Speaker 1: Zone Media. Hello and welcome to Better Offline. I'm your
Speaker 1: host and chief romance officer, ed Ze Tron. In the
Speaker 1: last episode, I dug into the fundamental weaknesses and open
Speaker 1: AI the supposed leader in the genital of AI boom,
Speaker 1: and today I'm going to get into a much larger,
Speaker 1: more systemic, more terminal problem and the signs that things
Speaker 1: are really really falling apart. And as ever, I will
Speaker 1: have links to everything I'm talking about in the episode notes,
Speaker 1: so you know I'm not making it up, which one
Speaker 1: person suggested I did once and it bothered me a
Speaker 1: great deal. But back to the actual stuff. The problems
Speaker 1: that open ai is facing are those faced by the
Speaker 1: entire generative AI industry. One's born of their sole focused
Speaker 1: on the transformer based architecture underlying large language models like
Speaker 1: chat GPT open A issues besides the fact that they're
Speaker 1: in a terrible business as discussed in the last episode,
Speaker 1: is that generative AI, and by extension, the model GPT
Speaker 1: in the product Chat GPT, doesn't really solve complex problems
Speaker 1: that would justify the massive costs behind it. It is
Speaker 1: these massive intractable challenges that are a result of these
Speaker 1: models being probabilistic, meaning that they don't know anything. They're
Speaker 1: just generating an answer based on maths and training data,
Speaker 1: something that model developers are running out of at an
Speaker 1: incredible pace. Hallucinations, which occur when models authoritatively state something
Speaker 1: that isn't true, or, in the case of an image
Speaker 1: or a video, makes something that just looks wrong. Well,
Speaker 1: they're impossible to resolve without new branches of maths, and
Speaker 1: while you might be able to reduce or mitigate them,
Speaker 1: their existence makes it hard for business critical applications to
Speaker 1: truly rely on this kind of AI. I don't even
Speaker 1: know if i'd call it an AI, but regardless, we
Speaker 1: go forward, and even tech's most dominant players can't seem
Speaker 1: to turn generative AI into any kind of real business line.
Speaker 1: The Information reported in early September that customers of Micross
Speaker 1: three sixty five Suite are barely adopting its AI powered
Speaker 1: copilot products, with somewhere between zero point one percent and
Speaker 1: one percent of the four hundred and forty million paying
Speaker 1: people who pay for Microsoft three sixty five, which is
Speaker 1: about thirty to fifty dollars a person, by the way,
Speaker 1: are willing to pay for AI, and just to be clear,
Speaker 1: I muddied that little. It's thirty to fifty bucks per
Speaker 1: person per head to add this stuff. I'll get into
Speaker 1: it in a minute. One firm, according to the information,
Speaker 1: was testing the AI features and was quoted as saying
Speaker 1: that most people don't find it that valuable right now,
Speaker 1: and others are saying that many businesses haven't seen breakthroughs
Speaker 1: in productivity or other benefits, and that they're not sure
Speaker 1: that they will. In an internal presentation provided to be
Speaker 1: by a source, users of Microsoft SharePoint copilot complained that
Speaker 1: Microsoft chatbot kept getting questions wrong, sometimes failing to provide
Speaker 1: references even for correct answers, with another complaining that the
Speaker 1: copilot was and I quote using content not connected as
Speaker 1: a document resource to answer questions. And by the way,
Speaker 1: the whole point of share point is that it's your
Speaker 1: data informing everything. I assume it was drawing from its
Speaker 1: training data or perhaps the internet anyway, genuinely not useful.
Speaker 1: And you'd think that with these new services that don't
Speaker 1: seem that useful, that are questionably useful, that Microsoft would
Speaker 1: be doing people a deal right wrong. How much is
Speaker 1: Microsoft charging for these services? Thirty dollars a seat per
Speaker 1: person on top of what you are already paying, are
Speaker 1: as much as fifty dollars a month extra for specialist
Speaker 1: products like co pilots for sales, Microsoft is effectively asking
Speaker 1: customers to double their spend, and by the way, that's
Speaker 1: with an annual commitment for products that don't seem to
Speaker 1: be that helpful. And really, that is kind of the
Speaker 1: state of generative AI, the literal leader and productivity in
Speaker 1: business software, cannot seem to find a product that will
Speaker 1: make people more productive and that they will then pay for.
Speaker 1: And it's in part because the results are kind of mediocre,
Speaker 1: and also that the costs are so burdensome that there's
Speaker 1: no way for Microsoft to avoid charging a premium. And really,
Speaker 1: if Microsoft needs to charge this much, it's either because
Speaker 1: Sachin Adela is really desperate to hit half a trillion
Speaker 1: dollars in revenue by twenty thirty, or that the costs
Speaker 1: are too high to charge much less. Maybe it's a
Speaker 1: little bit of both. And this all only serves to
Speaker 1: shed further light on just the mediocrity of generative AI
Speaker 1: and how limited large language models are. And all of this,
Speaker 1: by the way, is existentially threatening to Open AI because
Speaker 1: they've coasted to one hundred and fifty seven billion dollar valuation,
Speaker 1: almost entirely based on hype. And so it's that company's
Speaker 1: always tried to tell us that the future of AI
Speaker 1: will blow us away, that the next generation of large
Speaker 1: language models are eminent and they're going to be incredible.
Speaker 1: And the artificial general intelligence, where machines can reason and
Speaker 1: act beyond human capabilities, that's just around the corner. And
Speaker 1: by the way, all of that is in part thanks
Speaker 1: to the media sloping it down and just assuming that
Speaker 1: they get it right. But until now that that's all
Speaker 1: they've really had to do. But I think we're finally
Speaker 1: getting the rubber meeting the road with this. I previously
Speaker 1: said one of the pale horses of the AI apocalypse
Speaker 1: is when a big stupid magic trick was necessary, a
Speaker 1: product that someone shoves out the door in hopes it
Speaker 1: will impress people and keep them believing in the magical future.
Speaker 1: And you'd think that they'd have something really good right
Speaker 1: now because open ai just raised all this money and
Speaker 1: the practical applications just are obviously not there, except well,
Speaker 1: you know, no, no, no, no, this is open AI.
Speaker 1: They wouldn't make a big, stupid mistake, would they. I mean,
Speaker 1: one of the things I always tell clients of mine
Speaker 1: in pr is not to shove a product out the
Speaker 1: door before it's ready, and to also make sure it's
Speaker 1: really obvious why people should pay for it. Otherwise you're
Speaker 1: just kind of launching something into the ether and hope
Speaker 1: people will find a reason to sell it for you.
