Speaker 1: All media, soul of an angel, body of a devil,
Speaker 1: chosen by God and perfected by science. This is better
Speaker 1: offline and I'm your host ed Zitron. Now we're working
Speaker 1: on my newsletter. Last week I was chatting with my
Speaker 1: friend friend of the show casey Kagawa about generative AI,
Speaker 1: and we kept coming back to one thought, where's the money?
Speaker 1: Where is it? Not really where is the money? Where
Speaker 1: is the money that this supposedly revolutionary, world changing industry
Speaker 1: is making and of course we'll make in the future.
Speaker 1: And the answer is simple. After hours of hours of
Speaker 1: grinding through earnings, of grinding through media articles, of grinding
Speaker 1: through all sorts of things, I just don't believe it
Speaker 1: really exists. It's real, but it's small. Generative AI lacks
Speaker 1: the basic unit economics, product market fit, or market penetration
Speaker 1: associated with any meaningful software boom, and outside of open AI,
Speaker 1: the industry may be pathetically hopelessly small or while providing
Speaker 1: few meaningful business returns and constantly losing money. I'm going
Speaker 1: to be pretty straightforward with everything I say in this
Speaker 1: two parter because the numbers and the facts in my
Speaker 1: hypotheses are pretty fucking damning of both the generative AI
Speaker 1: industry and its associated boosters. You're going to get this episode.
Speaker 1: Then there's going to be a monologue about something else
Speaker 1: or something related. I really haven't got to it yet,
Speaker 1: and then a second part which I'm recording immediately after
Speaker 1: this one, a little behind the curtain there for you. Anyway,
Speaker 1: in reporting this analysis, I've done everything I can to
Speaker 1: try and push back against my own work, and I've
Speaker 1: saw evidence to counter the things that I've seen, like
Speaker 1: the revenue and the business models of these companies. Yet
Speaker 1: in doing so, I've only become more convinced of the
Speaker 1: flimsiness of generative AI and the associated industry, and the
Speaker 1: likelihood of this bubble bursting in a way that kneecaps
Speaker 1: tech valuations for a prolonged period, or worse, hits the
Speaker 1: major stock market. Now, I really had originally written a
Speaker 1: far more jocular and perc script, but while I was
Speaker 1: writing it, I realized I really had to be blunt
Speaker 1: because what I'm describing is a systemic failure. Venture capital
Speaker 1: has propped up Open AI and Anthropic, two companies that
Speaker 1: burned are combined ten point five billion dollars in twenty
Speaker 1: twenty four, and that number is set to double or
Speaker 1: more in twenty twenty five. The tech media has allowed
Speaker 1: Sam Mortman to twist them to validate completely fictional ideas
Speaker 1: as a means of propping up this unprofitable, environmentally destructive
Speaker 1: software company, and big tech has become so disconnected from
Speaker 1: reality that it is incapable of seeing how little actual
Speaker 1: returns there are in generative AI, and they're failing. By
Speaker 1: the way. As I'll walk you through in these episodes,
Speaker 1: the GENERATIVEAI industry is very small, with the consumer market
Speaker 1: of the entire American GENERATIVEAI industry outside of chat GBT
Speaker 1: barely cracking one hundred million monthly active users, which puts
Speaker 1: them below a lot of free to play games that
Speaker 1: you get on your iPhone. Hyperscalers have already spent hundreds
Speaker 1: of billions of dollars in capital expenditures for an AI
Speaker 1: industry that has the combined monthly active users of a
Speaker 1: free to play mobile game. I really must repeat myself,
Speaker 1: it's insane, But unlike most mobile games, GENERATIVEAI doesn't really
Speaker 1: make any money. And for those of you wondering if
Speaker 1: selling access to AI models is the solution, it's important
Speaker 1: to know that open AI the market leader in generative AI,
Speaker 1: made less than a billion dollars an API calls in
Speaker 1: twenty twenty four, and that's when people plug their models
Speaker 1: them For those of you who don't understand, so it's
Speaker 1: the difference between you to load up the chat GPT
Speaker 1: app or someone has an AI generative AI like chat
Speaker 1: gpt plugged into it. Now, Microsoft pays open Ai a
Speaker 1: revenue share of twenty percent on them selling open AI's models,
Speaker 1: so two hundred billion dollars. So this means that Microsoft
Speaker 1: likely when he makes a billion dollars in revenue from
Speaker 1: API calls themselves, this is a pathetic amount of money
Speaker 1: and suggests there really isn't significant demand at all or
Speaker 1: they're not charging enough. Neither of these are great, And honestly,
Speaker 1: I'm sick and tired of hearing people prop up this
Speaker 1: fucking industry. In these episodes, I will explain as calmly
Speaker 1: as possible how the generative AI industry barely exists outside
Speaker 1: of open Ai, And honestly, in writing this, I've become
Speaker 1: completely disgusted at Silicon Valley at the waste. Why is
Speaker 1: nobody talking about the revenues, Why is nobody sharing real
Speaker 1: user numbers other than open Ai. Well, I believe it's
Speaker 1: because there isn't that much money, and there certainly aren't
Speaker 1: that many users. Nobody is making a profit from this
Speaker 1: other than consultants, and that's because this is a hype
Speaker 1: driven movement. What you see on TV and in the
Speaker 1: newspaper is not the advent of a revolutionary piece of technology.
Speaker 1: It's a cynical marketing campaign for one company, open Ai.
