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.
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.
Donald Trump’s reelection is not just a political victory—it is the beginning of a seismic realignment of American power. Unshackled by the need for reelection and surrounded by a cadre of contrarian advisors, Trump stands ready to rewrite the rules of domestic governance, global trade, and national security. Not since the mid-20th century has a U.S. presidency promised such a fundamental overhaul of the nation’s operating system.
This moment introduces a high-variance environment where volatility is the new norm and uncertainty both a risk and an opportunity. Trump’s method turns conventional wisdom on its head: predictability, once prized, is now a vulnerability; unpredictability, a calculated asset. This inversion compels domestic institutions, foreign governments, multinational corporations, and investors to abandon old assumptions and prepare for a new, uncharted era of American leadership.
Strategic Unpredictability
In conventional politics, predictability reinforces trust and stabilizes alliances. Trump turns this formula on its head. Borrowing from his business roots, he treats governance like an endless high-stakes negotiation, refusing to be pinned down by familiar rules. Instead of relying on time-honored frameworks—NATO’s ritualistic guarantees, half-century-old trade deals, bureaucratic inertia—Trump embraces a sophisticated combinatorial approach to decision-making. He experiments with countless permutations of strategies and tactics, making his next move virtually impossible to predict.
This unpredictability, often mistaken for chaos, is calculated. Trump breaks traditions, mixes signals, and never commits fully to a single position. The discomfort this causes among media, diplomats, and policymakers arises from their inability to slot him neatly into known categories. As allies and adversaries scramble to decode shifting signals, they must now renegotiate assumptions and adapt on the fly. Formerly stable trading partners can no longer rely on a static understanding of U.S. policy, and institutions once considered untouchable must re-justify their relevance.
The benefits for Trump’s agenda can be substantial: unthinkable reforms, renegotiated pacts more favorable to U.S. interests, and revived domestic industries. The risk, however, is perpetual uncertainty—markets can rattle, trust erode, and miscalculations prove costly. Yet by keeping the world off-balance, Trump preserves maximum strategic freedom, forcing every stakeholder to engage on his terms. This approach reveals Trump as a leader who, far from being misguided or simplistic, demonstrates a rare creative intelligence—one that thrives on complexity, defies convention, and redefines the limits of political possibility.
A Presidency with Succession Plan
No longer seeking reelection, Trump’s ambitions transcend short-term popularity. He envisions a legacy enduring centuries, a future where his descendants inherit a reshaped America. This shift in time horizon is profound. It emboldens him to attempt structural overhauls that others fear as political suicide. He can endure short-term pain, criticism, and even chaos if he believes it sets a foundation that benefits future generations.
Rather than governing for one election cycle, Trump is orchestrating a multi-decade realignment aimed at reviving stagnant industries, redrawing global trade patterns, and consolidating a durable political base. Central to this strategy is J.D. Vance, a sharp and versatile leader who will command broad appeal if the administration delivers on its promises. As a policy entrepreneur who blends conservative instincts with selective progressive ideas, his potential appeal across party lines sets him apart from orthodox politicians. If he can claim credit for tangible improvements—such as a resurgent manufacturing corridor in the Midwest—Vance’s path to the presidency in 2028 becomes clearer, ensuring policy stability that stretches well beyond Trump’s final day in office.
Beyond J.D. Vance, Trump’s succession plan includes other high-potential figures who could easily extend his vision well into the 2030s. Robert F. Kennedy Jr., with his unique blend of populist appeal, independent thinking, and a growing base across traditional party lines, emerges as a natural complement to Trump’s coalition. His presence signals a broader ideological realignment, bridging gaps between disillusioned Democrats, independents, and Republicans. Additionally, Trump’s children – particularly Donald Trump Jr. and Ivanka Trump – are well positioned to inherit both the political machinery and cultural influence their father has cultivated. Together, this combination of J.D. Vance, RFK Jr., and the Trump family creates a formidable roster of successors, capable of sustaining Trump’s disruptive agenda for 12 years, or even two decades.
This multi-generational continuity is the most important possibility to internalize. Waiting Trump out is no longer an option. Institutions and foreign governments cannot bank on a swift return to pre-Trump norms. Instead, they must recognize the likelihood of an enduring disruption and recalibration. Again, even if Trump only succeeds in reviving the Rust Belt, it seems likely that the U.S. will spend the coming decade dismantling, digitizing, and rebirthing its institutions, forging a state that sets new efficiency standards and redefines global power.
Trump x Musk
In the tense aftermath of the assassination attempt on Donald Trump, Elon Musk’s swift and unequivocal endorsement stunned the public. Within minutes, his bold show of confidence galvanized millions of hesitant voters, emboldening them to step forward and voice their support.
Musk went further, warning that if Trump, armed with superior policies, a seasoned team, and lessons from his first term, still failed to defeat a weak Democratic challenger, it would mark America’s last truly meaningful election. While dramatic, this message was less prophecy than critique—an attack on the creeping institutional inertia in Washington. In Musk’s view, the real danger laid not in some North Korean style regime but in the emergence of a system that, like California’s one-party politics, renders elections mere formalities. If entrenched bureaucracy could outlast Trump’s best efforts, democracy would become ritual rather than reality, and the nation’s political destiny would drift beyond the voter’s reach.
The Musk – Milei Connection
Elon Musk’s fascination with Argentina’s libertarian president, Javier Milei, adds an unexpected dimension to the Trump-Musk relationship. Milei’s reforms, centered on relentless deregulation and led by a powerful Ministry of Deregulation dismantling barriers at lightning speed, offer a live test bed for the libertarian governance Musk envisions and Trump might embrace. The Argentine experiment—wielded by the sharp intellect of Federico Sturzenegger’s ministry—cuts one to five obstacles a day, shrinking a bloated state into a lean, innovation-ready apparatus.
This bold agenda resonates strongly with Musk, who has hinted at parallel efforts in the U.S. through his proposed Department of Government Efficiency (DOGE). Both he and Milei share a taste for smashing outdated frameworks, allowing decentralized markets to flourish and forcing institutions to justify their existence. Milei’s admiration for Trump as a “true warrior” and “viking” cements this ideological triangle. It suggests a cross-pollination of ideas—Milei’s ruthless pruning of state power, Musk’s efficiency crusade, and Trump’s willingness to rewrite the rulebook—potentially softening Trump’s reliance on tariffs and energizing his push for structural reform.
An Alliance of Consequence
Elon Musk’s transition from outside visionary to an influential policymaker is more than a new addition to Trump’s arsenal—it’s a force multiplier. Musk’s wide-angle, multi-planetary perspective infuses fresh intellectual rigor into a governance style defined by volatility, turning unpredictable impulses into purposeful experimentation. But his influence no longer stands alone. The Milei effect now permeates these corridors of power, seeding radical ideas about deregulation and streamlined government that are not theoretical but field-tested in Argentina’s bold experiment.
With Milei’s blueprint as a proof of concept, Musk and Trump find tangible models for dismantling entrenched bureaucracies. Instead of grappling with intangible theories, they can point to real results—economic barriers torn down at a breakneck pace, the state machinery pared back without collapsing the social fabric. Argentina’s evidence emboldens Musk’s push for sweeping reforms—faster permitting, leaner agencies, a dynamic redefinition of public service—and helps Trump justify riskier moves that traditional politics once deemed unthinkable.
The result is not mere chaos, but a calculated recalibration. As Musk invests time shaping innovation policies and operational efficiencies, he draws on lessons from Milei’s successes to justify even bolder undertakings. These new frameworks, influenced by both Musk’s contrarian brilliance and Milei’s radical pragmatism, feed back into Trump’s governance style. Each actor accelerates the others, creating a self-reinforcing cycle of disruption and renewal.
The result: a triad of global disruptors—Trump, Musk, Milei—whose ideological synergy could reshape how governments function and markets evolve. Argentina’s libertarian revolution provides a clarifying lens into what future American reforms might look like: radical, data-driven, and unapologetically free-market, with global ripples challenging stagnation wherever it takes root.
The DOGE Experiment
The synergy between Trump, Musk, and the lessons drawn from abroad now converges within the Department of Government Efficiency (DOGE). Freed from traditional templates, DOGE seeks to simplify tax codes, automate administrative procedures, and use technology to slash bureaucratic dead weight at breakneck speed.
