Speaker 1: Zone Media Hell, and welcome to Better Offline. I'm your
Speaker 1: host ed Zeitron. Subscribe to the newsletter by the merchandise.
Speaker 1: It's all in the notes. And we're on the second
Speaker 1: installment of our three part Hater's Guide to the AI
Speaker 1: bubble and the cracks within the generative AI industry and
Speaker 1: how they're becoming bigger and scarier and the potential economic
Speaker 1: meltdown course by a collapse in generative AI spending. Well,
Speaker 1: it's not really general if AI spending. It's literally just
Speaker 1: fucking GPUs, and I think it might be sooner and
Speaker 1: likelier than many think. Toward the end of the last episode,
Speaker 1: we talked about one of the inane comparisons we hear
Speaker 1: between today's nation state size spending on jen ai capital
Speaker 1: expenditures and the investments that Amazon made when scaling Amazon
Speaker 1: Web Services, which was literally the foundation of Dow Computing
Speaker 1: at scale. I would say someone's going to email and
Speaker 1: say I'm wrong, not going to read it, and I
Speaker 1: had to cut things short because we ran out of time.
Speaker 2: But I want to.
Speaker 1: Continue the conversation because I think it's important to examine
Speaker 1: this comparison thoroughly, if not just to explain why it
Speaker 1: doesn't work. It's also I want to stop. I want
Speaker 1: to stop hearing it. I want when people say it
Speaker 1: to me, I just want to send them this fucking
Speaker 1: episode and say leave me alone, buddy boy.
Speaker 2: But but bye.
Speaker 1: But the first point I want to make in this
Speaker 1: episode is that generative AI and large language models do
Speaker 1: not resemble Amazon Web Services or the greater cloud compute boom,
Speaker 1: and generative AI is not infrastructure. Now, some people compare
Speaker 1: llms and their associated services to Amazon Web Services or
Speaker 1: services like Microsoft Zero or Google Cloud, their giant, multi
Speaker 1: billion dollar operations that basically share their server capacity with
Speaker 1: companies wanting to run stuff on the Internet or within
Speaker 1: their own within their own systems. A very fudgy way
Speaker 1: of putting. They help make sure that applications work online.
Speaker 1: These are very very useful services, and by the way,
Speaker 1: people are wrong to make the comparison between them and
Speaker 1: the l lms. As I'll get into now. Amazon Web
Speaker 1: Services when it launched, comprised of things like and forgive
Speaker 1: me how much I'm going to dilute this, Amazon's Elastic
Speaker 1: Compute Cloud EC two, where you rent space in Amazon
Speaker 1: service to run applications in the cloud, or Amazon Simple
Speaker 1: Storage S three, which is enterprise level storage for applications
Speaker 1: and storing things, is not just like a simple hard drive.
Speaker 1: It's redundancy, it's making sure it's copied in places, so
Speaker 1: latency comes down tons of other things. But in simpler terms,
Speaker 1: if you were providing a cloud based service, you used
Speaker 1: Amazon to both stored a stuff that the service needed
Speaker 1: and the cloud actual cloud based processing. So of compute,
Speaker 1: so like your compute loads and runs applications, but delivered
Speaker 1: the thousands of millions of people online.
Speaker 2: And this is a huge industry.
Speaker 1: Amazon Web services are alone brought in WHEB revenues are
Speaker 1: over one hundred billion dollars in twenty twenty four. And
Speaker 1: while Microsoft and Google don't break out their cloud revenues,
Speaker 1: they're similarly large parts of their companies, and Microsoft is
Speaker 1: used as zero and the past to patch over shoddy growth.
Speaker 1: These services are also selling infrastructure. You aren't just paying
Speaker 1: for compute, but the ability to access storage and deliver
Speaker 1: services with low lateent so users have a snappy experiences
Speaker 1: wherever they are in the world. And I know I
Speaker 1: just said a snappy experiences. I'm not editing it. The
Speaker 1: subtle magic of the Internet is that it works at all,
Speaker 1: and a large part of that is the cloud compute
Speaker 1: infrastructure and oligopoly of the main cloud providers. Having such
Speaker 1: a vast data centers, this is much cheaper than doing
Speaker 1: it yourself, and to a certain point, Jobbox moved away
Speaker 1: from Amazon Web Services is it at scale, for example,
Speaker 1: but this also allows someone to take care of the
Speaker 1: maintenance of the hardware and make sure it actually gets
Speaker 1: your stuff to your customers. You also don't have to
Speaker 1: worry about spikes and usage because these things are usage based,
Speaker 1: hence the elastic and you can always add more compute
Speaker 1: to meet demand or just have it in a particular time.
Speaker 1: There is, of course nuance, security specific features, content specific
Speaker 1: delivery services, data based services. There's nuance behind these clouds.
Speaker 1: You're buying into the infrastructure of the infrastructure provider, and
Speaker 1: the reason these products are so profitable is that in
Speaker 1: part you are handing off the problems and responsibility to
Speaker 1: somebody else. And also, most web applications are not that
Speaker 1: demanding of cloud compute. They might be at scale expensive
Speaker 1: to provide to millions of people, but Facebook was not
Speaker 1: a super complex, I don't know website depending on thousands
Speaker 1: or millions of GPUs, and based on the idea, there
Speaker 1: are multiple product categories you can build on top of
Speaker 1: something like edblys Because ultimately cloud services are about Amazon,
Speaker 1: Microsoft and Google running your infrastructure for you. Large language
Speaker 1: models and their associated services are completely different, despite these
Speaker 1: companies attempting to prove otherwise. And it starts with a
Speaker 1: very very simple problem. Why did any of these companies
Speaker 1: build these giant data centers and why did they fill
Speaker 1: them full of GPUs? Amazon Web Services was created out
Speaker 1: of necessity. Amazon's infrastructure needs were so great that it
Speaker 1: effectively had to build out the software and hardware necessary
Speaker 1: to deliver a store that sold theoretically everything, the theoretically anywhere,
Speaker 1: handling both the traffic and customers, delivering the software that
Speaker 1: runs Amazon dot Com quickly and reliably and well, making
Speaker 1: sure things kept working, making sure they were stable. And
Speaker 1: it didn't need to come up with a reason for
Speaker 1: people to run web applications. They were already running applications
Speaker 1: client side on their computers. They realized that doing so
Speaker 1: at scale would be cool, or they were already doing
Speaker 1: so in a way that was likely not particularly cost effective.
