Dr Jonathan Frost

Month: January 2026

  • Ethical Framework For Using LLMs In Non-Fiction

    How can we use AI for good, ethically?

    Front matter: what this is, how it was made, and limits

    What this document is: A practical ethics + workflow guide for using LLMs to draft and edit non-fiction.

    How this document was made (LLM disclosure): This guide was drafted with an LLM (ChatGPT) and then tightened against a small set of public, primary references listed at the end, overseen by Jonathan Frost.

    Important limits (read this):

    • Not legal advice. Copyright, privacy, and disclosure duties vary by jurisdiction and contract (publisher/client/platform terms). If your work is high-stakes or commercial, get qualified review.
    • “Publicly available” ≠ “free to reuse.” Copyright and website terms can still apply.
    • LLMs can fabricate facts and citations. Build your process assuming errors will occur. [2]

    1) Purpose and scope

    Scope: Any output readers might treat as factual: reports, books, articles, policy briefs, educational content, explainers, biographies, case studies.

    Goal: Use LLMs to improve productivity without weakening:

    • factual accuracy,
    • source integrity,
    • rights compliance,
    • reader trust.

    Baseline rule: An LLM is a drafting/synthesis tool, not an authority.

    2) Core principles (non-negotiables)

    1. Truth over fluency
      • If you can’t verify a claim, label it as uncertain or remove it.
      • Don’t publish “sounds right” facts.
    2. Traceability (“no source, no claim”)
      • Every material factual claim should be traceable to a source you actually accessed.
      • Keep a source log and a claim-to-source map (template below).
    3. Transparency
      • Disclose meaningful LLM involvement where it affects trust (research, synthesis, summaries, translation, or substantive rewriting).
    4. Rights respect
      • Don’t use an LLM to copy or lightly paraphrase copyrighted text, evade paywalls, or “launder” proprietary material.
    5. Human accountability
      • A human author/editor owns responsibility for accuracy, attribution, and harm reduction—consistent with risk management expectations for AI use. [3]

    3) Sourcing and referencing rules

    A. Evidence tiers (useful for enforcement)

    • Tier 1 (strong): primary documents, official datasets, peer-reviewed research, transcripts/recordings.
    • Tier 2 (medium): reputable journalism, well-sourced books, institutional reports.
    • Tier 3 (weak): unsourced blogs, anonymous posts, single unverified claims.

    Policy: Tier 3 cannot support major claims; Tier 2 needs corroboration for high-stakes assertions.

    B. Referencing rules you can actually follow

    • Direct quotes: only from sources you personally retrieved and checked. (Never quote text “as given by the model” unless you verified it against the original.)
    • Paraphrases: require a source and must preserve meaning (no “citation laundering” where you find a vaguely related source after the fact).
    • Attribution: for interpretation, dispute, or uncertainty, attribute clearly (“According to…”, “X argues…”).

    4) Publicly available information: ethical use constraints

    Rule: Online access does not equal reuse rights. Even if content is “public,” it may still be copyrighted, contract-restricted (terms of service), or privacy-sensitive.

    Privacy & harm minimization:

    • Avoid publishing sensitive personal info (contact details, medical info, etc.) unless there’s a strong public-interest justification and you minimize harm (redact/aggregate where possible).
    • For private individuals, use a higher bar than “it’s online.”

    5) Hallucinations and error risk: process design

    LLMs can generate plausible falsehoods and fabricated citations; verification is mandatory for factual work. [1] [2]

    A. Verification-first workflow (recommended)

    1. Outline claims first: separate what must be factual from narrative/interpretation.
    2. Collect sources first: gather the documents you’ll rely on.
    3. Use the model to draft from those sources: structure, summarize, propose wording, identify gaps.
    4. Verify and annotate: check each material claim against sources; mark confidence.
    5. Final editorial pass: look for overreach, missing caveats, misleading framing.

    B. High-risk claim categories (extra checks)

    Require double-checking (and ideally a second reviewer):

    • numbers/statistics, dates, names/titles,
    • direct quotes,
    • medical/legal/financial statements,
    • allegations about people or organizations.

    C. “Never do this” list

    • Don’t publish citations the model generated unless you verified they exist and support the claim.
    • Don’t “reconstruct” quotes you can’t locate.
    • Don’t let the model be the only “researcher.”

    6) IP and rights: rules + jurisdiction flags

    A. Baseline IP ethic

    • Don’t copy protected text (or close-paraphrase) without permission or a defensible exception.
    • Avoid output that could substitute for the original work (especially paid content).

    B. Jurisdiction-dependent frameworks (you must label this in your policy)

    • United States (fair use): evaluated case-by-case under statutory factors; no fixed “safe” word count rule. [4]
    • United Kingdom (fair dealing): purpose-specific exceptions; “fair dealing” is judged by context. [5]
    • International baseline (quotation right concept): quotations are generally permitted only if compatible with “fair practice” and limited to what the purpose justifies (implementation varies by country). [6]

    C. Text and data mining (TDM) and “public web” content (jurisdiction-dependent, evolving)

    • In the EU, the DSM Directive includes TDM exceptions with a rights reservation/opt-out concept for certain uses; online rights reservations may be required to be machine-readable (details depend on implementation and interpretation). [7]
    • In the UK, government consultation materials discuss approaches including opt-out style mechanisms and transparency measures (policy not static). [8]

    Practical policy: Treat training/crawling/large-scale ingestion as a separate legal/contractual question from “quoting and citing.” Don’t assume you can ingest or reuse just because it’s online.

    D. Style imitation and misrepresentation

    • Don’t publish work that implies a human expert, journalist, or witness did reporting they didn’t do.
    • Avoid “in the voice of a living author” for publication; it risks deception and brand/rights issues.

    7) Disclosure to readers: when and how

    Default: Disclose LLM use when it meaningfully affects trust: research assistance, source summarization, translation, substantive drafting, or rewriting.

    Example disclosure text (short):

    “This work used language-model assistance for drafting/editing. All factual claims and quotations were verified against the cited sources by the author.”

    Stricter disclosure triggers:

    • the piece resembles investigative reporting,
    • the topic is high-stakes,
    • the model generated any material factual claims that required verification.

    8) Operational controls (so it’s enforceable)

    A. Required artifacts (lightweight but effective)

    1. Source log (what you read and used)
    2. Claim table (what you assert and why)

    B. Two-pass review

    • Pass 1: factual integrity (claims, numbers, quotes, attributions)
    • Pass 2: interpretive fairness (framing, omissions, loaded language)

    C. Corrections policy

    Publish a visible mechanism for error reporting and correction; keep a change log for factual fixes.