Speaker 1: And yeah, that's exactly what they did. It happened. On
Speaker 1: September twelfth. Open Ai launched OH one, which had been
Speaker 1: code named Strawberry, with all of the excitement as a
Speaker 1: trip to the Proctologist. Across a series of tweets, CEO
Speaker 1: Samuel and described one as open ayes most capable and
Speaker 1: aligned models, yet then immediately conceded that O one was
Speaker 1: still flawed, still limited, and it still seems more impressive
Speaker 1: on its first use than it does after you spend
Speaker 1: more time with it. Oh my god, he admitted. He
Speaker 1: then promised it would deliver more accurate results when performing
Speaker 1: the kinds of activities where there's a definitive right answer,
Speaker 1: like coding maths or answering science questions. One might think
Speaker 1: that he'd walk in with I don't know, like a
Speaker 1: product built on top of one or like an use
Speaker 1: case or thing that would make the audience go, wow,
Speaker 1: I could build something with this. He didn't. I don't
Speaker 1: think he wants to try. I don't think he hasn't
Speaker 1: had to try that hard. So far people have been
Speaker 1: sloping down his slop happily. This boy may not have
Speaker 1: any tricks left. But let's talk about how O one works.
Speaker 1: And I'm going to introduce you to a bunch of
Speaker 1: new concepts here, but I promise I won't get too
Speaker 1: deep into the weeds. And I really want you to
Speaker 1: know how these machines work. It's critical for critiquing these companies.
Speaker 1: And the big way they take advantage of you is
Speaker 1: that they claim all of this is black magic, that
Speaker 1: you could never possibly understand it. You absolutely can. And
Speaker 1: if you want their explanation, I'm going to have it
Speaker 1: in the show notes. Okay. When presented with a problem,
Speaker 1: OH one breaks it down into individual steps that hopefully
Speaker 1: would lead to a correct answer in a process called
Speaker 1: chain of thought. Again, these things are not thinking. They're
Speaker 1: not thinking, but this is the term. It's also a
Speaker 1: little easier if you think of OH one as two
Speaker 1: parts of one model. On each step, one part of
Speaker 1: the model applies something called reinforcement learning with the other one,
Speaker 1: which is the model actually outputting things rewarded or punished
Speaker 1: based on the perceived correctness of their progress. And this
Speaker 1: is what is called reasoning, by the way, even though
Speaker 1: it really doesn't match human reasoning at all, and then
Speaker 1: based on the reward of the punishment, it generates a
Speaker 1: final answer from this chain of thought consideration. This is
Speaker 1: different to how other large language models work in the
Speaker 1: sense that the model is generating outputs than actually looking
Speaker 1: back at them then ignoring or approving what it thinks
Speaker 1: are good steps to get to an answer, rather than
Speaker 1: just generating one and saying here's the answer. This may
Speaker 1: seem like a big breakthrough or even another step towards
Speaker 1: artificial general intelligence, and it isn't, And you can tell
Speaker 1: that by the fact that open ai opt to release
Speaker 1: O one as its own standalone product rather than something
Speaker 1: built into GPT. It's also telling that the examples demonstrated
Speaker 1: by open AI, like maths and science problems, are the
Speaker 1: ones where the answer can be known ahead of time
Speaker 1: and a solution is either correct or false, thus allowing
Speaker 1: the model to guide the chain of thought through each
Speaker 1: step towards that answer, rather than actually having to produce
Speaker 1: something where they might not necessarily be one. Open ai
Speaker 1: didn't show the one model trying to tackle complex problems
Speaker 1: such as high end mathematical equations or otherwise where the
Speaker 1: solution isn't known in advance by its own admission. Open
Speaker 1: AI has heard reports that one is actually more prone
Speaker 1: to hallucinations than GPT four H, and the model is
Speaker 1: less inclined to admit when it doesn't have the answer
Speaker 1: to a question when compared to other previous models. This
Speaker 1: is because despite there being a model that checks the
Speaker 1: work of the model, the work checking part of the
Speaker 1: model is still capable of hallucinations. It's kind of like
Speaker 1: a kid being taught something by a teacher who just
Speaker 1: occasionally gets things horribly wrong. That child, though they may
Speaker 1: mostly get right answers, will learn bad things. Now, learning
Speaker 1: here isn't really what's happening, but the output of the
Speaker 1: end will be informed by a model that makes the loocinations.
Speaker 1: It's like, I don't know, got a town full of dogs.
Speaker 1: You get a bunch of baboons in to get rid
Speaker 1: of the dogs. The baboons succeed and getting rid of
Speaker 1: the dogs Now you just got a bunch of baboons,
Speaker 1: so you get in, aren't no robots? Robots destroy the baboons.
Speaker 1: At this point, you've got robots. If the robots are autonomous,
Speaker 1: they start taking over the town. So they need to
Speaker 1: find a bigger robot to take over the town from
Speaker 1: the robots. Now you've just got an escalating problem where
Speaker 1: things are only going to get worse. And if you
Speaker 1: work open ai and that sounds accurate, please email me anyway.
Speaker 1: According to open ai, OH one also, thanks to this
Speaker 1: chain of thought process, feels more convincing to human users
Speaker 1: because it provides more detailed answers, and thus people are
Speaker 1: more inclined to trust the outputs even when they're completely wrong. Now,
Speaker 1: if you think I'm being overly hard on open ai,
Speaker 1: consider the ways in which the company is marketed. One
Speaker 1: open ai described O one's reinforcement training as thinking and
Speaker 1: reasoning when it's making guesses and then guessing on the
Speaker 1: correctness of these guesses at each step. Where the end
Speaker 1: destination is often something that can be known in advance.
Speaker 1: Generative AI does not know anything. These are still probabilistic models.
Speaker 1: This thing is not thinking at all. There is no reasoning.
Speaker 1: It's got a model, reading a model, giving a model
Speaker 1: answers from it's a mess, and it's an insult to people,
Speaker 1: actual human beings who, when they think, are acting based
Speaker 1: on many, many complex factors, their experience, their knowledge, the
Speaker 1: knowledge they've accumulated over years of experiences, their brain chemistry,
Speaker 1: so on and so forth. Well, we may to guess
Speaker 1: about the correctness of each thing we're guessing at, and
Speaker 1: we may reason through a complex problem. All of this
Speaker 1: is based on something concrete. When we get something wrong,
Speaker 1: it's based on actual experience versus training data and probabilistic models.
Speaker 1: This shit is not thinking at all, and by god
Speaker 1: is it expensive. Pricing for one preview, which is the
Speaker 1: first model, is fifteen dollars per million input tokens and
Speaker 1: sixteen per million output tokens. In essence, it's three times
Speaker 1: as expensive as their most expensive model GPT four O
Speaker 1: for input and four times is expensive for output. And
Speaker 1: then there's a hidden cost. Data scientist Max Wolf reported
Speaker 1: the open ayes reasoning tokens the output it uses to
Speaker 1: get you to the final answer where it says, Okay,
Speaker 1: I need to find out the solution to this problem.
