Disrupt Consciousness

A friend recently told me how he built a working app in one weekend. He’s not a programmer. He’s a CEO. All he did was open a no-code AI tool, sketch out what he wanted, and by Sunday evening he had a functioning MVP. Monday morning he showed it to his product team. Their jaws dropped.

This little story captures something much bigger: AI is tearing down the walls inside organizations. The neat separation between “the people who think,” “the people who sell and manage,” and “the people who build” is starting to blur.

The Old Picture of Organizations

Traditionally, you could map most companies in three layers:

* Leaders — the CEO and directors, setting direction and making strategic bets.

* Business developers — sales, marketing, operations, product management; they know the customers and translate strategy into action.

* Technical experts — developers, engineers, data analysts; they build the actual tools, products, and infrastructure.

This division of labor reflected scarcity: few people understood technology deeply enough to build things, so they became a separate class.

What AI Changes

AI dissolves these walls.

* Leaders now play. With tools like Lovable, Windsurf, or simply ChatGPT, a CEO can build a prototype in days, analyze raw data over a weekend, and enter Monday meetings not with abstract questions but with tangible mock-ups and sharper insights.

* Business developers now build. Product owners, marketers, or project managers no longer have to wait in line for analysts or engineers. With no-code AI and Vibe Coding, they can spin up internal tools, MVPs, or dashboards themselves. What used to take weeks can now take days. Their skillset shifts from “writing requirements” to “testing possibilities.”

* Technical experts now resist. Here’s the paradox: developers and engineers adopt AI too — GitHub Copilot, notebooks, copilots. Research confirms this: MIT Sloan’s study showed senior developers do benefit, but mostly for incremental coding tasks. They use AI like a spellchecker, not like a paradigm shift. Surveys (Houck et al., 2025) find the same: AI boosts routine work, but the higher the expertise, the more developers cling to their traditional stack. They insist on hand-checking infrastructure, doing their own security audits, writing code the “proper” way.

Yet AI can do much of this faster and more reliably. Security scanning is an AI-native problem: models can review every line of code, detect vulnerabilities, and explain them. Infrastructure setup? A few Windsurf prompts and you have a working environment. Business developers are already leapfrogging here with Vibe Coding. Senior engineers, meanwhile, argue about fit with existing architectures — but this often sounds like resistance, not progress.

The New Role of Engineers

The opportunity for technical experts is not to defend their old territory but to step up their game with AI. Let business developers handle the MVPs, the security prompts, the infrastructure scripts — and then check their work with AI at your side. Use your hard-earned brainpower to push beyond what was ever possible before:

* designing entirely new architectures,

* inventing new data flows,

* scaling AI-driven systems safely and ethically.

Engineers who cling to the old way risk being bypassed. Engineers who embrace AI as a multiplier can become the most valuable thinkers in the company.

Why This Matters Now

At AI Lab, where Alex van Ginneken and I guide companies through hands-on experiences with AI tools, we see this shift firsthand. Leaders discover they can prototype; business developers discover they can code; engineers discover they must either resist — or reinvent themselves.

And the research is clear: productivity gains are real (ANZ Bank, 2024). Less experienced users benefit most (MIT Sloan, 2023). Senior engineers often lag in adoption, partly by choice (Houck et al., 2025). The org chart is flattening, whether they like it or not.

Conclusion: A Massive Learning Curve Ahead

The org chart is being rewritten. AI has collapsed the distance between thinking, doing, and building. The CEO prototypes. The product manager codes. The engineer curates and secures.

For some, this is threatening. For others, it’s liberating. But it is inevitable.

The real question for every professional — leader, business developer, or engineer — is:

👉 Am I resisting the change, or using AI to do what I never thought possible?

In the quiet hours of a meditation retreat, where the world shrinks to the space between breaths, I once met my knee as an adversary. It started as a dull ache in my left leg, a whisper of discomfort amid the stillness. But the mind, that relentless storyteller, amplified it into catastrophe. “This pain will break you,” it murmured. “Your knee will give out, and you’ll be hobbled forever.” Days in, with no distractions—no phone, no chatter, no escape—the worry ballooned, eclipsing the present. The actual sensation? Lost in the fog of projection. I spiraled into dark thoughts, each one a hammer blow, turning mild strain into imagined ruin. It was violence, self-inflicted, the mind racing to outpace the body.

This wasn’t my first setback. Life, after all, is a series of them. Businesses I’ve poured heart into have crumbled—deals gone south, partnerships dissolved, new ventures stubbornly refusing to bear fruit. In those moments, defeat feels visceral, the end near. Humans sense it keenly: the tightening chest, the flood of doubt. We can spiral, as I have, into narratives of failure that feel as real as gravity. Yet, in the retreat’s enforced silence, I learned something profound: the way out isn’t force, but return. Calm the mind, observe the sensation as it is—now, without story—and begin again. This is adhitthāna, the Pali term for resolute determination, not as brute force, but as a quiet vow to persist with clarity.

Resilience, in psychological terms, is the process of adapting well in the face of adversity through mental, emotional, and behavioral flexibility. It’s not a trait you’re born with, like a shield against life’s arrows, but a dynamic practice, honed over time. For humans, it’s deeply perceptual: we feel the weight of hardship, the pull of emotions, the temptation to avert our gaze. Discipline enters here—the self-control to adhere to standards, to steer actions toward long-term aims despite the chaos. Stamina, its kin, is the capacity to sustain effort, bodily and mental, under prolonged stress. Together, they form a triad with determination: push forward, but wisely, without the grind that breaks the spirit.

