AGI vs. ASI: How Far Are We, Really?

TL;DR for leadership: Treat AGI with P50 arrival in the early 2040s and P10 in the late 2020s as the planning envelope. Move into the “fast track” posture only when (1) held-out generalist eval suites hit human parity, (2) agentic stacks run autonomously for ≥72 hours with auditable logs, and (3) frontier CAPEX plus regulation signal permissive deployment. Until then, stage investments across talent pipelines, evaluation infrastructure, and compliance-by-design so you can accelerate or brake without panic.

Predictions about artificial general intelligence (AGI) and artificial superintelligence (ASI) swing from "five years away" to "never happening", leaving executives unsure how to plan. Instead of chasing quotes, this guide distills the most credible forecasts, technical blockers, and policy signals so you can brief leadership with evidence—not vibes.

Scenario: Board Briefing for Horizon Planning

You're the CTO of a fintech preparing a 10-year roadmap. Directors want to know:

• When AGI (human-level capability) might arrive, according to labs, academics, and market analysts.
• How quickly ASI (superhuman capability) could follow.
• Which technical bottlenecks and governance moves could accelerate or delay those timelines.
• How to allocate R&D and risk budgets under uncertainty.

We will walk through five steps to build that briefing.

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Step 1: Align on Definitions and Verification

Term	Working definition	Operational trigger	Independent verification
Narrow AI	Excels at single or tightly scoped tasks	Production models such as GPT-4, Claude 3, Gemini	Benchmark scores published + reproducible by external labs
AGI	Matches or exceeds median human performance across most cognitive domains	ARC benchmark ≥85% and parity on held-out, open eval suites; multi-modal reasoning; autonomy ≥72 hours with ≤1 operator nudge/day	Third-party audits observe eval runs, confirm no prompt fiddling or tool-stitching
ASI	Vastly exceeds best human in depth, speed, adaptability	Demonstrated ability to self-improve, solve novel science problems, or coordinate large-scale actions faster than expert teams	Independent panel replicates results across ≥3 novel domains with transparent logs

Definitions only matter if they are testable by someone other than the lab making the claim. Bake auditability into every trigger.

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Step 2: Compare Credible Timelines

Source	P10 (AGI)	P50 (AGI)	P90 (AGI)	ASI lag (conditional)	Evidence class
Lab leadership (OpenAI, 2024–25 statements)	2026	2029–2032	2036	2–5 years	Public interviews/talks [OpenAI-2024]
DeepMind (Hassabis, Legg)	2027	2030–2035	2038	3–10 years	Talks & research notes [DeepMind-2024]
Anthropic / safety researchers	2030	2040	2060	5–15 years	Alignment-focused memos [Anthropic-2024]
Large-N expert survey (1,700 researchers)	2027	2047	2090+	10–20 years	Peer-reviewed survey [FHI-2023]
Macro analysts (McKinsey, ARK)	2030	2040	2050	5–10 years	Market research reports [McKinsey-2024]
Composite (weighted)	2029	2039–2043	2060+	3–10 years	60% weight on peer-reviewed surveys, 40% on lab leadership

Takeaways

1. Single-digit probabilities this decade: Leading labs publicly place low-double-digit probabilities on AGI before 2030; boards should treat it as a tail, not the base case.
2. Baseline in early 2040s: Surveys and conservative labs converge on P50 ≈ 2040–2045 due to open problems in grounding, memory, and robustness.
3. Conditional fast follower: ASI follows quickly only if access to compute scales, autonomy compounding is demonstrated, and safety gates allow tight deployment cadence.

Always brief with P10/P50/P90 ranges and label sources. Replace anecdote with percentile math.

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Step 3: Map Blockers, Accelerants, and the Risk Matrix

Risk / Driver	Likelihood (12–24 mo)	Impact on timeline	Notes / mitigations
H100 / GB200 supply constraints	Medium	+2–4 years	Multi-sourcing, long-term pre-buys
Eval contamination & overfitting	High	+1–3 years	Held-out benchmarks, audit logging
Algorithmic efficiency jump (program-of-thought hybrids)	Medium	−1–3 years	Track scaling-law deltas, fund research partnerships
Safety gating / licensing regimes	Medium	+1–5 years	Build compliance engineering muscle early
Sovereign megaclusters coming online	Low-Med	−1–4 years	Partnership negotiations, access agreements
Synthetic data quality breakthroughs	Medium	−0–2 years	Govern data provenance, bias checks

Plot these on a 4×4 impact vs likelihood matrix for board decks. Sponsors immediately see which levers they can pull and which require policy engagement.

