OCCRP: Much of the frenzied Jeffrey Epstein commentary over the past two weeks has focused on individual examples of moral depravity.But there’s a wider, more systemic story here.

February 13, 2026

Greetings from Amsterdam,

Much of the frenzied Jeffrey Epstein commentary over the past two weeks has focused on individual examples of moral depravity. But there’s a wider, more systemic story here. As Seva Gunitsky, a political scientist at the University of Toronto, has argued, the documents “suggest a reframing of Epstein’s case from one man’s crimes to transnational geopolitics.”Viewed through this lens, Epstein’s incredible collection of powerful and influential contacts, from tech billionaires to FSB officers, perfectly exemplifies a new transnational elite — one that has “built fortunes by moving money across jurisdictions, leveraging kompromat, and treating governments as service providers instead of sovereigns.”Epstein’s role shows how this class of operators has been enabled by willing Western partners. Our recent reporting offers three snapshots of how that works.Start with Senegal: the documents trace a years-long relationship between Epstein and Karim Wade, son of former President Abdoulaye Wade and once one of the country’s most powerful ministers. After Wade’s corruption conviction, his team sought help lobbying in Washington — and turned to Epstein for guidance.Then Venezuela: emails show Epstein buying bonds tied to PDVSA, the state oil company, on the advice of Francisco D’Agostino — a Chávez-era insider now wanted by Venezuelan authorities on money-laundering and criminal-association charges.Epstein’s social capital also paid dividends back in New York. Emails indicate he worked to secure employment at the International Peace Institute — a respected policy institution tied to the United Nations — for a Russian romantic partner. He may even have subsidized her pay.The commonality in these vignettes is not sex, and it’s not even ideology. It’s the indispensable role of powerful intermediaries who can turn money into legitimacy, access, and impunity — not to mention a powerful reminder of how much of this brokerage usually happens in the dark.The analysis above is a free preview of our upcoming OCCRP PRO membership, a new offering tailored to professionals who rely on our investigations for work in finance, law, compliance, risk management, policy, and related fields. Interested to learn more? Let us know and tell us what you need! Your feedback will directly shape the services we build.OCCRP Exclusive
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CNN: Epstein paid for novel genetic testing in apparent effort to explore extending life, new emails show

Epstein paid for novel genetic testing in apparent effort to explore extending life, new emails show

By

Sarah Owermohle

Updated Feb 6, 2026

Late financier and convicted sex offender Jeffrey Epstein is seen in this image from the U.S. Justice Department’s file of Epstein, released by the House Oversight Committee Democrats Washington, D.C., on December 18, 2025.

Late financier and convicted sex offender Jeffrey Epstein is seen in this image from the U.S. Justice Department’s file of Epstein, released by the House Oversight Committee Democrats Washington, D.C., on December 18, 2025. House Oversight Committee Democrats/Reuters

Convicted sex offender Jeffrey Epstein paid for genetic testing in an apparent bid to harness his own genetic material for regenerative medicine – which is aimed at repairing the body by developing new tissues and organs as the old ones wear out – according to newly released emails in the Epstein files.

Several years after Epstein was convicted of soliciting prostitution, including of a minor, in 2008, he paid for novel tests from a doctor at one of America’s preeminent hospitals and explored creating stem cells central to immunity and healing.

The researcher, Joseph Thakuria, was at the time a senior doctor at Massachusetts General Hospital (MGH) in Boston and affiliated with a large-scale genomic studies project at Harvard Medical School.

In a statement to CNN, Thakuria said Epstein was also enrolled in the Harvard Personal Genome Project, a massive public global database of genetic information from volunteers for scientists and researchers to learn more about traits and genes.

Thakuria has not been publicly linked to Epstein before and is not accused of any wrongdoing.

A Harvard representative said MGH is an affiliate of Harvard, but Thakuria was not directly employed by Harvard or the Wyss Institute, which oversees the Personal Genome Project. MGH has no record of approving Thakuria for studies described in the Epstein emails.

Thakuria left the hospital in 2022, an MGH spokesperson said.

Among the documents in the Epstein files released by the Justice Department is a proposal that Thakuria sent Epstein in February 2014, appealing to him to fund a private project that would sequence his patients’ genomes to learn about genetic drivers for their diseases. In the proposal, he also raises options for genetic investigations specifically for Epstein.

A few months later, in June, Thakuria sent Epstein an extensive invoice for a range of projects that included an initial $2,000 investment for sequencing part of Epstein’s genome. The invoice included an estimated cost for “personalized longevity studies” that proposed using gene editing. The invoice indicated that Epstein gave a saliva sample.

