Marius Schober

  • The Exploitation Behind Legalizing Organ Trade

    I recently saw a debate on whether organ trade and an organ market should be legal. Here’s my take on the issue.

    Yes, there’s a clear mismatch between supply and demand. But who would be the ones selling their organs? People in precarious situations, without the luxury of long-term choices. The wealthy have no reason to sell their organs—it’s the poor and those in debt who would. An organ market would systematically create an incentive to exploit the economically vulnerable, turning their bodies into commodities.

    This brings us to the concept of autonomy. People facing financial desperation have little autonomy. A wealthy individual who has never experienced financial hardship wouldn’t sell their kidney—there’s simply no need (perhaps they’d donate it to a family member or close friend). But the poor don’t have that choice. Their “choice” isn’t voluntary; it’s coerced by poverty. And that is not only economically disastrous but morally catastrophic.

    The very idea that body parts could be marketable contradicts the essence of human dignity. It reduces the most vulnerable members of society to mere commodities. An organ market would lead us straight into a form of slavery—though subtler, more insidious. It’s a slavery packaged as economic freedom. It may look like freedom, but it’s nothing more than exploitation.

  • Running Out of Our Circle

    Running in circles is an expression that is often used to express when no matter what we do, nothing changes. We run in a circle, always ending up where we started.

    Imagine the circle lines as boundaries, not physical boundaries but mental barriers. In our life, we often run within our circle of possibilities. Anything outside our circle seem impossible. Outside the circle is anything that seems unattainable.

    For some, healing a chronic disease may seem unattainable, for others it is a nice house, finding one’s soulmate, or merely financial abundance.

    Over our lifetime, through our upbringing, we have defined our circle of possibilities. We have defined what is within our possibility and what is outside our possibility.

    But this is just a line we drew. It is a mental barrier that does not exist outside our mind. In order to attain what seems unattainable we have to expand our circle of possibilities. We have to pull what is outside our circle inside.

    Imagine it like this: anything that is within our circle is easy and comes effortless. For example making a coffee, driving a car are within our circle of effortless possibilities.

    Other things seem out of reach. They are outside our circle of possibilities. They look extremely hard and impossible to reach.

    What we need to do is reframe our understanding of what is within and what is without our circle. We pull seemingly impossible things inside our circle and thereby we are expanding the size of our circle exponentially. We do this by following our excitement.

    Not everything can be pulled inside our circle. But anything we are absolutely excited and passionate about can be pulled inside and made attainable.

    You might think that you want to be the founder and CEO of a large successful company. But if this is merely a desire that comes from mimesis – in other words a desire that we have because we see other people have or desire it – not from our true inner being.

    We will try forever to pull this inauthentic desire inside our circle, but we will fail because it is against our nature. Listening to our true excitement is key. We have to follow what is truly authentic to us – what we are truly excited about from our whole heart – and pull it inside our circle.

    You may find true excitement and joy playing the piano or researching a certain subject. But true mastery of the piano or earning a livelihood with it may seem like an impossibility. Don’t let this hold you back. If this is what excites you the most, make the decision to pull it into your circle, define it is easily attainable, possible.

    Inside our circle, doing and attaining our desires is as natural and easy as making a cup of coffee.

    Our inner circle represents our current reality. It is both endless and limiting. Endless in terms of repetition and confinement of boundaries.

    Think again of walking in a cricle, you always end up in the same spot, never really advancing. We try to improve the conditions within our circles, but improvements within our circles is like improving a prison cell.

    True freedom comes from expanding that circle. Or stepping out of that circle into an entirely new one.

    That is difficult because we are like fish in an aquarium unaware of the world behind. We only see what is familiar, what is within our circle, and everything beyond that feels alien or unattainable even though we desire it.

    The real truth is that it takes the same energy to live and operate within our current circle as it takes to live within a much larger circle or to step. It takes the same effort to be in our current circle as it takes to be in a completely different, much larger circle.

    First, you need to identify that you are inside of a circle. What are your current habits and goals? What is your current reality?

    Once you are aware, the next step is to identify what is outside your circle. What is it that you desire but looks unattainable, impossible?

    Now we define a new circle. In this new circle, our goals, our habits are aligned with our authentic aspirations, our true excitement. We create a new reality.

    Stepping out of this circle requires risk. It means breaking free from the familiar, and pursing something that may seem uncomfortable or unattainable.

    We leave our circle – we leave our comfortzone.

    We can do this in small, consistent steps or we can make a sharp turn–an instant shift in our approach to living, like flipping from being chased to becoming the one who chases.

    The real key to escaping our limiting circle is focus.

