Marius Schober

Embracing the Mysteries, Unveiling the Realities

A polymathic exploration of innovation, philosophy, and the mystical.

Author: Marius Schober

  • The First Principles of a Post-AGI Business

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    8 min read

    OpenAI released its new o3 models and numerous people argue that this is in fact Artificial General Intelligence (AGI) – in other words, an AI system that is on par with human intelligence. Even if o3 is not yet AGI, the emphasis now lies on “yet,” and – considering the exponential progression – we can expect AGI to arrive within months or maximum one to two years.

    According to OpenAI, it only took 3 months to go from the o1 model to the o3 model. This is a 4x+ acceleration relative to previous progress. If this speed of AI advancement is maintained, it means that by the end of 2025 we will be as much ahead of o3 as o3 is ahead of GPT-3 (released in May 2020). And, after achieving AGI, the self-reinforcing feedback loop will only further accelerate exponential improvements of these AI systems.

    But, most anti-intuitively, even after we have achieved AGI, it will for quite some time look as if nothing has happened. You won’t feel any change and your job and business will feel safe and untouchable. Big fallacy. We can expect that after AGI it will take many months of not 1-2 years for the real transformations to happen. Why? Because AGI in and of itself does not release value into the economy. It will be much more important to apply it. But as AGI becomes cheaper, agentic, and embedded into the world, we will see a transformation-explosion – replacing those businesses and jobs that are unprepared.

    I thought a lot about the impact the announced – and soon to be released – o3 model, and the first AGI model are going to have.

    To make it short: I am extremely confident that any skill or process that can be digitized will be. As a result, the majority of white-collar and skilled jobs are on track for massive disruption or elimination.

    Furthermore, I think many experts and think tanks are fooling themselves by believing that humans will maintain “some edge” and work peacefully side-by-side with an AI system. I don’t think AGI will augment knowledge workers – i.e. anyone working with language, code, numbers, or any kind of specialized software – it will replace them!

    So, if your job or business relies purely on standardized cognitive tasks, you are racing toward the cliff’s edge, and it is time to pivot now!

    Let’s start with the worst. Businesses and jobs in which you should pivot immediately – or at least not enter as of today – include but are not limited to anything that involves sitting at a computer:

    • anything with data entry or data processing (run as fast as you can!)
    • anything that involves writing (copywriting, technical writing, editing, proofreading, translation)
    • most coding and web development
    • SAAS (won’t exist in a couple of years)
    • banking (disrupted squared: AGI + Blockchain)
    • accounting and auditing (won’t exist as a job in 5-10 years)
    • insurance (will be disrupted)
    • law (excluding high-stake litigation, negotiation, courtroom advocacy)
    • any generic design, music, and video creation (graphic design, stock photography, stock videos)
    • market and investment research and analysis (AI will take over 100%)
    • trading, both quantitative and qualitative (don’t exit but profit now, but expect to be disrupted within 5 years)
    • any middle-layer-management (project and product management)
    • medical diagnostics (will be 100% AI within 5 years)
    • most standardized professional / consulting services

    However, I believe that in high-stakes domains (health, finance, governance), regulators and the public will demand a “human sign-off”. So if you are in accounting, auditing, law, or finance I’d recommend pivoting to a business model where the ability to anchor trust becomes a revenue source.

    The question is, where should you pivot to or what business to start in 2025?

    My First Principles of a Post-AGI Business Model

    First, even as AI becomes infallible, human beings will still crave real, raw, direct trust relationships. People form bonds around shared experiences, especially offline ones. I believe a truly future-proof venture leverages these primal instincts that machines can never replicate at a deeply visceral level. Nevertheless, I believe it is a big mistake to assume that humans will “naturally” stick together just because we are the same species. AGI might quickly appear more reliable, less selfish than most human beings, and have emotional intelligence. So a business build upon the thesis of the “human advantage” must expertly harness and establish emotional ties, tribal belonging, and shared experiences – all intangible values that are far more delicate and complex than logic.