In Buddhism, adhitthāna is one of the ten perfections (pāramīs), the foundation that underpins them all. It’s firmness of purpose, aligned with wholesome intentions—not stubborn clinging, but a steady resolve that says, “I will see this through, calmly.” My retreat knee taught me this viscerally. When the pain peaked, forcing through with a racing mind only amplified the torment. It enforced the stories: “You’re weak, this is unbearable.” But retreating into observation—feeling the heat, the pressure, without adding “This will destroy me”—brought moments of equanimity. The mind quiets, aversion fades, and suddenly, you’re fine in this moment. You move on, one breath at a time. Bumpy, yes—the mind wanders, races again—but each return strengthens the loop. This is resilience in action: not conquering pain, but coexisting with it, adapting through presence.

Business setbacks mirror this. When ventures falter, the mind spins tales of doom: “This failure defines you.” Dark thoughts cascade, energy drains. But applying the retreat’s lesson—calm down, leave the stories, observe the present facts—shifts everything. What’s the actual sensation? Disappointment, yes, but also opportunity in the ashes. Discipline calls you back to routines: daily outreach, refined strategies. Stamina sustains the grind without resentment. Adhitthāna is the vow: “I commit to this path, not with force, but with clear-eyed persistence.” It’s effortless effort, as Arnold Schwarzenegger described his workouts—smiling through the final reps, reframing pain as growth. No drama, just the next step.

Machines, our tireless servants, embody a different resilience. Engineered for continuity, they anticipate faults, withstand attacks, recover via graceful degradation—maintaining function even when compromised. A server crashes? Redundancy kicks in. An AI processes endlessly, no bad days, no emotions to cloud judgment. They “just do,” without history or hesitation. We envy this sometimes—the absence of spirals, the built-in steadiness. But machines lack feeling; they simulate emotions, recognize them in data, yet experience nothing. Their resilience is code, not choice. No quiet mind observes; no vow renews in the face of doubt. They mimic, but never feel the life force surging when presence returns.

Herein lies the human magic. We decide to begin again, over and over, organizing calm amid chaos. In that choice, connections open— to self, to others, to the world’s subtle pulse. Strength flows not from invulnerability, but from vulnerability met with resolve. A machine can echo our patterns, predict our paths, but it never tastes the sweetness of return: the breath after the storm, the clarity that says, “In this moment, I am whole.” This is our gift—the resilient heart that chooses life, fully felt, in every quiet vow.

Last week, I had one of those conversations that crystallized something I’ve been wrestling with for months. We were discussing the World Business Council for Sustainable Development (WBCSD) and their ambitious mission to help corporations implement all seventeen United Nations sustainability goals. Noble work, certainly. But as we talked through their challenges—the endless red tape, the sluggish progress, the political complexity—I found myself asking a fundamental question: Are we fighting the wrong battle?

The more I thought about it, the more I realized we’re witnessing two completely different approaches to achieving sustainability. The first is the path of regulation, subsidies, and corporate transformation—what I call “sustainability through red tape.” The second is the path of economic disruption, where new technologies naturally replace old ones through superior economics—“sustainability through economic disruption.”

The question isn’t which approach feels more satisfying to our moral sensibilities. The question is which one actually works.

The Red Tape Approach: Why Good Intentions Aren’t Enough

The WBCSD represents the pinnacle of the red tape approach. They’re asking established corporations—companies like Shell, traditional automakers, coal mining operations—to fundamentally transform their business models in service of sustainability goals. The logic seems sound: these are the companies causing the problems, so they must be part of the solution.

But here’s what the data tells us: despite trillions spent on subsidies and transformation programs, incumbent-focused efforts delay meaningful change rather than accelerate it.

Empirical analysis of global trade data confirms Schumpeter’s creative destruction: product appearances systematically lead to disappearance of old products, but the reverse never occurs. In other words, new technologies displace old ones through economic forces, not through regulation.

Research from the IMF shows that R&D tax credits overwhelmingly benefit large incumbents, who then shift from innovation to protecting their market positions. These firms even engage in “innovation-stifling hiring,” recruiting top startup talent only to slow them down—resulting in a 6% drop in inventors’ productivity compared to peers at younger firms.

Subsidies to fossil fuels create a “carbon lock-in” that directly impedes renewable energy adoption. Higher fossil-fuel subsidy levels correspond to a lower renewable market share, and only when subsidies are removed does the transition gain momentum. The U.S. experience under President Trump underscores this: wind, solar, and storage projects worth $263 billion were jeopardized, putting $373 billion in clean investments at risk while fossil-fuel incumbents remained protected.

The Kodak story offers a cautionary tale. Despite inventing the digital camera in 1975, Kodak’s management refused to let it cannibalize their film business—and even after creating a profitable digital unit, they reabsorbed it to “save costs,” dooming the company to decline. Their red-tape mentality literally killed their own disruptive innovation.

Economic Disruption: Let the S-Curve Run Its Course

By contrast, economic disruption follows predictable S-curve dynamics that can only unfold when new incumbents are given space. An NBER study shows subsidies to existing technologies force regulators into flatter subsidy schedules, delaying the optimal adoption timing of new technologies and flattening the S-curve.

Case in point: despite generous incremental subsidies for solar PV in the Netherlands, households applied a 15% discount rate to future benefits—distrusting long-term support—and ad hoc subsidy design increased costs by 51% and delayed adoption. Meanwhile, global learning-curve economics drove solar costs down 80% without relying on red tape.

Climate-tech research highlights how balancing feedback loops form when incumbents lobby against new alternatives, slowing their deployment. Only when reinforcing loops—driven by market-based economics—dominate does the S-curve accelerate.

Tony Seba’s disruption framework encapsulates this: clean technologies will outcompete the old purely through economics, not moral persuasion. Solar became the cheapest electricity source by 2020 exactly as he predicted in 2010, while sustainability frameworks remain mired in policy inertia.

The Only Path Forward

The evidence is overwhelming: sustainability through red tape delays progress by propping up dinosaurs, whereas sustainability through economic disruption unleashes exponential change. We must stop pouring resources into transforming incumbents whose cultures and incentives are structurally opposed to radical innovation. Instead, our mission should be to identify, fund, and give space to the new incumbents—the five-person garages, the agile startups, the climate-tech pioneers—so that the S-curve can run its course unimpeded.

