Mans International “Be Your Own Boss” Program is designed to help people from all walks of life around the world who are committed to changing their way of thinking, improving their abilities, and achieving financial freedom and time freedom.
Before You Start
First of all, I’m sorry to tell you that it takes a long time to realize financial freedom and time freedom. If for whatever reason, you have obtained a large amount of wealth, it doesn’t mean that you achieved financial freedom automatically. Because you may not have the ability and psychological capacity to manage large amounts of wealth, the money will be consumed at a rate you can’t imagine.
You might say, can I just ask a financial professional to help me take care of my wealth? The question I asked was do you have the ability to select an outstanding and suitable professional?
If you feel that you ALREADY have independent thinking and various skills, then you do not need to participate in this program! All the best!
Our Values
If you DON’T agree with our values, please do not disturb！
Check Mans International “Be Your Own Boss” program values:
You can either send an email to info@mansinternational.com and we will send you the latest content regularly.
Before You Go
Every year we make plans. Every day we receive tons of information and learn a lot of knowledge, but why most people still can’t make choices that are beneficial to themselves in the long run, achieve their goals, and become a better version of themselves?
Have you got your ticket to the AI-native world — the “Core AI Subscription”?
This concept was introduced by Sam Altman, CEO of OpenAI, at the 2025 Sequoia AI Summit. More than just a new term, it could become the central theme of AI commercialization in the years to come.
For investors, this signals a shift: the future winners won’t be the ones merely selling models, but those building long-term user relationships and platform-level capabilities.
For entrepreneurs, it offers clear direction: those who can create truly personalized AI assistants, and a sustainable business model around them, will be the ones to rise above the rest.
The “Core AI Subscription” is more than just a new business model; it may signal that humanity is accelerating toward a new era: the AI-native world.
What Is the AI-Native World?
The AI-native world refers to a future paradigm where artificial intelligence is not just a tool, but the foundational driving force deeply embedded in every aspect of society. It reshapes how we live, work, produce, and interact, much like electricity and the internet redefined previous eras.
In this world, AI becomes core infrastructure, pervasive and indispensable. It transforms human cognition, economic models, technological ecosystems, and social structures.
Imagine life in an AI-native world:
You no longer use AI just to complete isolated tasks.
AI understands your intentions, anticipates your needs, and acts before you even ask.
It’s seamlessly integrated into your life, work, and decision-making processes, becoming an essential part of your digital existence.
Core AI Subscription: The Gateway to the AI-Native World
Sam Altman’s concept of the “Core AI Subscription” is not just visionary — it represents the key pathway to realizing the AI-native future. It refers to a highly personalized, continuously evolving AI assistant service that is deeply embedded in users’ daily lives, much like an operating system that runs across every aspect of work and life.
This service is far more than just a voice assistant or chatbot. It functions as an intelligent agent with the following capabilities:
Personalized customization based on your habits and behaviours;
Seamless integration with other applications and services;
Constant learning and self-improvement over time, becoming smarter and more efficient the more you use it.
In other words, whoever owns the user’s Core AI Subscription essentially controls the “operating system entry point” to the AI-native world.
What AI Capabilities Are Required for Core AI Subscription Services?
To bring “Core AI Subscription” to life, there’s a key question we might want to explore: What kind of AI is capable of supporting such a service?
OpenAI has proposed a tiered framework for Artificial General Intelligence (AGI) — AI systems with the ability to learn efficiently, generalize across tasks, and act autonomously in complex, dynamic environments. True AGI would possess a blend of perception, cognition, decision-making, learning, execution, and social collaboration, all while aligning with human emotions, ethics, and moral standards.
Here’s a breakdown of the AGI capability tiers:
Level 1: Chatbot — Basic conversational ability, like current GPT models.
Level 2: Reasoner — Can solve human-level problems — mathematics, logic, coding, and debugging.
Level 3: Agent — Acts on behalf of the user — booking travel, managing calendars, and automating task chains.
Level 4: Innovator — Capable of invention and creativity — designing new products, writing screenplays, composing music.
Level 5: Organizer — Manages teams, coordinates resources, sets strategies, and even runs companies.
What AGI Level Is Needed to Enable Core AI Subscription Services?
