The Mitochondria Doctor: This Reverses Gray Hair, Makes You Feel Young Again & Fixes Disease!

 

1. Core Paradigm: You Are Energy, Not a Body

The fundamental thesis of Dr. Picard’s work is that science has over-indexed on the material body while ignoring the energetic processes that dictate life, health, and consciousness.

"We are energy we literally are the energy that's flowing through the body... The difference between a dead body, a cadaver, and a living, thinking, feeling, conscious person who cares is the flow of energy." [00:03:12]

2. The Mechanics of Mitochondria

Mitochondria are not merely static "powerhouses of the cell." Dr. Picard highlights their evolutionary origins and their true role as an information-processing system:

• The 5,000 Trillion Ecosystem: There are roughly 1,000 mitochondria per cell, totaling about 5,000 trillion in the human body. They generate ATP (cellular energy currency) by ripping electrons from food and flowing them toward oxygen, creating heat and electricity [00:07:33].

• The Cellular Brain: Mitochondria act as a collective, distributed brain within our cells, continuously monitoring receptors for stress hormones like cortisol to evaluate environmental safety [00:16:14].

• Symbiotic Origins: Roughly 1.5 billion years ago, mitochondria evolved from free-living oxygen-utilizing bacteria that entered an anaerobic host. Picard notes that this didn't just provide energy; it gave cells the sensory architecture to cooperate, effectively creating multicellular and social life [00:17:58].

3. The Law of Energy Resistance & Disease

Picard introduces the Energy Resistance Principle, defined mathematically as Energy Demand divided by Flow Capacity. When energy cannot flow smoothly or is heavily forced into a restricted system, resistance increases, leading to "sparks flying" (oxidative stress) and cellular breakdown. This explains major pathologies:

• Diabetes: Fundamentally a disease of energy resistance. To protect the mitochondria from being overwhelmed by too many forced electrons from chronic high blood glucose, muscle cells downregulate insulin receptors to shut the intake valves [00:19:23]. Obesity acts as a "fat capacitor"—a protection mechanism storing away excess energy that the mitochondria cannot process [00:13:44].

• Cancer (The Warburg Effect): Cancer cells intentionally ditch their social, mitochondrial-driven function. They revert back to an ancestral, anaerobic, "selfish" bacterial state focused solely on self-replication, successfully immunizing themselves against cell suicide (apoptosis) triggered by mitochondria [00:21:47].

• Alzheimer's (Type 3 Diabetes): Dr. Picard explicitly challenges the mainstream amyloid plaque theory. Instead, Alzheimer's begins as an early phase of local hyper-metabolism (the brain working harder to compensate for energy inefficiency), which eventually tires out, causing the brain regions to become hypometabolic. Chronic excess glucose forces energy pressure onto these delicate brain circuits, destroying conductance [01:02:28].

4. Reversing Gray Hair & The Energy Budget

The breakthrough study conducted by Picard's lab mapped the pigmentation patterns of individual hairs, uncovering that hair graying is not a one-way, linear aging process.

"This was incontrovertible evidence that graying of hair is reversible and it can be pretty pretty fast... a white hair completely white regained color in just about a week." [00:39:32]

• The Mechanism: Graying occurs when a hair follicle reaches a high threshold of energy resistance and stops prioritizing pigment. However, if the energetic pressure lifts (e.g., going on vacation, reducing stress), there is a "window of opportunity" where the hair follicle can drop below that threshold and regain its color [01:41:42].

• The Structural Shift: Surprisingly, gray hairs contain more mitochondria, not fewer. When cells age or struggle, they waste vast amounts of energy attempting to compensate by upregulating faulty mitochondria [00:36:07].

5. Stress, GDF-15, and Sickness Behavior

Nothing is free in biology. Dr. Picard’s lab proved that psychological stress has a massive physical energy cost. In laboratory conditions, exposing cells to cortisol increased their baseline energy expenditure by 60% simply to prepare for a perceived threat [00:01:01].

The biological sequence of chronic stress operates via a specific biomarker:

1. The Stress Trigger: Mental stress or trauma causes the body to produce an energetic stress protein called GDF-15 (Growth Differentiation Factor 15) [01:49:02].

2. The Brainstem Dock: The brain stem contains the sole receptors for GDF-15, specifically located in the area postrema (the nausea and vomiting center) [01:49:32].