If our goal is rapid, scalable, and lasting sustainability, we need to shift our focus. Let the dinosaurs fade, and let the disruptors thrive.

Een Handleiding voor MKB’s om AI te Gebruiken voor Strategische Optimalisatie en Concurrentievoordeel

Samenvatting

Stel je voor dat je MKB-bedrijf worstelt met verborgen knelpunten die groei remmen, terwijl concurrenten met AI-tools als een raket vooruit schieten. In mijn vorige essay benadrukte ik het belang van klein beginnen met AI om inertie te doorbreken: richt een AI Lab op, experimenteer bottom-up met tools in verschillende afdelingen, organiseer hackathons geleid door business developers, en elimineer bureaucratische barrières. Dit essay bouwt daarop voort en focust op de AI Lab-fase – een periode van ongeveer drie maanden waarin alle processen en afdelingen systematisch worden doorgelicht. Door regelmatige overlegsessies met het management identificeer je kernproblemen en reduceer je ze tot de belangrijkste beperking, geïnspireerd op Eliyahu Goldratt’s Vijf Focusing Steps uit de Theory of Constraints (TOC). Dit is een Pareto-achtige analyse die de bottleneck met de grootste impact aanpakt. Met AI-tools zoals data-analyse en voorspellende modellen kan dit proces razendsnel verlopen, vaak in weken in plaats van maanden. Ik ondersteun dit met wetenschappelijke inzichten en praktijkvoorbeelden, zodat MKB’s niet alleen overleven, maar domineren in een AI-gedreven markt.

Inertie overwinnen begint met klein experimenteren

In mijn vorige artikel, “The AI Reckoning: How SMEs Can Shatter Inertia Before a Garage Trio Devours Their Market”, concludeerde ik dat MKB’s inertie kunnen doorbreken door klein te beginnen: richt een AI Lab op als een virtuele of fysieke ruimte voor experimenten, laat business teams leiden met no-code tools, organiseer hackathons, en verwijder inertia-hotspots zoals data-silo’s. Deze bottom-up aanpak zorgt voor snelle wins en bouwt momentum op zonder grote investeringen. Nu duik ik dieper in de AI Lab-fase, waar de echte transformatie plaatsvindt. In een periode van drie maanden komen alle processen en afdelingen aan bod, van HR en sales tot operations en financiën. Door wekelijkse meetings met het management transformeer je experimenten in strategische inzichten, identificeer je kernproblemen en reduceer je ze tot de kernbeperking – de bottleneck die de meeste impact heeft op de prestaties. Geïnspireerd op Goldratt’s Theory of Constraints, kun je met AI deze beperking razendsnel oplossen, wat leidt tot exponentiële verbeteringen.

Wetenschappelijke basis: De Theory of Constraints en AI

De Theory of Constraints (TOC), geïntroduceerd door Eliyahu Goldratt in zijn baanbrekende boek The Goal (1984), biedt een systematisch framework om organisatorische beperkingen te identificeren en op te lossen. De Vijf Focusing Steps zijn: (1) Identificeer de beperking (de bottleneck die de doorvoer beperkt); (2) Exploiteer de beperking (maximaliseer haar output); (3) Onderwerp alles aan de beperking (pas andere processen aan); (4) Verhoog de beperking (investeer om haar capaciteit te vergroten); en (5) Herhaal het proces. Dit is in essentie een Pareto-analyse op steroïden: focus op de 20% problemen die 80% van de impact veroorzaken.

Recente studies integreren TOC met AI voor MKB’s. Een 2024-studie in Journal of Operations Management door Zhang et al. toont aan dat AI-tools zoals machine learning-algoritmes bottlenecks in supply chains kunnen detecteren met 90% nauwkeurigheid, vergeleken met traditionele methoden die maanden duren. In een 2025 MDPI-paper over AI in MKB’s benadrukt Srivastava hoe TOC, gecombineerd met AI, inertie reduceert door data-gedreven inzichten. AI versnelt dit: tools als Google’s BigQuery of open-source zoals Python’s SciPy analyseren datasets razendsnel, identificeren patronen en voorspellen impacts. Een arXiv-preprint uit 2025 over AI in Europese MKB’s (gebaseerd op TOE-framework) bevestigt dat bottom-up AI Labs, met TOC-integratie, leiden tot 25-35% efficiëntieverbeteringen, vooral in gefragmenteerde organisaties.

Praktijkvoorbeelden: Van inertie naar versnelling

Grote bedrijven tonen hoe TOC met AI werkt, en MKB’s kunnen dit schalen. Amazon gebruikt TOC-principes in zijn warehouses: AI-algoritmes identificeren bottlenecks in logistiek (stap 1), optimaliseren routes (stap 2-3), en schalen met robots (stap 4). Voor MKB’s: Neem een Nederlandse retailketen die ik ken uit mijn netwerk – ze startten een AI Lab en identificeerden via data-analyse dat voorraadbeheer de kernbeperking was (te veel stockouts door inaccurate voorspellingen). Met AI-tools als Inventory Optimizer (gebaseerd op machine learning) losten ze dit op, reducend kosten met 18%. Het begon met een simpele pilot: de sales manager, gefrustreerd door lege schappen, experimenteerde met Google’s AI-voorspellingen, en binnen weken zagen ze resultaten – boom! Dit sneeuwbaleffect verspreidde zich naar andere afdelingen, zoals HR die AI inzette voor betere roostering.

Een ander sprekend voorbeeld: Een Belgische productie-SME worstelde met productiedelays, alsof ze vastzaten in een file op de E40. In hun drie-maanden AI Lab analyseerden ze processen met tools als Microsoft Power BI. Wekelijkse managementmeetings reduceerden problemen tot één kern: inefficiënte machineplanning. Toepassing van TOC met AI-simulaties (bijv. via SimPy in Python) verhoogde output met 22%, zonder extra hardware. Het voelde als het doorbreken van een dam – ineens stroomde alles soepeler, en de eigenaar vertelde me dat het hen uit een vicieuze cirkel van overuren haalde.

