Speaker 1: Fall Zone Media.
Speaker 2: Hello and welcome to Better Offline. I'm your host ed
Speaker 2: z tron. It's been just under a year and a
Speaker 2: half since chat gpt, an AI powered chatbot launched by
Speaker 2: so called nonprofit open Ai, ushered in a new investor
Speaker 2: in media hype cycle around how AI would change the world.
Speaker 2: Chat GPT's instant success made both open Ai and its CEO,
Speaker 2: Sam Mortman overnight celebrities. As a result of chat GPT's
Speaker 2: alleged intelligence, which seemingly allowed you to do everything from
Speaker 2: generate an entire essay from a simple prompt to writing
Speaker 2: entire reams of software code. You can theoretically ask it
Speaker 2: anything and it will spit out an intelligent sounding response
Speaker 2: thanks to being trained on terabides of text data, like
Speaker 2: a search engine that's able to think for itself. Ah.
Speaker 2: Big problem is chat gpt doesn't think at all. It
Speaker 2: doesn't know anything. Chat gpt is actually probabilistic. It uses
Speaker 2: a statistical model to generate the next piece of information
Speaker 2: in a sequence. If you ask it what an elephant is,
Speaker 2: it'll guess that the most likely answer to that prompt
Speaker 2: is that an elephant is a large mammal, and then
Speaker 2: perhaps describe its features, such as a long trunk. Chat
Speaker 2: GPT doesn't know what an elephant is, or what a
Speaker 2: trunk is, or what the alefant Haantai family is. It's
Speaker 2: simply ingested enough information to reliably guess what an elephant
Speaker 2: is meant to be, or indeed know that that's what
Speaker 2: you're asking it. This is the technology underpinning the latest
Speaker 2: artificial intelligence boom. It's called generative artificial intelligence, and it's
Speaker 2: powered by large language models, and they underpin tools like
Speaker 2: open ais, chat, GPT, Anthropics, claude x dot COM's horrifying
Speaker 2: chatbot Grock, and of course Google's Gemini. Essentially, they're AI
Speaker 2: systems that ingest vast quantities of written text or other data,
Speaker 2: and then, through mathematics, try and identify patterns of relationships
Speaker 2: between words or symbols, or basically any meaning from the
Speaker 2: text or thing they're being fed. And it almost seems
Speaker 2: like magic because it's able to generate this plausible, seeming
Speaker 2: almost human content at this remarkable speed. These models are
Speaker 2: now capable of generating text, images, and even video in
Speaker 2: response to simple chat prompts or by learning the patterns
Speaker 2: and structures of their data. Yet underneath the hood, there's
Speaker 2: always something a bit wrong. Generative AI, at times authoritatively
Speaker 2: spits out incorrect information, which can range from funny like
Speaker 2: telling you that you can melt an egg, to outright dangerous,
Speaker 2: like when the recently launched AI powered New York City
Speaker 2: chatbot for small business owners started telling them that it
Speaker 2: was legal to fire somebody for refusing to cut their dreadlocks.
Speaker 2: This is why you'll see strange glitches in images generated
Speaker 2: by AI hands with too many fingers, horrifying looking people
Speaker 2: in the back of realistic looking photos, and so on
Speaker 2: and so far. Because these models don't actually know what
Speaker 2: anything is. They don't have meaning, they don't have consciousness
Speaker 2: or intelligence. They're guessing, and when they guess, they sometimes hallucinate,
Speaker 2: which I'll get too soon. And while they might be
Speaker 2: really really good at guessing, there are effectively a very
Speaker 2: very powerful version of auto complete. I don't know anything.
