TL;DR
Modern large models behave like a cortex-style pattern engine. Add (1) durable memory, (2) an offline "sleep" cycle for self-improvement, (3) sensors and action modules (software tools now; robots soon), and (4) an executive layer to orchestrate them—and you approach general capability in digital environments. Whether this is AGI depends on definitions: economic (outperform most human work) [1-2] versus cognitive (human-like fluid reasoning on novel tasks). On the latter, ARC-AGI-2 shows humans can solve all tasks while frontier systems remain single-digit—so fluid reasoning is still a frontier [16].
1) First principles: what counts as "AGI" and "ASI"?
- • AGI (OpenAI Charter): highly autonomous systems that outperform humans at most economically valuable work [1].
- • GPAI (EU policy): a governance label for broadly usable models, with a 2025 Code of Practice guiding providers—useful for compliance, not a capability threshold [2].
- • ASI (Bostrom): much smarter than the best human brains in practically every field—a philosophical yardstick, not an engineering spec [3].
Implication: "Already AGI" is credible if you adopt the economic lens; under a cognitive lens (fluid abstraction, causal learning), not yet.
2) How neuroscientists carve up the brain (and why that matters)
Brains are systems of systems, not one module.
- • Large-scale networks in cortex—Default Mode, Frontoparietal/Executive, Salience, Attention, Sensorimotor, Visual—are reproducible across people (7/17-network maps) [4]. The salience system helps switch between self-referential (DMN) and task-focused executive control [5-6].
- • Key subsystems with clear roles:
- • Basal ganglia for action selection and reinforcement-learning-like gating across motor and cognitive sequences [7-8].
- • Cerebellum for coordination and cognition/affect (CCAS/Schmahmann) [9-11].
- • Thalamus as a cortical hub that shapes information flow, attention and state [12].
These maps give us a principled template for engineering analogues.
3) Your thesis, formalised: "stitch the missing pieces"
Proposition: A large model is a cortex-like pattern engine. Add long-term memory, an offline "sleep" loop, embodied I/O (eyes/ears/tools/robots), and an executive that orchestrates them—and you have practical AGI for software environments; scaled across many specialities, you approach ASI-like breadth.
Brain → Engineering mapping
| Brain system (coarse) | Today's engineering analogue |
|---|---|
| Cortical networks (perception/abstraction/language) | LLM/Multimodal LLM (text, code, images, audio) |
| Basal ganglia (action selection, RL) | Planner/policy choosing the next tool/skill chain |
| Hippocampus ↔ cortex (episodic→semantic consolidation) | Long-term memory: vector stores + knowledge graphs + retrieval |
| Sleep (replay; synaptic homeostasis) | Nightly replay, evaluation, pruning; light fine-tuning/LoRA [13-15] |
| Salience + executive networks (switching) | Orchestrator that routes tasks by uncertainty/priority and escalates |
| Embodiment (sensors/actuators) | Software tools now; VLA-style robot policies increasingly capable [17-20] |
Why the "sleep" loop matters: Non-REM dynamics (spindles/ripples) support systems-level consolidation; Synaptic Homeostasis suggests global renormalisation—clean metaphors for nightly self-optimisation [13-15].
4) Are we there yet?
- • Economic lens: Tool-using systems already execute research, coding, analysis, design and customer workflows at professional levels in many domains—consistent with the policy spirit of AGI [1-2].
- • Cognitive lens: ARC-AGI-2 explicitly targets fluid abstraction on novel tasks. Humans solve 100% of tasks (pass@2); frontier "reasoning" systems score single digits; pure LLMs ~0% [16]. That's a real gap.
Verdict: Near-AGI in software under an economic view; not yet on human-level fluid reasoning.
5) A brain-inspired architecture you can ship today
- 1. Cortex-like core: A strong multimodal model for text/code/tables/images/audio.
- 2. Executive/salience layer: An uncertainty-aware scheduler that routes tasks among agents and tools; handles mode-switching (cf. salience ↔ executive ↔ DMN) [5-6].
- 3. Action selection (basal-ganglia analogue): RL/bandit policy that picks the next tool/skill chain, with explicit stop/escalate criteria tied to business KPIs [7-8].
- 4. Long-term memory:
- • Episodic (append-only logs of interactions/outcomes)
- • Semantic (distilled knowledge graph + vector index with provenance)
- 5. Sleep cycle (offline self-improvement): Replay from episodic logs → targeted re-tests → dataset pruning → light LoRA refresh → redeploy [13-15].
- 6. Embodiment:
- • Now: safe, auditable tool-use (search, spreadsheets, CRM, code, RPA).
