CZM Rewind: The Case Against Generative AI (Part 3)

Summary

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Better Offline — "CZM Rewind: The Case Against Generative AI (Part 3)"

Host: Ed Zitron
Podcast: Better Offline (Cool Zone Media / iHeartPodcasts)
Date: December 31, 2025

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Overview

In the third installment of his comprehensive four-part series, Ed Zitron continues his critical exploration of the generative AI industry. This episode dissects the financial realities and failed promises fueling the current AI bubble, exposing the unsustainable economics, deceptive business strategies, and persistent myths about AI’s capability—especially in coding and enterprise settings. Zitron marshals industry data, media analysis, direct quotes from engineers, and candid commentary to argue that generative AI is not only failing to deliver on its grandest promises, but doing so at massive and accelerating cost.

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Key Discussion Points and Insights

1. The Illusion of Revenue and the Harsh Economics of AI

• AI’s Supposed Profitability Debunked
  Zitron unpacks the “piss poor revenue” of generative AI companies, even among top players like OpenAI and Anthropic, both of which are burning billions yearly.
  • Example: AnySphere (creator of Cursor) peaks at $500M annualized, but that is dwarfed by industries seen as less revolutionary, e.g., smartwatches ($32B).
  • “That’s some piss poor revenue for an industry that’s meant to be the future of software.” — Ed Zitron [03:00]
• Skyrocketing Costs, Negative Margins
  Most AI companies are negative gross margin businesses—meaning they lose money for each additional user due to uncontrollable GPU/token costs.
  • Perplexity burns 164% of revenue just on infrastructure.
  • Replit’s gross margins: 23%, excluding the heavy costs of free users.
  • “Every user loses you money in Generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
• Unstoppable Token Burns
  Attempts to cap user costs (via rate-limiting or “all-you-can-eat” subscriptions) falter; some users manage to rack up tens of thousands of dollars in generated token costs under $200/month plans (see “CC Usage” and “Vibrank” examples).

2. Broken Business Models and User Exploitation

• Anthropic’s Claude Code and Burn Dynamics
  Zitron details how even best-in-class AI firms hemorrhage cash serving heavy users. One “capitalist apex predator” user burned ~$50,000 in a month on a $200 Claude Code subscription.
  • “Even if Anthropic’s costs are half…the margins are devoured by a few wild users. This is not a real business.” — Ed Zitron [09:50]
• Replit’s Agent 3 Pricing Fiasco
  • Change to opaque, “effort-based” pricing surges user costs, with tasks leaping from a few dollars to hundreds; some report $1000 weekly bills.
  • “Agent 3 has been a disaster… another user complained that costs skyrocketed without any concrete results.” — [10:30]
  • The company’s subsequent backpedaling (letting users select agent “autonomy”) is seen as a “tacit admission you can’t trust coding LLMs to build software.”

3. The Core Problem: Coding with LLMs Doesn’t Work at Scale

• Software Economics vs. AI Economics
  Traditional SaaS relies on scaling at low marginal cost. LLM apps, by contrast, have rising marginal costs with each user as token inference costs can’t be effectively capped.
  • “AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
• Opportunity Costs, Monitoring, and Inaccuracy
  • LLM-powered coding multiplies errors and additional queries, increasing infrastructure costs.
  • Caching and using smaller models don’t offset expenses meaningfully.

4. Myth-Busting: LLMs Are Not Killing Developer Jobs

• Media Myths Corrected
  • Zitron forcefully rebuts claims that LLMs are “replacing” engineers, calling them “grotesque, manipulative, abusive and offensive lies… propagated through the entire business and tech media.” — Ed Zitron [18:00]
  • Invokes real software engineers, who liken LLMs to “a slightly below average computer science graduate,” not a replaceable dev.
• Engineer Voices (Direct Quotes):
  • Carl Brown (Internet of Bugs, guest on previous episode):
    “[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.” [17:12]
  • Nick Suresh:
    “The effort of articulating so much… in plain English and hoping the LLM emits code I find acceptable is more work than just writing the code. For most problems the hardest part is the thinking, and LLMs don’t make that part any easier.” [19:31]
  • Colt Vogi:
    “LLMs often function like a fresh summer intern… They lack the experience to be wholly trusted. And trust is the most important thing you need to fully delegate coding tasks.” [20:20]
• LLMs can generate code but can’t actually engineer software.
  • “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.” — Ed Zitron [20:45]
  • “If you’re printing this in a media outlet… you are fucking up.” — Ed Zitron [23:10]

5. Coding Versus Real-World Software Engineering

• Coding ≠ Engineering
  Zitron analogizes to translation—true translation (like true engineering) is creative, contextual, and irreplaceable by LLMs lacking any genuine understanding.
  • “Coding is not just a series of text that programs a computer, but a series of interconnected characters that refers to other software … must also function now and explain on some level to someone who has never ever seen the code before why it was done in this way.” [27:00]
• No Tangible Proof of LLMs Replacing Engineers
  • Coding seemed like the most direct use case, but LLMs amplify complexity-induced hallucinations, generate more errors, and make complex tasks harder.