Speaker 2: I really mean that these things aren't even intelligent. But
Speaker 2: because these models seem like they know stuff, and they
Speaker 2: seem to be able to do stuff, and the things
Speaker 2: that they create almost seem right. The media and the
Speaker 2: Vocal Investor class on Twitter have declared that large language
Speaker 2: models would change everything to them. Llms like chat GPT
Speaker 2: would upend entire business models, render once unassailable tech giants nunerable,
Speaker 2: and rewrite our entire economic playbook by turning entire industries
Speaker 2: into something you tell a chatbot to do. In a sentence,
Speaker 2: you know, it doesn't really matter that genera ive AA
Speaker 2: is mostly good at reams of generic slop, and that
Speaker 2: it's also clogging services like Amazon's Kindle eBookstore. And I
Speaker 2: guess the rest of the Internet with generative content that
Speaker 2: doesn't matter at all, because it's kind of good. Obviously,
Speaker 2: I'm being sarcastic. This is all very, very bad. In
Speaker 2: the last year, there have been hundreds of muling articles
Speaker 2: about how AI will replace everything from drive through workers
Speaker 2: to medical professionals. Theoretically, you could just feed whatever information
Speaker 2: a potential customer could ask for into a vast database
Speaker 2: and have an AI chew it up, and then they
Speaker 2: could just generate exactly the answer you'd need. AI would
Speaker 2: just naturally slip into areas of disorganization and inefficiency and
Speaker 2: spit out remarkable new ideas, all with minimum human input.
Speaker 2: In every one of these stories carries with them a
Speaker 2: shared belief one might even call it a shared hallucination.
Speaker 2: And they all believe that generative AI will actually be
Speaker 2: able to do these things, that it will actually be
Speaker 2: able to replace people. And what we're seeing today is
Speaker 2: just the beginning of our glorious automated future. But what
Speaker 2: if it's not. What if generative AI can't actually do
Speaker 2: much more than it can today? What if we're actually
Speaker 2: at peak AI. In the next two episodes, I'm going
Speaker 2: to tell you why I think that is. I'm going
Speaker 2: to tell you how I think this whole thing falls apart.
Speaker 2: AI's media hype train has been fairly relentless since November
Speaker 2: twenty twenty two, when chat GBT launched, and AI champions
Speaker 2: like open Ai CEO Sam Mortman have proven all too
Speaker 2: willing to grease its wheels, making bold promises about what
Speaker 2: AI could do and how today's problems are so easily overcome.
Speaker 2: It's also helped that the tech media has largely accepted
Speaker 2: these promises without asking basic questions like how and when
Speaker 2: will it do this stuff? And can it do this stuff?
Speaker 2: Don't believe me, Go and look at any interview with
Speaker 2: Sam Moltman from the last few years, watch any of them.
Speaker 2: In fact, just look at any AI figurehead getting interviewed
Speaker 2: and count the amount of times they've actually received any
Speaker 2: pushback or been asked to elaborate on any specific issue.
Speaker 2: It's actually very rare. Let me play you one of
Speaker 2: the few times that anyone's actually interrogated an AI person.
Speaker 2: Specifically Joanna Stern of The Wall Street Journal, who you
Speaker 2: might remember from the Vision Pro episode A Better Offline.
Speaker 2: She interviewed open AI's Chief Technology Officer, Mirror Marathi about Sora,
Speaker 2: which is open aiy's video based version of chat GPT
Speaker 2: where you can theoretically ask it to generate videos. Just
Speaker 2: to be clear, it's unreleased and unclear whether it'll ever
Speaker 2: actually get released, and the videos look good at first,
Speaker 2: then they look really weird. But just listen to this
Speaker 2: particular question. It's Joanna asking Mirror, the CTO of open
Speaker 2: ai and eighty billion dollar AI company, Hey, did you
Speaker 2: train on YouTube? What data was.
Speaker 3: Used to train?
Speaker 1: We used publicly available data and licensed data, so videos
Speaker 1: on YouTube?
Speaker 2: Now, I encourage you to go and look up this
Speaker 2: clip because at this point Maurti makes the strangest face
Speaker 2: I've ever seen in a tech interview.
Speaker 1: I'm actually not.
Speaker 3: Sure about that okay, videos from Facebook, Instagram.
