Speaker 1: Media, Hello and welcomes a better offline. I'm, of course
Speaker 1: your host ed Zitron. We're in the third episode of
Speaker 1: our four part series where I give you a comprehensive
Speaker 1: explanation as to the origins of the AI bubble, the
Speaker 1: mythology sustaining it, and why it's destined to end really,
Speaker 1: really badly. Now, if you're jumping in now, please start
Speaker 1: from the very beginning. The reason why this is a
Speaker 1: four part my first ever, is because I want it
Speaker 1: to be comprehensive, and because this is a very big
Speaker 1: subject with a lot of moving parts and even more bullshit.
Speaker 1: A few weeks ago, I published a premium newsletter that
Speaker 1: explained how everybody is losing money on generative AI, in
Speaker 1: part because the costs of running AI models is increasing,
Speaker 1: and in part because the software itself doesn't do enough
Speaker 1: to warrant the costs associated with running them, which are
Speaker 1: already subsidized and unprofitable for the model providers. Outside of
Speaker 1: open and to a lesser extent, Anthropic, nobody seems to
Speaker 1: be making much revenue, with the most successful company being
Speaker 1: any Sphere, makers of AI coding tool Cursor, which hid
Speaker 1: five hundred million dollars have annualized so forty one point
Speaker 1: six million in one month a few months ago, just
Speaker 1: before Anthropic and open ai jacked up the prices for
Speaker 1: priority processing on enterprise queries, raising their operating costs as
Speaker 1: a result. In any case, that's some pissport revenue for
Speaker 1: an industry that's meant to be the future of software.
Speaker 1: Smart Watchers are projected to make thirty two billion dollars
Speaker 1: this year, and as I've mentioned in the past, the
Speaker 1: Magnificent Seven expect to make thirty five billion dollars or
Speaker 1: so in revenue from AI this year, and I think
Speaker 1: in total, when you're throw in core even all them,
Speaker 1: it's barely fifty five billion dollars in total. Even Anthropic
Speaker 1: and open Ai seem a little lethargic, both burning billions
Speaker 1: of dollars while making by my estimates, no more than
Speaker 1: two billion dollars in Anthropics case this year so far
Speaker 1: and six point six two six billion dollars in twenty
Speaker 1: twenty five so far for open Ai, despite projections of
Speaker 1: five billion dollars and thirteen billion dollars respectively. Outside of
Speaker 1: these two AI startups are floundering, struggling to stay alive
Speaker 1: and raising money in several hundred million dollar versus their
Speaker 1: negative gross margin businesses flounder as they dug into. A
Speaker 1: few months ago, I could find only twelve AI powered
Speaker 1: companies making more than eight point three million dollars a month,
Speaker 1: with two of them slightly improving their revenue, specifically AI
Speaker 1: search company perplexd, which is now here one hundred and
Speaker 1: fifty million dollars an ur in or twelve point five
Speaker 1: million dollars a month, and AI coding startup Replayer, which
Speaker 1: has hit the same amount. Both of these companies burn
Speaker 1: ridiculous amounts of money. Paplexd burned one hundred and sixty
Speaker 1: four percent of its revenue on Amazon web services, open
Speaker 1: Ai and Anthropic last year, and while replet hasn't leaked
Speaker 1: its costs, the information reports its gross margins in July
Speaker 1: but twenty three percent, which doesn't include the cost of
Speaker 1: its free users, which you simply have to do with llms,
Speaker 1: as free users are capable of costing you a shit
Speaker 1: ton of money. And some of you might say that's
Speaker 1: how they do it in software, Well, guess what software
Speaker 1: doesn't usually connect you to a model that can burn
Speaker 1: I don't know ten cents twenty cents every time they
Speaker 1: touch it, which may not seem like much, but when
Speaker 1: you're making three dollars on someone and they don't convert,
Speaker 1: it does problematically. Your paid users also cost you more
Speaker 1: than they bring in as well. In fact, every user
Speaker 1: loses you money in Generative AI because it's impossible to
Speaker 1: do cost control in a consistent manner. A few months ago,
Speaker 1: I did a piece of Anthropic losing money on every
Speaker 1: single claud code subscriber. And now I'm going to walk
Speaker 1: you through the whole story in a simplified fashion because
Speaker 1: it's quite important. So claud Code is a coding environment