6. Enterprise Generative AI Is Fizzling

• Microsoft Copilot: Sales Disappoint, Discounts Rampant
  • Despite 440M Microsoft 365 users, only ~8M “active” Copilot licenses (1.81% conversion rate, and many non-active).
    • “This would amount to… $2.88 billion annual revenue for a product category that makes $33 billion a fucking quarter… That’s piss poor.” — Ed Zitron [32:00]
  • “Active” defined generously—any action in 28 days.
  • Widespread discounting further erodes gross revenue.
• Failure of Enterprise Adoption
  • Massive sales teams, huge partner ecosystem cannot “sell AI.”
  • Quotes customer saying: “It’s easy for an employee to say yes, this will help me, but hard to quantify how. And if they can’t quantify how it’ll help them, it’s not going to be a long discussion over whether the software is worth paying for.” [33:26]
  • GPU utilization lags, and example features (like SharePoint Copilot) are largely ignored even by massive user bases.

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Notable Memorable Quotes (with Timestamps)

• “Every user loses you money in generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
• “A customer paying $200 a month ran up $50,000 in costs, immediately devouring the margin of any user running the service that day, that week, or even that month.” — Ed Zitron [09:48]
• “Agent 3 has been a disaster… I spent $1K this week alone.” — Quoting a Replit Register user, via Ed Zitron [10:30]
• “AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
• “Coding LLMs 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.” — Carl Brown, via Ed Zitron [17:41]
• “The effort of articulating so much of the design work in plain English and hoping the LLM emits code that I find acceptable is frequently more work than just writing the code.” — Nick Suresh [19:33]
• “LLMs often function like a fresh summer intern. … They lack the experience to be wholly trusted.” — Colt Vogi [20:20]
• “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.” — Ed Zitron [20:45]
• “You can’t replace a software engineer with them.” — Ed Zitron [21:30]
• “You are doing software engineers dirty.” — Ed Zitron [23:40]
• “The media has also been quick to suggest that AI writes software, which is true in the same way that ChatGPT writes novels.” — Ed Zitron [27:35]
• “That’s piss poor, buddy. That’s piss poor. That’s pissy. It sucks. It’s bad. Doo doo. Well, I just said pp, I guess. Anyway, very serious, Very serious podcast.” — Ed Zitron [32:25]
• “If Microsoft’s doing this badly, I don’t know how anyone else is doing well. And they’re not. They’re all failing.” — Ed Zitron [34:28]

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Important Segments and Timestamps

• Introduction & Series Recap: [02:15]
• AI Company Revenue and Cost Analysis: [02:15–08:32]
• Claude Code & Token Burn Explainer: [08:32–11:46]
• Replit Agent 3 Pricing Debacle: [10:21–11:46]
• AI Economics vs SaaS Reality: [11:46–15:58]
• Media Lies about Replacing Software Engineers: [16:00–21:44]
• Real Engineers’ Views on LLM Capabilities: [17:12–21:44]
• The Software/Translation Analogy: [26:58–28:50]
• Failure of Enterprise GenAI (Microsoft Copilot): [29:40–34:30]
• Closing and Preview of Next Part: [34:30–35:23]

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Tone and Language

Ed Zitron's style is frank, irreverent, and confrontational—calling out media, tech leaders, and industry narratives with a blend of humor and exasperation. He frequently swears for emphasis, employs colorful analogies, and relies on direct evidence and firsthand testimony.

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Conclusion

This episode of Better Offline delivers a thorough takedown of generative AI’s economics and the overheated hype about its capabilities—especially the notion that AI will replace human developers or revolutionize enterprise productivity. Ed Zitron uses data, candid interviews with engineers, and sharp wit to expose the stark realities: Generative AI businesses are unsustainable, foundational coding tasks are still out of AI’s league, and adoption in enterprise is failing to materialize at meaningful scale. With a final warning that “it really only gets worse from here,” Zitron sets up the series finale as a must-listen for anyone invested in the future of tech.