Speaker 1: I need you to understand how precarious this all is.
Speaker 1: So much money has been wasted propping up an industry
Speaker 1: that only burns money, that does not have mass market appeal.
Speaker 1: Chat GPT is not significant enough, or useful enough, or
Speaker 1: meaningful enough to justify spending nine billion dollars to lose
Speaker 1: five billion dollars. And yes, those are the raw economics
Speaker 1: of open Ai. Now you may say, well, Ubber lost
Speaker 1: a lot of money, didn't it, Edward? Guess what? Uber
Speaker 1: lost about six point two billion dollars in one year,
Speaker 1: and that was in twenty twenty when they couldn't run
Speaker 1: their bloody service. Uber is a very different company. I
Speaker 1: will gladly if you email me, I will explain this
Speaker 1: to you. But Uber is not a comparison. There is
Speaker 1: no comparison to what open ai is doing, what Anthropic
Speaker 1: is doing. I sound crazy as ever, but you're going
Speaker 1: to understand why when I'm done. I am deeply worried
Speaker 1: about this industry, and I need you to know why.
Speaker 1: But in this first episode, I'm going to focus on
Speaker 1: one specific thing, and that's the capitalist delusion known as
Speaker 1: open Ai, a company that encompasses almost all of the traffic, funding,
Speaker 1: and attention in generative AI, and I believe they die
Speaker 1: without a constant flow of venture capital and hyperscale or welfare.
Speaker 1: And I actually don't know why I said I believe
Speaker 1: that they will. They cannot survive without that money. But okay,
Speaker 1: let's take a second. Let's talk about open as unit economics.
Speaker 1: Putting aside the high the blaster, open Ai, as with
Speaker 1: all GENERATIVAI model developers, loses money on every single prompt
Speaker 1: and output. Its products do not scale like traditional software,
Speaker 1: in that the more users it gets, the more expensive
Speaker 1: its services are to run because its models are so
Speaker 1: compute intensive. For example, Chat GPT having four hundred million
Speaker 1: weekly active users is not the same thing as a
Speaker 1: traditional app like Instagram or Facebook having that many users.
Speaker 1: Or indeed Uber. The cost of serving a regular user
Speaker 1: of an app like Instagram is significantly smaller because these
Speaker 1: are effectively websites with connecting APIs, images, videos, and user interactions.
Speaker 1: These platforms aren't innately compute heavy, and so you don't
Speaker 1: need to have the same level of infrastructure to support
Speaker 1: the same amount of people. Conversely, GENERATIVAI requires expensive to
Speaker 1: buy and expensive to run and expensive to maintain graphics
Speaker 1: processing units GPUs, both for inference and training the models themselves.
Speaker 1: These GPUs must be run at full tilt for both
Speaker 1: inference and training, which organs their lifespan while also consuming
Speaker 1: ungodly amounts of energy. And by the way, inference is
Speaker 1: just the thing that happens when you tell chat GPTs.
Speaker 1: I think it infers the meaning of the prompt. And
Speaker 1: the training is what they do when they throw all
Speaker 1: the training data to make the model huh smart. Not
Speaker 1: really though, And by the way, surrounding the GPU in
Speaker 1: there isn't that the GPUs just kind of hang out.
Speaker 1: There's the rest of a computer, which is usually highly
Speaker 1: specked and incredibly difficult to cool and thus very very expensive.
Speaker 1: These generative AI models also require endless amounts of training
Speaker 1: data and supplies of that training data have been running
Speaker 1: out for a long time. While synthetic data might bridge
Speaker 1: some of the gap, there are likely diminishing returns due
Speaker 1: to the sheer amount of data necessary to make a large,
Speaker 1: loud and quich model even larger, as much as more
Speaker 1: than four times the size of the Internet. This is insane.
Speaker 1: There is not enough data, and it already kind of sucks,
Speaker 1: and it's not getting better. These companies also must spend
Speaker 1: hundreds of millions of dollars on salaries to attract and
Speaker 1: retain AI talent, as much as one point five billion
Speaker 1: dollars a year in open AI's case, and that's before
Speaker 1: stock based compensation. In twenty sixteen, Microsoft claimed that top
Speaker 1: AI talent could cost as much as an NFL quarterback
Speaker 1: to hire, and that some has likely only increased since
Speaker 1: then given the GENERATIVAI frenzy and the fact they're overpaying
Speaker 1: quarterbacks as in the side. One analyst told The Wall
Speaker 1: Street Journal that companies running generative AI models could and
Speaker 1: I quote be utilizing half their capital expenditures because all
Speaker 1: of these things could break down. That's that's really bad.
Speaker 1: These costs are not a burden on open ai or Anthropic,
Speaker 1: but they absolutely are on Microsoft, Google and Amazon. This
Speaker 1: shit's crazy anyway. As a result of the costs of
Speaker 1: running these services, a free user of Chat GPT is
Speaker 1: a cost burden on open Ai, as is every single
Speaker 1: free customer of Google's Gemini, Anthropics, Clawed, Perplexity, or any
Speaker 1: other generative AI company. Said costs are also so severe
Speaker 1: that even paying customers lose these companies' money. Even the
Speaker 1: most successful company in the business appears to have no
Speaker 1: way to stop burning cap And as I'll explain, there's
Speaker 1: only really one real company in the industry, Open Ai,
Speaker 1: and open ai is not a real business. But let's
Speaker 1: start with a really, really important fact. If you forget
Speaker 1: everything I say, I want you to remember this. Open
Speaker 1: Ai spent nine billion dollars to make just under four
Speaker 1: billion dollars in twenty twenty four, and the entirety of
Speaker 1: their revenue that's about four billion dollars, is spent on compute,
Speaker 1: two billion dollars to run models and three billion dollars
Speaker 1: to train them. That is completely and utterly fucking insane.
