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Author: devanshu0987

content/docs/world/WorldView/impact_of_ai.md

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+---
+title: Impact of Agentic AI on the world
+weight: 1
+---
+# Impact of Agentic AI on the world
+
+- Source 1: [https://x.com/thedankoe/status/2010751592346030461](https://alapshah1.substack.com/p/the-global-intelligence-crisis)
+- Source 2: https://www.citriniresearch.com/p/2028gic
+
+## Step-by-step logic
+
+### Phase 0: Foundation
+1. **Core assumption of the economy:** Human intelligence is the scarce, expensive input that turns raw materials into goods and services.
+2. **Agentic AI:** Not chatbots — systems that do **multi-step, autonomous work**. METR: unaided task duration doubling every ~6–7 months (~14.5 hours now → trend to ~1 month by mid-2028).
+3. **Substitution, not complement:** Past tech augmented humans; agentic AI **replaces** cognitive labor. So “tech destroys jobs then creates more” may not hold — new roles still need humans; AI is getting good at those too.
+
+### Phase 1: Rational adoption
+4. **Per-firm logic:** Adopt AI → cut headcount → margins and earnings up. Firms that don’t adopt lose on cost and competitiveness. So adoption is **rational** for each company.
+5. **Early evidence (Article 2):** Late 2025 agentic coding step-change → “replicate mid-market SaaS in weeks.” Procurement in 2026 asks “build in-house?” → vendor pricing power collapses (e.g. 30% discount; long-tail SaaS crushed).
+6. **ServiceNow reflexivity (Article 2):** ServiceNow sells **seats**. Clients cut 15% headcount → cancel 15% licenses. So the same AI-driven layoffs that help **customer** margins **mechanically** destroy **vendor** revenue. Workflow-automation vendor is disrupted by better workflow automation → cuts headcount and invests in that same tech. **Collective result:** every dollar saved on labor flows into AI that enables the next round of cuts.
+
+### Phase 2: Negative feedback loop — no natural brake (both)
+7. **Loop:** AI improves → companies need fewer workers → white-collar layoffs → displaced workers spend less → margin pressure → firms invest more in AI → AI improves. **No self-correcting mechanism.**
+8. **OpEx substitution (Article 2):** It’s not “less CapEx.” Spend shifts: e.g. $100M labor + $5M AI → $70M labor + $20M AI. **Total spend falls, AI spend rises.** So demand can fall while AI buildout continues.
+9. **“Ghost GDP” (Article 2):** Output and productivity rise in national accounts, but **machines don’t spend.** Velocity of money and the **human-centric consumer economy** (70% of GDP) weaken. Single GPU cluster replacing 10,000 Manhattan workers = “economic pandemic” more than panacea.
+
+### Phase 3: Spending concentration (both)
+10. **Who gets displaced:** White-collar, high earners — the same people who **drive spending.** Article 1: top 20% ≈ 65% of US consumer spending; Article 2: top 10% >50%, top 20% ≈ 65%.
+11. **Leverage of job loss on demand:** A **small** % drop in white-collar employment → **large** % drop in discretionary spending (e.g. 2% employment → ~3–4% hit to discretionary spend). Plus **lag:** high earners use savings for a few quarters, then spending breaks (Article 2: initial claims 487k, then S&P -6%).
+
+### Phase 4: Intermediation layer dismantled (both)
+12. **Moat = human friction:** Decades of value built on: limited time, patience, habit, willingness to accept bad prices. **Trillions** of enterprise value on that.
+13. **Agents remove friction (Article 2):** Subscriptions, travel, insurance (passive renewals), financial advice, tax, routine legal, real estate (commissions 2.5–3% → <1%, “agent on agent”). “Human relationships” often = “friction with a friendly face.”
+14. **Habitual intermediation:** DoorDash-style moat (“app on home screen”) doesn’t exist for agents; they compare all options. Coding agents also **lower entry barriers** → many thin-margin competitors → margins crushed.
+15. **Payments (Article 2):** Agents optimize away fees → 2–3% interchange targeted → stablecoins / instant settlement, fractions of a penny. Mastercard Q1 2027: “agent-led price optimization”; card-centric banks and mono-line issuers hit (Amex, Synchrony, Cap One, Discover). **Moat = friction; friction → zero.**
+
+### Phase 5: Labor market and wage cascade (both)
+16. **Core white-collar jobs (Article 1):** Stagnant/declining since 2023; +4% vs pre-pandemic (6 years) vs +5% population, +11% real GDP. Information sector already ~8% below peak.
+17. **Displaced downshift (Article 2):** Senior PM $180k → Uber $45k. Overqualified labor floods service/gig → **wage compression** there too. Then AVs and autonomous delivery hit that gig layer. Sector-specific disruption → **economy-wide** wage and job pressure.
+
+### Phase 6: Financial daisy chain (both)
+18. **Private credit (Article 2):** >$2.5T by 2026; big share in software/tech LBOs underwritten on **ARR that stays recurring**. AI makes ARR not recurring (e.g. Zendesk: support automated, no tickets). Marks 100→92→85 while public comps 50. Moody’s downgrades; Zendesk covenant breach → largest private-credit software default. “Permanent capital” = **annuity policyholder money** in same paper; when that paper defaults, life insurers (Apollo/Athene, etc.) and offshore SPVs create **opacity** and regulatory stress. **Recognition** of losses, not just losses, turns it systemic.