Speaker 1: So here are the thirty steps I've gone through. Yeah,
Speaker 1: those are actually generated using the most expensive tokens, the
Speaker 1: output tokens. So the more it has to think, the
Speaker 1: more expensive it gets. All of the things it generates
Speaker 1: to consider an answer are also charged for, which means
Speaker 1: the more complex it is, the more expensive it's going
Speaker 1: to be worse. Still, if you integrate this model, open
Speaker 1: ai does not show you what it's reasoning. All of
Speaker 1: that calculation happens in the background, and they still charge
Speaker 1: you for it. You just don't know how much every
Speaker 1: oh one step is charged to you in an indeterminate way,
Speaker 1: and open ai claims that they can't show you because
Speaker 1: of competitive reasons. Ugh, nasty company, really greasy and they're
Speaker 1: still gonna burn Okay, okay, though it's different GPT four.
Speaker 1: Oh and it's really expensive. But is it better? Of
Speaker 1: course it must be better, right, right? It sounds great.
Speaker 1: It's thinking, right, it's reasoning right. No, No, it's not,
Speaker 1: it's not. It's worse. This crap's worse. Let's talk about accuracy.
Speaker 1: On how can news the reddit styles I owned by
Speaker 1: am Horman's former alum y Combinator, one person complained about
Speaker 1: O one hallucinating libraries and functions when presented with a
Speaker 1: programming task, and making mistakes when asked questions where the
Speaker 1: answer isn't readily available in the Internet. On Twitter, Henrik Nyberg,
Speaker 1: a startup founder and former game developer, asked OH one
Speaker 1: to write a Python program that multiplied two numbers, then
Speaker 1: calculated the expected output of said program. While OH one
Speaker 1: correctly wrote the code, although said code could have been
Speaker 1: more succinct, the actual result was wildly incorrect. Karthig Cannon, himself,
Speaker 1: a founder of an AI company, tried a programming task
Speaker 1: on O one where it also hallucinated a non existent
Speaker 1: command for the API he was using. Another person, Sashi Yanshin,
Speaker 1: tried to play a game of chess with O one,
Speaker 1: and it hallucinated an entire piece onto the board and
Speaker 1: then it lost. And because I'm a little shit, I
Speaker 1: also tried asking one to listen a number of states
Speaker 1: with A in the name. After contemplating for eighteen seconds,
Speaker 1: it provided the names of thirty seven states, including Mississippi,
Speaker 1: you know, the classic state with an A in it.
Speaker 1: By the way, there are thirty six states that have
Speaker 1: in them, just in case you're curious. I then asked
Speaker 1: for a list of states with the letter W in
Speaker 1: the name, and then it sat and it thought for
Speaker 1: eleven seconds, and then included North Carolina and North Dakota.
Speaker 1: Great stuff. By the way, I also asked tho one
Speaker 1: to count the number of times the letter R appears
Speaker 1: in the word strawberry, which is the pre release code
Speaker 1: name for this. It's said too, I would have hard
Speaker 1: coded that one. Personally. You can't give me that kind
Speaker 1: of joy now. Open AI claims that I one performs
Speaker 1: similarly to PhD students on challenging benchmark tasks in physics, chemistry,
Speaker 1: and biology, just not in geography, it seems, or basic
Speaker 1: elementary level English, or maths or programming. Also, I mean
Speaker 1: for the PhD listeners. I've met a few PhD people
Speaker 1: who authoritatively state things that are completely untrue that they
Speaker 1: know nothing about. This is not a broad stroke thing,
Speaker 1: but I get the sense that it's true anyway. This
Speaker 1: is I should know, the big stupid magic trick I
Speaker 1: predicted in the past. Open AI is shoving strawberry out
Speaker 1: the door as a means of proving to investors and
Speaker 1: the greater public. But they've still got it that the
Speaker 1: AI revolution is still here, that this thing is thinking,
Speaker 1: and what they actually have is a clunky, on exciting
Speaker 1: and expensive model that doesn't really seem to have any
Speaker 1: measurable improvement. Okay, I'm sorry. It has a measurable improvement.
Speaker 1: You can measure it on the weird rigged test they
Speaker 1: do for all of these things. And the thing is,
Speaker 1: at this point, you'd think that even Apple, when they
Speaker 1: pulled together a new thing, even when they had the
Speaker 1: first Apple Watch and it was not obvious why you
Speaker 1: had to own it, they still had apps that were
Speaker 1: connected to it. They still had things you could point
Speaker 1: out and go, oh, that's cool, I've got four square
Speaker 1: on this four square on there at the time. Nevertheless,
Speaker 1: they had apps to show. I just feel like open
Speaker 1: Ai has this deep contempt for Silicon Valley and for
Speaker 1: the world at large. They don't even have it in
Speaker 1: them to be like, Okay, we have this new model,
Speaker 1: and here is the new thing we built with it,
Speaker 1: and this thing does this and now you will see
Speaker 1: how important this company is. Instead, we get this crap.
Speaker 1: We just get this very boring crap. And sure, I'm
Speaker 1: sure someone technical is going to email me and say, ed, wow,
Speaker 1: chain thought reasoning. There are other companies that have been
Speaker 1: doing it already. Anthropic already had something like this, and
Speaker 1: even then they didn't do shit with it. Where's the product, man,
Speaker 1: where's the thing I meant to care about? Why should
Speaker 1: anybody give a shit about this? Well, sam Altman is
Speaker 1: likely trying to trump up the reasoning abilities of O one. Well,
Speaker 1: people you know, such as the people bankrolling him, will
Speaker 1: actually see he's a ten to twenty second waiting time
Speaker 1: for an answer which may or may not be correct.
Speaker 1: But you have a bit more detail, which isn't even
Speaker 1: the reasoning happening because open AI hides that bit. Nobody
Speaker 1: gives a shit about better answers anymore. They want generative
Speaker 1: AI to do something new, and I don't think the
Speaker 1: open AI has any idea how to make that happen.
Speaker 1: Sam Orman's limp shitty attempts to anthropomorphize I one by
Speaker 1: making it think can use reasoning obvious attempts to suggest
Speaker 1: that this is on how part of the path through AGI.
Speaker 1: But even the most staunch AI advocates, well, they can't
Speaker 1: seem to get excited about this. In fact, I kind
Speaker 1: of argue that O one shows that open AI is
Speaker 1: desperate and out of ideas. Now if you don't have
Speaker 1: any ideas, though, the following advertisements will be more than
Speaker 1: happy to fill your empty little brain with new ideas
Speaker 1: that involve giving someone money or downloading something. And I
Speaker 1: must implore you to just accept everything that follows. I
Speaker 1: don't endorse any of it because I don't know what
Speaker 1: it's going to be, but you must. And we're back.