Speaker 1: that people use used, or I should really say, try
Speaker 1: to use to build software using generative AI. It's available
Speaker 1: as part of Anthropics twenty dollars, one hundred dollars and
Speaker 1: two hundred dollars a month claud subscriptions, with the more
Speaker 1: expensive subscriptions having more generous rate limits. Generally, these subscriptions
Speaker 1: are all you can eat. You can use them as
Speaker 1: much as you want until you hit limits, rather than
Speaker 1: paying for the actual tokens you burn. When I say
Speaker 1: burn tokens and someone reached out saying I should specify this,
Speaker 1: I'm describing how these models are traditionally built. In general,
Speaker 1: you'll builded a dollar per million input tokens as in
Speaker 1: user feeding in data and output tokens the output created,
Speaker 1: so you wouldn't get one token built, so every million
Speaker 1: you get charged. So, for example, Anthropic charges three dollars
Speaker 1: per million input tokens and six million output tokens to
Speaker 1: use its clauds on it for model, and it's about
Speaker 1: I think, well, a word before tokens should really look
Speaker 1: that up. It's it also gets more complex as you
Speaker 1: get into things like generating code. Nevertheless, claud code has
Speaker 1: been quite popular, and a user created a program called
Speaker 1: cc usage which allowed you to see your token burn
Speaker 1: the amount of tokens you were using. You were actually
Speaker 1: burning using Anthropics models while using clawed code versus just
Speaker 1: getting charged a month and not knowing, and many were
Speaker 1: seeing that they were burning in the excess of their
Speaker 1: monthly spend. To be clear, this is the token price
Speaker 1: based on anthropics own pricing, and thus the cost of
Speaker 1: Anthropic are likely not identical. So I got a little
Speaker 1: clever using anthropics gross profit margins, I chose fifty five percent,
Speaker 1: and then a few weeks solved my article sixty percent
Speaker 1: was leaked. I found at least twenty different accounts of
Speaker 1: people costing Anthropic anywhere from one hundred and thirty percent
Speaker 1: to three thousand and eighty four percent of their subscription.
Speaker 1: There is also now a leader board called vibrank, where
Speaker 1: people compete to see how much they burn with the
Speaker 1: current leader burning and I sheit you not fifty two
Speaker 1: hundred and ninety one dollars of the course of a month.
Speaker 1: Anthropic is, to be clear, the second largest model developer
Speaker 1: and has some of the best AI talent in the industry.
Speaker 1: It has a better handle on its infrastructure than anyone
Speaker 1: outside of big tech and open AI, and it still
Speaker 1: cannot seem to fix this problem even with weekly rate
Speaker 1: limits brought in at the end of August. While one
Speaker 1: could assume that Anthropic is simply letting users run wild,
Speaker 1: my theory is far simpler. Even the model developers have
Speaker 1: no real way of limiting user activity, likely due to
Speaker 1: the architecture of generative AI. I know it sounds insane,
Speaker 1: but at the most advanced level. Even there, modeled providers
Speaker 1: are still prompting their models, and whatever rate limits may
Speaker 1: be in place appear to at times get completely ignored,
Speaker 1: and there doesn't seem to be anything they can do
Speaker 1: to stop it now. Really, Anthropic counts amongst its capitalist
Speaker 1: apex predators one lone Chinese man who spent fifty thousand
Speaker 1: dollars to their compute in the space of a month
Speaker 1: fucking around with glord code. Even if Anthropic was profitable,
Speaker 1: it isn't, and we'll burn billions of dollars this year.
Speaker 1: A customer paying two hundred dollars a month ran up
Speaker 1: fifty thousand dollars in costs, immediately devouring the margin of
Speaker 1: any user running the service that day, that week, or
Speaker 1: even that month. Even if Anthropics costs are half the
Speaker 1: published rates, they're not. By the way, one guy amounted
Speaker 1: to one hundred and twenty five US is worth of
Speaker 1: monthly revenue. This is not a real business. That's a
Speaker 1: bad business without of control costs, and it doesn't appear
Speaker 1: anybody has these costs under control and face with the
Speaker 1: grim reality ahead of them, these companies are trying nasty
Speaker 1: little tricks on their customers to douce more revenue from them.