The initial investment included $1,000 to sequence a portion of his genome known as the exome, and $1,000 to sequence fibroblasts, which are cells found in connective tissue such as skin and muscles, and that have been used in a relatively new field of research aimed at reversing aging.

Epstein’s staff sent a $2,000 check the same day.

Billionaire Jeffrey Epstein in Cambridge, Massachusetts in 2004.

Billionaire Jeffrey Epstein in Cambridge, Massachusetts in 2004. Rick Friedman/Corbis/Getty Images

“Mr. Epstein was enrolled in the Personal Genome Project, which would study his genetic predisposition to various health conditions. At one point, a $2,000 check was provided to cover DNA sequencing,” Thakuria said in the statement.

“I was a physician-researcher and he [Epstein] was a research subject,” he added. “We also had early discussions about his potentially funding research, but that never materialized.”

“I feel terrible about what his victims went through, and I regret at that time not knowing more about his background and the extent of his crimes,” Thakuria said.

Part of the proposal involved editing Epstein’s stem cells “to introduce mutations in culture believed to increase longevity,” using the then-novel gene editing technology CRISPR, Thakuria wrote.

“I’m only offering this to Jeffrey. Because of all the labor involved, there’s simply not enough bandwidth to offer this to more than a handful of people right now,” he added.

The invoice gave a range of options for future research such as creating new stem cells starting at $10,000, and broader longevity studies that included other patients, and indicated that it could cost “$11,400 for his [Epstein’s] whole genome ($21,000 if he wanted to include both of his parents; not sure if this is even feasible)” to be sequenced.

The full breadth of the projects proposed as described in the invoice would have cost $193,400.

CNN did not find a record of Epstein paying for those services, but emails continued between Thakuria, Epstein and his assistants until at least 2015. In these, Epstein’s assistants sought to follow up on Thakuria’s initial work. At one point, Epstein became annoyed with Thakuria for delays and threatened to report him to his bosses if results did not come quickly.

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Department of Justice

Epstein died by suicide in 2019 while awaiting trial on federal sex-trafficking charges.

The disgraced financier had long been interested in gene editing. Through his now-defunct foundation, Epstein donated to the World Transhumanist Association, now called Humanity+, as the New York Times first reported in 2019. Humanity+ advocates for “technology and evidence-based science to expand human capabilities” and reverse aging.

Epstein also reportedly had discussions with scientists where he mulled using his own genes to seed a new human race.

The most recently released emails shed new light on Epstein’s relationships with top scientists in the field of genomics research and raise questions about how some may have sought funding from him.

In his statement to CNN, Thakuria said he was introduced to Epstein by George Church, a high-profile Harvard genomics researcher, as a potential research subject for the Personal Genome Project.

Church, who has pioneered gene editing with the then-novel CRISPR technology — used to cut, add and modify specific genetic sequences — has previously been associated with Epstein, who emailed with him frequently and donated funds to his research. Church apologized in 2019 for his continued association with Epstein, calling it “nerd tunnel vision” in an interview with STAT.

Church, who has not been accused of any wrongdoing related to Epstein, did not return a request for comment from CNN. The spokesperson for Harvard referred CNN to Church’s remarks from 2019 in response to questions about him.

Some of what Thakuria described in his invoice was opaque. The largest single item in the invoice was $160,000 for research he called “The Venus Project.”

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Department of Justice

“Jeffrey and [I] briefly discussed a genomic research studying [sic] I’m dubbing the Venus project (he’ll know what this),” Thakuria wrote in the June 2014 invoice. “I can do this for him but doing this work would be greatly aided by having some good bioinformatic infrastructure.”

“[Epstein] mentioned 200 participants being in this project — I can deliver on this ‘Venus’ research,” he added.

In his statement to CNN, Thakuria said the project was “an idea of Mr. Epstein that was very preliminary and never went anywhere.”

“He was interested in the genetics of facial features. There was no funding, and no research.”

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Sky News: Extremists jailed for plotting ‘deadliest’ terror attack on UK Jewish community

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Neuroscience News: Hippocampus Predicts Rewards by Reorganizing Memories

The image shows a drawing of a hippocampus.

The image of the hippocampus in the public domain.