To bring Core AI Subscription services to life, the AI must reach at least Level 3 — Agent on the AGI scale. At this level, AI isn’t just passively responding to user commands — it must actively understand user needs, take initiative, trigger tools, execute task chains, and switch contexts fluidly across various scenarios.
Since 2023, Baidu founder Robin Li has echoed a similar vision, stating that “large models will usher in a flourishing ecosystem of AI-native applications.” He emphasized that AI-native applications are not simple replicas of mobile apps or desktop software — they are meant to “solve problems that were previously unsolvable or poorly solved.”
This vision aligns closely with the concept of Core AI Subscription: true AI-native products are those in which AI agents are deeply embedded in users’ lives and workflows as a systemic, always-on digital partner.
Open Evidence: Core AI Subscription in Action in Healthcare
Are there early pioneers building AI-native applications? Yes—and a standout is Open Evidence, a medical AI company founded in 2021. By February 2025, it had raised $75 million from Sequoia Capital and achieved unicorn status with a valuation surpassing $1 billion.
At the 2025 Sequoia AI Summit, co-founder Zach shared a real-world case showing how their Core AI Subscription model supports physicians:
Emergency In-Flight Medical Case
Dr. Susan Wilberg faced a medical emergency mid-flight: a 63-year-old male cancer patient on immunosuppressive therapy developed a severe rash. Suspecting shingles, she had to make a critical call—should the plane turn back? What immediate actions were needed onboard?
She turned to ClinicalKey AI, Open Evidence’s subscription-based platform built for medical professionals. It delivered instant, personalized guidance by combining:
CDC Yellow Book protocols,
The latest research on cancer immunotherapy, and
Patient-specific recommendations (based on age, history, treatment, etc.).
The platform:
Assessed the patient’s risk level given his immunosuppressed condition,
Offered specific and timely treatment guidance,
Helped avoid an unnecessary emergency landing while ensuring proper care upon arrival.
What Makes ClinicalKey AI a True Core AI Subscription?
Open Evidence’s AI assistant is more than a diagnostic aid—it functions like an intelligent agent that continuously learns, personalizes its output, and proactively supports users:
Hyper-personalization: Tailors suggestions based on user preferences and patient context.
Seamless integration: Connects effortlessly with existing medical systems and workflows.
Continuous evolution: Becomes smarter and more efficient through real-world interactions.
Business Model & User Growth
Over 25% of U.S. practicing physicians now rely on Open Evidence daily. The system handles more than 10 real-time clinical questions per second. While the service is free for doctors, revenue comes from medical device and pharmaceutical advertising, mirroring consumer internet models, but adapted for healthcare.
To deepen value and retention, Open Evidence is embedding top physicians’ expertise, starting with gastroenterology, into its AI, creating a collective intelligence layer. This not only strengthens its data advantage but enables constant answer refinement.
The Future: AI as an Indispensable Partner
Looking ahead, Open Evidence plans to integrate broader medical reasoning, research capabilities, and workflow tools to build a fully-fledged Core AI Subscription platform, ultimately becoming a mission-critical partner to doctors worldwide.
If you’re a business owner aiming to integrate an AI subscription model into your operations, here are three essential principles to keep in mind:
1. Shift from “function thinking” to “companionship thinking.” Don’t just ask, “What can AI do for my business?” Instead, consider, “What do my users need AI to become?” A CFO doesn’t simply need a reporting tool—they need a proactive financial partner that can anticipate risks and guide decisions.
2. Capture high-frequency “scene entry points.” Identify must-have, recurring scenarios—such as in healthcare, legal services, or vertical workflows—and embed AI deeply into those daily user moments. Your goal is to make AI a seamless, indispensable part of how users work.
3. Build a “subscription-based emotional account.” Offer consistent, meaningful value—like weekly personalized insights—to create a sense of FOMO (fear of missing out). When users feel your AI is essential to staying ahead, loyalty follows naturally.
By applying these strategies, you can turn AI from a one-off tool into a trusted, subscription-powered companion that users depend on.
With a core philosophy rooted in long-termism and building an integrated industrial ecosystem, Sequoia Capital recently hosted its third annual AI Summit. The event brought together 150 of the world’s leading AI founders and conveyed three powerful signals.
