• Energy As The Life Force: Human existence is defined by the flow of energy, as the presence of physical components is common to both living beings and dead bodies.
• Mitochondrial Role Redefined: Rather than just cellular powerhouses, mitochondria act as information processors that adaptively manage energy distribution based on environmental and psychological demands.
• Energy Resistance Principle: Health relies on maintaining optimal resistance levels within the body's internal biological circuits, where excessive or insufficient resistance leads to dysfunction and disease.
• Metabolic Cost Of Stress: Chronic stress forces the body to prioritize immediate survival over long-term maintenance, significantly increasing energy expenditure and accelerating cellular aging.
• Psychological And Biological Interdependence: Mental states and mitochondrial function exist in a continuous feedback loop, suggesting that human health is a manifestation of how energy moves through both the brain and body.

“When you compare a dead body with a living one, the only difference is the presence of energy — the physical machinery, the DNA, the proteins, the skin, the organs, it’s all still there.”

I was surprised by Martin Picard’s choice of words. Evoking a lifeless image to start a conversation about energy flow was counterintuitive, but the image lingers and proves his point. Cadavers have all the “stuff” we associate with being human. The only thing missing, the Columbia professor suggests, is the flow of energy. He calls this the “potential for change,” and it’s what defines us, gives us vitality, and shapes our experience.

“We are not molecular machines, but energetic beings,” he tells me, “and we relate to one another on an energetic dimension.” It’s a succinct but provocative idea, one Picard believes could not only reshape how we understand the human experience but also lead to new treatments for a variety of diseases.

Every process in the body exists downstream of energy flow.

It’s also what drew me to his research in the first place. A self-professed “high-energy” individual, I’ve always identified with that quality in myself more than any other, but I’ve also struggled to reconcile something so fluid with the more concrete ways we’re taught to understand ourselves. Picard was the first researcher I’d encountered who placed that intuition within a scientific frame — and I wanted to hear more.

From philosophy to measurable science

“We are energy” sounds like something you’d be more likely to hear in Eastern philosophy than a modern research lab. Yet Picard doesn’t speak as a guru, but as an Ivy League professor who publishes in top journals and tests his ideas empirically. Still, he welcomes the comparison. “I don’t know what chi or prana are,” he says, “but the idea that we are deeply interdependent with the flow of energy does align with those philosophies, and it’s something researchers have to remain open to.”

To move from abstraction to measurement, Picard focuses on something concrete: mitochondria, the organelles that generate and regulate the energy that powers our cells. At Columbia, he leads a lab focused on mitochondrial psychobiology — a term he coined to describe how psychological states interact with biological processes within mitochondria. This framework allows Picard to empirically study how lived experience manifests physiologically, including areas biology has struggled to explain: aging, the cost of stress on health, and how thoughts and emotions affect physiology.

If you see yourself energetically, it changes your behavior. You begin to see yourself as interdependent with the natural world and to view relationships as energetic exchanges.

Martin Picard

Partway through a technical explanation of his work, Picard pauses and briefly looks away. “I just really love mitochondria,” he says when he looks back at me, breaking into a laugh mid-sentence. He then reaches behind him, pulls a small wooden model of a mitochondrion from a shelf, and holds it up to the camera like a proud father. 

For all the talk of energy, it’s fitting that Picard himself seems indefatigable — publishing papers, writing articles, and sharing ideas at a steady pace. The energy driving him, it seems, comes from the potential of this research. “If you see yourself energetically, it changes your behavior,” he says. “You begin to see yourself as interdependent with the natural world and to view relationships as energetic exchanges. That shift can ripple outward, from individuals to households to institutions.”

Beyond the “powerhouse of the cell”

Students are taught in middle school that mitochondria are the “powerhouses” of cells and that nearly every cell in the body contains hundreds to thousands of them (more energy-intensive tissues, like those in the heart, land in the higher range). Picard pushes back on that description because it suggests that mitochondria simply keep the lights on when, in reality, the complex organelles play a far more influential role.

Yes, they do convert the food we eat into usable energy and heat, but as the body’s demands shift, mitochondria adapt. They communicate with one another, change their number and structure, and adjust how they produce and distribute energy. They are the only organelles with their own genetic material, mitochondrial DNA (mtDNA), which is inherited exclusively from mothers.
Rather than “powerhouses,” Picard prefers to call mitochondria the “information processors” of cells: “They’re not just permissive — they don’t simply create the energy that allows life to happen. They’re instructive — they integrate how we live into the flow of energy through the body.”

Returning to first principles 

Scientists have proposed many “master explanations” for health, pointing to diet, genetics, hormones, the microbiome, and more as the factor that outweighs any other. So why does Picard focus his energy on energy?

While those other factors are important, every process in the body exists downstream of energy flow, Picard says — for cortisol to rise, for neurons to fire, for the microbiome to function, energy has to move through the system. Interventions that support health — like sleep, diet, exercise, and meditation — are ways of increasing and redistributing energy within the body.

Our modern health framework, which views humans as collections of biochemical parts, has struggled to explain basic questions, like why stress harms us or how mental states shape physical health.

Martin Picard

Every time I ask Picard something, he peels the question itself back to a more basic layer. He approaches his research the same way, reasoning from first principles, the basic laws that govern living systems. From that perspective, energy is a natural place to begin. Genes, hormones, and the microbiome vary from person to person, but energy follows physical principles that cause it to behave the same way across living systems. 

**“**We tend to see humans as collections of biochemical parts, and our medical practices focus on what goes wrong with the structure of humans,” Picard says. “But that framework has struggled to explain basic questions, like why stress harms us or how mental states shape physical health.” 