Speaker 1: And the ways that we're doing so, they were inflexible,
Speaker 1: and they required specially skills and indeed physical infrastructure personnel.
Speaker 1: They were quite expensive. So Amazon Web Services took something
Speaker 1: that people already did and what there was actually a
Speaker 1: proven demand for, and made it better and scaled it. Eventually,
Speaker 1: Google and Microsoft copied done because that's all they can do.
Speaker 1: And that appears to be the only similarity with generative AI.
Speaker 1: That due to the ridiculous costs of both data centers
Speaker 1: and GPUs necessary to provide these services, it's largely impossible
Speaker 1: for others to enter the market.
Speaker 2: You know.
Speaker 1: After that, generative AI feels more like a feature of
Speaker 1: cloud infrastructure rather than the infrastructure itself. AWS and similar
Speaker 1: medic clouds are versatile, flexible, and multi faceted. Generative AI
Speaker 1: does what generative AI does well, that's about it. You
Speaker 1: can run lots of different things in AWS. What are
Speaker 1: the different things you can run using large language models?
Speaker 1: What are the different use cases and indeed user requirements
Speaker 1: that make this the supposed next big thing. Perhaps the
Speaker 1: argument is that generator of AI is the next AWS
Speaker 1: or similar cloud service because you can build the next
Speaker 1: great companies on the infrastructure of others. The models of
Speaker 1: say open AI and anthropic and the service of Microsoft. Okay, okay,
Speaker 1: let's humor this point too. You can build the next
Speaker 1: great AI startup, and you have to build it on
Speaker 1: one of the megaclouds because they're the only ones that
Speaker 1: can afford to build the infrastructure. One inc wincteny small problem.
Speaker 1: Companies built on top of large language models don't make
Speaker 1: much money, and in fact they're almost all deeply unprofitable.
Speaker 1: But let's establish a few flats to get going. I said, flats, flats,
Speaker 1: Jesus Christ.
Speaker 2: Facts.
Speaker 1: Here are the flats I'm establishing. Outside of one exception,
Speaker 1: mid Journey, which claimed it was profitable in twenty twenty two,
Speaker 1: which may not still be the case. I've actually reached
Speaker 1: out to ask them and they didn't get.
Speaker 2: Back to me.
Speaker 1: Every single LLM model is company is unprofitable, often wildly so.
Speaker 1: Outside of open ai and oropic, in any sphere which
Speaker 1: makes the AI coding app cursor, there are no large
Speaker 1: language model companies either building models or services on top
Speaker 1: of others models that make more than five hundred million
Speaker 1: dollars in annualized revenue meaning month times twelve. Outside mid
Speaker 1: Journeys two hundred million arr and Iron clouds one hundred
Speaker 1: and fifty million arr Also fucking perplexity, there are only
Speaker 1: twelve generative AI powered companies making one hundred million dollars
Speaker 1: annualized or eighteen point three million dollars a month in revenue.
Speaker 1: The database then, this is the Information's AI. Generative AI
Speaker 1: database doesn't have replt, which also announced it hit one
Speaker 1: hundred million in analyzed revenue. I've included it in my
Speaker 1: statement of facts. Of these companies, two of them have
Speaker 1: been acquired, move Works acquired by service Now in March
Speaker 1: twenty twenty five after the company shit the Big Big Time,
Speaker 1: and Windsurf, which was acquired by Google and Cognition in
Speaker 1: July twenty twenty five and one of the most annoying
Speaker 1: deals of all time. But for the sake of simplicity,
Speaker 1: I've left out companies like Surge, Scale, Cheering, and Together,
Speaker 1: all of whom run consultancies selling services and training stuff
Speaker 1: for training models. Otherwise, there are seven companies total that
Speaker 1: make fifty million dollars or more annual recurring revenue, which
Speaker 1: is four point one six million dollars a month.
Speaker 2: Now, none of this is to say.
Speaker 1: That one hundred million dollars isn't a lot of money
Speaker 1: to you and me.
Speaker 2: I just want to be clear.
Speaker 1: If you want to give me one hundred million dollars,
Speaker 1: I'll do anything. I'll wink like a pig for you anyway.
Speaker 1: But in the world of software as a service or
Speaker 1: enterprise software, this is jump change HubSpot At revenues are
Speaker 1: two point six three billion dollars in its twenty twenty
Speaker 1: four financial year. Three years into this crap, and Generative
Speaker 1: AI's highest grossing companies outside of open Ai ten billion
Speaker 1: annualized as of June and Anthropic four billion annualized as July.
Speaker 1: Don't like saying that word. Both of them loose billions
Speaker 1: a year after revenue. There are really three problems here.
Speaker 1: Businesses powered by Generative AI do not seem to be popular,
Speaker 1: Those businesses that are remotely popular are deeply unprofitable, and
Speaker 1: even the less popular generative AI powered businesses are also
Speaker 1: deeply unprofitable. But I want to start somewhere because I
Speaker 1: keep hearing about fucking Cursor. Fucking's start with any sphere
Speaker 1: and Cursor and their app Cursor. It's an AI powered
Speaker 1: coding app and they have five hundred million dollars of
Speaker 1: annualized revenue.
Speaker 2: Pretty great, right, ha.
