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Speaker 1: Already and this is this is the daily This is

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Speaker 1: the Daily OS. Oh, now it makes sense. Good morning

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Speaker 1: and welcome to the Daily OS. It's Wednesday, the twenty

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Speaker 1: ninth of January. I'm Sam, I'm Zara. What if creating

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Speaker 1: artificial intelligence programs was actually cheaper than we thought? How

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Speaker 1: would AI companies, governments and investors react if something faster,

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Speaker 1: more accurate, and cheaper than the current AI giants like

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Speaker 1: chatcha Et suddenly burst onto the scene. Well, today we're

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Speaker 1: going to talk about Deep Seek, this new company that

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Speaker 1: has really shaken up the stock market and the way

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Speaker 1: that we're all feeling about artificial intelligence. It's pretty remarkable,

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Speaker 1: stories areah.

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Speaker 2: I think at the beginning of this week, the number

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Speaker 2: of people that could name, identify, and explain what Deep

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Speaker 2: Seek is would have been few and far apart at

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Speaker 2: myself included. I'd say potentially you included in that as well.

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Speaker 2: And now it's all anyone's talking about. It is literally

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Speaker 2: dominating every single headline across the world today. Sam, take

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Speaker 2: us back to the beginning. AI is a very very

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Speaker 2: complicated space. But what exactly is Deep Seek and why

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Speaker 2: the hell are we hearing so much about it?

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Speaker 1: So DC is a Chinese AI company that just launched

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Speaker 1: its latest AI model called R one. Now when I

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Speaker 1: say model, just think of a brain.

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Speaker 3: I can do that.

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Speaker 1: All of these AI companies are trying to work out

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Speaker 1: how to create the best AI brain. Some are better

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Speaker 1: at solving mathematical problems, others are better at expressing, reasoning,

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Speaker 1: or being emotional, trying to mimic human emotion. And of course,

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Speaker 1: the goal for every AI company is to try and

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Speaker 1: build a model or a brain that can do everything.

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Speaker 2: Okay, And so when you're talking about AI companies, an

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Speaker 2: example of that would be open Ai, Creator, Chat ChiPT exactly.

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Speaker 1: But there are many companies and there are many companies

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Speaker 1: that are in the AI space without making models, and

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Speaker 1: we'll talk about that later as well. Now, what's fascinating

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Speaker 1: about deep Seek is that they've built a model that

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Speaker 1: they say performs as well as the best American AI systems,

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Speaker 1: but at a fraction of the cost. So to put

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Speaker 1: that in context, just last week we saw major US

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Speaker 1: companies like Microsoft, Oracle and open Ai, which you just mentioned,

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Speaker 1: pledge five hundred billion US dollars to build new AI infrastructure.

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Speaker 1: Then we also had an announcement from Meta they came

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Speaker 1: out separately and said that they were committing sixty five

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Speaker 1: billion US dollars to build infrastructure. There is so much

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Speaker 1: money being spent on this technology at the moment. Then

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Speaker 1: deep Seat comes along and they say they built their

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Speaker 1: system for about six million dollars, so million versus billion.

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Speaker 1: One expert I was reading called it a joke of

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Speaker 1: a budget.

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Speaker 2: So they're clearly a lot of differences between these companies.

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Speaker 2: But all of the companies that you were just talking

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Speaker 2: about before are American companies. Deep Seek is a Chinese.

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Speaker 1: Chinese AI company that say it can do what American

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Speaker 1: companies can do for a fraction of the cost.

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Speaker 2: Okay, So then tell me more about deep Seek, like,

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Speaker 2: how is it possible that they're doing this?

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Speaker 3: Are they telling the truth? Where have they come from?

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Speaker 1: It's a really interesting story. So it started as a

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Speaker 1: side hustle project of a Chinese it's an absurd side hustle,

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Speaker 1: it's crazy of a Chinese hedge fund called high Flog.

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Speaker 3: Usually like a jewelry brand.

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Speaker 1: So these are coders who had been employed to build

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Speaker 1: systems that would trade stocks and be able to predict

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Speaker 1: for clients when to buy and sell. And they started

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Speaker 1: playing around on the side of that with different ways

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Speaker 1: to build AI products that could be used by consumers,

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Speaker 1: So by you and me. The founder then spun off

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Speaker 1: Deep Seek as a separate company to this hedge fund

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Speaker 1: only in twenty twenty three.

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Speaker 3: Oh wow, so very recent.

