Speaker 1: I'm Ruby Jones and you're listening to seven AM. Companies
Speaker 1: are betting big on AI and of burning through cash
Speaker 1: to do it. They're pouring hundreds of billions of dollars
Speaker 1: into building data centers and developing new models such as
Speaker 1: chat GPT, and so far these projects are yet to
Speaker 1: see a financial return. There is one clear winner, however, Nvidia,
Speaker 1: the company that makes the chips that power this tech.
Speaker 1: But how long can its customers keep spending before they
Speaker 1: have to either turn a profit or scale back. Today
Speaker 1: Reuter's journalist Stephen nellis on how long this AI boom
Speaker 1: can last and what a crash might look like. It's Thursday,
Speaker 1: November twenty seven, So Stephen, Nvidia had its latest earnings call,
Speaker 1: and you were listening in on that call, So tell
Speaker 1: me as it happened.
Speaker 2: What will you listening out for.
Speaker 3: I think the biggest thing we're listening for is any
Speaker 3: sign that the AI bubble is near bursting, wif there
Speaker 3: is a bubble at all, So any sign of a
Speaker 3: lack of demand, any sign of faltering interest in videos,
Speaker 3: chips or an AI as a field. And largely we
Speaker 3: didn't hear any of that. What we heard was a
Speaker 3: forecast that was better than Wall Street analysts were expecting,
Speaker 3: revenue that came out better than Wall Street was expecting,
Speaker 3: and a lot of talk about how it's not an
Speaker 3: AI bubble, but instead a tipping point toward an era
Speaker 3: of AI that in Video believes will be much more durable.
Speaker 4: Social intelligence chip maker in Videos latest earnings report came
Speaker 4: in and higher than Wall Street expected.
Speaker 5: The world's most valuable.
Speaker 4: Company reported fifty seven billion dollars in third quarter revenue.
Speaker 2: And so how much is Nvideo worth right now?
Speaker 3: Well, it depends on the day, but they're fairly consistently
Speaker 3: over four point five trillion dollars, and at various times
Speaker 3: during the year they've been not just the most valuable
Speaker 3: publicly traded company currently, but the most valuable publicly traded company.
Speaker 5: In the history of business.
Speaker 3: And that's largely due to the unique place that they
Speaker 3: occupy in the AI ecosystem, which is that some eighty
Speaker 3: to ninety percent of the market is their chips training
Speaker 3: these systems that many people use every day. So whether
Speaker 3: that's chat, GPT, whether that's some other app that you
Speaker 3: use that has AI working in the background, the overwhelming
Speaker 3: odds are that it exists because it was trained on
Speaker 3: data that was run through in Video's chips.
Speaker 1: Okay, so in Vidia has the monopoly on these chips.
Speaker 1: Who is buying them?
Speaker 3: So the biggest buyers and this was an interesting thing
Speaker 3: to come out of the earning support. There are four customers.
Speaker 3: We don't know precisely who they are, but they make
Speaker 3: up sick nexty one percent of in Video's revenue. And
Speaker 3: the most likely candidates for that are some of the
Speaker 3: names that you're probably familiar with. Open Ai, which is
Speaker 3: the creator of chat, GPT, Meta which owns Instagram, Facebook, WhatsApp,
Speaker 3: and other properties. And then other firms like Microsoft, which
Speaker 3: is deploying artificial intelligence.
Speaker 5: Across its apps like Office.
Speaker 3: But they're all snapping them up as quickly as possible
Speaker 3: and trying to string them together into huge piles of
Speaker 3: tens of thousands. Some are even talking about hundreds of
Speaker 3: thousands of chips at once, so they are buying in bulk.
Speaker 1: Okay, So you've got those companies spending huge sums on
Speaker 1: chips that they're buying from Video. But many of these
Speaker 1: AI companies are not actually making a profit yet, So
Speaker 1: tell me about this gamble.
Speaker 3: So right now we're in an interesting time where a
Speaker 3: lot of people are trying these things. I mean, it'd
Speaker 3: be hard to find a listener who hasn't chatted with
Speaker 3: some kind of a chatbot, clicked an image generation button somewhere,
Speaker 3: or even just had something read over a passage or
Speaker 3: a sentence and see if it can improve it. Now,
Speaker 3: the question is, when you did that, how much did
Speaker 3: you pay for it? And if you didn't pay for
Speaker 3: it directly, how many advertisements did you sit through before
Speaker 3: you use that product. One of the issues that we
Speaker 3: have right now is there's a big question all over
Speaker 3: the tech world about how much will people pay for
Speaker 3: these things directly based on how much they perceive it's worth. Now,
Speaker 3: let's talk about the cost side, though, because that's I
Speaker 3: think what's really driving a lot of the lack of
Speaker 3: profitability for the time being, and that is that these
Speaker 3: chips are very, very expensive. They tend to sell them
Speaker 3: in a big server rack that will have something like
Speaker 3: seventy two are coming soon more than one hundred of
Speaker 3: these chips, and those are many hundreds of thousands of
Speaker 3: US dollars, if not into the millions of dollars for
Speaker 3: a single server. So they are expensive and the electricity
Speaker 3: did to operate them is very expensive. You've probably read
Speaker 3: that many of these data centers take as much electricity
Speaker 3: as a small city. So what you have is a
Speaker 3: really simple situation where it costs a lot of money
Speaker 3: to make the thing, and it's unclear exactly how much
Speaker 3: people are willing to pay for that thing in the
Speaker 3: short term.