Speaker 1: It hit two hundred million dollars in annualized revenue in
Speaker 1: March and then hit five hundred million in June after
Speaker 1: raising nine hundred million dollars. That's amazing, ed, ed it's
Speaker 1: time walk to the garage. ED, it's over for you. Wrong,
Speaker 1: it's a mirage. Cursor's growth was the result of an
Speaker 1: unsustainable business model that it's now had to replace with
Speaker 1: opaque terms of service, dramatically restricting access to models, and
Speaker 1: rate limits that effectively stop its users using the product
Speaker 1: at the price point they were used to go to
Speaker 1: Arsnash Cursor on red app Take a look. Take a
Speaker 1: look at how happy everyone is.
Speaker 2: I want to know one.
Speaker 1: My peers in the media don't seem to have the
Speaker 1: ability to talk to actual fucking customers. It's ridiculous. This
Speaker 1: company is circling the drain, and nobody seems to want
Speaker 1: to talk about it, despite how big a deal that is.
Speaker 2: Oh.
Speaker 1: Also, Curse is horribly unprofitable, and I believe there are
Speaker 1: a sign of things to come in generative AI. A
Speaker 1: couple of weeks weeks ago, I wrote up the dramatic
Speaker 1: changes that Cursor made to its service in the middle
Speaker 1: of June or my premium newsletter and discovered that they
Speaker 1: timed these changes precisely with Anthropic and open Ai to
Speaker 1: a lesser extent, adding service tiers and priority processing, which
Speaker 1: is tech language for pay us extra if you have
Speaker 1: a lot of customers or face rate limits or service delays.
Speaker 2: Asshole.
Speaker 1: These price ships have also led to companies like replt
Speaker 1: having to make significant changes to their pricing model that
Speaker 1: disfavors users. People are finding in really simple terms that
Speaker 1: what they used to get for twenty bucks is much much, much,
Speaker 1: much much smaller curse the users hit rate limits. Replit
Speaker 1: users are hitting rate limits, and even then when they
Speaker 1: try and do the same things, they're spending way more
Speaker 1: money if they go pay as you go. It's a
Speaker 1: complete fast But I'm going to repeat some of this
Speaker 1: stuff from the premium newsletter because there is a time
Speaker 1: of events that I believe are going to be in
Speaker 1: the big short to AI Boogloo all right. In or
Speaker 1: around May fifth, twenty twenty five, Cursor closed the nine
Speaker 1: hundred million dollar funding round in a Around May twenty second,
Speaker 1: twenty twenty five, Anthropic launched Clawed four Opus and new
Speaker 1: models with Sona and Opus, both of them kind of
Speaker 1: well known for coding, and on May thirtieth, twenty twenty five,
Speaker 1: they added service tiers, including priority pricing specifically focused on
Speaker 1: cash heavy products like Cursor and the cash is when
Speaker 1: you put stuff that you're going to be looking at regularly,
Speaker 1: take a look at it, and you can use it
Speaker 1: more readily. Cash is the CAC eight G, by the way,
Speaker 1: is generally something that's for efficiency.
Speaker 2: The idea that you.
Speaker 1: Would add a toll onto the cash is fucking disgusting
Speaker 1: and only targeted coding startups. But on May thirtieth, twenty
Speaker 1: twenty five, Reutter's reported the Anthropics annualized revenue hit three
Speaker 1: billion dollars, with a key driver being code generation. This
Speaker 1: translates to around two hundred and fifty million dollars in
Speaker 1: monthly revenue. June ninth, twenty twenty five, CNBC reported open
Speaker 1: Ai'd hit ten billion dollars in annualized revenue. And yeah,
Speaker 1: when they said ann your recurring revenue, they meant annualized.
Speaker 1: But the very same day they cut the price of
Speaker 1: their three model by eighty percent, which competes directly with
Speaker 1: Clawed four Opus by the way, and This was a
Speaker 1: direct and aggressive attempt to force Anthropic to kind of
Speaker 1: like make too ether lower prices or compete. It's just
Speaker 1: shtheads fuckinging around with assholes. But on or around June sixteen,
Speaker 1: twenty twenty five, Cursor changed its pricing, added a new
Speaker 1: two hundred dollar a month Ultra tier that, in their
Speaker 1: own words, was made possible by multi year partnerships with
Speaker 1: open Ai, Anthropic, Google, an Xai, which translates to multi
Speaker 1: year commitments to spend which can be amortized as monthly amounts.
Speaker 1: A day later, on June seventeenth, Cursor dramatically changed its
Speaker 1: offering to it for its twenty dollars a month subscriptions
Speaker 1: to usage base, where one got at least the value
Speaker 1: of their subscription, so a twenty bus a month person
Speaker 1: would get more than twenty dollars of API course in compute,
Speaker 1: along with arbitrary rate limits and unlimited access to Cursor's
Speaker 1: own slow model that its users hey. Then on June eighteenth,
Speaker 1: Repler and other vibe coding company that I had previously
Speaker 1: mentioned announced their effort based pricing increases that were massive.
Speaker 2: July first, the Information reported.
Speaker 1: The Anthropic hit four billion dollars of annualized revenue making
Speaker 1: three hundred and thirty million dollars a month, an increase
Speaker 1: of eighty three million dollars a month. We'll just under
Speaker 1: twenty five percent in the space of a month.
Speaker 2: Hmm.
Speaker 1: Where could that money have come from? In simpler terms,
Speaker 1: Cursor raised nine hundred million dollars and very likely had
Speaker 1: to hand large amounts of that money over to Open
Speaker 1: Air and Anthropic to keep doing business with them, then
Speaker 1: immediately change the terms of service to make them worse
Speaker 1: for their customers. And as I said at the time,
Speaker 1: and this is a direct quote from my news there,
Speaker 1: while some met, no, I can't do the Kevin Ruth's
Speaker 1: voice and doing my own stuff, pardon me. While some
Speaker 1: may believe that Open AI and Anthropic hitting annualized revenue
Speaker 1: milestones is good news, you have to consider how these
Speaker 1: milestones were hit. Based on my reporting, I believe that
Speaker 1: both companies are effectively doing steroids, forcing massive infrastructural costs
Speaker 1: onto big customers as a means of covering the increasing
Speaker 1: costs of their own models. There is simply no other
Speaker 1: aid to read this situation. By making these changes, Anthropic
Speaker 1: is intentionally making it harder for its larger costs largest
Speaker 1: customer to do business. By the way, Cursor is their
Speaker 1: largest customer, creating the extra revenue by making Cursors product
Speaker 1: worse by proxy. What's sickening about this particular situation. It
Speaker 1: doesn't really matter if Curs's customers are happy or sad.