Speaker 1: That is bonkers. That is crazy. That is completely nuts.
Speaker 1: This is not a real company. It is insane. We're
Speaker 1: allowing this. Everyone should be screaming this at everyone. We
Speaker 1: live in an alternate reality where this is acceptable. There
Speaker 1: has been no precedent for this. Not Amazon Web Services,
Speaker 1: not Uber, not anyone. No one has done this, and
Speaker 1: it's sickening and wasteful that we continue to. And in
Speaker 1: the past I've repeatedly said that open ai lost five
Speaker 1: billion dollars after revenue. Now that is true. By the way,
Speaker 1: that is completely true. They made money and they lost money,
Speaker 1: but ended up losing five billion either either way. However,
Speaker 1: I really just I can't in good conscience suggest that
Speaker 1: open ai only spent five billion dollars. It's time to
Speaker 1: be honest about these numbers. While it's fair to say
Speaker 1: that their net losses are five billion, they spent nine
Speaker 1: billion dollars to lose five billion dollars. Let's really get
Speaker 1: down into the nitty gritty of these numbers. So, as
Speaker 1: discussed previously, according to the reporting by the Information, open
Speaker 1: AI's revenue was likely somewhere in the region of four
Speaker 1: billion dollars in twenty twenty four. Their burn ray, according
Speaker 1: to the Information, was five billion dollars after revenue in
Speaker 1: twenty twenty four, excluding stock based compensation, which open Ai,
Speaker 1: like other startups, uses as a means of compensation on
Speaker 1: top of cash. Nevertheless, the more it gives away, the
Speaker 1: less it has for capital raises. And these are technically costs,
Speaker 1: though they're not real money unless there's a liquidity event,
Speaker 1: but that's our you don't need to worry about that.
Speaker 1: To put this in blunt terms, based on reporting by
Speaker 1: the Information, and I'm repeating myself here, but I really
Speaker 1: need you to remember this, running open Ai cost nine
Speaker 1: billion dollars in twenty twenty four. The cost of the computer,
Speaker 1: to the compute to train models alone three billion dollars
Speaker 1: obliterates the entirety of their subscription revenue, which was about
Speaker 1: three billion dollars by the way, and the compute from
Speaker 1: running models two billion dollars takes the rest and then some.
Speaker 1: They actually end up losing an extra billion on top
Speaker 1: of that. Sam Altman's net worth is a billion dollars.
Speaker 1: By the way, Casey Gogawa has now used this as
Speaker 1: the the Altman Index, so it's like you've lost one
Speaker 1: Sam Altman. That's a billion dollars. But just to be clear,
Speaker 1: it doesn't just cost more to run open Ai than
Speaker 1: they make. It costs them a billion dollars more than
Speaker 1: the entirety of their revenue to run the software they
Speaker 1: sell before any other costs. Why are we not more
Speaker 1: concerned about this company now? Something else to note is
Speaker 1: that open ai also spends another line amount of money
Speaker 1: on salaries, over seven hundred million dollars in twenty twenty
Speaker 1: four before you consider that compensation from stock, a number
Speaker 1: that will also have to increase because open ai is growing,
Speaker 1: which means hiring as many people as possible, and they're
Speaker 1: paying through the nose for them. But let's talk about
Speaker 1: how open ai makes money. Open Ai sells access to
Speaker 1: its models via its API and selling premium subscriptions to
Speaker 1: chat gpt. The majority of its revenue over seventy percent,
Speaker 1: comes from subscriptions to premium versions of chat GPT. The
Speaker 1: Information also reported that open ai now has fifteen point
Speaker 1: five million paying customers, though it's unclear what level of
Speaker 1: the service they're paying for or how sticky these customers
Speaker 1: are is in how luckily they are to stick around,
Speaker 1: or the cost of acquiring these customers are really any
Speaker 1: other metric to tell them tell us how valuable these
Speaker 1: customers are to the bottom line. Nevertheless, open ai loses
Speaker 1: money on every single paying customer, just like its free users.
Speaker 1: Increasing paid subscribers to open ai services somehow increases open
Speaker 1: AI's burn rate. This is not a real company. Now,
Speaker 1: The New York Times reports the open ai projects it
Speaker 1: will make eleven point six billion dollars in twenty twenty five,
Speaker 1: and assuming that open ai burns at the same rate
Speaker 1: it did in twenty twenty four, spending two point two
Speaker 1: five dollars to make one dollar, open ai is on
Speaker 1: course to bone over twenty six billion dollars in twenty
Speaker 1: twenty five for a loss of fourteen point four billion dollars.
