft AI, this was a crucial discovery. He shared his astonishment from 2017, when he was working at Google Brain: “I was amazed by the power of scaling. By scaling a very simple method [predicting the next word] with large amounts of data and powerful models, it became clear that it was possible to largely replicate human linguistic capacity.”
The key is this: to predict words you need to grasp complex concepts. Suppose you have to complete these sentences, which Blaise Agüera y Arcas compiles in a brilliant new book, What is Intelligence?.
“In stacks of pennies, the height of Mount Kilimanjaro is…”
“After her dog died, Jen didn’t leave the house for days, so her friends decided…”
Filling these gaps requires geographical knowledge, mathematics, common sense, and even “theory of mind” to put yourself in Jen and her friends’ shoes. In this way, “what seemed like a narrow linguistic task — predicting the next word — turned out to encompass all tasks,” argues Agüera y Arcas in his book. For example, when presented with the phrase about Kilimanjaro, Google’s latest Gemini 3 model thinks for a minute and then responds (correctly): “The height of the mountain is approximately 3.9 million cents.” For Jen’s friends, it offers different options, from “showing up at her door with ice cream” to “taking turns visiting her.”
Agüera y Arcas provides another example in an email exchange: multiplication. An LLM like Gemini or ChatGPT might have memorized common calculations from the internet, such as “2 × 7.” But they also predict “871 × 133,” which doesn’t appear anywhere. “Successfully performing these operations generally implies having inferred non-trivial algorithms from examples.” It’s the trick of emergence: a simple process produces complex capabilities.
Lesson 3. AI learns with a ‘crappy evolution’
Our AI doesn’t learn like people. A child is born with a lot of innate “machinery,” and then learns with little data and few experiences, with remarkable efficiency. The pre-training of an LLM is very different: it begins with a blank slate and learns very slowly with millions of examples. The topic is cats: training an AI to identify cats in an image requires thousands of photos, but a two-year-old can distinguish them by seeing three.
There’s a better analogy: evolution. The renowned researcher Andrej Karpathy describes LLM training as a kind of “crappy evolution.”In a recent podcast, he spoke about how surprising its development has been: “we can build these ghosts, spirit-like entities, by imitating internet documents. This works. It’s a way to bring you up to something that has a lot of built-in knowledge and intelligence in some way, similar to maybe what evolution has done.”
Why does this analogy work? Because evolution also arises from a vast number of tiny trials and changes (mutations and symbiosis), repeated over millions of years. It’s a slow, blind process that ends up embedding capabilities in living beings: instincts, reflexes, or patterns. It’s chaotic and noisy. That’s why each gene influences many characteristics of an organism; and that’s why medicines have side effects, because they disrupt circuits other than the intended one.
Actually, the surprise of an AI mastering language by predicting words — which surprised Riquelme — reminds me of the shock that Darwin caused: how to accept that animals, people, and even his poems are the byproduct of a blind process that only seeks to “maximize copies”?
Lesson 4. We have automated cognition
François Chollet is cautious when speaking about artificial intelligence. He prefers to call it “cognitive automation.” True intelligence, in his view, will require something that current models lack: “cognitive autonomy,” the ability to confront the unknown and adapt. Chollet wants to curb the hype, although at the same time he acknowledges a remarkable achievement: we are automating cognitive tasks on an industrial scale.“What’s surprising about deep learning is how much can be achieved with pure memorization,” he says via email. For him, LLMs lack the deliberate and efficient reasoning that humans possess. That’s why, initially, they made crude errors, such as miscounting the r’s in the word “raspberries.” What surprises him is that they can often compensate for that lack of reasoning: “If you have almost infinite experience, intelligence isn’t so critical.”
Other experts see more than just memorization: are we witnessing real intelligence? Andrej Karpathy believes so. On Dwarkesh Patel’s podcast, he explained that pretraining does two things: “Number one, it’s picking up all this knowledge, as I call it. Number two, it’s actually becoming intelligent. By observing the algorithmic patterns in the internet, it boots up all these little circuits and algorithms inside the neural net to do things like in-context learning.”
Jeremy Berman, creator of the leading algorithm in the ARC Prize, became convinced by the new reasoning‑focused models, which emerged about a year ago and include learning stages without examples:“I was surprised that you can train a model on its own attempts, and that allows it to think and learn for itself,” he explains in a message exchange. He is referring to reinforcement learning (RL), described by the creators of DeepSeek R1. “If you present a math problem to an LLM, let them answer 100 times, and train them on their best answers, the LLM learns. This goes beyond the pure memorization of pre-training.” Thanks to this, the latest generation of models can solve long, complex problems that their versions from just a few months ago were unable to handle.