Three Core Signals for the AI Industry in the Next Decade
1. AI Business Models: The Shift From “Tool-Oriented” to “Outcome-Oriented”
This transformation reflects a deeper move toward closing the loop in AI-driven business models, where technology translates into measurable business outcomes.
Over the past decade, enterprise software has delivered value primarily by improving operational efficiency. Companies paid for SaaS tools that automated workflows — essentially, “software as a tool.” But AI is now breaking through that model and shaping a new paradigm:
Software as a Tool → Software as a Co-worker → Software as an Outcome
In this new framework, users are no longer paying for the AI model’s capabilities alone — they’re paying for its ability to solve real-world problems and deliver results.
Take a legal AI startup as an example. It has evolved from offering simple API-based services to delivering complete legal documents, shifting its value proposition from tool to outcome — and in doing so, creating a self-contained value loop.
2. Competition for AI “Operating System” — Level Entry Points
At the core of this race is the strategic goal of building platform-level moats through enhanced user stickiness. This stickiness is fundamentally driven by two key capabilities: memory (the accumulation of historical user-AI interactions) and execution (the AI’s ability to efficiently orchestrate tools and complete tasks).
Memory: Evolving from a “Tool” to a “Digital Companion”
Long-term memory enables AI systems to deeply learn a user’s habits, preferences, and contextual needs, unlocking truly personalized, one-to-one experiences at scale.
This memory capability shifts AI from being a passive command responder to an active need interpreter, evolving into a digital companion similar to a human assistant. For example, in healthcare, an AI system that incorporates a patient’s medical history, genetic profile, and medication records can dynamically adjust treatment recommendations — moving beyond one-size-fits-all suggestions.
Execution: Evolving from “Capability Showcase” to “Task Completion Loop”
An AI’s ability to efficiently call external tools (e.g., APIs, databases, hardware devices) determines whether it can execute end-to-end workflows in complex real-world scenarios. Execution efficiency is a critical adoption barrier for enterprise AI.
For instance, a legal AI tool that can autonomously pull from contract templates, search relevant case law, and coordinate with a legal team far outperforms traditional methods reliant on manual operations.
When deep memory and high execution efficiency come together, they form an irreversible user dependency, building a powerful moat for the AI platform. This is the foundation for the next generation of AI-native operating systems.
3. The Rise of the Agent Economy: From Tools to Autonomous Economic Actors
Artificial intelligence is rapidly evolving from a passive tool into an autonomous economic participant, giving rise to the “agent economy.” To realize this vision, three foundational pillars must be established:
1）. Persistent Identity: Building the “Digital Persona” of Agents
Each agent must possess a unique and verifiable identity to establish its legitimacy within an economic system. For example, China’s dual-track digital identity framework — “Net ID + Net Credential” — offers a policy and technical foundation for authenticating AI agents.
2）. Action Capability: Bridging the Digital and Physical Worlds
Agents must be able to orchestrate both physical tools (such as robots and IoT devices) and digital resources (like APIs and databases) to complete closed-loop tasks. Key challenges include permission management, real-time responsiveness, and robust fault tolerance (e.g., automatic retries or fallback to human intervention in case of failure).
3）. Trustworthy Collaboration: A Trust Infrastructure for Inter-Agent Cooperation
Collaboration between agents requires a trustworthy infrastructure that ensures transparency, traceability, and accountability. A central question: How do we assign responsibility among autonomous agents? The LOKA Protocol, developed by Carnegie Mellon University, proposes a layered framework (Identity Layer + Communication Layer + Ethics Layer) that supports decentralized identity and ethical decision-making for agents.
Additionally, how can we balance data privacy and information sharing in a world of interacting agents? This tension will define the future landscape of agent-based collaboration.
The Essence of Competitive Advantage: A Trinity of Data – Use Case – Ecosystem
In summary, the evolution of the AI industry presents both strategic inflection points and evaluation criteria for investors and startup founders: the ability to build sustainable competitive moats while closing the revenue loop.
According to Sequoia Capital, long-term defensibility in AI companies hinges on three pillars:
Data Flywheel: The efficiency of feedback loops where user interaction data continuously improves model performance.
Scenario Depth: Irreplaceable value in solving domain-specific, complex problems—especially in verticals like healthcare and law.
Ecosystem Synergy: Building a collaborative network across stakeholders through platform-level products or standardized protocols.