The cost of being alive

To investigate those questions, Picard argues, we need to look beyond structure to the dynamics that support it. Unsurprisingly, when he thinks about what questions matter most, he starts with a foundational principle: “Nothing in biology is free, so how much energy does something cost?”

Energy follows the same basic laws in biological systems that it follows everywhere else: It cannot be created or destroyed, only transformed. Ecologists use this principle to understand how energy moves through ecosystems and even why large animals typically live longer than small ones. But in medicine, where the focus often falls on genes and molecules, energy budgets are regularly overlooked.

Like many of Picard’s ideas, this framing aligns with a basic intuition. We know that our bodies can’t just create energy — we need to consume something to fuel up. This limited supply is then spread across competing demands. If I go for a long run in the morning, I often find that I can’t focus as well later. It doesn’t matter that one activity is physically exhausting, while the other is cognitive — both require energy.

Picard and his colleagues applied this question — how much energy something costs — to one of the most poorly understood yet universally experienced drains on the body: stress. 

Chronic stress doesn’t just “wear us down” metaphorically. It reallocates energy from repair to survival.

In the lab, they exposed human cells to signals similar to those of cortisol — the hormone the body releases during stress — to mimic chronic stress in the body. “You can think of a stress response like an activation; it takes energy to mobilize,” Picard says. “We found that cells increased their energy expenditure to roughly 60% above baseline, which is a significant metabolic drain.”

That extra energy has to come from somewhere, and his team demonstrated that cells with a higher stress demand age faster. This suggests that activating a stress response siphons energy away from long-term maintenance processes. Chronic stress doesn’t just “wear us down” metaphorically. It reallocates energy from repair to survival, leaving less available for the processes that keep cells healthy over time.

When you consider life as perpetually managing an energy budget, other familiar physical experiences start to click. Many people lose their appetite when they’re sick, which seems paradoxical. Wouldn’t your body need more fuel to fight the infection? But digestion is expensive, requiring roughly 10% of your daily energy budget. By suppressing appetite, the body can direct more energy toward the immune response. Reducing the amount of energy spent on digestion may also help explain why fasting can make some people feel more energized.

The right amount of resistance

As Picard points out, how we feel and function isn’t just about “how much” energy we have, but also how well energy is flowing through our system. To explore that, Picard turns again to first principles. “At its core, the organism behaves like an electric circuit,” he says. “Electrons flow from food to oxygen to sustain life.”

In biological terms, that flow runs through the mitochondria. When you eat, you take in electrons stored in carbon-based molecules. Inside your cells, those electrons move through a series of reactions toward oxygen, the final electron acceptor. As the electrons move, they release energy that mitochondria capture and convert into adenosine triphosphate (ATP), the form of usable energy that fuels cellular processes. 

This flow always encounters some resistance; how much depends on oxygen availability, cellular demand, and the integrity of the system itself, including the number of mitochondria in the cell and the condition of their membranes. Picard and his colleagues argue that maintaining an optimal level of resistance to this flow is vital. “Health depends on maintaining energy resistance in a ‘Goldilocks’ level — not too high, not too low, but just right.”

How we feel and function isn’t just about “how much” energy we have, but also how well energy is flowing through our system.

Picard and his colleague Nirosha Murangan formalized this idea as the energy resistance principle. If resistance is too low, energy passes through the mitochondria but isn’t transformed into anything useful. It’s like pedaling a bike with the chain off — your legs are spinning, but the bike isn’t moving forward. But when resistance is too high, when something constrains electron flow through the mitochondria, the system backs up. This increases oxidative stress and contributes to inflammation, cellular damage, and other hallmarks of disease. Picard and Murangan argue that many features of aging and disease reflect disruptions in how energy is flowing through mitochondria.

The idea of a “Goldilocks” level of resistance appears across biology. Lift weights at just the right level, and muscles grow stronger. Doing too much leads to injury, while doing too little produces no change. Psychologically, when you engage deeply with a problem, you encounter resistance that focuses effort and transforms it into structured thought. But too much resistance, and you might get discouraged and give up on trying to solve it.

In each case, resistance doesn’t block growth. So long as it’s paired with periods of lower resistance, like meditation, sleeping, or resting, it encourages it. 

Picard’s lab has begun identifying molecular signals that reflect disruptions in this system. One of them, growth differentiation factor 15 (GDF15), increases when energy flow becomes strained. “If GDF15 is high,” he says, “it’s a signal that the system is under energetic stress.” He suggests that GDF15, which can be measured in saliva, could eventually serve as a proxy for the quality of energy flow in an individual.

The mind and body connection 

We already know that thoughts and emotions affect physiology. Take the placebo effect: Simply believing a health intervention might help can produce positive outcomes. And many of us are familiar with the racing heart that comes when we simply ruminate about something negative happening. Picard’s work suggests these experiences may be reflecting shifts in how energy is moving through the body.

“The brain is a pattern of energy,” Picard says. It’s a simple statement, but a radical one. The brain is an energy-intensive system, consuming roughly 20% of the body’s total energy. It follows that mitochondrial function could play a central role in shaping mental states. “The human psychological experience is incredibly diverse,” Picard says. “You can wake up feeling refreshed and energized, like life is beautiful. Or you can wake up feeling depressed, like you don’t want to get out of bed. We began to wonder: Could subjective experiences reflect differences in how energy moves through the brain and body?”

Across several studies, Picard found a continuous feedback loop between the brain and mitochondria: Mental states may influence mitochondrial biology, and mitochondrial function may in turn shape psychological processes. Supported by this evidence, Picard proposes that individual variations in mitochondrial biology may shape health and disease risk across a suite of issues, including mental disorders. 