Speaker 1: So I think now is a good time to get
Speaker 1: back to the root of the generative AI problem. Generative
Speaker 1: AI is being sold to you on multiple lives that
Speaker 1: it's AI, it's actually artificial intelligence, it's going to get better,
Speaker 1: that this will become artificial general intelligence, that this will
Speaker 1: become the thinking computer, and all of this is inevitable.
Speaker 1: Putting aside terms like performance, as they're largely us as
Speaker 1: a means of generating things accurately or faster, rather than
Speaker 1: being good at anything. Large language models have effectively platowed
Speaker 1: more powerful never seems to mean does more, and more
Speaker 1: powerful often means more expensive to run or more expensive
Speaker 1: for you as the user to access, meaning that you've
Speaker 1: just made something that doesn't do more and does cost
Speaker 1: more to run. If the combined forces of every venture
Speaker 1: capitalist and big tech hyperscaler have yet to come up
Speaker 1: with a meaningful use case that lots of people will
Speaker 1: actually pay for. I just don't see one coming. Large
Speaker 1: language models and yes, that's where all of these billions
Speaker 1: of dollars are going. Are not going to magically sprout
Speaker 1: new capabilities a big Tech and open Ai burn another
Speaker 1: one hundred and fifty billion dollars. And yes that number
Speaker 1: isn't hyperbole. It's actually pretty close to the amount being
Speaker 1: plowed into these companies when you include things like investments
Speaker 1: in companies like Anthropic and open Ai. And they're genuinely
Speaker 1: and sane amount of capex from the likes of Google,
Speaker 1: Amazon and Microsoft going into expanding data centers and buying GPUs.
Speaker 1: Nobody seems to be trying to make these things more efficient,
Speaker 1: or at the very least nobody's succeeded in doing so,
Speaker 1: because I think if they had, they'd be shouting it
Speaker 1: from the rooftops and as on the side. By the way,
Speaker 1: the biggest sign that no one's actually making money from
Speaker 1: this is that no one's talking about how much money
Speaker 1: they're making. Microsoft and all of these companies they love
Speaker 1: talking about making profit. They love doing that. Beyond earnings.
Speaker 1: They love talking about it instead whenever they're asked to go, oh, hey,
Speaker 1: I will do some things in the future, I need
Speaker 1: to take a phone CALLT and then they kind of
Speaker 1: disappear from the room. Amy heard CFO of Microsoft classic
Speaker 1: bullshit artists dancing around Yeah, oh net revenue increase checking
Speaker 1: a watch. It's just really sad. It's really sad because
Speaker 1: what we have here is a shared delusion, a shared
Speaker 1: delusion about a dead end technology that runs on copyright theft,
Speaker 1: one that requires a CONTINUALUS supply of capital to keep
Speaker 1: running as it provides services that are at best in
Speaker 1: essential sold to US dressed up as a kind of
Speaker 1: automation that does not exist, and it doesn't provide, costing
Speaker 1: billions and millions of dollars and continuing to do so
Speaker 1: im perpetuity. Generative AI doesn't run on money or cloud
Speaker 1: credit so much as it does on faith. And the
Speaker 1: problem is that faith, like investor capital, is actually a
Speaker 1: finite resource. And that's where I bring you one of
Speaker 1: my biggest anxieties about this industry, because I think we're
Speaker 1: in the midst of a SUBPRIMEI crisis where thousands of
Speaker 1: companies have integrated this stuff into their software at prices
Speaker 1: that are far from stable and even further from profitable
Speaker 1: for the services providing them. This concern, by the way,
Speaker 1: isn't unfounded. At the latest open ai dev Day, they
Speaker 1: said that they'd slash prices for their APIs by ninety
Speaker 1: nine percent over the previous two years, largely as tech crunchies.
Speaker 1: MAXT theorized due to price pressure from Meta and Google,
Speaker 1: both of whom want to take that API access for
Speaker 1: I assume some reason. Anyway, almost every AI powered startup
Speaker 1: uses large language model features is based on some combination
Speaker 1: of GPT or chlaud so open Ai or Anthropics models.
Speaker 1: These models are built by two companies that are deeply unprofitable.
Speaker 1: Open Ai they can lose five billion this year, Anthropic
Speaker 1: is on course to lose two point seven billion this
Speaker 1: year on much less revenue, and they all have pricing
Speaker 1: design to get more customers through the door than make
Speaker 1: any kind of profit. Open Ai, as mentioned, is subsidized
Speaker 1: by Microsoft, both in cloud credits they received in the
Speaker 1: twenty twenty three investment and the preferential pricing Microsoft offers
Speaker 1: for their cloud services about a quarter of the price
Speaker 1: of what everyone else pays. And these companies willow open
Speaker 1: Ai and Anthropic. Their pricing is entirely dependent on the
Speaker 1: support of big tech in the case of open ai,
Speaker 1: Microsoft's continued support. In the case of Anthropic, Amazon and
Speaker 1: Google both as investors and service providers. Based on how
Speaker 1: unprofitable these companies are. I hypothesized that if open ai
Speaker 1: or Anthropic charge prices closer to their actual costs, they'll
Speaker 1: be a ten to one hundred times increase in the
Speaker 1: price of API calls, though it's impossible to say how
Speaker 1: much without the actual numbers of direct burn from these companies. However,
Speaker 1: Let's consider for a moment that the numbers reported by
Speaker 1: the Information Estimate the open AI's server costs with Microsoft
Speaker 1: will be four billion dollars in twenty twenty four, which
Speaker 1: I add are over two and a half times cheaper
Speaker 1: than what Microsoft charges others. It's like about four dollars
Speaker 1: in something and they pay them out a dollar something
Speaker 1: per GP per hour. And then consider, after knowing that
Speaker 1: they're getting this massive discount, that open ai still loses
Speaker 1: over five billion dollars a year. Open ai is more
Speaker 1: than likely charging only a small percentage of what it
Speaker 1: likely costs to run its models, and can only continue
Speaker 1: to do so if it's able to continually raise more
Speaker 1: venture funding than has ever been raised ever and continue
Speaker 1: to receive preferential pricing from Microsoft, a company that recently
Speaker 1: mentioned that it considers open Ai a competitor and has
Speaker 1: complete access to its IP and research. While I can't
Speaker 1: say for certain, I would think it's reasonable to believe
Speaker 1: that Anthropic receives a similarly preferential pricing package from both
Speaker 1: Amazon Web Services and Google Cloud. Both of those companies,
Speaker 1: by the way, put billions into them. Assuming that Microsoft
Speaker 1: gave open ai ten billion dollars of cloud credits and
Speaker 1: it spent four billion on server costs and let's say
Speaker 1: two three billion dollars on training costs, that are both
Speaker 1: short to increase. With new models, open Ai will either
Speaker 1: need more credits will have to pay actual cash to
Speaker 1: Microsoft sometime in twenty twenty five, and Microsoft did participate
Speaker 1: in the latest round, by the way, but it's not
Speaker 1: obvious how much, and it was much less than last time,
Speaker 1: which was I believe ten billion, mostly in cloud credits.