+19. **Mortgages (both):** ~$13T. Underwriting assumes **stable employment/income for 30 years**. Borrowers are **prime** (780 FICO, 20% down). **2008:** loans bad at origination. **Here:** loans good at origination; **world changed after.** Income expectations structurally impaired → “are prime mortgages money good?” Delinquencies rise first in tech/finance-heavy ZIPs (SF, Seattle, Austin, Manhattan). Trajectory is the risk; full crisis still avoidable if policy and income stabilize in time.
+
+### Phase 7: Fiscal trap (both)
+20. **Revenue = tax on human work.** As payrolls and white-collar income fall, **receipts drop** (Article 2: 12% below CBO). Labor share of GDP: 64% → 56% → **46%** in four years. Output no longer flows through households → not through IRS.
+21. **Outlays rise** (unemployment, transfers) exactly when **receipts fall.** Automatic stabilizers assume **temporary** job loss and reabsorption. Displacement is **structural**; many won’t be reabsorbed at prior wages. Government must **transfer more** at the moment it **collects less.**
+
+### Phase 8: Policy and time (both)
+22. **Policy lags and is politicized:** “Transition Economy Act” (transfers + tax on AI compute), “Shared AI Prosperity Act” (public claim on AI returns). Splits: redistribution vs growth, taxing compute vs “handing lead to China,” regulatory capture, deficits, GFC-style austerity. **Real constraint:** AI and markets move faster than institutions and ideology.
+23. **Conclusion of both:** This is the **unwind of the intelligence premium.** Economy can find a **new equilibrium**, but only if we build **new frameworks** (tax, safety nets, labor, credit). Article 2’s twist: “You’re reading in February 2026” — **canary still alive;** time to prepare.
+
+---
+
+## Combined key points (concise)
+
+| # | Point |
+|---|--------|
+| 1 | Economy is built on **scarce human intelligence**; agentic AI makes intelligence **abundant** and substitutable. |
+| 2 | **Negative feedback loop** has no natural brake: AI → fewer workers → less spending → more AI investment → more displacement. |
+| 3 | **Per-firm rationality** (cut labor, invest in AI) produces **collective catastrophe** (systemic job loss and demand shock). |
+| 4 | **Spending is concentrated** in the same white-collar earners who are displaced → small employment loss, large demand loss, with a **lag**. |
+| 5 | **Intermediation** (software, consulting, insurance, travel, real estate, payments) is a **friction moat**; agents make friction → zero. |
+| 6 | **“Permanent capital”** in private credit was partly Main Street (annuities) in PE/software paper; when that paper repriced, **recognition** of losses spread through insurers and SPVs. |
+| 7 | **Prime mortgages** ($13T) assume stable income for 30 years; loans were good at origination; **income expectations** changed after. |
+| 8 | **Fiscal structure** (tax on labor, stabilizers for temporary unemployment) is wrong for **structural** displacement. |
+| 9 | **Policy** is too slow and polarized; the “villain” is **time** — capability and markets outpace institutions. |
+| 10 | **First time** the most productive asset (intelligence) produces **fewer** jobs at scale; old frameworks don’t fit; **new frameworks** are needed. |
+
+---
+
+## Combined action items (integrated)
+
+**Personal / career**
+- Treat your job as **replaceable by agents** in a 12–24 month window; shift toward judgment, relationships, accountability, and tasks agents don’t yet do well.
+- **Use agentic AI daily** (coding, research, analysis) so you’re on the multiplier side and your skills stay relevant.
+- **Stress-test your industry:** Is it intermediation (friction), “seats,” or ARR that assumes human labor? If yes, assume repricing and plan exit or pivot.
+
+**Household / financial**
+- **Reduce dependence on “prime borrower forever”:** Less leverage; don’t assume current income for 30 years; larger emergency buffer.
+- **Diversify income** (skills that work in essential services, gig, or roles less exposed to agent substitution).
+- **Assume** a possible **prolonged** demand shock and **repricing** of risk (equities, credit, real estate in tech-heavy metros).
+
+**Investing / portfolio**
+- **Audit “daisy chain” exposure:** Revenue and margins that assume white-collar employment, ARR stickiness, interchange, or intermediation margins staying intact.
+- **Hedge or underweight:** Private credit/PE software, card-centric issuers, intermediation-heavy business models, real estate in tech/finance-heavy ZIPs.
+- **Differentiate** “AI infrastructure” (convex to adoption) vs “economy that depends on human spending” (concave).
+
+**Business / leadership**
+- **Model reflexivity:** If your customers cut headcount (or reduce friction), what happens to your “seats,” ARR, or take rate? Don’t assume you’re only a beneficiary of AI.
+- **Plan for both** productivity gains **and** demand shock: slower top-line, margin pressure, and political/regulatory risk (transfers, compute taxes, public claim on AI).
+
+**Civic / policy**
+- **Support and debate** serious proposals: transfers for displaced workers, funding (e.g. deficit, compute tax, or shared returns from AI), and automatic stabilizers redesigned for **structural** displacement.
+- **Vote and advocate** for candidates who treat this as a **structural** transition, not a normal business cycle.
+
+**Mindset**
+- **Combine both articles:** One is “from the future” (2028); the other is “from 2026.” Use the **combined** step-by-step logic above as your mental model: adoption → reflexivity → loop → intermediation → labor → financial daisy chain → fiscal trap → policy race.
+- **Use “canary still alive”:** If you treat the present as “before the loop has fully kicked in,” the priority is **assessment and preparation** (career, leverage, portfolio, policy) rather than panic.