Speaker 1: You know, if they were publicly available available, publicly available
Speaker 1: to use, there might be the data, but I'm not sure.
Speaker 1: I'm not confident about it.
Speaker 3: What about Shutterstock, I know you guys have a deal
Speaker 3: with them.
Speaker 1: I'm just not going to go into the details of
Speaker 1: the data that was that was used, but it was
Speaker 1: publicly available or licensed data.
Speaker 2: The remarkable part about this interview is that it's a
Speaker 2: relatively simple question. You, as the CTO of an eighty
Speaker 2: billion dollar AI company, what training data did you use
Speaker 2: to train your model? Did you use YouTube? So yes
Speaker 2: or no? Question? Mirror mirror, answer the bloody question, mirror
Speaker 2: all right, right? The answer is, of course Open Ai
Speaker 2: likely trained it's video generating model, Sora on YouTube videos,
Speaker 2: which might be why they're yet to launch it. And
Speaker 2: the videos generated by Soora also feature some remarkably similar
Speaker 2: images to say, SpongeBob SquarePants, and I wouldn't be surprised
Speaker 2: if they carry with them multiple weird biases about race
Speaker 2: and gender that we'll see in the future. But also
Speaker 2: when you watch these videos, much like most generative AI content,
Speaker 2: there's something a bit off about them. In The Wall
Speaker 2: Street Journal's interview. You get to see some of the
Speaker 2: prompts that were used and some of the videos that
Speaker 2: came out, and you see crazy things happening, like a
Speaker 2: robot completely changing shape as it turns, cars disappearing and
Speaker 2: appearing behind the robot. It's not very good. It seems
Speaker 2: cool at first. If you squint really hard, it looks real,
Speaker 2: but there's always something off. And that's because, as I've
Speaker 2: said before, these models don't know anything and don't know
Speaker 2: what at robot looks. They can make a really good
Speaker 2: guess though. Anyway, Sterne's interview with Marati of Open AI
Speaker 2: is a great example of how the entire AI artifst
Speaker 2: falls apart at the slightest touch, because it's fundamentally flawed
Speaker 2: and not actually able to deliver the society defining promises
Speaker 2: that Sam Morltman and the venture capital sect would have
Speaker 2: you believe. In a year and a half, despite billions
Speaker 2: of dollars of investment, despite every major media outlet claiming otherwise,
Speaker 2: generative artificial intelligence has proven itself incapable of replacing or
Speaker 2: even meaningfully enhancing human work. And the thing is, all
Speaker 2: of these problems I'm talking about with generative AI all
Speaker 2: of these hallucinations, all of these weird artifacts that are
Speaker 2: popping up throughout these videos, the weird mistakes that the
Speaker 2: texts that are popped out by chat GPT have, all
Speaker 2: of these. The problems are problems that aren't necessarily just technological.
Speaker 2: Their physics, they're mathematics. These aren't things you can just outrun.
Speaker 2: And I believe that there are four intractable problems that
Speaker 2: will stop generative AI from progressing much further than it
Speaker 2: is today. The first is, of course, its energy demands,
Speaker 2: the massive amounts of power requires. The second are its
Speaker 2: computational demands, the amount of compute power it requires to
Speaker 2: even crunch the simplest things out of chat GPT, It's hallucinations,
Speaker 2: the authoritative failures it makes when it spits out nonsense
Speaker 2: or creates a human hand with eighteen fingers, and of
Speaker 2: course the fact that these large language models have an
Speaker 2: insatiable hunger for more training data. Now, let me break
Speaker 2: that down. Large language models are extremely technologically and environmentally demanding.