Speaker 1: While it might be possible that Microsoft, Amazon and Google
Speaker 1: extend their preferred pricing indefinitely, the question is whether these
Speaker 1: transactions are profitable for them in any way. As we
Speaker 1: saw following Microsoft's most recent quarterly earnings, there's growing investor
Speaker 1: concern over how capex is being spent and the amount
Speaker 1: that's being required to build the infrastructure for generative AI,
Speaker 1: with many voicing skepticism about the potential profitability of the technology,
Speaker 1: including Jim Cavello of Gold and Sex. And what we
Speaker 1: really don't know is how on profit generative AIS for hyperscalers,
Speaker 1: because they baked those costs into other parts of their ownings.
Speaker 1: What we can't know for sure, I imagine this stuff
Speaker 1: is if this stuff was in any way profitable, they'd
Speaker 1: be talking about it all the time. They would never
Speaker 1: shut up. This would be their new golden goose, and
Speaker 1: they're not. In fact, the most concrete information we have
Speaker 1: about open AI's balance sheet comes from leaked reports, well
Speaker 1: sourced reporters at places like The New York Times, and
Speaker 1: the information and invested prospectuses that found a wider audience
Speaker 1: than Altman perhaps would have liked. So you may remember
Speaker 1: from a few months ago that the markets have become
Speaker 1: a little skeptical of the generative AI boom and Nvidia
Speaker 1: CEO Jensen Huang had no real answers about AI's return
Speaker 1: and investment from his latest earnings, which led to a
Speaker 1: historic two hundred and seventy nine billion dollar drop in
Speaker 1: Nvidia's market cap in a single day. This, by the way,
Speaker 1: was the largest route in US market histories. The total
Speaker 1: value lost is equivalent of nearly five Layman Brothers Its
Speaker 1: peak value. They've recovered some of it, but nevertheless, that's
Speaker 1: what we in the business called are not so good.
Speaker 1: At the beginning of August, Microsoft, Amazon, and Google all
Speaker 1: took a similar beating for the markets for their massive
Speaker 1: capital expenditures related to AI, and all three of them
Speaker 1: will face the wheel next quarter in a couple weeks
Speaker 1: in fact, if they can't show a significant increase in
Speaker 1: revenue from the combined one hundred and fifty billion or
Speaker 1: more in capex that they put into new data centers
Speaker 1: in the Nvidio GPUs. What's important to remember here is
Speaker 1: that other than AI, bigtech really doesn't have any other ideas.
Speaker 1: There are no more hypergrowth markets left, and as firms
Speaker 1: like Microsoft and Amazon begin to show signs of declining growth,
Speaker 1: so too does their desperation to show the markets that
Speaker 1: they've still got it. Google, a company almost entirely sustained
Speaker 1: by multiple at risk monopolies in search and advertising, also
Speaker 1: needs something new and sexy to wave in front of
Speaker 1: the street. Except none of this is working because the
Speaker 1: products aren't that useful, and it appears most of its
Speaker 1: revenue comes from companies trying out AI and then realizing
Speaker 1: it wasn't worth it. And if you think back to
Speaker 1: what I was saying about open aised cloud costs, they're
Speaker 1: making what eight hundred to a billion on this? How
Speaker 1: much does Google make? Probably much less considering their multiple
Speaker 1: stories about people not really caring about Gemini. But at
Speaker 1: this point there are really two eventualities. Big Tech realizes
Speaker 1: that they've gotten in way too deep in this, and
Speaker 1: out of the deep fear of pissing off the street,
Speaker 1: chooses to reduce capital expenditures related to AI, or the
Speaker 1: second one, Big Tech, desperate to find a new growth hog,
Speaker 1: decides instead to cut costs to sustain their stupid fucking ideas,
Speaker 1: laying off workers and reallocating capital from other operations as
Speaker 1: a means of sustaining this death march nowhere, it's unclear
Speaker 1: which will happen if Big Tech accepts that generative AI
Speaker 1: is in the future. I don't really have anything else
Speaker 1: to waive at Wall Street, but they could do their
Speaker 1: own version of from twenty twenty two. Meta did this
Speaker 1: Year of Efficiency thing, which involved reducing capital expenditures and
Speaker 1: laying off thousands of people while also promising to slow
Speaker 1: down a little with investment. This, by the way, is
Speaker 1: the most likely path for Amazon and Google, who, while
Speaker 1: desperate to make Wall Street happy, they still kind of
Speaker 1: have their profitable monopolies now at least. Nevertheless, there really
Speaker 1: needs to be some kind of revenue growth from AI
Speaker 1: in the next few quarters. That has to be material.
Speaker 1: It can't just be this thing about AI being a
Speaker 1: maturing market or how annualized run rates have improved, and
Speaker 1: said material contribution will have to be magnitudes higher if
Speaker 1: capex has increased along with it. I just don't think
Speaker 1: it's going to be there, whether it's Q four twenty
Speaker 1: twenty four or Q one twenty twenty five, or maybe
Speaker 1: a little later. Wall Street's going to punish big tech
Speaker 1: for this the sin of lust, and the punishment is
Speaker 1: going to be to savage these companies, even more harshly
Speaker 1: than Nvidia, which, despite Jensen Huang's bluster and empty platitudes
Speaker 1: is pretty much the only company that's actually making money
Speaker 1: on AI, and that's because you do need their chips
Speaker 1: to do all this. But I worry more than anything
Speaker 1: that option two is more possible. I think these companies
Speaker 1: are really capable of committing to AI as the future
Speaker 1: and the cultures are so disconnected from the creation of
Speaker 1: actual value or like software or solving problems that actual
Speaker 1: people face, that they're willingly start laying people off if
Speaker 1: it means bankrolling these operations. I really really worry about that.
Speaker 1: By the way, the mass layoffs that could come from
Speaker 1: this will be horrifying, because otherwise it's just going to
Speaker 1: be feeding profit into this, and at this point they're
Speaker 1: feeding in pretty much all their profits. And all of this,
Speaker 1: by the way, could have been stopped if the media
Speaker 1: had actually held the leaders of tech companies accountable. This
Speaker 1: narrative was sold through the same con as the previous
Speaker 1: hype cycles, and the media assumed that these companies would
Speaker 1: just work it out like they did with crypto and
Speaker 1: the metaverse, despite the fact that it was blatantly obvious
Speaker 1: that they wouldn't work this out. You think I'm a duma, Well,
Speaker 1: ask to me this, what's the plan, what does generative
Speaker 1: AI do next? If your answer is that they'll work
Speaker 1: it out or that they have something behind the scenes
Speaker 1: that is incredible, you're an absolute mark. You're a participant
Speaker 1: in a marketing scheme. It's time to wake up. It
Speaker 1: is time to wake up to how stupid this is.
