The Case Against Generative AI (Part 3)

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2:09We're in the third episode of our four-part series where I give you a comprehensive explanation as to the origins of the AI bubble, the mythology sustaining it and why it's destined to end really, really badly. Now, if you're jumping in now, please start from the very beginning. The reason why this is a four-parter, my first ever, is because I want it to be comprehensive and because this is a very big subject with a lot of moving parts and even more bullshit. A few weeks ago, I published a premium newsletter that explained how everybody is losing money on generative AI, in part because the costs of running AI models is increasing, and in part because the software itself doesn't do enough to warrant the costs associated with running them, which are already subsidized and unprofitable for the model providers.

2:48Outside of OpenAI and, to a lesser extent, Anthropic, nobody seems to be making much revenue, with the most successful company being AnySphere, makers of AI coding tool Cursor, which hit$500 million of annualized, so$41.6 million in one month, a few months ago, just before Anthropic and OpenAI jacked up the prices for priority processing on enterprise queries, raising their operating costs as a result. In any case, that's some piss-poor revenue for an industry that's meant to be the future of software. Smartwatches are projected to make$32 billion this year. And as I've mentioned in the past, the Magnificent Seven expect to make$35 billion or so in revenue from AI this year.

3:25And I think in total, when you throw in CoreWeave and all them, it's barely$55 billion in total. Even Anthropic and OpenAI seem a little lethargic, both burning billions of dollars while making, by my estimates, no more than$2 billion in Anthropics case this year so far, and$6.26 billion in 2025 so far for OpenAI, despite projections of$5 billion and$13 billion respectively. Outside of these two, AI startups are floundering, struggling to stay alive and raising money in several hundred million bursts as their negative gross margin businesses flounder. As I dug into a few months ago, I could find only 12 AI-powered companies making more than$8.3 million a month, with two of them slightly improving their revenues, specifically AI search company Perplexity, which has now hit $150 million in ARR or$12.5 million a month, and AI coding startup Replit, which has hit the same amount.

4:17Both of these companies burn ridiculous amounts of money. Perplexity burned 164 % of its revenue on Amazon Web Services, OpenAI, and Anthropic last year. And while Replit hasn't leaked its costs, the information reports its gross margins in July were 23%, which doesn't include the cost of its free users, which you simply have to do with LLMs, as free users are capable of costing you a shit ton of money. And some of you might say, that's how they do it in software. Well, guess what? Software doesn't usually connect you to a model that can burn, I don't know, 10 cents, 20 cents every time they touch it, which may not seem like much, but when you're making free dollars on someone and they don't convert, it does.

4:55Problematically, your paid users also cost you more than they bring in as well. In fact, every user loses you money in generative AI because it's impossible to do cost control in a consistent manner. A few months ago, I did a piece on Anthropic losing money on every single ClaudeCode subscriber, and now I'm going to walk you through the whole story in a simplified fashion because it's quite important. So ClaudeCode is a coding environment that people used, or I should really say tried to use, to build software using generative AI. It's available as part of Anthropix$20,$100, and$200 a month Claude subscriptions, with the more expensive subscriptions having more generous rate limits.

5:34Generally, these subscriptions are all you can eat. You can use them as much as you want until you hit limits, rather than paying for the actual tokens you burn. When I say burn tokens, and someone reached out saying I should specify this, I'm describing how these models are traditionally billed. In general, you're billed at a dollar per million input tokens, as in user feeding in data, and output tokens, the output created. So you wouldn't get one token built. So every million you get charged. So for example, Anthropic charges$3 per million input tokens and 6 million output tokens to use its clawed sonnet for model.

6:08And it's about, I think, well, a word before tokens? I should really look that up. It also gets more complex as you get into things like generating code. Nevertheless, clawed code has been quite popular. and a user created a program called CCUsage which allowed you to see your token burn. The amount of tokens you were using, you were actually burning using Anthropix models while using clawed code versus just getting charged a month and not knowing and many were seeing that they were burning in excess of their monthly spend. To be clear this is the token price based on Anthropix own pricing and thus the cost to Anthropix are likely not identical.

