Executive summary (no sugarcoating)
- AGI (Artificial General Intelligence) means systems that can perform most economically valuable cognitive tasks at or above human level, adapt across domains, and self-improve. If we only reach “near-AGI” (very capable, but not fully general) by 2030, the macro effects will still be massive.
- Baseline serious estimates for pre-AGI generative AI imply +1–1.5pp to annual productivity growth for a decade and ~+7% to global GDP in level terms—if adoption is real, not theater.
- Labor markets will see large task reshuffling: advanced economies are more exposed (cognitive work share is higher), while distributional effects will be uneven without policy.
- If early AGI emerges before 2030, everything accelerates—both gains and shocks: faster capital deepening, steeper skill premiums for those who can direct AI, and shorter business cycles as firms retool.
- Bottom line: by 2030 you either ride the productivity wave—or get run over by it. The constraint isn’t the tech; it’s adoption, complements (data/process), and policy.
1) What do we mean by AGI?
Operational definition: a system that can (a) learn any cognitive task humans can, (b) transfer that learning across domains with minimal prompting, and (c) reliably achieve human-level outcomes in real-world workflows.
Practical 2030 reality: Firms won’t wait for textbook AGI. Near-AGI models that can handle 80–90% of white-collar task bundles will be enough to move macro needles. The bar for macro impact is lower than the philosophical bar for “true” AGI.
2) The productivity channel: the only lever that matters at scale
Serious macro studies of pre-AGI generative AI already imply large gains:
- Goldman Sachs: potential ~7% lift to global GDP and ~1.5pp to productivity growth over 10 years with broad adoption.
- McKinsey: $2.6–$4.4T in annual value from gen-AI use cases; that’s an incremental boost to the existing AI impact. Translation: material total factor productivity (TFP) upside if firms actually redesign work.
- Goldman (2025 update): fully absorbed, gen-AI could raise labor-productivity levels ~15% in advanced economies; expect a temporary unemployment uptick during transition.
Coach’s take: These are not freebies. Without process reengineering, data pipelines, and change management, your “AI pilot” is a cost center, not a growth driver.
3) Labor markets: where the shock lands
- Exposure is highest in advanced economies (more knowledge work to automate/augment). IMF expects faster and deeper impacts in those markets, with benefits and risks both amplified.
- Net jobs? Theory says ambiguous: displacement effect vs productivity & reinstatement effects (new tasks and complementary roles). Short-run frictions are real; long-run depends on reskilling speed and policy.
- Entry-level pressure: firms automate junior workflows first; this risks a broken talent pipeline unless companies redesign apprenticeship models. (Recent employer surveys reflect this bias.)
Coach’s take: If you’re a policymaker or a CEO, assume task churn is the norm through 2030. Build internal mobility + training now, or you’ll pay in churn, wages premia for AI-complementary skills, and execution delays.
4) Prices, profits, and inequality
- Inflation: One-off productivity gains are disinflationary; transition frictions (capex, labor mismatch) can be inflationary. Net impact by 2030 likely mildly disinflationary if adoption sticks.
- Profits: Early adopters in data-rich, process-heavy sectors capture margin expansion (replace contractor hours; compress SG&A; speed cycle time). Laggers give that margin to competitors.
- Inequality: Without policy, widening dispersion—capital owners and AI-complementary workers gain; routine cognitive workers lose bargaining power. IMF flags sizable distribution risks.
5) Sector-by-sector heat map to 2030
- Software & IT services: highest immediate lift (coding copilots, test, ops). Expect higher output per engineer and lower time-to-market.
- Professional services (legal, accounting, consulting): big task automation; price compression where outputs become standardized; premium shifts to judgment + client context.
- Finance & insurance: underwriting, compliance, modeling, and CX become AI-first; headcount mix tilts to risk, data, and oversight.
- Healthcare & life sciences: clinical documentation, triage, and R&D literature mining accelerate; regulatory gating slows full automation.
- Manufacturing & supply chain: design, quality, predictive maintenance; robotics pace sets ceiling.
- Public sector & education: massive productivity upside; adoption lags due to procurement and governance debt.
(Patterns consistent with employer surveys and cross-industry use-case sizing.)
6) Investment cycle and capital deepening
- Capex mix shifts: data infrastructure, model hosting, orchestration, workflow automation, security, and trust layers.
- M&A: data-rich niches command premiums; distressed junior-labor-intensive providers face consolidation.
- TFP accounting: Expect a multi-year lag between spend and measured productivity—until workflows are redesigned and bottlenecks (governance, latency, accuracy) fall.
7) Trade, geopolitics, and concentration risk
- Model and chip supply chains become strategic choke points. Countries with compute, data, and talent capture surplus; others import intelligence like they import energy.
- Platform concentration can slow pass-through of productivity to wages/consumer prices. Antitrust and interoperability standards matter for diffusion.
8) Policy to actually capture the upside (and not blow up the labor market)
International institutions converge on the same to-do list:
- Human-capital sprint: fund reskilling toward AI-complementary skills; redesign education for applied problem-solving + tooling.
- Diffusion over demos: tax incentives tied to measured adoption (workflow hours shifted, cycle times cut), not PR pilots.
- Competition + portability: prevent lock-in; mandate data/model portability and auditability for high-risk domains.
- Social insurance & transition support: wage insurance, mobility stipends, and apprenticeship pipelines to replace the crumbling “entry-level ladder.”
- Public-sector modernization: digitize services with AI-native workflows to set the floor for diffusion.
Coach’s take: Don’t spray money at “innovation.” Tie support to verifiable productivity gains and worker transition outcomes.
9) Three scenarios for 2030 (and what they imply)
A) High-adoption near-AGI (most likely)
- Macro: +1–1.5pp productivity growth for 5–10 years; ~+7% GDP level vs counterfactual by early 2030s. Temporary rise in structural unemployment during reallocation.
- Markets: higher profit share near-term; multiple compression possible if rates stay high and AI premia normalize.
B) Slow diffusion
- Macro: Gains capped (<0.5pp); “pilot purgatory.” Inequality still rises (automation pockets), but growth underdelivers.
C) Early AGI breakout (tail risk)
- Macro: explosive TFP; big redistribution from labor to capital unless policy intervenes; potential financial instability from rapid obsolescence of incumbents and skills.
- Policy stress test: safety, alignment, and compute governance become macro-critical infrastructure issues.
10) What to do now (for executives and policymakers)
- Pick 3–5 workflows where AI can remove entire task clusters, not sprinkle autocomplete; measure hours saved / cycle time / error rate monthly.
- Re-architect org design: create AI product owners inside every P&L; budget data engineering > model experiments.
- Talent barbell: fewer juniors doing grunt work; more operators who can both prompt and program; rebuild apprenticeships explicitly.
- Governance that scales: centralized model registry, evaluation harnesses, and incident response. If you can’t audit, you can’t deploy at scale.
- Policy side: tie tax credits to measured productivity, fund mobility + reskilling, and mandate interoperability to avoid moat-driven stagnation.
The honest forecast
By 2030, with just near-AGI, the world probably sees meaningful productivity acceleration and a visible GDP level step-up—if firms do the hard, boring work (data cleanup, process surgery, change management). If early AGI arrives, amplify everything: the upside and the social risk. Your hedge is simple: execute adoption with discipline, and build the human complements now. Waiting is the most expensive strategy you can choose.
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