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Step 4: Turn Signals into KPIs

KPI	Target / threshold	Owner
Frontier CAPEX (4-quarter rolling)	Alert ≥ $60B → upgrade to Scenario A watch	CFO / Corp Dev
Export-control lattice score (−2 tightening … +2 loosening)	Alert at −1 or lower	Policy / Gov Affairs
Frontier license status (US/EU)	Track proposed → passed → enforced	Legal / Compliance
Hiring delta in autonomy & alignment (top 5 labs)	YoY % change; alert if +20% sustained	Talent Intelligence

Publish a monthly dashboard. When a KPI crosses a threshold, revisit scenarios immediately instead of waiting for annual planning.

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Step 5: Build Scenario Frameworks with Tripwires

Scenario	Tripwires (all must fire)	Actions
Scenario A – Fast track (AGI P50 ≤ 2032)	T1: Held-out generalist eval suite parity on ≥6 domains. T2: Autonomous agent runs ≥1 week with reproducible audits. T3: Publicly confirmed training run ≥10× prior cycle or sovereign cluster online.	Freeze risky deployments > Tier-2, double eval spend, form joint safety review with counsel, pre-negotiate access to frontier clusters.
Scenario B – Baseline (AGI P50 ≈ 2040)	Status quo KPIs within thresholds.	Continue staged automation, invest in talent pipelines, maintain compliance-by-design cadences.
Scenario C – Slow roll (AGI P50 ≥ 2060)	T1: Hardware supply constrained >36 months. T2: Regulatory licensing requires model audits before release. T3: Eval breakthroughs stall (<5% YoY gains).	Focus on domain-specific AI, hedge with alternative tech, prioritize incremental tooling ROI.

Tripwires eliminate debate: when they flip, move budgets accordingly.

Guardrails: How to Falsify an “Early AGI” Claim

Before you accept a press release, validate:

• Eval hygiene: Was the benchmark held out, run by an independent auditor, and free from tool-stitching?
• Human intervention: Did humans guide prompts mid-run or patch tool outputs?
• Distribution shift: Does performance degrade on fresh tasks or non-English inputs?
• Traceability: Are logs reproducible and auditable end-to-end?

If any answer is “no”, treat the claim as marketing, not a strategy trigger.

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Implementation Checklist

• Adopt shared AGI/ASI definitions and measurable thresholds.
• Maintain a living timeline table that captures 10/50/90 percentile forecasts.
• Update blocker/accelerant risk matrix quarterly.
• Create a policy signal dashboard and assign owners.
• Refresh scenario planning assumptions during annual strategy reviews.

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Thought Experiments to Challenge Your Plan

1. Flash-Forward 2029: A frontier lab open-sources an agentic stack that can write, test, and deploy software autonomously. Do you treat this as a partner, competitor, or existential risk? Which controls trigger?
2. Slow Burn 2045: Progress stalls because compute supply is rationed and regulation requires licensing for any model above 10B parameters. How do you stay competitive when bespoke domain AI becomes the only viable route?
3. Regulation Leads the Race: Governments mandate AI model audits similar to financial audits. Does that extend your AGI timeline or accelerate trust, letting you ship faster than rivals who resist?
4. Human Talent Crunch: Alignment and safety experts become the scarcest resource, not GPUs. What incentives and training programs guarantee you can evaluate frontier systems?
5. ASI Emergence in a Niche: Superhuman capability first appears in a narrow science domain (e.g., materials discovery) and quickly spills over. Are your legal, compliance, and R&D teams ready to negotiate access or defend IP?

Use these prompts in leadership offsites to stress-test the assumptions from Steps 1-5. The goal is not to predict perfectly, but to rehearse responses so you can move faster than the news cycle.

Keeping leadership aligned on evidence-based timelines turns the AGI/ASI debate from science fiction into pragmatic risk management.

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Appendix: Board Slide Starter Pack

1. Definitions & Verification: Table from Step 1.
2. Composite Timeline: P10 / P50 / P90 mini-table plus method note.
3. Risk Matrix Snapshot: Top six blockers/accelerants placed on 4×4 grid.
4. Scenario Trigger Chart: A/B/C tripwires with associated actions.