Speaker 2: The New York Are reported in March twenty twenty four
Speaker 2: the CHATGBT uses more than half a million kis what
Speaker 2: hours of electricity to respond to the two hundred million
Speaker 2: requests it receives in a day, or seventeen thousand times
Speaker 2: the amount that the average American household uses in a day,
Speaker 2: and others have suggested it might be as high as
Speaker 2: thirty three thousand households worth. Generative AI models demand specialist
Speaker 2: chips called graphics processing units, typically a souped up version
Speaker 2: of the technology used to drive the graphics in a
Speaker 2: gaming console, albeit at a much higher cost, with each
Speaker 2: one costing tens of thousands of dollars each. They do
Speaker 2: this because large language models like chat GPT are highly
Speaker 2: computationally intensive. I'm going to break that down. Don't worry.
Speaker 2: When you ask chat GPT a question, it tokenizes it,
Speaker 2: breaking it down into smaller parts for the model to understand.
Speaker 2: It then feeds these tokens into various mechanisms that help
Speaker 2: it understand the meaning of the thing you asked it
Speaker 2: to do based on the parameters that it learned in training.
Speaker 2: Chat GPT generates a response by predicting the most likely
Speaker 2: sequence of things that you might want it to do.
Speaker 2: An answer to a question, an image, so on, and
Speaker 2: so forth. Each one of these steps is extremely demanding,
Speaker 2: processing hundreds of billions of these parameters learned patterns from
Speaker 2: ingesting training data such as how the English language works
Speaker 2: or what a dog looks like to produce even the
Speaker 2: simplest thing. Training these models is equally intensive, requiring chat
Speaker 2: GPT to process massive amounts of data. Another problem I'll
Speaker 2: get to in a bit adjusting those hundreds of billions
Speaker 2: of parameters and developing new ones based on what the
Speaker 2: data says as it quote unquote learns more. Though as
Speaker 2: we're clear, chap GPT doesn't learn anything. It just makes
Speaker 2: new parameters to read things. A model like chat GBT grows,
Speaker 2: making it more complex, which in turn requires more data
Speaker 2: to train on and more compute power to both ingest
Speaker 2: the data, create more parameters and turn it into something
Speaker 2: resembling an answer, And because it doesn't know anything, it's
Speaker 2: suggesting the most likely to be correct answer, which leads
Speaker 2: it to hallucinating and correct things that, based on proper ability,
Speaker 2: kind of seem like the right thing to say. These
Speaker 2: hallucinations are the dirty little secret of generative AI, and
Speaker 2: are impossible to avoid thanks to the fact that every
Speaker 2: single thing these models say is a mathematical equation rather
Speaker 2: than any kind of intellectual exercise. If you ask CHATGPT
Speaker 2: how many days there are in a week. It doesn't
Speaker 2: know that there are seven days, but it's been trained
Speaker 2: on patterns of language and generates a result based on
Speaker 2: those patterns, which at times can be correct and can
Speaker 2: also be wrong. There's no way of fixing this problem.
Speaker 2: You can mitigate it, you can make it less likely
Speaker 2: it will mess up, but hallucinations will happen because there
Speaker 2: is no consciousness. It is not learning anything. This thing
Speaker 2: has no knowledge. More computing power would allow it more
Speaker 2: parameters to give it more rules, so that a generative
Speaker 2: AI will be more likely to give a correct answer,
Speaker 2: but there's no eliminating them, and doing so may require
Speaker 2: more computing power than actually exists or is possible without
Speaker 2: an AI of consciousness, an impossible dream known as average
Speaker 2: generalized intelligence that Sam Mortman would have you believe is imminent.
Speaker 2: There's really no solving hallucinations. When you answer questions using probability,
Speaker 2: you're always going to have mistakes because you're not actually
Speaker 2: answering them using knowledge, intellect, or experience. You're using dice rolls.