Speaker 1: A few weeks ago, Replet, an unprofitable AI coding company,
Speaker 1: released a product called Agent three, which promised to be
Speaker 1: ten times more autonomous and offer infinitely more possible abilities,
Speaker 1: testing and fixing its code, constantly improving your application behind
Speaker 1: the scenes in a reflection loop. Sounds very real, sounds
Speaker 1: extremely real, It's so real, but actually it isn't. In reality.
Speaker 1: This means you go and tell the model to build something,
Speaker 1: and it would go and do it, and you'll be
Speaker 1: shocked to hear that these models can't be relied upon
Speaker 1: to go and do anything. Please note that this was
Speaker 1: launched a few months after Replet raise their prices, shifting
Speaker 1: to obfiscated effort based pricing that would charge the full
Speaker 1: scope of the agent's work. And if you're wondering what
Speaker 1: the fuck that means, so are their customers. Agent three
Speaker 1: has been a disaster. Users found the tasks that previously
Speaker 1: cost a few dollars were spiraling into the hundreds of dollars,
Speaker 1: with the register reporting one customer found themselves within one
Speaker 1: thousand dollars bill after a week, and I quote them,
Speaker 1: I think it's just launch pricing adjustment. Some tasks on
Speaker 1: new apps ran over an hour and forty five minutes
Speaker 1: and only charged four to six dollars, but editing pre
Speaker 1: existing apps seems to cost most overall. I spend one
Speaker 1: K this week alone, and they told that to the register.
Speaker 1: By the way, another user comp that costs skyrocket without
Speaker 1: any concrete results, and they quote the register here. I
Speaker 1: typically spent between one hundred dollars and two hundred and
Speaker 1: fifty dollars a month. I blew through seventy dollars in
Speaker 1: a night at Agent three launch, and another redditor wrote
Speaker 1: alleging the new tool also performed some questionable actions. One
Speaker 1: prompt brute forced its way through authentication, redoing auth and
Speaker 1: hard resetting users password to what it wanted to perform
Speaker 1: app testing on a form. The user wrote, I realized
Speaker 1: that's a little nonsensical, but long story short, it did
Speaker 1: a bunch of shit. It wasn't asked to. As I
Speaker 1: previously reported, in late May early June, both open ai
Speaker 1: and Anthropic cranked up the pricing on their enterprise customers,
Speaker 1: leading Replet and Cursor both shifting their prices upward. This
Speaker 1: abuse is now trickled down to the customers. Report has
Speaker 1: now released an update. Unless you choose how autonomous you
Speaker 1: want Agent three to be, which is a tacit admission
Speaker 1: that you can't trust coding elms to build software replets.
Speaker 1: Users are still pissed off, complaining that report is charging
Speaker 1: them for an activity when the agent doesn't do anything,
Speaker 1: a consistent problem I've found across redditors. While Reddit is
Speaker 1: not the full summation of all users of every company everywhere,
Speaker 1: it's a fairly good barometer of user sentiment and man
Speaker 1: a user's piss and now here's why this is bad. Traditionally,
Speaker 1: Silicon Valley startups have relied upon the same model, have
Speaker 1: grow really fast and burn a bunch of money, then
Speaker 1: turn the profit lever. AI does not have a profit
Speaker 1: lever because the raw costs of providing access to AI
Speaker 1: models are so high and they're only increasing that the
Speaker 1: basic economics of how the tech industry sell software don't
Speaker 1: make sense. I'll reiterate something I wrote a few weeks ago.
Speaker 1: A large language model users infrastructural burden varies wildly between
Speaker 1: users and use cases. While somebody asking chat gpt to
Speaker 1: summarize an email might not be much of a burden,
Speaker 1: somebody asking chat gpt to review hundreds of pages of
Speaker 1: documents at once. A core feature of basically any twenty
Speaker 1: dollars a month subscription could eat up to eight GPUs
Speaker 1: at once. To be very clear, a user that pays
Speaker 1: twenty dollars a month could run multiple queries like this
Speaker 1: a month and there's not really a way to stop them.