Imagine the U.S. government as an advanced operating system: blockchain-based audits instead of paper trails; AI-driven licensing to eliminate red tape; simpler, unified tax codes; algorithms to streamline procurement. DOGE aims for order-of-magnitude improvements in efficiency, cutting decades of accumulated friction.
The United States is no fragile backwater; its immense global influence and deeply entrenched institutions mean that any disruption reverberates across markets, alliances, and long-standing treaties. As the world’s largest economy and a cornerstone of geopolitical stability, the U.S. cannot afford large-scale missteps. Yet the DOGE initiative adopts a startup mentality—rapid iteration and high-stakes trial and error. The potential upside is transformative: streamlined public services, productivity-boosting incentives, and a leaner, more efficient government. The risk, however, is equally profound. Removing critical structural supports without care could destabilize the system, triggering unintended and potentially catastrophic consequences.
This tension underscores the importance of the existing talent housed within the U.S. bureaucracy. Unlike Argentina’s historically disorganized public sectors, Washington’s institutional apparatus holds deep reservoirs of domain expertise—i.e. in foreign affairs. The DOGE mandate is to harness this knowledge, not extinguish it. Musk’s first-principles logic demands that old frameworks pass rigorous stress tests: if a structure can’t be justified, it goes. But he must also ensure valuable specialists remain engaged, transforming inertial complexity into dynamic competence.
The outcome is radical uncertainty. Markets should expect breakneck policy pivots, unconventional alliances, and sudden regulatory changes. The winners will be those who anticipate Musk’s logic: simplify processes, reduce friction, solve root problems, and think big. Those who rely on slow-moving bureaucracies and incrementalism may find themselves outpaced.
Tax Reforms: Toward Radical Simplification
Trump’s envisioned tax overhaul—steeped in campaign promises of cuts, credits, and targeted relief—now faces a deeper metamorphosis under the influence of Elon Musk’s DOGE. While conventional analysis fixates on marginal rates and brackets, Musk approaches taxation like a first-principles engineering problem, stripping away centuries of incremental complexity.
This perspective challenges the old narrative. Instead of parsing line items—tips, overtime, tariffs—Musk demands a wholesale reset: a flattened structure free of intricate carve-outs and sector-specific giveaways. Such radical simplification acknowledges a central truth: complexity breeds corruption, invites rent-seeking, and rewards the nimble few at the expense of the many. If America’s fractal tax code now favors professional tax strategists and corporate accountants fluent in loopholes, Musk wants a system comprehensible to any citizen with a smartphone.
The most likely outcome? A streamlined tax regime that reduces friction across the entire economy. Imagine minimal categories of income, uniform treatment of earnings, and a largely automated compliance process. Smart contracts and digital ledgers could replace annual filings with instantaneous settlements, neutering the bureaucratic machinery that has grown around tax enforcement. These changes would make it harder for both corporations and governments to hide inefficiencies—an outcome that resonates with Trump’s broader ambition to strip away outdated infrastructure.
Yet this simplicity harbors profound implications. A truly flat, transparent system would expose the real winners and losers of American policy choices. If protectionism endures, tariffs would stand naked as a parallel tax, visible in real time rather than obfuscated by a maze of deductions and rebates. Politicians, accustomed to cloaking redistribution in complexity, might find it harder to pass off subtle forms of patronage as populism. In essence, a maximally simplified tax code removes the camouflage that has protected vested interests for decades.
Of course, simplicity will backfire if introduced bluntly. Entire industries, from tax advisory firms to lobbyists, depend on complexity’s shelter. Abruptly leveling the landscape will produce short-term chaos as entrenched players scramble for new footing. Moreover, while Musk’s logic-driven approach promises elegance, reality may resist tidy solutions. Certain incentives—promoting green energy or encouraging domestic manufacturing—might still demand nuance. But the starting point is no longer incremental tinkering; it’s a clean slate, forcing every tax provision to justify its existence from zero.
Tariffs: Negotiation Leverage
Once dismissed by orthodox economists as blunt and inefficient, tariffs now stand at the center of Trump’s global playbook—not as a fixed doctrine, but as a tactical lever. Free market idealists champion free trade as the route to optimal outcomes, yet real-world markets rarely start on equal footing. Nations tilt the field with subsidies, currency manipulation, and hidden regulatory hurdles. In such an environment, tariffs become a strategic scalpel that can reset terms, enforce reciprocity, and pry open previously closed markets.
For Trump, a sweeping 60% duty on Chinese imports is no final blueprint—it’s an opening offer designed to shock the system. The message: negotiate, adjust, or pay the price. This unpredictability unsettles long-standing assumptions. Allies and adversaries alike must recalibrate, as stable supply chains give way to fluid production networks in Vietnam, India, or Mexico. If done well, these shifts yield a more balanced distribution of manufacturing and reduce America’s vulnerabilities to single-source suppliers. In this sense, tariffs can foster resilience and diversification, mitigating the geopolitical choke points that free trade theory never fully acknowledged.
Yet these weapons must be wielded with surgical precision. Mishandled tariffs risk alienating key partners, rattling markets, and sparking inflation. They can morph into a hidden tax on consumers, undermining the very domestic revitalization they promise. Elon Musk’s perspective offers a cautionary note: restructuring supply chains is no quick fix. Shifting factories and retraining workers takes years. Abrupt, across-the-board tariffs can fracture critical production systems overnight. Prudence suggests a phased approach, signaling intentions early, allowing industries time to adapt, and using threats as negotiation chips rather than sledgehammers.
Trump’s coalition of advisors—visionaries like Musk, pragmatists like Howard Lutnick—emphasizes targeted action over blunt force. Lutnick proposes a formulaic approach: match a trading partner’s tariffs, impose them only where the U.S. can compete, and use them as a bargaining chip rather than an end state. Paired with Musk’s operational realism, this strategy tempers political showmanship with economic feasibility.
Instead of uniform duties, expect a tiered system: minimal tariffs for allies who reciprocate, moderate rates for neutral partners, and punishing levies for strategic rivals until fair terms emerge.
Under this lens, tariffs become a negotiating language—a means of translating America’s industrial resurgence into concrete policy outcomes. Politically, these moves resonate with the Rust Belt and other regions hungry for manufacturing revivals. Economically, they remain high-risk experiments, vulnerable to miscalculation. But the goal is not permanent protectionism; it’s to restore equilibrium. If tariffs coax other nations toward true free trade—removing their own barriers—they ultimately may lead to a more open global system than before.
In short, Trump’s tariff agenda is less about ideology and more about leverage. Done right, tariffs serve as corrective scalpel, not crude club—enforcing fairness where laissez-faire rhetoric has failed. In a world of asymmetric rules and systemic imbalances, this may be the stark, contrarian truth: without the threat of tariffs, free trade’s promised harmony remains a chimera.
Renegotiating the World
For over seven decades, America’s alliances and institutions have rested on the scaffolding erected in the aftermath of the Second World War. NATO, Bretton Woods, the UN—these once-bold innovations now feel like aging load-bearing beams creaking under their own weight. They have delivered stability and prosperity, but also complacency and moral hazard. As the world fragments into multipolar tension—Tehran, Moscow, Kiev, Jerusalem, Taiwan, India-Pakistan—Donald Trump’s second term thrusts these pillars into a stress test. His approach is simple yet radical: prove your worth or face demolition.
This contrarian posture rattles allies accustomed to American predictability. For decades, Europe has invested minimally in its own defense under the U.S. umbrella. Now, NATO members must confront the possibility that American guarantees are no longer unconditional. The same logic extends to trade blocs, security treaties, and bilateral pacts formed in a bygone era. By challenging their continued relevance, Trump invites allies and adversaries alike to recalibrate. In this environment, alliances cease to be moral endowments and become contingent bargains that must demonstrate current strategic value.
This renegotiation is risky. The global order no longer pivots neatly around a stable U.S.-Soviet axis, nor is it the unipolar moment of the 1990s. Today’s order is an uneven chessboard of nuclear weapons, resource competition, and ideological fragmentation. Overturning familiar architectures could yield unexpected cascades. Pushing NATO partners to shoulder more responsibility might strengthen the alliance—or fracture it. Pressuring countries reliant on U.S. market access may secure fairer deals—or encourage them to form new blocs that exclude Washington. Each move is a high-stakes bet, where skillful statecraft could produce more honest and balanced arrangements or trigger crises that even superpowers struggle to contain.