6:43So I got a little clever. Using Anthropix gross profit margins, I chose 55 % and then a few weeks after my article, 60 % was leaked, I found at least 20 different accounts of people costing Anthropic anywhere from 130 % to 3 ,084 % of their subscription. There's also now a leaderboard called VibeRank, where people compete to see how much they burn, with the current leader burning, and I shit you not,$51 ,291 over the course of a month. Anthropic is, to be clear, the second largest model developer, and has some of the best AI talent in the industry. It has a better handle on its infrastructure than anyone outside of big tech and open AI.

7:21And it still cannot seem to fix this problem, even with weekly rate limits brought in at the end of August. While one could assume that Anthropic is simply letting users run wild, my theory is far simpler. Even the model developers have no real way of limiting user activity, likely due to the architecture of generative AI. I know it sounds insane, but at the most advanced level, even there, modeled providers are still prompting their models, and whatever rate limits may be in place appear to at times get completely ignored, and there doesn't seem to be anything they can do to stop it. Now really, Anthropic counts amongst its capitalist apex predators one lone Chinese man who spent$50 ,000 of their compute in the space of a month fucking around with clawed code.

8:02Even if Anthropic was profitable, it isn't, and will burn billions of dollars this year, a customer paying$200 a month ran up$50 ,000 dollars in costs, immediately devouring the margin of any user running the service that day, that week, or even that month. Even if Anthropics costs are half the published rates, they're not, by the way. One guy amounted to 125 users' worth of monthly revenue. This is not a real business. That's a bad business with out-of-control costs, and it doesn't appear anybody has these costs under control. And, faced with the grim reality ahead of them, these companies are trying nasty little tricks on their customers to juice more revenue from them.

8:40A few weeks ago, Replit, an unprofitable AI coding company, released a product called Agent 3, which promised to be 10 times more autonomous and offer infinitely more possibilities, testing and fixing its code, constantly improving your application behind the scenes in a reflection loop. Sounds very real. Sounds extremely real. It's so real, but actually it isn't. In reality, this means you'd go and tell the model to build something, and it would go and do it, and you'll be shocked to hear that these models can't be relied upon to go and do anything. Please note that this was launched a few months after Replit raised their prices, shifting to obfuscated effort-based pricing that would charge the full scope of the agent's work, and if you're wondering what the fuck that means, so are their customers.

9:21Agent 3 has been a disaster. Users found the tasks that previously cost a few dollars were spiraling into the hundreds of dollars, with the register reporting one customer found themselves with a$1 ,000 bill after a week, and I quote them. I think it's just launch pricing adjustments. Some tasks on new apps ran over an hour and 45 minutes and only charged$4 to$6, but editing pre-existing apps seems to cost most overall. I spent$1k this week alone. And they told that to the register, by the way. Another user complained that costs skyrocketed without any concrete results, and I quote the register here.

9:53I typically spent between$100 and$250 a month. I blew through$70 in a night at Agent 3 launch, another editor wrote, alleging the new tool also performed some questionable actions. One prompt brute forced its way through authentication, redoing auth and hard resetting a user's password to what it wanted to perform app testing on a form, the user wrote. I realize that's a little nonsensical, but long story short, it did a bunch of shit it wasn't asked to. As I previously reported in late May, early June, both OpenAI and Anthropic cranked up the pricing on their enterprise customers, leading Replit and Cursor both shifting their prices upward.

10:27This abuse has now trickled down to the customers. Replit has now released an update that lets you choose how autonomous you want Agent 3 to be, which is a tacit admission that you can't trust coding LLMs to build software. Replit's users are still pissed off, complaining that Replit is charging them for an activity when the agent doesn't do anything, a consistent problem I've found across Redditors. While Reddit is not the full summation of all users of every company everywhere, it's a fairly good barometer of user sentiment, and man, are users pissy. And now here's where this is bad. Traditionally, Silicon Valley startups have relied upon the same model of grow really fast and burn a bunch of money, then turn the profit lever.

11:04AI does not have a profit lever because the raw costs of providing access to AI models are so high, and they're only increasing, that the basic economics of how the tech industry sells software don't make sense.

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12:26Toyota, the official automotive partner of the NFL. Visit toyota.com slash NFL now to learn more. But the humility in knowing that life is this classroom that we should never graduate from is what is going to keep you growing. And that's all that matters. World Mental Health Day is around the corner. And on my podcast, Just Heal with Dr. J, I dive into what it really means to care for your mind, body, and spirit. from breaking generational patterns to building emotional capacity. Healing is a journey and wholeness is the destination. I'm going to walk away feeling very healed and feeling like, yes, I'm going to continue my healing journey and I'm going to get some keys from you.