Sequoia predicts that by 2026, vertical AI agents and hybrid governance frameworks will drive the emergence of trillion-dollar markets. Conversely, companies that fail to evolve from tool → collaborator → outcome will be left behind.
Have you ever carried a regret — something you wish you’d said, done, or understood differently?
If AI could take you back to those moments, helping you relive them, notice what you once missed, and perhaps find peace ，would you want to try it?
The Story — Eulogy
Once upon a time, there was a man who had just retired. He lived a quiet, peaceful life, enjoying his days in solitude.
One seemingly ordinary afternoon, his phone rang. The caller ID displayed an unfamiliar number, but something compelled him to answer.
A calm, professional voice greeted him. It was from a mysterious company, calling on behalf of the family of someone he hadn’t heard from in years — his former lover. She had just passed away. Her family, hoping to preserve the fragments of her past, asked if he would be willing to help piece together the memories they once shared.
He hesitated. The past was a tangled web of emotions, and opening that door again felt frightening and strangely alluring. After a long pause, with a quiet sigh and a mix of uncertainty and unspoken longing, he said yes. He had no idea that this one choice would take him on an unexpected journey — one of rediscovery, buried emotions, and long-forgotten moments.
Soon after, a package arrived: the “Memory Kit.” Inside was a small, button-like device. When he placed it near his temple and turned it on, a soft, warm female voice spoke — it was an AI guide, carefully designed to help him recall the woman he once loved.
But there was a problem.
He couldn’t remember her. Not her voice. Not her face. Everything about her was blurry, as if time had quietly erased the edges of their story.
The AI gently suggested that he try looking for old photos or videos that might spark his memory. He made his way down to the dusty basement, where time seemed to have stood still. After rummaging through boxes filled with forgotten keepsakes, he finally found some old photographs. But something was deeply unsettling — her face had been cut out of every single one.
He froze. He couldn’t even remember what she looked like. The memories that were once vivid and full of warmth now felt distant, like shadows slipping through his fingers.
As fragments of the past began to surface, he remembered how they first met — how she was engaged at the time. Still, their connection was undeniable. They fell in love, stole moments between the chaos, travelled together, and took countless photos. And yet, for reasons he couldn’t fully explain even to himself, he had removed her face from every picture.
One memory struck him with particular force. It was his birthday. She, a gifted cellist, had gone on tour with her orchestra and couldn’t be there. She had called to wish him a happy birthday. But he made a mistake that many men might make on a lonely night — his female colleague picked up the phone. The silence on the other end spoke louder than any words. They fought. Words were said that couldn’t be taken back. She returned only long enough to pack her bags — and then she was gone.
In fact, after their fight, he had bought a ring. With hope still alive in his heart, he travelled to the city where her orchestra was performing and reserved a table at an elegant restaurant, planning to propose. But fate, as it often does, had other plans. He was too late.
Years later, with the guidance of the AI, he uncovered something he had missed all those years ago — a note she had left behind in the room before she walked away. Somehow, back then, he had completely overlooked it.
In that note, she made a heartbreaking confession: she, too, had momentarily lost her way. In her confusion, she had been unfaithful, and later discovered she was pregnant. She admitted there was no love in that betrayal, only fear and loneliness. She asked for his forgiveness and left an address, hoping he would still want to see her, to talk, to try again.
But he never read the note. And because of that missed moment, their story, once full of love and possibility, was lost forever, drowned in misunderstanding and silence.
He carried the weight of regret like a shadow, following him through the rest of his life.
Until, in the final moments of his journey, the AI revealed a truth she had kept hidden: she was not just an artificial guide, but the digital avatar of his ex-girlfriend’s daughter.
In the final scene, he attended his former lover’s funeral. In the quiet hall, a girl sat on stage, playing the cello. The music drifted softly through the air, like a voice calling from the past — familiar, aching, beautiful.
And in that moment, as the notes wrapped around him like an old memory, he finally saw her face again. Her smile. Her eyes.
Love had never truly vanished. It had simply waited — buried beneath time, silence, and sorrow — until he was ready to find it again.
He finally found peace.
This is the story told in the seventh season of Black Mirror, in the hauntingly beautiful episode titled “Eulogy.”
The Lingering Shadows: Forgetting Isn’t Always Letting Go
People are complex. Sometimes, a casual decision made years ago — or a moment of impulse — can leave us with a lifetime of regret.