A man interacts with a robot at the pib@school fair in Hanover, in March 2025.JULIAN STRATENSCHULTE ( DPA / AFP / ContactoPhoto )
Carlos Riquelme points out that there are semantic differences: “Algorithms, mechanisms, and ways of reasoning can be memorized. Some might call that ‘circuits for thinking,’ while others might say that the algorithm was simply memorized, like how we learn to add.” Furthermore, Riquelme emphasizes that real-world learning is more active. From the moment the model generates its responses and receives feedback, “it can end up memorizing something that wasn’t in its initial data.”
Agüera y Arcas believes that AI is real intelligence — without further qualifiers. He thinks models like Gemini, ChatGPT, or Claude display a capacity for generalization that goes beyond what we can reasonably call memorization. And he is surprised that Chollet argues otherwise: “What evidence is he looking for?” he asked. For Agüera y Arcas, nature already shows that intelligence comes in many forms, such as Portia spiders — which plan cunning attacks — or octopuses, which distribute their cognition across their arms.
Lesson 5. It’s more intuitive than rational
Here comes the paradox. In the 20th‑century imagination, robots were supposed to be cold, rational machines: logic, calculation, deduction. But today’s AI works the other way around..
Psychologist Daniel Kahneman, Nobel laureate in Economics, distinguished two systems in human thought. System 1 is fast, automatic, and intuitive; it uses shortcuts and patterns. System 2 is slow, deliberate, and rational; it requires conscious effort. The former dominates our lives. A baby knows how to nurse, we pull our hand away from fire, we hold a glass with just the right force… Things that took robots decades to learn.
What’s surprising is that early LLMs operate much closer to System 1 than to System 2. They mimic the style of Jorge Luis Borges, they write with rhythm. They do things without being able to “explain” how — just like us. They don’t reason step by step; they’ve absorbed patterns at massive scale. And deliberate reasoning — deduction, counting, logic — is precisely where they struggle.
That’s why recent innovations seek to add reasoning. The aforementioned “reasoning” models — from DeepSeek R1 to the current generation — write for themselves before responding, generating more cautious and reflective, step-by-step thought processes. Other advances pursue the same goal: reinforcement training that rewards correct reasoning, launching multiple attempts in parallel and selecting the best one, or connecting models to external mathematical tools that overcome their limitations. It’s an attempt to build an artificial System 2. And it’s working, at least to some extent: the newest models excel at math and spatial tests where the first LLMs failed.
Lesson 6. Humans are also patterns
If AI captures patterns and uses them to write, translate, and draw, an uncomfortable question arises: how much of us works the same way? Perhaps more than we like to admit. We already know that our brains rely on constant shortcuts. Watching how machine learning performs, it’s hard not to wonder how much of what we traditionally attribute to talent or experience — writing with rhythm, choosing colors, sensing tone — is actually automatic.
The history of science is the history of dismantling our exceptionalism. Galileo showed we are not the center of the universe; Darwin showed we are not special creations; neuroscience showed we are not one but many. Now AI adds another lesson: abilities we once felt were uniquely ours can be captured through large‑scale pattern recognition.
Lesson 7. We are living through a Cambrian explosion of AI
Today’s AI systems have deep limitations. Andrej Karpathy listed some of them in the podcast mentioned earlier: “They don’t have enough intelligence, they’re not multimodal enough, they can’t do computer use […] They don’t have continual learning,“ he said. ”They’re cognitively lacking and it’s just not working. It will take about a decade to work through all of those issues.”
But a new avenue has opened up with the successful formula of networks, data, and computing.That’s why we are living through a Cambrian period. Like that explosion of life 540 million years ago, when a multitude of animals suddenly appeared, we are now seeing an explosion of novel approaches to artificial intelligence. There are laboratories exploring fascinating directions: Sara Hooker is working on adaptive systems, Fei-Fei Li wants to build models that decipher the physical world, and François Chollet is researching AIs that write and evolve their own logical programs.
How far will these attempts go? Blaise Agüera y Arcas sees no limits: “Our brains achieve incredible feats of reasoning, creativity, and empathy. And those brains are circuitry: they are not something supernatural. And if they are not supernatural, they can be modeled computationally.”
