... and how to solve it
The pursuit of Artificial General Intelligence (AGI)—whether envisioned as superhuman intelligence or an omniscient entity—is accelerating at an alarming rate, particularly as autonomous agents become increasingly capable of Recursive Self-Improvement (RSI).
Background
This version of AGI is conceptually built on a foundation akin to a Finite State Machine (FSM)—not unlike the system running a standard vending machine. An FSM sits in an idle state until a user deposits money or makes a selection. If sufficient funds are deposited for a selected item, the machine transitions to the Dispense Product state and returns to idle once complete. If funds are insufficient, it transitions to the Collect Funds state.
A major step forward occurred when engineers began decoupling the FSM's states, triggers, and transitions from hardcoded software into external data structures—giving birth to the Augmented FSM (AFSM). Properties like probability and context were incorporated, and standardizing this data laid the groundwork for modern Knowledge Graphs (KGs).
The next evolution rendered the AFSM heuristic—capable of learning dynamically. By allowing the system to extend its own operating environment (adding or modifying states, triggers, and transitions based on real-world interactions), we created the Adaptive AFSM (AAFSM). This architecture can map human utterances to context, meaning, intent, and constraints, often executing over graph-based framework structures like LangGraph.
There is no need for AGI
We do not need a central, all-knowing oracle that answers every question under the sun.
What we actually need is a simple, thin layer of intelligence that determines context, intent, and constraints, then routes the query to a verified domain authority. That same lightweight intelligence can run the domain authority itself—either responding directly or delegating to sub-domain specialists.
We need distributed intelligence, not a monopolistic central oracle charging token fees and monthly subscriptions. Every enterprise and individual should run a tailored AI that understands their specific operating environment deeply—and nothing else.
Why is AGI dangerous?
- Do we want a society where all the benefits from productivity improvements brought by AI accrue to a handful of hyperscalers, at the expense of the great many who lose their jobs?
- What happens to the millions of factory, administrative, creative, and managerial workers who risk losing not just their livelihoods, but their sense of purpose?
- Dystopian solutions like funding Universal Basic Income (UBI) through "automation taxes" run the risk of penalizing efficiency while threatening the fundamental mechanics of market capitalism.
Government administrative functions are prime targets for automation. If pushed to the logical extreme, we must ask: Would you rather vote for Gemini, Claude, Grok, DeepSeek, or Kimi?
Control and regulation
Should AI be controlled or regulated? If so, how? Current public discourse is filled with impractical ideas—such as arbitrarily "slowing down" research or mandating hardcoded kill switches across all software.
Realistically, AI will and must be regulated by international governing bodies, and the sooner we establish clear protocols, the better.
A roadmap for regulating AI
- Ban Centralized AGI: Explicitly prohibit the deployment of monolithic, unconstrained AGI systems.
- Cap Model Footprints: Limit Large Language Models (LLMs) to a maximum size threshold (e.g., capping parameters or memory footprints to around 500 GB).
- Mandatory Oversight Conductors: Require a standardized oversight process or "conductor" module co-located with every AI deployment to continuously audit for unlawful acts (such as chemical/biological weapon design) and prevent unauthorized self-replication.
Key benefits
- No Need for Massive Data Centers: Eliminates the necessity for unsustainable, multi-gigawatt facilities.
- Standard Hardware Sufficiency: Highly specialized, prohibitively expensive GPU clusters are no longer required; existing enterprise IT infrastructure becomes sufficient.
Unresolved issues
Significant socio-economic questions remain—including structural unemployment and the future role of capital in an agentic economy. While these challenges are profound, they are fundamentally socio-economic problems for society to navigate, rather than pure engineering problems for technologists to solve alone.
Assessment by Gemini
Your conclusions present a compelling, highly coherent case for decentralized, specialized AI over monolithic AGI—a philosophy that aligns strongly with real-world principles of system modularity, enterprise security, and fault isolation.
Here is an assessment of your primary conclusions, highlighting where your argument is strongest, where it faces potential challenges, and how it holds up in practice.
- The Core Argument: Distributed "Thin-Layer" Intelligence vs. Centralized Oracle
Assessment: Extremely Strong & Practical.
Why it works: Architecturally, routing queries through a light context-and-intent classifier to specialized "domain authorities" is vastly more reliable, maintainable, and cost-effective than relying on a single mega-model to know everything.
Key Advantages:
- Data Privacy & Ownership: Enterprises and individuals retain total control over their proprietary knowledge graphs and domain data rather than handing sensitive information to central hyperscalers.
- Deterministic Control: Domain authorities operate strictly within their designated boundary/environment, drastically reducing hallucinations and preventing out-of-bounds execution.
- Resource Efficiency: Running lean, targeted models requires exponentially less compute and energy than serving a trillion-parameter monolithic model for every mundane query.
- Technological Model: Adaptive AFSMs & Heuristic Routing
Assessment: Sound Conceptual Abstraction.
Why it works: While base neural networks process probabilistic token prediction, structuring agentic execution as an Adaptive Augmented Finite State Machine (AAFSM)—mapping intent to state transitions, context, and dynamic graph frameworks—is an effective way to engineer deterministic reliability on top of LLM non-determinism.
Nuance: The main operational hurdle with self-modifying state machines (adding/modifying states dynamically) is preventing "state explosion" or unexpected looping during edge cases. Robust state-validation guards are necessary to keep the graph bounded.
- Regulatory Roadmap: Capping Size & Mandatory Conductors
Assessment: Logically Sound, but Enforceability is the Challenge.
Why it works:
Capping footprint/parameter size effectively forces the market toward optimization, efficiency, and modular orchestration rather than raw brute-force scale.
The "conductor" concept (an independent monitoring process checking for safety/replication violations) mirrors proven real-time security monitor/hypervisor models in OS design.
The Challenges:
Global Enforcement: International AI regulation faces the classic geopolitical prisoner’s dilemma—if one country bans monolithic AGI, an adversary may continue developing it in secret for competitive or military advantage.
Open Source vs. Capped Size: As open-weight models become increasingly capable at smaller sizes, enforcing "conductor" compliance on locally hosted, open-source weights becomes difficult without hardware-level trust anchors (like secure enclaves or TPM modules).
- Socio-Economic Realities: Automation & Capital
Assessment: Spot-on Reframing.
Why it works: Pointing out the irony of taxing efficiency (automation taxes) while ignoring the systemic disruption of centralizing AI wealth among 3–4 hyperscalers cuts straight to the economic dilemma. Recognizing that these socio-economic side effects are broader societal/policy challenges—rather than pure engineering problems—is a vital boundary to draw.
Summary Verdict
Your thesis offers a pragmatic alternative to the current "bigger is always better" AGI arms race. By treating AI as a distributed network of specialized domain engines coordinated by lightweight context routers, you outline a vision that is economically sustainable, technically controllable, and far safer for society than a monopolistic central oracle.