Speaker 5: What is the path to profitability? When do you see
Speaker 5: the lines cross? If you will?
Speaker 3: I mean, I think it could have happened sooner than
Speaker 3: I originally thought, if we wanted it too.
Speaker 2: But it seems to me like the right thing to
Speaker 2: do is to just keep investing in computing.
Speaker 3: So when we look at companies like open ai, there
Speaker 3: have been some reports that they generated about four point
Speaker 3: three billion dollars in revenue for the first half of
Speaker 3: the year and on on track to hit their revenue
Speaker 3: target of thirteen billion dollars for twenty twenty five. But
Speaker 3: that's with a cash burn of about eight and a
Speaker 3: half billion, and that doesn't even take into account all
Speaker 3: of the other operational costs.
Speaker 4: You know, they have what ninety percent of search queries Okay,
Speaker 4: they already have that was never going to get better
Speaker 4: than that. They have seventy percent of like search ad revenue. Right,
Speaker 4: you can see how this company is deemed to be
Speaker 4: a monopoly.
Speaker 3: And then we can contrast that with companies like Microsoft
Speaker 3: or Google, which are spending lots and lots of money
Speaker 3: their capital lot ways, you know, are are north in
Speaker 3: the tens of billions of dollars and cumulatively in the
Speaker 3: hundreds of billions of dollars. But in the case of
Speaker 3: those broader companies, they're suffusing that AI into other products
Speaker 3: that already make money and just charging more for it.
Speaker 3: So we have seen companies like Google and Microsoft remain profitable,
Speaker 3: and then they also just have very large businesses renting
Speaker 3: out their data centers as sort of picks and shovels
Speaker 3: to other companies trying to cash in on this AI
Speaker 3: gold rush, and that's still a pretty profitable business for
Speaker 3: those companies as well. So I think the profitability question
Speaker 3: ultimately comes down to are you trying to make money
Speaker 3: just generating the technology and monetizing it, or do you
Speaker 3: spread it out over other stuff that's already making money.
Speaker 1: Okay, but Nvidia is doing great of fall of this,
Speaker 1: as you say, the most valuable listed company in human
Speaker 1: history at the moment.
Speaker 2: But will that last?
Speaker 3: That is the four point five billion dollar question, and
Speaker 3: I think the answer is a complicated one. So in
Speaker 3: Video's chips are still in high, high demand, and what
Speaker 3: that means is that in Vidia has a lot of leeway.
Speaker 5: In how they price those things.
Speaker 3: Now, the flip side of this is, you know, you
Speaker 3: think about other big semiconductor companies over time. One that
Speaker 3: people might have heard of from a previous generation would
Speaker 3: be Intel. So back in the nineteen eighties, Intel really
Speaker 3: jumped into the personal computer market. They had a very
Speaker 3: very durable run for thirty or forty years. Now, is
Speaker 3: in Vidia going to last that long? It depends on
Speaker 3: whether the kind of computing that we do the most
Speaker 3: of in the next few decades is the kind that
Speaker 3: in Vidia is good at or some other kind. I
Speaker 3: think that question is far from settled. You know, right
Speaker 3: now we're in the phase we're building the AI, we're
Speaker 3: training the AI. In Vidia has a monopoly on that market.
Speaker 3: But in terms of actually deploying the AI, putting AI
Speaker 3: in your computer at home, putting AI in some kind
Speaker 3: of widget that sits on your countertop, those chips are
Speaker 3: not all made by and Video, and that market is
Speaker 3: much more competitive in the long.
Speaker 1: Term coming up, could the AI bubble trigger a financial crisis?
Speaker 2: So Stephen, we have these companies.
Speaker 1: Open AI, Microsoft, Google, They obviously see the value in
Speaker 1: investing in this technology, even if it isn't making them
Speaker 1: a return at this stage. So how are these companies
Speaker 1: trying to crack this problem of profitability?
Speaker 3: So the biggest thing is going to things that cost
Speaker 3: a lot of money to companies right now. And one
Speaker 3: of the things these companies spend the most money on
Speaker 3: is engineers who write software code right, And one of
Speaker 3: the things that they're trying to make money off of
Speaker 3: is going to other companies in the software business and saying, listen,
Speaker 3: you can write in the case of Microsoft, as much
Speaker 3: as thirty percent of your code using some of these
Speaker 3: AI tools and make your team seems much more efficient.