Looking through an energy lens

Picard hopes that energy will soon become a core dimension of health that doctors and researchers assess alongside genetics, lifestyle choices, and other biomarkers. But that vision requires overcoming an obvious challenge: How do you measure something as dynamic as energy?

One approach is to estimate how much energy different processes consume and use that information to get an idea of the body’s overall energy budget. Picard says he can imagine people measuring markers of energy flow and resistance. “I can envision wearables that track GDF15, which raises when you’re experiencing strong energy resistance, to give you an ‘energy score.’” Laughing, he points out one potential application: “You can see if the new person in your life energizes or drains you.”

More pressing than measuring energy, Picard argues, is adopting the mindset needed to study it. He urges researchers across disciplines to see humans as systems of energy flow, not just collections of molecular parts. In a recent Nature article, he and co-author Christopher P. Kempes call on biomedical researchers to “look at central questions energetically, from first principles,” linking these dynamics to diseases like Alzheimer’s and cancer. He’s also working on a book, Energy: The New Science of Vitality, Healing, and Transformation, expected in 2027, that explores how to tune into our energy.

Picard proposes that individual variations in mitochondrial biology may shape health and disease risk across a suite of issues, including mental disorders. 

When I ask Picard how people might apply his ideas today, he points to interventions we already know are beneficial: a healthy diet and sufficient amounts of sleep, exercise, and relaxation. The key, he says, is to consider how these activities align with our lives as energetic beings, shaping where we get our energy and how we distribute it, respectively. “When you see these behaviors through an energy lens, they become more motivating,” he says. “You understand what they’re doing for you.”

He suggests alternating high-resistance states, like exercise or sustained cognitive effort, and lower-resistance states, such as rest, meditation, or even periods of fasting. He also encourages paying attention to how you get energy. “The ketogenic diet has completely changed [some] people’s lives, and for others, it doesn’t work,” Picard says. “It’s about tuning into your energy, not just with diet, but across your life.”

He then characteristically began to zoom out, away from specifics and back toward first principles. “We should make decisions based on how they affect our energy,” he says. “You, as an energetic system, are the most sensitive instrument you have to know whether the content of your life — where you gain energy, what you do for purpose, who you are with — is aligned with who you are.”

I perked up when Picard said this, recognizing my own imperfect habits in his words. I have always tried to follow my energy — even when I couldn’t fully explain what that meant. Still, I trust the pull and, whenever possible, I make choices that feed my energy and avoid those that drain it. What Picard offers me, and everyone, is a reason beyond intuition to see that instinct as something real, measurable, and even fundamental.

If he’s right, then health isn’t just something we build through better inputs — better diets, better habits, better routines — but something we experience as the quality of energy moving through us. And the difference between feeling alive and merely being alive may come down to how well we learn to notice and work with that flow.

• Research Methodology: Investigators analyzed surveys from 1,316 US adults to classify viewing habits into three distinct groups based on the timeline between initial exposure and regular engagement.
• Early Engagement Patterns: Approximately 67 percent of participants began regular viewing by age 18, showing a correlation with increased psychological distress, reliance on more intense material, and elevated symptoms of substance use or gambling.
• Moral Incongruence Effects: A group of Late Engagers, who established viewing habits in adulthood, reported high levels of anxiety and depression attributed to the internal conflict between personal religious values and their behavioral choices.
• Late Engagement Findings: Participants who experienced early exposure but did not develop a regular habit until later in life demonstrated the lowest levels of general distress and mental health challenges.
• Evolutionary Factors: The study notes that rapid transitions from accidental exposure to frequent consumption mimic addiction development, though the observational nature of the data precludes confirmation of direct causation.

Understanding how people develop habits around viewing adult content can help identify potential psychological risks later in life. Researchers identified three distinct patterns of how adults start viewing sexually explicit material, revealing that establishing a regular habit at a young age is linked to higher rates of mental health struggles. The findings were published in the journal Computers in Human Behavior.

Viewing adult entertainment is a highly common behavior across varied age groups. Many adolescents see sexually explicit images or videos unintentionally, perhaps through internet advertisements or links shared by peers. Researchers separate this initial exposure from the point at which an individual decides to seek out the material on a regular schedule.

In the field of addiction science, healthcare professionals observe that starting to drink alcohol or gamble at a young age is associated with a higher likelihood of developing a behavioral disorder in adulthood. Psychology researchers wanted to see if the timeline of viewing adult content followed a similar pattern. They hypothesized that a shorter gap between initial exposure and regular viewing might correlate with negative psychological outcomes.

Problematic viewing habits often involve feeling a loss of control, craving the material, experiencing disruptions in daily life, and using the media to avoid negative emotions. Repeated struggles to control the viewing habit can eventually interfere with an individual’s work commitments and personal relationships. Psychologists refer to these symptoms as signs of distress or behavioral impairment.

Bailey M. Way, a psychology researcher at the University of Nevada, Las Vegas, led a team to investigate this timeline. Way and colleagues noted that many existing studies only gathered data on the age of first exposure. By asking individuals about both their first exposure and their first regular engagement, the team hoped to paint a more nuanced picture of behavioral development.

The investigators relied on survey data from 1,316 American adults. The sample matched demographic norms for the United States, accurately reflecting the broader population in terms of age, gender, geographic region, race, and household income. Participants answered questions about when they first saw sexually explicit material and when they began viewing it frequently.

The survey also asked respondents about their current viewing habits, including how often they watch and the duration of their typical sessions. Additional questionnaires screened the adults for symptoms of depression, anxiety, and suicidal ideation. The team also evaluated habits related to alcohol use, cannabis consumption, and gambling.