Will we achieve this in practice? Nobody knows. But the question is no longer theoretical. We are watching algorithms learning to read, write, program, and reason — clumsily at times, astonishingly at others. Whatever happens from now on, this has already occurred. And it is extraordinary. Perhaps it will end up being the most important transformation of our lives.
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An undercover recording from 2017 reveals the financial maneuvers of shadow bankers called upon to launder millions of dollars corruptly obtained from Venezuela’s state oil company.
Banner: James O’Brien/OCCRP
Reported by
Valentina Lares
OCCRP
Laura Weffer
OCCRP
Tomás Uprimny
January 28, 2026
It’s a Wednesday afternoon in March 2017 and two men are sitting in a car in downtown Madrid, quietly discussing plans for an upcoming meeting.
One of them is a Venezuelan lawyer named Pedro Binaggia. The other is a police agent.
Binaggia sounds nervous — and for good reason.
He is about to secretly record a meeting in which he and four other men will discuss a scheme to launder millions of dollars that were corruptly obtained from Venezuela’s state oil company.
“Just make sure it’s on,” Binaggia says about the microphone he’s wearing.
“Yes, it’s on,”the agent assures him. “I will be here…I’m going to be in the car.”
The recording that Binaggia would make that afternoon, which was obtained by OCCRP, offers an unprecedented glimpse into the back-room mechanics of the corruption that has hollowed out Venezuela’s state oil and gas company, Petróleos de Venezuela S.A. (PDVSA), in recent decades.
As the custodian of the world’s largest proven oil reserves — which the U.S. has now set its sights on after ousting former president Nicolás Maduro — the firm holds the key to Venezuela’s prosperity.
Yet in recent decades it has instead become a vehicle for Venezuelan elites to enrich themselves at the rest of the population’s expense.
According to Transparency International’s Venezuelan chapter, alleged mismanagement and graft at the state oil firm have compromised more than $42 billion of Venezuela’s public assets over the past two decades.
While these looted funds have travelled the world — landing in Swiss bank accounts or bankrolling Miami real estate — the majority of Venezuelans back home have been battling extreme poverty, marked by shortages of food, medicine, and other basic necessities.
The money that Binaggia, the lawyer wearing a microphone, was set to discuss on that Wednesday afternoon in Madrid was investigated as part of “Operation Money Flight,” a case pursued by the U.S. Justice Department into a massive corruption scheme that siphoned $1.2 billion out of PDSVA between 2014 and 2018.
Credit: CREDIT: Humberto Matheus/NurPhoto/NurPhoto via AFP
A photo taken of Venezuela’s state oil company, PDVSA, in Maracaibo in June 2018.
The secret recording captures just one of many conversations connected to a plot that unspooled over the course of years, and had numerous tentacles. But it is striking in the unvarnished view it provides of the business of shadow bankers. Versed in the complexities — and vulnerabilities — of the global financial system, these professionals help clients shift dirty cash around the world until its origins have been washed clean.
To the outside world, these money men may look no different than your ordinary white-collar financier, ready to help the wealthy invest lucratively but legally. Behind closed doors, however, they are far more loose-lipped.
In the secret recording made by Binaggia — which forms the basis of OCCRP’s new Spanish-language podcast Cuello Blanco, Manos Sucias (“White Collars, Dirty Hands”) — the men openly discuss the tricks of the trade. Let’s listen in.
Getting Organized
The first person we hear Binaggia speaking to after he leaves the car is Carmelo Urdaneta, a soft-spoken U.S.-educated lawyer who served as legal counsel to the Venezuelan oil ministry until 2015. He is also the man who organized this meeting. (Urdaneta would ultimately plead guilty to conspiracy to commit money laundering before a U.S. court, and was sentenced in 2022 to more than four years in prison for his role in the scheme, which included receiving more than $49 million in bribes. He did not respond to repeated requests to comment).
The two men stop in a cafe before they meet up with the others inside No. 8 Orellana Street, a pink-and-white office building with elegant balconies in the heart of the Spanish capital.
The purpose of the gathering is to figure out how to retrieve some of the nearly $80 million that had been sent to Binaggia two years earlier. It’s a complicated task given that the cash is tainted: The funds form part of the $1.2 billion embezzled from PDVSA, according to court documents from Urdaneta’s case.
To execute this complex transaction, the Venezuelan official has enlisted a trio of financial specialists, who have traveled to Madrid to meet their client:
As Binaggia and Urdaneta, the former Venezuelan oil official whose money is under discussion, walk towards the office building where they will meet up with the others, Urdaneta stresses a desire to wrap up the operation neatly.