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Transcript

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Call zone media. Hello and welcome to Better Offline. I'm of course your host, Ed Zitron. Better Offline we'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 unprofit model providers. Outside 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 and as I've mentioned in the past, the Magnificent Seven expect to make $35 billion or so in revenue from AI this year. And I think in total when you throw in core, we've 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 Anthropic's case this year so far, and $6.626 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 dollar 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 revenue, specifically AI search company Perplexity, which is now here $150 million in AR or $12.5 million a month, and AI coding startup Replir, which has hit the same amount. Both 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. Problematically, 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 of nonthropic losing money on every single Claude Code subscriber and now I'm going to walk you through a the whole story in a simplified fashion because it's quite important. So Claude code 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 Anthropic's $20, $100 and $200 a month Claude subscriptions, with the more expensive subscriptions having more generous rate limits. Generally, 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 without 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 build, so every million you get charged. So for example, Anthropic charges $3 per million input tokens and 6 million per output tokens to use its Claude Sonnet 4 model. And it's about, I think, well, a word before tokens. I should really look that up. It's. It also gets more complex as you get into things like generating code. Nevertheless, Claude code has been quite popular and a user created a program called CC Usage which allowed you to see your token burn. The amount of tokens you were using, you were actually burning using Anthropic's models while using CLAUDE 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 Anthropic's own pricing, and thus the costs to Anthropic are likely not identical. So I got a little clever using Anthropic's 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 is also now a leaderboard called Vibrank 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 OpenAI, and 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 use activity, likely due to the architecture of generative AI. I know it sounds insane, but at the most advanced level, even there model 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. Not really. Anthropic counts amongst its capitalist apex predators one lone Chinese man who spent $50,000 of their computer in the space of a month fucking around with clawed code. Even 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 in costs immediately devouring the margin of any user running the service that day, that week or even that month. Even if Anthropic's 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. A 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. Agent 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 thousand dollar bill after a week and I quote them. I think it's just launch pricing adjustment. Some tasks on new apps an hour and 45 minutes and only charge four to six dollars. 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 cost skyrocketed without any concrete results. And I quote the register here, I typically spent between $100 and $250 a month. I blew through $70 in a night at Agent 3 launch and 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. 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. This 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. REPL DOT's users are still pissed off complaining that Relet is charging them for an activity when the agent doesn't do anything. A consistent problem I 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. AI does not have a profit lever because the raw costs of providing access to AI models are so, 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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[Ed Zitron]

Reiterate something I wrote a few weeks ago. A large language model user's infrastructural burden varies wildly between users and use cases. While somebody asking ChatGPT to summ summarize an email might not be much of a burden, somebody asking Chat GPT 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 when 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, or the model must make a further generation or generations. And even with caching, which Anthropica's editor told 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. The 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 or 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 part of the input. As 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, Anthropic's 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 31, 2025. The 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, it's 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, discussing with multiple different engineers, that Claude code had become ubiquitous with anything to do with LLMs and coding. To be clear, Anthropic, 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 send it. Microsoft's GitHub Co pilot 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 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 200amonth, 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 Amade says, you'd think that the reason that software engineers are having trouble finding work is because their jobs are being replaced by AI. This 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 code members of the media, I am begging you. Stop. Stop doing this. Stop publishing these fucking headlines. You're embarrassing yourself. Every asshole 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 have 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 an 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. Now, 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 40 years have been designing safer and better ways of tying these blocks together to create large 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. This is where AIs fall down. The amount of context required to hold the interconnections between these blocks quickly grows beyond the AI. 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 in grade. That'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 Suresh 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 brain power. 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. For most problems, the hardest part is the thinking, and LLMs don't make that part any easier. I also talked to Colt Vogi 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 learned 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. In 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 at once so you can't replace a software engineer with them. If you are printing this in a media outlet and have heard this sentence, you are fucking up. You really are fucking up. I really need members of the media hearing this.

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You need to change.

[Ed Zitron]

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

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[Trainer Games Host]

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[Ed Zitron]

This is where mindset comes in.

[Trainer Games Host]

Someone will be eliminated.

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Pressure is coming down.