Speaker 1: And I'm sure some of you will say, oh, oh,
Speaker 1: you're going to look so stupid in six months. People
Speaker 1: were telling me that six months ago. And I still
Speaker 1: don't look stupid other than the ways they do, and
Speaker 1: they're unrelated to the podcast. But let's get back to
Speaker 1: the real problem, and let's get back to the really
Speaker 1: worrying stuff, because I believe that the very least Microsoft
Speaker 1: will begin reducing costs in other areas of its business
Speaker 1: as a means of sustaining the AI boom. In an
Speaker 1: email shared with me by a source from earlier this year,
Speaker 1: Microsoft's senior leadership team requested in a plan that was
Speaker 1: eventually scrapped, reducing power requirements from multiple areas within the
Speaker 1: company as a means of freeing up power for GPUs,
Speaker 1: including moving other services compute to other countries as a
Speaker 1: means of freeing upseid capacity, specifically for AI. On the
Speaker 1: Microsoft section of anonymous social network Blind, where you're required
Speaker 1: to verify that you have a corporate email of the
Speaker 1: company in question. One Microsoft worker complained in mid December
Speaker 1: twenty twenty three that AI was taking their money, saying
Speaker 1: that the cost of AI is so much that it
Speaker 1: is eating up pay raises and that things will not
Speaker 1: get better. In mid July twenty twenty four, another shared
Speaker 1: their anxiety about how it was apparent to them that
Speaker 1: Microsoft had and I quote a borderline addiction to cut
Speaker 1: costs in order to fund in Video's stock price with
Speaker 1: operational cash flows, and that doing so had and I
Speaker 1: quote damaged Microsoft's culture deeply. Another added that they believe
Speaker 1: that copilot is going to ruin Microsoft's FY twenty five,
Speaker 1: referring of course to their financial year twenty twenty five,
Speaker 1: adding that the f y twenty five copilot focus is
Speaker 1: going to massively fall in f y twenty five, and
Speaker 1: they knew of big co pilot deals in their country
Speaker 1: that have less than twenty percent usage after almost a
Speaker 1: year of integration, adding that corpor is too much and
Speaker 1: that Microsoft's huge AI investments are not going to be realized.
Speaker 1: While Blind is anonymous, it's kind of hard to ignore
Speaker 1: the fact that there are many, many posts that tell
Speaker 1: a tale of a kind of cultural cancer in Microsoft,
Speaker 1: with disconnected senior leadership, the only funds projects if they
Speaker 1: have AI takeed onto the side. Many posts the men
Speaker 1: satching the Della's words salard approach and complain of a
Speaker 1: lack of bonuses or upward mobility, and an organization focused
Speaker 1: on chasing an AI boom that may not exist. And
Speaker 1: at the very least, there's a deep cultural sadness there
Speaker 1: with the many posts I've seen oscillating between I don't
Speaker 1: like working at Microsoft and I don't know where we're
Speaker 1: putting so much into AI, and then someone replying with
Speaker 1: get used to it, SAJA doesn't get a shit, and
Speaker 1: it all feels so ridiculous because there's so many signs
Speaker 1: that these products don't have a product market fit. At
Speaker 1: the start of this episode, I mentioned an article from
Speaker 1: the Information about a lack of adoption of Microsoft's AI features.
Speaker 1: Buried within that one was a particularly worrying thought about
Speaker 1: the actual utilization of their data centers for this AI
Speaker 1: and it said, and I quote around March of this year,
Speaker 1: Microsoft had set aside enough server capacity in its data
Speaker 1: centers for three sixty five copilot to handle daily users
Speaker 1: of the AI system in the low millions. According to
Speaker 1: someone with direct knowledge of those plans, it couldn't be
Speaker 1: learned how much of that capacity was used at the time.
Speaker 1: Based on the information's estimates, Elsewhere, Microsoft has somewhere between
Speaker 1: four hundred thousand and four million years of its office
Speaker 1: Copilot features, meaning that there's a decent chance that Microsoft
Speaker 1: has built out capacity that isn't getting used. Now. One
Speaker 1: could argue that it's building with the belief that the
Speaker 1: product category will grow. But here's another idea. What if
Speaker 1: it doesn't. Huh ah, what do you think? What if?
Speaker 1: And this is crazy, Microsoft, Google and Amazon built out
Speaker 1: these massive data sentences to capture demand that may never arrive.
Speaker 1: I realized that sound a little crazy saying this, But
Speaker 1: back in March I made the point that I could
Speaker 1: find no companies that had integrated generative AI in a
Speaker 1: way that was truly benefited their bottom line, And just
Speaker 1: under six months later, I'm still looking. The best that
Speaker 1: I can find is that big companies appear to have
Speaker 1: done is stapled AI onto existing products and hoping that
Speaker 1: that helps them shift them something that does not seem
Speaker 1: to be working either. It doesn't work for Microsoft, doesn't
Speaker 1: work for Box. It does seem to be working anywhere
Speaker 1: as I'm not sure any of these AI upgrades give
Speaker 1: any kind of significant business value. Now. While there may
Speaker 1: be companies integrating AI that are driving some degree of
Speaker 1: spend on Microsoft as your Amazon Web Services and Google Cloud,
Speaker 1: I don't know how much it is, considering the last
Speaker 1: episode saying about how open ai was only making about
Speaker 1: a billion dollars licensing out their models, and I hypothesize
Speaker 1: that any of this demand is driven by investor sentiment,
Speaker 1: because companies are right now everywhere in the economy being
Speaker 1: pushed to invest in AI without really knowing if it
Speaker 1: will work, or whether it's useful, or whether their users
Speaker 1: will like it. Nevertheless, these companies have spent a great
Speaker 1: deal of time and money baking generative AI features into
Speaker 1: their products, and I think they're going to face one
Speaker 1: of a few different scenarios. Scenario the first, after developing
Speaker 1: and launching these features, these companies are going to find
Speaker 1: customers don't want to pay for them, as Microsoft's finding
Speaker 1: with three sixty five Copilot and if they can't find
Speaker 1: a way to make them pay for it. Now, they're
Speaker 1: going to be really hard pressed when nobody's telling them
Speaker 1: to get in on AI. And there's the same scenario.
Speaker 1: After developing and launching these features, these companies can't find
Speaker 1: a way to get users to pay for them, or
Speaker 1: at least pay extra for them, which means that everyone
Speaker 1: is going to have to bake the same thing into
Speaker 1: their products. Everyone's going to have to do this because
Speaker 1: none of these companies are able to function without copying
Speaker 1: their competitors, which will turn Generative AI into a kind
Speaker 1: of parasite. Now, just to broaden out what I mean here,
Speaker 1: I looked across most of the software as a service
Speaker 1: industry and a previous newsletter and I was looking and
Speaker 1: most of them are doing much the same thing. It's
Speaker 1: document summarization, document search, generation of staff, so emails and
Speaker 1: the like, and summarization summarization can be emails can be documents.