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14:31I'll reiterate something I wrote a few weeks ago. A large language model uses infrastructural burden varies wildly between users and use cases. While somebody asking ChatGPT to summarize an email might not be much of a burden, somebody asking ChatGPT to review hundreds of pages of documents at once, a core feature of basically any$20 a month subscription, could eat up to eight GPUs at once. To be very clear, a user that pays$20 a month could run multiple queries like this a month, and there's not really a way to stop them. Unlike most software products, any errors in producing an output from a large language model have a significant opportunity cost.

15:06When a user doesn't like an output or the model gets something wrong, which it's guaranteed to do, or the user realizes they forgot something, the model must make a further generation or generations, and even with caching, which Anthropoc has added a toll to, there's a definitive cost attached to any mistake. Large language models are, for the most part, lacking in any definitive use cases, meaning that every user is, even with an idea of what they want to do, experimenting with every input and output. In doing so, they create the opportunity to burn more tokens, which in turn creates an infrastructural burn on GPUs, which cost a lot of money to run.

15:38The more specific the output, the more opportunities there are for monstrous token burn, and I'm specifically thinking about coding with LLMs. The token-heavy nature of generating code means that any mistakes, suboptimal generations, or straight-up errors will guarantee further token burn. Even efforts to reduce compute costs by, for example, pushing free users or those on cheap plans to smaller, less intensive models have dubious efficacy. As I talked about in a previous episode, OpenAI's splitter model in the GPT version of ChatGPT requires vast amounts of additional compute in order to route the user's request toward the appropriate model, with simpler requests going to smaller models and more complex ones being shifted to reasoning models, and it makes it impossible to cache as part of the input.

16:18As a result, it's not really clear whether it's saving OpenAI any money, and indeed, kind of suggests it might be costing them more. In simpler terms, it's very, very, very difficult to imagine what one user, free or otherwise, might cost, and thus it's hard to charge them anything on a monthly basis, or tell them what a service might actually cost them on average. And this is a huge, huge problem with AI coding environments. But let's talk about Claude Code again, Anthropics Code Generator Tool. According to the information, Claude Code was driving nearly$400 million in annualized revenue, roughly doubling from a few weeks ago on July 31st, 2025.

16:54The annualized revenue works out to about$33 million a month in revenue for a company that predicts it will make at least$416 million a month by the end of the year, and for a product that has become, for a time, the most popular coding environment in the world, from the second largest and best-funded AI company in the world. Is that it? Is that fucking it? Is that all that's happening here? $33 million, all of which is unprofitable, after it felt, at least based on social media chatter and discussing with multiple different engineers, that Claude Code had become ubiquitous with anything to do with LLMs and coding.

17:29To be clear, Anthropic's Sonnet and Opus models are consistently some of the most popular for programming an open router, an aggregator of LLM usage, and Anthropic has been consistently named as the best at coding. Whether or not I feel that way is irrelevant. Some bright spark out there is going to say that Microsoft's GitHub Copilot has 1.8 million paying subscribers, and guess what? That's true. In fact, I reported it. Here's another fun fact. The Wall Street Journal reported that Microsoft loses on average$20 a month per user, with some users costing the company as much as$80. bucks and that's for the most popular product but wait wait wait wait hold up wait i read some shit in the newspaper aren't these llm code generators replacing actual human engineers and thus even if they cost way more than 20 100 or 200 a month they're still worth it right they're replacing an entire engineer oh my sweet summer child if you believe the new york times or other outlets that simply copy and paste whatever anthropic ceo wario amaday says you'd think that the reason that software engineers are having trouble finding work is because their jobs are being replaced by AI.

18:30This grotesque, manipulative, abusive, and offensive lie has been propagated through the entire business and tech media without anybody sitting down and asking whether it's true, or even getting a good understanding of what it is that LLMs can actually do with code. Members of the media, I am begging you, stop. Stop doing this. Stop publishing these fucking headlines. You're embarrassing yourself. Every arsehole is willing to give a quote saying that coding is dead and that every executive is willing to burp out some nonsense about replacing all of their engineers but i'm fucking begging you to either use these things yourself or speak to people that do i am not a coder i cannot write or read code nevertheless i'm capable of learning and i've spoken to numerous software engineers in the last few months and basically i've reached a consensus of this is kind of useful sometimes however one time a very silly man with a increasingly squeaky voice said that i don't speak to people who use ai tools so i went and spoke to three notable, experienced software engineers, and asked them to give me the straight truth about what coding LLMs can do.