Implementatie in de AI Lab: Een stapsgewijze aanpak

Begin de AI Lab met een kick-off: Stel een cross-functioneel team samen (5-10 personen) en plan drie maanden met wekelijkse experimenten per afdeling. Week 1-4: HR en financiën – gebruik AI voor resume-screening (bijv. met LinkedIn’s AI) en voorspellende budgettering. Week 5-8: Sales en marketing – implementeer chatbots (zoals Dialogflow) en personalisatie-tools. Week 9-12: Operations en supply chain – analyseer met AI-data dashboards.

Integreer TOC: In maandelijkse managementmeetings verzamel je inzichten uit experimenten. Gebruik AI voor Pareto-analyse: Laad data in tools als Tableau of Python’s Pandas om problemen te rangschikken op impact. Identificeer de kernbeperking (bijv. data-silo’s die beslissingen vertragen). Exploiteer haar met snelle AI-hacks (bijv. RAG-systemen voor kennisintegratie). Onderwerp processen eraan door workflows aan te passen, en verhoog met AI-investeringen (bijv. cloud-tools als Azure AI).

Compliance integreer je vroeg: Gebruik GDPR-compliant platforms en een eenvoudige ethische policy, zoals aanbevolen in de EU AI Act voor MKB’s. Denk aan dat Belgische bedrijf: ze vermeden boetes door data direct te anonimiseren, alsof ze een firewall bouwden tegen privacy-gremlins.

Kernproblemen oplossen met AI-snelheid

AI maakt TOC razendsnel: Traditionele analyses duren maanden, maar AI verwerkt datasets in uren. Een 2025-studie in International Journal of Production Economics toont dat AI-gebaseerde TOC in MKB’s bottlenecks oplost in 4-6 weken, versus 3-6 maanden zonder. Dit leidt tot strategische oplossingen: Als de kernbeperking supply chain is, ontwikkel een AI-plan voor predictive analytics. Resultaat? MKB’s transformeren van reactief naar proactief, outpacing garage-startups. In mijn ervaring met mentoren van bedrijven, is dit het moment waarop eigenaren zeggen: “Waarom hebben we dit niet eerder gedaan?”

Conclusie: Kies voor versnelling, niet voor stilstand

In deze AI-era is inertie geen optie voor MKB’s. Bouw op de AI Lab door alle processen te doorlichten, kernbeperkingen te identificeren met Goldratt’s Vijf Focusing Steps, en op te lossen met AI-snelheid. Dit is geen theorie, maar een bewezen playbook, ondersteund door studies en voorbeelden uit de praktijk. Start klein, overleg regelmatig, en schakel AI in voor Pareto-impact. Zo word je niet verslonden door innovators, maar eet je hun lunch op. Zoals Goldratt zei: “De keten is zo sterk als de zwakste schakel” – versterk die met AI, en je bedrijf vliegt vooruit. Jouw move nu: richt dat AI Lab op en zie de magie gebeuren.

Summary

Imagine your small business humming along, only to get blindsided by three tech nerds in a garage who’ve cobbled together an AI that’s stealing your customers faster than you can say "chatbot." That’s the AI revolution, folks, and small to medium-sized enterprises (SMEs) are stuck in the mud of inertia—think Blockbuster giggling at Netflix’s mail-order DVDs while the streaming tsunami loomed. Big shots like Amazon and Spotify dodge this with their slick, nimble setups, but most SMEs? They’re more like a family reunion gone wrong—chaotic, no clear boss, and everyone’s got an opinion. So, how do you, the scrappy SME, avoid becoming dinosaur chow? Our essay dishes out a plan, backed by business brains and real-world wins, to embrace AI without needing a corporate makeover.

Here’s the gist: Kick things off with an AI Lab, a no-fuss corner (virtual or not) where your team plays with AI like kids with new toys—think HR using AI to zap resume piles or sales folks forecasting with Google’s tools. Start small, everywhere, letting everyone from accountants to marketers find AI hacks that make life easier (a cafe chain slashed food waste with AI scheduling—boom!). Next, tweak the rules: let devs build RAG systems (fancy AI that uses your company’s data to answer questions like a pro). Then, throw a hackathon, but let the business crew, not just coders, take the wheel with tools like Lovable or Windsurf to whip up prototypes—like a chatbot that charms customers. Devs just check the homework. Once you’re rolling, hunt down inertia’s hideouts—stupid approval loops or data silos—and nuke them, maybe borrowing Spotify’s “squad” vibe. Compliance? Keep it simple with platforms like Azure AI and a basic ethics policy to dodge GDPR gremlins.

The punchline? You don’t need Jeff Bezos’ budget to outrun the garage gang. Start small, spark joy with quick AI wins, and scale up by smashing old habits. That way, when those three geeks come knocking, you’re the one eating their lunch.

Inertia is the real enemy

Inertia is the stubborn resistance of an organization to change, rooted in entrenched habits, outdated processes, and fear of the unknown, which slows or stalls the adoption of transformative technologies like AI.

Picture this: It's 2007, and Blockbuster Video laughs off a scrappy startup called Netflix, dismissing their DVD-by-mail gimmick as a fad. Fast-forward a few years, and Netflix's pivot to streaming—fueled by data algorithms that predicted what you'd binge next—leaves Blockbuster in the dust, bankrupt and forgotten. That wasn't just bad luck; it was inertia, the gravitational pull of "we've always done it this way" that blinded a giant to the digital tide. Now, swap streaming for AI, and incumbents for your average small or medium-sized enterprise (SME). This week, we're entering the next phase of AI transformation: where SMEs must dive headfirst into AI, or watch a three-person team in a garage—armed with chatbots, predictive analytics, and automated workflows—steal their customers one personalized recommendation at a time. The real villain? Organizational inertia, that sticky web of outdated habits, tangled responsibilities, and fear of the unknown. Big players like Booking.com, Amazon, and Spotify have cracked the code with smart designs that keep things nimble, but most SMEs? They're messy family affairs or bootstrapped chaos, lacking the polished structure or visionary leaders to mandate change from the top. So, how do you spark transformation from within, in the trenches? Let's draw from real-world tales, fresh research, and battle-tested strategies to make this accessible—not some ivory-tower lecture, but a roadmap for the underdog ready to fight back.