Using a mathematical sorting method, the researchers grouped participants based on common developmental timelines. The statistical model grouped the adults into three distinct categories. The authors named these groups Early Engagers, Casual Engagers, and Late Engagers.

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Early Engagers made up the largest portion of the sample, accounting for nearly 67 percent of respondents. These individuals typically saw adult material for the first time around age 14 and began a regular viewing habit by age 18. This group reported the highest current viewing frequency and the longest viewing sessions.

This early onset group also explored more intense or niche material compared to the other groups. They reported higher rates of viewing nonmainstream categories, ranging from violent material to extreme fetishes. The researchers suggested that early viewers might seek out more extreme content over time to achieve the same level of arousal.

The transition into more intense material mimics patterns seen in chemical tolerance. As a person becomes desensitized to standard visual stimuli, they sometimes require stronger or more unusual imagery to achieve the desired psychological effect. This behavioral escalation often serves as a red flag for clinicians attempting to diagnose an occupational or psychological impairment.

Mentally and emotionally, Early Engagers reported the highest rates of psychological distress. They scored higher on screening tools for depression, anxiety, and suicidal thoughts than the other groups. The same group also endorsed more symptoms related to problematic drinking, cannabis use, and gambling.

Casual Engagers mapped out a completely different timeline. They represented just 7 percent of the participants and did not see sexually explicit material until an average age of 28. They established a regular viewing routine around age 36.

Current viewing among Casual Engagers was the lowest of all three groups, yet they reported symptoms of depression and anxiety at levels comparable to the Early Engagers. They also reported feeling distressed regarding their limited viewing habits. The researchers noted that these individuals ranked highly on measures of religious devotion and frequent church attendance.

The research team observed that identifying as deeply religious often correlates with lower overall viewing rates but higher feelings of guilt. Casual Engagers answered specific survey questions indicating that faith played a central role in their daily routines. They reported attending religious services regularly and ranked spirituality as highly important to their personal identities.

The psychological burden seen in Casual Engagers likely stems from a concept known as moral incongruence. This phenomenon occurs when a person’s behavior contradicts their deeply held personal or religious values. The internal conflict can cause an individual to view a relatively rare behavior as a severe personal failure, generating intense anxiety.

The third group, Late Engagers, shared an early exposure timeline with the first group, seeing adult content around age 14. Unlike the first group, they did not transition into regular viewing habits until an average age of 38. This group exhibited the lowest average levels of depression, anxiety, and general distress.

The contrast between the groups highlights that casual exposure alone is not the primary factor linked with later distress. Instead, the rapid transition from accidental exposure to a dedicated habit seems to carry the strongest association with psychological struggles. The results mirror observations in substance use research, where early and frequent engagement suggests a vulnerability to addiction.

Demographic background also shaped group membership. Men were more likely than women to fall into either the early or late onset groups. Heterosexual respondents and white participants were highly represented among the Late Engagers.

Conversely, individuals identifying with diverse sexual orientations were highly represented among the early onset group. The researchers suggest this demographic overlap might relate to young people exploring their evolving sexual identities online. Finding representation and answering questions about sexuality on the internet is a common experience for many diverse youths.

The observational nature of the survey means the results cannot prove that early viewing causes mental illness. It is entirely possible that young people experiencing early symptoms of depression or anxiety use adult entertainment as a coping mechanism. If sexually explicit media is used to regulate negative emotions, the behavior may become entrenched as a lifelong habit.

Generational differences in technology access also influenced the development of these three profiles. Older adults in the sample grew up without home internet or smartphones, making regular engagement difficult during their teenage years. Younger participants had readier access to online media, which could explain the accelerated timeline of the first group.

The study relied entirely on retrospective memory, asking adults to remember specific ages from decades past. Human memory regarding childhood events is often imprecise and subject to individual bias. A cross-sectional survey like this also captures only a single moment in a person’s life, rather than tracking their psychological health as it develops.

To build on these observations, researchers plan to conduct long-term studies that follow young people over many years. Tracking actual behavior as it happens provides a more accurate dataset than relying on childhood memories. In the meantime, the investigators advise mental health professionals to ask clients about both their age of first exposure and their timeline of regular use when assessing behavioral risks.

The study, “Early exposure and emerging risk: A latent profile analysis of pornography use trajectories and their psychological correlates,” was authored by Bailey M. Way, Todd L. Jennings, Joshua B. Grubbs, Kris Gunawan, and Shane W. Kraus.

• Mortality Risk Reduction: Epidemiological studies indicate that engaging in strength training at least twice weekly is associated with up to a 30% lower risk of all-cause mortality in adults over age 65.
• Biological Strength Indicators: Measures of muscular strength, such as grip strength, serve as significant predictors of all-cause and cardiovascular mortality, often surpassing traditional health markers in long-term cohort studies.
• Sarcopenia Mitigation: Resistance training acts as a primary intervention to counter the natural age-related decline in muscle mass and strength, helping to prevent frailty, metabolic disorders, and loss of independence.
• Systemic Metabolic Benefits: Contracting muscle functions as an endocrine organ, releasing myokines that regulate inflammation, insulin sensitivity, and blood pressure, thereby reducing factors linked to chronic diseases.
• Cognitive And Functional Health: Systematic resistance exercise supports cognitive longevity by promoting neuronal health while improving physical metrics like balance, gait speed, and functional mobility across all age ranges.