    Micro claim-to-source table (self-compliance)

    Claim used in this guideWhy it mattersRef
    LLMs can produce plausible falsehoods (“hallucinations”).Justifies verification-first workflow.[1]
    LLMs may fabricate citations/references.Supports “don’t trust model citations” rule.[2]
    Responsible AI use calls for governance/accountability.Supports “human accountability” and controls.[3]
    US fair use is case-by-case; no fixed word-count rule.Prevents fake certainty in IP rules.[4]
    UK fair dealing is context-specific.Prevents overgeneralizing UK exceptions.[5]
    International quotation norm includes “fair practice” and purpose-limited extent.Grounds quote/extent guidance.[6]
    EU DSM Directive includes TDM exceptions and rights reservation.Grounds “public web isn’t free to mine” warning.[7]
    UK copyright-and-AI policy is under consultation/active development.Justifies “evolving” caveat.[8]

    References

    1. OpenAI — Why language models hallucinate (explains hallucinations as a model behavior and why it happens).
    2. OpenAI Help Center — Does ChatGPT tell the truth? (notes errors and hallucinations; cautions about reliability).
    3. NIST — AI Risk Management Framework (AI RMF 1.0) (risk management, governance, accountability concepts for trustworthy AI).
    4. U.S. Copyright Office — Fair Use (fair use overview; emphasizes case-by-case analysis, no fixed “safe” amount).
    5. UK Government — Exceptions to copyright (fair dealing and other exceptions guidance).
    6. Berne Convention (via Cornell LII) — Article 10 on quotations and “fair practice” (international baseline concept; implementation varies).
    7. EUR-Lex — Directive (EU) 2019/790 (DSM Directive) (includes TDM provisions and rights reservation concepts).
    8. UK Government — Copyright and artificial intelligence consultation materials (shows policy is active/evolving).

    Meta Check on ChatGPT

    ChatGPT can’t reliably list the “original material it’s based on” because it isn’t a system that stores documents and retrieves them by title. During training, the model’s internal parameters (“weights”) are adjusted so it becomes better at predicting likely text. What it retains is a distributed statistical representation of patterns across a huge amount of text—not a library of identifiable passages with a usable index back to specific books, articles, or webpages. OpenAI describes this as models not storing or retaining copies of the training data, and instead learning via parameter updates. [1]

    Even when the model produces something that resembles a known phrase, there’s usually no clean, inspectable trail like “this sentence came from source X, page Y.” A response is generated from many overlapping influences in those weights plus the immediate prompt, not from a deterministic lookup. This is also part of why the model can sound confident while being wrong: the system is optimized to produce plausible continuations, and standard training/evaluation incentives can reward “best guess” answers over explicitly stating uncertainty—leading to hallucinations (confident, false outputs) that are not grounded in any real source at all. [2]

    Finally, the training pipeline is typically a mixture of data types and stages (e.g., broad pretraining plus later fine-tuning and safety tuning), and providers often describe it at a high level rather than enumerating every item, in part because exhaustive lists would be enormous and can involve licensed or otherwise restricted material. OpenAI’s system cards, for example, focus on capability and safety characteristics and describe the development process at a high level, not as a per-document bibliography. [3]

    Meta Check References

    1. OpenAI Help Center — How ChatGPT and our language models are developed (explains that models do not store/retain copies of training data; learn via parameter updates).
    2. OpenAI — Why language models hallucinate (explains hallucinations and how training/evaluation can reward guessing over uncertainty).
    3. OpenAI — GPT-4 System Card (example of high-level model development/safety documentation rather than an itemized source list).

  • The Dominance Of U.S.-Based Global Tech Platforms

    The dominance of U.S.-based global tech platforms stems from a powerful, self-reinforcing combination of structural, financial, cultural, and regulatory advantages that Europe has not matched. Here are the core reasons:

    1. Venture Capital Scale & Culture: 

    The U.S., particularly Silicon Valley, has a deep, risk-tolerant, and massively scaled venture capital ecosystem. Funding is more abundant, faster to deploy, and comfortable with funding “blitzscaling” (prioritizing hyper-growth over immediate profitability) and moonshot ideas. Europe’s VC scene, while growing, is more fragmented, conservative, and risk-averse.

    2. “Single,” Large, Homogeneous Market: 

    The U.S. offers a unified market of 330 million people with one language (predominantly), one set of federal regulations, one currency, and a common consumer culture. This allows startups to scale to massive size quickly and cost-effectively before going international. Europe is a patchwork of 27+ distinct nations with different languages, cultures, regulations, and (until recently) currencies, making scaling complex and expensive from day one.

    3. Regulatory & Litigation Environment: 

    The U.S. has historically taken a more “permissionless innovation” approach, allowing new business models to launch and scale with relatively light initial regulation (though this is changing). The European approach is often “precautionary principle”-based, establishing comprehensive regulatory frameworks (like GDPR, DMA, DSA) upfront, which can slow innovation and create higher compliance burdens from the start.

    4. Talent Mobility & Concentration: 

    The U.S. attracts and retains top global tech talent due to:

    • University Ecosystem: Elite, well-funded research universities (Stanford, MIT, etc.) closely tied to industry.
    • Stock-Based Compensation: The standard use of stock options aligns with high-risk, high-reward Silicon Valley culture, creating immense wealth for early employees.
    • “Go Global” Mindset: A cultural focus on building for the world from the outset.
    • Labor Mobility: Easier for talent to move within the U.S. to tech hubs. Europe faces greater linguistic and professional qualification barriers to internal mobility.

    5. First-Mover Advantage & Network Effects: 

    U.S. platforms (e.g., in search, social, OS) achieved critical mass first. The “winner-takes-most” nature of network effects creates impregnable moats. European competitors, arriving later, cannot dislodge entrenched global users and their data.

    6. Entrepreneurial Culture & Mindset: 

    U.S. culture celebrates entrepreneurial risk-taking, tolerates failure as a learning experience, and idolizes tech founders. Failure in many European societies still carries a heavier stigma, and the ambition to build a global monopolistic platform is often viewed with more suspicion than admiration.

    7. Government Procurement & Military-Industrial Complex: 

    Historically, massive U.S. defence and space spending (e.g., DARPA, NASA) provided early funding and demand for foundational technologies (internet, semiconductors, GPS). This created a fertile R&D base for later commercial spin-offs.

    The Result: 

    A virtuous cycle for the U.S.: success breeds more capital, attracts more talent, inspires more founders, and increases lobbying power to shape favourable regulations. Europe, meanwhile, has excelled at business-to-business (B2B), industrial, and deep-tech innovation, but has failed to create consumer-facing platform giants due to this compounded set of disadvantages.

    Strategy for Europe to Redress the Tech Platform Balance

    Europe cannot and should not try to replicate Silicon Valley. The goal is not to create identical U.S.-style giants, but to build a self-sustaining ecosystem where European tech platforms can scale to global relevance. This requires brutal prioritization and politically difficult choices.

    Core Strategic Pillars:

    1. Create a Genuine Single Market for Digital Scale (The Non-Negotiable Foundation)


    The current “Digital Single Market” is a regulatory framework, not a functional reality. Fix it.