    Where we focus our mental energy on determines the reality we will experience. By only focusing on improving our current reality, we remain locked in. But by expanding our vision to something outside our current circle, outside our current reality, we open up the possibilities of stepping into a new, much larger circle of possibilities.

    What seems impossible now, becomes as effortless as making a cup of coffee.

    The decision to break free starts with the realization that we are contained in a circle and the decision that we are ready to stop running in circles.

  • Understanding: Human vs. Machine

    Whether we understand a text depends on several factors. First, do we recognize and understand the alphabet? Do we understand the language? Assuming both, we can read the words that are written. But this doesn’t mean we understand the text. Understanding what is written depends on whether we have the necessary contextual knowledge and conceptual framework to interpret the meaning behind each word. On a ‘word level’ alone, language is more than a sequence of symbols. Each word and each combination of words conveys in and of itself ideas that are shaped by cultural, historical, and experiential factors.

    Consider the word “football”. In the United States, “football” refers to American football, a sport with an oval ball and heavily physical play. In the UK (and most of the world), “football” is a game played primarily with the feet, a round ball, and two rectangle goals. The same word triggers entirely different images and cultural associations depending on the context in which it is used.

    Or consider the word “gift”. In English, “gift” means a present, something given voluntarily to another person. In German, “Gift” means poison. The same word evokes – again – entirely different meanings depending on the language.

    Even if we can read and comprehend the literal meaning of words, true understanding requires an ability to grasp the underlying concepts, nuances, and intentions, as well as to connect the information to prior knowledge or experiences. If we don’t have these deeper connections, we may be able to read the text, but fail to genuinely “understand” it in a meaningful way.

    When we talk about “understanding” a text, we are simply processing patterns of language based on previous experiences and context. Meaning emerges when we can connect the symbols to prior knowledge and concepts we have already internalized. In other words, the idea of “meaning” arrives from a vast database of stored experiences.

    This becomes clear when we deal with complex technical, scientific, or philosophical texts. Understanding these require not only familiarity with the language, but also a deeper technical or conceptual foundation.

    For example, take a physics paper discussing “quantum entanglement.” The words themselves may be understandable to anyone familiar with basic English, but without a solid grasp of quantum mechanics and concepts like wave-particle duality, superposition, or the mathematical formalism behind quantum states, the meaning of the text is lost. The read can follow the sentences, but the true meaning remains obscure.

    In essence, understanding a text – especially a complex one – goes beyond recognizing words or knowing their dictionary definitions. It depends on an interplay between language and thought, where meaning is unlocked through familiarity with the underlying concepts, cultural context, and prior knowledge. True understanding is furthermore a learning process. Understanding not only demands a proper intellectual preparation, but also the ability to integrate new information from the text with what we already know.

    With that in mind, can a machine understand text in the same way humans do?

    A large language model (LLM) also processes patterns of language, recognizing text based on vast amounts of data. On a surface level, it mimics understanding by assembling words in contextually appropriate ways, but does this equate to “understanding” in the human sense?

    When humans read, we don’t just parse symbols, we draw from a rich background of lived experiences, emotional intelligence, and interdisciplinary knowledge. This allows us to understand metaphors, infer unstated intentions, or question the credibility of the text.

    Back to our example of “quantum entanglement”. When a trained physicist reads the physics paper, they relate the written sentences to physical phenomena they’ve studied, experiments they’ve conducted, and debates he is involved in.

    By contrast, a LLM operates by recognizing patterns from its vast training data, generating contextually relevant responses through probabilistic models. While it does this impressively, we might argue that for true understanding, a LLM lacks the aforementioned deeper conceptual and experiential framework that humans develop through real-world experience and reasoning.

    While it is obvious that LLMs do not experience the world as humans do, this does not mean that LLM are not or will never be capable of understanding and reasoning.

    LLMs do engage in a form of reasoning already, they manipulate patterns, make connections, and draw conclusions based on the data they’ve encountered. The average LLM of today can process abstract ideas like “quantum entanglement” – arguably – more effectively than the average human merely by referencing the extensive patterns in its data, even though they are not capable of linking this to sensory and emotional experience.

    Sensory and emotional experiences, such as the joy of scoring a first goal in a 4th grade sports class or the sorrow of watching one’s favorite team suffer a 0:7 defeat on a cold, rainy autumn day, create deep personal and nuanced connections to texts about “football.” This allows humans to interpret language with personal depth, inferring meaning not just from the words themselves, but from the emotions, memories, and sensory details attached to them.

    The absence of emotional grounding may limit LLMs in certain ways, but does it mean they cannot develop forms of understanding and reasoning that, while different, can still be highly effective?