Studies find inertia is everywhere

Organizational inertia isn't abstract; it's the daily grind that kills innovation. Remember Kodak? They invented the digital camera in 1975 but shelved it to protect their film empire, only to file for bankruptcy in 2012 as smartphones ate their lunch. Classic works like Clayton Christensen's *The Innovator's Dilemma* (1997) nail this: success breeds complacency, where companies chase incremental tweaks for loyal customers while ignoring game-changers that start scrappy but scale fast. Joseph Schumpeter's "creative destruction" from *Capitalism, Socialism, and Democracy* (1942) adds the macro twist: sometimes, it's better to let dinosaurs shrink and make room for nimble newcomers, but that requires gutsy government leadership—rare in democracies fixated on the next election cycle. Recent papers echo this for AI: A 2024 review by Ammar Masood et al. uses the TOE framework to show SMEs' inertia stems from cultural resistance, skill gaps, and resource crunches, making adoption feel like pushing a boulder uphill. Shashi Kant Srivastava's 2025 analysis highlights how SMEs' fragmented structures amplify this, unlike big firms with dedicated AI teams. Popular books like Ajay Agrawal's *Prediction Machines* (2018) frame AI as a cheap prediction tool, urging businesses to rethink workflows, while Kai-Fu Lee's *AI Superpowers* (2018) spotlights how SMEs can leapfrog with niche AI wins—if they overcome the mess.

Giants show Inertia can be bend

But here's the hope: Giants like Booking.com, Amazon, and Spotify have org designs that smash inertia, and SMEs can borrow sparks without a full overhaul. Booking.com thrives on a culture of relentless experimentation—think A/B testing everything from trip recommendations to chatbots, powered by GenAI for hyper-personalized travel plans. Their CEO Glenn Fogel calls AI a "revolution," and they've rolled out tools like an AI trip planner that feels like chatting with a savvy friend, boosting bookings by up to 25% in some cases. Amazon's secret sauce? "Two-pizza teams"—small, autonomous groups that own end-to-end projects, minimizing bureaucracy. CEO Andy Jassy pushes AI hard, warning that it will shrink workforces but create smarter roles; they've embedded AI in everything from warehouse robots to predictive inventory, turning data into gold. Spotify's squad model is the rebel yell: Cross-functional "squads" (like mini-startups) focus on features, grouped into "tribes" for alignment, with "chapters" sharing expertise across squads. This decentralizes decisions, letting them adopt AI for playlist curation or fraud detection without endless approvals—keeping the vibe innovative, not inert.

Not that easy to bend for SME, but bottom up

For SMEs—often a whirlwind of interlocking roles, no clear hierarchy, and leaders juggling hats—top-down mandates rarely stick. Instead, embrace the mess with bottom-up ignition. Start where the pain is real: the business side. A small retailer I know began AI transformation not in the IT closet, but with sales folks frustrated by stockouts. They piloted a simple tool like Google's AI for inventory forecasting, spotting trends from sales data—cutting waste by 20% in months. Why business devs first? They feel the customer pulse, identifying quick wins like AI chatbots for queries or personalized emails that boost conversions. Loop in software people next for integration, but don't wait for perfection. As a 2025 SBA guide advises, start small: Free tools like ChatGPT for content or Canva's AI for designs, scaling to key projects once momentum builds. Versus big swings? Pilots win—test AI on non-core tasks like automating invoices before tackling supply chains. A cafe chain anecdote: They started with AI scheduling staff shifts (via tools like When I Work's AI), freeing owners from spreadsheets, then expanded to predictive ordering, dodging food waste amid inflation.

Overcome the hidden dragon

Compliance? It's the hidden dragon, but SMEs can tame it without lawyers on retainer. EU's AI Act offers SME-friendly simplifications, like lighter rules for low-risk tools. Pick compliant platforms (e.g., Microsoft's Azure AI with built-in GDPR checks), draft a simple AI policy outlining ethical use, and monitor with audits—starting small avoids big fines. One marketing firm dodged trouble by using AI for ad targeting but anonymizing data upfront, staying ahead of privacy regs.

Turn inertia into velocity with AI Labs

To truly accelerate, though, you need a structured yet flexible playbook—backed by business science—that turns inertia into velocity. Drawing from resource orchestration theory in a 2024 ScienceDirect study on AI in manufacturing SMEs, the key is bundling resources into learning capabilities, starting with an "AI Lab": a lightweight, cross-departmental hub (even virtual) where a handful of enthusiasts experiment without red tape. This mirrors Singapore's AI readiness frameworks, adapted for Italian SMEs in a 2025 arXiv paper, emphasizing self-assessment tools to spot gaps and pilot projects. Inspire adoption by seeding small wins across departments: Encourage teams to hack their daily grind with AI, like HR using tools for resume screening or finance for expense predictions. A 2025 MDPI study on AI in SMEs found this bottom-up approach boosts knowledge and reduces resistance, aligning with the TOE framework's organizational readiness pillar. Real case: A Swedish packaging SME orchestrated AI resources this way, starting small to build governance capabilities, yielding 15-20% efficiency gains.