But from Trump’s vantage point, the old frameworks no longer align with American interests. They’re relics of a unique historical anomaly—the post-1945 order—when America’s unmatched might and nuclear stalemate enforced a global architecture. That anomaly, he argues, is over. In an age where strategic rivals like China and Russia test the boundaries with greater subtlety, clinging to outdated agreements is not strategy but inertia.
Critics warn that eroding trust and predictability drains American soft power, making it harder to rally allies in crises like pandemics or climate shocks. True enough, unpredictability can sabotage diplomacy. But predictability can also foster free-riding and entrench dysfunction. Trump’s gamble is that by shaking old alliances to their core, he can force genuine renewal. Perhaps NATO will finally modernize and balance its burden-sharing. Perhaps trade compacts will shed legacy constraints and become truly reciprocal.
The outcome is uncertain. Renegotiating the world order in real time risks overreach and unintended consequences. Yet standing pat means risking slow decline under ossified structures that no longer serve American interests or global stability. In a world of rising stakes and diminished certainties, Trump’s challenge to the old order represents a radical, contrarian attempt to forge a more honest equilibrium—one in which every alliance, every treaty, and every institution must earn its keep.
The Miscalculation Threat
Modern leaders, Trump included, have never personally witnessed the horrors of full-scale war. They grew up in an era defined by contained conflicts, drone strikes, and managed escalations rather than battles that raze cities and reorder civilizations. Without scars from industrial-scale bloodshed, they treat war as a toolkit, negotiable and bounded—a game where one can bluff, push, and recalibrate at will.
This war amnesia skews judgment. Absent the visceral memory of trenches or mushroom clouds, today’s statesmen and strategists assume that rational actors will always stop short of catastrophe. But true rationality erodes when survival is at stake. Corner a nuclear-armed power—Russia over Ukraine, China over Taiwan—and the logic of controlled brinkmanship can unravel. The difference between a shrewd gamble and a disastrous misread shrinks to a razor’s edge.
Trump’s unpredictability, in theory, can shatter diplomatic inertia and open unprecedented avenues for deal-making. Yet the same volatility can push adversaries beyond their comfort zones. Misread signals and cultural blind spots can amplify misunderstandings. In a world of intertwined alliances and nuclear tripwires, the room for error narrows to nothing. A single miscalculation could cascade toward irreversible chaos.
Compounding the problem is a distorted concept of strength. Without the crucible of large-scale war, leaders conflate bluster with courage. Posturing and chest-thumping replace the tempered resolve forged in battle. This masculinity crisis encourages leaders to prove their mettle through brinkmanship, pushing strategic tensions to the brink under the assumption that someone else will blink first.
Yet history warns us. Before World War I, European leaders believed war would be short and decisive. They lacked the mental model for industrial slaughter. The result was unimaginable carnage. Today’s faith in rational deterrence and limited warfare is equally untested against nuclear thresholds. The risk: assuming that what has never happened cannot happen—until it does.
For investors and policymakers, these tail risks matter. Even a tiny probability of nuclear exchange dwarfs conventional cost-benefit calculations. Markets often discount extreme events, but the logic here fails: one nuclear flash, and investment theses vanish. Realist scenario planning must treat the unthinkable as possible, building robust hedges and diplomatic channels that anticipate irrational moves.
Leaders must confront the fragility behind their confident theories. They can run hard-nosed simulation exercises exposing the realities of nuclear war, engage historians for depth, and deliberately cultivate humility. The aim: to ensure that strategic unpredictability—useful for realigning outdated frameworks—is anchored by a genuine appreciation for the catastrophic potential of miscalculation.
The stakes transcend any single presidency. Trump’s style highlights an underlying vulnerability in the global order: the illusion that every escalation can be managed. Without conscious effort to re-inject war’s existential reality into policymaking, we risk turning bravado and guesswork into the architects of our undoing.
An American Renaissance
Amid volatility, uncertainty, and the rattling of old foundations, the United States finds open ground for reinvention—fertile space where scientific audacity, inventive genius, and fearless exploration can flourish without constraint. Freed from the constraints of incrementalism, the United States can embrace the role of a frontier civilization once again: a nation unafraid to ask audacious questions, challenge sacred doctrines, and test the limits of the possible.
For decades, America’s once-thriving innovation engine has stalled, suffocated by excessive regulation, rigid academic dogmas, and bureaucratic inertia. Critical fields—from theoretical physics to biotechnology—have languished behind walls of entrenched interests and outdated paradigms. Now, with Trump’s second term shaking the foundations of the status quo and Elon Musk’s contrarian vision gaining traction, the United States faces a rare chance to reignite its pioneering spirit. Instead of tinkering at the margins, Trump and his team propose far-reaching reforms: radically simplified tax codes, streamlined regulations, and reimagined immigration policies designed to attract the brightest global talent and unleash their creative potential.
This intellectual and cultural thaw reverberates through the sciences. The same nation that once sent men to the Moon now contemplates multi-planetary homesteading. If the old gatekeepers who have stalled theoretical physics for half a century can be bypassed, research into next-generation propulsion, dark chemistries, and new fundamental frameworks beyond the standard model can finally flourish. The tyranny of stagnant string theory, the deep entrenchment of cautious committees, and the decades of intellectual ossification may give way to what some call “cowboy science”: a return to risk-taking, intuition-led breakthroughs, and the heroic ethos of individual genius.
As these reformist energies spread, the U.S. can leverage a more fluid, reciprocal global trading landscape. Realigned alliances and supply chains engineered for resilience—not just cost-minimization—create fertile conditions for deep-tech ventures, advanced AI labs, and next-generation energy systems. Investors, entrepreneurs, and scientists will gravitate toward America’s rejuvenated ecosystem, drawn by the promise of intellectual freedom and the exhilarating possibility of rewriting fundamental laws of physics. Under these conditions, even concepts dismissed as far-fetched—interstellar travel, room-temperature superconductors, and quantum computing at scale—begin to feel tangible rather than utopian.
Culturally, a merit-driven ethos replaces hollow credentialism. With intellectual courage in fashion and bold ideas encouraged rather than stifled, the private and public sectors unite in a grand experiment of renewal. The old narrative that the 20th century’s greatest leaps cannot be repeated or surpassed is discarded. Instead, the horizon expands: the stars become destinations, the atom a playground, and the genome a toolkit.
Of course, nothing guarantees success. The same high-variance environment that enables breakthroughs also courts failure. But the alternative—endless stagnation under rigid orthodoxies—is far less appealing. Risk and reward remain inseparable. Yet if America seizes this rare moment of disruption, the outcome could be a cultural and scientific flourishing that defines the 21st century. The world would witness an America not just rearranging old furniture but remodeling the entire house of knowledge and capability.
My Perspective
Embrace the uncertainty. Legacy frameworks, linear forecasts, and predictable policy arcs disintegrate before our eyes. In this new environment, strategic thinking must center on asymmetry, adaptability, and an appetite for chaos. The stable handrails of the past—fossilized alliances, orderly trade pacts, incremental reforms—no longer guide us. Instead, we confront a world where each assumption must be retested, each relationship retooled.
Short-term, don’t be fooled by today’s optimism. A global recession in 2025 looks increasingly plausible. Just as radical tariff policies and gutting government agencies shake domestic supply chains, weakened global demand may trigger market shocks.
I expect immediate disappointment in the headlines: over-leveraged sectors are at risk, euphoria is unsustainable, and cracks beneath Bidenomics’ veneer are about to surface. Yet in this churn also lies profound opportunity. High-variance environments punish rigidity and stagnation, while rewarding those who sense the underlying logic: volatility can be harnessed, not merely weathered. Consider three critical asymmetries shaping the investment and business landscape:
1. Bidenomics Masked Fragility
Beneath surface-level confidence, America’s economic foundations have softened. Over 60% of recent jobs growth is pinned to government expansion, residual pandemic adjustments, and immigration—rather than genuine private-sector dynamism. Key signals such as spiking credit rejection rates and record-high consumer credit APRs (averaging 23.4%) expose deep vulnerabilities.