19:28Now, for the purposes of brevity, I'm going to use select quotes from what these people said, but if you want to read the whole thing, you can check out the newsletter. First, I'm going to read what Carl Brown of the Internet of Bugs said, and I had him on the show a few months back. He's fantastic. So, most of the advancements in programming languages, technique, and craft in the last four years have been designing safer and better ways of tying these blocks together to create larger and larger programs with more complexity and functionality. Humans use these advancements to arrange these blocks in logical abstraction layers, so we can fit an understanding of the layers interconnections in our heads as we work, diving into blocks temporarily as needed.

20:03This is where AIs fall down. The amount of context required to hold the interconnections between these blocks quickly grows beyond the AI's effective short-term memory, in practice much smaller than its advertised context window size, and the AIs lack the ability to reason about the abstractions as we do. This leads to real-world code that's illogically layered, hard to understand, debug and maintain. Carl also said, Code generation AIs, from an industry standpoint, are roughly the equivalent of a slightly below-average computer science graduate fresh out of school without any real-world experience, only ever having written programs to be printed and graded.

20:37That's bad, because as he pointed out, whereas LLMs can't get past this summer intern stage, actual humans get better. and if we're replacing the bottom rung of the labor market, there won't be any mid-level or senior developers later down the line. Next, I asked Nick Shoresh of I will fucking pile drive you if you mention AI again what he thought. LLMs, he said, will sometimes solve a thorny problem for me in a few seconds, saving me some brainpower. But in practice, the effort of articulating so much of the design work in plain English and hoping the LLM omits code that I find acceptable is frequently more work than just writing the code.

21:11For most problems, the hardest part is the thinking. And LLMs don't make that part any easier. I also talked to Colt Volge of, no, AI is not making AI engineers 10x is productive, who we also had on the show recently, and he said this. LLMs often function like a fresh summer intern. They're good at solving the straightforward problems that coders learn about in school, but they are unworldly. They do not understand how to bring lots of solutions to small, straightforward problems together into a larger whole. They lack the experience to be wholly trusted, and trust is the most important thing you need to fully delegate coding tasks.

21:41in simpler terms llms are capable of writing code but can't do software engineering because software engineering is the process of understanding maintaining and executing code to produce functional software and llms do not learn cannot adapt and to paraphrase something carl brown said to me break down the more of your code and variables you ask them to look at once so you can't replace a software engineer with them if you are printing this in a media outlet that and have heard this sentence, you are fucking up. You really are fucking up. I'm really, members of the media hearing this, you need to change.

22:13You need to change on this one. You are doing software engineers dirty.

22:36And as the number one podcaster, iHeart's twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus, only iHeart can extend your message to audiences across broadcast radio. Think podcasting can help your business? Think iHeart. Streaming, radio, and podcasting. Let us show you at iHeartAdvertising.com. That's iHeartAdvertising.com. Hey, this is Matt Jones. And I'm Drew Franklin. And this is NFL Cover Zero. We're just here to try to give you an NFL perspective a little bit different. Did you see the Colts pretzel? That was my other big takeaway from that game.

23:11What was that? Oh, my. We think NFL coverage should be informative and entertaining. And twice a week, that is exactly what you're going to get. Listen to NFL Cover Zero with Matt Jones and Drew Franklin on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

23:31Toyota, the official automotive partner of the NFL. Visit toyota.com slash NFL now to learn more. December 29th, 1975. LaGuardia Airport. The holiday rush. Parents hauling luggage. Kids gripping their new Christmas toys. Then, at 6.33 p.m., everything changed. There's been a bombing at the TWA terminal. Apparently the explosion actually impelled metal glass. The injured were being loaded into ambulances. Just a chaotic, chaotic scene. In its wake, a new kind of enemy emerged. And it was here to stay. Terrorism. Law & Order Criminal Justice System is back. In Season 2, we're turning our focus to a threat that hides in plain sight.

24:25That's harder to predict and even harder to stop. Listen to the new season of Law & Order Criminal Justice System on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.