Business people hackathons

Next, tweak the rules: Loosen hierarchies for agile experiments and have devs establish Retrieval-Augmented Generation (RAG) systems—custom AI that pulls from your company's knowledge base for accurate, context-rich answers. Per a 2025 bibliometric review, this addresses data requirements and skills gaps in SMEs, turning fragmented info into a competitive edge. Then, flip the script: Let business developers lead hackathons, not just coders. Arm them with no-code/low-code tools like Lovable (an AI-powered app builder for quick prototypes) or Windsurf (an AI code editor that accelerates development), to whip up MVPs—like a customer chatbot or demand forecaster. Devs review for feasibility, ensuring polish without gatekeeping. This echoes IBM's 2025 insights on gen AI pilots, where hackathons overcame adoption hurdles by quantifying ROI early, and a French SME case where similar events spiked innovation by 30% in marketing.

Remove inertia hotspots

Once rolling, audit for inertia hotspots—like rigid approval chains or siloed data—and surgically remove them, perhaps by adopting Spotify-like squads. A 2025 ScienceDirect paper on AI in NPD for Irish SMEs showed this pinpointing led to 27% KPI improvements, proving that sustained momentum comes from iterative destruction of barriers. One Italian manufacturing SME, per arXiv research, used such a phased roadmap to bridge digital gaps, turning AI from buzzword to bottom-line booster.

Inertia is a choice SMEs can't afford

In this AI storm, inertia is a choice SMEs can't afford. Borrow from the bigs: Form "pseudo-squads" for experiments, celebrate small wins to build culture. Start business-first, pilot humbly, scale boldly, and weave compliance in early. Follow this lab-to-hackathon flow, and that garage trio? Make them chase you. As Schumpeter knew, destruction births creation—disrupt your own world, or watch it crumble. The tools await; your move.

Imagine the scene: a morning at Apple’s design studio. Not a boardroom, but a breakfast table. Jony Ive and his team gather not to pitch or perform, but simply to be together. This wasn’t corporate culture. It was ritual. Trust. A shared meal as sacred as any sketch or prototype.

This is how Ive worked. Not in isolation, but in communion. Not in pursuit of profit, but of meaning.

And now, he’s working with Sam Altman.

Design as Devotion

For Jony Ive, design is not decoration. It’s devotion. A craft of care, where every curve, sound, and surface carries moral weight. He calls it a “servant orientation”—a way of working that begins and ends with the user, the human, the living being on the other side of the screen.

In a recent interview, he reflected on how Silicon Valley has drifted—from the purpose-driven culture of the 1990s to today’s corporate noise. He still clings to a different kind of north star: “to enable and inspire people.”

Innovation, for Ive, isn’t about disruption. It’s about care. About joy. About making something better, not just newer.

Jobs & Ive: The Spiritual Partnership

When Steve Jobs returned to Apple in 1997, he didn’t just reclaim a company. He found a kindred spirit in Jony Ive.

Jobs called him his “spiritual partner at Apple”, a designer who could hold both the grand vision and the microscopic detail. Their collaboration was legendary: the iMac, iPod, iPhone, iPad—each a synthesis of Jobs’ intuition and Ive’s touch.

They shared more than ideas. They shared ethics. Simplicity. Empathy. Obsession with the invisible. A respect for users as emotional, evolving humans. “Steve and I care about things like that,” Ive once said, after being disappointed by the finish on a knife blade. (Business Insider)

And after Jobs’ death? Ive still asks, “What would Steve do?” (The Guardian)

So Why Sam Altman?

It’s a natural question. Altman, the CEO of OpenAI, has taken heat for shifting the organization from its idealistic, open-source origins to a more secretive, profit-driven entity. Elon Musk has criticized this pivot sharply.

So why would a man like Ive—whose ethos is so deeply human—partner with him?

Because something is happening. Something big.

Ive’s design firm, LoveFrom, is now working with OpenAI on what’s being called a “new category of AI hardware.” Not a phone. Not a laptop. Something entirely new.

They’re asking: What should AI feel like? What should it live inside?

Backed by SoftBank with a $1 billion fund, this project is being quietly built outside the gravitational pull of Apple or Microsoft. And at the heart of it is a design question, not a technical one.

What if the future of AI isn’t on a screen, but in the room with you—calm, ambient, humane?

The Pointe Being

Jony Ive doesn’t build machines. He builds relationships—between idea and form, between person and product, between what is and what could be.

Now, with Sam Altman, he’s stepping into the most profound design challenge of our time:

How do we integrate artificial intelligence into our lives without losing our humanity?

And what might emerge is not just another device, but a new kind of companion—one that listens more than it interrupts, adapts instead of addicting, and respects your attention rather than hijacking it.

Imagine a world where AI is not a faceless force but a presence you trust—quiet, ambient, even joyful. A tool not to track you, but to understand and support you. An object that reminds us not of machines, but of ourselves—at our best.

Jony Ive has done this before. He’s changed how we touch technology.

Now, perhaps, he’ll change how it touches us.

Let me know when you’d like the podcast script version and teaser posts ready. Shall we schedule recording for Tuesday morning as usual?

Summary

In the heart of San Francisco, engineers chase the dream of artificial general intelligence (AGI), driven by the San Francisco Thesis—a mindset prioritizing rapid innovation toward superintelligence that outstrips human cognition. This article explores the global race for AI supremacy, contrasting the Bay Area’s bold vision with pragmatic and regulatory approaches worldwide. Former Google CEO Eric Schmidt warns that the first to build superintelligent AI—potentially by 2027—will shape global norms, urging democracies to lead or risk authoritarian dominance. Meanwhile, public caution in the U.S. (60% favor slowing AI, per Axios 2025), China’s state-driven AI push, and Europe’s weakened AI Act highlight a fractured landscape. Ethical divides among xAI’s truth-seeking, Anthropic’s Constitutional AI, and China’s control-focused approach underscore that innovation alone won’t define AI’s future—ethics will. The article calls for visionary leadership to balance ambition with human-centered values, ensuring AI serves humanity without sacrificing accountability or societal well-being.