My Perspective: Be careful and consider shorting sectors drunk on euphoria and leverage. Hedge through defensive allocations in utilities, select commodities, and volatility instruments. Expect the market’s reality-check to be swift and severe.
2. Trump’s Shock Therapy
Trump’s proposed moves – >60% tariffs on Chinese imports, mass deportations, a dramatic agency cull – risk near-term upheaval. Inflation may flare as re-shored supply chains struggle with labor gaps and capacity constraints. Yet these same policies could, over time, liberate America’s productive energy. Leaner agencies, streamlined regulations, and targeted immigration reforms might unleash a “productivity renaissance.”
My Perspective: As the tariff storm gathers, go long on domestic industrial plays, automation tech, and logistics hubs that stand to benefit from re-shoring. Short inflation-sensitive assets and prepare for a recessionary downdraft. Hedge with precious metals, critical commodities, and volatility products. Meanwhile, larger positions in frontier technologies poised to flourish in a liberated innovation environment.
3. The Geopolitical Pivot: The Dollar and BRICS
China’s ascendancy as the dominant trade partner for over 120 nations, along with BRICS’ rising economic heft, indicates a shifting global gravity. Mounting U.S. refinancing needs and reduced foreign appetite for Treasuries challenge American financial stability. Yet, the U.S. retains unmatched capital markets and remains the ultimate safe haven in moments of panic. Trump’s readiness to deploy financial sanctions and trade barriers could paradoxically reinforce dollar dominance.
My Perspective: Diversify currency exposure. Maintain core holdings in dollar-denominated assets but add hedges: gold, Bitcoin, rare-earth ETFs, and neutral currencies like the Swiss franc (CHF) or Singapore dollar (SGD).
Conclusion: Engaging with the New Parameters
We have entered a period that defies simple narratives. Trump’s reelection announces to the world: comfortable equilibrium is over. His brand of strategic unpredictability invites us to reimagine what American power, governance, and global influence can be. However, the end of safe assumptions means the start of dynamic possibilities.
While the near-term disruptions will test even the most resilient systems, the long-term vision is undeniably bright for those who play the horizon. A renaissance of innovation, deep-tech breakthroughs, and industrial re-shoring is not only plausible but increasingly probable as legacy constraints fall away.
A revitalized America, unafraid to challenge stagnation, could emerge as a global leader in space exploration, advanced physics, AI, and frontier sciences. Trump’s recalibration provides the foundation for a leaner, more dynamic economy capable of driving exponential progress.
I believe: for investors, entrepreneurs, and visionaries, this is the time to look beyond the turbulence and focus on the extraordinary opportunities waiting on the other side of disruption.
Investment Guidance
Identifying Signals and Triggers
Short-Term (< 12 Months):
Prioritize Liquidity & Intelligence: Maintain higher cash reserves and invest in geopolitical risk analysis and scenario modeling. Deploy specialized teams or AI-driven tools to monitor trade policy changes, alliance realignments, and tariff announcements in near-real time.
Trade Shock Indicators: Watch for a surge in container freight rates or abrupt commodity price spikes as tariffs hit. When the Baltic Dry Index or forward freight agreements jump unexpectedly, it’s a sell signal for overly exposed consumer goods equities and a prompt to rotate into logistics-tech and North American manufacturing automation.
Sovereign Debt & Currency Pressure: Keep a close eye on U.S. Treasury auctions. If foreign participation dips below historical averages by more than 20%, prepare to adjust currency hedges. Add allocations to “safe haven” currencies (CHF, SGD), selected gold or rare-earth ETFs, and volatility indices. Reduce reliance on sectors heavily tied to stable policy (e.g., heavily subsidized industries) and increase optionality in energy metals and critical supply chain components.
Medium-Term (2–3 Years):
Innovation Inflection Points: Direct capital into exponential technologies and applied sciences; advanced materials, quantum computing, biotech, and AI-driven compliance tools. As bureaucratic complexity shrinks, these sectors stand to benefit outstandingly from faster innovation cycles and greater capital efficiency. A spike in private venture rounds in fields such as advanced materials or ultra-capacitor energy storage signals imminent ecosystem tipping points.
Geographical Differentiation: Identify markets that handle uncertainty well—e.g., countries with robust legal systems, flexible labor markets, and strong digital infrastructure. These places can serve as operational hubs from which you can rapidly scale or contract as global policies shift.
Regulatory Overhauls: Monitor legislative dockets. If immigration reforms fast-track visas for STEM PhDs and if R&D tax credits deepen annually, expect a 2–3 year lag before the next wave of intellectual capital floods U.S. labs. Increase exposure to biotech and quantum computing startups shortly after such reforms pass.
Long-Term (5+ Years):
Global Power Reorder: If emerging markets, spurred by U.S. unpredictability, coalesce around alternative trade blocs that stabilize after 5+ years, that’s your cue. Prepare for a world of modular alliances. Align long-horizon infrastructure bets with these new power centers.
Scenario-Based Policy and Investments
“China Retaliation” Scenario: As soon Beijing imposes further capital controls and technology export bans, pivot quickly:
Reduce exposure to companies dependent on Chinese rare-earths.
Expand positions in U.S. rare-earth suppliers and recycling tech (long specialized recycling firms).
Increase cyber-insurance and cybersecurity equity holdings as cyber-warfare risks peak.
Secure put options on major indices; a 15–20% market drop in a flash-crisis scenario can be mitigated by well-structured options positions.
Reassess treasury holdings and ensure a diversified emergency liquidity plan—short-duration U.S. debt, gold, and stablecoins backed by reputable custodians.
Positioning for the Innovation Renaissance
Initiate a strategic allocation into venture funds, selected stocks, and indexes focusing on deep tech; quantum sensors, quantum-safe encryption, next-gen propulsion (for aerospace), and synthetic biology platforms.
Monitor and partner with universities and national labs. The moment immigration policies simplify STEM recruitment, double down on early-stage biotech and materials R&D firms that secure top-tier postdoctoral talent.
Embrace a Layered Risk Architecture
Create a layered defense:
Core stable assets (30–40%)
Growth equities and frontier tech (10–20%)
Defensive hedges in commodities, currencies, and volatility instruments (5–10%)
Agile, tactical allocations that adjust quarterly based on policy signals (remainder)
Cultural and Organizational Adaptation
In your firm and institution:
Launch scenario planning committees that simulate tariff impacts, alliance breakdowns, or regulatory leaps.
Recruit analysts with backgrounds in geopolitics, physics, and biotech—do not rely on MBAs and economists.
Encourage experimentation within your decision-making processes—pilot new portfolio strategies on a small scale before scaling up.
For Non-U.S. Founders & Foreign Firms:
Incorporate in the U.S. and use reputable U.S. startup accelerators and venture networks to navigate evolving immigration policies and establish a strong launchpad.
Focus on mid-tier American cities seeking innovation and talent inflows, where streamlined approvals and incentives provide a foothold.
Build solutions that complement, stabilize, or enhance U.S.-based production and logistics systems, emphasizing resilience and cost-effectiveness.
Offer platforms or products that facilitate seamless cross-border transactions, digital collaboration, or remote operations as global markets rewire.
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.
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.
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.
In the summer of 1995, Netscape went public, igniting the dot-com boom and ushering in the Internet age. That moment marked a fundamental shift in how businesses were built and run. Today, we are on the cusp of an equally transformative moment: the dawn of the AI era.
Imagine a world where a startup founder wakes up, grabs a coffee, and sits down not with a co-founder or a team of bleary-eyed developers, but with an AI. This AI isn’t just a tool or an assistant; it’s a full-fledged partner in the entrepreneurial journey. It helps generate and validate business ideas, build and manage teams, develop products, and make strategic decisions in real time. All while keeping the company small, agile, and fiercely focused on its mission.
In this essay, inspired by this Tweet from Paul Graham, we’ll explore how exponential AI – artificial intelligence that is rapidly increasing in power and capability – will fundamentally transform the way startups operate. We’ll challenge the long-held belief that successful companies must inevitably become large. Instead, we’ll examine how AI might enable a new breed of startup: the Sovereign AI Startup.