Speaker 1: They like open AI's Enterprise Priority Access API Anthropic in
Speaker 1: this case, require a long term commitment which involves a
Speaker 1: minimum through put of tokens per second as part of
Speaker 1: their tiered access program. If Curs's customers drop off, both
Speaker 1: Anthropic and open Ai still get their cut, and if
Speaker 1: curses customers somehow out spend those commitments, they'll either still
Speaker 1: get rate limited or any sphere willkin cur more costs.
Speaker 2: Why do you care about this?
Speaker 1: Well, Cursor is the largest and most successful genetive AI
Speaker 1: company by far. In these aggressive and desperate changes to
Speaker 1: its products suggest that a that its products are deeply unprofitable,
Speaker 1: and b that its current growth was the result of
Speaker 1: offering a product that it was not the one it
Speaker 1: would sell in the long term. Cursor misled its customers,
Speaker 1: and its current revenue is as a result, highly unlikely
Speaker 1: to stay at this level. Worse still, two anthropic engineers
Speaker 1: left from the the Clawed Code team to go and
Speaker 1: work at Cursor two weeks ago, and they have already
Speaker 1: come back. This heavily suggests that whatever they saw over
Speaker 1: there wasn't compelling enough to make them stay. As I
Speaker 1: also said, while Cursor may have raised nine hundred million dollars,
Speaker 1: it was really open Aianthropic XAI and Google that got
Speaker 1: that money. At this point, there are no profitable price
Speaker 1: AI startups, and it's highly unlikely that the new pricing
Speaker 1: models by both Cursor and Replet are going to help.
Speaker 2: These are now the.
Speaker 1: New terms of doing business with the big model companies,
Speaker 1: a shakedown where you pay for priority access or tears,
Speaker 1: or face indeterminate delays or rate limits. Any start up
Speaker 1: scaling into an enterprise integration of General AVII, which means
Speaker 1: in this case anything that requires a level of service
Speaker 1: uptime has to commit to both a minimum amount of
Speaker 1: months and the throughput of tokens, which means that the
Speaker 1: price of starting an AI company that gets any kind
Speaker 1: of real market traction just dramatically increased. Well, one could say, oh,
Speaker 1: perhaps you don't need priority access. The need here is
Speaker 1: something that can be entirely judged by anthropic and open
Speaker 1: ai in a totally opaque manner. They can and they
Speaker 1: will throttle companies that are two demanding on their systems.
Speaker 1: It's proven by the fact that they've done this to
Speaker 1: curse them multiple times. But okay, why does curse them
Speaker 1: out so much? And it's simple. Generative AI will not
Speaker 1: get big on selling consumer software without an enterprise SaaS story,
Speaker 1: they're dead And I realize, I know, okay, folks, it's
Speaker 1: kind of a little boring hearing about software as a
Speaker 1: service despite the fact that it's a huge, several hundred
Speaker 1: billion dollar industry. But this is the only place where
Speaker 1: generative AI can really make money. Companies buying hundreds of
Speaker 1: thousands of seats or how industries that rely on compute
Speaker 1: grow and without that growth, they're going nowhere. To give
Speaker 1: you some context, Netflix makes about thirty nine billion dollars
Speaker 1: a year in subscription new from consumers, and Spotify about
Speaker 1: eighteen billion.
Speaker 2: These are the.
Speaker 1: Single most popular consumer software subscriptions in the world, and
Speaker 1: open ai is fifteen point five million subscribers. Suggest that
Speaker 1: open ai can't rely on them for the kind of
Speaker 1: growth that would actually make the company worth three hundred billion.
Speaker 2: Dollars or more.
Speaker 1: Cuzer, as it stands, is the one example of a
Speaker 1: company thriving using GENERATIVEA a software company selling software, and
Speaker 1: it appears its rapid growth was the result of selling
Speaker 1: a product at a massive loss. As it stands today,
Speaker 1: Curs's product is significantly worse and it's ready it's full
Speaker 1: of people furious at the company for the changes. In
Speaker 1: simpler terms, Curser was the company that people mentioned to
Speaker 1: prove that startups could make money by building on top
Speaker 1: products on top of open AI and Anthropics models. Yet
Speaker 1: the truth is the only way to do so is
Speaker 1: to grow, and grow is to burn tons of money.
Speaker 1: While the tempting argument is to say that Curs's customers
Speaker 1: are addicted and will keep paying, this is clearly not
Speaker 1: the case, nor is it an actual business model. Like
Speaker 1: people that say this, I've never had a drug addiction,
Speaker 1: but I know people that do it. It's nothing like software.
Speaker 1: Stop making that comparison. It's insulting to the victims of addiction.
Speaker 1: But anyway, this story showed that open A and Anthropics
Speaker 1: are actually their bigger the biggest threats to their customers
Speaker 1: and will actively rent seek can punish any of their
Speaker 1: success stories, looking to loose as much as they can from.
Speaker 2: Them before they copy their products.
Speaker 1: To put it bluntly, curses growth story was a fucking lie.
Speaker 1: It reached five hundred million dollars in annulif revenue selling
Speaker 1: a product it can no longer afford to sell and
Speaker 1: could not afford to sell long term, suggesting material weakness
Speaker 1: in its business and any and all coding startups. It's
Speaker 1: also remarkable, in the shocking failure of journalism that this
Speaker 1: isn't in every single article about any sphere. I'm doing
Speaker 1: this part time? Why am I in the asshole here?