Speaker 1: Unlike most software products, any errors in producing an output
Speaker 1: from a large language model have a significant opportunity cost.
Speaker 1: When a user doesn't like an output, or the model
Speaker 1: gets something wrong which it's guaranteed to do, or the
Speaker 1: user realizes they forgot something, the model must make a
Speaker 1: further generation or generations, and even with caching which anthropic
Speaker 1: is added are told to there's a definitive cost attached
Speaker 1: to any mistake. Large language models are for the most
Speaker 1: part lacking in any definitive use cases, meaning that every
Speaker 1: user is even with an idea of what they want
Speaker 1: to do, experimenting with every input and output. In doing so,
Speaker 1: they create the opportunity to burn more tokens, which in
Speaker 1: turn creates an infrastructural burn on GPUs, which cost a
Speaker 1: lot of money to run. The more specific the output,
Speaker 1: the more opportunities there are of a monstrous token burn.
Speaker 1: And I'm specifically thinking about coding with l elms. The
Speaker 1: token heavy nature of generating code means that any mistakes,
Speaker 1: suboptimal generations, or straight up errors will guarantee further token burn.
Speaker 1: Even efforts to reduce compute cors by, for example, pushing
Speaker 1: free users or those on cheap plans, the small or
Speaker 1: less intensive models have dubious efficacy. As I talked about
Speaker 1: in a previous episode, open ai split a model in
Speaker 1: the GPT version of CHET. GPT requires vast amounts of
Speaker 1: additional compute in order to route the user's request or
Speaker 1: the appropriate model, with simpler requests going to smaller models
Speaker 1: and more complex ones being shifted to reasoning models, and
Speaker 1: it makes it impossible to cash part of the input.
Speaker 1: As a result, it's not really clear whether it's saving
Speaker 1: open ai any money, and indeed, kind I suggest it
Speaker 1: might be costing them more. In simpler terms, it's very,
Speaker 1: very very difficult to imagine what one user free or
Speaker 1: otherwise might cost, and thus it's hard to charge them
Speaker 1: anything on a monthly basis or tell them what a
Speaker 1: service might actually cost them on average. And this is
Speaker 1: a huge, huge problem with AI coding environments. But let's
Speaker 1: talk about claud Code again. Anthropics code generate a tool.
Speaker 1: According to the information claud code was driving nearly four
Speaker 1: hundred million dollars in annualized revenue, roughly doubling from a
Speaker 1: few weeks ago on July thirty first, twenty twenty five.
Speaker 1: The annualized revenue works out to about thirty three million
Speaker 1: dollars a month in revenue for a company that predicts
Speaker 1: it will make at least four hundred and sixteen million
Speaker 1: dollars a month by the end of the year, and
Speaker 1: for a product that has become for a time the
Speaker 1: most popular coding environment in the world from the second
Speaker 1: largest and best funded AI company in the world. Is
Speaker 1: that it is that fucking it is that all that's
Speaker 1: happening here thirty three million dollars, all of which is
Speaker 1: unprofitable after it felt, at least based on social media
Speaker 1: chatter and discussing with multiple different engineers, that claud code
Speaker 1: have become ubiquitous with anything to do with LLLMS and coding.
Speaker 1: To be clear, Anthropics, so on It and Opus models
Speaker 1: are consistently some of the most popular for programming an
Speaker 1: open router, an aggregator of LM usage, and Anthropic has
Speaker 1: been consistently named as the best at coding. Whether or
Speaker 1: not I feel that way is irrelevant. Some bright spark
Speaker 1: out there is going to send it. Microsoft's get hub
Speaker 1: copilot at one point eight million paying subscribers, and guess
Speaker 1: what that's true? In fact, I reported it. Here's another
Speaker 1: fun fact. The Wall Street Journal report that Microsoft loses
Speaker 1: on average twenty dollars a month per use, with some
Speaker 1: users costing the company as much as eight bucks. And
Speaker 1: that's for the most popular product. But wait, wait, wait, wait,
Speaker 1: hold up, wait, I read some shit in the newspaper.