These Sovereign AI Startups will stay small by design, leveraging AI to achieve outsized impact with minimal headcount. They’ll operate with unprecedented efficiency and agility, free from the bureaucratic bloat that typically comes with growth. Most importantly, they’ll empower founders to focus on what truly matters: the vision, the strategy, and the relentless pursuit of creating something new and valuable in the world.
But to understand why this shift is so revolutionary, we first need to grapple with a counterintuitive truth: companies tend to get worse as they get bigger. I call this The Size Theory of Company Decay. By examining why this happens, we’ll see how AI offers a potential cure for this seemingly inevitable decline.
We’ll then explore how AI will reshape every aspect of the entrepreneurial process, from ideation to execution, from team-building to go-to-market strategies. We’ll look at a real-world example of a company that has achieved remarkable results with a small core team, and imagine how AI could supercharge these approaches.
Along the way, we’ll consider the broader implications of this shift. How will it change the nature of work and creativity? Will it democratize entrepreneurship, allowing underdogs from anywhere in the world to compete on a global stage? And what new legal and regulatory frameworks will we need to support these AI-native companies?
But first, let’s take a step back and understand a common misconception: that successful startups must get big, why we believe that, and how AI will change it.
The Size Theory of Company Decay
The idea that successful startups must grow into large companies is deeply ingrained in our entrepreneurial culture. We’ve been conditioned to equate success with scale – more employees, more offices, more layers of management. This belief stems from a pre-digital, pre-AI era when growth often did require a proportional increase in human resources. But it’s a model that’s showing its age.
Consider the traditional growth trajectory: a startup begins with a small, scrappy team. As it gains traction, it hires more people to handle increased demand, expand into new markets, or develop new products. Before long, what started as a lean, agile startup becomes a sprawling organization with hundreds or thousands of employees. Along the way, it often loses the very qualities that made it successful in the first place – speed, flexibility, and a laser focus on solving customer problems. This is what I call company decay.
At the heart of company decay lies a paradox: the very things that drive a startup’s initial success become the seeds of its eventual decline. It’s as if success itself carries within it the DNA of failure. But why?
Think of a startup as a finely tuned machine, where every part knows its function and works in perfect harmony with the others. Now imagine that machine growing larger and more complex with each passing day. What happens?
First, communication breaks down. In a small startup, information flows freely. Everyone knows what everyone else is doing. But as the company grows, the number of potential communication channels explodes exponentially. Suddenly, you need meetings to plan other meetings. Information gets stuck in departmental silos. The machine starts to sputter.
Then there’s the cultural shift. In the early days, everyone is a true believer, united by a shared mission to change the world. But as you add more people, that sense of purpose gets diluted. New hires are there for a job, not a crusade. The machine loses its soul.
This cultural erosion bleeds into the company’s vision. Peter Thiel calls it the loss of “definite optimism.” The bold question of “How can we change the world?” gets buried under layers of management and short-term thinking. It morphs into “How can we protect what we have?” The machine forgets why it was built in the first place.
As if these internal changes weren’t enough, external pressures mount. Public companies face relentless pressure to meet quarterly targets. Long-term investments in innovation are sacrificed on the altar of short-term gains. The fear of a stock price drop drives decisions that are poison to the company’s long-term health.
But perhaps the most insidious change is in decision-making. In a small startup, decisions are made quickly by people close to the problem. In a large company, decision-making becomes a bureaucratic nightmare. No one wants to make a tough call for fear of repercussions. Responsibility becomes so diffuse that no one feels truly accountable. The machine grinds to a halt.
All of these factors – and many more – compound each other, creating a vicious cycle of inefficiency and stagnation. It’s as if there’s an invisible force pulling successful companies towards mediocrity, much like how gravity inevitably pulls objects back to earth.
How bad can it be?
Firing 12 Floors
Carl Icahn once told a hilarious story of him acquiring a company called ACF Industries in the early 1980s. Upon taking control, he visited their New York office, which occupied 12 floors of prime real estate. As he tried to understand what each floor did, he lost himself in a miracle of bureaucracy and unclear job functions. Despite spending days going from floor to floor, Icahn couldn’t figure out what these people actually did for the company.
Frustrated, Icahn decided to visit the company’s manufacturing operation in St. Louis. There, he met with Joe, the head of operations, who gave him a clear picture of how the business actually worked. When Icahn asked Joe how many of the New York office staff he needed to support his operation, Joe responded: “minus 30”.
Unsure what to do, Icahn paid a couple of consultants $250,000 to find out what these people in New York actually do. Three weeks later, the consultants came back with hundreds of pages and the blunt answer: “we don’t know what they do either.”
Icahn ended up firing everyone in the New York office – all 12 floors. The company continued to operate without a hitch. Icahn said that he never received a single complaint or inquiry – it was as if those 12 floors of people never existed.
This story sounds so ridiculous (I highly recommend watching the 8.5 minute video) that it raises a valid question for discussion: Even without AI – how many employees in large companies are actually productive and necessary for the core operations of the business?
As companies grow, particularly during periods of hyper-growth fueled by large capital infusions, they often accumulate layers of middle management, support staff, and specialized roles that may not directly contribute to the bottom line. The pressure to allocate capital quickly can lead to hasty hiring decisions and the creation of positions that look good on paper but add little real value. It’s easy to justify each hire individually, but harder to step back and question whether the overall organizational structure is truly optimal.
I assume that leaders often know that their organizations have become bloated, but they delay taking action due to the psychological toll of firing employees. Firing is extremely difficult, both for those making the decision and for those losing their jobs. This emotional barrier can lead companies to maintain inefficient structures far longer than is economically justified, fooling themselves into believing that all roles are necessary.
Carl Icahn’s story of firing 12 floors of employees without any noticeable impact on the company’s operations illustrates how inefficient large organizations can become. But it is not limited to industrial corporations.
At its peak, WeWork had over 12,500 employees, Uber over 32,000 employees – we have to wonder: how many of these people are truly essential to the core business?
It’s easy to fall into the trap of equating headcount with productivity or success. The job of a founder and executive is not to build empires of employees, but to lead and solve problems efficiently. Sometimes, that means taking a hard look at your organization and asking yourself: do I really need all these 12 floors?
Elon Musk, like Carl Icahn, not only asked this question as he acquired Twitter (now X) – he acted. When Elon Musk acquired the company in 2022, it had over 7,500 employees. In a move that shocked many, he promptly laid off about 80% of the workforce, leaving the company with roughly 1,500 employees.
In an interview with WSJ, Elon Musk said that Twitter had “a lot of people doing things that didn’t seem to have a lot of value,” and that “Twitter was in a situation where you’d have a meeting of 10 people and one person with an accelerator and nine with a set of brakes, so you didn’t go very far.”
He didn’t think that this was unique to Twitter and continued that other big tech companies could cut jobs without impacting productivity.
Conventional wisdom suggested that such a drastic reduction would cripple the platform’s ability to function, let alone innovate. Yet – just as ACF Industries – X has not only continued to operate but has arguably accelerated its pace of innovation. This suggests that a significant portion of Twitter’s previous workforce may have been redundant or focused on non-essential tasks.
The Example of Telegram
The bloat we see in companies like Twitter, Uber, and WeWork isn’t just a problem for established tech giants. More importantly is it a cautionary tale for every startup founder. These companies, once lean and agile, fell into the trap of equating headcount growth with progress. But what if the next generation of startups can avoid this fate entirely?
Imagine a startup that can scale to serve millions of users without the historical explosion in headcount. This isn’t science fiction. Telegram is already a prime example of how a small core team of 60 team members – of which 30 are engineers – can serve more than 900 monthly users.
In an interview with Tucker Carlson, Pavel Durov, Telegram’s founder, described in greater detail how he built Telegram by combining a clear vision with ruthless efficiency.
Pavel Durov has crafted an organizational structure so lean it borders on ascetic. He’s the sole director, equity holder, and product manager, working directly with every engineer and designer. There’s no HR department; instead, Durov recruits through coding contests, identifying top talent through performance rather than resumes. This isn’t just cost-cutting; it’s a fundamental rethinking of how a tech company can operate. Telegram has never run an ad, yet it’s challenging giants like WhatsApp and WeChat.
