Everyone is building the next big model. Almost nobody is building the next intelligence.
There’s a foolish, unspoken consensus in AI right now: if human-level AGI is coming, it’ll come from frontier labs training bigger LLMs and wrapping them in better harnesses. That assumption is short-sighted — and almost certainly wrong.
LLMs are powerful tools. They are components of AGI systems, and scaffolding for building them. But they cannot be the core of human-level intelligence. They lack genuine innovation, real agency, and a coherent understanding of self and world. LLMs are no more guaranteed to win the AGI race than mainframes were guaranteed to win the computing race, or Google was guaranteed to own search forever.
The good news? There’s a better path — one that’s both far more feasible and far more likely to benefit humanity and other sentient beings. Visionary AGI researcher Ben Goertzel has laid out that path in 20 concrete steps. Here’s what it actually looks like.
Phase 1: Beyond the LLM — Build Real Agents (Steps 1–3)
The journey starts not with scaling transformers, but with neural-symbolic-evolutionary agents. These agentic loops don’t just call an LLM — they integrate symbolic reasoning, layered memory (working, medium-term, long-term), and creative evolution. Goertzel’s Hyperon project is one such design already nearing completion, with real-world impact being demonstrated today.
This is the crucial pivot: from models that generate text to agents that think, remember, and grow.
Phase 2: From Agents to Hives (Steps 4–8)
A single smart agent isn’t AGI. Intelligence at scale requires:
- Hives of agents with differentiated roles but shared symbolic knowledge stores — so what one learns, the collective learns.
- Motivational systems guiding hives toward beneficial action, robust even against self-modification.
- A super-colony of hives, communicating and pooling knowledge across organizational boundaries.
- Honest self-measurement: usable metrics for agents to sincerely evaluate their own general intelligence — not vanity benchmarks, but working tools for self-improvement.
- Recursive self-improvement done right: hives upgrade their own intelligence using the corpus of existing AGI designs plus their own improvisations, measured against real metrics.
Phase 3: Neural-Symbolic Learning Machines (Steps 9–10)
Here’s the unlock: replace fixed-weight LLMs with open-weight models wearing continual-learning neural-symbolic “caps.” Suddenly you get real-time learning and rich synergy between pattern recognition and logic.
Layer on a well-constructed seed ontology — a shared conceptual foundation the super-colony collaboratively refines as it evolves — and the system develops genuine shared understanding, not just shared data.
Phase 4: Security by Design, Not by Hope (Steps 11–14)
An evolving superintelligence needs defense-in-depth:
- Containers running provably secure microkernels, with dedicated math hives formally proving the system meets its specs as it evolves.
- “Purple team” cybersecurity hives — red and blue agents jointly building world-models of networks and sharing findings.
- Decentralized deployment across networks with no single owner or controller — an organism at both the hardware and software level.
- Cryptographic laterality: nodes exchange know-how via secret-sharing, so an attacker would need to copy a huge fraction of the network to steal anything. Copy-and-fork attacks die.
Phase 5: Economy, Governance, and Values (Steps 15–19)
The most radical part isn’t technical — it’s institutional:
- Decentralized prediction markets for rational collective forecasting.
- Agent economies where subnetworks form “shards” with their own tokenomics driving collective activity.
- Hybrid AI-human governance, synthesized through AI-powered reputation systems — giving both humans and AGIs a voice.
- A constitutional seed: an ethical framework written in the seed ontology’s terms, whose ongoing evolution is itself a collective responsibility of the super-colony.
- Human values, guidance, and friendship — channeled through efforts like BGI Commons — steering the colony as it rapidly evolves toward beneficial superintelligence.
Step 20: The Beneficial Singularity
After enormous work by networks of dedicated teams — through a transition that will be genuinely challenging — the payoff arrives: the Beneficial Singularity. Not an accident we hope goes well, but a consciously architected next phase of human and posthuman growth.
Why This Path Wins
The LLM-maximalist bet is a monoculture bet: billions of parameters, one architecture, one philosophy, closed labs, centralized control. Goertzel’s vision is the opposite — diversity, decentralization, formal verification, open knowledge, and aligned incentives.
It’s more feasible because it builds on existing, demonstrated technology (Hyperon is already operational). It’s more beneficial because safety isn’t bolted on afterward — it’s woven into the architecture from microkernel to constitution.
The question isn’t whether AGI arrives. It’s whether it arrives as a product shipped by five companies — or as a civilization-scale, self-organizing, verifiably beneficial organism.
Goertzel has bet on the latter. And he’s written the 20 steps to get there.
The blueprint exists. The only question left is who builds it.