Speaker 1: Who knows what their actual cost will be. Now you've
Speaker 1: probably heard about soft Bank coming in. Soft Bank's going
Speaker 1: to feed the money, and soft banks said they're going
Speaker 1: to spend money on this, that, and the other. That
Speaker 1: round has not closed yet. Masayoshi Son a complete fucking
Speaker 1: idiot who's lost thirty odd billion dollars for soft Bank,
Speaker 1: the Japanese make a conglomerate. He's dedicating billions of dollars
Speaker 1: of revenue to buying open ai services, unless this is
Speaker 1: a straight up trade where he's just sending money before
Speaker 1: the services come in. I don't know if it happens,
Speaker 1: and I'm going to get into things like agents later,
Speaker 1: but the information reported that open ai expects to make
Speaker 1: three billion dollars in revenue from agents. By the end
Speaker 1: of this episode. You're going to realize how fucking stupid
Speaker 1: that sounds. We'll get their layer. It's also important to
Speaker 1: note that open AI's costs are partially subsidized by its
Speaker 1: relationship with Microsoft, which provides cloud compute credits for its
Speaker 1: zero cloud service. Not super technical, it's just when they
Speaker 1: host people's software and files and such and the compute
Speaker 1: to run these models, and they also offer this a steep,
Speaker 1: steep discount to open Ai. Or put another way, it's
Speaker 1: like open ai got paid with air miles, but the
Speaker 1: airline lowered the redemption cost of booking a flight with
Speaker 1: those air miles, allowing it to take more flights than
Speaker 1: any other person with the equivalent amount of points. Until recently,
Speaker 1: open ai exclusively used Microsoft as Zuo services to train
Speaker 1: host and run its models, but recent changes to its
Speaker 1: deal means that open ai is now working with Oracle
Speaker 1: to build up further data centers to train host and
Speaker 1: run its models. It's unclear whether this partnership will work
Speaker 1: in the same way as the Microsoft deal with open
Speaker 1: Ai provided credits and discounts like before. If not, open
Speaker 1: AI's operating costs will only go up. For previous reporting
Speaker 1: from the information, open Ai pays just over twenty five
Speaker 1: percent of the cost of a zero's GPU compute part
Speaker 1: of their deal with Microsoft, and that's about a dollar
Speaker 1: thirty per GPU per hour versus the regular is your
Speaker 1: cost of three dollars and forty cents to four dollars
Speaker 1: an hour. I know that this sounds really technical, but
Speaker 1: in very short they're getting a sweet deal from Microsoft,
Speaker 1: and if anything happens in that, they're completely fucked. They're
Speaker 1: fucked anyway. They don't have They're burning billions of dollars.
Speaker 1: It's insane. But let's talk about user numbers, because open
Speaker 1: ai has quite a few. They recently announced that they
Speaker 1: have four hundred million weekly active users. Now, weekly active
Speaker 1: users is a wanky number and a very strange one
Speaker 1: for a company like this. Open Ai may pretend to
Speaker 1: be a consumer company, but the majority of their revenue
Speaker 1: comes from monthly subscriptions, making them kind of a cloud
Speaker 1: software company. Classically cloud software companies report monthly active users.
Speaker 1: That way, you can, I don't know, compare one number
Speaker 1: which is the amount of active users you have with
Speaker 1: the paid users you have, and then say, oh, that's
Speaker 1: a good business. That's a good business, right there. Man,
Speaker 1: guess what open ai isn't given their monthly active users.
Speaker 1: Don't worry, I might estimated it. When I asked open
Speaker 1: ai to define what a weekly active user was, it
Speaker 1: responded by pointing me to a tweet by chief operating
Speaker 1: officer Brad light Cap that said chat gpt recently crossed
Speaker 1: four one hundred million weekly active users. We feel very
Speaker 1: fortunate serve five percent of the world every week. What
Speaker 1: a fucking liar. It's extremely questionable that open ai refuses
Speaker 1: to define this core metric. By the way, and without
Speaker 1: a definition, in my opinion, there is no way to
Speaker 1: assume anything other than open ai is actively gaming its numbers. Now.
Speaker 1: There's likely two reasons they focus on weekly active users.
Speaker 1: One has described these numbers are easy to game, you
Speaker 1: can choose any seven day period. And also the majority
Speaker 1: of open AI's revenue comes from paid subscriptions to chat gpt.
Speaker 1: And that latter point is crucial because it suggests open
Speaker 1: ai is not doing anywhere near as well as it
Speaker 1: seems based on the very basic metrics used to measure
Speaker 1: the success of a software product. The information reported on
Speaker 1: January thirty first, open Ai, like I mentioned, had fifteen
Speaker 1: point five million monthly paying subscribers, and they added in
Speaker 1: this piece that this was less than a five percent
Speaker 1: conversion rate of open aiy's weekly active users, a statement
Speaker 1: that's kind of like dividing the number fifty two budd
Speaker 1: a letter A. This is not an honest or reasonable
Speaker 1: way to evaluate the success of chat GPT's still unprofitable
Speaker 1: software business, because the actual metric, like I mentioned, would
Speaker 1: have been to definde paying subscribers by monthly active users
Speaker 1: or the other way around. I guess a number that
Speaker 1: would be considerably higher than four hundred million. And the
Speaker 1: reason they don't need to do that, by the way,
Speaker 1: is because you would divide them and see that they
Speaker 1: have a piss poor conversion rate. Good conversion rate is
Speaker 1: way higher than five percent, by the way, and theirs
Speaker 1: is definitely lower. But don't worry. I'm a sneaky little shit.
Speaker 1: So I went and looked some stuff up, and I
Speaker 1: talked to some people based on data from the market
Speaker 1: intelligence firm Center Tower. Open AI's chat gpt app on
Speaker 1: Android and iOS is estimated to have more than three
Speaker 1: hundred and thirty nine million monthly active users, and based
Speaker 1: on traffic data for market intelligence company similar web chatgpt
Speaker 1: dot com had two hundred and forty six million unique
Speaker 1: monthly visitors, and these were in January twenty twenty five.