Durov hasn’t just built a messaging app; he’s created a blueprint for how startups can scale to enormous impact with minimal headcount. In doing so, he’s not just saving on salaries; he’s eliminating the communication overhead and bureaucratic friction that leads to the decay most companies experience as they grow.
I believe Telegram isn’t an anomaly – it is a glimpse into the future of what companies can achieve when they reject conventional wisdom about organizational structure and embrace radical efficiency. And by bringing AI into the equation, I believe this is the near future of entrepreneurship.
Telegram is a great example that companies don’t have to get big after all. Yet, how small is big enough?
Teams Smaller Than Dunbar’s Number
Robin Dunbar, a British anthropologist, suggests that the conscious decision to stay small has real advantages. In his first paper, “Neocortex size as a constraint on group size in primates,” Dunbar proposed that humans can comfortably maintain only about 150 stable relationships. This limit, known as Dunbar’s Number, is becoming fascinatingly relevant to startups, especially as AI begins to enable startups to operate extremely efficiently with fewer than 150 employees.
Scientifically, Dunbar’s number makes sense. The neocortex, the part of the brain responsible for conscious thought and language, can only process so much social information. Beyond 150 relationships, we struggle to keep track of the complex web of who knows whom and how they relate. In a startup, where relationships and culture are paramount, exceeding this number can lead to breakdowns in communication and cohesion – leading to company decay.
Psychologically, smaller teams are more conducive to trust and intimacy. With fewer people, it’s easier to understand each person’s strengths, weaknesses, and quirks. This understanding creates psychological safety – the confidence that you can take risks and be vulnerable without fear of embarrassment or retribution. Psychological safety is critical for the kind of innovative, out-of-the-box thinking that startups need to thrive.
Philosophically, too, there’s an elegance to the idea of a small, tight-knit team taking on Goliath challenges. It’s the story of David and Goliath, the rebel against the empire. Small teams can be more agile, more adaptable, more resilient. They can make decisions quickly without getting bogged down in bureaucracy. You can pivot on a dime when circumstances change.
Startups that stay below Dunbar’s number indefinitely – can avoid company decay. But how can a small team hope to compete with the resources and scale of a large corporation?
The Era of Sovereign AI Startups
The book The Sovereign Individual predicted that the information revolution would empower individuals over institutions. Now, 27 years after it was first published, I believe this trend is accelerating, especially in entrepreneurship. Just as the personal computer and the internet gave rise to The Sovereign Individual, exponential AI will give rise to what we might call The Sovereign AI Startup.
Today, a single founder armed with nothing more than a laptop can conceive, validate and launch a new business in a matter of days. Add a Starlink Internet connection and they can do it from anywhere in the world. AI will accelerate and simplify this process even further:
With generative AI, you can quickly prototype new products or services and iterate based on real-time customer feedback.
With predictive AI, you can identify untapped market niches and optimize their offerings for maximum impact.
And with autonomous AI agents, you can automate everything from customer support to supply chain management, allowing them to scale their operations with minimal overhead.
In this AI-first world, a team of five might wield the capabilities of what once required 500. Imagine a customer support ‘department’ that’s a hyper-intelligent AI, learning and improving with each interaction, available 24/7 without a single human on the payroll. Envision data analysis so sophisticated and instantaneous that it feels like precognition, surfacing insights before you even know to look for them. Consider project management AI that doesn’t just track deadlines, but anticipates bottlenecks, suggests optimal resource allocation, and even mediates team conflicts with the wisdom of a seasoned executive.
AI will become the antidote to corporate decay, taking over many of the routine tasks that often justify additional hiring in growing companies. With AI as a force multiplier, a small team can accomplish big things. From data analysis and report generation to customer support and project management, AI will perform a significant portion of the work that currently requires human employees. This will allow companies to increase their output and impact without increasing their headcount proportionately. They can target their efforts with laser precision, focusing on the areas where human ingenuity is most needed. You can respond to customer needs and market changes with the speed and personalization that only a small, nimble team can deliver.
Sovereign AI Startups, unencumbered by legacy systems and bureaucratic inertia, will be able to outmaneuver established players, disrupt industries, and create entirely new markets. They will be able to tap into a global pool of talent and resources and collaborate with other sovereign entities in fluid, ad-hoc networks that transcend geographic and institutional boundaries.
The Convergence of Exponential Technologies
It is not just AI as a technology that will change the way startups operate. The convergence of AI with other exponential technologies will revolutionize hardware development, enabling smart teams to achieve what once required armies of engineers and massive factories.
For example, advanced robotics in fully automated factories will allow sovereign AI startups to access world-class manufacturing on demand, to prototype, iterate, and even manufacture complex devices with minimal human involvement.
3D printing – for example – is evolving at breakneck speed, is already producing not just plastic prototypes but fully functional electronic components – which in the future will integrate seamlessly with AI-designed circuitry.
In the future, a Sovereign AI Startup will be able to conceptualize a groundbreaking medical device, have AI optimize its design for both function and manufacturability, simulate its performance across millions of virtual scenarios, and then set autonomous robots to work building and testing physical prototypes. Machine learning algorithms will analyze test results in real-time, suggesting improvements that can be immediately implemented in the next iteration. The entire process – from idea to market-ready hardware product – could happen in weeks rather than years.
This will lower the barriers to entry for hardware startups, allowing a proliferation of niche products tailored to specific needs that big companies might overlook. We’ll see an explosion of creativity as inventors are freed from the constraints of traditional manufacturing.
I believe a world in which small teams can rapidly bring complex hardware to market will accelerate the pace of technological progress exponentially. The next world-changing invention might not come from a tech giant or a well-funded lab, but perhaps from a handful of determined individuals in a Sovereign AI Startup.
The AI-Native Organizational Design
As AI continues to advance, we can expect to see a rise in Sovereign AI Startups – companies built from the ground up with AI as a core part of their DNA – each hyper-focused on solving a specific problem or serving a niche market. These startups will be characterized by small, agile teams that – like Telegram – stay below Dunbar’s number and leverage AI to achieve outsized impact.
The shift will bring with it a new paradigm of organizational design. One in which companies leverage AI not just as a tool, but as a key stakeholder and a core system that is intricately woven into every facet of a startup’s existence.
The founder and visionary will be at the heart of the Sovereign AI Startup, providing the idea, overall direction, and purpose. The founder will work with a human core team, consisting of a small group of highly skilled individuals who focus on strategic, creative, and uniquely human tasks.
An AI Core System will not just be a set of tools – as we know it today – but a central part of the organization, handling a wide range of operational, analytical, and decision-support functions.
An important element of The Sovereign AI Startup will be its external network, a fluid ecosystem of on-demand talent, partners, and contributors that the company can tap into as needed.
A structure like this allows for maximum flexibility and efficiency, enabling the company to stay lean while accessing a broad range of capabilities. It will allow the founder to keep the team size below Dunbar’s number with a human core team, while leveraging AI and a distributed external network to achieve scale.
This organizational design challenges the traditional notions of what constitutes a company, blurring the lines between internal and external, human and machine. As a result, AI entrepreneurs can move faster, decide smarter, and tackle challenges of unprecedented scope and complexity – independent of their physical location.
Post-AI Organizational Collaboration
With AI becoming an integral and core part of any organization, we will not only have to rethink how startups are organized internally, but also how organizations collaborate with each other.
Benoit Vandevivere, who commented on Paul Graham’s post, argued that our current models of business organization are relics of a pre-digital, pre-AI era. This makes sense as we are arguably still operating with organizational structures and legal frameworks that were designed for a world of physical offices, face-to-face meetings, and human-only decision making.
Benoit mentioned the idea of “artificial neural networks interconnecting natural neural networks” – the idea sounds complicated yet is a powerful idea for a future where the boundaries between companies become more fluid, with AI systems facilitating seamless collaboration and information flow across organizational lines.
In the future, a startup might not just be a discrete entity, but a node in a larger network of interconnected businesses, each specializing in what they do best and relying on AI to coordinate their efforts. The “company” as we know it might evolve into something more akin to a dynamic, AI-mediated coalition of talent and resources, assembling and reassembling as needed to tackle specific challenges or opportunities.
AI-Native Jurisdictions
As we reimagine the nature of companies in the AI era, we must also consider the legal and regulatory frameworks that will enable these new organizational structures to thrive. Traditional jurisdictions, with their legacy laws and regulations, may struggle to accommodate the fluid, borderless nature of AI-native startups. This is where innovative legal zones like the Catawba Digital Economic Zone or a “network state” – as proposed by Balaji Srinivasan – come into play.