Speaker 1: Aren't these LLLM code generators replacing actual human engineers? And thus,
Speaker 1: even if they cost way more than twenty dollars one
Speaker 1: hundred dollars or two hundred dollars a month, they're still
Speaker 1: worth it. Right, They're replacing an entire engineer. Oh my
Speaker 1: sweet summer child. If you believe the New York Times
Speaker 1: or other outlets that simply copy and paste whatever anthropic
Speaker 1: CEO Warrio Ama Day says, you'd think that the reason
Speaker 1: that software engineers are having trouble finding work is because
Speaker 1: their jobs are being replaced by AI. This grotesque, manipulative, abusive,
Speaker 1: and offensive lie has been propagated through the entire business
Speaker 1: and tech media without anybody sitting down and asking whether
Speaker 1: it's true, or even getting a good understanding of what
Speaker 1: it is that elms can actually do with code. Members
Speaker 1: of the media, I am begging you stop stop doing this,
Speaker 1: Stop publishing these fucking headlines. You're embarrassing yourself. Every asshole
Speaker 1: is willing to give a quote saying that coding is
Speaker 1: dead and that every execut if he is willing to
Speaker 1: burp out some nonsense about replacing all of their engineers.
Speaker 1: But I'm fucking begging you to either use these things
Speaker 1: yourself or speak to people that do. I am not
Speaker 1: a coder. I cannot write or read code. Nevertheless, I'm
Speaker 1: capable of learning, and I've spoken to numerous software engineers
Speaker 1: in the last few months, and basically I've reached a
Speaker 1: consensus of this is kind of useful sometimes. However, one time,
Speaker 1: a very silly man with an increasingly squeaky voice said
Speaker 1: that I don't speak to people who use AI tools.
Speaker 1: So I went and spoke to three notable experienced software
Speaker 1: engineers and ask them to give me the straight truth
Speaker 1: about what coding lllms can do. Now, for the purposes
Speaker 1: of brevity, I'm going to use select quotes from what
Speaker 1: these people said. But if you want to read the
Speaker 1: whole thing, you can check out the newsletter first. I'm
Speaker 1: going to read what Carl Brown of the Internet of
Speaker 1: Bugs said, and I had him on the show a
Speaker 1: few months back. He's fantastic. So most of the advancements
Speaker 1: in programming languages, technique and craft in the last four
Speaker 1: years have been designing safer and better ways of tying
Speaker 1: these blocks together to create large and larger programs with
Speaker 1: more complexity and functionality. Humans use these advancements to arrange
Speaker 1: these blocks in logical abstraction layers so we can fit
Speaker 1: an understanding of the lairs interconnections in our heads as
Speaker 1: we work. Diving into blocks temporarily is needed. This is
Speaker 1: where AIS fall down. The amount of context required to
Speaker 1: hold the interconnections between these blocks quickly grows beyond the
Speaker 1: AI's effective short term memory, in practice much smaller than
Speaker 1: its advertised context windows size, and the AIS like the
Speaker 1: ability to reason about the abstractions as we do. This
Speaker 1: leads to real world code that's illogically layed, hard to understand, debug,
Speaker 1: and maintain. Carl also said code generation AIS, from an
Speaker 1: industry standpoint, are roughly the equivalent of a slightly below
Speaker 1: average computer science graduate fresh out of school without any
Speaker 1: real world experience, only ever having written programs to be
Speaker 1: printed and graded. That's bad because, as he pointed out,
Speaker 1: whereas llms can't get past this summer, in turn stage,
Speaker 1: actual humans get better, and if we're replacing the bottom
Speaker 1: rung of the labor market, there won't be any mid
Speaker 1: level or senior developers later down the line. Next, I
Speaker 1: asked Nick Sharesh of I will fucking pile drive you
Speaker 1: if you mention AI again what he thought. Llms, he said,
Speaker 1: will sometimes solve a thorny problem for me in a
Speaker 1: few seconds, saving me some brain power. But in practice,
Speaker 1: the effort of articulating so much of the design work
Speaker 1: in plain English and hoping the LM emits code that
Speaker 1: I find acceptable is frequently more work than just writing
Speaker 1: the code. For most problems, the hardest part is the thinking,
Speaker 1: and lllms don't make it that part any easier. I
Speaker 1: also talked to Colvogi of no AI is not making
Speaker 1: AI engineers ten X is productive. We also had in
Speaker 1: the show recently, and he said this, llms often function
Speaker 1: like a fresh summer intern. They're good at solving the
Speaker 1: straightforward problems that code has learned about in school. But
Speaker 1: they are unworldly. They do not understand how to bring
Speaker 1: lots of solutions to the small, straightforward problems together into
Speaker 1: a larger hole. They lack the experience to be wholly
Speaker 1: trusted and trust this is the most important thing you
Speaker 1: need to fully delegate coding tasks. In simpler terms, lms
Speaker 1: are capable of writing code, but can't do software engineering
Speaker 1: because software engineering is the process of understanding, maintaining and
Speaker 1: executing code to produce functional software, and lms do not learn,
Speaker 1: cannot adapt, and to paraphrase something Carl Brown said to me,
Speaker 1: break down the more of your code and variables you
Speaker 1: ask them to look at at once, so you can't
Speaker 1: replace a software engineer with them. If you are printing
Speaker 1: this in a media outlet and have heard this sentence,
Speaker 1: you are fucking up. You really are fucking up. I'm
Speaker 1: really neat members of the media here in this You
Speaker 1: need to change. You need to change on this one.