Speaker 1: There's likely some crossover with people using both the mobile
Speaker 1: and web interfaces, though how big nut group is is
Speaker 1: kind of hard to tell and remains uncertain. Though every
Speaker 1: single person that visits chatgpt dot com might not become
Speaker 1: a user, it's safe to assume that chat GPT's monthly
Speaker 1: active users are somewhere in the region of five hundred
Speaker 1: to six hundred million. That's good, right, Its actual users
Speaker 1: are higher than officially claimed, right, that's good. No, it's bad.
Speaker 1: First of all, each user that uses chat gpt for
Speaker 1: free is a drain on the company, whether they're free
Speaker 1: or not, honestly, but either way, their free ones definitely are.
Speaker 1: It would also suggest that the real conversion rate is
Speaker 1: somewhere in the neighborhood of two point five eight three
Speaker 1: percent from free paid users on chat GPT, which is
Speaker 1: astonishingly bad, and it's a fact that's made worse by
Speaker 1: the fact that every single user, regardless of whether they
Speaker 1: pay or not, loses the money either way. And while
Speaker 1: it's quite common for Silicon Valley companies to play fast
Speaker 1: than loose with metrics, this particularly one is well was
Speaker 1: deeply concerning, and I hypothesize that open ay is choosing
Speaker 1: to go with the weekly versus monthly active users in
Speaker 1: an intentional attempt to avoid people calculating the conversion rate
Speaker 1: of its subscription products. As I will continue to repeat,
Speaker 1: these subscription products lose the company money every single time.
Speaker 1: Now let's talk product strategy, shall we, because I don't
Speaker 1: think open ai really has one. Open Ai makes most
Speaker 1: of its money from subscriptions approximately three billion dollars in
Speaker 1: twenty twenty four, and the rest on API access to
Speaker 1: its models approximately a billion. As a result, open Ai
Speaker 1: is chosen to monetize chat GBT in its associated products
Speaker 1: in it all you can eat software subscription model, or
Speaker 1: otherwise make money by other people productizing in and just
Speaker 1: to be clear, in both of these scenarios, open Ai
Speaker 1: loses money on every transaction. Open AI's products are not
Speaker 1: fundamentally differentiated or interesting enough to be sold separately. It
Speaker 1: has failed, as with the rest of the generative AI industry,
Speaker 1: to meaningfully productize its models due to the massive training
Speaker 1: and operational costs and a lack of any meaningful killer
Speaker 1: app use cases for large language models. The only product
Speaker 1: that OpenAI has succeeded in scaling to the mass market
Speaker 1: is the free version of chat GBT, which loses the
Speaker 1: company money with every single prompt and output. This scale
Speaker 1: isn't a result of any kind of product market fit,
Speaker 1: by the way, It's entirely media driven, with reporters making
Speaker 1: chat GPT synonymous with artificial intelligence, a thing they regularly
Speaker 1: write about without thinking. As a result, I do not
Speaker 1: believe that the generative AI industry is real. It's not
Speaker 1: a real industry, which I will define as one with
Speaker 1: multiple competitive companies with sustainable or otherwise growing revenue streams
Speaker 1: and meaningful products with actual market penetration. And I feel
Speaker 1: this way because this market is entirely subsidized by a
Speaker 1: combination of venture capital and hyperscaler cloud credits, and well
Speaker 1: real money. I guess chat GPT is popular because it's
Speaker 1: the only well known product one that's mentioned in basically
Speaker 1: every article on ai. If this were a real industry,
Speaker 1: other competitors would also be mentioned all the time. They
Speaker 1: would have similar scale, especially those run by hyperscalers. But
Speaker 1: as I'll get to later, they suggest that open ai
Speaker 1: is the only company with any significant user base in
Speaker 1: the entire generative AI industry, and it's still wildly unprofitable
Speaker 1: and unsustainable. Open AI's models have also been entirely commoditized.
Speaker 1: Even its reasoning model OH one has been commoditized by
Speaker 1: both deep seats are one model and Perplexities agonizingly named
Speaker 1: are one seventeen seventy six model, both of which have
Speaker 1: similar outcomes at a much discounted priced open ais oh one.
Speaker 1: Though it's unclear and unlikely in my opinion, that these
Speaker 1: models are profitable anyway. Open Ai as a company, well,
Speaker 1: they just piss poor at product. It's been two years
Speaker 1: in chat gpt mostly does the same thing, still costs
Speaker 1: more to run than it makes an ultimately does the
Speaker 1: same thing as every other LLM chatbot from every other company.
Speaker 1: The fact that nobody has managed to make a mass
Speaker 1: market product by connecting open AI's models also suggests that
Speaker 1: the use cases just aren't there. Furthermore, the fact that
Speaker 1: API access is such a small part of its revenue
Speaker 1: suggests that the market for actually implementing large language models
Speaker 1: is relatively small. If the biggest player in the space
Speaker 1: only made a billion dollars in selling access to its
Speaker 1: models unprofitably, and that amount is the minority of its revenue,
Speaker 1: there might not actually be a real industry here. And
Speaker 1: I must be clear. If there was user demand, this
Speaker 1: would be where it was in the APIs, it would
Speaker 1: be doing gangbusters because people wouldn't be able to help themselves.