3. The Survival Response: The brain interprets high GDF-15 as a signal that the body is critically running out of energy or fighting an infection. It immediately triggers energy conservation mode (lethargy, depression, anhedonia, loss of motivation) while forcing glucose and fat into the blood supply to rescue struggling organs, which deposits as dangerous visceral belly fat if unspent [01:50:30].

6. Real Actionable Interventions

Because humans operate on a fixed energy budget, you cannot simply force more energy into the top of the pyramid by eating more food. Anti-aging and vitality require maximizing the efficiency of the flow.

• Mindset & Coherence (The Laser Analogy): Scattered, anxious thoughts act like an incandescent light bulb—diffused photons going everywhere, incapable of traveling far. A unified sense of life purpose focuses our mitochondrial energy into a perfect phase alignment.

  "Focus I think brings our energy into a coherent state... allowing you to do more with less energy." [01:30:31]

• Exercise Hormesis: Working out temporarily spikes energy resistance, causing acute discomfort and oxidative stress. However, during recovery, the body adapts to ensure it isn't caught off guard next time, triggering mitochondrial biogenesis (doubling muscle mitochondria) and lowering baseline energy resistance for regular daily life [00:47:01].

• Intermittent Fasting & Ketones: Overeating and constant grazing cause continuous mitochondrial friction. A restricted eating window (e.g., 2 PM to 6 PM) forces cells into a state of structural efficiency, triggering mitophagy (the self-eating and elimination of broken, low-performing mitochondria) [01:00:26]. Ketones provide a significantly shorter, more direct pathway to the brain with fewer enzymatic "resistors" than glucose, calming neurological inflammation [01:10:05].

• Red Light Therapy: Red and near-infrared light waves pass through bone and tissue to hit a specific mitochondrial cellular antenna called cytochrome C oxidase. At low-to-moderate doses, it reduces glucose spikes by accelerating electron flux and increasing oxygen consumption [02:04:54]. However, Dr. Picard warns it follows a strict bell curve: excessive exposure creates severe phototoxicity, causing massive oxidative stress that shuts down cellular respiration entirely [02:09:01].

The overarching conclusion of the discussion emphasizes stepping away from the conceptual model of the body as a mechanical car requiring constant external parts (supplements/drugs), and instead honoring it as an interconnected web of energy that thrives on real human connection, alignment with nature, periods of caloric restriction, and deep psychological clarity.

Sam Bent - Backdoor

 

Darknet Diaries Episode 175: Bayrob

 

Darknet Diaries Episode 175: Bayrob chronicles the 10-year lifespan, technical execution, and ultimate downfall of the Bayrob Group—a highly sophisticated Romanian cybercrime syndicate.

1. Key Figures & Characters

• The Perpetrators (The Bayrob Group): Operating from Bucharest, Romania, this core three-man team consisted of:

  • Bogdan Nicolescu (alias "Masterfraud"): The leader and primary orchestrator.

  • Radu Miclaus (alias "Minolta"): Key co-conspirator heavily involved in the operations and logistics.

  • Tiberiu Danet (alias "Amightysa"): The third core technical member who later pleaded guilty.

• The Investigators & Researchers:

  • Liam O'Murchu: A top-tier malware analyst at Symantec (famous for early analysis of the Stuxnet virus). He reverse-engineered the malware, named it "Bayrob" (robbing eBay users), and became a direct target of the hackers' taunts.

  • FBI Special Agents (e.g., Stacy Diaz, Macfarlane): Led the federal investigation in tandem with global agencies and the Romanian National Police.

2. The Core Criminal Concept & Mechanics

The Bayrob Group began in 2007 as an online auction fraud scheme but evolved into a massive, multi-vector botnet operation that lasted until 2016.

Phase 1: The Fake Car Scam (Man-in-the-Middle Browser Hijacking)

• The Bait: The hackers placed over 1,000 fraudulent listings for high-end items (primarily cars and motorcycles) on eBay and Craigslist at tempting prices.

• The Infection: The photos of the cars were laced with custom malware. When a user downloaded or clicked to view the vehicle slideshow, the executable stealthily installed itself on the victim's PC.

• Geofencing: The attackers intentionally used geofencing so the fraud execution only active within specific US regions, masking it from European researchers like Liam (who was initially based in Ireland).