    First Principle: Operate in the Physical World

    • If your product or service can be fully digitalized and delivered via the cloud, AGI can replicate it with near-zero marginal cost
    • Infuse strategic real-world constraints (logistics, location-specific interactions, physical limitations, direct relationships) that create friction and scarcity – where AI alone will struggle

    Second Principle: Create Hyper Niche Human Experiences

    • The broader audience, the easier it is for AI to dominate. Instead, cultivate specialized groups and subcultures with strong in-person and highly personalized experiences.
    • Offer creative or spiritual elements that defy pure rational patterns and thus remain less formulaic

    Third Principle: Emphasize Adaptive, Micro-Scale Partnerships

    • Align with small, local, or specialized stakeholders. Use alliances with artisan suppliers, local talents, subject-matter experts, and so on.
    • Avoid single points of failure; build a decentralized network that is hard for a single AI to replicate or disrupt

    Fourth Principle: Embed Extreme Flexibility

    • Structured, hierarchical organizations are easily out-iterated by AI that can reorganize and optimize instantly
    • Cultivate fluid teams with quickly reconfigurable structures, use agile, project based collaboration that can pivot as soon AGI-based competition arises

    Opportunity Vectors

    With all of that in mind, there are niches that before looked unattractive, because less scalable, that today offer massive opportunities – let’s call them opportunity vectors.

    The first opportunity vector I have already touched upon:

    • Trust and Validation Services: Humans verifying or certifying that a certain AI outcome is ethically or legally sound – while irrational, it is exactly what humans will insist on, particularly where liability is high (medicine, finance, law, infrastructure)
    • Frontier Sectors with Regulatory and Ethical Friction: Think of markets where AI will accelerate R&D but human oversight, relationship management, and accountability remain essential: genetic engineering, biotech, advanced materials, quantum computing, etc.

    The second opportunity vector focuses on the human edge:

    • Experience & Community: Live festivals, immersive events, niche retreats, or spiritual explorations – basically any scenario in which emotional energy and a human experience is the core product
    • Rare Craftsmanship & Creative Quirks: Think of hyper-personalized items, physical artwork, artisanal or hands-on creations. Items that carry an inherent uniqueness or intangible meaning that an AI might replicate in design, but can’t replicate in “heritage” or provenance.

    Risk Tactics

    Overall, the best insurance is fostering a dynamic brand and a loyal community that invests personally and emotionally in you. People will buy from those whose values they trust. If you stand for something real, you create an emotional bond that AI can’t break. I’m not talking about superficial corporate social responsibility (nobody cares) but about authenticity that resonates on a near-spiritual level.

    As you build your business, erect an ethical moat by providing “failsafe” services where your human personal liability and your brand acts as a shield for AI decisions. This creates trust and differentiation among anonymous pure-AGI play businesses.

    Seek and create small, specialized, local, or digital micro-monopolies – areas too tiny or fractal for the “big AI players” to devote immediate resources to. Over time, multiply these micro-monopolies by rolling them up under one trusted brand.

    Furthermore, don’t avoid AI. You cannot out-AI the AI. So as you build a business on the human edge moat, you should still harness AI to do 90% of the repetitive and analytic tasks – this frees your human capital to build human relationships, solve ambiguous problem, or invent new offerings.

    Bet on What Makes Us Human

    To summarize, AI is logical, combinatorial intelligence. The advancements in AI will commoditize logic and disrupt any job and business that is mainly build upon logic as capital. Human – on the other hand – is authenticity. What makes human human and your brand authentic are elements of chaos, empathy, spontaneity. In this context, human is fostering embodied, emotional, culturally contextual, physically immersive experiences. Anything that requires raw creativity, emotional intelligence, local presence, or unique personal relationships will be more AI resilient.