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Ask almost anyone how to stay young, and they’ll rattle off skincare products, fad diets, or the latest “longevity pill.” What few realize, yet what science now makes unmistakably clear, is that the most powerful anti-aging intervention doesn’t come in a bottle or capsule at all. It comes from a barbell. A major epidemiological study published in JAMA Network Open that tracked more than 115,000 adults over age 65 for nearly eight years found that those who did strength training at least twice weekly had up to 30 % lower risk of dying during the follow-up period compared with those who didn’t, even when aerobic exercise and other health behaviors were accounted for. This survival benefit was consistent independent of how much walking or cycling participants reported doing.

That’s not longevity marketing copy—that’s real-world science.

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Muscle: The Lifespan Predictor We’ve Ignored

Muscle isn’t vanity. It is biology. Multiple longitudinal cohort studies have found that grip strength and overall muscular strength predict all-cause mortality more powerfully than many traditional health markers, including blood pressure and cholesterol. A large prospective cohort of adults aged 20 to 80 showed that men with higher measured muscular strength experienced significantly lower all-cause and cardiovascular mortality over long-term follow-up.

Even among the oldest old, adults over age 90 from 27 European countries, greater muscle strength was associated with lower mortality risk, controlling for age, sex, and health status. More than half of study participants died during the follow-up period, but those with stronger grip strength were less likely to die in that time frame, suggesting a dose-response relationship between strength and survival. In younger cohorts, similar patterns emerge. In a massive analysis of the U.S. National Health and Nutrition Examination Survey (NHANES), low muscle strength was independently linked with a ~65 % greater risk of all-cause mortality compared with individuals with higher strength, even after adjusting for age, sex, chronic disease, and lifestyle factors.

Put simply: Being stronger predicts living longer. The magnitude of these associations rivals, and sometimes exceeds, classic risk factors like smoking and obesity.

Strength Training Counters the Aging Process at Its Core

As we age, muscle mass and strength naturally decline, a process called sarcopenia. Beginning in our 30s, adults can lose 1-3 % of muscle mass per year, accelerating in later decades. Sarcopenia isn’t cosmetic; it’s a core driver of frailty, falls, metabolic disorders, and loss of independence.

The good news? This process is not inevitable.

Strength training is one of the most robust interventions to counteract sarcopenia. A recent systematic review of randomized controlled trials in older adults with sarcopenia found consistent improvements in muscle mass and strength with resistance training programs, sometimes in as little as six to 16 weeks, compared with non-training controls.

These aren’t subtle changes. Participants in these trials show statistically significant increases in functional metrics like grip strength, chair-stand performance, and skeletal muscle mass, all of which are directly linked to better survival, mobility, and quality of life as people age.

Strength Is Medicine for the Metabolic System

Muscle is not inert flesh: It’s an endocrine organ.

Resistance training triggers the release of myokines, hormones produced by contracting muscle that regulate inflammation, glucose metabolism, and fat storage. This plays a direct role in reducing the risk of type 2 diabetes, cardiovascular disease, and metabolic syndrome, all major causes of mortality. Multiple mechanistic studies confirm that strength training improves insulin sensitivity, lowers blood pressure, and favorably alters lipid profiles.

Unlike many interventions that target only one system, strength training simultaneously tunes multiple biological pathways—metabolism, immune regulation, nervous system function, and even cellular repair processes associated with aging.

Ollyy / Shutterstock

Brain Strength: Lifting for Cognitive Longevity

Physical strength training doesn’t just build muscle; it stimulates the brain.

Coordinating complex lifts engages motor planning, balance, and proprioception. Emerging research ties resistance exercise with improvements in executive function and slower cognitive decline later in life. While the exact mechanisms are still being explored by neuroscientists, animal and human trials suggest that strength training increases neurotrophic factors, biochemicals that promote neuronal health and plasticity.

This means strength training is both a physical and cognitive investment in aging well, protecting not just the body you live in, but the brain you live with.

The Myth of Extremes vs. the Power of Consistency

Modern fitness culture is awash in extremes: high-intensity interval training, metabolic conditioning, gram-perfect macros, and wearable metrics that count every heartbeat.

None of these rival the systemic impact of strength training.

While cardio remains valuable for heart health and endurance, it cannot substitute for the anabolic stimulus that resistance training provides to muscle tissue. Studies find that even modest amounts of strength training, and I’m talking as little as twice weekly, confer longevity benefits independent of aerobic activity.

Forty-five minutes with dumbbells or suspension bands can outweigh hours on a treadmill when it comes to preserving functional body systems.

How much longer do you want to let time dictate your health?

PeopleImages

Recovery: The Secret Anti-Aging Ingredient

Strength training works by applying stress. What transforms that stress into lasting benefit happens between workouts.

Recovery, facilitated by sleep, nutrition, and metabolic balance, is where adaptation occurs. Muscle hypertrophy, hormonal regulation, immune recalibration, and neural repair all happen during rest. Inadequate recovery isn’t performance failure; it’s a longevity failure.

This is why anti-aging strategies that focus solely on biomarkers or supplements without considering recovery miss the point. Resistance training, paired with intelligent recovery, recalibrates the body’s response to stress, a core driver of biological aging.

A Lifelong Prescription: Strength as a Habit, Not a Phase

Perhaps the most remarkable aspect of strength training is its universality and adaptability.

From age 25 to 95, humans can gain strength, improve muscle quality, and enhance functional performance with appropriate training. There is no age cutoff; only a choice to act. Older adults starting resistance training programs show significant gains in strength, balance, gait speed, and quality of life — outcomes directly linked to reduced mortality and better health span.

Strength isn’t an elite domain. It is accessible to anyone willing to progress deliberately, from chair rises and resistance bands to barbells and kettlebells.

Conclusion: The Medicine We Already Hold

Here’s the paradox of modern aging: The most powerful anti-aging therapy isn’t a patent, a pill, or a superfood, it’s a movement prescription.