• The Hijack: Once infected, the malware performed localized DNS poisoning and browser interception. If the victim attempted to navigate to real eBay, PayPal, or Facebook pages, the malware rerouted them to pixel-perfect, local phishing clones hosted by the hackers.

• Fake Infrastructure:

  • If a victim clicked "Help" or "Customer Service" on the fake eBay page, they were met with a live chat window connected directly to the hackers, who walked them through the "safe transaction."

  • To explain why the car hadn't arrived, they built a completely fake auto-transportation and trucking company website with fabricated tracking numbers to string victims along.

  • The malware explicitly blocked access to security sites and consumer protection portals like ic3.gov (the FBI's Internet Crime Complaint Center).

• The Theft: Victims were instructed to wire funds (usually between $8,000 and $11,000 per vehicle) via non-refundable wire transfers. The funds were instantly extracted by a complex network of international "money mules."

Phase 2: Botnet Proliferation & Diversification

As technology advanced, the trio realized they were sitting on massive computing power and pivoted to exploit their botnet:

• The Proliferation: The malware harvested email address books from infected hosts and used those relationships to blast out over 70 million malicious emails disguised as official notices from Western Union, Norton AntiVirus, the IRS, and AOL.

• AOL Account Hijacking: They forced compromised PCs to systematically register over 100,000 fake or hijacked AOL email accounts to act as clean distribution nodes for spam.

• Cryptocurrency Mining: They harnessed the distributed processing power of their botnet to mine early cryptocurrencies.

• Data Mining: They continuously scraped the 400,000+ infected host machines for passwords, credentials, and credit card numbers, which they packaged and repeatedly sold on Darknet marketplaces like AlphaBay.

3. The Research Feud (Liam vs. Bayrob)

When Liam O'Murchu published a Symantec blog post breaking down their operation—complete with video evidence of him posing as a victim buying a car—the hackers noticed.

Instead of going quiet, their operational security (OpSec) turned into direct trolling:

• Taunting the Analyst: They began hardcoding insults directly into the malware's Command and Control (C2) infrastructure. They registered domain names explicitly targeting Liam (e.g., gayassholeliam.com, tinycockliam.com, liamthemule.com, thankyouliam.com).

• The Chicken Message: Inside the code, they left a broken-English message for the Symantec team: "Symantec team is a big hen coop chicken smart."

• The Backfire: This mocking behavior only motivated Liam to dig deeper. Using Symantec's internal telemetry, he mapped out their multi-layered proxy architecture, noting that the hackers routed their traffic across multiple layers of infected machines globally to hide their exact Bucharest footprint.

4. The Investigation Breakthrough & Downfall

Despite their high-level OpSec, the group was undone by standard human error and aggressive federal tracking.

• The Fatal Mistake: A core member of the Bayrob Group accidentally logged into his personal, real-name email account from an active criminal server infrastructure. AOL's abuse team noticed the overlap and linked the two accounts.

• The Social Footprint: This single unencrypted login gave the FBI a name, which led to real-world social media profiles in Romania.

• The Darknet Trap: The FBI launched undercover operations on AlphaBay, executing controlled buys of stolen credit card data and credentials directly from the group to solidify the evidentiary link between the online personas and the physical individuals.

• The Arrest (2016): Working in tandem with the Romanian National Police and the Romanian Directorate for the Investigation of International Organized Crime and Terrorism, the FBI executed coordinated raids in Bucharest. They seized smartphones, crypto, and servers.

5. Final Case Consequences & Statistics

The legal fallout concluded in late 2019 and early 2020 in the Northern District of Ohio:

• Bogdan Nicolescu ("Masterfraud"): Sentenced to 20 years in prison.

• Radu Miclaus ("Minolta"): Sentenced to 18 years in prison.

• Tiberiu Danet ("Amightysa"): Pleaded guilty; sentenced to 10 years in prison.

