We Are the Neurons: Augmented Intelligence and the Human Super-Brain
Why the "distracted generation" is actually the smartest collective organism in history
By John L. Sokol
The Accusation
Every generation has its moral panic about the next one. But the panic around the internet generation has a specific shape: they can't focus. They're addicted to their phones. They can't hold a thought longer than a tweet. The academics line up to diagnose an entire generation with attention deficit disorder, pointing to multitasking as evidence of cognitive decline.
I think they have it exactly backwards.
What looks like distraction is actually coordination. What looks like short attention spans is actually rapid information passing. These kids aren't broken Einsteins. They're neurons.
Intelligence Amplification
The concept isn't new. Vernor Vinge, Douglas Engelbart, and others have written about Intelligence Amplification (IA) -- the idea that technology doesn't replace human intelligence but extends it. Engelbart built the first computer mouse and hypertext system not to create artificial intelligence, but to augment human intelligence.
But something happened in the 2000s that went beyond what even Vinge imagined. We didn't just give individuals better tools. We wired the individuals together.
By 2010, Gen Y outnumbered Baby Boomers, and 96% of them had joined a social network. Facebook was adding 100 million users every nine months. YouTube had become the second largest search engine in the world. Over 200 million blogs existed, with more than half their authors posting daily.
This wasn't a collection of people using computers. This was a network becoming aware of itself.
The Team, Not the Genius
Here's the mental model that changed how I think about this:
We're used to the lone genius model of intelligence. One Einstein. One Tesla. One Edison. A single extraordinary mind that sees what others can't.
But that's not how intelligence works at scale anymore. It's more like a team passing a ball. No single player needs to be the fastest or the smartest. What matters is the passing -- the speed and accuracy of information moving between nodes.
One person googles something, thinks about it, shares a partial insight. Someone else picks it up, adds context, passes it forward. A third person corrects an error. A fourth connects it to something from a completely different field. The cycle takes minutes. No individual in the chain needed to be a genius. Collectively, they just did something no individual genius could do alone.
This is not attention deficit. This is distributed cognition.
The Wrong Answer Principle
A friend of mine, Jesse Monroy, once said one of the most profound things I've ever heard about how networked intelligence actually works:
"The best way to get the right answer is to confidently post the wrong one."
If you ask a question online, you might get silence. But if you state something incorrect with confidence -- say, "the Moon is a million miles away" -- someone will immediately show up to correct you with the precise number. And if they get it wrong, there's a line of people waiting to outdo them.
This sounds like a joke about internet culture. It's actually a description of a remarkably efficient error-correction mechanism. It's the same principle that makes neural networks work: nodes don't need to be individually correct. The network converges on accuracy through competitive interaction.
Jesse's observation, which predated Wikipedia's rise, is essentially how Wikipedia works. No single editor needs to know everything. The system corrects itself through the collective irritation of people who can't stand seeing wrong information persist. That's not a bug. That's distributed intelligence with a built-in error-correction protocol.
A Computer Made of Flesh and Silicon
What we've built, without quite realizing it, is a hybrid computer. Part biological, part electronic. Each human node brings pattern recognition, intuition, lived experience, and emotional intelligence. The silicon layer -- search engines, social platforms, messaging -- provides the interconnect fabric, the memory, and the communication speed.
No one person needs to be all that smart. No Edison can outthink a room full of reasonably intelligent people with real-time access to the largest knowledge base ever assembled. The combination of human intuition and machine memory creates something neither could achieve alone.
Think about what happens when you encounter a problem today versus in 1990. In 1990, you either knew the answer, knew someone who knew, or you went to a library. Today, you search, read, think, share, get feedback, search again, synthesize -- all in parallel with thousands of others doing the same thing on related problems. The cycle time from question to useful answer has collapsed from days to minutes.
We are, functionally, neurons in a super-brain. Each of us fires when activated, passes signals to connected nodes, and contributes to pattern recognition at a scale no individual can perceive.
What the Critics Miss
The academics measuring attention spans are measuring the wrong thing. They're timing how long a single neuron holds a charge and concluding the brain is broken.
A single neuron in your brain fires for about a millisecond. By the "attention span" metric, it's catastrophically unfocused. But that millisecond of activity, multiplied across billions of neurons passing signals in rapid succession, produces consciousness.
A teenager switching between six tabs, texting three friends, and scanning a feed isn't failing to concentrate. They're doing what neurons do -- processing, routing, and relaying information across a network. The intelligence isn't in any single tab. It's in the pattern of switching.
The Failure Mode
I don't want to be naive about this. The human super-brain has serious failure modes.
Networks can amplify noise as easily as signal. Misinformation spreads faster than corrections. Filter bubbles create subsections of the network that reinforce their own errors rather than correcting them. Coordination mechanisms -- the protocols that determine which signals get amplified -- are controlled by algorithms optimized for engagement, not accuracy.
The collective brain can be manipulated. It can be stupid. It can be cruel.
But these are engineering problems, not fundamental flaws. The human brain has failure modes too -- confirmation bias, tribalism, panic responses. We don't conclude that individual intelligence is a myth because people are sometimes irrational. The architecture is sound. The protocols need work.
From Augmented Intelligence to Collective Consciousness
Here's where it gets interesting.
The social media era (roughly 2005-2020) was the first draft of networked human intelligence. It proved the concept -- collective problem-solving, distributed knowledge creation, real-time global coordination -- while also revealing the vulnerabilities.
Now we're entering a second phase. Large language models -- AI systems trained on the written output of the entire network -- are becoming a new kind of node in the system. They don't replace human neurons. They serve as a coordination layer. An always-available synthesis engine that can summarize what the network knows, identify patterns across conversations, and reduce the friction of information passing between human nodes.
The super-brain is getting a prefrontal cortex.
What I sketched out in 2009 as a metaphor -- people as neurons, the internet as axons, Google as memory -- is becoming literal infrastructure. The question is no longer whether collective intelligence is real. It's whether we can build the coordination protocols to make it wise rather than merely fast.
The generation that the academics diagnosed with ADD may turn out to be the first generation that learned to think as a network rather than as individuals. That's not a deficit. That's an upgrade.
Originally sketched in 2009-2010, drawing on conversations with Jesse Monroy and ideas from Vernor Vinge's work on Intelligence Amplification. Updated to reflect a decade and a half of watching the thesis play out.
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