    For example, a mathematician can solve an equation without needing to “experience the numbers”, meaning they don’t need to physically sense what “2” or “π” feels like to perform complex calculations. Their understanding comes from abstract reasoning and logical rules, not from emotional or sensory connection.

    While a LLM cannot yet solve mathematical problems, in a transferred sense, a LLM might “understand” a concept by connecting ideas through data relationships without needing direct experience. It recognizes patterns and derives logical outcomes, like a mathematician working through an equation.

    One example for this is language translation. While a professional human translator might rely on personal cultural experience to choose the right phrasing for nuance, in many cases, LLMs are already able to process and translate languages with remarkable accuracy by identifying patterns in usage, grammar, and structure across million of texts. They don’t have personal experience of what it is like to live in each culture or speak a language natively, they nevertheless outperform humans in translating text (think of speed).

    Understanding, then, is the process of combining knowledge, reasoning, and in our human case, personal experience. In that sense, is it impossible for LLMs to understand and reason, or lies the difference more in what LLM ground their reasoning on?

    Humans reason through real-life experience, intuition, emotions, and sensory input, like the joy of scoring a goal or the gut-feeling resulting from a suspicious facial expression. LLMs, on the other hand, don’t have this kind of grounding, they operate purely on data.

    Again, does this mean LLMs cannot reason? LLMs – despite lacking this personal grounding – still show early forms of reasoning. This reasoning is powerful, especially in cases where personal experience is not required or less important. In fact, understanding may not even require physical or emotional experiences in the same way humans are biologically conditioned to need them. If reasoning is fundamentally about making accurate predictions and drawing logical conclusions, then LLMs are – arguably – already surpassing humans in certain domains of abstract reasoning.

    With advancements in AI architecture, it is likely that LLMs will one day develop a form of “conceptual grounding” based purely on data patterns and logical consistency. We will arrive at new forms of understanding and reasoning that differ from, but rival, human cognition.

    The limitations of LLM are what makes human human: an inherent drive to pursue truth and question assumptions. While LLMs – arguably – reason by connecting dots and generating solutions, they lack the intentionality and self-awareness that drives human reasoning.

    Ultimately, the question of whether machines can in fact understand and reason is less about how accurately it is replicating human cognition and more about recognizing and harnessing a new form of intelligence.

  • Government as an Investor?

    There is a real debate going on whether the German government should provide Lilium – a 9-year-old, publicly-listed money loosing eVTOL company, without a single successful realistic test-flight – a €150M loan. (It decided not to, good.)

    The real issue I see is that public funding socializes risk and losses, forcing taxpayers—like my parents (!!)—to cover the bets of government employees who lack skin in the game, all under the guise of ‘deep tech’ and ‘innovation.’

    Worse, I don’t want German taxpayers’ money supporting a so-called ‘German’ company headquartered in the Netherlands and listed in the USA.

    IF a company seeks public loans, all its shareholders and executives should be personally liable for the full amount (and what they promise)—no exceptions. Only then can we can talk about a loan from tax-payers.

    Furthermore, it seems that private and institutional investors are not willing to provide any more funding to achieve the alleged test flights in 2025. If private investors are not willing to put any more money into the company – why should the tax payer?

    What should Germany do? Lower taxes. Deregulate. But don’t become a VC.

  • Fertility Rates and Real Estate

    Global fertility rates are plummeting. Countries like the U.S. (1.64), China, Japan, and Spain (all below 1.2) face drastic population reductions – up to 80% over three generations. South Korea’s rate of 0.7 could trigger a 96% decline. This is not only a demographic issue but also an economic time bomb.

    For real estate, fewer people means fewer homes needed. An aging population will favor downsizing and specialized housing, while larger family homes sit vacant. Urban areas may initially absorb the shock, but even cities will face declining demand. Property values and rental incomes will inevitably fall, hurting investments and slowing construction. Immigrant-driven growth, which propped up Europe’s housing markets for decades, is no longer a reliable cushion as the fertility rate plunges across the globe and across ethnicities.

    As demand shifts, so will the nature of housing. Assisted living, multi-generational homes, and adaptive reuse projects will dominate, while sprawling suburban developments could become ghost towns. Governments may attempt to incentivize higher birth rates or attract foreign buyers, but the long-term trajectory points toward overcapacity and falling values.

  • “Researched Climate Models”

    So-called “researched climate models” are nothing more than digital crystal balls, fed by human arrogance and mere morsels of data. These models are as “researched” as the deep ocean – we’ve barely scratched the surface.