    • One Rulebook, One Enforcer: Move beyond harmonizing rules to true federal-level digital regulation. A European startup should file one compliance form for all 27 states, overseen by a single EU digital authority, not 27 separate negotiations.
    • Standardize Stock Options & Bankruptcy Law: Mandate a pan-European model for employee stock option plans with tax treatment that makes them a real incentive (align with U.S. competitiveness). Reform bankruptcy laws to treat honest failure as a learning event, not a career-ending stigma.
    • Digital Identity & Payments: Accelerate and mandate the adoption of a single, secure European Digital Identity (eIDAS 2.0) and instant payment system (like SEPA Instant). Make them the default for all public and private sector services.

    2. Reorient Capital: Force “Scale-Up” Capital into Existence

    European capital is deep but conservative. Policy must redirect it.

    • Pension Fund Mandate: Legislate that a minimum percentage (e.g., 3-5%) of EU-based institutional and pension fund assets must be allocated to high-growth, pan-European venture capital funds. This unlocks trillions in dormant capital.
    • Founder-Friendly “Scale-Up” IPOs: Create a dedicated, streamlined EU stock exchange listing track for high-growth tech firms with governance structures that protect founder control during the critical scaling phase, akin to the dual-class shares common in the U.S.
    • Massive, Outcome-Based Public Procurement: Use the EU’s €2+ trillion annual public procurement budget strategically. Issue large-scale, multi-year contracts for innovative digital solutions (e.g., in health, energy, logistics) with clear “scale to Europe” requirements, creating instant, revenue-generating champions.

    3. Win the War for Talent with Aggressive Meritocracy

    • EU Blue Card 2.0: Create a “Tech Talent Visa” with a 2-week fast-track, granting immediate work and residency rights across the entire EU for qualified tech professionals and their families. Sponsor relocation.
    • Elite University-Industry Clusters (The “European DARPA” Model): Don’t just fund research. Fund missions. Identify 5-10 strategic areas (e.g., AI for climate, privacy-enhancing tech, industrial IoT platforms). Create mandated consortia between elite universities (ETH, TU Delft, etc.) and companies, with U.S.-level salaries for researchers and a mandate to commercialize in Europe.
    • Tax the Talent Drain: Consider a punitive “tech talent poaching” tax on large, non-EU tech firms that hire beyond a certain threshold of graduates from EU-funded elite programs within, say, 3 years of graduation. Make them pay for the R&D subsidy.

    4. Play Europe’s Structural Strengths, Not Silicon Valley’s Game

    • Industrial & Sovereignty Platforms: Europe dominates complex B2B industries (automotive, energy, pharma, fintech). Mandate the creation of open, interoperable, and sovereign industrial data platforms in these sectors. GDPR gives a trust advantage—build “Privacy-by-Design” platforms for sensitive data (health, finance) that U.S. giants cannot easily replicate.
    • Regulate to Innovate, Not Just to Restrain: Use the DMA/DSA not only to tame U.S. giants but to actively carve out space for competitors. Enforce interoperability mandates aggressively to allow European apps to plug into dominant platforms’ core functionalities (e.g., messaging, social graphs).
    • “Scale from Day One” Mindset: Foundational EU funding (like Horizon Europe) must require a “scale to the Single Market” plan from applicants. Stop funding brilliant local solutions with no path to continental dominance.

    5. Cultivate a New Cultural Narrative

    • Celebrate Scale Ambition: Government and media must actively celebrate European tech founders aiming for global dominance, treating them as strategic assets, not just profit-seekers.
    • Create “Founder Rehab”: Publicly fund and endorse programs for “second-time founders” who have experienced failure, turning them into mentors and investors.

    The Trade-Offs (The Political Cost):

    This strategy will require Europe to:

    • Accept inequality as a temporary byproduct. Successful scaling will create billionaires and concentrated wealth. Tax policy must balance incentives with social equity, but punishing success will kill the strategy.
    • Embrace “strategic hypocrisy.” Promote open markets globally while protecting and building its own scaling champions internally, just as the U.S. and China do.
    • Centralize power. This demands a significant transfer of national regulatory sovereignty to EU-level bodies. Member states must surrender digital industrial policy to Brussels.
    • Be prepared to fail publicly. Not all bets will win. The public and media must tolerate high-profile, expensive failures as the cost of competing.

    Bottom Line: Europe has the money, the talent, and the market size. What it lacks is the integrated operating system for scaling. This strategy is about installing that operating system by force of political will, redirecting capital flows, and playing ruthlessly to its inherent structural strengths in B2B, privacy, and complex industry. It is a 20-year project, not a 5-year one. The alternative is permanent digital vassalage.

    How U.S. Policy Shifts Could Catalyse European Tech Ascent

    This is a potential inflection point. A hostile or erratic U.S. policy environment doesn’t automatically benefit Europe—it could cripple the global tech ecosystem for everyone. However, if Europe executes the aggressive strategy outlined previously, these U.S. shifts could create openings. Here’s how, bluntly:

    1. Reduced Immigration & Mass Deportations: Europe’s Talent Window

    • The Opportunity: The U.S. has been the world’s premier talent sink for decades. If it actively restricts the flow of high-skilled immigrants (H-1B, OPT, etc.), a massive pool of frustrated global talent—from Indian engineers to Iranian AI researchers—will seek alternatives.
    • Europe’s Required Move: Aggressively poach. Europe must have its “Tech Talent Visa” (from previous strategy) ready to go. Launch a global marketing campaign: “Your American Dream is Denied? Build Your European Future.” Fast-track visas, recognize qualifications, and offer a path to citizenship. This is a once-in-a-generation chance to re-route the global talent pipeline.

    2. Unpredictable Tariffs & “Imperial” Policy: The Sovereignty Pitch

    • The Opportunity: U.S. volatility undermines its role as a stable, rules-based platform for global business. Tariffs and extraterritorial sanctions create fear among allies about over-dependence on U.S.-controlled tech stacks (cloud, payments, apps).
    • Europe’s Required Move: Double down on “Strategic Autonomy” and “Digital Sovereignty.” Market European platforms as the stable, predictable, rules-based alternative. To governments and corporations worldwide, especially in allied democracies and the “Global South,” the pitch becomes: “You cannot bet your national infrastructure on a platform whose access can be cut by a U.S. presidential tweet or sanction. Choose sovereign European tech.” This is particularly powerful for government cloud services, financial infrastructure, and trusted communications.

    3. Close Trump-Tech “Bromance”: The Regulatory & Trust Vacuum

    • The Opportunity: A perceived cronyism between a U.S. administration and Big Tech (e.g., relaxed antitrust enforcement, favourable rulings, shared ideology) destroys the remaining veneer of these platforms as neutral public squares. It validates the European regulatory critique and alienates users/employees who disagree politically.
    • Europe’s Required Move:
      • For Consumers/Users: Amplify the narrative that U.S. platforms are now political instruments, not neutral utilities. Push European alternatives (e.g., Mastodon, Element, next-gen privacy apps) as truly community-governed and free from capricious political influence.
      • For Talent: Target the significant portion of the U.S. tech workforce (in blue states/companies) who are alienated by this political alignment. Launch recruitment drives in Silicon Valley and Austin: “Come build ethical tech in a democratic system.” Leverage Europe’s stronger worker protections and social safety net as selling points.
      • For Regulation: Use the moment to lead a global coalition of regulators. If the U.S. FTC/DOJ goes dormant, the EU’s DMA/DSA enforcers become the de facto global sheriff. This attracts jurisdictions seeking a counterbalance.