Speaker 1: They'd just be all over this generative AI share. But
Speaker 1: they're not. Now. I want to address one counterpoint. Some
Speaker 1: might argue that open ai has a new series of
Speaker 1: products that could open up new revenue streams, such as
Speaker 1: operator It's agent product and deep Research their research products.
Speaker 1: And I'm so fucking tired of hearing about agents. Whenever
Speaker 1: you hear someone say agent, really look at what they're saying,
Speaker 1: because they want you to think autonomous bit of software.
Speaker 1: What they're actually talking about is either a chatbot or
Speaker 1: well the dogshit the open ai and Anthropic have warmed up.
Speaker 1: You'll get too shit. But first let's talk costs. Both
Speaker 1: of these products are very compute intensive. Operator uses open
Speaker 1: AI's computer using agent they see u weigh, which combines
Speaker 1: open aies models with virtual machines that take distinct actions
Speaker 1: on web pages in this extremely unreliable and costly way
Speaker 1: where they take screenshots as they scroll down, and it
Speaker 1: just doesn't fucking work. I had a whole thing about
Speaker 1: Casey Newton writing about this. It's just it was just
Speaker 1: so bad. Like the case Newton, you please go outside, challenge,
Speaker 1: just just go outside, Casey stop, stop with the computer.
Speaker 1: You don't know what you talked about. But failures with
Speaker 1: these and remember these models, pretty much all of them
Speaker 1: are inconsistent. And the more in depth the thing you
Speaker 1: ask them to do, the more likely there's going to
Speaker 1: be a problem with it. So think about it like this.
Speaker 1: Failures from something you've asked them to do will either
Speaker 1: increase the amount of attempts you make to get the
Speaker 1: thing you want or make users not use it at all.
Speaker 1: Not a really great idea. Now, let's stalk deep research.
Speaker 1: They use a version of open Aiyes Oh three reasoning model,
Speaker 1: which is a model so expensive because it spends more
Speaker 1: time to generate a response based on the model, reconsidering
Speaker 1: and evaluating steps as it goes. The open Ai will
Speaker 1: no longer launch O three as a standalone model, and
Speaker 1: that's really a good thing when you see a company
Speaker 1: be like, yeah, you can't touch it. It's too expensive.
Speaker 1: In short, these products are extremely expensive to run, and
Speaker 1: this means that any time their outputs aren't perfect, which
Speaker 1: is to say a lot of the time, there's a
Speaker 1: high likelihood that they'll be triggered again, which will in
Speaker 1: turn spend more compute. But let's talk about the product
Speaker 1: market fit, because this is really important to use. Operator
Speaker 1: or Deep research currently requires you to pay two hundred
Speaker 1: dollars a month for open AI's Chat GPT Pro, a
Speaker 1: two hundred dollars a month subscription which Sam Altman recently
Speaker 1: revealed still loses the money because people are using it
Speaker 1: more than expected, and that is a quote. Furthermore, even
Speaker 1: on chat GPT pro, deep research is currently limited to
Speaker 1: one hundred queries per month, adding that it is very
Speaker 1: compute intensive and slow. Though Altman has promised the chat
Speaker 1: GPT Plus and Free users will eventually get access to
Speaker 1: a few deep research queries a month. Well, that's not
Speaker 1: good for their cash burn. That's actually bad for the
Speaker 1: cash burn. I'm not sure it's going to make them.
Speaker 1: Not really sure how that turns into money anywhere. But
Speaker 1: let's talk about Operator. Operator is this agent product where
Speaker 1: you're meant to be able to be like, hey, look,
Speaker 1: go and look something up for me, and it only
Speaker 1: works like thirty percent of the time, and it takes.
Speaker 1: It's just very bad. And as I covered in my
Speaker 1: Newslater a few weeks ago, this product and it claims
Speaker 1: to control your computer and does not appear to be
Speaker 1: able to do so consistently. It's not even ready for
Speaker 1: the prime time, and I don't think it has a market.
Speaker 1: The way they're selling this is that you'll be able
Speaker 1: to make it do distinct tass in the computer. But
Speaker 1: even Casey Newton in his article was like, yeah, it
Speaker 1: only works sometimes, and the things it works on are
Speaker 1: like searching trip Advisor. Imagine this if you will. What
Speaker 1: if for the cost of boiling a lake and throwing
Speaker 1: an entire zoo into the lake and boiling the animals
Speaker 1: inside it, you could sometimes be able to search trip
Speaker 1: Advisor in two minutes versus ten or like five seconds.
Speaker 1: The future is so cool. I love living in it.
Speaker 1: But let's talk about Deep Research for a say. It's
Speaker 1: already being commoditized perplexed. The AI and Xai have launched
Speaker 1: their own versions immediately, and Deep Research itself is not
Speaker 1: a good product. As I covered in my newsletter last week,
Speaker 1: the quality of the writing that you received from Deep
Speaker 1: Research is really piss poor, and it's rivaled only by
Speaker 1: the appalling quality of its citations, which include forum posts
Speaker 1: and search engine optimized content instead of actual news sources.
Speaker 1: These reports are neither deep nor well researched, and cost
Speaker 1: open Ai a great deal of money to deliver, and
Speaker 1: just to give you a primary. Deep Research is meant
Speaker 1: to be you meant to be able to type something in,
Speaker 1: and it does like a three thousand word report. It's gobbledygook,
Speaker 1: it's nonsense, it's bullshit. I really if you should go
Speaker 1: and look up go to my newsletter. Where's your head?
