Bigger isn't always better when it comes to AI.

From American Public Media, this is Marketplace Tech.

I'm Megan McCarty Carrino.

By now, you probably know the term large language models. They are the systems that

underlie AI chatbots like chatchipT. And they're called large because typically the more data

you feed into them, like say all the text on the internet, the better those models perform. But in recent

months, there's been a lot of chatter about the prospect that ever bigger models might not

deliver the transformative performance gains hoped for. Enter small language models.

MIT Technology Review recently listed the systems as one breakthrough technology to watch in 2025.

We spoke to executive editor Neil Firth about why they made the list.

It's only of the last sort of year or so that they started putting out these models.

And I think this is the,

from now on is where we're going to see how they're actually going to be used. And one of the issues of these large language models, is apart from them being such an energy suck and drain,

environmentally damaging as well, they're also really expensive to run. And for businesses,

particularly who want to, like engage in AI, sticking into their business, they want to have something that's a bit more efficient. And for businesses, particularly, who want to engage in AI, sticking into their business,

they want to have something that's a bit more efficient, and they can train it to do just

the thing they want. If you want an AI to do your one pass, it'd be like a customer service

chatbot or something, you don't need the entire internet's worth of information in there.

You just need something that's really good

at answering questions about like insurance products or something. The thing about them being

really smaller is they couldn't be able to run them on our devices, which you can't do the

moment. So if you've got, might have the chat, you BT app on your phone, but when you speak to

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