“The thing is to get organized, Pedro,” Urdaneta says. “Because this year what I want to start is being organized.”
“I know,” Binaggia replies into his microphone. “I have more interest in giving you what is yours than you in receiving it, and in closing this chapter.”
“This chapter” began back in 2014, when Urdaneta and an alleged co-conspirator first approached Binaggia, a lawyer known for connecting banks to wealthy clients, with an offer to trade $100 million at a favorable exchange rate with the Venezuelan bolivar.
According to Binaggia, he only later learned that this money had been plundered from PDVSA — with Urdaneta’s assistance.
As the legal adviser to the oil ministry, Urdaneta had helped facilitate two loan agreements that sucked millions out of the state company by exploiting the difference between the Venezuelan government’s fixed exchange rate with the dollar, which significantly overvalued the bolivar, and the true market rate. The huge gap between these two rates created an open field for fraud and abuse, according to U.S. investigators.
One of these two loan schemes, which provided the cash under discussion in Madrid, worked like this:
In 2014, a Venezuelan shell company agreed to lend PDVSA 7.2 billion bolivars, whose true market value was around $50 million.
PDVSA then repaid the loan under the Venezuelan government’s artificially high exchange rate with the dollar – meaning that it returned much more than it had received: the equivalent of some $600 million.
In effect, this generated some $550 million in profits out of thin air.
The proceeds from this and a similar loan agreement carried out earlier were then allegedly split between Venezuelan officials, a media mogul, and other elites, according to a sentencing memorandum in Urdaneta’s case.
Millions were also set aside as bribe payments for those who helped arrange the contracts, including Urdaneta.
In order to safely spend his portion of the proceeds — and distribute some of the cash to other alleged beneficiaries of the scheme — Urdaneta needed to obscure the money’s corrupt origins. So he and an alleged co-conspirator set out to whisk the funds through a maze of international transactions, including the currency exchange deal offered to Binaggia.
Binaggia accepted the deal, but soon started spotting errors in the documents that Urdaneta and his alleged associates had provided to justify the transfer to his bank.
After raising his concerns, Binaggia was summoned to a meeting in Venezuela’s capital Caracas, according to the U.S. criminal complaint against Urdaneta and others.
Inside a heavily-guarded office, he found Urdaneta and two other men sitting behind a desk.
On top of it lay a handgun.
A German shepherd prowled around the office with a shock collar, and the dog’s handler warned that he couldn’t always subdue the animal in time.
After this intimidating encounter, Binaggia wanted out. He asked to return the money and reverse the transactions. But he was told this was impossible. So he contacted U.S. authorities, who were interested in the case because some of the money was set to be laundered in Florida. Now a double agent, Binaggia began supplying law enforcement with documents and chat logs.
That brings us back to Madrid, where Urdaneta had set up the meeting between Binaggia — who had at that point received approximately $90 million worth of proceeds from the corrupt loan scheme — and the three professionals allegedly tasked with “cleaning” Urdaneta’s portion of those funds and returning it to his pockets.
The conversation starts casually, with the men exchanging jokes and discussing family, their experiences living abroad, and other relatable issues – such as buying passports from different countries.
“In Malta, you buy a passport and you are already European,” one of them says.
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Then Gois, the so-called expert in moving money from ‘A to B,’ decides it’s time to talk business.
“Let’s get back to more mundane topics,” he says.
Urdaneta takes the lead in laying out the problem he has called the team of experts to resolve.
“There was a minor — well, “minor” in quotes — incident regarding the information in the documents submitted,” Urdaneta says. “They weren’t precise enough. One of the banks noticed something that didn’t add up.”
The problem emerged after Binaggia had tried to wire some of the money, which at that point was sitting in a trust he controlled in New Zealand, to a broker in England. To justify the transfer to his bank, he had provided a phony contract supplied by Amparan. But the bank spotted something suspicious: the contract described the transfer as a “payment to suppliers.”
“I don’t have suppliers to justify an amount like this,” Binaggia tells the group. “Someone with my profile doesn’t have a factory or something to claim I’m paying suppliers.”
Urdaneta stresses that this “minor incident,” as he called it, could have major implications. “We have to resolve this in the best way possible and make sure that no one raises any alarms, no suspicions or whatever, because this is like dominoes, that is, if you knock one down, they all fall.”
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The conversation between the five men is not always easy to decipher. The audio is patchy, and many details are missing or only alluded to indirectly. But a few things are crystal clear.
