Marius Schober

Progress Through Consciousness

Embracing the Mysteries, Unveiling the Realities

  • Remote Viewing: Deutsche Bundestagswahl

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    9 min read

    Without a lot of preparation I tried my very first remote viewing with the German election as a target.

    Below is a scanned version of my remote viewing session with a summary and possible interpretation of what I foresaw.

    Remote Viewing Bundestagswahl 20250223_20250222-1104Download

    1. The Remote Viewing Session

    I became interested in remote viewing and during my research I found a simple remote viewing template on RemoteViewed.com. I decided to just follow the instruction and do my first session to see where it leads me.

    The Ideogram

    In the ideogram (page 1) I felt chaos. My reflexive drawing showed a lot of zig-zag and noise.

    Sensory Impressions

    • Sounds: loud, noise
    • Textures: stiff
    • Temperature: hot
    • Colours: red, orange
    • Luminescence: bright
    • Smells: rotton, sweat
    • Tastes: milky
    • Dimensions: large, bold
    • Aesthetic Impact: chaotic, no structure

    Sketching

    1. My sketch started with the chaos and noise that I already drew in the Ideogram on page one. A swirling, noisy mass possibly symbolizing the raw, unfiltered political turbulence and uncertainty surrnounding the election
    2. I then overlaid, bolder, distinct layers below the chaos, possibly representing the various political parties, each emerging from the chaos. These layers may be the established and emerging parties.
    3. After that, I reflexively drew a diagram that weaves through and connects the layers. One or two layers/parties were below the x axis, possibly indicating not making it into the Bundestag. Three layers/parties are above the x axis, where they meet the chaos/noise
    4. The diagram seems to bring order into the chaos. It brings or suggests connections, relationships, realignments, interactions among the layers/parties and the chaos.

    In summary, the sketch captures a transition from a state of disorder to one where clear, dynamic structures/alliances begin to take shape amid political upheaveal.

    Summary

    The Chaotic Foundation

    The sketch begins as a swirling, noisy mass, representing the raw political turbulence and uncertainty permeating the election night. Maybe, this chaos symbolizes the initial flood of emotions, unfiltered voter sentiment, the unexepcted shocks that will accompany the unfolding vote counts on Sunday night.

    The intense, noisy chaos forsees the election night as a period of upheaval. As the votes come in, there will be a surge of raw energy. A mix of elation, shock, even protest while the voters’ deep-seated dissatisfaction and desire for change manifest in real time.

    Emergence of Distinct Layers

    Over the chaotic backdrop, I overlaid bold, distinct layers most likely representing the various political parties. These could be established forces (Union, SPD, and others) and emerging parties. The positioning below the initial chaos suggest that they are attempting to extract order and meaning from the turbulent environment, each vying to define its identity and appeal to a shifting electorate.

    The Reflexive Diagram

    The subsequent diagram, with clear x- and y-axis interlinks these layer, and is a critical element.

    • Below the x-axis: One or two layers indicate that at least one party will not make it into the Bundestag, another party is starting above the x-axis, yet falls below it as x increases. This party may start in the first extraploitation > 5%, yet as the night progresses, falls below the 5% and not make it. Or it might mean this party very barely secure the 5%.
    • Above the x-axis: three key layers are visible. Yet only two of these layers are bolder than the third; possibly meaning that only two parties have the majorities to form a coalition. These three layers directly interface with the chaotic environment of the vote.
    • The diagram suggests that relationships between the parties exist and possibly allow a realignment – meaning even amid the chaos, there is an emergent order

    The diagram division is telling. One or two parties will fall short (symbolized by layer below the x-axis), two major forces will emerge as decisive parties; meaning that only two parties gain enough public mandate to engage in serious coalition negotiations. One third force is above the x-axis, yet declining, which might represent an established polticial forces that loses ground (like SPD or Greens). This paves the wave for an unexpected, transformative coalition – possibly including elements from parties previously sidelined (like AFD).

    The reflexive diagram suggests that the chaotic energy isn’t random but a structure emerging within it. This structure may be a new coalition, realignment, and relationships among parties. The choatic energy is channeled into forging connections that, though unorthodox, are robust enough to redefine the government’s foundation.

    The overall impression is one of radical transformation. Traditional alliances crumble, and a new, dynamic order takes shape. This could include unexpected partnerships (like the AFD gaining leverage) and the entry of emergent forces that upend conventional politics. The emering light in my sensory sketch might be the embodiment of a new political mandate born from the voter’s desire to break with the past.

    2. Interpretation

    My remote viewing session describes the current political moment, where chaos and transformation coalesce into a dynamic vision of change.

    The initial ideogram conveys a landscape dominated by raw, unfiltered turbulence: a swirling, noisy mess rendered in bold reds and oranges that represent both passion and danger.

    The chaotic canvas, felt with a sensation of intense heat, stiffness, and an overwhelming sound of clamor, represents the charged atmosphere permeating the election night.

    The environment feels almost tangible: a hot, bright, and palpable space imbued with the odors of decay and sweat, suggesting that the old order is rotting ander pressure even as something new stirs to life.

    Overlaying this turbulent backdrop, my sketch introduces distinct, bold layers that represent the various political parties emerging from this maelstrom. These layers looked random at first, but were drawn below an – yet – invisible threshold – the x-axis – hinting at parties that might fail to reach the Bundestag.

    Conversely, three prominent layers rise above this line, symbolizing those factions robust enough to engage directly with the prevailing chaos and claim their place in the new order.

    The reflexive diagram weaves through these layers, hinting at emergent connections and realignments, suggesting that even amid the cacophony of dissent and uncertainty, there is a nascent structure forming.