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Speaker 1: Yeah, and they started only recently playing around with old GPUs,

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Speaker 1: which what is one of the key tools you need

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Speaker 1: to build an AI machine that they had available to them.

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Speaker 3: What's what's GPUs?

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Speaker 1: So let's break it down. I'm going to use a

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Speaker 1: car analogy. Let's pretend that Ferrari were spending billions of

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Speaker 1: dollars a year on new car engines to try and

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Speaker 1: figure out how to win the Formula One, and then

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Speaker 1: all of a sudden, engineers at Holden, you know, a

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Speaker 1: really un sexy car company, started playing around with a

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Speaker 1: Ferrari part made fifteen years ago, and they approached the

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Speaker 1: challenge of making the fast car from a different perspective

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Speaker 1: with these old parts, and actually they've made a faster car.

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Speaker 1: That's basically what's happened here.

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Speaker 2: Okay, So what you're saying is that this Chinese company,

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Speaker 2: Deep Seek has created this basically beast of Nai machine,

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Speaker 2: are using old parts, old GPUs, which is just the

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Speaker 2: little chips yeah sure, rather than building new ones.

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Speaker 1: And getting exactly the same results.

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Speaker 3: Okay, And is that why the cost is lower?

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Speaker 1: Exactly? And so they say though that it's not actually

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Speaker 1: the new GPUs, the new chips that you need to

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Speaker 1: make really good AI, it's their innovative training techniques. So

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Speaker 1: they say they've found ways to make AI models think

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Speaker 1: more efficiently. And there was one really key part of

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Speaker 1: the research that they say makes their AI different, and

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Speaker 1: that's recognizing that AI has a heart moments. This is

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Speaker 1: actually what they say. They say that AI has a

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Speaker 1: heart moments where they start processing a trying to answer

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Speaker 1: a question you put to them, and then they realize

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Speaker 1: something that might not be on the exact path they

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Speaker 1: were following. They've trained their AI to take that route,

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Speaker 1: so they say, oh, I've actually just thought of this, this,

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Speaker 1: and this, and then go down that route. So instead

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Speaker 1: of it explicitly trying to get to the answer as

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Speaker 1: fast and direct as possible, it almost kind of goes

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Speaker 1: with the flow a bit more. And I'm really simplifying

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Speaker 1: this here.

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Speaker 3: This train is legitimately heading.

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Speaker 2: So what you're saying here is that they are suggesting

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Speaker 2: that their technology is superior because they've trained it to

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Speaker 2: think differently.

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Speaker 1: Essentially, I think that we're probably summarizing artificial intelligence. Yeah,

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Speaker 1: and I think we've probably summarized like twenty five years

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Speaker 1: of quantum physics and quantum computing into six sentences. But yes, essentially,

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Speaker 1: they've figured out how to make old technology think differently

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Speaker 1: and be as good as new technology. Okay, that's what

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Speaker 1: you need to know. Okay, Sure, And so the USAI

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Speaker 1: companies get wind of this, and they start testing this

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Speaker 1: Chinese AI yep, and they give deep Seek and open

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Speaker 1: AI's top models, so the best chatch apt models that

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Speaker 1: you can possibly get. They give the both models the

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Speaker 1: same questions, and deep Seek produces similar, if not better answers.

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Speaker 1: When was this this week? Last week?

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Speaker 3: A panic?

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Speaker 1: Exactly? Now there is an important caveat that I want

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Speaker 1: to mention here. These us AI companies have said that

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Speaker 1: whilst the deep Seek model is impressive at writing in

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Speaker 1: general problem solving, it does struggle with certain specific tasks,

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Speaker 1: so things like you know, booking and a play, Yeah,

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Speaker 1: you're you do struggle with certain specific tasks numbers. So

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Speaker 1: now there's going to be a whole independent testing phase

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Speaker 1: that will give us some clearer results.

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Speaker 3: Okay.

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Speaker 2: The reason that the AI industry basically is freaking out

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Speaker 2: is because this new players come along is doing something

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Speaker 2: for the fraction of the cost, is getting similar results,

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Speaker 2: and it's non an American company.

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Speaker 1: Yeah, okay, and it challenges this fundamental assumption that we

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Speaker 1: have about AI development, which is this conventional thinking that

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Speaker 1: you needed the most expensive, the most cutting edge computer chips,

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Speaker 1: particularly those made by one company in the video, which

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Speaker 1: is one of the most valuable companies in the world.