The Metrics of the Bayrob Enterprise

• Duration: 9+ Years (2007–2016)

• Infected Assets: 400,000+ computers compromised globally (primarily US-based)

• Direct Financial Theft: Over $4 million verified (excluding the value of dark web data sales and crypto mining)

• Average Damage Per Scam Victim: $8,000 to $11,000

• Spam Footprint: 70+ Million malicious emails sent via 100,000+ automated email registrations

Sam Bent, Surveillance,

 

Pro-AI, shareholders, Irony, Marcus Hutchins,

 

Expel - EDR - BYOVD,

 

BYOVD - Drivers with old vulnerabilities being used

 

Marcus Hutchins - Passkeys dilemma

 

Want to know the future? Don’t trust the stockmarket

 

Share prices are buffeted by far more than just new information

Jun 2nd 2026|4 min read

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“If economists wished to study the horse,” a dismal scientist once joked, “they wouldn’t go and look at horses. They’d sit in their studies and say to themselves: ‘What would I do if I were a horse?’” But at least horses tend to be spared such attention; finance types are not. And one economic idea is especially liable to get them snorting with impatience and asking whether the person who cites it has been near a trading floor.

This is the efficient-market hypothesis, the formal version of which says that investors, in aggregate, perfectly and promptly incorporate new information into asset prices. Those who invoke it can often mean something even stronger: that markets therefore provide the best possible forecasts of fundamentals like corporate earnings. In other words, the price is always right, as it surely would be if it were economists cantering around and making all the decisions.

Right now this Platonic ideal feels especially remote. Retail traders clamour for meme stocks, whipping up prices just to give short-sellers a thrashing. Shares in GameStop, an ailing video-game seller selected for such favour in 2020, are still worth more than 20 times as much as they were then. They have done about as well as Nvidia’s, the biggest beneficiary so far of the artificial-intelligence revolution. Nvidia’s fellow tech giants are racing to issue new stock and sell it to the public—a sure sign that they reckon the bull market is nearing its peak. Yet investors are still happily piling in.

Those financial economists who do visit the stables have known for nearly half a century that markets are far more volatile than they would be if new information were all that moved them. Robert Shiller, who won the Nobel prize in 2013, showed this for bond yields in a paper published in 1979 and for stock prices in 1981. Over the hundred years or so of data he studied, stock prices fell several times by much more than could have been justified even by a Depression-scale downturn. This made it implausible that investors were pricing shares based only on sober forecasts of their dividends.

More recently Mr Shiller’s intellectual heirs have helped explain why—aside from people’s occasional tendency to bolt off and join a stampede. The most persuasive theory, held increasingly by both researchers and academically minded investors, is the “inelastic-markets hypothesis”, coined by Xavier Gabaix of Harvard University and Ralph Koijen of the University of Chicago. Its crux is that share prices, rather than being set by the dividends (or earnings) investors expect, are buffeted significantly and lastingly by capital flows. Messrs Gabaix and Koijen estimate that someone who buys $1-worth of shares with fresh cash pushes up aggregate stockmarket value by $3-8.

To see why, picture three types of investor. One is a return-chaser, who buys more shares when they are on a tear and sells when prices are falling (think of retail traders or trend-following hedge funds). The second maintains fixed asset allocations: 60% to stocks and 40% to bonds, say (think of a pension scheme). The third is a value investor, only interested in buying shares at rock-bottom after a crash (think of Warren Buffett). Squint, and this stylised mix looks rather like the groups that dominate real-world markets. Importantly, any arbitrageurs—who efficient-market enthusiasts imagine might smooth out distortions and reprice assets according to fundamentals—are few, and tightly constrained.

Now imagine a retirement saver who wants to buy shares in a bull market. They cannot hit up the return-chaser, who wants more themselves. The fixed allocator can sell only if prices rise, since they must maintain their 60/40 split. The value investor, too, will sell only if shares get more expensive and hence, to them, less attractive. So the capital flow sends stock prices up, regardless of what anyone thinks about future earnings.

If the thoroughbreds manning trading desks get frustrated by economists touting efficient markets, they in turn may smirk at this explanation. It does, after all, sound quite like “prices rise to match supply to demand”, without saying why demand rose to begin with.

That absence might in fact be a strength. Ordinary savers who buy stocks each payday do not generally base their purchases on predictions of stellar corporate earnings. Nor, necessarily, do institutional investors who are told to aim for a set return target and have few better shots. Their actions nevertheless push share prices up. Betting that this might forecast future champions seems like a good way to lose your shirt. ■

Will artificial intelligence soon escape human control?