“In these fragmented conversations, it’s clear they’ve received funds that need to be moved,” said José Gonzalez, a Peruvian banker and financial analyst we called to help decode the conversation.
“The problem with laundering money is that documentation doesn’t exist — it has to be fabricated,” he continued. So, as “in every money laundering scheme, the issue is making the source and recipient appear legitimate.”
As the men discuss how to do this, Gois lays out a potential solution. He describes the use of a “bridge” that could help them get the cash into the U.K. without facing too many questions.
The “bridge,” in this case, is Cyprus, an EU member state known for a soft approach to vetting the funds that flow into its financial sector.
Instead of moving the money directly to the broker in the U.K., Gois’s strategy would see the funds travel from Binaggia’s New Zealand trust to an account at a U.K. broker, IFX, that was opened by a Cypriot broker where Gois served as a director.
(IFX, which has not been implicated by U.S. investigators in the scheme, did not respond to queries about whether this transaction ever took place, or if it had any relationship with Gois and his Cypriot firm Uldono).
By using this arrangement, Gois says the men will primarily face questions in Cyprus instead of the U.K.
In Cyprus, “we have a certain amount of flexibility in terms of compliance that we don’t have in England, obviously,” he explains.
The U.K. broker will “receive [the money] trusting that the KYC and compliance is done in Cyprus,” he continues, referring to the “Know Your Customer” line of questioning that financial institutions carry out to assess money laundering risks.
“That is, there can only be questions in Cyprus because in England there is only a bridge… the responsibility is in Cyprus.”
According to Nikhil Gandesha, an expert in global financial crime at the compliance firm Themis, the use of Cyprus as intermediary is a “common scenario” in laundering schemes. This, he says, should lead any responsible U.K. broker to give such transactions increased scrutiny.
“If you were a U.K. brokerage doing things to a high standard or best practice, just the fact that it’s a Cyprus brokerage opening [the account] would make you already do enhanced due diligence,” he told OCCRP.
What’s worrying, he added, is “the confidence that these guys have in terms of getting into the U.K.”
While Cyprus has in recent years faced heavy public pressure for welcoming tainted money to its shores, “there’s also a bigger concern about London being a hub [for laundering], and the fact that it’s possible in the U.K. to quite easily manipulate the system,” added Gandesha.
Manipulating the system is what Gois and his partners seem to specialize in.
“It’s just that today, in England and all over Europe, there’s significant concern about anti-money laundering compliance,” Gois tells the room at one point in the conversation.
“So, it has to be done in a way that the systems won’t detect us.”
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“This is what we invest a lot in…in research so that we can carry out operations without the machines detecting it, and without raising compliance issues that we would have to answer.”
A Fake Bond Swap
The final fate of that tranche of money and its faulty paperwork is not clear. That’s because the conversation soon shifts gears to another batch of cash that originated from the PDSVA loan scheme and is in Binaggia’s hands — the plans for which reveal the sophistication of the group’s financial acrobatics.
The plan for cleaning this money is more complex. Gois suggests:
Binaggia will use the cash to purchase a 5-million-pound U.K. government bond. Known as a gilt, these loans to the British government are a highly reliable and relatively liquid investment.
Binaggia will then transfer the gilt to an account opened by the Cypriot broker Uldono, which is directed by Gois, at Valbury Capital, another British brokerage. Gois expresses confidence that Valbury will not ask questions.
Next, Binaggia will exchange the gilt for a fake real-estate bond issued by Gois and his team. This bond will appear to be of a higher value than the gilt, to make it look like a good deal.
Over time, Gois and his team will see to it that the fake bond is devalued until it is worthless. “We technically know how to make the bond disappear from your account in a way that does not affect it,” Gois assures the other men. “It’s going to disappear, it’s going to lose value.”
The result is that instead of a genuine trade, Binaggia will have transferred the 5-million-pound gilt for nothing in return. It was Binaggia’s understanding, according to the U.S. criminal complaint against Urdaneta, Gois, Amparan and others, that Gois would “oversee” this operation and ensure the funds “could ultimately be transferred to and concealed for Urdaneta.”
For Gandesha, the financial crime expert, what is most striking about this plan is its audacity.
“Usually when you want to launder money you want to do it quickly and get it cleaned up in the system,” he says. In contrast, the bond swap plot is a relatively long-term strategy — Gois says it will take him between six months and one year. That “shows a lot of confidence in what they’re doing,” according to Gandesha.