Speaker 1: You are doing software engineers dirty. Look, and I understand
Speaker 1: why too. It's very easy to believe that software engineering
Speaker 1: is just writing code, but the reality is that software
Speaker 1: engineers maintain software, which includes writing and analyzing code, amongst
Speaker 1: a vast array of different personalities and programs and problems.
Speaker 1: Good software engineering harkens back to Brian Merchant's interviews with translators.
Speaker 1: While some may believe the translators simply tell you what
Speaker 1: words mean, true translation is communicating the meaning of a sentence,
Speaker 1: which is cultural, contextual, regional, and personal and often requires
Speaker 1: the exercise of creativity and novel thinking. And on top
Speaker 1: of that, while translation is the production of words, you
Speaker 1: can't just take code and look at it. You actually
Speaker 1: need to know how code works and functions and wide functions.
Speaker 1: In that way, using an LLM, you'll never know because
Speaker 1: the LM doesn't know anything either. Now, my editor Matt
Speaker 1: Hughes gave an example of this in his newsletter, which
Speaker 1: I think i'll paraphrase. He used to live in France
Speaker 1: and the French speaking part of Switzerland, and sometimes he
Speaker 1: will read French translations of books to see how awkward
Speaker 1: bits of prose are translated. Doing those awkward bits requires
Speaker 1: a bit of creative thinking. And I quote take Harry
Speaker 1: Potter in French, Hogwarts is boudlard, which translates into bacon lice.
Speaker 1: Why did they go with that instead of a literal translation?
Speaker 1: Of Hogwarts, which would be Verus Spork. I'm sorry to
Speaker 1: anyone who can actually read languages, no idea, but I'd
Speaker 1: assume it is something to do with the fact that
Speaker 1: Poolard that Poudlard sounds a lot better than Veru Spork,
Speaker 1: and both of them, I can say flawlessly. Someone had
Speaker 1: to actually think about to translate that one idea. They
Speaker 1: had to exercise creativity, which is something that an AI
Speaker 1: in is inherently incapable of doing. Similarly, coding is not
Speaker 1: just a series of texts that program as a computer,
Speaker 1: but a series of interconnected characters that refers to other
Speaker 1: software in other places that must also function now and
Speaker 1: explain on some level to someone who has never ever
Speaker 1: seen the code before why it was done in this way.
Speaker 1: This is, by the way, while we're still yet to
Speaker 1: get any tangible proof that AI is replacing software engineers,
Speaker 1: because it isn't replacing software engineers, and now we need
Speaker 1: to understand why this is so existentially bad for generative AI.
Speaker 1: Of all the fields supposedly at risk from AI disruption,
Speaker 1: coding fields or felt the most tangible, if only because
Speaker 1: the answer to can you write code with LMS wasn't
Speaker 1: an immediate unilater or no The media has also been
Speaker 1: quick to suggest that AI writes software, which is true
Speaker 1: in the same way that chat GBT writes novels. In reality,
Speaker 1: lms can generate code and do somewhere some sort of
Speaker 1: software engineering adjacent tasks, but like all large language models,
Speaker 1: break down and go totally in saying hallucinating more and
Speaker 1: more as the tasks get more complex, and software engineering
Speaker 1: is extremely complex. Even software engineers who can read code
Speaker 1: and have done so for decades will find problems they
Speaker 1: can't solve just by looking at the code. And as
Speaker 1: I pointed out earlier, software engineer is not just coding.