    4. “Imperial Style Land Grabs” & Global Distrust: The Alliance Opportunity

    • The Opportunity: Actions perceived as coercive or unilateral (e.g., attempts to ban TikTok, seizure of assets, demands for data) spook other nations. It confirms fears of U.S. digital hegemony.
    • Europe’s Required Move: Position itself as the trusted, coalition-building partner. Instead of banning TikTok, Europe could mandate its interoperability and data governance rules, offering a “third way” between U.S. control and Chinese law. Actively build alternative digital alliances—for example, a “D-10 (Democratic 10) Data & Cloud Pact” with trusted partners like Japan, South Korea, Canada, and the UK, based on European regulatory principles and infrastructure.

    Critical Caveats & Risks for Europe:

    • Capital Flight Risk: U.S. instability could scare capital globally, hurting European startups just as much. Europe must be a safe harbor for investment, not just talent.
    • The China Factor: The beneficiary of U.S. isolationism could easily be China, not Europe. Europe must offer a clearly superior alternative to both American volatility and Chinese authoritarianism.
    • Execution, Execution, Execution: This is all theoretical if Europe remains fragmented, slow, and risk-averse. The U.S. self-harming is not a strategy. It merely opens a window. Europe must have the unified political will to climb through it.
    • Cutting Off the Nose: If Europe retaliates with its own protectionism, it destroys the “stable, open” brand it needs to sell. Tariffs should be surgical and on goods, not on data/services where Europe can win with rules.

    Final Assessment: 

    A hostile U.S. turn is a necessary but insufficient condition for European tech platform rise. It creates a crisis of confidence in the incumbent. Europe’s success depends entirely on its ability to present itself as the competent, trustworthy, and ambitious alternative—and to back that up with the integrated market, capital, and aggressive talent grabs outlined in the core strategy. The moment would be historic, but Europe’s own inertia is still its primary enemy.

    Approximate annual wealth transfer from Europe to USA due to the USA based nature of top tech platforms

    There is no single, precise, or officially tracked figure for the annual “wealth transfer” from Europe to the U.S. due to dominant tech platforms. However, we can construct a credible, high-level estimate by aggregating key channels. The total is substantial, easily in the hundreds of billions of euros annually, and permeates the European economy in multiple ways.

    Here is a breakdown of the major channels and approximate annual ranges:

    1. Direct Profits & Tax Base Erosion (Most Visible)

    • What it is: Profits earned in Europe by U.S. tech giants (Alphabet/Google, Meta/Facebook, Apple, Amazon, Microsoft, etc.) that are largely booked in low-tax jurisdictions (like Ireland, Luxembourg, Netherlands) and ultimately repatriated or shifted to U.S. parent companies.
    • Approximate Annual Range:€50-€100+ Billion.
      • Derivation: These companies generate hundreds of billions in revenue in Europe. Their aggregate profit margins often exceed 20-30%. Even after recent tax reforms (like the global minimum tax), a significant portion of this economic profit ultimately flows to U.S. shareholders and the U.S. tax base.

    2. Data Value & Strategic Control (Intangible, but Critical)

    • What it is: The value of European user data harvested to train AI, refine algorithms, and build dominant advertising and service models. This is not a direct cash flow but represents a massive transfer of a strategic asset (data) that fuels further U.S. dominance.
    • Approximate Annual Range: Impossible to quantify directly, but value is astronomical. This is the “payment” for “free” services. The control of this data means Europe does not own the digital intelligence derived from its own citizens and markets, crippling its own AI and platform development.

    3. Digital Advertising Revenue Drain (A Direct Pipeline)

    • What it is: The majority of online ad spending in Europe flows to Google and Meta. This directly transfers wealth from European businesses (who pay for ads) to U.S. corporate coffers.
    • Approximate Annual Range:€60-€80 Billion.
      • Derivation: Europe’s digital ad market is ~€100-€120 billion annually. Google and Meta consistently capture a combined ~65-75% of this market.

    4. Platform Fees & Cloud Services (Infrastructure Rent)

    • What it is: Apple/Google’s 15-30% fees on app stores and in-app purchases; AWS, Azure, and Google Cloud’s dominance in European cloud infrastructure. European companies and developers pay this “toll” to access their own customers.
    • Approximate Annual Range:€30-€50 Billion.
      • Derivation: App store fees alone were estimated at ~€10-€15B in Europe pre-DMA. The cloud market in Europe is ~€70-€90B, with U.S. giants holding ~70%+ share, generating enormous recurring revenue.

    5. Capital Markets & Investment Returns

    • What it is: European institutional and pension funds invest heavily in U.S. tech stocks as they are the prime global growth assets. Dividends and capital gains flow back to the U.S., and Europe misses out on hosting the growth companies itself.
    • Approximate Annual Range: €20-€40 Billion+ (in dividends, buybacks, and unrealized gains accruing to U.S., not European, ecosystems).

    6. Talent Drain & Lost Innovation Potential

    • What it is: Thousands of top European STEM graduates moving to U.S. tech firms, either locally or by emigrating. The net present value of their lifetime high-productivity earnings and future entrepreneurial potential is lost to Europe.
    • Approximate Annual Range: €10-€20 Billion+ in lost high-value GDP contribution and future founder potential.

    Synthetic Total Annual Estimate: €200 – €300+ Billion

    Important Caveats:

    • This is not a simple “bill.” Europe receives valuable “free” services (search, social connectivity, cheap cloud tools) in return. The argument is about strategic dependency, lost sovereignty, and the extraction of the most valuable layers of the digital economy (profits, data, strategic control).
    • It’s a systemic transfer. The true cost is the opportunity cost: the European startups that never scale, the AI that isn’t developed, the business models that aren’t invented, and the corporate tax base that is permanently diminished.
    • The DMA/DSA aim to change this by forcing interoperability, limiting self-preferencing, and enabling competition to claw back some of this value for European businesses and consumers.

    Conclusion:

     
    While pinning down an exact number is impossible, the order of magnitude is clear: hundreds of billions of euros per year. This represents a continuous, structural draining of economic value and strategic leverage from Europe to the United States, cementing a 21st-century dependency that is far more profound than traditional trade deficits.

  • The Six Players in Politics Blame Culture

    Politics blame culture is a machine that turns disappointment into certainty and complexity into villains. When outcomes are bad – services fail, inequality persists, trust collapses – each group can tell a story where they’re the rational one and someone else is the obstacle. Those stories aren’t always wrong. They’re just incomplete in exactly the same way: they protect identity, shift risk, and trade responsibility for rhetoric.

    The six players are:

    General public, Career politician, Local community representative, Lobbyist, Social change activist, Commentator.

    They argue like enemies. They behave like a system.