Speaker 1: Like I'm I don't know, really, though, I do have
Speaker 1: a question. Where are all the consumer AI starts? I'm
Speaker 1: genuinely serious.
Speaker 2: What have you got for me?
Speaker 1: Perplexity Perplexity. Perplexity only has one hundred and fifty million
Speaker 1: dollars in the annualized revenue, and they spent one hundred
Speaker 1: and sixty seven percent of their revenue in twenty twenty four,
Speaker 1: or fifty seven million dollars of spending on revenues of
Speaker 1: thirty four million dollars on computer services from Anthropic, Open
Speaker 1: AI and Amazon. They lost sixty eight million dollars and
Speaker 1: worse still, they still have no path to profitability and
Speaker 1: it's not even making anything new. They're a search engine,
Speaker 1: they have an AI browser. But don't worry. Professional gas
Speaker 1: bag Alex Heath just did this insane and flumm mixing
Speaker 1: interview with CEO Aravins Ravinas, who, when asked how it
Speaker 1: perplexed you would become profitable, appeared to experience what seems
Speaker 1: to be a stroke like I'm about to read something
Speaker 1: to you and it's gonna sound strange, but this is
Speaker 1: exactly what was said. Maybe let me give you another example.
Speaker 1: You want to put an ad on meta Instagram, and
Speaker 1: you want to look at ads done by similar brands,
Speaker 1: pull that, study that, or look at AdWords pricing of
Speaker 1: one hundred different keywords and figure out how to price
Speaker 1: your thing comparatively. These are tasks that could definitely save
Speaker 1: you hours and hours and maybe even give you up
Speaker 1: an arbitrage over what you could do yourself, because AI
Speaker 1: is able to do a lot more and at scale.
Speaker 1: If it helps you to make a few million bugs,
Speaker 1: does it not make sense to spend two thousand dollars
Speaker 1: for that prompt? It does, right, So I think we're
Speaker 1: going to be able to monetize in many more interesting
Speaker 1: ways than chatbots for the browser. I want to be
Speaker 1: fucking clear about something. Alex seems like a nice guy.
Speaker 1: If someone said that to me, I'd ask them if
Speaker 1: they could smell toast. I'd be like, Aravin, Mate, are
Speaker 1: you okay? How many fingers I'm holding up?
Speaker 2: Aravin? You're right?
Speaker 1: Did you hit your head on something? The ceilings don't
Speaker 1: seem that low in here. But mate, you're just spewing
Speaker 1: utter fucking nonsense. I've read this paragraph multiple times. I
Speaker 1: do not know what he's getting at. I think he's
Speaker 1: suggesting something about how you could ask it to tell
Speaker 1: you what to do with ads.
Speaker 2: I don't know. I don't know.
Speaker 1: This is the big probably the biggest consumer AI company
Speaker 1: that isn't open AI, and they speak like they're an
Speaker 1: insane person or a stupid person. Check out the Business
Speaker 1: Idiot Trilogy for what I think there. I also mentioned
Speaker 1: them earlier, but I don't I don't want you to
Speaker 1: talk to me about AI browsers. Anyone humoring AI browsers
Speaker 1: is being an imbecile for some reason. They are not
Speaker 1: a business model. How are people going to make money
Speaker 1: on the browser. Hm hmm, what do these products actually do?
Speaker 2: Oh? They can poorly automate accepting linked invites. Wow. Wow,
Speaker 2: it's like God himself has personally best my computer. A
Speaker 2: big fucking deal.
Speaker 1: In any case, it doesn't seem like you can really
Speaker 1: build a consumer AI startup that makes any real money
Speaker 1: or approach being a real company other than chat GPT,
Speaker 1: I guess, and that's because the GENERATIVEAI software market is small,
Speaker 1: with little room for growth and no profits to be seen. Arguably,
Speaker 1: the biggest sign that things are are troubling in the
Speaker 1: generative AI spaces that we use the term annualized revenue
Speaker 1: at all, which, as I've mentioned repeatedly, means multiplying a
Speaker 1: month by twelve and saying that's our annualized baby. The
Speaker 1: problem with this number is that, well, people cancel things.
Speaker 1: While your June might look great, if ten percent of
Speaker 1: your subscribers churning a bad month due to a change
Speaker 1: in your terms of service, for example, that's a huge
Speaker 1: chunk of your annualized revenue gone and likely gone forever.
Speaker 1: But the worst sign is that nobody is saying the
Speaker 1: monthly figures, mostly because the monthly figures fucking suck. One
Speaker 1: hundred million dollars of anualized revenue is eight point three
Speaker 1: three million dollars a month. To give you some scale,
Speaker 1: Amazon Web Services hit one hundred and eighty nine million
Speaker 1: dollars fifteen point seventy five million dollars a month in
Speaker 1: revenue in two thousand and eight, two years after founding,
Speaker 1: and while it took until twenty fifteen to hit profitability,
Speaker 1: it actually hit break even in two thousand and nine,
Speaker 1: though were invested in cash and growth for a few
Speaker 1: years later. And I should be clear them doing that
Speaker 1: justified so many startups burning cash, so many starts like yeah,
Speaker 1: look at aws. They were investing in growth, which is
Speaker 1: a fair thing for companies to do. But I'm being
Speaker 1: an asshole. But right now there is not a single
Speaker 1: generative AI software company that's profitable, and none of them
Speaker 1: are showing the signs of the kind of hypergrowth that
Speaker 1: previous big software companies had or Cursor technically is the
Speaker 1: fastest growing software as a service company of all time.
Speaker 1: It got there by basically lying. Cursor is never bringing
Speaker 1: back the product at the twenty dollars price point that
Speaker 1: they were selling. They're never doing it. The money they
Speaker 1: earned was earned it's not fraud because they didn't do it.
Speaker 2: I guess it was deceptive, but it's not really to
Speaker 2: the it's just fucking lying.