    Therefore, a Post-AGI business must involve:

    1. Tangibility: Physical goods, spaces, unique craftsmanship
    2. Human Connection: Emotional, face-to-face, improvisational experiences
    3. Comprehensive Problem Solving: Complex negotiations, messy real-world situations, diverse stakeholder management

    The inverse list of AGI proof industries involve some or multiple aspects of that:

    • Physical, In-Person, Human-Intensive Services
      • Healthcare: Nursing, Physical therapy, Hands-on caregiving
      • Skilled trades & craftsmanship
    • High-Level Strategy & Complex Leadership
      • Diplomacy, Negotiation, Trust building
      • Visionary entrepreneurship
    • Deep Emotional / Experiential Offerings
      • Group experiences, retreats, spiritual or therapeutic gatherings
      • Artistic expression that thrives on “imperfection”, physical presence, or spontaneous creativity
    • Infrastructure for AGI
      • Human-based auditing/verification
      • Physical data center operations & advanced hardware
      • Application and embedment of AI in the forms of AGI agents, algorithmic improvements, etc. to make it suitable for everyday tasks and workflow

    The real differentiator is whether a business is anchored in the physical world’s complexity, emotional trust, or intangible brand relationships. Everything pure data-driven or standardized is on the chopping block – imminently.

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  • AI-to-AI Communication

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    1 min read

    Nowadays, most emails I receive – including technical and legal ones – are undoubtedly written by ChatGPT. Which I’m okay with – but I find it rather funny that I now have to read what an AI has written only to input the context myself into my AI system. We are effectively constraining AI systems to communicate via human intermediaries – which is a laughably stupid and cognitively inefficient approach.

    I think it is wasted energy to make AIs even better at mimicking human communication – this energy is better used in developing AI-to-AI communication protocols that bypass human language entirely. Instead of exchanging emails written in human language, AIs should directly exchange action items, structured data, intent vectors, or probabilistic models. How valuable is it really in making AI communication more human-readable? I believe it is about freeing AIs to communicate in their “native language” while humans simply set high-level objectives and constraints. No latency, no information loss, no mental drainage, more time for actual human communication and interaction.

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  • The Future Belongs to the Intuitive

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    7 min read

    Everything looks as if the future belongs entirely to machines, where decisions will be driven solely by logic and data. This makes sense from a logical perspective. AI can already shift through terabytes of real-time data in seconds. It can identify patterns the human eye cannot see. As these systems become more sophisticated and continue to improve exponentially, it is fair to predict that in the near-term future we will not only push data- and logic-driven decision-making to a point of saturation, we will also experience a natural tendency to lean heavily on logic-based recommendations from advanced AI systems.

    I fear that the more we rely on these data-driven arguments, the more we risk sidelining a crucial element of decision-making: human intuition. We risk that algorithms and AI systems become the default arbiters of choice. The more powerful their capabilities become, the higher will be the temptation to dismiss our intuition. We will end up making decisions purely on logic, with every action optimized by data.

    Here is the contrarian truth: as AI systems gets better at advanced reasoning, processing even more data, and identifying patterns, pure logical and knowledge based analysis becomes commoditized.

    We are already in a world where decisions are made for us by algorithms and AI systems. Not only do they decide which video we should watch next on YouTube, they also provide decision makers with data and insights – whether it is in finance, trading, marketing, hiring, or medicine. And why not? AI systems process data faster, more accurately, and with few biases than any human being ever could. They can recognize patterns that would take humans years to discern. Advanced algorithms spit out logical predictions based on mathematical conclusions. For tasks like optimizing logistics, predicting customer behavior, and analyzing stock market trends, it is a no-brainer–AI wins.

    It seems logical to assume that pure data and computation will lead to the best decisions. But this is flawed because there is something missing in this equation. Decisions are not always about logic. The most important decisions in life and business are anything but logical. They are guided by subtle, almost imperceptible signals we cannot fully explain, but we feel. This is intuition, the gut feeling we experience when something just feels right or feels wrong. While it is tempting to dismiss these feelings as irrational, they often turn out to be right.

    Optimizing decisions based on more data and more logical reasoning is thereby flawed, and I fear that the more we lean on AI systems to guide our choices, the more we risk sidelining the most powerful tool humans possess: intuition.