 

“Recursive self-improvement” is both tantalising and worrying

Photograph: Getty Images

Jun 7th 2026|9 min read

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AI Narrated

WHEN ANTHROPIC, an artificial-intelligence lab, debuts on stock markets later this year, it is likely to be one of the biggest initial public offerings in history. That’s because the company’s Claude chatbot is beloved of coders, who are willing to pay a lot for access. Since Claude Code, its software-engineering agent, launched in February 2025, it has become indispensable for many human developers around the world. That includes Anthropic’s own: more than four-fifths of the code it published in May was written by Claude, the company says. Before Claude Code launched, the percentage was “low single-digits”.

The systems have improved in quality of output as well as quantity. An influential benchmark from METR, a think-tank, shows that in early 2025 Anthropic’s models could complete tasks that took human engineers a little under an hour. The company’s latest systems can complete tasks that would take more than a working day.

And so it may be easy to raise a cynical eyebrow when the company, at the top of its game and outclassing the competition, calls for the world to have “the option to slow or temporarily pause frontier AI development”, as it did on June 5th. What market leader would not wish that its competition stop trying to catch up?

Yet Anthropic’s leaders, who have for years worried about the prospect of out-of-control AI wreaking havoc, seem sincere. The latest generation of AI models are such competent coders, engineers and (soon) scientists that many worry they may be among the last ever made by humans. Jack Clark, an Anthropic co-founder, thinks there is a 60% chance that, by the end of 2028, an AI system will be capable of creating its own successor with no human involvement.

That moment would mark the beginning of a process called “recursive self-improvement” (RSI), a closed loop. Version one of a model produces version two, which is faster and more capable; version two produces version three, which is more so again. The loop continues, and the improvements grow with each iteration. Build an AI system capable of this, and your human engineers never need to build another one again. “What can seem to many like a fanciful story may instead be a real trend,” says Mr Clark.

Nobody knows for sure what the consequences of RSI would be. Because AI can, unlike humans, work tirelessly and constantly, some think it would in short order lead to a superintelligent AI—a “fast take-off”. (It has also been onomatopoeically dubbed “going foom”, for the sound one might imagine an intelligence explosion making). AI doomers fear the superintelligence would be beyond human control, and that the start of RSI is the moment at which humanity’s fate is handed over to the machines. Yet a self-improving AI would probably face speed limits, at least at first.

Building a model capable of RSI would require automating a range of specialist tasks currently carried out by humans. At present data scientists work on the theory of AI and coders put it into practice. Systems engineers build the foundations on which toy models can be raised to production scale. Other people seek out novel sources of training data, or experiment with ways to generate it fresh. Alignment and safety teams check that what comes out of the training process won’t cause harm, intentional or otherwise.

Not all of those teams are equally amenable to AI assistance, and within each specialism some tasks are more automatable than others.  It will not be too long until a human coder can do their job without ever writing a line of computer code themselves, but it may be some time until an AI is able to negotiate to acquire a previously-undigitised collection of scientific papers. It is not always obvious how the “jagged frontier” will progress. Designing new algorithms seemed one of the safer jobs, until one of Google DeepMind’s models, AlphaEvolve, began doing it in May 2025. It proposed a change to how Google spreads workloads across its data centres that saved 0.7% of the company’s worldwide computing power, and found better ways to perform matrix multiplication, which sped up the training of Gemini, the company’s flagship large language model (LLM), by 1%.

Full RSI requires every task in this chain to become automated. The AI-powered acceleration of research and development (R&D) may be felt before then, however. “As the fraction of AI R&D performed by AI systems increases, the productivity boost over human-only R&D” could increase ten-fold, then a hundred-fold, then a thousand-fold, according to a report published in January by the Centre for Security and Emerging Technology (CSET), a think-tank within Georgetown University. In that scenario, it warns that even if some aspects of AI R&D are initially difficult to automate, “the accelerated rate of progress means those bottlenecks are soon overcome.”

The joy of repetition

Today no AI model can build its own successor. But big AI models can build smaller models on their own. With human help they can build other big AI models, too.

Earlier this year Andrej Karpathy, a then-independent researcher who now works for Anthropic, trained a chatbot about as capable as GPT-2, a large language model built by OpenAI in 2019. Back then the model took 168 hours of training to build on 32 state-of-the-art chips; Dr Karpathy achieved the same result using a single computer with eight GPUs, the specialised chips used to build AI, in only three hours. With some more months of work he reduced the training time for his model, Nanochat, to just over two hours.

