Tag: Insight

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AI AI: Large Language Models

The Architecture of Unpredictability

There is a special understanding that comes from looking too closely at a map of a massive network or a large city. There is a point where the individual components vanish, and something else—something “other”—takes over.

Niall Ferguson captures this beautifully in The Square and the Tower:

“Large networks are complex systems which have ‘emergent properties’ – the tendency of novel structures, patterns and properties to manifest themselves in ‘phase transitions’ that are far from predictable.”

We like to believe we are the architects of our systems. We build platforms, we codify laws, and we design cities with the intent of order.

But Ferguson points out that once a network crosses a certain threshold of complexity, it enters a state of “phase transition.” It’s like water reaching 100°C; it doesn’t just get “hotter”—it becomes steam. It changes its fundamental nature.

We see this most vividly today in the trajectory of Artificial Intelligence. An LLM is, at its core, a gargantuan network of weights and probabilities. We understand the math of the individual neuron, yet we cannot fully explain how, at a certain scale, these systems begin to exhibit reasoning, humor, or theory of mind. These are not explicitly programmed “features”; they are emergent properties—the ghost that moves into the machine once the network becomes sufficiently dense.

Dario Amodei, CEO of Anthropic, describes this phenomenon through the lens of scaling:

“The thing that is so surprising about these models is that as you scale them up, they just keep getting better at things you didn’t explicitly train them to do… there’s this sense in which the model is ‘learning’ the structure of the world just by being forced to predict the next word.”

This is the “emergent property.” It is the intelligence of the beehive that no single bee possesses. It is the sudden, viral revolution that no single activist could have ignited. These properties are far from predictable because they don’t live in the nodes of the network; they live in the relationships between them.

The philosophical weight of this is humbling. It suggests that our world is governed by a structural momentum that defies linear logic.

When we find ourselves in these moments of societal or personal transition, perhaps the goal isn’t to control the outcome, but to understand the new physics of the system we’ve helped create.

We aren’t just parts of the network; we are the medium through which the unpredictable manifests.

Questions to Ponder

  • If your own consciousness is an emergent property of your neural network, where does “you” actually reside?
  • In the social networks we inhabit daily, what properties are emerging that we haven’t yet named?
  • As AI continues its phase transition, are we creating a tool, or are we witnessing the birth of a new kind of physics?
Categories
Curiosity

The Neutral Ground of Curiosity

We live in a time that demands certainty. We are constantly pressured to have a stance, to pick a team, to decide—right now—whether something is good or bad, right or wrong. It is exhausting. It feels like standing in a courtroom where you are forced to be both the lawyer and the judge.

But there is a quieter, more fertile ground we can stand on. Rick Rubin, writing in The Creative Act, describes it like this:

“The heart of open-mindedness is curiosity. Curiosity doesn’t take sides or insist on a single way of doing things. It explores all perspectives. Always open to new ways, always seeking to arrive at original insights.”

I love the idea that curiosity “doesn’t take sides.” It implies that curiosity is a neutral party. It isn’t there to win an argument; it is there to understand the argument.

When we approach the world with judgment, our vision narrows. We look for evidence that confirms what we already believe. But when we approach the world with curiosity, the lens widens. We stop asking, “Is this right?” and start asking, “What is this?”

Rubin reminds us that the goal isn’t to be correct; the goal is to be original. And you cannot arrive at an original insight if you are walking the same worn path of binary thinking. You have to be willing to wander off the trail, to listen to the opposing view not to defeat it, but to learn the shape of it.

I remind myself to try to drop the gavel. To stop judging the events of my day and simply witness them. To be the explorer, not the jury. Oh, and along the way, embrace serendipity!

I’m reminded of a couple of friends and colleagues. One seems to listen briefly but rapidly reach a black/white conclusion. Another seems to always want to explore further, asking questions to go deeper. One is much more enjoyable to be around. The other a lot less so! Which one can I be? Which one am I?