The debate over "Is AGI here yet?" is largely semantic. In economic terms we operate with operational generality: composite stacks (model + retrieval + tools + memory + evaluators) competently perform most routine digital cognitive tasks. What remains is deep strategic autonomy, grounded causal modeling, and durable alignment under pressure. This spectrum clarifies where capability is substantive versus still aspirational.
1. Key Definitions (Updated 2025)
- ANI: Narrow optimization for a bounded task family.
- Operational Generality (Current State): Orchestrated systems delivering broad cognitive labor coverage with rapid adaptation and transfer across domains.
- Strategic AGI: Adds stable long-horizon planning, reliable world modeling, causal inference, persistent calibrated memory, goal management.
- ASI Trajectory: Potential future phase where strategic foresight + innovation loops outpace coordinated human expert networks.
2. Capability Axes
- Breadth: Distinct task families handled without architecture change.
- Adaptation Latency: Time/data to acquire new behavior.
- Autonomy Depth: Consecutive tool / decision layers without brittle failure.
- Reliability & Alignment Robustness: Stability under distribution shift / adversarial prompts.
- Self-Improvement Leverage: Ability to draft tests, critique, refine, and integrate new tools.
Orchestration ≠ Illusion
Human cognition is itself an orchestrated system (external memory, instruments, collaboration). Current AI stacks legitimately deliver functional generality; the engineering focus now shifts to reliability, governance, and equitable diffusion.
3. Mid 2025 Reality
- Coverage: >80% of routine digital knowledge workflows augmentable/end-to-end (research synthesis, code scaffolding, summarization, planning, drafting).
- Adaptation: Few-shot specification or micro fine-tune in hours yields new role competence.
- Evaluator Loops: Models drafting validators/tests reduce human review bandwidth.
- Localization: Small (1–3B) distilled models enable private, offline operation.
- Gaps: Multi-week autonomous project pursuit, grounded physical simulation without external engines, robust causal reasoning, value stability under adversarial pressure.
4. Next-Phase Indicators
- Cryptographically verifiable long-horizon memory with policy-governed forgetting.
- Autonomous tool discovery + integration beyond pre-registered APIs.
- Hierarchical objective decomposition with measurable subgoal verification.
- Causal inference benchmarks (counterfactual stability) approaching expert parity.
- Continuous self-alignment reports (drift detection instrumentation).
5. Risk Surface Evolution (Recast)
| Phase | Primary Value | Primary Risk | Mitigation Focus |
|---|---|---|---|
| Operational Generality (Now) | Broad cognitive labor coverage | Reliability gaps, subtle hallucination | Evaluator stacking, provenance, model tracing |
| Strategic Autonomy (Emerging) | Multi-week goal pursuit | Spec drift, reward hacking | Goal sandboxing, formal objective specs |
| Robust AGI | Self-directed complex project execution | Misaligned long-term strategies | Alignment interpretability, constraint architectures |
| Early ASI Trajectory | Accelerated scientific innovation | Capability concentration | Coordination, pacing frameworks |
6. Practical Team Framework
- Task Mapping: Inventory workflows by impact & risk.
- Guardrails: Structured output schemas + automatic validators.
- Telemetry: Log prompts, tool calls, evaluator scores (privacy-aware).
- Reliability Thresholds: Expand autonomy windows only after metric gates.
7. Strategic Imperative
Treat functional generality as present. Focus policy & engineering on reliability infrastructure, alignment transparency, memory/data governance, equitable access, and pacing before strategic autonomy consolidates.