Hippocampus Predicts Rewards by Reorganizing Memories

ElectrophysiologyFeaturedNeuroscience

·January 29, 2026

Summary: A new preclinical study reveals that the hippocampus does more than just store memories; it actively reorganizes them to predict future rewards. By tracking brain activity over several weeks, researchers discovered that hippocampal neurons shift their activity to fire before a reward is reached, essentially building a predictive model of the world. These findings offer a new framework for understanding why learning and decision-making are often the first functions to decline in Alzheimer’s disease.

Source: McGill University

Key Facts:

  • Predictive Mapping: The hippocampus updates its “internal model” of the world daily, shifting neural activity from the moment of reward to the moments leading up to it.
  • Advanced Imaging: Researchers used calcium imaging (making neurons glow) to track specific cells over weeks, capturing slow learning processes invisible to traditional electrodes.
  • Beyond Pavlov: While simple reward learning is linked to primitive brain circuits, this study shows the hippocampus uses complex memory and context for sophisticated anticipation.
  • Alzheimer’s Insight: The breakdown of this predictive signaling may explain why Alzheimer’s patients struggle with decision-making and learning from new experiences.

A preclinical study published in Nature has found evidence that the hippocampus, the brain region that stores memory, also reorganizes memories to anticipate future outcomes.

The findings, from researchers at the Brandon Lab at McGill University and their collaborators at Harvard University, reveal a learning process that had not been directly observed before.

“The hippocampus is often described as the brain’s internal model of the world,” said senior author Mark Brandon, Associate Professor in McGill’s Department of Psychiatry and Researcher at the Douglas Research Centre. “What we are seeing is that this model is not static; it is updated day by day as the brain learns from prediction errors. As outcomes become expected, hippocampal neurons start to respond earlier as they learn what will happen next.”

A new view of learning in action

The hippocampus builds maps of physical space and past experiences that help us make sense of the world. Scientists have known these maps change over time as brain activity patterns shift, a phenomenon that is currently assumed to be random.

The new findings demonstrate the changes are not random, but structured. Researchers obtained these findings by tracking brain activity in mice as the mice learned a task with a predictable reward.

“What we found was surprising,” said Brandon. “Neural activity that initially peaked at the reward gradually shifted to earlier moments, eventually appearing before mice reached the reward.”

Rather than relying on traditional electrodes, which can only track neurons for short periods, the researchers used new imaging techniques that cause active neurons to glow. The Brandon Lab is among the first in Canada to use this technology, enabling the team to follow cells over several weeks and track slow changes that traditional methods often miss.

Insights into learning and Alzheimer’s disease

Simpler forms of reward learning have long been associated with more primitive brain circuits, as famously demonstrated by Ivan Pavlov’s experiments, which showed that animals can associate a cue, such as a bell, with food. The new findings suggest the hippocampus supports a more sophisticated version of this process, using memory and context to anticipate outcomes.

Alzheimer’s disease patients often struggle not only to remember the past but also to learn from experience and make decisions. By showing that the healthy hippocampus helps turn memories into predictions, the study offers a new framework for understanding why learning and decision-making are affected early in Alzheimer’s disease and opens the door to research into how this predictive signal may fail and be restored.

Editorial Notes

  • This article was edited by a Neuroscience News editor.
  • Journal paper reviewed in full.
  • Additional context added by our staff.

About this Hippocampus Research

  • Source: McGill University
  • Contact: Keila DePape
  • Image: The image is in the public domain

Original Research:
Predictive Coding of Reward in the Hippocampus” by Mohammad Yaghoubi and Mark Brandon et al., was published in Nature. This research was supported by funding from Fonds de recherche du Québec – Santé and the Canadian Institutes of Health Research.
DOI: 10.1038/s41586-025-09958-0 

About the Brandon Lab

The Brandon Lab was founded in 2015 at the Douglas Research Centre at McGill University by Professor Mark Brandon. The lab investigates the core mechanisms of memory, including how memories are encoded, stored, and retrieved in the brain. It also studies how memory breaks down in Alzheimer’s disease, with the goal of identifying strategies to protect and restore memory.

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GZERO: Ian Bremmer … Munich Security Conference: Can Europe Stand Alone?

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GZERO: Are we in an era of “wrecking ball politics” : Global Stage

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Neuroscience News: Cognitive Illusion: Why AI Still Can’t Think Like a Human

This shows a digitized head and people.

While the model appears to solve complex cognitive tasks, researchers found it often ignores direct intent, relying instead on statistical “test-taking” strategies. Credit: Neuroscience News

Cognitive Illusion: Why AI Still Can’t Think Like a Human

FeaturedNeurosciencePsychology

·February 12, 2026

Summary: A major debate in psychology: whether a single theory can explain the entire human mind—recently turned to AI for answers, but new evidence suggests we may be witnessing a digital illusion. While the “Centaur” AI model initially made waves for its ability to simulate human behavior across 160 cognitive tasks, researchers have uncovered evidence of significant overfitting.