Speaker 1: For the most part, that's what everyone is doing. The
Speaker 1: problem is that everyone doing the same thing means that
Speaker 1: no one can really make money off of it. And
Speaker 1: Jim Cavello out of Gold and Sex made the same
Speaker 1: worrying we had the same thought as me, which makes
Speaker 1: probably him smarter than me. I shouldn't think about that
Speaker 1: too much anyway. I mentioned previously in the last episode
Speaker 1: the commoditization effect of these large language models, and I
Speaker 1: think there's going to be a further commoditization of these
Speaker 1: effects themselves, of these features. If everyone summarizes email, now
Speaker 1: you have to do it too, because otherwise the customer
Speaker 1: can go, there's another feature. I'm going to pay for
Speaker 1: this one because it's got more stuff in it, except
Speaker 1: the feature in questions more expensive. It's very worrying, But
Speaker 1: in general, what I fear is a kind of cascade effect.
Speaker 1: I believe that a lot of businesses right now are
Speaker 1: trying AI, and once those trials end, and Gartner predicts
Speaker 1: that thirty percent of GENERAVIAI projects will be abandoned after
Speaker 1: the proof of concept by the end of twenty twenty five,
Speaker 1: these companies are going to stop paying for the extra
Speaker 1: features or stop integrating GENERATEVII into their products. If this happens,
Speaker 1: it will reduce the already kind of shitty revenue flowing
Speaker 1: to the hyperscalers providing cloud computer or access to models
Speaker 1: for generat AI, which in turn could create more price
Speaker 1: pressure on these companies. They're already negative margins sour. At
Speaker 1: that point, Open AI and Anthropic will almost certainly have
Speaker 1: to raise prices. And what's fun is they're already not
Speaker 1: making that much money from this. So we're in this
Speaker 1: weird situation where it isn't obvious which it's going to be.
Speaker 1: Is it that they're going to have to raise prices,
Speaker 1: or that no one wants to pay them, or some
Speaker 1: combination of both. It's also important to note that the
Speaker 1: hyperscalers are also terrified of pissing off Wall Street. I
Speaker 1: really mean that one of them will eventually blink. And
Speaker 1: while they could theoretically do the layoffs and cost cutting
Speaker 1: measures I've mentioned, these are short term solutions that don't
Speaker 1: really work against burning billions tens of billions, like more
Speaker 1: than half more than fifty billion a year for each
Speaker 1: of them. How are you going to cut enough to
Speaker 1: bankroll that? But in any case, putting aside the amount
Speaker 1: of money they're having to invest, it might be time
Speaker 1: to accept that there really isn't money here in Generative AI.
Speaker 1: It might be time to stop and take stock of
Speaker 1: the fact that we're in the midst of what our
Speaker 1: third delusional epop our third stupid idea that everyone claims
Speaker 1: the future, But unlike cryptocurrency, in the metaverse, everyone seems
Speaker 1: to have joined this pie, and everyone's decided to burn
Speaker 1: as much money as he mainly possible on this unsustainable, unreliable, unprofitable,
Speaker 1: environmentally destructive bullshit sold to customers and businesses as artificial
Speaker 1: intelligence to law may everything without ever having a path
Speaker 1: to do so. Because that's the thing, none of this
Speaker 1: is even AI. This is an automation. It's generation generation
Speaker 1: in different hats, and it burns the world around us
Speaker 1: to provide it. But you know, I don't think the
Speaker 1: following is going to burn the world. In fact, I
Speaker 1: think it could really make your life better. And I
Speaker 1: need you to directly and vacifiously engage with the following
Speaker 1: advertisements and we're back. See might ask why does this
Speaker 1: keep happening? Why do we keep getting these stupid movements?
Speaker 1: Why did they tell us that cryptocurrency was the future.
Speaker 1: Why did they tell us the metaverse was the future?
Speaker 1: Why are they telling us that generative AI is the
Speaker 1: future when none of these things from the very beginning
Speaker 1: looked like the future. There were signs from GPT through
Speaker 1: like oh cool, you can generate entire things, and like
Speaker 1: a minute, wow, that's crazy. But past that point, past
Speaker 1: that moment of oh you can do that, I guess
Speaker 1: what was there? And why does this keep happening. It's
Speaker 1: the natural result of a tech industry that's become entirely
Speaker 1: focused on making each customer more valuable rather than providing
Speaker 1: more value to the customer in exchange for I don't know,
Speaker 1: money or attention. The products you're being sold today almost
Speaker 1: certainly tried to wed you to a particular ecosystem when
Speaker 1: owned by Microsoft, Apple, Amazon or Google as a consumer
Speaker 1: at least and internally increase the burden of lead things
Speaker 1: said ecosystem. Imagine trying to move all of your subscribe
Speaker 1: and save shit off of Amazon. Imagine trying I mean
Speaker 1: moving iOS to Android or It's not that easy. And
Speaker 1: that's by design. Everything is about further monetization, about increasing
Speaker 1: the dollar per head value of each customer, be it
Speaker 1: through keeping them doing stuff on the platform, to show
Speaker 1: them more advertising, upselling them new features that are only
Speaker 1: kind of useful or previously we're free, or creating some
Speaker 1: new monopoly or oligopoly where only those with the massive
Speaker 1: war chests a big tech can really play, and very
Speaker 1: very little about this is about delivering any kind of
Speaker 1: real value or utility or thing that you, the customer
Speaker 1: might like. Generative AI might not be super useful, but
Speaker 1: it's really easy to integrate into stuff and make new
Speaker 1: things happen, creating all sorts of new things that the
Speaker 1: company could theoretically charge for, both for a customer and
Speaker 1: an enterprise customer. Sam Molton was smart enough to realize
Speaker 1: that the tech industry needed a new thing, a new
Speaker 1: technology that everybody could take a piece of and sell.
Speaker 1: And while he might not really understand technology, all one
Speaker 1: understands growth and the lust that the economy has for growth,
Speaker 1: and he's productized Transformer based architecture is something that everybody
Speaker 1: could sell, a magical tool that could plug into things
Speaker 1: and kind of connect to an ephemeral concept like AI.
Speaker 1: The problem is that the desperation to integrate genera if
Speaker 1: AI everywhere has shown a pretty nasty light on how
Speaker 1: disconnected these companies are from actual consumer needs or even
Speaker 1: running good companies. Like really, I'm not even being facetious.
Speaker 1: I would genuinely like it if this stuff was useful.
Speaker 1: I like useful things. There would be ethical concerns about
Speaker 1: the copyright theft and such, but I would at least
Speaker 1: tip my hat to them if I could find something,
Speaker 1: anything that I looked at and could say, Wow, that's
Speaker 1: really useful in my daily life. I got nothing, and
Speaker 1: I've really looked. You can email me easy that's echoes
Speaker 1: Abetter offline dot com if you have one. But I've
Speaker 1: yet impressed by one of those emails, So please try harder.