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Speaker 1: We've talked about it on this podcast before, to build

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Speaker 1: the best AI systems, and that's why tech companies like

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Speaker 1: Google and Meta have been spending insane amounts of money

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Speaker 1: building the infrastructure that they say they need to produce

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Speaker 1: these AI systems, and investors have gone with it.

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Speaker 2: Okay, But I guess the reason that the tech world

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Speaker 2: is in so much disarray this week is because that

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Speaker 2: type of logic might not necessarily be the only.

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Speaker 1: Way exactly, and Deep Seek says that might not be true.

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Speaker 1: And what's more, Deep Seek have released their program as

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Speaker 1: open source, and what that means is they're an open book.

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Speaker 1: They've taken the hold and around the racetrack popped open

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Speaker 1: the hood and said, whoever wants to come and have

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Speaker 1: a look as much as.

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Speaker 3: You want, Why can't they just be copied?

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Speaker 1: They say that they don't care really if they're copied.

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Speaker 1: That this technology almost think about it, like you know,

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Speaker 1: there's been a lot of examples in the last twenty

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Speaker 1: four hours about the space race. They've kind of said,

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Speaker 1: everyone's trying to get to the moon. Here's how we're

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Speaker 1: doing it, hoping that the entire industry becomes more profitable.

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Speaker 1: It's just a different way of doing business. It's very

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Speaker 1: common in the tech world to release something out to

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Speaker 1: the public. And I want to bring up one new

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Speaker 1: term here, and that's the idea of a company having

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Speaker 1: a moat, a mote e moche. So think about a

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Speaker 1: competitive advantage that surrounds a company and literally think about

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Speaker 1: a castle with emotion around it and moat. Yeah yeah,

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Speaker 1: And that's what makes a company hard to copy. So

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Speaker 1: if a company has I feel that far ahead, yeah,

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Speaker 1: but if you have a wide moat, then other companies

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Speaker 1: can't copy you quickly and easily. And so for these

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Speaker 1: AI companies, these really valuable stocks. Their moats are not

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Speaker 1: only the knowledge they have and the expertise, but just

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Speaker 1: how much cash they've got, because the common reasoning has

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Speaker 1: always been to build great AI, you need fifty sixty

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Speaker 1: seventy one hundred, five hundred billion.

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Speaker 3: I understand.

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Speaker 2: So you're saying these companies they're competitive advantages that they've

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Speaker 2: raised in dollars of money, and therefore the average Joe

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Speaker 2: on the street can't go and build the same tech

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Speaker 2: as them because they can't raise a bajillion.

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Speaker 1: Dollars until now, until now when DeepC comes out and.

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Speaker 2: Says, actually, you don't need a bajillion dollars, you only

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Speaker 2: a little bit, only six million, and you can build

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Speaker 2: something just as good.

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Speaker 3: Okay.

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Speaker 2: So what has all of this kind of knowledge and

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Speaker 2: information done to the market, because there has been quite

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Speaker 2: a significant economic kind of market impact.

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Speaker 1: Yeah, it's been really dramatic. So, as I mentioned earlier

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Speaker 1: in the video, the company that makes the computer chips

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Speaker 1: that AI companies buy to build these big AI brains,

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Speaker 1: they lost a whopping get ready, five hundred and eighty

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Speaker 1: nine billion US dollars in the market value on the

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Speaker 1: US stock market yesterday. They went down sixteen point one percent,

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Speaker 1: and the overall US market went down three point one

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Speaker 1: percent crazy. You have to remember that it's not just

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Speaker 1: companies that are directly related to AI, but there are

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Speaker 1: so many adjacent companies that have business in AI. You know,

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Speaker 1: for example, energy companies, they would be relying on the

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Speaker 1: fact that everyone's building these big data centers. They're going

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Speaker 1: to need to plug that all into a wall somewhere.

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Speaker 1: So their stocks went down. The entire market really did

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Speaker 1: freak out about the fact that there's this new way

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Speaker 1: of doing things.

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Speaker 2: And it's not just the market that responds. It is

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Speaker 2: also this kind of geopolitical angle. I've mentioned that deep

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Speaker 2: Seek is Chinese and Open Ai and the other AI

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Speaker 2: companies are American. Talk me through that geopolitical angle.