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[Ed Zitron]

Look, and I understand why too it's very easy to believe that software engineering is just writing code, but the reality is that software engineers maintain software which includes writing and analyzing code amongst a vast array of different personalities and programs and problems. Good software engineering harkens back to Brian Merchant's interviews with translators. While some may believe the translators simply tell you what words mean, true translation is communicating the meaning of a sentence which is cultural, contextual, regional and personal, and often requires the exercise of creativity and novel thinking. And on top of that, while translation is the production of words, you can't just take code and look at it. You actually need to know how code works and functions and why it functions in that way. Using an LLM, you'll never know because the LLM doesn't know anything either. Now, my editor Matt Hughes gave an example of this in his newsletter, which I think I'll paraphrase. He used to live in France, in the French speaking part of Switzerland, and sometimes he'll read French translations of books to see how awkward bits of prose are translated. Doing those awkward bits requires a bit of creative thinking, and I quote take Harry Potter in French, Hogwarts is poutlard, which translates into bacon lice. Why did they go with that instead of a literal translation of Hogwarts, which would be veru spork. I'm sorry, to anyone who can actually read languages, no idea. But I'd assume it was Something to do with the fact that Poulardo, that Poodlard sounds a lot better than Veru Spork. And both of them, I can say flawlessly. Someone had to actually think about how to translate that one idea. They had to exercise creativity, which is something that an AI is inherently incapable of doing. Similarly, coding is not just a series of text that programs a computer, but a series of interconnected characters that refers to other software in other places that must also function now and explain on some level to someone who has never ever seen the code before why it was done in this way. This is, by the way, why we're still yet to get any tangible proof that AI is replacing software engineers, because it isn't replacing software engineers. And now we need to understand why this is so existentially bad for generative AI. Of all the fields supposedly at risk from AI disruption, coding feels or felt the most tangible, if only because the answer to can you write code with LLMs wasn't an immediate unilateral no. The media has also been quick to suggest that AI writes software, which is true in the same way that ChatGPT writes novels. In reality, LLMs can generate code and do somewhere some sort of software engineering adjacent tasks. But like all large language models, break down and go totally insane, hallucinating more and more as the tasks get more complex. And software engineering is extremely complex. Even software engineers who can read code and have done so for decades will find problems they can't solve just by looking at the code. And as I pointed out earlier, software engineering is not just coding. It involves thinking about problems, finding solutions to novel challenges, designing stuff in a way that can be read and maintained by others, and that's ideally scalable, available and secure. The whole fucking point of an AI is that you hand off to it. That's what they've been selling it as. That's why Jensen Huang told kids to stop learning to code. As with AI, there's no point. And it was all a fucking lie. Generative AI can't do the job of a software engineer, and it fails while also costing an abominable amount of money. Coding large language models seem like magic at first because they, to quote a conversation with Carl Brown, make the easy things easier, but they also make the harder things harder. They don't even speed up engineers. There's a study that showed they make them slower. Yet coding is basically the only obvious use case for LLMs. Oh, I'm sure you're going to say, but I bet the enterprise is doing well and you're also very, very wrong. Microsoft, if you've ever switched on a TV in the past two years, has gone all in on Generative AI. And despite being arguably the biggest software company in the world, at least in terms of desktop operating systems and productivity, software has made almost no traction in popularizing Generative AI. It has thousands, if not tens of thousands of salespeople and thousands of companies that literally sell Microsoft services for a living. And it can't sell AI. I've got a real fucking scoop here. I'm so excited and I buried it in the third part of a four part episode and truly twisted but a source that has seen materials related to sales has confirmed that as of August 2025, Microsoft has around 8 million active licensed so paying users of Microsoft 365 copilot amounting to a 1.81 conversion rate across 440 million Microsoft 365 subscribers, must be clear that 365 is their big cash cow. This would amount to if each of these users paid annually at the full rate $30 a month to about $2.88 billion in annual revenue for a product category that makes $33 billion a fucking quarter. This productivity and business unit for Microsoft. And I must be clear, I am 100% sure these users aren't all paying $30 a month.

[Odoo Advertiser]

Month.