Speaker 2: It's a bloody game of dungeons and dragons. We turned
Speaker 2: in Carter into dungeons and dragons anyway. A newly published
Speaker 2: paper by Tepo Felon and Matthias Holweg of the University
Speaker 2: of Oxford agrees funding that large language models like chat
Speaker 2: GPT are incapable of generating new knowledge. It's a remarkably
Speaker 2: in depth rundown of the fundamental differences between a large
Speaker 2: language model and a human brain, and it combines both
Speaker 2: psychological and mathematical research going back to child psychology as
Speaker 2: well the basic building blocks of how we consume and
Speaker 2: learn things and how we make decisions. As a result,
Speaker 2: the paper title theory is or You Need AI Human
Speaker 2: Cognition and Decision Making argues that AI's data and prediction
Speaker 2: based orientation is an incomplete view of human cognition and
Speaker 2: the forward thinking theorizing of the human mind. In Layman's terms,
Speaker 2: the mess of the information we've learned over our lives,
Speaker 2: our experiences, and our ability to look forward and consider
Speaker 2: the future is just fundamentally different to a model that
Speaker 2: predicts things only based off of past data. Think of
Speaker 2: it like this, If you've read a book and you
Speaker 2: might think about writing a new book based on those ideas,
Speaker 2: you're not remembering every part of the book. You don't
Speaker 2: have a perfect memory, and you're also constantly thinking about
Speaker 2: things as your day goes on. The human brain is
Speaker 2: a goddamn mess. Generative AI is in some level stuck
Speaker 2: in amber. Though the billions of parameters might change, the
Speaker 2: data never does the way it consumes the data. May
Speaker 2: be that the data doesn't change. In essence, generative AI
Speaker 2: is held back by the fact that it can't consider
Speaker 2: the future and is actually permanently mired in the data
Speaker 2: of the past. Their largest problem might be a fast,
Speaker 2: simpler one, a farcillio, or a kind of an ironic one.
Speaker 2: There might not be enough data for these bloody things
Speaker 2: to actually train on. While the Internet may at times
Speaker 2: feel limitless, a researcher recently told The Wall Street Journal
Speaker 2: that only a tenth of the most commonly used web
Speaker 2: data set, the common Crawl, are freely available. Two hundred
Speaker 2: and fifty billion page dump of the web's information is
Speaker 2: actually of high enough quality data for large language models
Speaker 2: like CHATGBT to actually train on. Putting aside the fact
Speaker 2: that I can't find a single definition of what high
Speaker 2: quality actually means, the researcher pat Blow Vilobos suggested that
Speaker 2: the next version of chat GBT would require more than
Speaker 2: five times the amount of data it took to train
Speaker 2: in its previous version, GPT four. The new one is
Speaker 2: called GPT five. By the way, and other researchers have
Speaker 2: suggested that AI companies are going to run out of
Speaker 2: training data in the next two years. Now. That sounds dire,
Speaker 2: but don't worry. They've come up with a very funny
Speaker 2: and extremely stupid idea to fix it. One specifically posed
Speaker 2: by The Wall Street Journal is that the AR companies
Speaker 2: are going to create their own synthetic data to train
Speaker 2: their models, a computer science version of inbreeding. The researcher
Speaker 2: Jason Stadowski calls habsburg AI. This is, of course, an
Speaker 2: absolutely terrible idea. A research paper from last year found
Speaker 2: that feeding model generated data into models to train them
Speaker 2: creates something called model collapse, a degenerative learning process where
Speaker 2: models start forgetting improbable events over time as the model
Speaker 2: becomes poison with its own projection of reality. The paper,
Speaker 2: called the Curse of recursion. Training on generated data makes
Speaker 2: models forgam highlights an example where feeding a generative AI
Speaker 2: its own data eventually destroys its ability to answer questions,
Speaker 2: and within nine generations. One answered a simple prompt about
Speaker 2: architecture with an insane screed about jack rabbits full of
Speaker 2: at symbols and weird characters. So not to worry again.