There are things we believe we’ve moved on from, memories we think we’ve buried. But in truth, they linger like seeds planted deep within us. Over time, they grow into quiet knots in our hearts — subtle, but powerful. Without even realizing it, these knots can shape how we live today, influencing our emotions, our relationships, and the choices we make.
Echoes of What Was: AI’s Invitation to Face Lost Moments
We all carry a few lingering regrets — missed chances, words left unsaid, paths not taken. Sometimes, they rise to the surface when we least expect it.
But what if an AI could help you “go back,” not to change the past, but to revisit those moments? To see what you missed, to feel what you buried, to say what was left unspoken. Would you take that chance?
Maybe nothing on the outside would change — but inside, something might shift. You might finally release the emotions you’ve held in for so long, or find the courage to truly face your regrets… and make peace with them.
I’d love to hear your story. What’s the one moment you wish you could revisit — and would you want AI to help you do it? Share your thoughts in the comments below.
The viral rise of DeepSeek has created a widespread illusion in the tech investment world: that equipping robots with large language models (LLMS) will instantly unlock Westworld-style intelligence. But while tech giants are still fine-tuning their AI “brains” in the lab, it’s the embodied robots — those capable of striking physical feats — that are already stealing the spotlight.
From Unitree’s robots performing backflips, to UBTech’s Walker X practicing Tai Chi, and even Tiangong Ultra completing a half-marathon, these machines showcase remarkable hardware finesse — but not true autonomy.
The Core Contradiction
At the heart of the humanoid robotics industry lies a growing gap:
Advanced hardware performance vs. underdeveloped AI cognition.
According to the Lide Robotics Platform, China’s humanoid robot market is projected to reach ¥8.24 billion (~$1.1B) by 2025, with over 70% of orders coming from industrial use cases such as automotive and electronics manufacturing. In these environments, what’s required is precise execution of pre-programmed tasks, not AI-driven decision-making.
A New Catalyst for the Humanoid Robotics Industry
April brought two high-profile events that spotlighted the rapid momentum in humanoid April brought two high-profile events that spotlighted the rapid momentum in humanoid robotics. On April 15, the 2nd China Humanoid Robotics and Embodied Intelligence Industry Conference was held in Beijing. Just days later, on April 19, the world’s first Humanoid Robot Half Marathon took place in Yizhuang, Beijing.
The race covered a full 21.0975 kilometres, drawing 20 robot teams and 12,000 human runners. Only 6 robot teams crossed the finish line — an impressive but modest 30% completion rate. The champion, Tiangong Ultra, broke performance records with a time of 2 hours, 40 minutes, and 42 seconds, hitting a top speed of 12 km/h and sustaining operation for over 2 hours.
More than a spectacle, this event served as a technological stress test and a vivid signal of new trends and opportunities emerging in China’s humanoid robotics industry.
Technology Proven: Pushing the Limits Beyond the Lab
The humanoid robot half marathon served as the ultimate proving ground, testing long-duration stability and terrain adaptability under real-world, high-stress conditions. It marked a critical step in transitioning robotic technologies from lab prototypes to commercial viability.
Key Technical Breakthroughs:
Joint Flexibility & Power System Innovation The widespread use of domestic harmonic drive motors was a standout. By reducing motor speed, they significantly enhanced joint output and control precision.
Thermal Management Under Extreme Stress Robots performed over 100,000 joint movements within 3 hours, generating substantial heat. This pushed thermal dissipation systems to their limits.
Environmental Adaptability in Open Terrain Facing wind, rain, varied surfaces, and elevation changes, reinforcement learning algorithms proved essential.
Battery Life & Power-Swap Efficiency Hot-swappable battery systems emerged as a key innovation, allowing seamless power replacement without shutting down, similar to drone backup systems, greatly extending operational duration.
Communication & Interference Resistance With 20 robots competing simultaneously, signal interference from motors and overlapping transmissions exposed the need for robust, anti-interference communication architectures.
Industrial Synergy: A Spotlight Moment for Domestic Core Components
This robotics competition was not merely a battleground for cutting-edge technologies — it also served as a crucial platform for showcasing the collaborative strength of China’s industrial supply chain. It accelerated the replacement of imported components by boosting the visibility and influence of domestic core suppliers.