    The shared DNA of politics blame culture

    1) Everyone believes they’re constrained and others are corrupt or clueless

    Each role has a built-in excuse generator:

    • General public: “Nothing changes; they’re all the same.”
    • Career politician: “You can’t govern with slogans – trade-offs are real.”
    • Local representative: “We’re closest to the problem, but we don’t control the budget.”
    • Lobbyist: “We’re just providing information and representing interests.”
    • Social change activist: “Power never concedes without pressure.”
    • Commentator: “I’m just calling it like it is.”

    Same move, different words: my limits are legitimate; your failures are moral.

    2) Everyone is rewarded more for performance than results

    Politics is the ultimate “looks like work” industry.

    • The public is rewarded socially for being outraged, savvy, or cynical – not for sustained civic engagement.
    • Politicians are rewarded for winning the news cycle, avoiding gaffes, and fundraising – not for long-term reforms.
    • Local reps are rewarded for visibility and responsiveness – not for structural fixes they may not control.
    • Lobbyists are rewarded for access and influence – not for public value.
    • Activists are rewarded for attention, purity, and mobilization – not for compromise-heavy implementation.
    • Commentators are rewarded for certainty, conflict, and hot takes – not for being careful or correct.

    When incentives favour theatre, blame becomes a career strategy.

    3) Everyone keeps a scapegoat on standby

    When outcomes disappoint, each role has a default villain:

    • Public scapegoat: “politicians,” “the system,” “elites”
    • Career politician scapegoat: “populism,” “the opposition,” “voters won’t accept trade-offs”
    • Local representative scapegoat: “central government,” “bureaucracy,” “unfunded mandates”
    • Lobbyist scapegoat: “regulatory complexity,” “economic reality,” “bad policy design”
    • Activist scapegoat: “corporate capture,” “structural injustice,” “cowardice”
    • Commentator scapegoat: “media bias,” “wokeness,” “ignorance,” “polarization” (pick your flavour)

    Scapegoats are often partly true. They become toxic when they’re used to avoid the next question: what will you do with the power you actually have?

    4) Everyone fears paying the cost of being early, honest, or specific

    Blame culture is a protection racket against risk.

    • The public fears being the only one to care, vote, volunteer, or sacrifice.
    • Politicians fear losing elections for telling unpleasant truths.
    • Local reps fear upsetting either constituents or party leadership.
    • Lobbyists fear losing access if they push too aggressively – or not aggressively enough.
    • Activists fear compromise will be treated as betrayal.
    • Commentators fear nuance will be treated as weakness (and will lose audience share).

    So everyone drifts toward positions that are emotionally satisfying and reputationally safe: certainty, outrage, and deflection.

    5) Everyone cherry-picks a time horizon that makes them look right

    Politics is a battle of clocks.

    • Activists operate on moral urgency: “Now.”
    • Politicians operate on electoral cycles: “Soon, but not too soon.”
    • Local reps operate on service delivery: “This month, this budget year.”
    • Lobbyists operate on quarterly impacts and long-run industry strategy.
    • The public operates on daily cost-of-living reality.
    • Commentators operate on the next segment, the next clip, the next viral moment.

    Same tactic everywhere: choose the clock that makes your constraint reasonable and someone else’s approach reckless.

    6) Everyone prefers purity over responsibility

    Purity isn’t just an activist thing. It’s everywhere; it just wears different clothes.

    • Public purity: “I’m not naïve – I won’t get played.”
    • Politician purity: “I’m principled” (often means loyal to party)
    • Local rep purity: “I’m for my community” (even when it blocks essential trade-offs)
    • Lobbyist purity: “I’m simply representing shareholders”
    • Activist purity: “No compromise with injustice”
    • Commentator purity: “I tell the truth others won’t”

    Purity is attractive because it reduces accountability. If your stance is morally clean, outcomes can stay messy without threatening your identity.

    The six shields of politics blame culture

    The General Public Shield: “They’re all useless.”

    This Shield feels protective: cynicism as armour. It avoids disappointment by expecting nothing. The problem is it also removes your leverage – because disengagement is oxygen for the very dysfunction you’re condemning.

    The Career Politician Shield: “Governing is hard, so accept less.”

    Sometimes that’s reality. Often it’s also a license for vagueness: never name losers, never price policies, never commit to enforcement, and always blame “constraints” when promises evaporate.

    The Local Community Representative Shield: “We’re closest to the pain.”

    Often true – and local reps can be the most honest actors in the chain. But the shield can also become perpetual grievance: all responsibility upstream, all credit local, and no ownership of trade-offs inside the community (housing, zoning, taxes, policing, services).

    The Lobbyist Shield: “We’re just part of the process.”

    Lobbyists do provide expertise and representation. But this shield launders power: influence becomes “information,” self-interest becomes “stakeholder engagement,” and the public interest becomes an optional extra.

    The Social Change Activist Shield: “Pressure is the only language power understands.”

    This shield is often correct historically. But it can also trap movements in permanent escalation, where compromise is taboo and winning becomes less important than staying pure, visible, and angry.

    The Commentator Shield: “I’m above it all.”

    This shield sells certainty: sharp takes, villains, and easy stories. But it often converts politics into sport – more heat than light – because conflict is profitable and complexity is boring.

    The core loop: the “Real Lever” shell game

    Each player points outward:

    • Public: “Fix it.”
    • Politicians: “Let us govern / blame the other side.”
    • Local reps: “Fund us / stop tying our hands.”
    • Lobbyists: “We’re not the decision-makers.”
    • Activists: “Force them.”
    • Commentators: “Expose them.”

    Everyone identifies a real problem. Together, they create diffusion of responsibility: all diagnosis, no ownership.

    Blame culture isn’t just moral failure. It’s a coordination failure with incentives attached.

    What breaks the cycle: responsibility at the edge of your power

    The antidote isn’t “be nicer.” It’s to replace vague blame with specific ownership.

    One “hard move” per role:

    • General public: trade cynicism for participation – vote, show up locally, tolerate trade-offs, and reward honesty over theatre.
    • Career politician: tell the truth about costs and enforcement, even when it hurts; stop selling miracles without mechanisms.
    • Local representative: be explicit about what you can control; lead local trade-offs instead of outsourcing them upward.
    • Lobbyist: disclose interests clearly; separate expertise from pressure; accept rules that reduce capture even if it limits you.
    • Social change activist: pair pressure with implementation demands and measurable wins; build coalitions that survive compromise.
    • Commentator: downgrade certainty; elevate evidence; cover governance like engineering, not like boxing.

    Politics improves when each player stops asking, “Who can I blame?” and starts asking, “What can I own that actually changes the incentives of the system?”

  • The “Fortress America” Fantasy

    How to Brick Your iPhone, Prang Your Pickup, and Get Strangled by a Soybean

    Let’s play a game. You’re a red-blooded, flag-waving, “America First” patriot. You’ve had it with the globalist cabal. You want the drawbridge up, the factories home, and the foreigners out. You dream of an isolated, self-sufficient USA, a fine fortress of freedom, untethered from the greasy, grasping hands of the harsh world.