Speaker 2: The tech overlords have come up with a great idea
Speaker 2: to fix this problem. Their common retort to the problem
Speaker 2: of synthetic data is that you could use another generative
Speaker 2: AI to monitor the synthetic data being fed into all
Speaker 2: model to make sure it's right. At this point, I'd
Speaker 2: like to get slightly angry. Are you kidding me? Are
Speaker 2: you fucking kidding me? You're saying that the way to
Speaker 2: make sure the data generated by an unreliable generative AI
Speaker 2: is to use another generative AI, one with the same
Speaker 2: goddamn problems, which also hallucinates information that knows nothing. You're
Speaker 2: going to use that AI to monitor whether the data
Speaker 2: that is created by an AI is any good? Are
Speaker 2: you completely insane? You insane? You're going to feed the
Speaker 2: crap from the crap machine into another crap machine to
Speaker 2: make it not make crap? Why am I reading journalists
Speaker 2: credulously printing this ridiculous solution in the New York Goddamn
Speaker 2: Times every time? Every time, these bubbles are inflated because
Speaker 2: tech executives are able to get their half ass, half
Speaker 2: baked solutions parroted by reporters who should know better. You
Speaker 2: don't have to give them the better affair of the
Speaker 2: goddamn fucking doubt. This is how we got the bloody
Speaker 2: meta us. Pardon me, I've calmed down now. Anyway. Anyway,
Speaker 2: if you're worried about model collapse, you're already too late,
Speaker 2: as these models are likely already being fed their own data.
Speaker 2: You see, these models are trained on the web, as
Speaker 2: I previously told you, and they're desperate. They need data.
Speaker 2: They need more stuff. They need more stuff to ingest
Speaker 2: so they can spit out more stuff. The problem is
Speaker 2: that these machines are purpose built to make a lot
Speaker 2: of content, and so the Web's already being filled with
Speaker 2: generative AI. Generative AI is already spamming the Internet. A
Speaker 2: report from four oh four Media from last week said
Speaker 2: that Google Books has already started to index several different
Speaker 2: works that were potentially written by AI, featuring the hallmark
Speaker 2: generic writing tropes of these models. Four or four Media
Speaker 2: also reports that the same thing is happening over at
Speaker 2: Google Scholar their index of scientific papers with one hundred
Speaker 2: and fifteen different articles featuring the phrase as of my
Speaker 2: last knowledge update a specific phrase spat out by generative models.
Speaker 2: This is really bad, by the way, and this is
Speaker 2: only going to get worse. When you have an Internet
Speaker 2: economy that is built so that the people that can
Speaker 2: put the most out there will probably get the most traffic.
Speaker 2: They're going to use these tools. These tools are great
Speaker 2: for that. If you don't give a rap fuck about
Speaker 2: the quality, this is the best thing in the world
Speaker 2: for you. And that's the thing. This is a problem
Speaker 2: both created and caused by these models. You see. The
Speaker 2: other dirty little secret of generative II is that these
Speaker 2: models unashamedly plagiarize the entire web, leading outlets like The
Speaker 2: New York Times and authors like John Grisham to sue
Speaker 2: open ai for plagiarism. While open ai won't reveal exactly
Speaker 2: what their training data is, the New York Times is
Speaker 2: able to successfully make chat gpt reproduce content from the newspaper,
Speaker 2: and the company has repeatedly said that it trains on
Speaker 2: publicly available data from the Internet, which will naturally include
Speaker 2: things like Google scholar and Google Books. The Times also
Speaker 2: reports that open ai has become so desperate for data
Speaker 2: that they've used their whisper tool to transcribe YouTube videos
Speaker 2: into texts to feed into chat GPT's training data. Pretty sure,
Speaker 2: that's plagiarism, but who am I to tell you? And
Speaker 2: as the web gets increasingly pumped full of this generative content,
Speaker 2: these models are going to just start eating their own swill,
Speaker 2: slowly corrupting themselves in a kind of ironic death. According
Speaker 2: to Zakhar Schumelov, one of the authors of the model
Speaker 2: collapsed paper at the University of Cambridge, the unique problem
Speaker 2: that synthetic data creates is that it lacks human errors.