The Catawba Digital Economic Zone (CDEC), established on Native American tribal land in South Carolina, is pioneering a regulatory environment tailored for digital businesses and cryptocurrencies. It offers a streamlined business registration process, favorable tax treatment, and regulations that are more attuned to the needs of AI and Web3 startups. But it’s not alone. For over a decade, Estonia’s e-Residency program allows digital entrepreneurs to start and run a business in the EU from anywhere in the world. Wyoming has positioned itself as a crypto-friendly state with laws recognizing DAOs (Decentralized Autonomous Organizations) as legal entities. And in the Caribbean, Próspera in Honduras is creating a charter city with regulations designed for the digital age.
These jurisdictions are fundamentally rethinking governance for the AI and Web3 era. They’re creating environments where smart contracts have legal standing, where AI agents could potentially hold rights and responsibilities, and where the lines between human and machine decision-making are acknowledged and accommodated in law.
For founders building AI-native startups, these new jurisdictions offer more than just tax benefits or easier registration. They provide a legal and regulatory sandbox to experiment with new forms of organization and governance. They allow startups to operate in a framework that understands and supports their unique needs, from data sovereignty issues to the complexities of AI-human collaboration.
In the coming years, the most successful AI startups may not just be those with the best technology or the most efficient operations, but those that have strategically positioned themselves in jurisdictions that truly understand and support their needs.
The Rise of the Underdogs
The rise of Sovereign AI Startups incorporated in AI-Native jurisdictions is a game-changer for entrepreneurs of smaller and underprivileged countries who don’t have access to talent pools or the legal infrastructure that exists in ‘top-tier’ countries like the United States, Singapore, or Hong Kong.
Traditionally, they have been at a disadvantage in the global economy, unable to compete with larger countries that have deeper reservoirs of skilled workers and more favorable legal systems.
But this is changing. By leveraging AI, making use of the remote talent pool, and favorable jurisdictions, a small team in a ‘developing country’ could potentially outperform a much larger team in Silicon Valley. Why? Because AI can level the playing field, handling tasks that once required specialized expertise. A founder in a remote country no longer needs to recruit a team of world-class engineers, data scientists, and marketers. Instead, they can leverage AI agents, on-demand experts, and freelance specialists to handle much of this work. By digitally setting up a LLC or C Corp in the Catawba Digital Economic Zone, they have access to a respected legal entity that can compete globally.
Furthermore, we can expect AI to evolve into a bona fide co-founder. Founders who live outside of major startup ecosystems can struggle to find the right co-founder for their business idea. In the future, instead of looking for a human co-founder, founders will first set-up an AI Co-founder. AI will also take on other supportive roles that have traditionally been filled by humans – like mentors and advisory boards.
Already today, smart entrepreneurs use advanced AI prompting in tools like ChatGPT or Claude to have a one-on-one mentoring session with Paul Graham, solve engineering problems with Richard Feynman, or to assemble an entire virtual advisory board of industry titans to stress-test their business strategy, overcome biases, and make smarter decisions.
In addition, the rise of remote work means these startups can tap into a global talent pool for specialized skills they do need, without requiring relocation. They can build truly decentralized teams while maintaining a lean local presence. This could lead to a new wave of innovation coming from unexpected places, as entrepreneurs in these underdog countries leverage their unique perspectives and local knowledge to solve global problems.
Unleashing Human Creativity
Smaller, agile companies and a lower barrier to entry is only one dimension of AI entrepreneurship. What is even more important is how AI has the potential to unleash and amplify human creativity.
At its core, entrepreneurship is about creating something new and valuable in the world. It’s about seeing possibilities that others miss, and having the courage and determination to make them real. This is a fundamentally creative act, one that requires not just technical skill but also imagination, intuition, and a deep understanding of the human condition.
As AI takes over more of the routine tasks of starting and running a business, I believe it will free entrepreneurs to focus more on this creative core. Instead of getting bogged down in the mechanics of incorporation, accounting, and HR, founders will be able to devote their energy to the higher-level work of envisioning new products, services, and business models.
This is important not just for individual founders, but for society as a whole. In a world of increasing automation and AI, we’ll need more than ever the uniquely human capacity for creativity, intuition, and imagination. We’ll need entrepreneurs who can dream up new industries and new ways of creating value.
The AI-Assisted Pursuit of Passion
When successful entrepreneurs are asked about their recipe for their success, there is one word that comes up more frequently than anything else: passion. While “following one’s passion” is simple but less practical advice, I believe the underlying spiritual idea is correct. By pursuing our passion – what excites us most – we tap into a wellspring of creativity, motivation, and fulfillment. We do our best work, make our greatest contributions, and live our most meaningful lives.
Historically, however, following one’s excitement has been a privilege reserved for a lucky few. For most people, work has been a matter of necessity, not passion. We’ve had to take jobs that pay the bills, even if they leave us feeling bored, unfulfilled, or worse. The demands of survival have often trumped the pursuit of excitement.
But what if AI will change this equation? What if, by automating the boring, repetitive, and unexciting tasks that consume so much of our time and energy, AI can free us to focus on what truly excites us?
In the future, AI will handle the drudgework of data entry, scheduling, and email management while robotics will increasingly take over physically demanding work. This will leave us humans with more time and headspace for creativity and problem-solving. Where AI takes over the tedious aspects of research and analysis, it allows us to focus on high-level insights and ideas. Where AI automates the mundane tasks of manufacturing and logistics, it enables us to pour our energy and creativity into design and innovation.
In this future, work will be an opportunity to pursue our passions, to explore the frontiers of our curiosity, to create and contribute in ways that truly excite us.
The Rise of AI-Enabled Polymath
AI taking over mundane and uninspiring work will free individuals to pursue a much wider range of their inherent interests and passions. No longer constrained by the need to specialize in a single area to make a living, people will be able to explore multiple domains, cultivating a diverse set of skills and knowledge. In fact, I believe in the emerging era of AGI it will be crucial for individuals to pursue and master knowledge and skills in multiple domains.
This, in turn, will lead us to a new era of polymaths – individuals who excel in multiple fields, bringing together insights and ideas from disparate areas to solve complex problems and create new innovations. Just as the Renaissance gave rise to legendary polymaths like Leonardo da Vinci and Galileo, the AI revolution will unleash a new generation of multi-talented thinkers and creators.
In the future, a single person can be a skilled artist, a savvy entrepreneur, and a cutting-edge scientist all at once, using AI tools to handle the routine aspects of each pursuit while they focus on the creative and strategic work they truly enjoy. Or a brilliant engineer could also be a passionate philosopher and a gifted musician. This kind of cross-pollination of ideas and expertise – together with AI as our partner – could lead to breakthroughs and innovations that we can hardly imagine today.
Conclusion
In this essay, we’ve explored a range of ideas about how exponential AI will transform the landscape of entrepreneurship and work. We’ve seen how AI could enable startups to stay small and agile, lowering the barriers to entry and enabling a Cambrian explosion of new ventures. We’ve considered how AI could amplify human creativity, freeing entrepreneurs to focus on the visionary and strategic work of building the future. And we’ve imagined how AI, by taking over mundane and uninspiring tasks, could unleash a new era of polymaths, empowered to pursue their passions and bring cross-disciplinary insights to bear on the world’s challenges.
Now let’s bring these threads together and consider how exponential AI will supercharge the way startups are run in the future.
At its core, a startup is a vehicle for turning an idea into reality, for bringing something new into the world. It’s a crucible of innovation, a space where creativity and ambition collide to generate breakthroughs and create value.
Historically, however, the process of starting and scaling a company has been fraught with friction and inefficiency. Founders have had to spend countless hours on mundane and repetitive tasks, from bookkeeping and scheduling to customer support and data entry. They’ve had to navigate the complexities of hiring, management, and bureaucracy, often at the expense of focusing on their core vision.
Exponential AI promises to change all that. By automating the routine and the mundane, AI will enable founders to operate with unprecedented efficiency and agility. They’ll be able to test and iterate on ideas at lightning speed, using generative AI to rapidly prototype products and predictive AI to optimize go-to-market strategies. They’ll be able to scale their operations with minimal overhead, relying on AI-powered systems to handle everything from supply chain management to customer service.