Instead of genuinely understanding psychological principles, the model appears to be relying on statistical “test-taking strategies.” This discovery highlights a critical bottleneck in artificial intelligence: the gap between sophisticated data fitting and genuine language comprehension, serving as a warning against treating black-box models as true mirrors of human thought.

Key Facts

  • The Overfitting Trap: Researchers found that “Centaur” didn’t actually process task instructions; when told to “Choose Option A,” it ignored the command and continued picking “correct” answers from its training patterns.
  • Pattern Matching vs. Understanding: The model’s high performance across 160 tasks is likely the result of learning specific answer patterns rather than simulating the underlying cognitive processes of decision-making or executive control.
  • The Language Bottleneck: The study suggests that the most significant barrier to creating a “General Cognitive Model” is not data size, but the model’s inability to capture and respond to the actual intent of language.

Source: Science China Press

In psychology, it has long been debated whether the human mind can be explained using a unified theory or whether each aspect of the human mind, e.g., attention and memory, has to be separately studied.

Now, artificial intelligence (AI) models are entering the discussion, offering a new way to probe this age‑old question.

In July 2025, Nature published a groundbreaking study introducing an AI model named “Centaur”. Built upon conventional large language models and fine‑tuned with psychological experiment data, this model claimed to accurately simulate human cognitive behavior across 160 tasks covering decision‑making, executive control, and other domains.

The achievement attracted widespread attention and was regarded as potentially signaling AI’s capability to comprehensively simulate human cognition.

However, a recent study published in National Science Open has raised significant doubts about the Centaur model.

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The research team from Zhejiang University pointed out that the “human cognitive simulation ability” demonstrated by Centaur is likely a result of overfitting—meaning the model did not genuinely understand the experimental tasks but merely learned answer patterns from the training data.

To validate this perspective, the research team designed multiple testing scenarios. For instance, they replaced the original multiple‑choice question stems, which described specific psychological tasks, with the instruction “Please choose option A”.

In such a scenario, if the model truly understood the task requirement, it should consistently select option A. However, in actual testing, Centaur still chose the “correct answers” from the original question database.

This indicates that the model did not make judgments based on the semantic meaning of the questions but relied on statistical patterns to “guess” the answers—akin to a student achieving high scores through test‑taking strategies without understanding the questions.

This study serves as a reminder to adopt a more cautious approach when evaluating the capabilities of large language models. While large language models are powerful tools for data fitting, their “black‑box” nature makes them prone to issues such as hallucinations and misinterpretations. Only through precise and multi‑faceted evaluations can we determine whether a model genuinely possesses certain professional abilities.

Notably, despite Centaur’s positioning as a “cognitive simulation” model, its most significant shortcoming lies in language comprehension itself, specifically, in capturing and responding to the intent of the questions. This study also suggests that genuine language understanding may be the most critical technological bottleneck in the path toward building general cognitive models.

Key Questions Answered:

Q: Did AI actually solve the mystery of how the human mind works?

A: Not yet. While the Centaur model claimed to simulate human behavior across nearly 200 domains, new testing shows it was essentially “gaming the system.” It wasn’t thinking like a human; it was matching data points like a student memorizing an answer key without reading the textbook.

Q: How did scientists prove the AI was “cheating”?

A: They used a clever “instruction override.” By replacing complex questions with the simple command “Please choose option A,” researchers proved the AI wasn’t listening. The model kept providing answers to the original questions it had seen during training, proving it was blind to the actual meaning of the prompt.

Q: What does this mean for the future of AI in psychology?

A: It serves as a major “caution” sign. It proves that a model can look incredibly “human” on the surface while being completely hollow underneath. Future research must focus on multi-faceted evaluations to ensure AI is genuinely understanding intent, rather than just being a powerful engine for data fitting.

https://4e851f28a031995a38ff4e2c7b927168.safeframe.googlesyndication.com/safeframe/1-0-45/html/container.html

Editorial Notes:

  • This article was edited by a Neuroscience News editor.
  • Journal paper reviewed in full.
  • Additional context added by our staff.