Speaker 1: Not at the it's the piece before the ones that's
Speaker 1: going to come out when these episodes come out. I
Speaker 1: forget the name exactly. You need to go and look
Speaker 1: at how shit Deep Research is. It's incredible that this
Speaker 1: money losing juggernaut piece of shit thinks that this is
Speaker 1: a real product. And it's insulting to the intelligence of
Speaker 1: readers that people at Casey Newton claimed it was good.
Speaker 1: But now we've established that both of these products are expensive, commoditized,
Speaker 1: and don't work very well. Let's talk about how they
Speaker 1: make money or don't. Both operate and deep research, like
Speaker 1: I told you, currently require you to pay two hundred
Speaker 1: dollars a month to a company that loses money all
Speaker 1: the time, that also loses money on the two hundred
Speaker 1: dollars a month. Neither product is sold in its own
Speaker 1: and while they may drive revenue to the chat GPT
Speaker 1: pro product has said before, said product loses open AI money.
Speaker 1: These products are also compute intensive and have questionable outputs,
Speaker 1: making each prompt very likely to create another follow up prompt.
Speaker 1: And the problem is you're asking something that doesn't know
Speaker 1: anything that probabilistically generates answers to research something. So as
Speaker 1: a result, the research isn't going to be any good.
Speaker 1: It's not like it's going to research it and go, hey,
Speaker 1: what would be a good source. It's going to say
Speaker 1: what matches the patterns? What matches all the patterns that
Speaker 1: are being trained on? Eh, that's fine, Who gives a shit?
Speaker 1: It's like having the world's worst intern, except the intern
Speaker 1: gets a concussion every ten minutes. But in summary, both
Speaker 1: Operator and deep research are expensive products to maintain, are
Speaker 1: sold through an expensive two hundred dollars a month subscription that,
Speaker 1: like every other service provided by open ai, loses the
Speaker 1: company money, and due to the low quality of their
Speaker 1: outputs and actions, are likely to increase user engagement to
Speaker 1: try and get the desired output, incurring further costs for
Speaker 1: open Ai. Well, you know, like ed, ED, you say, ED,
Speaker 1: you're just being You're just being a hater, right, just
Speaker 1: being a hater. Things don't look great today, but this
Speaker 1: early days. It isn't early days, but still edits early days.
Speaker 1: Things don't look great today. What about the future prospects
Speaker 1: for open Ai? Things can't be that bad, can they? Yeah?
Speaker 1: They can. A week or two ago, Sam Ortman announced
Speaker 1: the updated roadmap for GPT four point five and GPT five. Now.
Speaker 1: These are their next generation models that they've been hyping
Speaker 1: up for the best part of a year, except GPT
Speaker 1: four point five didn't exist before. It was always GPT five.
Speaker 1: Now GPT four point five will be open AI's last
Speaker 1: chain of thought model, referring to the core functionality of
Speaker 1: its reasoning models, where it checks the work as it goes,
Speaker 1: and it really it uses a model to ask another
Speaker 1: model whether the model's doing the right thing? Can they
Speaker 1: both hallucinate? Yes? GPT five will be and I quote
Speaker 1: Sam Mortman, a system that integrates a lot of open
Speaker 1: AIS technology, including three What the fuck are you talking about?
Speaker 1: Aortman also vaguely suggests that paid subscribers will be able
Speaker 1: to run GPT five at a higher level of intelligence,
Speaker 1: which likely refers to being able to ask the models
Speaker 1: to spend more time computing an answer. He also suggests
Speaker 1: that the GPT five and I quote will incorporate voice,
Speaker 1: canvas search, deeper search, and more. Fucking bed Bartham beyond motherfucker.
Speaker 1: Come on, my man, your company spent nine billion dollars
Speaker 1: to lose five billion dollars. Why is anyone taking this seriously?
Speaker 1: This is ridiculous. But both of these statements, all of
Speaker 1: these statements honestly vary from vague to meaningless. But I
Speaker 1: hypothesize the following GPT four point five will be an
Speaker 1: upgraded version of GPT four zero open AIS Foundation model
Speaker 1: you're probably using right now, and it's code named Orion
Speaker 1: GPT five, which used to be code named Ryan, could
Speaker 1: literally be anything. But one thing that Altman mentioned in
Speaker 1: the tweet is that open AI's model offerings have got
Speaker 1: too complicated. They'd be doing away with the ability to
Speaker 1: pick what model you used, gussieing this up Inese claiming
Speaker 1: it's unified intelligence. This fucking guy. If I said this
Speaker 1: shit to a doctor, they'd institutionalize me. They'd say, you
Speaker 1: sound like a lunatic. But anyway, as a result of
Speaker 1: doing away with the model picker, which is literally the
Speaker 1: thing you click and you choose GPT four O or
Speaker 1: GPT four O Mini or like the one reasoning things,
Speaker 1: I think they're going to attempt to moderate costs by
Speaker 1: picking what model will work best for a prompt A
Speaker 1: process it will automate. And if there's one thing I've
Speaker 1: noticed with open Ai, they're not very good at automating anything.