    The climate system is a labyrinth of countless variables and feedback loops:

    1. Solar cycles and variations in solar output
    2. Earth’s orbital changes (Milkankovitch cycles)
    3. Galactic cosmic rays influencing cloud formation
    4. Plate tectonics altering ocean currents and atmospheric circulation
    5. Volcanic activity injecting aerosols and gases
    6. Geomagnetic field fluctuations affecting atmospheric protection
    7. Deep ocean currents and heat distribution
    8. Ocean acidification
    9. Sea ice dynamics and albedo effects
    10. Greenhouse cas concentrations (CO₂, methane, water vapor)
    11. Aerosole distributions from natural and anthropogenic sources
    12. Ozone layer variations
    13. Forest cover changes affecting carbon sinks
    14. Soil microbiome dynamics influencing greenhouse gas emissions
    15. Phytoplankton populations and ocean sequestration
    16. Permafrost thawing releasing stored greenhouse gases
    17. Ice sheet stability and sea level changes
    18. Glacial retreat altering local climates
    19. Human greenhouse gas emissions from industry and agriculture
    20. Land use changes affecting albedo and local climates
    21. Geoengineering attempts (e.g. cloud seeding, stratospheric aerosol injection, etc.)
    22. Potential quantum influences on chemical reactions in the atmosphere
    23. Quantum entanglement in biological systems
    24. Schumann resonances and their potential climate impacts
    25. Ionospheric changes affecting atmospheric electricity
    26. Meteor impacts and dust influx
    27. Potential dark matter interactions with Earth’s core

    The climate system is a multi-dimensional, multi-scale phenomenon where microscopic quantum effects may cascade into global changes and cosmic events can trigger earthly responses. Our current models, focused primarily on greenhouse gases and simple feedback loops, are akin to trying to predict the outcome of a symphony by looking only at the trombone section.

    The sheer number of variables and their non-linear interactions make accurate long-term prediction an impossible task.

    MAYBE quantum computing and superintelligent AI might someday crack the climate code. But today’s models? They’re monuments to our stupendous arrogance. We’re using abacuses to calculate infinity, patting ourselves on the back for our “accuracy.” It’s not just misguided—it’s dangerously delusional.

    Our current understanding is but a drop in the ocean of what there is to know. Instead of boasting about “researched climate models,” we should humbly speak of “preliminary climate hypotheses.”

  • China’s Economy

    At the IAA TRANSPORTATION, the world’s leading trade fair for mobility, transport, and logistics, I talked to a Chinese salesman from Shandong province. He said since COVID-19, the Chinese economy is worsening every day, describing the Chinese economy and their order situation as “poorly”. He was working on a commission basis for an industrial robotics company. His base salary – he said – is only covering his “insurances”, nothing more. When a sales person like this talks, you listen. His livelihood depends on actual transactions, not theoretical models. The overall quiet at most supplier booths at the exhibition only amplified his concerns.

    Are we prepared for a future where the workshop of the world falls silent?

  • The Income-Wealth-Paradox

    Income and wealth often dance to different tunes.

    While a high income is seductive and provides comfort, it rarely leads to true wealth.

    The secret lies in equity–ownership that multiplies value over time. Equity in businesses or even ideas can generate generational wealth.

    It is not about how much you earn, but what you own that appreciates.

    To build real wealth, shift focus from earning more to owning more.

    Equity is the silent engine of wealth creation.

  • Trip to HTC Eindhoven

    Yesterday, at 7AM I was drinking an Espresso in the city center of Düsseldorf. Only a 1,5 hour drive later I arrived at the High Tech Campus (HTC) in Eindoven, the home of PHILIPS, Signify, and dozens of other technology companies and startups.

    This 90 minute drive felt like time traveling from the past into the present. The HTC greeted me with an infrastructure that reminded me of the Google Campus in Mountain View, California. The entire campus is designed car-free. Well not literally, because it has a direct access to the high way and offers generous free parking spaces in green car parks. From there you walk to your office or you can use bike sharing.

    It is surrounded by areas of water, which is typical for the Netherlands. On the clean, car-free roads, you can see self-driving delivery robots, you overhear at least as many English conversations as you can overhear Dutch conversations. An international business atmosphere you don’t have – for example – in Düsseldorf.

    The HTC in Eindhoven is not the future. The campus is already 25 years old. It is a reminder for city- and business park planners of how the present should look like. Overall, I feel that in the Netherlands you get a much better infrastructure for a comparable amount of taxes. The world can definetly learn from the Dutch.

  • iSame

    Apple released the iPhone 16. It’s just another iPhone. And that makes Apple the most risk-averse technology company in the world. Apple focusses on refinement over revolution. It prioritizes stability and incremental improvements. This strategy minimizes risk but stifles innovation. Can Apple continue to lead without daring to disrupt?

    Today, the Apple surely didn’t fall far from the tree-dition.