In the early days of computing, everyone thought about a big, colossal, mainframe computer

that had all the data. And eventually, the model switched. And what we ended up with is

billions of individual small computers, all networked and operating as a collective,

might AI go the same way, where we move from giant large language models owned by a few

companies to lots and lots of individual AI agents that are decentralized and operating together.

And what does this have to do with solving diseases

and why in the future we'll have an army of agents solving problems for us out there?

And what to make of the fact that we will have created a whole new species

that runs at a timescale a trillion times faster than we do.

Welcome to Inner Cosmos with me, David Eagleman. I'm a neuroscientist and author at Stanford,

and in these episodes, we sail deeply into our three-pound universe to understand why our lives look the way they do.

This is an I-Heart podcast.

This is an I-Heart podcast. Today's episode is about what happens when intelligence becomes decentralized.

When we talk about artificial intelligence, the conversation is typically about one

giant model, a mind-blowingly massive data set and a huge compute cluster so that we get one

singular model that takes care of everything. But the first thing to note is that's not how the brain

works. As I argued in my book Incognito, the brain is

best understood as a team of rivals. Inside your skull are scores or hundreds of specialized modules.

Some care about vision, hearing, touch, some about walking, so about processing faces, others track

moving objects in the world.

Other systems are working to predict what you should do next.

But the key is that these systems in the brain are not like pieces of a very efficient machine

...