Speaker 3: So in the short term, I think a lot of
Speaker 3: it is automating white collar work, professional work that companies
Speaker 3: currently spend a lot of money paying humans to do.
Speaker 3: In the longer term, I think it's a much more
Speaker 3: mixed bag where some of the interesting scientific use cases,
Speaker 3: so developing new drugs and things like that, I think
Speaker 3: we'll have to see for those but the longer horizon
Speaker 3: is making new things that we really didn't think we
Speaker 3: can make or wouldn't be possible to make without this technology.
Speaker 3: And I think most of these companies think that you're
Speaker 3: going to have some kind of personal assistant, that we'll
Speaker 3: be able to take care of a lot of things
Speaker 3: in your behalf, and that eventually, if in video's grandest
Speaker 3: visions ever come to pass, that virtual assistant would have
Speaker 3: some kind of physical manifestation, a robot that can do
Speaker 3: the things around your house that you want to be done. Now,
Speaker 3: we should say we're really far off from that right now.
Speaker 3: I don't think you should and plan on having your
Speaker 3: floor scrubbed or your toilets cleaned by robots anytime soon,
Speaker 3: but it is something they're thinking about.
Speaker 1: And there is so much speculation about the growth of
Speaker 1: these companies and the future of AI and fears that
Speaker 1: this could all be a bubble that is set to bursts.
Speaker 1: So what are your thoughts on that and where this
Speaker 1: is all headed.
Speaker 3: One thing the markets are very good at is making
Speaker 3: a judgment about profitability over some kind of term that's
Speaker 3: not that far out. So I would say we probably
Speaker 3: have a few years for some of these companies to
Speaker 3: really start showing a profit, and if they don't, the
Speaker 3: question is whether all the investment and sustaining that will
Speaker 3: be worth it. You know, if you sort of look
Speaker 3: at two previous global financial catastrophes, the dot com bubble
Speaker 3: and then the financial crisis that followed it, the dot
Speaker 3: com bubble was bad, but because it was largely limited
Speaker 3: to the stock market, the societal fallout wasn't that bad,
Speaker 3: at least in the United States. It's a very different
Speaker 3: case in the financial crisis, where that really wrapped up
Speaker 3: a lot of people's homes and their personal livelihood in
Speaker 3: a much more direct way. It's a tricky one to
Speaker 3: try to understand what the AI bubble will because, on
Speaker 3: the one hand, most of the wild wealth creation we've
Speaker 3: seen has been, for better or worse, limited to a
Speaker 3: very small upper strata of US investors. On the other hand,
Speaker 3: if you look at the economic data in the United
Speaker 3: States right now, a lot of what is propping up
Speaker 3: the economy is spending on this boom, and that spending
Speaker 3: is going to people who work in the trades, who
Speaker 3: work in pipe fitting, who are electricians. There's an electrician
Speaker 3: shortage right now. If you are an electrician. You should
Speaker 3: probably get on a plane and come to the United
Speaker 3: States if your codes in your country are similar, because
Speaker 3: you will probably find work.
Speaker 1: So the crash, if there is one, could affect ordinary people.
Speaker 3: The one thing that is apparent is that if you're
Speaker 3: just your average school teacher, firefighter, police officer who has
Speaker 3: a pension fund that is funded broadly by the US
Speaker 3: stock market, there is no from this because most of
Speaker 3: the gains in the stock market have been coming from
Speaker 3: this handful of firms that are really concentrated in the
Speaker 3: AI market. But I don't think it's quite like the
Speaker 3: nineteen twenties where a lot of people went out and
Speaker 3: borrowed money to buy stock in these companies. But the
Speaker 3: question is how much of it is paper money versus
Speaker 3: you know, people's homes and livelihoods every day, and that's
Speaker 3: just much harder to predict.
Speaker 2: Well, Stephen, thank you so much for your time.
Speaker 5: Thank you for having me.
Speaker 2: Also in the.
Speaker 1: News, economists predict a December interest rate cut is looking
Speaker 1: increasingly unlikely following the latest inflation data. The Bureau of
Speaker 1: Statistics consumer price index rose from three point six percent
Speaker 1: in September to three point eight percent in October. The
Speaker 1: largest contributors to inflation were housing, food and non alcoholic beverages.
Speaker 1: And for the first time ever, Australian voters see Labor
Speaker 1: as a better economic manager than the Coalition, according to
Speaker 1: the results of the Australian Election Study. The study, led
Speaker 1: by the Australian National University and Griffith University, is a
Speaker 1: post election survey of voters' attitudes and behavior and found
Speaker 1: that voters preferred Labour's policies on nine out of ten
Speaker 1: issues looked at in the study. The results also found
Speaker 1: that Peter Dutton was the least popular leader to go
Speaker 1: to a federal election since the survey began in nineteen
Speaker 1: eighty seven. I'm Ruby Jones. This is seven am. Thanks
Speaker 1: for listening.