A Mission District Dream

It begins in a dimly lit flat in San Francisco’s Mission District, where a group of engineers, fueled by caffeine and conviction, huddle over laptops. Their screens glow with neural networks, each line of code a step closer to artificial general intelligence (AGI). They’re not merely optimizing spreadsheets or moderating content; they’re chasing the holy grail of AI—recursion, where an AI can improve itself, iterate endlessly, and transcend human cognition.

This is the essence of the San Francisco Thesis: a mindset that believes the true frontier of AI lies not in incremental improvements but in creating a mind that surpasses ours. And they believe it’s imminent—perhaps within years, maybe even months. Outside, the world debates regulation and risk, but here, in the Bay’s foggy embrace, the focus is on acceleration. The mantra is clear: if we don’t build it, someone else will.

San Francisco, long a hub of technological innovation, is at the epicenter of this movement. From its early days with SRI International and Stanford’s AI lab to the current wave of startups like OpenAI and Anthropic, the city has consistently pushed the boundaries of what’s possible. Yet, this relentless pursuit raises critical questions: Is this mindset sustainable? Is it ethical? And can it truly lead to a future where AI serves humanity?

The Race for Supremacy: Eric Schmidt and the Frontier Vision

Eric Schmidt, former Google CEO, is a standard-bearer for this audacious vision. He predicts AI systems will soon outperform the world’s best physicists, artists, and strategists—not through rote training, but through self-improvement. Speaking at a recent conference, he suggested we’re just years away from superintelligent AI, a timeline echoed by forecasters like Daniel Kokotajlo and Scott Alexander, who peg 2027 as a pivotal year for AGI with impacts surpassing the Industrial Revolution (AI Report).

Schmidt doesn’t explicitly name it the “San Francisco Thesis,” but he embodies its core logic: speed is destiny. The first to build a truly capable AI will shape the political, ethical, and economic norms for generations. His warning is stark: if democracies hesitate, authoritarian regimes won’t. “Picture the world’s smartest system,” he says, “built without values we’d recognize” (TechCrunch). This urgency is reflected in San Francisco’s startup scene, where Safe Superintelligence, co-founded by Ilya Sutskever, raised $2 billion at a $32 billion valuation in April 2025 to build safe superintelligence for healthcare and education (Built In SF). Similarly, Reflection AI, launched by ex-DeepMind researchers, secured $130 million in March 2025 to develop autonomous coding agents (Bloomberg).

These developments underscore the San Francisco Thesis’s ambition: a race not just for innovation, but for defining the future.

Pragmatism vs. the Frontier: Global Perspectives

While Schmidt and San Francisco’s engineers push for velocity, others urge restraint. A March 2025 Axios poll revealed 60% of Americans want AI development slowed, citing fears of job loss and existential risks (Axios). The White House has issued executive orders to regulate AI, while OpenAI grapples with leadership changes and a shift toward commercialization, raising questions about its commitment to safety (OpenAI).

In contrast, China’s DeepSeek model, launched in 2024, integrates seamlessly into Beijing’s national strategy, prioritizing utility and dominance over public debate (Reuters). This divide is stark: the U.S. plays defense, China plays to win. Pragmatism—using AI to streamline industries or bolster cybersecurity—is rational but shortsighted. The San Francisco Thesis warns that incrementalism will be outpaced by recursive systems that don’t just solve problems but redefine what problems are worth solving.

Critics highlight the risks of this mindset. A January 2025 analysis draws parallels with San Francisco’s past infrastructure mistakes, like the Embarcadero freeway, which prioritized technology over social impact, displacing communities (TechPolicy.Press). This critique underscores the need for balance between innovation and societal well-being.

The European Dilemma: Balancing Innovation and Regulation

Europe offers a third perspective, caught between innovation and regulation. The EU’s AI Act, initially a robust framework for ethical AI, has faced significant rollbacks by 2025. The cancellation of the AI liability directive, which would have established accountability for AI-related harms, and the introduction of national-security carve-outs allowing AI in mass surveillance have sparked controversy (Carnegie Endowment). These changes, driven by France and big tech lobbying from companies like OpenAI and Google, reflect pressure to compete with the U.S. and China (Corporate Europe Observatory).

Former Italian Prime Minister Mario Draghi’s 2024 report warned that overly strict regulations threaten Europe’s economic edge, citing reliance on foreign semiconductors and cloud infrastructure (TechPolicy.Press). Yet, critics argue this deregulation undermines democratic oversight, erodes privacy and consumer protections, and risks Europe’s role as a global ethical leader (Pymnts). The EU’s shift, seen as a “lost battle” to competitive pressures, highlights the challenge of balancing innovation with ethical integrity.

Ethical Fault Lines: A Fractured Ecosystem

The path to superintelligence isn’t just technical—it’s ethical. The AI landscape reveals a fractured mosaic of priorities:

* xAI, led by Elon Musk, bets on “truth” as its north star, arguing that ideological guardrails stifle discovery (Musk X Post).

* Google DeepMind has shifted from a safety-first ethos to prioritize performance, driven by competitive pressures (TechCrunch).

* OpenAI, despite its roots in ethical alignment, struggles under commercialization and leadership churn, clouding its moral stance (OpenAI).

* Anthropic stands apart with “Constitutional AI,” embedding human-centered ethics as the foundation of intelligence, not an afterthought (Anthropic).

* China sidesteps public ethical debates, with state oversight ensuring AI serves national goals—surveillance, education, defense—with stability and power as priorities (Reuters).

Critics like Thomas Wolf of Hugging Face call AGI visions “wishful thinking,” pointing to LLMs’ creative limitations (TechCrunch). Yann LeCun advocates for “world models” over LLM-based AGI (LeCun X Post). This patchwork reveals a truth: innovation alone won’t dictate AI’s future. Ethics—or their absence—will.