    Scientific discoveries, for example, are not made as a result of logical reasoning. They are regularly the result of an “aha moment” of insight when knowledge seems to come from nowhere. Or think of the countless stories of entrepreneur who make bold decisions based on nothing but an intangible sense of certainty. Steve Jobs went against market research and expert advice when he decided to launch the iPhone. Elon Musk bet his fortune on SpaceX when logic screamed that the odds were against him. There are investors who pull out of a seemingly attractive opportunity just moments before it tanks, driven by nothing more than a gut feeling. Also, good music just comes to the musician, and it is not created by technical skill.

    Through intuition, we can feel the subtle energetic currents of events before they manifest. It’s the mother who knows something’s wrong with her child before receiving the call from school, or the traveler who avoids a particular flight, only to find out later it crashed.

    These aren’t coincidences or anomalies—they are examples of intuition at work. In these moments, we are not responding to what is, but we are aligning ourselves with what could be. We sense reality before it unfolds. This intuitive intelligence is more than a vague “gut feeling”; it is an ability to sense what isn’t in the data, to feel the reality before it is fully formed. With our intuition, we tap into a deeper field of information that transcends the conscious mind.

    Our rational mind is not very good at listening to our intuition. It is busy making sense of the things in our material world. It is busy with its endless internal monologue and anxiety. Our mind is constantly generating thoughts – and the more data we have access to (think of the infinite information feeds from social media, the news, and now generative AI) the more difficult it becomes to access our intuitive intelligence.

    Furthermore, I fear that, the more ubiquitous AI systems and the more convincing their logic-based arguments become, the more we will trust and rely on them blindly. When an algorithm presents a data-backed recommendation, it is hard to contradict it. The numbers add up, the patterns are clear – it feels almost reckless to go against the machine. But that is exactly the risk.

    The stronger the logical basis for decision-making becomes, the harder it will be to justify following your gut. The result? We will end up in a world where every decision is optimized for efficiency and logic – at the cost of creativity, foresight, and frankly, the human element.

    We risk entering a near-term future where we become slaves to the data, losing the ability to make decisions that transcend the immediate facts in front of us and instead tap into a deeper, more holistic understanding of reality.

    Exactly in fields that require the most crucial decisions, intuitive intelligence is of higher importance than pure data-driven logic. In business strategy, creative innovation, and geopolitical decisions, intuition plays a unique and uttermost important role. It tells use when an idea feels right, even if the numbers aren’t there to back it up, or we abandon a “logical” choice because something feels off.

    The best decisions aren’t made purely on logic or data. They’re made by integrating the analytical with the intuitive. AI will continue to become an ever more invaluable tool, but it’s just that – a tool. It processes the world as it is, based on observable facts and historical data. But intuition allows us to perceive the world as it could be. It taps into potential futures, subtle energetic shifts, and possibilities that aren’t visible in the data. Data can get us to the next step, but intuition lets us leap to entirely new paths. And as AI carves out logic’s territory, intuition becomes even more vital.

    I don’t say we should abandon data or AI systems – far from it. Intuitive decision makers aren’t anti data. They leverage data and logic without being trapped by it. They use it as a foundation, but they use their intuition to connect the dots and sense realities which machines cannot compute. They use data as a guide but trust their intuition to make the final decision. Their intuition will navigate the uncertainties and unknowns that lie beyond the reach of logic. The best leaders will be those who can access and trust their intuition even though logic is against it.

    Those who can access and act upon their intuitive intelligence will find themselves making the right decision when it matters the most—even if logic disagrees: preventing a nuclear conflict by sensing hidden motives when every visible sign points toward war, sparking a scientific breakthrough that defies conventional knowledge, designing a world-changing technology that others dismissed as impossible, or uniting adversaries to forge an unexpected, lasting peace against all rational odds.

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  • The Exploitation Behind Legalizing Organ Trade

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    2 min read

    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.

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  • Running Out of Our Circle

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    5 min read

    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.

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  • Understanding: Human vs. Machine

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    7 min read

    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.

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  • Government as an Investor?

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    1 min read

    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.

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  • Fertility Rates and Real Estate

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    1 min read

    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.

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  • “Researched Climate Models”

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    2 min read

    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.”

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  • China’s Economy

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    1 min read

    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?

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