Strength training is the closest thing we have to a universal longevity drug, with benefits that are measurable, replicable, and supported by decades of peer reviewed science. It improves not just muscle but metabolism, bone health, cognitive resilience, immune function, and mortality risk.

The prescription is simple:

• Lift consistently.
• Progress intelligently.
• Rest strategically.

Do that over decades, and aging stops being a passive decline. It becomes a process you engineer.

Strength training isn’t vanity. It is medicine in motion. It is longevity in action.

Rigor. Discipline. Results.

Topics:

• Aging
• Biohacking
• Body Health
• Health Benefits
• Health Tips
• Healthy Aging
• Healthy Habits
• Over 30
• Over 40
• Over 50
• Strength Training

Written by Dr. Nicole Rakowski, PhD

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• Strategic Infrastructure Investment: XTX Markets is constructing a $1 billion-plus data center complex in Finland to secure proprietary computing capacity for training financial forecasting models.
• Technological Focus: The firm utilizes deep learning and neural networks to automate trading, moving away from traditional high-frequency speed competitions toward predictive machine intelligence.
• Operational Scale: Overseeing a workforce of approximately 250, the company manages daily trading volumes averaging $250 billion across diverse asset classes including stocks, bonds, and electricity.
• Financial Performance: Reflecting high profitability in algorithmic trading, the firm's U.K. operations reported $2.3 billion in profit last year, benefiting from a lean structure without outside investors.
• Foundational Background: Founded by Alex Gerko, a mathematician who renounced his Russian citizenship in 2022, the firm maintains a focus on quantitative research and philanthropic support for education.

Alex Gerko founded XTX in 2015, naming the trading firm after a mathematical formula. XTX Markets

LONDON—Alex Gerko’s edge as a trader comes from a supercomputer powered by geothermal energy in Iceland.

Years before ChatGPT became a household name, his trading firm, XTX Markets, built an artificial-intelligence system aimed squarely at one goal: making money. 

That bold bet on AI has made XTX one of the most profitable players in the secretive world of algorithmic trading and turned the 46-year-old Gerko into one of Britain’s wealthiest people, with a fortune estimated at $12 billion.

Now, he is doubling down. XTX is this year set to start operations at the first of five planned data centers in Finland, expanding the firm’s ability to crunch vast quantities of financial data and train models to forecast price moves.

XTX is building the $1 billion-plus complex itself, rather than renting computing power, the more typical approach in finance. The idea is to ensure XTX can keep training its models cheaply even as the AI boom gobbles up ever more data-center capacity worldwide.

The project is also aimed at establishing a wider moat around XTX’s business as rivals pursue their own AI-based strategies. Earlier this month, Jane Street invested $1 billion in CoreWeave and signed a $6 billion deal to use the company’s AI infrastructure. Other firms are also spending big on computing power to refine their quantitative-trading prowess.

XTX is building a data-center complex in Finland. The first of five facilities is set to go live later this year. XTX Markets

It is a new arms race for a fiercely competitive industry. Years ago, electronic trading firms fought to build the fastest microwave networks so they could execute trades in milliseconds. Now, limited by physics from gaining meaningful new speed advantages, firms are fighting to be smarter.

Gerko was at the vanguard of the industry’s shift toward AI. He has long disparaged what he considers the wasteful, zero-sum speed race of high-frequency trading. XTX has lobbied for speed bumps—tiny delays to thwart ultrafast traders—angering some competitors.

“All these HFT firms…are trying to leapfrog each other by becoming faster and faster,” Gerko said in a 2019 discussion with students, a video of which was viewed by The Wall Street Journal. “We decided we’re just not a part of this game.”

Gerko often posts acerbic barbs on LinkedIn about XTX’s rivals, making him an unusually vocal figure in the hush-hush world of computerized traders. 

Last year, after Indian regulators accused Jane Street of market manipulation, Gerko dissected the case in LinkedIn posts: “The whole thing appears to stink very badly,” he wrote. Jane Street has denied the allegations.

Born in Soviet-era Moscow, Gerko attended a school for gifted math students before earning his Ph.D. in mathematics. He began his career as a trader at Deutsche Bank.

Gerko moved to London in the early 2000s and founded XTX in 2015, naming the firm after a mathematical formula. He now holds a British passport. A critic of Vladimir Putin’s war in Ukraine, he renounced his Russian citizenship in 2022.

The next year, Gerko topped the Sunday Times’ annual ranking of Britain’s largest taxpayers. As a philanthropist, Gerko has backed astronomical research and math education. 

One of his pet projects, the 1729 Maths School, is set to open in London in September. Geared toward mathematically gifted children, it is named after the Hardy-Ramanujan number, the smallest number that can be expressed as the sum of perfect cubes in different ways:

1,729 = 93 + 103 = 13 + 123

“I retained my interest in math education because this was the path from, let’s say, an underprivileged background to a very lucrative career,” Gerko told the Journal in 2023. 

XTX is based in London’s so-called Knowledge Quarter, near tech companies such as Google and Meta Platforms. Its office features a replica of the Apollo 11 command module and an authentic Enigma encryption machine from 1943—the German device cracked by Allied codebreakers including celebrated British mathematician Alan Turing.

A replica of the Apollo 11 command module in XTX's office in London. XTX Markets

None of XTX’s employees are traders in the conventional sense, deciding what assets to buy to sell. Instead, they oversee an army of algorithms that rely on “deep learning” models that predict price moves that can be milliseconds, minutes or hours away. 

Deep learning is a form of AI that uses layers of neural networks—computer programs that mimic structures in the brain—to solve complex problems.