Speaker 1: It's just lying. And who knows what happens to curser now.
Speaker 1: But you know what, I'm harping on cursor a bit.
Speaker 1: What other software startups are there?
Speaker 2: Glean, Glean, fucking Glean, Glean, everyone loves to talk about.
Speaker 1: Enterprise search company Glean, a company that uses AI to
Speaker 1: search and generate answers from your company's files and documents.
Speaker 1: Fun fact, also Salesforce's own Slack has now blocked them
Speaker 1: from searching Slack. Just arshole on arsehole violence. In December
Speaker 1: twenty twenty four, Glean raised two hundred and sixty million dollars,
Speaker 1: broadly stating that it had over five hundred and fifty
Speaker 1: million dollars in cash with best in class ARR growth.
Speaker 1: A few months later, in February twenty twenty five, Glean
Speaker 1: announced it achieved one hundred million dollars in annual recurring
Speaker 1: revenue in fourth arter FY twenty five, cementing its position
Speaker 1: is one of the fastest growing sas startups and reflecting
Speaker 1: a searching demand for AI powered workplace intelligence. In any case,
Speaker 1: AR could literally mean anything, as it appears to be
Speaker 1: based on quarters, meaning it could be an average of
Speaker 1: the last three months. I guess anyway. In June twenty
Speaker 1: twenty five, Glean announced it had raised another funding round,
Speaker 1: this time raising one hundred and fifty million dollars in
Speaker 1: It troublingly added that since its last round, it had
Speaker 1: surpassed one hundred million dollars in AR raw five months
Speaker 1: into the fucking year. And your revenue is basically the same.
Speaker 1: That isn't good. That isn't good at all. Also happened
Speaker 1: to that five hundred and fifty million dollars in cash?
Speaker 1: Why did Glean need more? Hey, wait a second, take
Speaker 1: a look at this. Glean announced their raise on June eighteenth,
Speaker 1: twenty twenty five, two days after Curses price increase, in
Speaker 1: the same day that Repler announced the similar price act.
Speaker 2: It's almost as if the.
Speaker 1: Dramatic pricing increase has affected them due to the introduction
Speaker 1: of Anthropic Service TRES and Opening Eyes priority processing.
Speaker 2: But I'm guessing. I know, I'm guessing.
Speaker 1: But it is kind of where that all of these
Speaker 1: companies raise money and all announced these things around the
Speaker 1: same time.
Speaker 2: Hey, that reminds me, I got another problem.
Speaker 1: I got another problem here because I think that there
Speaker 1: is another reason why the cycles kind of keep repeating.
Speaker 1: You get a company of that grows, and then they
Speaker 1: kind of go nowhere, because well, the company doesn't really
Speaker 1: seem to have a total addressable market much bigger than
Speaker 1: one hundred million AR and I think it's a little simple.
Speaker 2: It's quite simple.
Speaker 1: In fact, there really are no unique generative AI companies,
Speaker 1: and building a moat on top of l elms is
Speaker 1: near impossible.
Speaker 2: If you look a man, am I going to get
Speaker 2: some emails about this, but bring them on.
Speaker 1: If you look at what GENERATIVEAI companies do, now that
Speaker 1: the following is not a quality barometer, it's probably one
Speaker 1: of the following things. They're either chatbot one, either you
Speaker 1: ask questions or talk to This includes customer service bots, searching, summarizing,
Speaker 1: or comparing documents with increased amounts of complexity of documents
Speaker 1: or quantity of documents to be compared. This includes being
Speaker 1: able to ask questions of documents. Web search deep research,
Speaker 1: meaning long form web search that generates a document where
Speaker 1: some parts of it will inevitably be hallucinated or derived
Speaker 1: from low quality sources, generating text, images, voice, or in
Speaker 1: some rare cases video, Using AI to generative AII mean
Speaker 1: to write, edit or maintain code, transcription, translation, or photo
Speaker 1: and video editing. Every single generative AI company that is
Speaker 1: an open Aireanthropic and honestly kind of those two does
Speaker 1: one or a few of these things, and I mean
Speaker 1: every one of them. And it's because every single generative
Speaker 1: AI company uses large language models, which have inherent limits
Speaker 1: on what they can do. Llms can generate, they can search,
Speaker 1: they can kind of edit, they can sometimes transcribe accurately,
Speaker 1: and they can sometimes translate much more well, much less accurately.
Speaker 1: I guess within weeks of Curses changed to its services,
Speaker 1: Amazon and byte Dance release competitors that, for the most part, do.
Speaker 2: Exactly the same thing.
Speaker 1: Sure, there's a few differences in how they're designed, but
Speaker 1: design is not a moat, especially in a high cost,
Speaker 1: negative profit business were your only way of growing is
Speaker 1: to offer a product you can't sustain. The only other
Speaker 1: moat you can build is the services you provide, which,
Speaker 1: when your services are dependent on a large language model,
Speaker 1: are dependent on the model developer, who, in the case
Speaker 1: of open AI and Anthropic, could simply clone your startup,
Speaker 1: because the only valuable intellectual property is the models, and
Speaker 1: those models are theirs. You may say, well, nobody else
Speaker 1: has any ideas either, to which I say, I fully agree.