Speaker 1: It involves thinking about problems, finding solutions to novel challenges,
Speaker 1: designing stuff in a way that could be read and
Speaker 1: maintained by others, and that's ideally scalable and secure. The
Speaker 1: whole fucking point of an AI is that you handshit
Speaker 1: off to it. That's what they've been selling it as.
Speaker 1: That's why Jensen Huang told kids to stop learning to code.
Speaker 1: As with AI, there's no point and it was all
Speaker 1: a fucking lie. Generative AI can't do the job of
Speaker 1: a software engineer, and it fails. While also costing an
Speaker 1: abominable amount of money. Coding large language models seem like
Speaker 1: magic at first because they, to quote a conversation with
Speaker 1: Carl Brown, make the easy things easier, but they also
Speaker 1: make the harder things harder. They don't even speed up engineers.
Speaker 1: There's a study that showed that make them slower YEAT
Speaker 1: coding is basically the only obvious use case for lms. Oh,
Speaker 1: I'm sure you're gonna say, but I bet the enterprise
Speaker 1: is doing well, and you're also very, very wrong. Microsoft,
Speaker 1: if you've ever switched on a TV in the past
Speaker 1: two years, has gone all in on generative AI, and
Speaker 1: despite being arguably the biggest software company in the world
Speaker 1: at least in terms of desktop operating systems and productivity software,
Speaker 1: has made almost no traction in popularizing generative AI. It
Speaker 1: has thousands, if not tens of thousands of salespeople and
Speaker 1: thousands of companies that literally sell Microsoft services for a living,
Speaker 1: and it can't sell AI. I've got a real fucking scoopyeo,
Speaker 1: I'm so excited, and I buried it in the third
Speaker 1: part of a four pot episode. AAH and truly twisted.
Speaker 1: But a source that has CM materials related to Sales
Speaker 1: has confirmed that as of August twenty twenty five, Microsoft
Speaker 1: has around eight million active license so paying users of
Speaker 1: Microsoft three sixty five Copilot, amounting to a one point
Speaker 1: eight one percent conversion rate across four hundred and forty
Speaker 1: million Microsoft three sixty five subscribers. Must be clear that
Speaker 1: three sixty five is their big cash cow. This would
Speaker 1: amount to if each of these users paid annually at
Speaker 1: the full rate thirty dollars a month, to about two
Speaker 1: point eight eight billion dollars an annual revenue for a
Speaker 1: product category that makes thirty three billion dollars a fucking quarter.
Speaker 1: It's productivity and business unit for Microsoft, and I must
Speaker 1: be clear, I am one hundred percent sure these users
Speaker 1: aren't all paying thirty dollars a month. The Information reported
Speaker 1: a few weeks ago that Microsoft has been reducing the
Speaker 1: software's price, referring to Microsoft three sixty five with more
Speaker 1: generous discounts on the AI features. According to customers and salespeople,
Speaker 1: heavily suggesting discounts have already been happening. Enterprise software is
Speaker 1: traditionally sold at a discount anyway, or put a different way,
Speaker 1: with bulk pricing for those who sign up a bunch
Speaker 1: of users at once. In fact, I found evidence that
Speaker 1: they've been doing this for a while, with a fifteen
Speaker 1: percent discount on annual Microsoft three sixty five Copilot subscriptions
Speaker 1: for orders of ten to three hundred seats mentioned by
Speaker 1: an IT consultant back in late twenty twenty four, and
Speaker 1: another that's currently running through September thirtieth, twenty twenty five,
Speaker 1: with another Microsoft Cloud Solution Provider program. Yeah this, I've
Speaker 1: found tons of other examples too. A Microsoft three sixty
Speaker 1: five is the enterprise version where they sell things with
Speaker 1: like Word and PowerPoint and sometimes teams as well. This
Speaker 1: is them probably the most popular product, and by the way,
Speaker 1: they even manipulate the numbers a little bit there. An
Speaker 1: active user is someone who has taken one action on
Speaker 1: any Microsoft three sixty five app with Copilot in the
Speaker 1: space of twenty eight days, not thirty twenty eight. That's
Speaker 1: so generous, now, I know, I know that word active.