Speaker 1: And the really worrying part is that other than AI,
Speaker 1: many of these companies don't seem to have any other
Speaker 1: new products. What else is there? What are the things
Speaker 1: do they have to grow their companies? No? Really, what
Speaker 1: do they have? The new iPhone? I bought the new iPhone.
Speaker 1: I'm a little pig point cooin coin. I bought the iPhone.
Speaker 1: I bought the new one, and I've bought it every year.
Speaker 1: I am that guy I sell the old one about
Speaker 1: the new one. This is the first year. I think
Speaker 1: from the beginning where I bought it and being like
Speaker 1: why did I do that? Man? What does this do?
Speaker 1: And that's because I think we're hitting a wall. This
Speaker 1: is the rock combubble. I talked about a few months ago.
Speaker 1: They've not got anything. There's nothing, They've got nothing. And
Speaker 1: that really is the problem, because when everything falls, when
Speaker 1: everyone realizes, when the markets look at tech and say, wow,
Speaker 1: you're not going to grow forever. You're not going to
Speaker 1: come up with a new wiz bang that you can
Speaker 1: market to everyone and make billions in returns. You're not
Speaker 1: going to do that. No, they're not going to react
Speaker 1: well at all, because when you take away the massive
Speaker 1: growth that tech has, you have a very annoying industry
Speaker 1: full of annoying young people that will piss off the markets.
Speaker 1: They will piss off those with the money. The tech
Speaker 1: industry has a terrible rep with the government and a
Speaker 1: terrible rep with society. The reevaluation of these companies will
Speaker 1: be merciless, and there are very few friends left, and
Speaker 1: I think there will be a cascade down to the
Speaker 1: other companies in the tech space, just in the same
Speaker 1: way that it will hit workers who will get laid
Speaker 1: off when all of this falls apart. Despite none of
Speaker 1: these people doing anything wrong other than the people up
Speaker 1: top having no creativity, no real innovation, and no understanding
Speaker 1: of real people's problems. I hypothesize a kind of SUBPRIMEI
Speaker 1: crisis is brewing where almost the entire tech industry is
Speaker 1: brought in and a technology sold at this insanely discounted rate,
Speaker 1: heavily sentralized and subsidized by big tech companies like Microsoft, Amazon,
Speaker 1: and Google. At some point, this incredible toxic burn ray
Speaker 1: is going to burn through GENERATIVEAI and it's going to
Speaker 1: catch up with them. And when the price increases come
Speaker 1: or companies realize that these features are not that useful
Speaker 1: and they see the lack of user adoption, they're going
Speaker 1: to start getting nervous. But right now are in the
Speaker 1: piss take section of the economy. Right now we're seeing
Speaker 1: the egregious share like Salesforce charging two dollars a conversation
Speaker 1: for their new agent Force product. But eventually the markets
Speaker 1: will catch up because the money isn't there. And when
Speaker 1: these prices go up, I'm not confident that will have
Speaker 1: much of a generative AI industry left. And that's assuming
Speaker 1: that these companies still have enough money. It's assuming that
Speaker 1: open ai is able to raise another six and a
Speaker 1: half billion dollar round in the next six to eight months.
Speaker 1: How long can they do that? For? How many times?
Speaker 1: How many years? A VSA he's willing to prop up
Speaker 1: open AI. How many years is Microsoft ready to burn
Speaker 1: capital to make what a billion or two on Generative AI?
Speaker 1: This is embarrassing. It's bad business and it's bad product.
Speaker 1: Satch an Adela, Sundarpishi, sam Ortmon, the whole lot of them.
Speaker 1: They should be absolutely fucking ashamed of themselves. They're insult
Speaker 1: to innovation and insult Silicon Valley and insult to their consumers.
Speaker 1: And what happens, you tell me this when the tech industry,
Speaker 1: the entire tech industry, relies on the success of a
Speaker 1: kind of software that only loses money and doesn't create
Speaker 1: much value when it does so. And what happens when
Speaker 1: the heat gets too hot and these products become impossible
Speaker 1: to reconcile with, and that everyone realizes that none of
Speaker 1: these companies have anything else to sell. I really don't know.
Speaker 1: I'm scared. I'm not trying to do fud, doing a fud, fear, uncertainty,
Speaker 1: in doubt, told to spell these things out, but I
Speaker 1: am worried because really the only other alternative to what
Speaker 1: I'm saying is that they magically make this profitable, that
Speaker 1: they just keep doing this until it goes into the green,
Speaker 1: despite no one appearing to know how, despite their not
Speaker 1: being a path there. How willing are you to believe
Speaker 1: them after they've lied to you for so many years?
Speaker 1: How ridiculous is this really? How ridiculous have you been
Speaker 1: thinking this is? How much can you let them coast on?
Speaker 1: They'll work it out? Because they haven't. They haven't worked
Speaker 1: it out for a while. It's been over a decade
Speaker 1: since the last significance consumer tech innovation. It's been a
Speaker 1: ton on the chip side. But what is there for you?
Speaker 1: And I? Not really much? And I don't think there's
Speaker 1: much in this industry either, And I worry that the
Speaker 1: tech industry is building towards a really grotesque reckoning, with
Speaker 1: a total lack of creativity, enabled by an economy that
Speaker 1: rewards growth over innovation, and monopolization over loyalty, and management
Speaker 1: over those who actually build things. The people in control
Speaker 1: of the tech industry are not the ones who built it.
Speaker 1: These people are management consultants. Even Samultman is one of them.
Speaker 1: These people are superficially interesting and superficially smart, just like
Speaker 1: jat GPT. And I worry, I worry so much, So
Speaker 1: promise me, dear listener, then The next time someone tells
Speaker 1: you they'll work it out, that this stuff is the future,
Speaker 1: tell them some of this shit, send them the podcast,
Speaker 1: or just yell at them at the top of your voice.
Speaker 1: Don't even need to use words, but I'm so grateful
Speaker 1: to have you as listeners. Thank you for listening to
Speaker 1: Better Offline. The editor and composer of the Better Offline
Speaker 1: theme song is Matasowski. You can check out more of
Speaker 1: his music and audio projects at Mattasowski dot com, m
Speaker 1: A T T O S O W s ki dot com.
Speaker 1: You can email me at easy at Better Offline dot
Speaker 1: com or visit Better Offline dot com to find more
Speaker 1: podcast links and of course, my newsletter. I also really
Speaker 1: recommend you go to chat dot Where's youread dot at
Speaker 1: to visit the discord, and go to our slash Better
Speaker 1: Offline to check out I'll Reddit. Thank you so much
Speaker 1: for listening. Better Offline is a production of cool Zone Media.
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