Speaker 1: My rock com bubble thesis suggests that we're all out
Speaker 1: of hyper growth ideas, and yeah, I think we're out
Speaker 1: of ideas related to any large language models too. At
Speaker 1: this point, I think it's fair to ask, are there
Speaker 1: any good businesses you can build on top of generative
Speaker 1: AI or large language models. I don't mean ad features
Speaker 1: related to I mean an AI company that actually sells
Speaker 1: a product that people buy at scale that isn't called chat,
Speaker 1: GPT or claude. In previous tech booms, companies would make
Speaker 1: their own models, their own infrastructure, or the things that
Speaker 1: make them distinct from other companies. But the generative AI
Speaker 1: boom effectively changes that by making everybody build on stuff
Speaker 1: on top of somebody else's models, because training your own
Speaker 1: models is both extremely expensive and requires vast amounts of
Speaker 1: infrastructure and just pure power. As a result, much of
Speaker 1: this boom is about a few companies, really too, if
Speaker 1: we're honest, getting other companies to try and build functional
Speaker 1: software for them, and these companies Open ai and Anthropic
Speaker 1: are their customers weak point in a relationship that veers
Speaker 1: from symbiotic to parasitic at a moment's notice. I cannot
Speaker 1: stress enough how bad open ai and Anthropic are for
Speaker 1: their business customers. Their models are popular, by which I
Speaker 1: mean their customers customers will expect access to them, meaning
Speaker 1: the open ai and Anthropic can, as they did to Cursor,
Speaker 1: arbitrarily change pricing, service availability, and functionality based on how
Speaker 1: they feel that day or whether they need to pump
Speaker 1: their annualized revenue for investors.
Speaker 2: Don't believe me.
Speaker 1: Anthropic cut off access to AI coding platform Windsurf because
Speaker 1: it looked like they might get acquired by open Ai.
Speaker 1: They never were. They just harmed that business. They just
Speaker 1: cut a hole in them. Why because they might touch
Speaker 1: another business, the most anti competitive shit in the world.
Speaker 1: And everyone sat there clapping like a fucking seal. Disgusting
Speaker 1: even by big tech standards. This fucking sucks, and these
Speaker 1: companies will do it again. But you know what, Let's
Speaker 1: talk about the actual uses of generative AI, because the
Speaker 1: limited number of use cases are because large language models
Speaker 1: are all really really similar. Because all large language models
Speaker 1: require more data than anyone who's ever needed, including like
Speaker 1: four times the amount of data on the Internet. They
Speaker 1: all basically have to use the same thing, either taken
Speaker 1: from the Internet or bought from one of the few
Speaker 1: companies that scale surge during together or whoever. While they
Speaker 1: can get customized data or do customized training and reinforcement learning,
Speaker 1: these models are all transformer based and they all function similarly,
Speaker 1: and the only way to make them different is by
Speaker 1: training them, which doesn't make them that much different, just
Speaker 1: better things they already do. And good lord, is it
Speaker 1: so is general IFAI is so ungodly expensive and the
Speaker 1: training is as well. By the way, they have to
Speaker 1: pay real humans as well, which they hate doing. And
Speaker 1: even when they're paying outsourced labor and ken youre at
Speaker 1: two dollars a pop, they're still losing a ton of money.
Speaker 1: It's really crazy, actually, how badly built all of this is.
Speaker 1: And I already mentioned open AI and Anthropics costs as
Speaker 1: well as perplex The's fifty million dollar bill in a
Speaker 1: year to Anthropic Amazon and open Ai off of a
Speaker 1: easily thirty four dollars million dollars in revenue. These companies
Speaker 1: cost too much to run and their functionality doesn't make
Speaker 1: enough money to make them make sense. And the problem
Speaker 1: isn't just the pricing, but how unpredictable it is. As
Speaker 1: Matterscheer wrote for cio Dive last year, generative AI makes
Speaker 1: a lot of companies lives difficult for the massive spikes
Speaker 1: and costs that from the power users, with few ways
Speaker 1: to mitigate those costs. One of the ways that company
Speaker 1: manages their cloud bills is by having some degree of predictability,
Speaker 1: which is difficult to do with the constant sleu of
Speaker 1: new models and demands some new products to go with them,
Speaker 1: especially when send models can and can and do often
Speaker 1: cost more with subsequent iterations, not necessarily for much return,
Speaker 1: except if you're a company like a coding company, your
Speaker 1: customers are going to actually ask you for the new models.
Speaker 1: As a result, it's half AI companies to actually budge in.
Speaker 1: But ed, What was that? Ed?
Speaker 2: What about agents?
Speaker 1: Aren't they the thing that will eventually make the insane
Speaker 1: broken calculus behind generative AI actually work?
Speaker 2: What is your accent made? Anyway? Anyway?
Speaker 1: Let me tell you about agents. The term agent is
Speaker 1: one of the most egregious acts of fraud I've seen
Speaker 1: in my entire career writing about this crap, and that
Speaker 1: includes the metavers. When you hear the word agent, you
Speaker 1: were meant to think of an autonomous AI that can
Speaker 1: go and do stuff without oversight, replacing someone's job in
Speaker 1: the process. And companies have been pushing the boundaries of
Speaker 1: good taste and financial crimes in pursuit of them. Most
Speaker 1: egregious of them as Salesforce's Agent Force, which leads you
Speaker 1: deploy AI agents at scale. That's a quote, and brings
Speaker 1: digital labor to every employee, department and business process. Another
Speaker 1: quote from Salesforce's website. These are two blatant fucking lies.
Speaker 1: Agent Force is a goddamn chatbot program. It's a platform
Speaker 1: for launching chatbots. They can sometimes plug into APIs that
Speaker 1: allow them to access other information, but they're neither autonomous
Speaker 1: nor agents by any reasonable definition. Not only does Salesforce
Speaker 1: not actually sell agents, its own research shows that the
Speaker 1: agents and agents in general only achieve around fifty eight
Speaker 1: percent success rate on single step tasks. And I'm going
Speaker 1: to quote the register here. This means tasks that can
Speaker 1: be completed in the single step without needing follow up
Speaker 1: actions and more information or multi step tasks, So you know,
Speaker 1: most tasks they succeed a depressing thirty five percent of
Speaker 1: the time. Last week, open Ai announced its own chat
Speaker 1: GPT agent that can allegedly go and do tasks on
Speaker 1: a virtual computer. In its own demo, the agent took
Speaker 1: twenty one minutes or so to spit out a plan
Speaker 1: for a wedding with destinations of a cander and some
Speaker 1: suit options, and then showed a pre prepared demo of
Speaker 1: the agent preparing an itinery of how to visit every
Speaker 1: major league ballpark. And that's baseball for the non Americans
Speaker 1: out there. In this example's case, agents took twenty three
Speaker 1: minutes and produced arguably the most confusing map I've seen
Speaker 1: in my life. You can see the map in the
Speaker 1: newsletter version of this episode. It's hilarious. It missed out
Speaker 1: every single major ballpark on the East Coast, including Yankee
Speaker 1: Stadium and Femway Park, which are two of the most
Speaker 1: well known stadiums in sports, and added a bunch of
Speaker 1: roundom ones and like one in the middle of the
Speaker 1: Gulf of Mexico. What team is that, Sammy the deep
Speaker 1: Water Horizon Devils. Is there a baseball team in North Dakota?