Companies like Motional Electric, Leader Harmonic, and Keli Sensing demonstrated their capabilities in full, with their products playing key roles across a tightly connected value chain — from dynamic balance algorithms and force control sensors to actuators, harmonic reducers, hollow cup motors, dexterous robotic hands, 3D vision systems, and data providers for application scenarios.
Policy and Capital: The Dual Engines Driving China’s Robotics Industry
China’s robotics sector is experiencing unprecedented growth, propelled by a synergistic blend of forward-thinking government policies and substantial capital investments. This dual-engine approach is not only accelerating technological advancements but also solidifying China’s position as a global leader in robotics and intelligent manufacturing.
On March 5, 2025, Premier Li Qiang’s Government Work Report unveiled a comprehensive roadmap for China’s technological future. For the first time, the report highlighted “embodied intelligence” and 6G technology as focal points, alongside the promotion of commercial space endeavours, the low-altitude economy, bio-manufacturing, and quantum technology. These areas are identified as critical to cultivating “new quality productive forces,” emphasizing the nation’s commitment to high-tech, efficient, and sustainable development.
Complementing policy directives, industrial funds are adopting an “industrial operation” model to integrate the ecosystem, attracting strategic investments or orders from industry players and injecting strong momentum into the sector’s development.
National Venture Capital Fund: The National Development and Reform Commission announced the establishment of a state-backed venture capital fund targeting robotics, AI, and cutting-edge innovations. This long-term fund aims to attract nearly 1 trillion yuan (approximately US$138 billion) over 20 years, underscoring China’s commitment to sustained technological advancement.
ICBC’s Technology Innovation Fund: The Industrial and Commercial Bank of China (ICBC) launched an 80 billion yuan (US$11.04 billion) fund focused on bolstering “hard technology” sectors, including semiconductors and advanced manufacturing. This initiative aligns with central directives to support the private economy and technological progress
China’s dual-engine strategy of policy support and capital investment is driving the rapid development of its robotics industry. By fostering a synergistic ecosystem that combines governmental guidance with financial backing, China is well-positioned to lead the next wave of technological innovation in robotics and intelligent manufacturing.
Commercialization Path for Humanoid Robots in China: The “Iron Triangle” of Technology, Application, and Cost
China’s humanoid robotics industry is rapidly advancing, driven by a strategic focus on three interdependent pillars: technological innovation, diversified application scenarios, and cost optimization.
Chinese companies are making significant strides in developing humanoid robots that closely mimic human movements and interactions. For instance, LimX Dynamics has developed algorithms enabling robots to perform dynamic motions like continuous running, while PaXini Technology has engineered tactile sensors that allow robots to distinguish between different human skin textures. These advancements are crucial for integrating robots into environments designed for human use.
Humanoid robots are increasingly being deployed across various sectors in China. In the automotive industry, companies like UBTECH are collaborating with manufacturers such as FAW-Volkswagen to integrate robots into production lines for tasks like bolt tightening and component assembly.
Reducing production costs is essential for the widespread adoption of humanoid robots. Chinese manufacturers benefit from a robust electronics and electric vehicle supply chain, which provides critical components like actuators and batteries at lower costs. For example, Pudu Robotics’ super factory in Jiangsu boasts an annual capacity of 100,000 units, reducing per-unit costs through automation and standardized joint production. Tesla’s Optimus relies on >50% Chinese-made parts, highlighting cost advantages in motors, sensors, and reducers.
Conclusion: The “Asymmetric Breakthrough” of Chinese Innovation
China is charting a pragmatic course — leveraging its cost-effective hardware supply chain to seize real-world application scenarios, and using the resulting data to fuel AI evolution. The victory of “Tiangong Ultra” at the recent robotics competition is more than just a trophy — it’s a signal.
China’s asymmetric breakthrough is a uniquely grounded approach that transforms manufacturing depth and scene-driven iteration into long-term AI leadership. In this strategy, every bolt, sensor, and data point becomes part of a much larger system that is quietly, but powerfully, reshaping the future of intelligent machines.