    Congratulations. You’re an idiot. And your utopia would collapse before you could finish your third “Let’s Go Brandon” chant, undone by the brutal, hilarious poetry of global interdependence. Let’s take a tour of your new, isolated hellscape, using the one thing even you can’t ignore: stuff.

    Act I: The Great Tech-Tantrum


    You wake up in Fortress America. You reach for your phone—the sacred vessel of your memes and rage-tweets. It’s a brick. Why? Because we decided to stop importing those “commie rare earths” from China. No neodymium, no magnets. No magnets, no vibration motor, no speakers, and definitely no guidance systems for the missiles you think will keep you safe. Your prized F-35? It’s a $100 million paperweight. That “Made in USA” sticker is now just a sad monument to your ignorance. Your electric vehicle? A very expensive driveway ornament. You’ve successfully defended your sovereignty by reverting to a technology level slightly above two soup cans and a string.

    Act II: The Pickup Truck Paradox


    You storm out to your lifted F-150, your chariot of freedom, to go protest this nonsense. You turn the key. It starts (for now). You need gas. You pull into the station. It’s closed. Or it’s charging $15 a gallon. Because “energy independence” is a political slogan, not a physical reality. Refineries are tuned for specific crude blends. That cheap, easy Texas tea is great, but it’s not the only thing we run on. And good luck making plastic for… everything… without a complex global web of chemical feedstock, many of which start their life in places you’ve just sanctioned into oblivion. Your truck isn’t freedom. It’s a monument to global logistics. Cut the strings, and it’s a very heavy coffin.

    Act III: The Supermarket Massacre


    Fine, you’ll walk. You’re hungry. You head to the supermarket, a beacon of American plenty. You bypass the empty electronics aisle and head for the meat. The beef is gone. The chicken is astronomical. Why? Because we told Brazil and Argentina to go pound sand, and we’re not playing nice with Canada. But the real killer is the soybean. Or the lack thereof.

    See, we grow loads of soy. But in your isolationist wet dream, we’re not selling it to China—our biggest customer. Overnight, the bottom falls out of the farm economy. Iowa goes bankrupt. But it gets better. That cheap soy was what fed China’s pigs, which kept global pork prices low. No exports, Chinese herds shrink, global demand shifts to other meats, and prices everywhere—including for your precious bacon—go through the roof. You tried to own the Chinese and ended up getting owned by breakfast. The mighty American farmer, left holding a billion bushels of worthless beans. Poetic.

    Act IV: The “Bring It All Home” Delusion


    “We’ll just make it here!” you scream, spitting dip into a empty Mountain Dew bottle. Sure. Let’s mine rare earths! Never mind that the environmental impact statement alone would take a decade, the process is toxic as hell, and NIMBYs in Wyoming will fight it harder than they fight wolves. Let’s process all our own minerals! Just spin up a few hundred billion dollars in capital investment and find a generation of chemical engineers we haven’t outsourced or defunded. Let’s make all our own stuff! With what? The vacuum of your own smugness?

    Isolationism isn’t a policy; it’s a toddler’s tantrum. It’s the belief that you can take the world’s most complex, interwoven supply chain—a system so delicate that one stuck boat in the Suez Canal causes global panic—and just hack it apart with the blunt axe of nationalism, with no consequences.

    The truth is dirty, ugly, and inescapable: We are all tough traders, and it’s the only thing keeping the lights on. Your lifestyle is a fragile truce, a daily miracle of global trade where we send China soybeans so they can send us the magnets that go into the hard drives made in Thailand for the laptops assembled in Mexico that you buy to tweet about how much you hate global trade.

    The dream of going it alone is a fantasy for the simple-minded. The reality is a world where a protest in Chile lifts copper prices, a drought in Brazil spikes your coffee, and a decision in a Beijing ministry can determine if a factory in Ohio stays open. You can hate that reality. You can rage against it. But you can’t escape it. Trying to do so doesn’t make you a patriot. It makes you a passenger on a cruise ship, screaming that you’re going to build your own ocean. Good luck. You’re going to need it. And you’re going to have to import the materials to build it.

    From China with Love

    Focusing strictly on true raw materials—unprocessed or minimally processed commodities extracted from the earth—the list changes significantly. China is not a primary global supplier of classic raw commodities to the U.S.; it is instead the world’s primary processor of them.

    The most significant raw material imports from China are dominated by minerals and materials critical for modern technology and industry. Here are the top ten, based on trade data and strategic importance:

    1. Rare Earth Elements (Oxides, Carbonates, Metals): The most strategically critical raw material import. China controls over 80% of global rare earth processing. These are essential for permanent magnets (in EVs, wind turbines), defence technology, and electronics.
    2. Tungsten (Ores, Concentrates, Intermediate Products): A critical metal for its hardness and high melting point. Used in cutting tools, military armour-piercing rounds, and aerospace.
    3. Graphite (Natural, in Powder/Flake Form): A key anode material for lithium-ion batteries. China is the world’s dominant refiner of battery-grade graphite.
    4. Antimony (Ores, Concentrates): A brittle metal used primarily as a fire retardant synergist in plastics and textiles, and in lead-acid batteries. China is the top global producer.
    5. Barium (Natural Barium Sulphate – Barite): Primarily used as a weighting agent in drilling muds for oil and gas exploration. China is a major supplier.
    6. Strontium (Mineral Ores/Carbonates): Used in ceramic magnets for speakers, pyrotechnics (for the red colour in flares), and some aluminium alloys.
    7. Gallium & Germanium (Unwrought, Simple Forms): Critical semiconductor metals. In 2023, China imposed export controls on these, highlighting their strategic nature. Used in high-speed chips, LEDs, and fibre optics.
    8. Indium (Unwrought, Dross): A key element in thin-film coatings, notably for flat-panel displays (ITO – Indium Tin Oxide). China is a leading producer.
    9. Magnesium (Unwrought, Pure): While often considered a processed metal, primary magnesium is a crucial raw input for lightweight aluminium alloys (for automotive and aerospace) and as a reducing agent in titanium production. China dominates global output.
    10. Fluorspar (or Fluorite, Acid Grade): The primary source of fluorine. It’s processed into hydrofluoric acid, which is critical for refining gasoline, manufacturing fluorochemicals (like refrigerants), and etching glass.

    Crucial Clarifications:

    • Volume vs. Strategic Value: By sheer physical volume (tonnage), items like stone (granite, slate), crude fertilizers, and certain clays may rank high, but their economic and strategic value is far lower than the minerals listed above.
    • “Processing” is Key: The U.S. imports many of these materials in a “beneficiated” form (e.g., concentrated, roasted, chemically separated), not as pure ore straight from the ground. This semi-processed state is the last step before being considered a true “raw material” for industrial use.
    • Not Top Suppliers: For classic bulk raw materials like crude oil, iron ore, copper, bauxite, or lumber, the U.S. sources primarily from Canada, Latin America, Australia, and other regions—not China.