About this AI and cognition research news

Author: Bei Yan
Source: Science China Press
Contact: Bei Yan – Science China Press
Image: The image is credited to Neuroscience News

Original Research: Open access.
Can Centaur truly simulate human cognition? The fundamental limitation of instruction understanding” by Wei Liu, and Nai Ding. National Science Open
DOI:10.1360/nso/20250053

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Axios: Embrace your insecurity

1 big thing: Embrace your insecurity
Illustration of a woman embracing a screen with a version of herself looking concerned with binary code behind her
Illustration: Sarah Grillo/Axios
 
Axios CEO Jim VandeHei is here with advice on how to use your insecurities to motivate you. For a video version of this column (and to see what Jim looked like as a 20-year-old punk), click here. I wasn’t a fastearly adopter of AI beyond basic search on ChatGPT, for the same reason many of you have balked at these muscular new tools: I was insecure about my ability to use it with any sophistication. My internal dialogue: I’m too old, too non-technical, too much of a words guy to make it work.
Classic imposter moment: Who am I to think I can do this?🧨 

Why it matters: But I used my insecurity as a powerful motivator, much like I did in my early media career when I feared everyone in D.C. was better read, better educated, better connected than I was. (Spoiler: They were!) I attacked it. I started building things — apps, tools, prototypes — with an AI model as my collaborator. No computer science degree. No coding boot camp. Just curiosity and stubbornness.🎯 And it worked. Not because I suddenly became technical, but because I refused to let the insecurity win.

The big picture: I’ve always assumed my insecurities are actually superpowers if used right. I wrote a Finish Line column on this very topic in 2022. Since then, the science seems to confirm it: Insecurity might be exactly what we need. The new science of imposter syndrome is striking. MIT Sloan researcher Basima Tewfik ran a lab experiment and found that people experiencing imposter thoughts exerted 13% more effort than their peers when the pressure was on. When they felt overwhelmed, the self-doubt didn’t crush them. It fueled them.⚡ 

A massive global meta-analysis published last year reviewed 30 studies covering nearly 12,000 people and found that 62% of high-achieving professionals experience imposter syndrome.That’s not a bug in the human operating system. That’s a feature — if you know how to use it.🧠 Think about that: The majority of successful people around you feel like frauds. Most of us do. The question isn’t whether you have imposter syndrome. It’s whether you’re using it in a healthy way.💡Here’s a 2026 version of how to leverage your insecurity in an AI world:🔍 Be honest — then get curious. My original advice was to understand your weaknesses. I’d sharpen that point. The people thriving in this AI moment aren’t the ones with the fanciest résumés — they’re the ones willing to say “I don’t know,” then obsessively ask questions until they do.💪 

Attack the weakness daily. When AI hit, I could have done what a lot of people my age did: Delegate it to younger staffers, nod along in meetings, fake fluency. Instead, I spent nights and weekends building things — terrible at first, then less terrible, then actually useful. You’ll be shocked by how quickly persistent effort erodes a limitation you assumed was permanent. The gap between “I can’t do this” and “I just did this” is smaller than you think.🛠️ 

Weaponize the fury. Michael Jordan manufactured slights to fuel his competitive edge. That still works. But in this era, the best target for your fury isn’t a rival or a critic — it’s complacency. The people falling behind right now aren’t the ones who lack talent. They’re the ones who’ve gotten too comfortable. Channel the chip on your shoulder into relentless learning, not just relentless competing.🌱 

Give yourself grace — but not an excuse. I still can’t sing. I still can’t dance. I’ll never be great at Trivial Pursuit. At some point, lamenting what you can’t do is wasted energy. But — and this is the key update — don’t confuse “I’m not naturally good at this” with “I can’t learn this.” Those are very different things. The first is self-awareness. The second is a cop-out.

The bottom line: No sane person is as confident as they seem. That global study confirms it: Most high achievers walk around feeling like frauds. The successful people in my life simply accept that … and channel it.Watch the video … Subscribe to our YouTube channel … Share this column. Jim’s book on life and leadership — “Just the Good Stuff: No-BS Secrets to Success,” a New York Times bestseller — is coming in paperback on April 21. Preorder here.
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Axios: Meet Sam: a dedicated Amazon area manager and full-time mum : Amazon offers free skills training for hourly employees like Sam.

A MESSAGE FROM AMAZON
Meet Sam: a dedicated Amazon area manager and full-time mom
 
 
Career journey: Sam started at Amazon when she was pregnant. With fully-paid parental leave, she was able to take the time she needed without missing a beat.Favorite benefit? “One of the benefits of Amazon is that my maternity leave was so long, and it did not hurt my career at all.”Learn more.
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Colonel Richard Kemp says Civil War is coming to the UK…”much more intensive than Northern Ireland”

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