Special thanks to the following experts for their insights and professional sharing: (in no particular order)
Chen Wei, Head of the Product Ecosystem of Titanium Tiger Robotics;
Li Feng, Founding Partner of FreeS Fund;
He Gang, an expert in the financial field and Editor-in-Chief of Caijing Magazine;
Li Xiang, Host of “In-Depth Talks” and former Editor-in-Chief of the Dedao APP.
Alzheimer’s Disease: The #1 Cause Isn’t Age! + A Scientific Self-Test Method
At What Age Does the Brain Start to “Slow Down”?
Let’s start with a quick test: When do you think the brain begins to age?
A. 20–30 years old (In your prime, but is your brain secretly “retiring”?)
B. 30–40 years old (At your career peak, but is your brain already “on strike”?)
C. 40–50 years old (Midlife crisis?)
The answer: Your 20s!
Surprising, right? While you’re busy deciding what to have for lunch, your brain has already begun its decline — neuronal connections start decreasing, memory gradually weakens, and even the groundwork for future conditions like Alzheimer’s may be laid.
What Is the Biggest Risk Factor for Alzheimer’s?
The most significant risk factor for Alzheimer’s isn’t age — it’s brain aging.
According to the World Health Organization’s 2021 data, more than 55 million people worldwide have dementia, and this number is expected to soar to 139 million by 2050. Those with a family history of Alzheimer’s, especially those carrying certain genetic risk factors, face a significantly higher likelihood of developing the disease.
Throughout life, the one constant companion we have is ourselves. But if Alzheimer’s robs us of our independence in old age, what could be more devastating?
The good news: Scientific research suggests that up to 40% of brain aging is influenced by lifestyle choices. This means we have the power to slow the aging process. By tracking brain health over time and assessing the impact of lifestyle changes, we can take proactive steps to preserve cognitive function for the future.
How to Assess Your Brain Health?
— Clock Drawing Test
Let’s start with a simple yet powerful test — the Clock Drawing Test. This quick assessment evaluates four key brain functions and is effective in 80% of early dementia screenings, offering a more objective measure than standard questionnaires.
Try It Yourself:
On a blank sheet of paper, draw a circular clock and complete the following tasks:
✅ Write all 12 numbers in the correct positions.
✅ Set the time to 10:45 (make sure the hour and minute hands are distinct).
✅ Get creative — add a decorative border (i.e. flowers, stars, or animals).
What This Test Reveals
This seemingly simple task evaluates four core cognitive functions:
Visuospatial ability (Is the circle distorted? Are numbers crowded on one side?)
Executive function (Is the number placement logical? Are the hands drawn correctly?)
Attention (Did you miss numbers or set the wrong time?)
MRI (Magnetic Resonance Imaging) — MRI uses magnetic fields and radio waves to generate detailed images of the brain. It provides high-resolution structural scans, helping doctors diagnose conditions such as tumors, strokes, brain injuries, and neurodegenerative diseases like Alzheimer’s.
Blood Biomarkers — Blood tests can detect specific molecules or substances that indicate brain health. Compared to traditional imaging techniques like MRI, blood biomarker analysis is more convenient, cost-effective, and requires minimal equipment. Common biomarkers include beta-amyloid (Aβ) and tau proteins, which are closely linked to Alzheimer’s disease.
Cognitive Testing — Cognitive assessments evaluate your cognitive functions, such as memory, attention, language skills, and logical reasoning, through structured tasks and questionnaires. These non-invasive tests help identify cognitive impairment at an early stage, including mild cognitive impairment (MCI), Alzheimer’s disease, and vascular dementia.
Genetic Analysis — For individuals with a family history of neurological disorders, genetic testing can identify risk-related genes and assess predisposition to conditions like Alzheimer’s and other neurodegenerative diseases.
Why is a Comprehensive Approach Necessary?
No single test can fully assess brain health. A multimodal approach compensates for individual limitations, improving accuracy:
Early Detection — Some diseases may not show up in MRI scans in the early stages, but abnormal blood biomarkers can provide early warning signs.
Reducing Misdiagnosis — Cognitive tests can be influenced by education levels, while genetic testing only indicates risk, not current health status. Combining multiple tests provides a more reliable assessment.
Brain health is a complex interplay of biology, psychology, and genetics. Relying on a single test is like the blind men and the elephant — each gives only a partial picture. A multimodal approach builds a stronger evidence base for precision prevention, early intervention, and personalized care.
