Speaker 1: All Zone Media. Hi, and welcome to the very first
Speaker 1: Better Offline Monologue. This is going to be a short
Speaker 1: weekly episode where I take a quick look at something
Speaker 1: going on in the tech industry doesn't quite warrant a
Speaker 1: full episode. One might say, they're like quick bites of
Speaker 1: content quibis if you will, and this is a business
Speaker 1: model that's proven successful time and time again. This week,
Speaker 1: I'm going to give you a distilled rundown of a
Speaker 1: recent situation at Rock both the economy and the AI world.
Speaker 1: For those of you that either need a refresh or
Speaker 1: rejected the notion of a TUPAC podcast. At the end
Speaker 1: of January, something happened that radically overturned not just the
Speaker 1: AI industry status quo, but also called into question the
Speaker 1: dominance of the American tech industry. Our story starts on
Speaker 1: January twentieth, when a little known Chinese company called deep
Speaker 1: Seek released It's our one AI model, terrifying the Western
Speaker 1: tech behemoths that applowed over two hundred billion dollars combined
Speaker 1: into data centers in industrial grade graphics processing units GPUs
Speaker 1: for others to power generative AI models like those behind chat,
Speaker 1: GPT and anthropics. Claud like open aizo one model, deep
Speaker 1: seeks are one model is a reasoning model, which is
Speaker 1: a way to say that it works through problems step
Speaker 1: by step, showing the users the steps it took to
Speaker 1: reach its conclusion. Generally, when you make a request of
Speaker 1: a generative model, it generates an answer probabilistically, meaning it's
Speaker 1: guessing at each next bit based on the request you've made.
Speaker 1: In the case of open aizo one model, and indeed
Speaker 1: deep seeks are one model, the model thinks. They use
Speaker 1: that term loosely. These models do not know anything. They're
Speaker 1: not thinking. They have no consciousness, but I think through
Speaker 1: each step by generating it piece by piece and reviewing
Speaker 1: it piece by piece with separate parts of the model.
Speaker 1: In theory, this ability to reason means it's well suited
Speaker 1: for tasks where there's a definitive right and wrong answer,
Speaker 1: like logic and maths. It's also what it makes it
Speaker 1: different from the standard CHAT GPT or GPT four US,
Speaker 1: which is considerably faster, as it doesn't undertake this step
Speaker 1: by step thinking and thus is better suited for more
Speaker 1: open ended questions such as what would it be like
Speaker 1: if Garfield had a gun? To be clear, this doesn't
Speaker 1: mean the answers are any good now. Just a few
Speaker 1: weeks earlier, Deep Sea could release another model, albeit a
Speaker 1: far less fanfare, likely due to it being launched there
Speaker 1: after Christmas, of course, but nevertheless, it was called V
Speaker 1: three and it was still pretty impressive. V three competes
Speaker 1: with the same model that powers chat GPTs I just mentioned,
Speaker 1: which at the time of recording this is called GPT
Speaker 1: four zero, and that's a more general purpose kind of product.
Speaker 1: It can write code and solve maths problems, but it's
Speaker 1: better suited for tasks that are rooted in language, writing
Speaker 1: that term paper, summarizing a document, whatever it is you
Speaker 1: do with this. And it's also important to know that
Speaker 1: this is the most commonly used style of model. You're
Speaker 1: not really getting reasoning in everything, at least not yet,
Speaker 1: and I don't know how prevalent it'll ever be now.
Speaker 1: Deep seeks Tech didn't just match open ai and capabilities.
Speaker 1: It was also purportedly cheaper to train and to operate,
Speaker 1: whereas open AI's GPT four model reportedly costs one hundred
Speaker 1: million dollars to train. Some experts estimate the deep Seek's
Speaker 1: reasoning model, called R one cost a lot less than that,
Speaker 1: and their V three model actually costs less than six
Speaker 1: million dollars to train. This figure is open to some debate,
Speaker 1: but the big thing is about these models is they're
Speaker 1: dramatically cheaper. They can be run on your computer, though
Speaker 1: much slower, or they can be run another cloud infrastructure.
Speaker 1: And in the case of the V three model, the
Speaker 1: one that competes with chat GPT, it was actually about
Speaker 1: fifty times cheaper, and the Reasoning model are one about
Speaker 1: thirty which is crazy. Now, these are the prices that
Speaker 1: are run on the servers where deep Seak runs, but
Speaker 1: we're very quickly going to see as other people host
Speaker 1: them exactly how much cheaper they are. And they're more
Speaker 1: efficient too, which is crazy. They's so much more efficient.
Speaker 1: And it's also important to note that they train these
Speaker 1: models using older generation N video chips because they had
Speaker 1: sanctions on them from China. They got some of the
Speaker 1: newer ones too through weird resellers, but nevertheless this made
Speaker 1: it much harder for them to get GPUs in general,
Speaker 1: and thus they were able to kind of squeeze more
Speaker 1: power out than they had to come up with really
Speaker 1: interesting kind of assembly language level stuff where they did
Speaker 1: extra things with the GPUs, the well, the fat and
Speaker 1: happy tech executives never thought of, and Sam Altman and
Speaker 1: his ILK from open ai never really thought of, because well,
Speaker 1: why would they have to be why would they have
Speaker 1: to think of that they had the unlimited money cheap
Speaker 1: from the hyperscalers, like in the case of open Ai
Speaker 1: funded by Microsoft, in the case of Anthropic funded by
Speaker 1: Amazon and Google. And this is where the narrative has
Speaker 1: begun to kind of fall apart, because all of this
Speaker 1: has made it much harder to justify these companies building
Speaker 1: new data centers and buying new in video GPUs. This
Speaker 1: entire AI boom has been based off of the assumption
Speaker 1: that the only way to build powerful models was to
Speaker 1: get the biggest, most hugest chips from in video each year,
Speaker 1: and that there was just no way to make these
Speaker 1: models cheaper. Now as an aside, lost five billion dollars
Speaker 1: in twenty twenty four and all of their products are unprofitable,
Speaker 1: even their two hundred dollars a month open ai Chat
Speaker 1: GPT pro subscription. I hate these terms, by the way,
Speaker 1: They're all different. Nevertheless, everyone assumed that there was never
Speaker 1: going to be a more efficient model and I personally
Speaker 1: made the mistake of saying, well, if it was going
Speaker 1: to be more efficient, surely they would want it to
Speaker 1: be or they could do that, right, right, Maybe they
Speaker 1: just have to do this stuff even though it's stupid.