    Therefore, the defining characteristic of U.S. raw material imports from China is their concentration in the supply chain for critical and strategic minerals, where China holds a dominant position in mid-stream processing, making it the most viable source even for the raw or semi-processed forms.

    Back at You China

    The trade flow of raw materials from the USA to China is fundamentally different. The United States is a major exporter of agricultural commodities, hydrocarbon feedstocks, and mineral ores to feed China’s massive manufacturing and consumption engine.

    Here are the top ten true raw materials imported by China from the United States, based on trade value and strategic importance:

    1. Crude Oil: A foundational import. China, the world’s largest crude importer, sources significant volumes from the U.S., especially lighter shale oil, to diversify its energy supply.
    2. Liquefied Natural Gas (LNG): A critical energy and chemical feedstock raw material. U.S. LNG exports to China have grown substantially, used for power generation and as a raw material for plastics and fertilizers.
    3. Soybeans: The single most valuable agricultural raw material import. U.S. soybeans are essential for China’s animal feed industry (to produce pork, poultry, etc.) and for crushing into cooking oil.
    4. Coal (Thermal and Coking): Despite domestic production, China imports high-quality U.S. coking coal for steelmaking and thermal coal for power generation to supplement domestic supply.
    5. Copper Ores & Concentrates: The U.S. is a significant producer of copper. China, the world’s largest copper refiner and consumer, imports these concentrates to feed its smelters for wire, electronics, and construction.
    6. Wood Pulp (Dissolving & Paper Grades): A fundamental raw material for China’s massive paper and packaging industry. High-quality U.S. wood pulp is also key for producing viscose (rayon) for textiles.
    7. Aluminium Scrap & Waste (Unwrought): While not a virgin ore, this is a crucial secondary raw material. China imports vast quantities of scrap metal (especially before recent restrictions) to be remelted, reducing its need for energy-intensive primary aluminium production.
    8. Animal Hides & Skins (Raw): The U.S., with its large meat industry, is a top global supplier of raw hides, which China tans and manufactures into leather goods for global export.
    9. Cotton: The U.S. is a leading global exporter of high-quality raw cotton, which is spun and woven in China’s massive textile industry.
    10. Sorghum: An important feed grain, used as a partial substitute for corn in animal feed. Its trade volumes fluctuate based on Chinese agricultural policy and pricing.

    Key Context and Distinctions from the U.S. Imports from China:

    • Nature of Exports: U.S. exports to China are dominated by classic bulk commodities (energy, food, fibre, ores). In contrast, China’s exports to the U.S. are dominated by processed industrial materials and finished goods.
    • Strategic Dependence: China’s imports are driven by scale and necessity—feeding its population and powering/feeding its factories. The U.S. imports from China are driven by supply chain dominance in processing specific critical minerals.
    • Volume Leaders: By sheer tonnage and often value, Soybeans, Crude Oil, and LNG are the undisputed leaders. The mineral ores and metals are significant but often secondary in total trade value to the agricultural and energy sectors.
    • Recent Addition – Liquefied Petroleum Gas (LPG): While not in the top ten by value of the above, U.S. propane (LPG) is a major raw material export to China, where it is used as a critical feedstock for producing plastics (propylene).

    In summary, the United States acts as a key supplier of foundational agricultural and hydrocarbon resources to China, reflecting its role as a resource-rich “breadbasket and gas station,” while China supplies the U.S. with processed industrial minerals and manufactured components.

  • USA Leads Techno-Feudal Takeover

    A big chunk of global wealth drifts toward the US because the “toll roads” of the digital economy are overwhelmingly American. Some refer to this as techno-feudalism, named after the feudal land owners of mediaeval times.

    When the world communicates, searches, watches, shops, works, pays, or builds software, it often does so on US-owned platforms (cloud, operating systems, app stores, ad networks, payment rails, enterprise software). Those platforms sit in the middle of transactions and workflows, so they can charge fees, earn subscription revenue, take a cut of commerce, or monetize attention via ads. Even small percentages become enormous when applied to global volume. Like that little 2% on every credit card transaction.

    That cashflow doesn’t just stay as operating income. It turns into profits, stock buybacks, and dividends that primarily accrue to US shareholders and institutions. It also becomes high wages for US-based talent, and it attracts venture funding and startup formation into the same ecosystem, reinforcing the cycle.

    There’s a second-order effect too: once a platform becomes the default, everyone else must integrate with it. That creates switching costs and network effects that let the platform set terms (pricing, rules, ranking, access). Countries outside the US often end up as “rent payers” on infrastructure they don’t control—paying recurring platform taxes in exchange for participation in modern digital life.

    So the transfer isn’t a single event; it’s a continuous, compounding siphon: global usage → platform tolls → US corporate earnings → US capital markets and household wealth → more investment and dominance.

    Below are 20 “infrastructure-like” global platforms (payments, cloud, operating systems/ecosystems, business software, and network/compute rails) that a huge share of modern life and commerce effectively depends on. Turnover = latest reported annual revenue (or TTM where noted). For private companies, turnover is estimated.

    Platform (company)Country of originTurnover (latest)
    Apple (iOS/App Store ecosystem)USA$416.161B (FY2025)
    Microsoft (Windows/Azure/365/GitHub)USA$281.724B (FY2025)
    Alphabet / Google (Search/Android/Cloud)USA$385.477B (TTM to Sep 30, 2025)
    Amazon (AWS + commerce/logistics platform)USA$691.330B (TTM to Sep 30, 2025)
    Meta (Facebook/Instagram/WhatsApp messaging + ads rails)USA$189.458B (TTM to Sep 30, 2025)
    Netflix (global streaming distribution platform)USA$43.379B (TTM to Sep 30, 2025)
    Visa (card payments network)USA$40.0B net revenue (FY2025)
    Mastercard (card payments network)USA$31.474B (TTM to Sep 30, 2025)
    PayPal (online payments wallet/processor)USA$32.862B (TTM to Sep 30, 2025)
    Stripe (payments infrastructure)Ireland~$5.1B (2024, estimated)
    Xero (SMB accounting platform)New ZealandNZ$2.1B (FY2025, year to 31 Mar 2025)
    Oracle (database + enterprise platforms)USA$57.399B (FY2025)
    SAP (ERP/business backbone)Germany$40.364B (TTM to Sep 30, 2025)
    IBM (enterprise IT + hybrid cloud/services)USA$65.402B (TTM to Sep 30, 2025)
    Cisco (network infrastructure)USA$56.654B (FY2025)
    Salesforce (CRM + enterprise platform)USA$37.895B (FY2025)
    Adobe (document/content creation standards—PDF/Creative Cloud)USA$23.77B (FY2025)
    Tencent (WeChat “super-app” + payments/identity rails)ChinaRMB 660.3B / US$91.9B (FY2024)
    Alibaba (commerce + cloud platform)China$137.3B (FY2025)
    Samsung Electronics (mobile devices + chip supply platform)

    Here are 20 European(-origin) “infrastructure platform” competitors/alternatives that map pretty closely to the categories in your global list (cloud, payments rails, business software, telco/network gear, and semiconductor platforms). Turnover = latest reported annual revenue (or equivalent); for banks/fintechs this may be reported as net revenue or net operating income.