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Speaker 1: Yeah, So the US has been trying to maintain its

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Speaker 1: competitive lead in AI. They say there's a two horse

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Speaker 1: race between them and China, and the way that they're

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Speaker 1: doing that is by restricting the computer chips that are

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Speaker 1: allowed to enter China. So, to go back to our

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Speaker 1: Ferrari example, they are not letting in new models of

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Speaker 1: a Ferrari engine into China because they don't want Chinese

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Speaker 1: AI companies to rival them. But Deep Seek's success suggests

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Speaker 1: that that restriction might actually be backfiring in two possible ways.

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Speaker 1: Either the chips aren't as crucial as we all think

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Speaker 1: to building these models, or Chinese companies have worked out

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Speaker 1: ways to get around the restrictions.

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Speaker 3: Yeah. So interesting.

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Speaker 2: And when I hear about that, the one thing that

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Speaker 2: I think about is how Donald Trump would be responding

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Speaker 2: to this, because we all know that Trump's platform is

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Speaker 2: about growing specifically American companies and wealth. How has he

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Speaker 2: responded to this?

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Speaker 1: Well, this has become a core focus. I mean already

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Speaker 1: in his first week, full week of being president, he

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Speaker 1: announced a five hundred billion dollar program in collaboration with

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Speaker 1: some really big companies AI company. Yeah, and then just yesterday,

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Speaker 1: talking about Deep Seek, he said that this is a

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Speaker 1: wake up call for the AI industry and that we

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Speaker 1: need to be quote laser focused on competing to win.

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Speaker 3: Wow, it's really like Space Race.

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Speaker 1: It's very much the Space race. Trump's close advisor Elon

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Speaker 1: Musk he speaking of Space Race. Yes, he accused Deep

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Speaker 1: Seak of lying about how many chips it needed to

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Speaker 1: run system. So he essentially said, what you're doing is

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Speaker 1: simply impossible, but there is certainly a bit of panic there.

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Speaker 1: Trump said that he might use executive orders to increase

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Speaker 1: the amount of public reas sources like electricity and energy

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Speaker 1: supplies and building materials to just build these data centers

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Speaker 1: that USAI companies say they need as quick as they can.

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Speaker 2: Sam, I want to finish this chat by just bringing

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Speaker 2: it home. And I guess contextualizing this in an Australian setting,

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Speaker 2: what are the implications for Australia's tech sector? What does

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Speaker 2: this story mean for US?

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Speaker 1: Well, pretty much every country besides the US has had

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Speaker 1: the same reaction, including Australia, which is that this is

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Speaker 1: good news. We've all known that the US has a

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Speaker 1: major competitive advantage in AI, and AI is seen as

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Speaker 1: a really important part of any country's innovation strategy. So

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Speaker 1: our Industry and Science Minister Ed Husick. He said yesterday

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Speaker 1: that he expects to see more of these cheaper and

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Speaker 1: more creative AI solutions in years to come as this

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Speaker 1: becomes more mainstream, and that it kind of evens the

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Speaker 1: playing field a little bit because we don't need five

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Speaker 1: hundred billion dollars. We just need a side hustle in

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Speaker 1: our jobs.

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Speaker 3: How did an our side hustle lead to that?

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Speaker 1: I know, but I think, look, this is still a

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Speaker 1: privately held company Deep Seek, so deep Seak isn't listed anywhere.

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Speaker 1: We're going to see how the stock markets responded. The

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Speaker 1: stock markets in Australia that tech and AI companies were

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Speaker 1: hit pretty hard yesterday, but our stock market isn't as

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Speaker 1: reliant on those big tech giants as in the US.

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Speaker 1: But it's a really seismic shift in the way that

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Speaker 1: we think about AI and I'm going to have to

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Speaker 1: ask Chatcha and see what happens. Now.

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Speaker 2: Well, thank you for explaining that story. I actually feel

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Speaker 2: like I completely understand it now.

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Speaker 1: So you.

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Speaker 3: Well, there you go.

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Speaker 2: Thank you so much for joining us for another episode

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Speaker 2: of The Daily Ods and supporting our independent newsroom. We're

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Speaker 2: going to be back later today with the headlines, but

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Speaker 2: until then, have a great day.

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Speaker 1: My name is Lily Maddon and I'm a proud Arunda

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Speaker 1: Bungelung Calcottin woman from Gadigol Country. The Daily oz acknowledges

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Speaker 1: that this podcast is recorded on the lands of the

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Speaker 1: Strait Island and nations. We pay our respects to the

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Speaker 1: first peoples of these countries, both past and present,