Speaker 1: That was never the case, and deep Seek proved in crucially,
Speaker 1: deep Seak released its models under an open source license,
Speaker 1: meaning any company can reuse and repurpose its tech without
Speaker 1: having to pay anyone anything, any license fees or anything,
Speaker 1: or ask anyone for permission. Open Ai, by contrast, keeps
Speaker 1: its technology under lock and key. Despite their name, open
Speaker 1: ai is a deeply secretive organization open in name only.
Speaker 1: In summary, deep Seek has created a viable alternative to
Speaker 1: open AI's tech and indeed anthropics that's equally capable, vastly cheaper,
Speaker 1: an open source and proven that you don't need the
Speaker 1: most expensive and powerful chips to do so. And they
Speaker 1: kind of came out of nowhere. Well, deep Seek isn't
Speaker 1: exactly a tiny little startup. They're also not a Silicon
Speaker 1: Valley giant with billions of dollars of venture capital, or
Speaker 1: someone who's backed by one of the many different companies
Speaker 1: with a three trillion dollar market cap. They started off
Speaker 1: as a side project from a Chinese hedge fund. No,
Speaker 1: I'm not kidding now, still an eight billion dollars under
Speaker 1: management hedge fund. They're not small at all. It's so strange.
Speaker 1: It's a kind of cynical version of David versus Goliath,
Speaker 1: where David is a hedge fund baby and Goliath is
Speaker 1: several different hyperscalers taped together with a bad idea. But anyway,
Speaker 1: put yourself in the shoes of open Ai CEO and
Speaker 1: co founder Sam Mortmon. You've crafted this public perception of
Speaker 1: yourself as a visionary that isn't just bringing generative AI
Speaker 1: to the massives, but you're on the path that will
Speaker 1: bring about artificial general intelligence, which is to say, an
Speaker 1: AI that's as capable as a human being. You've crafted
Speaker 1: this myth not just about yourself, but about your company
Speaker 1: and what you'll do, and this has allowed you to,
Speaker 1: in essence, to fire the laws of physics when it
Speaker 1: comes to business. You can burn money at a rate
Speaker 1: unlike any tech company in history, with no hope of
Speaker 1: making a profit, or at least not in the short
Speaker 1: to medium term, and no real expectation that you'll do so,
Speaker 1: as investors will still line up to give you more money.
Speaker 1: With your company valued and even more ludicrous numbers seemingly
Speaker 1: every other month, you can say these outlandish things like
Speaker 1: you need seven trillion dollars to build the infrastructure and
Speaker 1: chip manufacturing capacity to bring your plans to life, and
Speaker 1: you don't get laughed out of the room if I
Speaker 1: said this shit, they'd asked me if I had a concussion.
Speaker 1: You can say stuff like I want to build five
Speaker 1: hundred billion dollars worth of data centers, and instead of
Speaker 1: people rolling their eyes, the world's largest tech companies and
Speaker 1: investors will say, damn man, that's sick, and then it
Speaker 1: turns out that you were wrong. You'd always assume that
Speaker 1: AI must be expensive, that the models used to power
Speaker 1: your apps like chat, GPT and Dally their image generator,
Speaker 1: they always cost more to build, they'd always cost more
Speaker 1: to run, they'd always require more powerful hardware, or maybe
Speaker 1: you just never thought about it too hard because you
Speaker 1: never have to worry about money and to grow to
Speaker 1: build more capable aiye moodels, you assume that you would
Speaker 1: always need more money, and so much more money than
Speaker 1: anyone's ever had, And then here comes this Chinese company
Speaker 1: didn't just replicate the functionality of your model. And on
Speaker 1: top of that, by the way, one is open ayes
Speaker 1: one moat. It was the one thing that people liked.
Speaker 1: It was their most sophisticated AI model. But this company
Speaker 1: came along and did it on a shoestring budget, both
Speaker 1: for actually training it even if the estimates are off
Speaker 1: by like factors of ten. But these things are more
Speaker 1: efficient too. And this company didn't even have access to
Speaker 1: the most capable GPUs. They didn't have the server architecture
Speaker 1: provided by Microsoft or Amazon or Google. And wow, and
Speaker 1: what did they do next with this thing they built
Speaker 1: that's competitive with you only real moat? They gave it away.
Speaker 1: Oh goodness me, Sammy, things aren't looking good at all.
Speaker 1: And this is where Sam Moultman's at. This is where
Speaker 1: open ai and the companies that are backed to it,
Speaker 1: and their competitors, this is where they're all at. The
Speaker 1: decisive lead they once enjoyed has like a puddle on
Speaker 1: a hot day, evaporated. And you'd see that happen a
Speaker 1: lot here in beautiful Las Vegas, Nevada. Now, don't get
Speaker 1: me wrong, open ai still burns money. But now when
Speaker 1: Sam Moretman dusts off his begging bowl. Investors will ask,
Speaker 1: perhaps for the first time, one very simple question, why