Some XTX trades involve market making, the strategy of standing ready to either buy or sell an asset and collecting a spread between the bid and offer price. Other trades involve spotting deviations from standard market patterns and betting that prices will snap back to normal. 

Overall, the firm trades an average of $250 billion daily across stocks, bonds, foreign exchange, derivatives and crypto. It recently expanded into electricity trading, people close to XTX said. 

To train its models, the firm has amassed 25,000 AI chips, mostly from Nvidia. Its new Finland data center is the size of three football fields, while a second facility next door is largely built and set to go live in 2027. The site’s northern location helps keep systems from overheating.

“The approach they seem to be taking is kind of a big, all-in bet on deep learning,” said Agustin Lebron, senior researcher at EquiLibre Technologies, a trading firm in Prague.

Last year, revenue at XTX’s U.K. business climbed 44% to $5.3 billion, company filings show. Profit rose 33% to $2.3 billion. Those figures don’t include the firm’s Singapore unit, whose 2025 financials aren’t yet available, and which previously earned a comparable amount of revenue as the U.K. operations.

While competitors such as Citadel Securities and Jane Street make more money, XTX is leaner, with about 250 employees rather than thousands.

XTX doesn’t have outside investors, meaning profit flows to a small group of partners—above all Gerko, who owns at least 75% of the firm, filings show. 

Former employees say Gerko is an exacting, hands-on manager. He is omnipresent on XTX’s Slack channels, where his avatar was previously a giant floating head from “Rick and Morty,” the animated science-fiction series, people familiar with the matter said. 

More recently he changed it to Thanos, the cosmic supervillain from the “Avengers” movies.

• System Achievement: The Ace Robot Successfully Competed In Peer Reviewed Matches Against Elite Human Table Tennis Players
• Performance Data: Peer Reviewed Results Included Three Victories Against Five Elite Players And One Game Win Against Professionals
• Technical Architecture: The System Integrates Nine Synchronized Cameras With Event Based Sensors To Track Ball Motion And Spin
• Hardware Integration: Success Relies On A Custom Eight Degree Of Freedom Platform With Millisecond Level Latency For Control
• Environmental Dependency: High Performance Is Achieved Through An External Sensor Rich Court Serving As The Robotic Nervous System
• Professional Claims: Sony Reports Later Versions Defeated Professional Players But These Claims Await Full Public Data Verification
• Operational Constraints: The Robot Demonstrates Specialized Physical Artificial Intelligence Rather Than General Purpose Autonomous Functionality
• Future Evaluation: Ongoing Assessment Requires Transparent Reporting Of Match Telemetry And Performance Under Varying Environmental Conditions

Sony AI's Ace robot has become the first peer-reviewed autonomous table-tennis system shown beating elite human players under close-to-official competition rules, according to a Nature paper published April 22 and Reuters reporting on the project. In April 2025 matches in Tokyo, Ace beat three of five elite players, lost both matches against professional players, and took one game out of seven from those professionals. Sony says later versions improved enough to beat professional players in December 2025 and March 2026, a claim Sony AI repeated around the Nature release.

That makes Ace real news. It also makes the easy version of the story too neat. The point is not that a robot now owns table tennis, or that general-purpose humanoids just moved closer by a year. The point is narrower and more useful: Sony built a wind tunnel for physical AI, then proved that a full-stack machine can beat highly trained humans inside it. If you want to know where embodied AI is actually advancing, watch the court around the robot, not just the paddle.

Key Takeaways

• Ace beat three of five elite players in peer-reviewed matches, while April 2025 professional matches were losses.
• Sony says later versions beat professionals, but those wins still need full public match data.
• The breakthrough is a sensor-rich court, spin estimation, RL control, and custom high-speed hardware.
• Ace points to constrained physical AI, not general-purpose robot deployment.

AI-generated summary, reviewed by an editor. More on our AI guidelines.

The court was the machine

Ace looks like a player, but it wins like an instrumented system. The robot uses nine synchronized cameras to locate the ball in three dimensions at 200 hertz, with 3.0 millimeters of average error and 10.2 milliseconds of average latency, according to the Nature paper. Three gaze-control systems then track the ball's logo with event-based vision sensors, mirrors, and tunable lenses to estimate spin at roughly 400 to 700 hertz.

That matters because table tennis is a hidden-state game. At high level, the ball is brutal: more than 20 meters per second, sometimes under half a second between shots, and spin that can touch 1,000 radians per second. A normal camera feed sees a blur. A player reads the opponent's wrist, torso, face, pressure, and habit. Ace reads the ball itself, with the court acting as its nervous system.

This is where corporate pride and scientific anxiety meet. Sony can claim a breakthrough because Ace did not merely rally with a cooperative human. It played umpired matches with standard balls, standard tables, licensed officials, and human opponents free to attack. Yet the achievement depends on an arena humans do not get. Ace's eyes sit around the court. That does not cheapen the result. It defines it.

The scoreline needs a split screen

The clean peer-reviewed number is three out of five against elite players. The professional number in the paper is harsher: zero matches out of two, with one game won out of seven. Sony's later professional wins may be real, and Reuters and AP both carried the company's post-review claim. The Guardian reported continued improvement after the Nature submission. Still, those later matches do not yet carry the same public data tables, shot distributions, and method detail as the Nature evaluation.

That distinction should shape the headline you carry away. Ace is not proven professional-dominant. It is proven elite-competitive, with Sony-reported professional wins after further tuning. The difference matters because robotics markets run on impatience. Investors want a clean line from sports victory to factory value. Engineers know the line bends through sensors, calibration, safety cases, cost, and repeatability.