Speaker 2: Clammy?
Speaker 1: Sammy Samy. I also should be clear this was a
Speaker 1: pre prepared example. This is the best they had. I
Speaker 1: want to see the cutting room footage on this, because
Speaker 1: you best bet that that map looked like straight dogshit,
Speaker 1: as with every large language model product, And yes, that's
Speaker 1: what this is, even if open ai won't talk about
Speaker 1: what model results are. Extremely variable agents are difficult because
Speaker 1: tasks are if for coal, even if they can be
Speaker 1: completed by a human being, that the CEO thinks is stupid.
Speaker 1: What open ai appears to be doing is using a
Speaker 1: virtual machine to run scripts that its models trigger regardless
Speaker 1: of how will it works, and it works very very
Speaker 1: very very poorly and inconsistently. It's also very likely expensive
Speaker 1: to run. In any case, every single company you see
Speaker 1: using the word agent is trying to mislead you. They're
Speaker 1: lying gleans ai agents to chatbots with If this, then
Speaker 1: that functions that trigger events using APIs, which means if
Speaker 1: an event happens, another thing will be triggered, not taking
Speaker 1: actual actions, because that is not what lms can do.
Speaker 1: Service Now's ai agents that allegedly act autonomously and proactively
Speaker 1: on your behalf are despite claiming they go beyond better
Speaker 1: chatbots still ultimately better chatbots that use APIs to trigger
Speaker 1: different events using if this, then that functions. Sometimes these
Speaker 1: chatbox can also answer questions that people might have or
Speaker 1: trigger an event somewhere. Oh right, that's literally the same thing.
Speaker 1: The closest we have to an agent is any kind
Speaker 1: of coding agent, which is they can make a list
Speaker 1: of things that you might do on our software project
Speaker 1: and go and generate code and push stuff to get
Speaker 1: help when you ask them to. And they can do
Speaker 1: so autonomously in the sense that you can just let
Speaker 1: them do what a model that doesn't know anything and
Speaker 1: has no consciousness thinks is right based on its corpus
Speaker 1: of data and the things you can access to. And
Speaker 1: it's about as safe as that sounds. When I say
Speaker 1: ask them to and go, and I mean that these
Speaker 1: agents are not intelligent at all. They do not have intelligence,
Speaker 1: and when let run rampant, fuck up everything and create
Speaker 1: a bunch of extra work or so. A study found
Speaker 1: that AI coding tools made engineers nineteen percent slower. Nevertheless,
Speaker 1: none of these products are autonomous agents, and anybody using
Speaker 1: the term agent likely means chat bond. And all of
Speaker 1: this is working because the media keeps repeating everything these
Speaker 1: companies say. It's a disgrace. We need to stop this.
Speaker 1: I realize we've taken a kind of a scenic route here, though,
Speaker 1: but I know he needed to lay the groundwork, because
Speaker 1: I really am alarmed. According to a UBS report from
Speaker 1: the twenty sixth of June, the public companies running AI
Speaker 1: services are making absolutely pathetic amounts of money from AI. Microsoft,
Speaker 1: according to the UBS, is making annual revenues of somehow
Speaker 1: less than the Information report at two point one billion dollars.
Speaker 1: Service now is making less than two hundred and fifty million,
Speaker 1: Adobe less than one hundred and twenty five million salesforce
Speaker 1: less than one hundred million now service now said two
Speaker 1: hundred and fifty million dollar ACV annual contract value. This
Speaker 1: may be one of the more honest explanations of revenue
Speaker 1: I've seen, putting them in the upper echelons of AI
Speaker 1: revenue and less. Of course, you think about it for
Speaker 1: a couple of seconds and think, are these all AI
Speaker 1: specific contracts or perhaps they're in contracts where you've taped
Speaker 1: AI on to the side.
Speaker 2: It gives a shit. It's also year.
Speaker 1: Long agreements that could churn, and according to Gartner, over
Speaker 1: forty percent of Agenetic AI products will be canceled by
Speaker 1: end of twenty twenty seven. And really, you gotta laugh
Speaker 1: at Adobe and Salesforce, both of whom to talk such
Speaker 1: a goddamn fuck ton about jenerit of Ai. And yeah,
Speaker 1: I have only made amazed the hundred twenty five million
Speaker 1: in analyzed revenue from it.
Speaker 2: Pathetic crap, dog shit.
Speaker 1: These aren't futuristic numbers, they're barely product categories, and none
Speaker 1: of this seems to include costs.
Speaker 2: Oh well, good grief.
Speaker 1: Look, a lot of what I've been saying is reminiscent
Speaker 1: the previous podcasts, and I've gone over this a lot,
Speaker 1: so I really want to make it clear that the
Speaker 1: signs are very troubling, and that the things I've warned
Speaker 1: you about the past couple of years are only getting worse,
Speaker 1: and the cliff's coming up things are only getting closer.
Speaker 1: When we double off of it, things may get really,
Speaker 1: really bad, And then the next episode we'll talk about
Speaker 1: how and what that tumble might look like and the
Speaker 1: noises I'm going to make when it happens.
Speaker 3: Thank you for listening to Better Offline, The editor and
Speaker 3: composer of the Better Offline theme song is Matasowski. You
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