    European platformClosest competitor(s) to…Country of originTurnover (latest)
    SAPOracle / Microsoft (enterprise backbone)Germany$40.364B (TTM to Sep 30, 2025)
    SpotifyNetflix (subscription media distribution)Sweden€15.673B (FY2024 revenue)
    OVHcloudAWS / Azure / Google Cloud (IaaS/PaaS cloud)France€1.0846B (FY2025 revenue)
    AdyenStripe / PayPal (merchant payments)Netherlands€2.0B (FY2024 net revenue)
    WorldlineStripe / Adyen / PayPal (payments processing)France€4.632B (FY2024 revenue)
    NexiVisa/MC ecosystem enablers + merchant acquiringItaly€3.514B (FY2024 net revenues)
    KlarnaPayPal / Affirm-style BNPL + checkoutSwedenSEK 25.4B (FY2024 total net operating income)
    RevolutPayPal-ish consumer fintech “super app”UK£3.1B (FY2024 revenue)
    WisePayPal/Xoom-style cross-border money transferUK£1.2B (FY2025 revenue)
    SageXero (SMB accounting + payroll)UK£2,332m (FY2024 underlying total revenue)
    Dassault SystèmesAdobe / Autodesk (design/engineering software standards)France€6.21B (FY2024 total revenue)
    Deutsche Telekom“The network” beneath everything (connectivity)Germany€115.8B (FY2024 total revenue)
    OrangeConnectivity + enterprise network servicesFrance€40.260B (FY2024 revenues)
    VodafoneConnectivity + enterprise network servicesUK€37.4B (FY2025 total revenue)
    NokiaCisco (network infrastructure & telecom equipment)Finland€19.220B (FY2024 net sales)
    EricssonCisco (telecom equipment / networks)SwedenSEK 247.9B (FY2024 net sales)
    ASML“Compute supply chain” underpinning Apple/Samsung/etc.Netherlands€28.3B (FY2024 total net sales)
    ArmDevice/edge compute platform used by Apple/Samsung/etc.UK$4.007B (FY ended 31 Mar 2025 revenue)
    InfineonSemiconductor platform (power/auto/IoT)Germany€14.955B (FY2024 revenue)
    STMicroelectronicsSemiconductor platform (auto/industrial/IoT)France/Italy (merged origins)$13.27B (FY2024 revenues)

  • David Shapiro – Do this over the next 5 years and you’re set

    David Shapiro – Do this over the next 5 years and you’re set

    He asks “How do I prepare for AI and what’s coming to jobs and the economy?”
    He frames the answer as four big areas you can act on: (1) where you live, (2) investments, (3) jobs, (4) lifestyle / higher purpose.

    1) Where you live: “location arbitrage” is a real lever

    • Remote work (accelerated by the pandemic) lets some people choose cheaper or more desirable places to live while keeping higher-paying work.
    • He argues a lot of return-to-office mandates are often a pretext for layoffs (though he acknowledges some teams truly benefit from in-person work).
    • As people leave expensive hubs (he mentions places like San Francisco), housing availability/prices may shift, creating opportunities for those who still want city life.
    • His personal stance: moving to a smaller town improved quality of life (community feel, less stress, more “village vibe”).

    Connection to AI: if AI disrupts jobs broadly, where you live and what it costs to live there matters more.

    2) Investments: the future shifts from “wage economy” to “capital economy”

    • He says we’re moving toward a world where labour earns less overall, and capital ownership/participation becomes the main way wealth gets distributed.
    • His personal strategy (as an example, not advice): dividend-producing ETFs so he doesn’t have to stress about trading—income comes via dividends.
    • He highlights typical household capital channels: stocks, bonds, real estate.
    • He points to “employee ownership” models as a bridge:
      • ESOPs (employee stock ownership plans) in the US
      • UK-style employee-owned trusts and similar European approaches
    • On crypto:
      • He’s sceptical of most crypto/DAOs (calls many scam/rug-pull risk).
      • He views Bitcoin more as a wealth-preservation asset than an income generator, and mentions The Bitcoin Standard as an argument for that view.

    Big claim: solving “how regular people gain capital if they have none” is not an individual problem—it requires policy change.

    3) Jobs: AI + robots squeeze both knowledge work and low-skill labour

    His core thesis: AI threatens high-paid knowledge work, and robots threaten many manual/service jobs, so the old “get skills → get stable job” model breaks down.

    What he thinks survives longer

    He proposes four job “buckets” that remain valuable because people still pay for humans:

    1. Attention jobs
      • Monetizing attention (YouTube, social media, etc.).
      • But he warns it’s winner-take-most and heavily luck-driven.
    2. Experience jobs
      • Work that facilitates lived experiences: tour guides, massage, event roles, “trip sitters,” hospitality/entertainment, etc.
      • People will keep wanting human-centred experiences, even if robots exist.
    3. Authenticity jobs
      • Roles where the customer/client specifically wants a real human presence (he mentions examples like therapists, politicians, etc.).
    4. Meaning jobs
      • Philosophers, spiritual leaders, mentors—people who help others make sense of life and change.
      • He positions himself partly here.

    The “use AI” middle path

    He describes a practical adaptation: become an AI power user (like his wife shifting from copywriting to broader marketing/strategy and using AI for research, planning, artifacts).
    The value becomes judgment + agency + client trust, not typing words.

    Trust and reputation matter more

    He gives an example of a fencing contractor:

    • Even if robots do the physical labour later, customers still hire the trusted name/brand.
    • Trust/reputation are “non-fungible” (can’t easily swap one human for another).

    Timeline / urgency

    He predicts a major societal labour crisis within 10–20 years, and even suggests it could hit before 2030 given the pace of innovation (in his view).

    4) Lifestyle and higher purpose: build agency and structure for a post-work world

    Assuming a future with some mix of UBI (cash) and universal basic capital / dividends, he asks: “What do you do with your time?”

    • He argues people will need purpose, not just income.
    • Key personal skill: agency (self-directed life).
      • Not just reacting to market opportunities, but creating your own path based on what you genuinely care about.
    • He emphasizes the need for structure when external structure (a job) fades.

    How to find a mission (his suggested starting point)

    • “Admit what you’re afraid to want.”
    • Once you acknowledge what you truly want (even if it risks judgment/failure), you can align choices and opportunities toward it.

    He also emphasizes that meaning doesn’t have to be career-shaped:

    • For some, purpose is family and being a good parent, building community, doing “village life” well.

    The talk’s bottom line in one paragraph

    Shapiro’s message is: AI and robotics will undermine both white-collar knowledge work and many service/manual jobs, pushing society toward a capital-based economy and forcing big policy changes. On a personal level, he suggests you prepare by optimizing where you live, building some form of capital participation if possible, steering toward work that depends on human attention/experience/authenticity/meaning, and developing agency, structure, and purpose so life still works even if traditional employment doesn’t.

    Source: https://youtu.be/cY–hKUWKX4