- • Next: VLA/generalist policies (RT-2, Open-X-Embodiment, Octo, OpenVLA) show transfer and broader dexterity across platforms [17-20].
6) What this buys you (and what it doesn't)
Strengths today
- • Breadth: With tools + memory, one stitched agent can complete many white-collar workflows end-to-end.
- • Multi-specialist behaviour: A single system can perform competently across multiple professions—rare for humans.
Gaps to close
- • Fluid abstraction on novel tasks: Still a differentiator for humans (ARC-AGI-2) [16].
- • Causal/world modelling: Active research; embodiment likely improves robustness [17-20].
- • Reliability & governance: Capabilities rise faster than controls—track GPAI-style obligations if operating at scale [2].
7) A pragmatic AGI scorecard (use both lenses)
Economic metrics
- • % of core tasks at human-parity or better
- • Profit/compute-hour vs fully loaded human cost
- • Time-to-competence on a new but typical workflow
Cognitive metrics
- • ARC-AGI-2 pass rate (zero-/few-shot) + sample efficiency [16]
- • Out-of-distribution tool use; transfer to new APIs without schema hints
- • Causal/abductive reasoning batteries from cognitive science
If you clear both, you've got a credible "AGI" under most reasonable definitions.
Conclusion: Treat AGI as an integration milestone
Stop asking if a single model is a person. Ask whether your stitched system—with memory, offline replay, and action—shows general, reliable competence across open-ended tasks. In software, we're close. In fluid reasoning and physical autonomy, we're not done.
For builders: prioritise orchestration, memory hygiene, offline replay, rigorous evaluation, and safety envelopes over model monotheism. Don't leave "part of a brain on the table".
References
Definitions & policy
- 1. OpenAI Charter (2018–): "AGI… highly autonomous systems that outperform humans at most economically valuable work."
- 2. European Commission (2025). General-Purpose AI (GPAI) Code of Practice (policy page, 10 July 2025).
Brain systems & networks
- 3. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford Univ. Press.
- 4. Yeo, B.T.T. et al. (2011). The organisation of the human cerebral cortex by intrinsic functional connectivity. J. Neurophysiol., 106, 1125–1165.
- 5. Menon, V. (2023). Large-scale brain networks and the triple-network model. Neuron, 111, 1776–1799.
- 6. Sridharan, D., Levitin, D.J., Menon, V. (2008). Right fronto-insular cortex in switching between executive and default-mode networks. PNAS, 105, 12569–12574.
- 7. Maia, T.V., Frank, M.J. (2011). From RL models to psychiatric & neurological disorders. Nat. Neurosci., 14, 154–162.
- 8. Jin, X., Costa, R.M. (2019). Basal ganglia in action sequence learning & performance. Neurosci. Biobehav. Rev., 94, 219–227.
- 9. Buckner, R.L. (2013). The cerebellum and cognitive function. Neuron, 80, 807–815.
- 10. Argyropoulos, G.P.D. et al. (2019). The Cerebellar Cognitive Affective/Schmahmann Syndrome. The Cerebellum, 19, 102–125.
- 11. Stoodley, C.J. (2018). Functional topography of the human cerebellum. Prog. Neurobiol., 168, 1–58.
- 12. Halassa, M.M., Sherman, S.M. (2019). Thalamocortical circuit motifs: a general framework. Neuron, 103, 762–770.
Sleep, consolidation & replay
- 13. Klinzing, J.G., Niethard, N., Born, J. (2019). Mechanisms of systems memory consolidation during sleep. Nat. Neurosci., 22, 1598–1610.
- 14. Foster, D.J. (2017). Replay in the medial temporal lobe. Nat. Neurosci., 20, 152–158.
- 15. Wilson, M.A., McNaughton, B.L. (1994). Reactivation of hippocampal ensembles during sleep. Science, 265, 676–679.
Benchmarks
- 16. ARC Prize Foundation (2025). ARC-AGI-2 (official benchmark page and summary).
Embodiment & VLA robotics
- 17. Brohan, A. et al. (2023). RT-2: Vision-Language-Action models transfer web knowledge to robotic control. arXiv:2307.15818 / PMLR 2023.
- 18. Open X-Embodiment Collaboration (2023–2024). Open-X-Embodiment datasets & RT-X models. arXiv:2310.08864 / OpenReview 2024.
- 19. Octo Model Team (2024). Octo: An open-source generalist robot policy. RSS 2024 + project materials.
- 20. Kim, M.J. et al. (2024). OpenVLA: An open-source Vision-Language-Action model. arXiv:2406.09246 (rev. 2024).
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