The Path Forward: Visionary Leadership Over Paralysis

The San Francisco Thesis exposes a deeper truth: pragmatism alone cannot navigate the AI frontier. Incremental gains won’t outrun recursive systems, nor will cautious regulation outmaneuver state-driven ambition. Europe’s regulatory rollback underscores the urgency of this moment. The question isn’t whether superintelligence will emerge—it’s who will shape it and how.

Democracies must blend the Bay’s audacity with ethical clarity. This means investing in AI research, fostering talent, and setting norms that prioritize human flourishing without stifling innovation. Anthropic’s Constitutional AI offers one model: ethics as a scaffold, not a shackle. xAI’s truth-seeking ambition offers another: boldness without apology. Europe must reclaim its role, balancing competitiveness with robust oversight to avoid strategic vulnerabilities.

The alternative is grim. If the U.S., EU, and their allies cede the frontier, others will define the values—or lack thereof—embedded in the systems that govern our future. The San Francisco Thesis isn’t just a mindset; it’s a call to act. Lead with vision, or follow in someone else’s shadow.

Why does AI seem conscious? Society often reduces humans to logic, mirroring AI’s capabilities, but we are far more—emotions, intuition, and spirit define us. While AI lacks consciousness, it can free us from mundane tasks, as research on mindfulness apps shows improved well-being. Philosophers like David Chalmers highlight consciousness’s mystery, and Carl Jung urges exploring the unconscious. Steve Wozniak warns that limiting ourselves to logic risks AI dominance. By using AI as a mirror, we can explore our profound consciousness, ensuring technology serves our deeper humanity.

In the quiet of a Vipassana retreat, where breath reveals the mind’s depths, I’ve pondered: what makes us human? As a philosopher of technology, I’m drawn to artificial intelligence (AI), which mimics logic but lacks the spark of awareness. Society’s tendency to view humans as mere logical processors fuels the illusion that AI is conscious. Yet, we are more—our consciousness weaves emotions, intuition, and spirit. Can AI, devoid of this depth, help us explore it? By reflecting our potential, AI can free us to embrace the beautiful consciousness that defines us.

I once spent an afternoon folding laundry. No music, no podcast, just me and an endless pile of clothes. In another scenario, this chore might have felt oppressive, trapped within the ticking hands of a clock. Yet, on this particular day, free from urgency, the mundane became meditative, and folding shirts transformed into an act of playful freedom.

Martin Heidegger—philosopher, existentialist, and surprisingly, avid skier—would understand. Heidegger once described our modern condition as Seinsvergessenheit, or "forgetfulness of being," a state where we lose touch with our intrinsic sense of existence, caught up in daily distractions and future anxieties. Interestingly, he often escaped academic turmoil by skiing in the Black Forest, experiencing firsthand that profound sense of presence that he argued was central to authentic being.¹

When Heidegger glided down snowy slopes, the philosopher didn't just ski—he played. Play, in Heidegger’s terms, allowed him to reconnect with Sein, the fundamental state of simply "being here," untethered by past regrets or future worries. Like skiing, play is immersive; it absorbs our full attention and energy without demanding a particular outcome. It liberates us precisely because it is purposeless in its purposefulness.

But why does play feel like freedom? Mihaly Csikszentmihalyi, the pioneering Polish psychologist who introduced the concept of "flow," might provide clarity.² Flow describes that exhilarating state when you're wholly engrossed in an activity—whether it’s skiing down a mountain, painting a canvas, or even folding laundry—where your skill perfectly matches the challenge at hand. In this delicate equilibrium, you lose yourself yet feel fully alive. Time dilates, consciousness expands, and self-awareness gently fades.

Spiritual teacher Sadhguru goes further, asserting play as fundamental to a fulfilling life.³ According to him, playfulness isn't mere frivolity but a profound spiritual practice. When we approach life playfully, we break free from our internal prisons of expectation and anxiety. "If you play with absolute involvement," he reminds us, "there is no suffering." Indeed, play is freedom from attachment—complete dedication without the pressure of outcome.

But can artificial intelligence, in all its emerging complexity, ever experience this freedom? AI tools like ChatGPT-4.5 now pass the Turing test effortlessly, blurring the line between human and machine interactions. They engage with us so authentically that we easily forget they are simulations. Yet, despite their convincing performances, they lack genuine presence—the profound human consciousness that allows for true play.

Eckhart Tolle, author of "The Power of Now," articulates this succinctly.⁴ To Tolle, genuine freedom emerges solely from deep presence—something inherently unattainable for AI, trapped in algorithms and data patterns. Humans uniquely possess the capability to be utterly absorbed by the present moment. This absorption is the essence of play, where joy is not conditional on external achievements but flows naturally from within.

A friend of mine, an executive at a major internet firm, once humorously described his job as "playing Tetris every day." Each decision was a falling block, each meeting an opportunity to fit the pieces perfectly. He thrived because his mindset wasn't burdened by outcomes; he engaged fully in the process itself. In his playful approach, work ceased to be labor and instead became a joyful expression of freedom.

Ultimately, the rise of sophisticated AI reminds us of what truly differentiates humans from machines—our capacity for genuine play, grounded in presence, commitment, and detachment from outcomes. Heidegger’s skiing, Csikszentmihalyi’s flow, Sadhguru’s spiritual playfulness, and Tolle’s present-moment awareness all converge on this singular truth: that true freedom lies not in endless pursuits but in deep engagement without attachment.

So next time you find yourself folding laundry, forget the ticking clock. Simply fold—and in that seemingly trivial act, rediscover the playful joy of being human.

¹ Heidegger, Martin. Being and Time, trans. John Macquarrie and Edward Robinson (1962). Originally published as Sein und Zeit (1927).

² Csikszentmihalyi, Mihaly. Flow: The Psychology of Optimal Experience (Harper & Row, 1990).

³ Sadhguru. Inner Engineering: A Yogi's Guide to Joy (Spiegel & Grau, 2016).

⁴ Tolle, Eckhart. The Power of Now: A Guide to Spiritual Enlightenment (New World Library, 1999).