    The network of lines implies that the political turbulence is not merely disorganized noise, but a transformative energy that is actively reordering alliances and carving out a new government landscape.

    My sensor impressions – loud, jarring sounds; a stiff, unyielding texture; intense heat, bright luminescence; and even the contrasting tastes and smells, enrich this narrative. They signify that this is a time of both decay and birth, where the old, rigid, structures are disintegrating, making space for a reimagined, more fluid political order.

    3. Foresight

    My remote viewing suggests:

    • We will see a dramatic and decisive political turning point
    • Initial chaos on election night – marked by intense public emotion, surging media noise
    • The established political order is forcibly redefined
    • Traditional power coalitions are losing their grip, unable to contain the torrent of public discontent, they fracture under the pressure
    • We will see an unexpected coalition of forces, one that defies conventional political taboos (possibly a CDU + AFD coalition)
    • One or two parties are unable to cross the critical threshold. It will look as if one party crossed the 5%, while during the election night they will fall below it.
    • There will be three parties prominently above the threshold
    • One potent new political party might surprisingly secure 10% of the vote
    • A new coalition will be formed that radically reorders the established dynamics; this might be an unexpected and cross-ideological alliance
    • For the AfD, the traditional “no-go” rules (such as the exclusion of the AfD from governing coalitions) may be overthrown by the force of public demand and unprecedented realignment
    • If the CDU prove unable to form a stable alliance, there is a possibility that even the AfD could be drawn into a coalition
    • The new coalition will be dynamic and willing to break with tradition, implementing bold policies that reflect the voter’s deep desire for systemic change
    • On Sunday night, we will birth a new political order, reshaping German governeance in a way that is revolutionary and pragmatic.
    • The public will react strongly – both in celebration and protest
    • The outcome might not replace the current government, but

    My intuitive interpretation:

    • The CDU/CSU will struggle to create a coalition and the AfD will enter the negotiation table.
    • If the CDU/CSU will not allow this, we will either see new elections or the CDU/CSU will enter a coalition that not only harms them but ultimately falters
    • The AfD will surprisingly enter the negotiation table, if it will not happen after this election, they will emerge even stronger in a new election making it impossible to govern without their participation
    • My remote viewing sees a new, unrecognized or marginal party break through the electral threshold with approximately 10% of the vote – based on current dynamics, this might be the Linke or BSW. If this is true, and we assume one of them reaches 10% and the other 5%, it will certainly mean the SPD will lose voters and come out at 12 or 13%.
    • From my remote viewing, I’d say that the FDP will not make it above 5%.
    • Furthermore, the AFD will likely surprise with a much stronger performance than predicted (possibly at 25%)
    • Both, a stronger far-left and a stronger right (AfD) will mean the conventional parties will come out worse than predicted in polls. The FDP and CDU will lose voters to AfD, while the SPD and Grüne lose voters to the Linke and BSW.
    • Based on my remote viewing and considering current polls, this might be a possible outcome:
      • SPD: 12%
      • Union: 29%
      • Grüne: 11%
      • FDP: < 4%
      • Linke or BSW: one will reach 10%, the other 5%
      • AfD: 29%

    Closing Words

    This was a fun exercise and my very first remote viewing session and interpetation. It will be fun to see how true or false it will turn out to be!

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  • The Era of Information Overload

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    2 min read

    One of the biggest problems of humanity is information overload.

    Think about how we used to get information just 50 years ago. We had to deliberately search for them.

    We had to engage in conversations, go to the library or bookshop to search relevant books or buy a newspaper.

    The internet gave birth to niche forums, and we got search engines which allowed us to find blogs and articles.

    Until social media arrived, it was a careful quest for information.

    Social media turned it around. Instead of searching for information, we now get bombarded with news, ideas, opinions – from anyone around the world, nonstop 24/7.

    With ChatGPT, we got a tool that not only bombarded us with human created information, we can now basically create our own information, endlessly.

    The worst: People now flood the internet and social platforms with content they didn’t even write themselves.

    What does this mean?

    It is now easier and cheaper than ever to access information. Which is great!

    But it is harder than ever to focus on what really matters to us.

    The now endless stream of information siphons our energy, distracting us from the intentional paths we truly wish to pursue.

    I think the best way to consume information consciously is to first have a clear picture of what we want to understand and know, and then to dedicate time for deep-reading and deep-writing.

    That means, not only searching for quick information on what truly interests you – but choosing one subject to study, research, and then write your own essay on it.

    Whether you publish that essay or not is irrelevant.

    Merely writing it keeps your thinking-ability alive.

    The important thing is to do it consciously.

    Use AI only as a research partner – not a ghostwriter.

    Pick what you want to master. Then dedicate time to actually master it – not only consume endless information on whatever the world decides is important now.

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  • Emerging from Limitations

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    1 min read

    I think we have an incomplete and false understanding of reality. Because of that, we are moving inside a tiny fraction of endless possibilities.

    An AGI system based on previous and current knowledge can only exploit what is possible within that tiny fraction. The missing link seems to be our mystical, uniquely human ability of intuition, which allows highly conscious humans to access knowledge outside our current fraction of possibilities – creating something (ideas, inventions, theories) entirely new, that has never been done before.

    Based on how AI systems are built, I expect them to create meaningful advancements within our current frame of understanding; but compared to what is actually possible, these will stay miniscule.

    To access what seems impossible, we shouldn’t look for logic and intellect. We should aim to understand consciousness; i.e., study Yogis, understand DMT, make sense of Psilocybin.

    Only by heightening our consciousness – and intuition seems to be the highest form of it – will we be able to emerge from current limitations. Because in the end, there are no limitations.

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  • 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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