Speaker 2: Human made training data, by the nature of it being
Speaker 2: written by a human, includes errors and imperfections, and models
Speaker 2: need to be robust to such errors. So what do
Speaker 2: we do if models are trained off of content created
Speaker 2: without them? Do we introduce the errors ourselves? How many
Speaker 2: errors are there? How do we can introduce them more?
Speaker 2: And indeed, what are the errors? What do they look like?
Speaker 2: Do we even know? Are we conscious of the errors
Speaker 2: in the human language that make us human? The models aren't? Well,
Speaker 2: maybe they are. It's kind of unclear. It's kind of
Speaker 2: tough to express how deeply dangerous the synthetic data idea
Speaker 2: is for AI models like chat, GPT and Claude are
Speaker 2: deeply dependent on training data to improve their outputs, and
Speaker 2: their very existence is actively impeding the creation of the
Speaker 2: very thing they need to survive. While publishers like Axel
Speaker 2: Springer have cut deals to license their companies data to
Speaker 2: chat GPT for training purposes, this money is flowing to
Speaker 2: the writers that create the content that open Ai and
Speaker 2: Anthropic need to grow their models much further. In fact,
Speaker 2: I don't think you're going to see more journalists get
Speaker 2: hired as a result of these deals, which kind of
Speaker 2: makes them a little bit stupid. This puts AI companies
Speaker 2: in a kind of Kafka esque bind, but they can't
Speaker 2: really improve a tool for automating the creation of content
Speaker 2: without human beings creating more content than they've ever created before,
Speaker 2: just as said tool actively crowds out human made data.
Speaker 2: It's a little silly. The solution to these problems, if
Speaker 2: you ask open AI's Sam Altman, is always more money
Speaker 2: and power, which is why the information reports he is
Speaker 2: trying to convince open ai investor Microsoft to build him
Speaker 2: and I'm not kidding, and one hundred billion dollars supercomputer
Speaker 2: called Stargate. This massive series of interconnected machines will require
Speaker 2: entirely new ways to mountain cool processing units and is
Speaker 2: entirely contingent on pen AI's ability to meaningfully improve chet GPT,
Speaker 2: something Sam Mortman claims isn't possible without more computing power.
Speaker 2: To be clear, open Air already failed to build a
Speaker 2: more efficient model, dubbed Arakis, which ended up getting mothballed
Speaker 2: because it wasn't more efficient. It's also important to note
Speaker 2: that every major cloud company now has inextricably tied themselves
Speaker 2: to the generative AI movement. Google and Amazon have invested
Speaker 2: billions into chat GPT competitor Anthropic, and both claim to
Speaker 2: be Anthropic's primary cloud provider, though isn't really obvious which
Speaker 2: one is. In doing so, they've guaranteed, according to a
Speaker 2: source of mind, about seven hundred and fifty million dollars
Speaker 2: a year of revenue for Google's cloud and eight hundred
Speaker 2: million dollars a year of revenue for Amazon Web Services
Speaker 2: the cloud service from Amazon by mandating that Anthropic uses
Speaker 2: their services to power their clawed model. This similar to
Speaker 2: the thirteen billion dollar investment that Microsoft gave open AI
Speaker 2: last year, most of which was made up of credits
Speaker 2: for Microsoft's cy or cloud, And I somehow doubt that
Speaker 2: Microsoft is going to be the noble party that goes
Speaker 2: on the earnings and says, well, we don't want to
Speaker 2: count the credits that we gave open out want to
Speaker 2: be fair. No, they're going to mash that shit right
Speaker 2: back into their revenue. Kind of a con kind of
Speaker 2: makes me angry when I think about it too. Anyway,
Speaker 2: let me just put that aside and not going to
Speaker 2: get pissed off again. Look, I'm surprised more people aren't
Speaker 2: really upset about this very incestuous relationship between big tech
Speaker 2: and this supposedly independent generative AI movement. Microsoft, Google, and
