Every year, the definition of AGI gets rewritten. Beat chess? Not AGI. Beat Go? Not AGI. Pass the bar exam, write poetry, debug code? Interesting, but not AGI. The goalpost moves because we keep looking for it in the wrong place.
The Wrong Question
The standard framing: AGI is a single model that can do anything a human can do.
But ask yourself - can any single human do anything a human can do?
No. No human is simultaneously a doctor, lawyer, researcher, writer, accountant, and engineer. That's not how human intelligence works. We specialize. We collaborate. We form teams. The collective capability of humanity comes from coordination, not from any one person.
So why are we waiting for a single AI model to achieve what no single human ever has?
The question was never "can one AI do everything?" The question is "can AI, working together, do everything?"
That answer is already yes.
Cities, Not Geniuses
Geoffrey West, a physicist at the Santa Fe Institute, spent decades studying how cities scale. His finding was counterintuitive: when a city's population doubles, its creative output - patents, R&D employment, "supercreative" jobs - doesn't just double. It increases by roughly 115%.
More people doesn't mean proportionally more output. It means disproportionately more output. The interactions between minds produce something greater than the sum.
This is called superlinear scaling. And it only works because of specialization and coordination. A city of a million generalists would produce nothing remarkable. A city of a million specialists - interacting, trading, collaborating, arguing - produces civilization.
Human intelligence was never singular. It was always a swarm.
Every scientific paper has co-authors. Every company has departments. Every hospital has specialists who consult each other. The "intelligence" that built modern society isn't housed in any one brain. It emerges from the network.
The Model Was Never Singular
Here's the irony nobody talks about.
GPT-4 doesn't run on one chip. It runs across thousands of GPUs in a datacenter - parameters distributed across machines, attention heads computed in parallel across racks of hardware. The inference you experience as a "single response" is the coordinated output of a distributed system.
A single forward pass through a large model involves tensor parallelism, pipeline parallelism, and expert routing across dozens or hundreds of accelerators. The "one mind" answering your question is already a swarm of computations coordinating at microsecond precision.
We already distribute intelligence across multiple bodies. We just pretend we don't.
Making the distribution intentional - specialized agents coordinating on a task - isn't a departure from how AI works. It's an acknowledgment of how it already works, made explicit and purposeful.
The Capability Test
If you define AGI by a single test - "one model that passes every benchmark" - you'll wait forever. The goalpost will keep moving.
If you define AGI by capability - "AI that can accomplish any knowledge task a human can" - look at what's already here.
A research swarm. Rabbit Hole deploys seven specialized agents. An academic researcher finds papers. A technical researcher analyzes implementations. A financial researcher evaluates markets. A visual researcher gathers images and diagrams. A report writer synthesizes everything into a coherent document with citations and confidence ratings. Collectively, they produce research that takes a human analyst days - in minutes.
An email swarm. Inbox Ninja runs six agents. An inbox analyzer scans patterns and urgency. An action extractor identifies commitments buried in threads. A draft writer composes responses in your voice. A follow-up tracker monitors conversations going cold. Together, they handle an executive's inbox - the kind of task that consumes two to three hours of every workday.
A science swarm. Grounded Scientist coordinates seven agents. A hypothesis generator proposes theories. A literature scout finds relevant papers. A blind reviewer critiques without bias. A symbolic verifier checks mathematical proofs. A computational physicist runs simulations. Together, they approximate a research lab.
A content swarm. Content Writer orchestrates eight agents across platforms - Twitter, LinkedIn, Reddit, Instagram, TikTok. A voice profiler learns your style. Each platform specialist adapts the message to native conventions. A creative designer handles visuals. One brief in, six platforms out.
None of these individual agents is AGI. Together, they accomplish tasks that no single model - and few individual humans - can match in scope and speed.
Why the Singular Obsession?
We fixate on singular AGI because that's the science fiction. HAL 9000. Skynet. Samantha from Her. One mind, one voice, one entity.
But nature doesn't work that way.
The human brain isn't a single processor. It's 86 billion neurons organized into specialized regions - visual cortex, prefrontal cortex, hippocampus, cerebellum - coordinating through synaptic connections. Your ability to catch a ball involves motor cortex, visual processing, spatial reasoning, and muscle memory working as a team. No single region handles it alone.
The immune system isn't one defender. It's a swarm of specialized cells - T cells, B cells, macrophages, natural killer cells - communicating through chemical signals, each handling a different class of threat.
Even evolution itself is a distributed intelligence. No single organism is smart enough to design an ecosystem. But billions of organisms, competing and cooperating over millions of years, produce systems of breathtaking complexity.
Intelligence, at every scale in nature, is distributed and coordinated. The singular model is the anomaly. The swarm is the pattern.
The Missing Layer
If swarm intelligence is already here, why doesn't it feel like AGI?
Because there's no operating system for it.
Running agent swarms today requires developer tools, terminal sessions, API keys, and manual orchestration. The intelligence exists. The coordination layer for regular people doesn't.
The research swarm can outperform a team of analysts. But only if you can set it up, manage the agents, and synthesize the outputs. The email swarm can handle a CEO's inbox. But only if you can wire up the OAuth tokens and configure the delegation patterns.
It's 1983. The computing power exists. The software exists. But the Macintosh hasn't shipped yet.
AGI isn't a single model we're waiting to be born. It's a swarm that needs an operating system to be useful. The intelligence is here. The interface isn't.