But the impact of AI on startups goes far beyond mere efficiency gains. By freeing founders to focus on their highest excitement and their deepest passions, AI will unleash a new wave of creativity and innovation in the startup world.
It is a world where the barriers to entry are low but the bar for success is high, where anyone with a great idea and the drive to pursue it can build something truly remarkable. It’s a world where work is not a means to an end, but an end in itself – an ongoing adventure of learning, growth, and impact. And it’s a world where the most successful startups are not necessarily the biggest or the most well-funded, but the ones that are most deeply aligned with their founders’ passions and most adept at harnessing the power of AI to bring their visions to life.
Of course, this doesn’t mean that entrepreneurship will become easy or that everyone will be able to do it. Even with AI tools, starting a successful business will still require grit, resilience, leadership, and a willingness to take risks. But it does mean that the playing field will be leveled, and that more people have the opportunity to participate in the creative process of entrepreneurship.
But to fully realize this potential, we’ll need to rethink many of our assumptions about entrepreneurship and its role in society. We’ll need to move beyond the narrow focus on unicorn IPOs and billion-dollar valuations, and recognize that the true value of entrepreneurship lies in its ability to solve problems and create meaning.
Ever since, I’m well known for being the curious guy who is always asking the challenging and often uncomfortable questions. Questions about life, philosophy, religion, science, health, politics, or business. It may be to optimize my life, to innovate, to think outside the box, or to call bullshit and detect and fight corruption. Ultimately, I ask questions to find the truth.
I believe, this is something everyone should do. We all should question everything around us. Because the only solution to all of the surrounding misery is ultimately the truth. And we can only get to the truth by asking the challenging and tough questions – about everything.
Children Intuitively Question
Children intuitively question everything they observe. As they explore and try to make sense of their environment, they ask countless questions. Before we can explain why the grass is green, they dive into the science and philosophy of life, space, and time.
With the example of children, we can see that by questioning – you explore complex ideas. But not only that. You also uncover their implicit assumptions, you expose deeply held beliefs, and you recognize hidden contradictions.
As we can observe in our children, curiosity, and questioning are part of our natural intelligence. Why is it so difficult for us adults to maintain this innate curiosity to question everything around us?
Our education system is a major reason why most people lose their childhood curiosity and their innate skepticism. As soon we are six years old, we enter an education system which is entirely based on dogma. In school and later in university, we are forced to memorize facts. Nobody teaches us to question these facts and discover everything around us. In fact, challenging the facts gets punished – not rewarded. And because we only memorize and never question what we are being lectured, we never really engage with this knowledge, and thus we can never build upon it.
Instead of lecturing, we should focus on questioning – again.
The Buddha encouraged questioning. It is seen as a fundamental skill which is still embraced in the practices of modern Buddhists today. Tibetan Buddhist monks often have a daily practice of “debate” where one monk continually questions the other monk for an entire hour. The purpose of this practice was to train logic, mental concentration and intense exchange.
Socrates was well known as the questioner of everything. He also used questioning as a teaching method to explore the unknown and evaluate the validity of an argument. To do so, he asked questions after questions until his students arrived at their own understanding. He rarely revealed or lectured opinions or knowledge on his own, rather, he taught his students to dissect their thoughts and ideas by questioning everything. Even his death embodied the spirit of questioning every assumption, as he was condemned for death penalty because of his teachings.
Quite similar is Chavrusa, a traditional Jewish learning method. Chavrusa challenges a small group of students to analyze and explain the learning material to each other, point out errors in their partners’ reasoning, and sharpen each other’s ideas by questioning them. By doing so, they often arrive at entirely new insights into the meaning of a text they are studying.
The Chavrusa is beautifully showing how questioning takes the familiar and makes it mysterious again. There is no teacher lecturing the meaning. There is nothing to memorize. It removes the comfort of “knowing”. Instead of memorizing, you explore complex ideas on your own. You uncover their implicit assumptions, you expose deeply held beliefs, you recognize hidden contradictions. You develop your own sense, think more clearly and change the way you see and perceive reality.
Philosophy and Science as Oneness
Our current education and university system is not only focussing on lecturing facts, they are also trying to categorize everything into small categories and subjects. Scientists and educators then look at these tiny subjects only independently of each other – and ultimately miss what’s really going on.
This narrow-minded thinking leads to very abstract science and philosophies. We focus purely on terminology and thereby divide the world into logic and creativity. By separating logic and creativity, we ultimately miss the existential truth encompassing all of it.
For example, let’s assume you understand everything about the brain: neurochemistry, neurobiology and so forth. Does it mean you understand consciousness? No. Looking at a separate subject alone is not sufficient. To really understand our world, we need to look at the whole.
Separating logic and creativity is therefore nonsense. The word creativity itself comes from create. It is not only art and philosophy which you create. You also create plans, you create logical rules, you create science, and you create inventions. Science and philosophy are one – but we separated it into tiny little subjects which we only look at separately. But this is wrong and has not always been the case.
Philosophy and science were once very closely connected and inseparably intertwined. Both: logical argument and creative thinking were renowned ways to explore and explain the natural world. There weren’t many “facts” that were known for certain. The idea of using experiments and data to understand the world only started to become popular in the middle of the second millennium. Since then, science and philosophy have grown apart – both – in subjects and methodologies.
Today, you’ll rarely see scientists and philosophers exchanging ideas. But it is precisely what we need. We need philosophers questioning scientists and scientists questioning philosophers. Even more, what we need are people who integrate all the aspects of art, science, philosophy, and practical creation into one unified art of science.
Because science, philosophy, art, and spirituality are all one, you always have to be open-minded. You should never categorize yourself into one category, for example: “I’m a scientist” or “I’m an artist”. Instead, you have to be everything. You are an artist, a scientist, a philosopher, and you are spiritual. All at the same – because otherwise you will miss the wholeness as you only look at the world from a very limited perspective.
As soon we can grasp the wholeness of everything again, innovation, re-thinking, or going from Zero-to-One will become natural states of our inner-being again – not some innovation workshops we have to attend.
To innovate and discover new things, we first have to forget all the beliefs which we have of ourselves, like: ”I’m a logical person, I’m not creative”. This is bullshit.
Do everything – to discover everything: Art and science are one.
If you describe yourself today as a logical person, you might want to learn an art or craft, such as making music or painting. By being creative, you’ll learn that there is more than the logical mind.
If you describe yourself today as a very creative person, you might want to learn mathematics and physics. By doing so, you’ll learn about the significance of logic.
Question Everything!
To make new discoveries and inventions, we finally have to start thinking for ourselves again. Many people believe they are thinking for themselves, which is gigantic bullshit. From the very first second of our lives, we have been conditioned with dogma and the desires of other people. People are naturally mimicking other people and other people’s desires.
Before we can make new discoveries, we have to first free ourselves from all the indoctrinated dogma we received. We have to free ourselves from all the limiting beliefs we have of ourselves. In other words: before we can discover new truths, we must start to think critically.
We have to have skepticism. We have to doubt our own experiences, our own standards, our own concepts. By questioning our own prejudices, beliefs, and conclusions, our mind becomes clearer and more active. We free our mind from conventional wisdom, from dogma, which helps us to discover what we want in life. It prevents us from doing the same which has already been done before. It prevents us from repeating mistakes and problems. It leads us to discover great new things – for our lives and the lives of others.
Discovery doesn’t mean that we have to endlessly sit and do research. For some people, yes. But for other people, discovery can also mean a practical mission to materialize things you envision.
This discovery process is a journey of life. In this journey, you need to be humble. Ultimately, it is about arriving at the truth. Still, we all have egos. Pay attention to it. People always want to be right. But trying to have the better argument prevents us from discovering truths.
Again: Question everything!
We are often afraid to ask the most challenging questions because when we challenge the core of our beliefs, we will have to admit to ourselves: “I know nothing and I have to start all over again.”
Questioning everything and being honest about it will hurt. It is worth it.
Ultimately, by questioning everything we see, read, know, and believe, we will enter a new age of great discoveries and thereby an abundance of prosperity and – most importantly – a lot of joy.