Speaker 2: Amazon have effectively handed cash to one or two companies
Speaker 2: that will eventually hand the cash back to them in
Speaker 2: exchange for cloud services that are necessary to make their
Speaker 2: companies work, and all three big tech firms are spending
Speaker 2: billions to expand their data center operations to capture this
Speaker 2: theoretical demand from generative AI. Every penny the open AI
Speaker 2: or anthropic makes will now flow back to one of
Speaker 2: three big tech firms, even more so in the case
Speaker 2: of open AI, because Microsoft's investment entitles Microsoft to a
Speaker 2: share of any future profits from open ai and chat GBT. Yeah,
Speaker 2: it doesn't even really matter if they make one, because
Speaker 2: Big tech wins either way. Anthropic has to use Google
Speaker 2: Cloud and Amazon Web services, open Ai has to use
Speaker 2: Microsoft's z your Cloud, and Microsoft is actively selling open
Speaker 2: AI's models to their zero cloud customers. And every time
Speaker 2: somebody uses open AI's models, that model is being run
Speaker 2: on a zero cloud, generating revenue for Microsoft. This is
Speaker 2: the rot economy in action, by the way, Big tech
Speaker 2: has funded its biggest customers for their next growth revenue stream,
Speaker 2: justifying this massive expansion of their data center operations because
Speaker 2: ai is the future and they're telegraphing growth to these
Speaker 2: brainless drones in the market who will buy any thing,
Speaker 2: who never think too hard about what they're actually investing in. Hey,
Speaker 2: i's this big, sexy, exciting and theoretically powerful way to
Speaker 2: centralized labor. And it's innovative sounding enough that it allows
Speaker 2: people to dream big about how it might change their lives. Now,
Speaker 2: it might help them not pay real people to do shit. Yeah,
Speaker 2: here's the biggest worry I have. Here's the real pickle,
Speaker 2: here's the thing that keeps me up at night. None
Speaker 2: of these companies seem to have appeared to consider something.
Speaker 2: What if generative AI can't actually do any of the
Speaker 2: things they're excited about. What if genera avii's content, as
Speaker 2: you probably seen from anything chet GBT spits out, isn't
Speaker 2: really good enough. Hey, is anyone checked if anyone's actually
Speaker 2: using these tools, if they're helpful to anyone? Is this
Speaker 2: actually replacing anyone's work? Huh, that's a bit worrying, mate,
Speaker 2: I didn't think about that before. Just kidding, I've been
Speaker 2: thinking about it for months. Look, here's the thing. I
Speaker 2: think that the big problem here is that Sam Altman
Speaker 2: and his cronies have allowed the media, the markets, and
Speaker 2: big tech to fill in the gaps of their specious messaging.
Speaker 2: They've allowed everybody to think that open AI can do
Speaker 2: whatever anyone dreams. Yeah, I don't think that generative AI
Speaker 2: can do much more than it is today. And also,
Speaker 2: from what I've seen, none of these generative AI companies
Speaker 2: actually make a profit, and with each new model they
Speaker 2: become less profitable, and I don't see that changing in
Speaker 2: the future. And so I've dug in a little more
Speaker 2: looking under the hood. All the demand that's spurring Microsoft, Google,
Speaker 2: and Amazon's data center operations might not actually be there.
Speaker 2: My friends, I think we're in the next tech bubble,
Speaker 2: and the next episode, I'm gonna walk you through how
Speaker 2: I think it might pop. Thank you for listening to
Speaker 2: Better Offline.
Speaker 4: The editor and composer of the Better Offline theme song
Speaker 4: is Matasowski. You can check out more of his music
Speaker 4: and audio projects at Matasowski dot com, M A T
Speaker 4: T O, S O W s ki dot com. You
Speaker 4: can email me at easy at Better offline dot com,
Speaker 4: or check out Better Offline dot com to find my
Speaker 4: newsletter and more links to this podcast. Thank you so
Speaker 4: much for listening.
Speaker 2: Better Offline is a production of cool Zone Media.
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