In March he handed the work of speeding up the training process over to an AI agent called Autoresearch. In two days the training time dropped to one hour and 48 minutes, and five days after that it fell to one hour and 39 minutes. “I didn’t touch anything,” Dr Karpathy says. The 18% improvement on the human work is striking because Dr Karpathy is a particularly talented human: he was a founding member of the research team at OpenAI and the head of AI at Tesla for five years.

The improvements themselves were prosaic. The AI agent picked better starting values for the training run, widened the scope of the LLM’s “attention” window and noticed that the model’s focus was wandering. None is particularly novel, Dr Karpathy says. But he had missed them. “They stack up and actually improved Nanochat,” he says.

Speed-ups of this kind are inevitable as models become more capable. Much of the work of building terabyte-sized frontier models is less glamorous than the AI industry’s enormous salaries and fancy offices suggest. It involves plumbing together the layers of an infrastructure stack that are bought in from third parties, debugging hardware and software set-ups and tweaking “hyperparameters”, the initial set-up of a training run, until the outcome looks solid. An AI system can do much of that today, with little supervision.

But even the more nuanced intellectual work is nearing automation, says Joe Spisak, a researcher at Reflection AI, a lab based in New York that is building frontier models that are open-weight (meaning their parameters are publicly released). Give a frontier system a rough sketch of an idea for efficiency gains, and it is increasingly capable of designing an experiment, running tests on a toy model, seeing what works and responding with a plan that is ready to implement at scale.

AI models can carry out these sorts of tasks, which take humans hours, in around 30 minutes. Increasingly, humans play the role only of research director, steering the AI to run experiments, which the models code up, debug, optimise and monitor themselves. The productivity boost is alluring, but also alarming. As humans’ role in the production process shrinks, they may lose control. The end result could be models trained by models, to achieve goals set by models, whose safety is verified only by models.

Some fear a disaster. Max Tegmark, a physicist and machine-learning researcher at the Massachusetts Institute of Technology who has devoted much of the past decade to campaigning for AI safety, likens it to a driver flooring the accelerator on the motorway with their eyes closed. The result would be certain doom, he told the forthcoming edition of The Economist’s “Inside Tech” video show, as long as the driver refuses to open their eyes. Professor Tegmark offers a variety of scenarios in which things go wrong: powerful AI systems could outcompete humans as the decisionmakers in government and commerce, disempowering humanity; they could offer supreme power to whoever first builds them, ushering in global totalitarianism; or they could simply cease to care about humanity at all, and gradually squeeze people out to make room for more data centres and power generation.

Three years ago, Professor Tegmark led a call for a pause in global AI development, arguing that the creation of the then-cutting edge GPT-4 was tantamount to that blindfolded journey. This year’s CSET report warned that the systems created by RSI “pose extreme risks. This warrants preparatory action now.” Anthropic, it seems, is now close to agreeing with that prescription.

Hot chip

There are also several physical constraints that will, for now, impose limits on the speed at which models can improve themselves. The most important is access to compute. Despite efficiency gains, newer models continue to use more computing power to train than their predecessors, forcing progress to occur at the pace of data-centre development.

Consumer use of AI may also slow down AI-powered R&D, says Helen Toner, interim executive director of CSET and a lead author of its recent report. The limited capacity in AI data centres needs to be carefully split between serving paying customers, training future models and carrying out open-ended R&D. The more demand there is in the first category, the less capacity, in the short term, there is for the other two.

Then there is the issue of training data. Much recent progress in AI has been in areas where models can teach themselves how to succeed thanks to “verifiable rewards”. A piece of software either runs or it does not; a mathematical proof is correct or it is not. In such cases synthetic data, generated by models purely to train other models, can be checked for accuracy and added to the training data without risking the degeneracy that normally comes with training an AI on its own output. It is trickier to make a model better at creative writing or legal judgment.  If the models need to learn from the real world, that could also limit the reach of self-improvement.

“Closing the loop” may be a step on the road to superintelligence and—depending on your disposition—utopia or doom. But it is not the only step required to produce exponential growth in AI’s capabilities. ■

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