[Ed Zitron]

The information reported a few weeks ago that Microsoft has been reducing the software's price, referring to Microsoft 365 with more generous discounts on the AI features according to customers and salespeople, heavily suggesting discounts have already been happening. Enterprise software is traditionally sold at a discount anyway, or put a different way with bulk pricing for those who sign up a bunch of users at once. In fact, I found evidence that they've been doing this for a While with a 15% discount on annual Microsoft 365 copilot subscriptions for orders of 10 to 300 seats mentioned by an IT consultant back in late 2024 and another that's currently running through 9-30-2025 with another Microsoft cloud solution provider program. Yeah, this I found tons of other examples too. And Microsoft 365 is the enterprise version where they sell things with like word and PowerPoint and sometimes teams as well. This is them probably their most popular product and by the way they even manipulate the numbers a little bit there. An active user is someone who has taken one action on any Microsoft 365 app with Copilot in the space of 28 days, not 3028 that's so generous. Now I know, I know that word. Active. Maybe you're thinking, Ed, this is like the gym model. There are unpaid licenses that Microsoft is getting paid for. Fine. Fine, Fine. Fucking fine. Let's assume that Microsoft also has, based on research that suggests this can be the case for some software companies, another 50%.4 million paying copilot licenses that aren't being used. That's still 12 million users, which is around 2.7% conversion rate. That's piss poor, buddy. That's piss poor. That's pissy. It sucks. It's bad. Doo doo. Well, I just said pp, I guess. Anyway, very serious, Very serious podcast. But why aren't people paying for Copilot? Well, let's hear from someone who talked to the information and I quote, it's easy for an employee to say, yes, this will help me, but hard to quantify how. And if they can't quantify how it'll help them, it's not going to be a long discussion over whether the software is worth paying for.

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[Ed Zitron]

Is that good? Is that good? Is that. Is that what? Is that what you want to hear? It isn't. It isn't. That's the. That's the secret. It's not. It's bad. It's really bad. It's all very bad. And Microsoft 365 copilot has been such a disaster that Microsoft will now integrate anthropics models to try and make them better. Oh, one other thing too. Sources also confirm GPU utilization. So how much the GPU set aside for Microsoft 365? Yeah, their enterprise copilot is barely scratching the 60%. I'm also hearing the SharePoint, which is an app they have with over 250 million users, has less than 300,000 weekly active users of their copilot features, suggesting that people just don't want to fucking use this. Those numbers are from August, by the way. And it's pathetic. And I must be clear, if Microsoft's doing this badly, I don't know how anyone else is doing well. And they're not. They're all failing. It's pathetic. But I've spent a lot of time today talking about AI coding because this was supposed to be the saving grace, the thing that actually turned this from a bubble into an actual money minting industry that changes the world. And I wanted to bring up Microsoft 365 because that's the place where Microsoft should be making the most money. It's their most ubiquitous software. It's their most. Well, known software and they're not 8 million people. 8 million people. I've run that by a few people and everyone's made the same oh God noise. It's quite weird the oh God noise and the numbers, but this just isn't happening. Things are going badly and it really only gets worse from here and I'm gonna tell you more tomorrow in the final part of our four parter. Thank you for your patience and thank you for your time. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski.

[Quince Spokesperson]

You can check out more of his.

[Ed Zitron]

Music and audio [e-mail] m a t t o s o W-K-I.com. you can email me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat wheresyoured at to visit the Discord and go to R betteroffline to check out our Reddit thank you so much for listening. Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, Visit.

[Trainer Games Host]

Our website coolzone media.com or check us out on the iHeartRadio app, Apple Podcasts or wherever you get your podcasts.

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[Trainer Games Host]

Will face the toughest job interview in fitness that will push past physical and mental breaking points. You are the fittest of the fit. Only one of you will leave here with an IFIT contract for $250,000.

[Ed Zitron]

This is where mindset comes in.

[Trainer Games Host]

Someone will will be eliminated.

[Podcast Announcer]

Pressure is coming down.

[Ryan Reynolds]

Trainer Games on Prime Video January 8th.

[Frank (Cosentyx Testimonial)]

Watch the trailer on trainergames.com did you.

[Ryan Reynolds]

Know Microsoft has officially ended Support for Windows 10? Upgrade to Windows 11 with an LG Gram laptop Voted PC Mag's Reader's Choice top laptop brand for 2025 Thin and ultra lightweight, the LG Gram keeps you productive anywhere, and Windows 11 gives you access to free security updates, updates and ongoing feature upgrades. Visit lgusa.com iheart for great seasonal savings on LG Gram laptops with Windows 11. PCMag reader's choice used with permission. All rights reserved.

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Season 2 of unrivaled basketball is here, and the talent is unreal. Paige Beckers, Nafiza Collier, Kelsey Plumb, Brianna Stewart and more are back to redefine the game. Unrivaled basketball season two, sponsored by Samsung Galaxy, tips off January 5th on TNT, TruTV and HBO Max. This is an iHeart podcast. Guaranteed human.