The useful calculation is simple. Ace needed 12 external optical systems to beat three elite players in the published test. That is not a flaw. It is the price of making a physical benchmark honest enough to count. The wind tunnel does not look like the open road, but it tells you whether the aircraft can fly.

Spin is the real frontier

The showy part of Ace is the racket moving faster than a human can parse. The better clue is spin. The Nature paper says Ace returned shots up to 14 meters per second consistently, sustained more than a 75 percent return rate up to 450 radians per second of spin, and returned human shots measured as high as 19.6 meters per second and 867 radians per second.

That is why the result lands beyond a lab demo. Earlier table-tennis robots could look impressive while avoiding the hardest part of the sport. Skilled players do not just hit hard. They hide spin, vary rhythm, set up third-ball attacks, and punish bland returns. Ace's event-camera stack let it see one of the variables that usually stays tactical.

Humans still found seams. Rui Takenaka told Reuters that complex-spin serves often came back complex, while simpler knuckle serves produced simpler returns he could attack. The Guardian reported that Ace also struggled earlier with slow, low-spin balls before improvements. That is not a minor footnote. It shows the gap between sensorimotor skill and match intelligence.

Physical AI, without the demo fog

Strategic AI news from San Francisco. No hype, no "AI will change everything" throat clearing. Just what moved, who won, and why it matters. Daily at 6am PST.

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Physical AI is getting a better test

Ace belongs in a line that starts long before this week. Researchers have been trying to solve robot ping-pong since John Billingsley's 1983 project made it a benchmark. Google DeepMind's 2024 table-tennis robot reached amateur-level competitive play, reporting a 45 percent win rate across 29 matches and stronger performance against intermediate players. Sony moved the ladder up by combining event-based spin sensing, simulation-trained control, a custom eight-degree-of-freedom platform, and official-style matches against stronger humans.

That stack is the lesson. Ace's controller queries a reinforcement-learning policy every 32 milliseconds, then turns those outputs into a one-kilohertz trajectory with optimization and safety fallbacks. It did not win because one model got big enough. It won because the system gave the model the right state, fast enough, then bounded what the robot could safely do with it.

If you build software, that may feel oddly old-fashioned. More data, bigger models, better prompts, new wrapper. Physical AI does not grant that shortcut. A missing spin estimate cannot be fixed after the ball is past the racket. A delayed actuator turns insight into a missed point. A safety fallback is not paperwork when a robot arm is swinging near a human athlete.

The market will overread the rally

Sony and others will point from Ace toward manufacturing, service robotics, sports training, entertainment, and safety-sensitive physical domains. Some of that is fair. Fast state estimation, real-time control, simulation-to-real training, and safe fallback motion all matter outside table tennis. A factory robot handling moving parts or a training machine reacting to an athlete could borrow pieces of this stack.

The leap to general robots is weaker. Jan Peters, a robotics professor who has worked on table-tennis robots, told the Guardian that table tennis does not solve object manipulation and that useful public robotics still requires engineering. AP also noted the fairness issue around Ace's external cameras and sensor-rich court.

That is the sober version investors may dislike and researchers may respect. Ace is a new kind of proof, not a product map. It proves that a tightly designed physical-AI system can cross an expert-human threshold in a fast sport. Laundry, warehouse bins, hospital assistance, city sidewalks. Those are different jobs, and Ace does not prove it can do them.

The next match is repeatability

The next evidence should be boring in the best way. Start with the December 2025 and March 2026 match package, not another highlight reel. Name opponents and rankings. Show full scores, serve order, shot speeds, spin buckets, return rates, rule exceptions, umpire credentials, and repeated matches after humans study the robot. Then vary the cameras and lighting to see how much performance depends on the wind tunnel.

That is where Ace can become more than a launch-day clip. The machine has already created pride inside Sony and envy across robotics labs. It should also create discipline. The harder match is not against a surprised elite player seeing a strange opponent for the first time. It is against scouting, replication, smaller sensor budgets, and transfer outside the court.

Ace did not make robots generally athletic. It made physical AI harder to dismiss.

Frequently Asked Questions

Did Sony's Ace beat professional table-tennis players?

In the peer-reviewed April 2025 Nature evaluation, no. Ace lost both professional matches and won one game out of seven. Sony says later versions beat professional players in December 2025 and March 2026, but those later wins are company-reported and need fuller public match data.

What made Ace technically different from earlier table-tennis robots?

Ace combines nine synchronized cameras, three event-camera gaze-control systems for spin, a reinforcement-learning rally policy, one-kilohertz trajectory execution, safety fallbacks, and a custom eight-degree-of-freedom robot platform. The result came from the full stack, not one model.

Why does spin matter so much in this result?

Spin changes the ball's flight, table bounce, and racket bounce. Skilled players use it to hide intent and create attacks. Ace's event-camera setup estimates spin fast enough to return high-spin shots that earlier robots often simplified away or missed.

Does Ace prove general-purpose robots are close?

No. Ace is a strong benchmark result in a structured, sensor-rich court. Homes, hospitals, warehouses, and streets have messier objects, goals, and safety problems. The transferable lesson is fast perception and control in constrained settings.

What should Sony publish next?

The key next step is full data for the December 2025 and March 2026 professional wins: opponent names, rankings, scores, serve order, telemetry, rule details, umpire credentials, and rematches after human players study the robot.

AI-generated summary, reviewed by an editor. More on our AI guidelines.

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Harkaram Grewal

New Delhi

Freelance correspondent reporting on the India-U.S.-Europe AI corridor and how AI models, capital, and policy decisions move across borders. Covers enterprise adoption, supply chains, and AI infrastructure deployment. Based in New Delhi.