Speaker 1: Maybe you're thinking ed, this is like the gym model.
Speaker 1: There are unpaid licenses that Microsoft is getting paid for. Fine, fine, fine,
Speaker 1: fucking fine. Let's assume that Microsoft also has based on
Speaker 1: research that suggests this can be the case for some
Speaker 1: software companies another fifty percent four million paying Copilot licenses
Speaker 1: that aren't being used. That's still twelve million users, which
Speaker 1: is around two point seven percent conversion rate. That's piss,
Speaker 1: poor buddy, that's piss, Paul, that's pissy. It sucks. It's bad, Doodoo.
Speaker 1: Well I just said pp I guess anyway, very serious,
Speaker 1: very serious podcast. But why aren't people paying for Copilot? Well,
Speaker 1: let's hear from someone who talked to the information and
Speaker 1: I quote, it's easy for an employee to say, yes,
Speaker 1: this will help me, but hard to quantify how. And
Speaker 1: if they can't quantify how it will help them, it's
Speaker 1: not going to be a long discussion over whether the
Speaker 1: software is worth paying for. Is that good? Is that good?
Speaker 1: Is that what you want to hear? It isn't. It isn't.
Speaker 1: That's that's the secret. It's not. It's bad. It's really bad.
Speaker 1: It's all very bad. And Microsoft through sixty five Copilot
Speaker 1: has been such a disaster that Microsoft will now integrate
Speaker 1: Anthropics models to try and make them better. Oh one
Speaker 1: other thing too. Sources also confirm GPU utilization, So how
Speaker 1: much the GPUs set aside for Microsoft through sixty five? Yeah,
Speaker 1: their enterprise codpile. It's barely scratching the sixty percents. I'm
Speaker 1: also hearing the share Point, which is an app they
Speaker 1: have with over two hundred and fifty million users, has
Speaker 1: less than three hundred thousand weekly active users of their
Speaker 1: copilot features, suggesting that people just don't want to fucking
Speaker 1: use this. Those numbers that from August, by the way,
Speaker 1: and it's pathetic, and it must be clear. If Microsoft's
Speaker 1: doing this badly, I don't know how anyone else is
Speaker 1: doing well, and they're not. They're all failing. It's pathetic.
Speaker 1: But I've spent a lot of time today talking about
Speaker 1: AI coding, because this was supposed to be the saving grace,
Speaker 1: the thing that actually turned this from a bubble into
Speaker 1: an actual money minting industry that changes the world. And
Speaker 1: I wanted to bring up Microsoft through sixty five because
Speaker 1: that's the place where Microsoft should be making the most money.
Speaker 1: It's the most ubiquitous software, it's their most well known software,
Speaker 1: and they're not eight million people eight million people. I've
Speaker 1: run that by a few people and everyone's made the
Speaker 1: same Oh God noise. It's quite weird, the old God
Speaker 1: noise and the numbers. But this just isn't happening. Things
Speaker 1: are going badly and it really only gets worse from here,
Speaker 1: and I'm going to tell you more tomorrow in the
Speaker 1: final part of our four part Thank you for your
Speaker 1: patience and thank you for your time.
Speaker 2: Thank you for listening to Better Offline. The editor and
Speaker 2: composer of the Better Offline theme song is Matasowski. You
Speaker 2: can check out more of his music and audio projects
Speaker 2: at Matasowski dot com, M A T T O S
Speaker 2: O W s ki dot com. You can email me
Speaker 2: at easy at Better Offline dot com or visit Better
Speaker 2: Offline dot com to find more podcast links and of
Speaker 2: course my newsletter. I also really recommend you go to
Speaker 2: chat dot Where's youreaed dot at to visit the discord,
Speaker 2: and go to our slash Better Offline to check out
Speaker 2: our reddit. Thank you so much for listening.
Speaker 1: Better Offline is a production of cool Zone Media. For
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