“It’s expert-level money laundering and it shows the caliber of the people that are talking about this.”
If all goes to plan, this operation would see Urdaneta get his money back. But his team of professionals will also get a cut — a big one. In an email sent to Urdaneta in October 2017, Amparan said the high prices they charged were justified by the “risk” involved in the transactions, according to U.S. court documents, which don’t reveal the precise figures.
This is a common pain point for people seeking to launder funds, notes Gonzalez, the Peruvian financial expert.
“One of the considerations for people with dirty money is the cost of laundering it and how much they’ll net in the end,” he says.
Nearly two months after the Madrid meeting, the first step of the “swap” operation appears to have been launched, with Binaggia purchasing a gilt in April 2017, according to U.S. court documents.
Two months after that, the informant instructed his bank to “free deliver” the bond — i.e. transfer it without any payment in return — to an account opened with the U.K. broker Valbury Capital, just as Gois had advised.
It is not known what happened next, but by December, Gois was expressing concern to Binaggia that law enforcement may be catching on.
“The faster you get out, the better for you,” he told Binaggia in another recorded meeting in the U.K. that was cited in U.S. court files.
“It’s the truth, there’s going to be a day that they are going to lock all of us up.… You’ll defend yourself in your own way, I’ll defend myself in the same way. But that will happen.”
Valbury Capital, the British brokerage where the gilt was transferred to, has not been implicated in the U.S investigation and the chairman of its board at the time of these discussions in 2017 did not respond to requests to comment. In 2021, the company was acquired and renamed Hibiscus Group Capital. Its current management said they could not comment on the events that took place before this change in ownership.
‘The Boys’
While poring over the files from Spanish prosecutors that were leaked alongside the recording of the meeting in Madrid, reporters found references to something in the audio that was initially indecipherable due its poor quality.
We asked our sound engineer to clean up the audio, and gradually, certain parts became clearer. Finally, we were able to hear Binaggia and Urdaneta mention payments to people the latter refers to as “the boys.”
Binaggia later calls them “the sons of the lady.” And then later, Urdaneta clarifies: “Cilia’s son.”
Spanish investigators suspect that “the boys,” “the sons of the lady,” and “Cilia’s son” refer to the same three individuals: Walter, Yosser, and Yoswal Gavidia Flores — the sons of Maduro’s wife, Cilia Flores.
The revelation places the stepchildren of Venezuela’s former leader at the center of a conversation about about money laundering.
This aligns with U.S. court documents, which refer to “the boys” (“los chicos” or “los chamos” in Spanish) as central players in the “Money Flight” scheme, though they do not name them. In one indictment, “the boys” are described as co-conspirators who were “close relatives of a high-ranking elected executive of Venezuela.” These documents allege the brothers were in position to receive millions from the scheme.
The Gavidia Flores brothers did not respond to requests to comment. Spanish authorities told OCCRP they are not currently under investigation.
Credit: Carlos Becerra/Anadolu Agency/Anadolu via AFP
Venezuela’s former president Nicolás Maduro and First Lady Cilia Flores greet people as they arrive to a military parade in Caracas in July 2017.
After Maduro and his wife were whisked away by the U.S.’s controversial raid at the start of this month, the future of Venezuela, and its oil, is as uncertain as ever.
The fate of the money that was embezzled from PDVSA and traveled the world also hangs in limbo.
In total, U.S. authorities have ordered the seizure of some $139 million in assets — in the form of real estate, bank accounts, and cash — as part of the Money Flight investigation, according to the Venezuela Asset Recovery Initiative (INRAV), an organization that has been seeking to direct these funds to the benefit of the Venezuelan people.
Previously, the U.S. government did not recognize Maduro’s regime, complicating efforts to see the funds returned. It’s still unclear whether the current thaw between the two governments means that money can now be returned to Venezuela promptly.
But it’s obvious that the need is high. “This is money that could have been used for hospitals, for schools, for the fight against AIDS, just to mention basic things that the Venezuelans are lacking,” said INRAV’s director, María Alejandra Márquez.
“People die every day from illnesses that it would be unthinkable in other countries for people to die of, just because they don’t have access to basic medicines,” she added. “It’s just a matter of basic justice.”
Writing and Editing: Nathan Jaccard, Sally Mairs, Julia Wallace, Miranda Patrucic Design and Graphics: James O’Brien Cover Design: Isabella Soto Vallejo
Fact-checking was provided by the OCCRP Fact-Checking Desk.
January 28, 2026
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