Speaker 1: So I expect this to be bad, and I believe
Speaker 1: that Altman announcing these things is a very bad omen
Speaker 1: for open Ai because Orion has been in the works
Speaker 1: for more than twenty months and was meant to be
Speaker 1: released at the end of twenty twenty four, but it
Speaker 1: was delayed due to multiple training runs that resulted in,
Speaker 1: to quote the Wall Street Journal, software that fell short
Speaker 1: of the results were searchers are hoping for. As in
Speaker 1: the side, the Wall Street Journal refers to Orion as
Speaker 1: GPT five. This was from several months back, but based
Speaker 1: on the copy in Aortman's comments, I believe Orion refers
Speaker 1: to a foundation model open ai, which is one to
Speaker 1: replace the core GPT one that powers chat GPT. Open
Speaker 1: Ai now appears to be calling a hodgepodge of different
Speaker 1: mediocre models something called GPT five. It's almost as if
Speaker 1: Altman's making this up as he goes along. Now. The
Speaker 1: Journal further adds that as of December, Orion performed better
Speaker 1: than open AI's current offerings, but hadn't advanced enough to
Speaker 1: justify the enormous costs of keeping the new model running
Speaker 1: with each six month long training run no matter how
Speaker 1: well it works, costing over five hundred million dollars. Open
Speaker 1: Ai also, like every generative AI company, is running out
Speaker 1: of high quality training data, the data necessary to make
Speaker 1: its model smarter. Based on the benchmark specifically made up
Speaker 1: to make l Limes seem smart, and I should note
Speaker 1: that being smarter means completing tests not new functionality or
Speaker 1: new things that it can do. Sam Mortman, deputizing Ryan
Speaker 1: from t GPT five to GPT four point five, suggests
Speaker 1: that open Ai has hit a war with making its
Speaker 1: new model, requiring hint to lower expectations for a model.
Speaker 1: Open Ai Japan president Tagao Nagasaki had suggested, would and
Speaker 1: I quote aim for one hundred times more computational volume
Speaker 1: than GPT four, which some took to me in one
Speaker 1: hundred times more powerful when it actually means it will
Speaker 1: take way more computation to train or run inference on it.
Speaker 1: I guess he was right. Now, if Sam Altman, who
Speaker 1: is a man who loves to lie, is trying to
Speaker 1: reduce expectations for a product, I think we should all
Speaker 1: be really, really worried. Now. Large language models, which are
Speaker 1: trained by feeding them massive amounts of training data and
Speaker 1: then reinforcing their understanding through further training runs, are hitting
Speaker 1: the point of diminishing returns in simple terms. To quote
Speaker 1: friend of the show, Max Zev of tech Crunch, everyone
Speaker 1: now seems to be admitting you can't just use more
Speaker 1: compute and more training data with pre training large language
Speaker 1: models and expect them to turn into some all knowing
Speaker 1: digital god max is a fucking legend. Open AI's real advantage,
Speaker 1: other than the fact it's captured the entire tech media,
Speaker 1: has been its relationship with Microsoft, because access to large
Speaker 1: amounts of compute and capital allowed it to corner the
Speaker 1: market for making the biggest, most hugest large language model.
Speaker 1: Now that it's pretty obvious this isn't going to keep working,
Speaker 1: open ai is scrambling, especially now deep seekers commoditized reacting
Speaker 1: models and prove that you can build lllms without the
Speaker 1: latest GPUs. It's unclear where what the functionality of GPT
Speaker 1: four point five or GPT five will be. Does the
Speaker 1: market care about an even more powerful large language model
Speaker 1: if said power doesn't do anything new or lead to
Speaker 1: a new product? Does the market care if unified intelligence
Speaker 1: just mean stapling together various models to produce more outputs
Speaker 1: that kind of look and sound the same. As it stands,
Speaker 1: open ai has effectively no mote beyond its industrial capacity
Speaker 1: to train large language models and its presence in the media.
Speaker 1: Open ai can have as many users as it wants,
Speaker 1: but it doesn't matter because it loses billions of dollars
Speaker 1: and appears to be continuing to follow the money, losing
Speaker 1: large language model paradigm guaranteeing it, or lose billions of
Speaker 1: dollars more if they're allowed to. This is the biggest
Speaker 1: player in the generative AI industry, both the market leader
Speaker 1: and the recipient of almost every single dollar of revenue
Speaker 1: that this industry generates. They have received more funding and
Speaker 1: more attention than any startup in the last few years,
Speaker 1: and as a result, their abject failure to become a
Speaker 1: sustainable company with products that truly matter is a terrible
Speaker 1: sign for Silicon Valley and an embarrassment to the tech media.
Speaker 1: In the next episode, I'm gonna be honest, I have
Speaker 1: far darker news. Based on my reporting, I believe that
Speaker 1: the generative AI industry outside of open Ai is incredibly small,
Speaker 1: with little to no consumer adoption and pathetic amounts of
Speaker 1: revenue compared to the hundreds of billions of dollars sunk
Speaker 1: into supporting it. This is an entire hype cycle fueled
Speaker 1: by venture capital and big tech hubris, with little real
Speaker 1: adoption and little hope for a turnaround. Enjoy Tomorrow's monologue
Speaker 1: and then the final part on Friday. Thank you for
Speaker 1: listening to Better Offline. The editor and compos of the
Speaker 1: Better Offline theme song is Matasowski. You can check out
Speaker 1: more of his music and audio projects at Matasowski dot com,
Speaker 1: M A T T O S O W s ki
Speaker 1: dot com. You can email me at easy at Better
Speaker 1: offline dot com or visit Better Offline dot com to
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